Arquivos da Tag: Matemática

Mathematical Models Out-Perform Doctors in Predicting Cancer Patients’ Responses to Treatment (Science Daily)

Apr. 19, 2013 — Mathematical prediction models are better than doctors at predicting the outcomes and responses of lung cancer patients to treatment, according to new research presented today (Saturday) at the 2nd Forum of the European Society for Radiotherapy and Oncology (ESTRO).

These differences apply even after the doctor has seen the patient, which can provide extra information, and knows what the treatment plan and radiation dose will be.

“The number of treatment options available for lung cancer patients are increasing, as well as the amount of information available to the individual patient. It is evident that this will complicate the task of the doctor in the future,” said the presenter, Dr Cary Oberije, a postdoctoral researcher at the MAASTRO Clinic, Maastricht University Medical Center, Maastricht, The Netherlands. “If models based on patient, tumour and treatment characteristics already out-perform the doctors, then it is unethical to make treatment decisions based solely on the doctors’ opinions. We believe models should be implemented in clinical practice to guide decisions.”

Dr Oberije and her colleagues in The Netherlands used mathematical prediction models that had already been tested and published. The models use information from previous patients to create a statistical formula that can be used to predict the probability of outcome and responses to treatment using radiotherapy with or without chemotherapy for future patients.

Having obtained predictions from the mathematical models, the researchers asked experienced radiation oncologists to predict the likelihood of lung cancer patients surviving for two years, or suffering from shortness of breath (dyspnea) and difficulty swallowing (dysphagia) at two points in time:

1) after they had seen the patient for the first time, and

2) after the treatment plan was made. At the first time point, the doctors predicted two-year survival for 121 patients, dyspnea for 139 and dysphagia for 146 patients.

At the second time point, predictions were only available for 35, 39 and 41 patients respectively.

For all three predictions and at both time points, the mathematical models substantially outperformed the doctors’ predictions, with the doctors’ predictions being little better than those expected by chance.

The researchers plotted the results on a special graph [1] on which the area below the plotted line is used for measuring the accuracy of predictions; 1 represents a perfect prediction, while 0.5 represents predictions that were right in 50% of cases, i.e. the same as chance. They found that the model predictions at the first time point were 0.71 for two-year survival, 0.76 for dyspnea and 0.72 for dysphagia. In contrast, the doctors’ predictions were 0.56, 0.59 and 0.52 respectively.

The models had a better positive predictive value (PPV) — a measure of the proportion of patients who were correctly assessed as being at risk of dying within two years or suffering from dyspnea and dysphagia — than the doctors. The negative predictive value (NPV) — a measure of the proportion of patients that would not die within two years or suffer from dyspnea and dysphagia — was comparable between the models and the doctors.

“This indicates that the models were better at identifying high risk patients that have a very low chance of surviving or a very high chance of developing severe dyspnea or dysphagia,” said Dr Oberije.

The researchers say that it is important that further research is carried out into how prediction models can be integrated into standard clinical care. In addition, further improvement of the models by incorporating all the latest advances in areas such as genetics, imaging and other factors, is important. This will make it possible to tailor treatment to the individual patient’s biological make-up and tumour type

“In our opinion, individualised treatment can only succeed if prediction models are used in clinical practice. We have shown that current models already outperform doctors. Therefore, this study can be used as a strong argument in favour of using prediction models and changing current clinical practice,” said Dr Oberije.

“Correct prediction of outcomes is important for several reasons,” she continued. “First, it offers the possibility to discuss treatment options with patients. If survival chances are very low, some patients might opt for a less aggressive treatment with fewer side-effects and better quality of life. Second, it could be used to assess which patients are eligible for a specific clinical trial. Third, correct predictions make it possible to improve and optimise the treatment. Currently, treatment guidelines are applied to the whole lung cancer population, but we know that some patients are cured while others are not and some patients suffer from severe side-effects while others don’t. We know that there are many factors that play a role in the prognosis of patients and prediction models can combine them all.”

At present, prediction models are not used as widely as they could be by doctors. Dr Oberije says there are a number of reasons: some models lack clinical credibility; others have not yet been tested; the models need to be available and easy to use by doctors; and many doctors still think that seeing a patient gives them information that cannot be captured in a model. “Our study shows that it is very unlikely that a doctor can outperform a model,” she concluded.

President of ESTRO, Professor Vincenzo Valentini, a radiation oncologist at the Policlinico Universitario A. Gemelli, Rome, Italy, commented: “The booming growth of biological, imaging and clinical information will challenge the decision capacity of every oncologist. The understanding of the knowledge management sciences is becoming a priority for radiation oncologists in order for them to tailor their choices to cure and care for individual patients.”

[1] For the mathematicians among you, the graph is known as an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).

[2] This work was partially funded by grants from the Dutch Cancer Society (KWF), the European Fund for Regional Development (INTERREG/EFRO), and the Center for Translational Molecular Medicine (CTMM).

Mathematics Provides a Shortcut to Timely, Cost-Effective Interventions for HIV (Science Daily)

Apr. 15, 2013 — Mathematical estimates of treatment outcomes can cut costs and provide faster delivery of preventative measures.

South Africa is home to the largest HIV epidemic in the world with a total of 5.6 million people living with HIV. Large-scale clinical trials evaluating combination methods of prevention and treatment are often prohibitively expensive and take years to complete. In the absence of such trials, mathematical models can help assess the effectiveness of different HIV intervention combinations, as demonstrated in a new study by Elisa Long and Robert Stavert from Yale University in the US. Their findings appear in the Journal of General Internal Medicine, published by Springer.

Currently 60 percent of individuals in need of treatment for HIV in South Africa do not receive it. The allocation of scant resources to fight the HIV epidemic means each strategy must be measured in terms of cost versus benefit. A number of new clinical trials have presented evidence supporting a range of biomedical interventions that reduce transmission of HIV. These include voluntary male circumcision — now recommended by the World Health Organization and Joint United Nations Programme on HIV/AIDS as a preventive strategy — as well as vaginal microbicides and oral pre-exposure prophylaxis, all of which confer only partial protection against HIV. Long and Stavert show that a combination portfolio of multiple interventions could not only prevent up to two-thirds of future HIV infections, but is also cost-effective in a resource-limited setting such as South Africa.

The authors developed a mathematical model accounting for disease progression, mortality, morbidity and the heterosexual transmission of HIV to help forecast future trends in the disease. Using data specific for South Africa, the authors estimated the health benefits and cost-effectiveness of a “combination approach” using all three of the above methods in tandem with current levels of antiretroviral therapy, screening and counseling.

For each intervention, they calculated the HIV incidence and prevalence over 10 years. At present rates of screening and treatment, the researchers predict that HIV prevalence will decline from 19 percent to 14 percent of the population in the next 10 years. However, they calculate that their combination approach including male circumcision, vaginal microbicides and oral pre-exposure prophylaxis could further reduce HIV prevalence to 10 percent over that time scale — preventing 1.5 million HIV infection over 10 years — even if screening and antiretroviral therapy are kept at current levels. Increasing antiretroviral therapy use and HIV screening frequency in addition could avert more than 2 million HIV infections over 10 years, or 60 percent of the projected total.

The researchers also determined a hierarchy of effectiveness versus cost for these intervention strategies. Where budgets are limited, they suggest money should be allocated first to increasing male circumcision, then to more frequent HIV screening, use of vaginal microbicides and increasing antiretroviral therapy. Additionally, they calculate that omitting pre-exposure prophylaxis from their combination strategy could offer 90 percent of the benefits of treatment for less than 25 percent of the costs.

The authors conclude: “In the absence of multi-intervention randomized clinical or observational trials, a mathematical HIV epidemic model provides useful insights about the aggregate benefit of implementing a portfolio of biomedical, diagnostic and treatment programs. Allocating limited available resources for HIV control in South Africa is a key priority, and our study indicates that a multi-intervention HIV portfolio could avert nearly two-thirds of projected new HIV infections, and is a cost-effective use of resources.”

Journal Reference:

  1. Long, E.F. and Stavert, R.R. Portfolios of biomedical HIV interventions in South Africa: a cost-effectiveness analysis. Journal of General Internal Medicine, 2013 DOI:10.1007/s11606-013-2417-1

In Big Data, We Hope and Distrust (Huffington Post)

By Robert Hall

Posted: 04/03/2013 6:57 pm

“In God we trust. All others must bring data.” — W. Edwards Deming, statistician, quality guru

Big data helped reelect a pesident, find Osama bin Laden, and contributed to the meltdown of our financial system. We are in the midst of a data revolution where social media introduces new terms like Arab Spring, Facebook Depression and Twitter anxiety that reflect a new reality: Big data is changing the social and relationship fabric of our culture.

We spend hours installing and learning how to use the latest versions of our ever-expanding technology while enduring a never-ending battle to protect our information. Then we labor while developing practices to rid ourselves of technology — rules for turning devices off during meetings or movies, legislation to outlaw texting while driving, restrictions in classrooms to prevent cheating, and scheduling meals or family time where devices are turned off. Information and technology: We love it, hate it, can’t live with it, can’t live without it, use it voraciously, and distrust it immensely. I am schizophrenic and so am I.

Big data is not only big but growing rapidly. According to IBM, we create 2.5 quintillion bytes a day and that “ninety percent of the data in the world has been created in the last two years.” Vast new computing capacity can analyze Web-browsing trails that track our every click, sensor signals from every conceivable device, GPS tracking and social network traffic. It is now possible to measure and monitor people and machines to an astonishing degree. How exciting, how promising. And how scary.

This is not our first data rodeo. The early stages of the customer relationship management movement were filled with hope and with hype. Large data warehouses were going to provide the kind of information that would make companies masters of customer relationships. There were just two problems. First, getting the data out of the warehouse wasn’t nearly as hard as getting it into the person or device interacting with the customers in a way that added value, trust and expanded relationships. We seem to always underestimate the speed of technology and overestimate the speed at which we can absorb it and socialize around it.

Second, unfortunately the customers didn’t get the memo and mostly decided in their own rich wisdom they did not need or want “masters.” In fact as providers became masters of knowing all the details about our lives, consumers became more concerned. So while many organizations were trying to learn more about customer histories, behaviors and future needs — customers and even their governments were busy trying to protect privacy, security, and access. Anyone attempting to help an adult friend or family member with mental health issues has probably run into well-intentioned HIPAA rules (regulations that ensure privacy of medical records) that unfortunately also restrict the ways you can assist them. Big data gives and the fear of big data takes away.

Big data does not big relationships make. Over the last 20 years as our data keeps getting stronger, our customer relationships keep getting weaker. Eighty-six percent of consumers trust corporations less than they did five years ago. Customer retention across industries has fallen about 30 percent in recent years. Is it actually possible that we have unwittingly contributed in the undermining of our customer relationships? How could that be? For one thing, as companies keep getting better at targeting messages to specific groups and those groups keep getting better at blocking their messages. As usual, the power to resist trumps the power to exert.

No matter how powerful big data becomes, if it is to realize its potential, it must build trust on three levels. First, customers must trust our intentions. Data that can be used for us can also be used against us. There is growing fear institutions will become a part of a “surveillance state.” While organizations have gone to great length to promote protection of our data — the numbers reflect a fair amount of doubt. For example, according to MainStreet, “87 percent of Americans do not feel large banks are transparent and 68 percent do not feel their bank is on their side.:

Second, customers must trust our actions. Even if they trust our intentions, they might still fear that our actions put them at risk. Our private information can be hacked, then misused and disclosed in damaging and embarrassing ways. After the Sandy Hook tragedy a New York newspaper published the names and addresses of over 33,000 licensed gun owners along with an interactive map that showed exactly where they lived. In response names and addresses of the newspaper editor and writers were published on-line along with information about their children. No one, including retired judges, law enforcement officers and FBI agents expected their private information to be published in the midst of a very high decibel controversy.

Third, customers must trust the outcome — that sharing data will benefit them. Even with positive intentions and constructive actions, the results may range from disappointing to damaging. Most of us have provided email addresses or other contact data — around a customer service issue or such — and then started receiving email, phone or online solicitations. I know a retired executive who helps hard-to-hire people. She spent one evening surfing the Internet to research about expunging criminal records for released felons. Years later, Amazon greets her with books targeted to the felon it believes she is. Even with opt-out options, we felt used. Or, we provide specific information, only to repeat it in the next transaction or interaction — not getting the hoped for benefit of saving our time.

It will be challenging to grow the trust at anywhere near the rate we grow the data. Information develops rapidly, competence and trust develop slowly. Investing heavily in big data and scrimping on trust will have the opposite effect desired. To quote Dolly Parton who knows a thing or two about big: “It costs a lot of money to look this cheap.”

The Mathematics of Averting the Next Big Network Failure (Wired)

BY NATALIE WOLCHOVER, SIMONS SCIENCE NEWS

03.19.13 - 9:30 AM

Data: Courtesy of Marc Imhoff of NASA GSFC and Christopher Elvidge of NOAA NGDC; Image: Craig Mayhew and Robert Simmon of NASA GSFC

Gene Stanley never walks down stairs without holding the handrail. For a fit 71-year-old, he is deathly afraid of breaking his hip. In the elderly, such breaks can trigger fatal complications, and Stanley, a professor of physics at Boston University, thinks he knows why.

“Everything depends on everything else,” he said.

Original story reprinted with permission from Simons Science News, an editorially independent division of SimonsFoundation.org whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Three years ago, Stanley and his colleagues discovered the mathematics behind what he calls “the extreme fragility of interdependency.” In a system of interconnected networks like the economy, city infrastructure or the human body, their model indicates that a small outage in one network can cascade through the entire system, touching off a sudden, catastrophic failure.

First reported in 2010 in the journal Nature, the finding spawned more than 200 related studies, including analyses of the nationwide blackout in Italy in 2003, the global food-price crisis of 2007 and 2008, and the “flash crash” of the United States stock market on May 6, 2010.

“In isolated networks, a little damage will only lead to a little more,” said Shlomo Havlin, a physicist at Bar-Ilan University in Israel who co-authored the 2010 paper. “Now we know that because of dependency between networks, you can have an abrupt collapse.”

While scientists remain cautious about using the results of simplified mathematical models to re-engineer real-world systems, some recommendations are beginning to emerge. Based on data-driven refinements, new models suggest interconnected networks should have backups, mechanisms for severing their connections in times of crisis, and stricter regulations to forestall widespread failure.

“There’s hopefully some sweet spot where you benefit from all the things that networks of networks bring you without being overwhelmed by risk,” said Raissa D’Souza, a complex systems theorist at the University of California, Davis.

Power, gas, water, telecommunications and transportation networks are often interlinked. When nodes in one network depend on nodes in another, node failures in any of the networks can trigger a system-wide collapse. (Illustration: Leonardo Dueñas-Osorio)

To understand the vulnerability in having nodes in one network depend on nodes in another, consider the “smart grid,” an infrastructure system in which power stations are controlled by a telecommunications network that in turn requires power from the network of stations. In isolation, removing a few nodes from either network would do little harm, because signals could route around the outage and reach most of the remaining nodes. But in coupled networks, downed nodes in one automatically knock out dependent nodes in the other, which knock out other dependent nodes in the first, and so on. Scientists model this cascading process by calculating the size of the largest cluster of connected nodes in each network, where the answer depends on the size of the largest cluster in the other network. With the clusters interrelated in this way, a decrease in the size of one of them sets off a back-and-forth cascade of shrinking clusters.

When damage to a system reaches a “critical point,” Stanley, Havlin and their colleagues find that the failure of one more node drops all the network clusters to zero, instantly killing connectivity throughout the system. This critical point will vary depending on a system’s architecture. In one of the team’s most realistic coupled-network models, an outage of just 8 percent of the nodes in one network — a plausible level of damage in many real systems — brings the system to its critical point. “The fragility that’s implied by this interdependency is very frightening,” Stanley said.

However, in another model recently studied by D’Souza and her colleagues, sparse links between separate networks actually help suppress large-scale cascades, demonstrating that network models are not one-size-fits-all. To assess the behavior of smart grids, financial markets, transportation systems and other real interdependent networks, “we have to start from the data-driven, engineered world and come up with the mathematical models that capture the real systems instead of using models because they are pretty and analytically tractable,” D’Souza said.

In a series of papers in the March issue of Nature Physics, economists and physicists used the science of interconnected networks to pinpoint risk within the financial system. In one study, an interdisciplinary group of researchers including the Nobel Prize-winning economist Joseph Stiglitz found inherent instabilities within the highly complex, multitrillion-dollar derivatives market and suggested regulations that could help stabilize it.

Irena Vodenska, a professor of finance at Boston University who collaborates with Stanley, custom-fit a coupled network model around data from the 2008 financial crisis. Her and her colleagues’ analysis, published in February in Scientific Reports, showed that modeling the financial system as a network of two networks — banks and bank assets, where each bank is linked to the assets it held in 2007 — correctly predicted which banks would fail 78 percent of the time.

“We consider this model as potentially useful for systemic risk stress testing for financial systems,” said Vodenska, whose research is financially supported by the European Union’s Forecasting Financial Crisis program. As globalization further entangles financial networks, she said, regulatory agencies must monitor “sources of contagion” — concentrations in certain assets, for example — before they can cause epidemics of failure. To identify these sources, “it’s imperative to think in the sense of networks of networks,” she said.

Leonardo Dueñas-Osorio, a civil engineer at Rice, visited a damaged high-voltage substation in Chile after a major earthquake in 2010 to gather information about the power grid’s response to the crisis. (Photo: Courtesy of Leonardo Dueñas-Osorio)

Scientists are applying similar thinking to infrastructure assessment. Leonardo Dueñas-Osorio, a civil engineer at Rice University, is analyzing how lifeline systems responded to recent natural disasters. When a magnitude 8.8 earthquake struck Chile in 2010, for example, most of the power grid was restored after just two days, aiding emergency workers. The swift recovery, Dueñas-Osorio’s researchsuggests, occurred because Chile’s power stations immediately decoupled from the centralized telecommunications system that usually controlled the flow of electricity through the grid, but which was down in some areas. Power stations were operated locally until the damage in other parts of the system subsided.

“After an abnormal event, the majority of the detrimental effects occur in the very first cycles of mutual interaction,” said Dueñas-Osorio, who is also studying New York City’s response to Hurricane Sandy last October. “So when something goes wrong, we need to have the ability to decouple networks to prevent the back-and-forth effects between them.”

D’Souza and Dueñas-Osorio are collaborating to build accurate models of infrastructure systems in Houston, Memphis and other American cities in order to identify system weaknesses. “Models are useful for helping us explore alternative configurations that could be more effective,” Dueñas-Osorio explained. And as interdependency between networks naturally increases in many places, “we can model that higher integration and see what happens.”

Scientists are also looking to their models for answers on how to fix systems when they fail. “We are in the process of studying what is the optimal way to recover a network,” Havlin said. “When networks fail, which node do you fix first?”

The hope is that networks of networks might be unexpectedly resilient for the same reason that they are vulnerable. As Dueñas-Osorio put it, “By making strategic improvements, can we have what amounts to positive cascades, where a small improvement propagates much larger benefits?”

These open questions have the attention of governments around the world. In the U.S., the Defense Threat Reduction Agency, an organization tasked with safeguarding national infrastructure against weapons of mass destruction, considers the study of interdependent networks its “top mission priority” in the category of basic research. Some defense applications have emerged already, such as a new design for electrical network systems at military bases. But much of the research aims at sorting through the mathematical subtleties of network interaction.

“We’re not yet at the ‘let’s engineer the internet differently’ level,” said Robin Burk, an information scientist and former DTRA program manager who led the agency’s focus on interdependent networks research. “A fair amount of it is still basic science — desperately needed science.”

Original story reprinted with permission from Simons Science News, an editorially independent division of SimonsFoundation.org whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.

Treating Disease by the Numbers (Science Daily)

Sep. 20, 2012 — Mathematical modeling being tested by researchers at the School of Science at Indiana University-Purdue University Indianapolis (IUPUI) and the IU School of Medicine has the potential to impact the knowledge and treatment of several diseases that continue to challenge scientists across the world.

Mathematical modeling allows researchers to closely mirror patient data, which is helpful in determining the cause and effect of certain risk factors. (Credit: Image courtesy of Indiana University-Purdue University Indianapolis School of Science)

The National Science Foundation recently recognized the work led by Drs. Giovanna Guidoboni, associate professor of mathematics in the School of Science, and Alon Harris, professor of ophthalmology and director of clinical research at the Eugene and Marilyn Glick Eye Institute, for its new approach to understanding what actually causes debilitating diseases like glaucoma. Their research could translate to more efficient treatments for diseases like diabetes and hypertension as well.

Glaucoma is the second-leading cause of blindness in the world, yet the only primary form of treatment is to reduce pressure in the patient’s eye. However, as many as one-third of the glaucoma patients have no elevated eye pressure, and the current inability to better understand what risk factors led to the disease can hinder treatment options.

Mathematical modeling, which creates an abstract model using mathematical language to describe the behavior of a system, allows doctors to better measure things like blood flow and oxygen levels in fine detail in the eye, the easiest human organ to study without invasive procedures. Models also can be used to estimate what cannot be measured directly, such as the pressure in the ocular vessels.

Through simulations, the mathematical model can help doctors determine the cause and effect of reduced blood flow, cell death and ocular pressure and how those risk factors affect one another in the presence of glaucoma. A better understanding of these factors — and the ability to accurately measure their interaction — could greatly improve doctors’ ability to treat the root causes of disease, Harris said.

“This is a unique, fresh approach to research and treatment,” Harris said. “We’re talking about the ability to identify tailor-made treatments for individual patients for diseases that are multi-factorial and where it’s difficult to isolate the path and physicality of the disease.”

Harris and Guidoboni have worked together for the past 18 months on the project. Dr. Julia Arciero, assistant professor of mathematical sciences at IUPUI, is a principle investigator on the project as well with expertise in mathematical modeling of blood flow.

The preliminary findings have been published in the British Journal of Ophthalmology and the research currently is under review in the Journal of Mathematical Biosciences and Engineering and the European Journal of Ophthalmology. The NSF recognized their work on Aug. 30 with a three-year grant to continue their research.

The pair also presented their findings at the 2012 annual meeting of the Association for Research in Vision and Ophthalmology (ARVO). Harris suggested that, out of the 12,000 ARVO participants, their group might have been the only research group to include mathematicians, which speaks highly of the cross-disciplinary collaboration occurring regularly at IUPUI.

“We approached this as a pure math question, where you try to solve a certain problem with the data you have,” said Guidoboni, co-director of the School of Science Institute for Mathematical Modeling and Computational Science (iM2CS) at IUPUI, a research center dedicated to using modeling methods to solve problems in medicine, the environment and computer science.

Guidoboni has expertise in applied mathematics. She also has a background in engineering, which she said helps her to approach medical research from a tactical standpoint where the data and feedback determine the model. She previously used modeling to better understand blood flow from the heart.

Harris said the potential impact has created quite a stir in the ocular research community.

“The response among our peers has been unheard of. The scientific community has been accepting of this new method and they are embracing it,” Harris added.

The group will seek additional research funding through the National Institute of Health, The Glaucoma Foundation and other medical entities that might benefit from the research. The initial success of their collaboration should lead to more cross-disciplinary projects in the future, Guidoboni said.

Also contributing are graduate students in mathematics, Lucia Carichino and Simone Cassani, and researchers in the department of ophthalmology, including Drs. Brent Siesky, Annahita Amireskandari and Leslie Tobe.

Ao menos 70% das espécies da Terra são desconhecidas (Fapesp)

Dando início ao Ciclo de Conferências 2013 do BIOTA-FAPESP Educação, Thomas Lewinsohn (Unicamp) falou sobre o tempo e o custo estimado para descrever todas as espécies do planeta (foto:Léo Ramos)

25/02/2013

Por Karina Toledo

Agência FAPESP – Embora o conhecimento sobre a biodiversidade do planeta ainda esteja muito fragmentado, estima-se que já tenham sido descritos aproximadamente 1,75 milhão de espécies diferentes de seres vivos – incluindo microrganismos, plantas e animais. O número pode impressionar os mais desavisados, mas representa, nas hipóteses mais otimistas, apenas 30% das formas de vida existentes na Terra.

“Estima-se que existam outros 12 milhões de espécies ainda por serem descobertas”, disse Thomas Lewinsohn, professor do Departamento de Biologia Animal da Universidade Estadual de Campinas (Unicamp), durante a apresentação que deu início ao Ciclo de Conferências 2013 organizado pelo programa BIOTA-FAPESP com o intuito de contribuir para o aperfeiçoamento do ensino de ciência.

Mas como avaliar o tamanho do desconhecimento sobre a biodiversidade? “Para isso, fazemos extrapolações, tomando como base os grupos de organismos mais bem estudados para avaliar os menos estudados. Regiões ou países em que a biota é bem conhecida para avaliar onde é menos conhecida. Por regra de três chegamos a essas estimativas”, explicou.

Técnicas mais recentes, segundo Lewinsohn, usam fórmulas estatísticas sofisticadas e se baseiam nas taxas de descobertas e de descrição de novas espécies. Os valores são ajustados de acordo com a força de trabalho existente, ou seja, o número de taxonomistas em atividade.

“No entanto, o mais importante a dizer é: não há consenso. As estimativas podem chegar a mais de 100 milhões de espécies desconhecidas. Não sabemos nem a ordem de grandeza e isso é espantoso”, disse.

Lewinsohn avalia que, para descrever todas as espécies que se estima haver no Brasil, seriam necessários cerca de 2 mil anos. “Para descrever todas as espécies do mundo o número seria parecido. Mas não temos esse tempo”, disse.

Algumas técnicas recentes de taxonomia molecular, como código de barras de DNA, podem ajudar a acelerar o trabalho, pois permitem identificar organismos por meio da análise de seu material genético. Por esse método, cadeias diferentes de DNA diferenciam as espécies, enquanto na taxonomia clássica a classificação é baseada na morfologia dos seres vivos, o que é bem mais trabalhoso.

“Dá para fazer? Sim, mas qual é o custo?”, questionou Lewinsohn. Um artigo publicado recentemente na revista Science apontou que seriam necessários de US$ 500 milhões a US$ 1 bilhão por ano, durante 50 anos, para descrever a maioria das espécies do planeta.

Novamente, o número pode assustar os desavisados, mas, de acordo com Lewinsohn, o montante corresponde ao que se gasta no mundo com armamento em apenas cinco dias. “Somente em 2011 foram gastos US$ 1,7 trilhão com a compra de armas. É preciso colocar as coisas em perspectiva”, defendeu.

Definindo prioridades

Muitas dessas espécies desconhecidas, porém, podem desaparecer do planeta antes mesmo que o homem tenha tempo e dinheiro suficiente para estudá-las. Segundo dados apresentados por Jean Paul Metzger, professor do Instituto de Biociências da Universidade de São Paulo (USP), mais de 50% da superfície terrestre já foi transformada pelo homem.

Essa alteração na paisagem tem muitas consequências e Metzger abordou duas delas na segunda apresentação do dia: a perda de habitat e a fragmentação.

“São conceitos diferentes, que muitas vezes se confundem. Fragmentação é a subdivisão de um habitat e pode não ocorrer quando o processo de degradação ocorre nas bordas da mata. Já a construção de uma estrada, por exemplo, cria fragmentos isolados dentro do habitat”, explicou.

Para Metzger, a fragmentação é a principal ameaça à biodiversidade, pois altera o equilíbrio entre os processos naturais de extinção de espécies e de colonização. Quanto menor e mais isolado é o fragmento, maior é a taxa de extinção e menor é a de colonização.

“Cada espécie tem uma quantidade mínima de habitat que precisa para sobreviver e se reproduzir. Não conhecemos bem esses limiares de extinção”, alertou.

Metzger acredita que esse limiar pode variar de acordo com a configuração da paisagem, ou seja, quanto mais fragmentado estiver o habitat, maior o risco de extinção de espécies. Como exemplo, ele citou as áreas remanescentes de Mata Atlântica do Estado de São Paulo, onde 95% dos fragmentos têm menos de 100 hectares.

“Estima-se que ao perder 90% do habitat, deveríamos perder 50% das espécies endêmicas. Na Mata Atlântica, há cerca de 16% de floresta remanescente. O esperado seria uma extinção em massa, mas nosso registro tem poucos casos. Ou nossa teoria está errada, ou não estamos detectando as extinções, pois as espécies nem sequer eram conhecidas”, afirmou Metzger.

Há, no entanto, um fator complicador: o período de latência entre a mudança na estrutura paisagem e mudança na estrutura da comunidade. Enquanto as espécies com ciclo curto de vida podem desaparecer rapidamente, aquelas com ciclo de vida longo podem responder à perda de habitat em escala centenária.

“Cria-se um débito de extinção e, mesmo que a alteração na paisagem seja interrompida, algumas espécies ficam fadadas a desaparecer com o tempo”, disse Metzger.

Mas a boa notícia é que as paisagens também se regeneram naturalmente e além do débito de extinção existe o crédito de recuperação. O período de latência representa, portanto, uma oportunidade de conservação.

“Hoje, temos evidências de que não adianta restaurar em qualquer lugar. É preciso definir áreas prioritárias para restauração que otimizem a conectividade e facilitem o fluxo biológico entre os fragmentos”, defendeu Metzger.

Colhendo frutos

Ao longo dos 13 anos de existência do BIOTA-FAPESP, a definição de áreas prioritárias de conservação e de recuperação no Estado de São Paulo foi uma das principais preocupações dos pesquisadores.

Os resultados desses estudos foram usados pela Secretaria Estadual do Meio Ambiente para embasar políticas públicas, como lembrou o coordenador do programa e professor do Instituto de Biologia da Unicamp, Carlos Alfredo Joly, na terceira e última apresentação do dia.

“Atualmente, pelo menos 20 instrumentos legais, entre leis, decretos e resoluções, citam nominalmente os resultados do BIOTA-FAPESP”, disse Joly.

Entre 1999 e 2009, disse o coordenador, houve um investimento anual de R$ 8 milhões no programa. Isso ajudou a financiar 94 projetos de pesquisa e resultou em mais de 700 artigos publicados em 181 periódicos, entre eles Nature e Science.

A equipe do programa também publicou 16 livros e dois atlas, descreveu mais de 2 mil novas espécies, produziu e armazenou informações sobre 12 mil espécies, disponibilizou e conectou digitalmente 35 coleções biológicas paulistas.

“Desde que foi renovado o apoio da FAPESP ao programa, em 2009, a questão da educação se tornou prioridade em nosso plano estratégico. O objetivo deste ciclo de conferências é justamente ampliar a comunicação com públicos além do meio científico, especialmente professores e estudantes”, disse Joly.

A segunda etapa do ciclo de palestras está marcada para 21 de março e terá como tema o “Bioma Pampa”. No dia 18 de abril, será a vez do “Bioma Pantanal”. Em 16 de maio, o tema será “Bioma Cerrado”. Em 20 de junho, será abordado o “Bioma Caatinga”.

Em 22 de agosto, será o “Bioma Mata Atlântica”. Em 19 de setembro, é a vez do “Bioma Amazônia”. Em 24 de outubro, o tema será “Ambientes Marinhos e Costeiros”. Finalizando o ciclo, em 21 de novembro, o tema será “Biodiversidade em Ambientes Antrópicos – Urbanos e Rurais”.

Programação do ciclo: www.fapesp.br/7487

Flap Over Study Linking Poverty to Biology Exposes Gulfs Among Disciplines (Chronicle of Higher Education)

February 1, 2013

Flap Over Study Linking Poverty to Biology Exposes Gulfs Among Disciplines 1

 Photo: iStock.

A study by two economists that used genetic diversity as a proxy for ethnic and cultural diversity has drawn fierce rebuttals from anthropologists and geneticists.

By Paul Voosen

Oded Galor and Quamrul Ashraf once thought their research into the causes of societal wealth would be seen as a celebration of diversity. However it has been described, though, it has certainly not been celebrated. Instead, it has sparked a dispute among scholars in several disciplines, many of whom are dubious of any work linking societal behavior to genetics. In the latest installment of the debate, 18 Harvard University scientists have called their work “seriously flawed on both factual and methodological grounds.”

Mr. Galor and Mr. Ashraf, economists at Brown University and Williams College, respectively, have long been fascinated by the historical roots of poverty. Six years ago, they began to wonder if a society’s diversity, in any way, could explain its wealth. They probed tracts of interdisciplinary data and decided they could use records of genetic diversity as a proxy for ethnic and cultural diversity. And after doing so, they found that, yes, a bit of genetic diversity did seem to help a society’s economic growth.

Since last fall, when the pair’s work began to filter out into the broader scientific world, their study has exposed deep rifts in how economists, anthropologists, and geneticists talk—and think. It has provoked calls for caution in how economists use genetic data, and calls of persecution in response. And all of this happened before the study was finally published, in the American Economic Review this month.

“Through this analysis, we’re getting a better understanding of how the world operates in order to alleviate poverty,” Mr. Ashraf said. Any other characterization, he added, is a “gross misunderstanding.”

‘Ethical Quagmires’

A barrage of criticism has been aimed at the study since last fall by a team of anthropologists and geneticists at Harvard. The critique began with a short, stern letter, followed by a rejoinder from the economists; now an expanded version of the Harvard critique will appear in February inCurrent Anthropology.

Fundamentally, the dispute comes down to issues of data selection and statistical power. The paper is a case of “garbage in, garbage out,” the Harvard group says. The indicators of genetic diversity that the economists use stem from only four or five independent points. All the regression analysis in the world can’t change that, said Nick Patterson, a computational biologist at Harvard and MIT’s Broad Institute.

“The data just won’t stand for what you’re claiming,” Mr. Patterson said. “Technical statistical analysis can only do so much for you. … I will bet you that they can’t find a single geneticist in the world who will tell them what they did was right.”

In some respects, the study has become an exemplar for how the nascent field of “genoeconomics,” a discipline that seeks to twin the power of gene sequencing and economics, can go awry. Connections between behavior and genetics rightly need to clear high bars of evidence, said Daniel Benjamin, an economist at Cornell University and a leader in the field who has frequently called for improved rigor.

“It’s an area that’s fraught with an unfortunate history and ethical quagmires,” he said. Mr. Galor and Mr. Ashraf had a creative idea, he added, even if all their analysis doesn’t pass muster.

“I’d like to see more data before I’m convinced that their [theory] is true,” said Mr. Benjamin, who was not affiliated with the study or the critique. The Harvard critics make all sorts of complaints, many of which are valid, he said. “But fundamentally the issue is that there’s just not that much independent data.”

Claims of ‘Outsiders’

The dispute also exposes issues inside anthropology, added Carl Lipo, an anthropologist at California State University at Long Beach who is known for his study of Easter Island. “Anthropologists have long tried to walk the line whereby we argue that there are biological origins to much of what makes us human, without putting much weight that any particular attribute has its origins in genetics [or] biology,” he said.

The debate often erupts in lower-profile ways and ends with a flurry of anthropologists’ putting down claims by “outsiders,” Mr. Lipo said. (Mr. Ashraf and Mr. Galor are “out on a limb” with their conclusions, he added.) The angry reaction speaks to the limits of anthropology, which has been unable to delineate how genetics reaches up through the idiosyncratic circumstances of culture and history to influence human behavior, he said.

Certainly, that reaction has been painful for the newest pair of outsiders.

Mr. Galor is well known for studying the connections between history and economic development. And like much scientific work, his recent research began in reaction to claims made by Jared Diamond, the famed geographer at the University of California at Los Angeles, that the development of agriculture gave some societies a head start. What other factors could help explain that distribution of wealth? Mr. Galor wondered.

Since records of ethnic or cultural diversity do not exist for the distant past, they chose to use genetic diversity as a proxy. (There is little evidence that it can, or can’t, serve as such a proxy, however.) Teasing out the connection to economics was difficult—diversity could follow growth, or vice versa—but they gave it a shot, Mr. Galor said.

“We had to find some root causes of the [economic] diversity we see across the globe,” he said.

They were acquainted with the “Out of Africa” hypothesis, which explains how modern human beings migrated from Africa in several waves to Asia and, eventually, the Americas. Due to simple genetic laws, those serial waves meant that people in Africa have a higher genetic diversity than those in the Americas. It’s an idea that found support in genetic sequencing of native populations, if only at the continental scale.

Combining the genetics with population-density estimates—data the Harvard group says are outdated—along with deep statistical analysis, the economists found that the low and high diversity found among Native Americans and Africans, respectively, was detrimental to development. Meanwhile, they found a sweet spot of diversity in Europe and Asia. And they stated the link in sometimes strong, causal language, prompting another bitter discussion with the Harvard group over correlation and causation.

An ‘Artifact’ of the Data?

The list of flaws found by the Harvard group is long, but it boils down to the fact that no one has ever made a solid connection between genes and poverty before, even if genetics are used only as a proxy, said Jade d’Alpoim Guedes, a graduate student in anthropology at Harvard and the critique’s lead author.

“If my research comes up with findings that change everything we know,” Ms. d’Alpoim Guedes said, “I’d really check all of my input sources. … Can I honestly say that this pattern that I see is true and not an artifact of the input data?”

Mr. Ashraf and Mr. Galor found the response to their study, which they had previewed many times over the years to other economists, to be puzzling and emotionally charged. Their critics refused to engage, they said. They would have loved to present their work to a lecture hall full of anthropologists at Harvard. (Mr. Ashraf, who’s married to an anthropologist, is a visiting scholar this year at Harvard’s Kennedy School.) Their gestures were spurned, they said.

“We really felt like it was an inquisition,” Mr. Galor said. “The tone and level of these arguments were really so unscientific.”

Mr. Patterson, the computational biologist, doesn’t quite agree. The conflict has many roots but derives in large part from differing standards for publication. Submit the same paper to a leading genetics journal, he said, and it would not have even reached review.

“They’d laugh at you,” Mr. Patterson said. “This doesn’t even remotely meet the cut.”

In the end, it’s unfortunate the economists chose genetic diversity as their proxy for ethnic diversity, added Mr. Benjamin, the Cornell economist. They’re trying to get at an interesting point. “The genetics is really secondary, and not really that important,” he said. “It’s just something that they’re using as a measure of the amount of ethnic diversity.”

Mr. Benjamin also wishes they had used more care in their language and presentation.

“It’s not enough to be careful in the way we use genetic data,” he said. “We need to bend over backwards being careful in the way we talk about what the data means; how we interpret findings that relate to genetic data; and how we communicate those findings to readers and the public.”

Mr. Ashraf and Mr. Galor have not decided whether to respond to the Harvard critique. They say they can, point by point, but that ultimately, the American Economic Review’s decision to publish the paper as its lead study validates their work. They want to push forward on their research. They’ve just released a draft study that probes deeper into the connections between genetic diversity and cultural fragmentation, Mr. Ashraf said.

“There is much more to learn from this data,” he said. “It is certainly not the final word.”

New Research Shows Complexity of Global Warming (Science Daily)

Jan. 30, 2013 — Global warming from greenhouse gases affects rainfall patterns in the world differently than that from solar heating, according to a study by an international team of scientists in the January 31 issue of Nature. Using computer model simulations, the scientists, led by Jian Liu (Chinese Academy of Sciences) and Bin Wang (International Pacific Research Center, University of Hawaii at Manoa), showed that global rainfall has increased less over the present-day warming period than during the Medieval Warm Period, even though temperatures are higher today than they were then.

Clouds over the Pacific Ocean. (Credit: Shang-Ping Xie)

The team examined global precipitation changes over the last millennium and future projection to the end of 21st century, comparing natural changes from solar heating and volcanism with changes from human-made greenhouse gas emissions. Using an atmosphere-ocean coupled climate model that simulates realistically both past and present-day climate conditions, the scientists found that for every degree rise in global temperature, the global rainfall rate since the Industrial Revolution has increased less by about 40% than during past warming phases of Earth.

Why does warming from solar heating and from greenhouse gases have such different effects on global precipitation?

“Our climate model simulations show that this difference results from different sea surface temperature patterns. When warming is due to increased greenhouse gases, the gradient of sea surface temperature (SST) across the tropical Pacific weakens, but when it is due to increased solar radiation, the gradient increases. For the same average global surface temperature increase, the weaker SST gradient produces less rainfall, especially over tropical land,” says co-author Bin Wang, professor of meteorology.

But why does warming from greenhouse gases and from solar heating affect the tropical Pacific SST gradient differently?

“Adding long-wave absorbers, that is heat-trapping greenhouse gases, to the atmosphere decreases the usual temperature difference between the surface and the top of the atmosphere, making the atmosphere more stable,” explains lead-author Jian Liu. “The increased atmospheric stability weakens the trade winds, resulting in stronger warming in the eastern than the western Pacific, thus reducing the usual SST gradient — a situation similar to El Niño.”

Solar radiation, on the other hand, heats Earth’s surface, increasing the usual temperature difference between the surface and the top of the atmosphere without weakening the trade winds. The result is that heating warms the western Pacific, while the eastern Pacific remains cool from the usual ocean upwelling.

“While during past global warming from solar heating the steeper tropical east-west SST pattern has won out, we suggest that with future warming from greenhouse gases, the weaker gradient and smaller increase in yearly rainfall rate will win out,” concludes Wang.

Journal Reference:

  1. Jian Liu, Bin Wang, Mark A. Cane, So-Young Yim, June-Yi Lee. Divergent global precipitation changes induced by natural versus anthropogenic forcing. Nature, 2013; 493 (7434): 656 DOI: 10.1038/nature11784

Understanding the Historical Probability of Drought (Science Daily)

Jan. 30, 2013 — Droughts can severely limit crop growth, causing yearly losses of around $8 billion in the United States. But it may be possible to minimize those losses if farmers can synchronize the growth of crops with periods of time when drought is less likely to occur. Researchers from Oklahoma State University are working to create a reliable “calendar” of seasonal drought patterns that could help farmers optimize crop production by avoiding days prone to drought.

Historical probabilities of drought, which can point to days on which crop water stress is likely, are often calculated using atmospheric data such as rainfall and temperatures. However, those measurements do not consider the soil properties of individual fields or sites.

“Atmospheric variables do not take into account soil moisture,” explains Tyson Ochsner, lead author of the study. “And soil moisture can provide an important buffer against short-term precipitation deficits.”

In an attempt to more accurately assess drought probabilities, Ochsner and co-authors, Guilherme Torres and Romulo Lollato, used 15 years of soil moisture measurements from eight locations across Oklahoma to calculate soil water deficits and determine the days on which dry conditions would be likely. Results of the study, which began as a student-led class research project, were published online Jan. 29 inAgronomy Journal. The researchers found that soil water deficits more successfully identified periods during which plants were likely to be water stressed than did traditional atmospheric measurements when used as proposed by previous research.

Soil water deficit is defined in the study as the difference between the capacity of the soil to hold water and the actual water content calculated from long-term soil moisture measurements. Researchers then compared that soil water deficit to a threshold at which plants would experience water stress and, therefore, drought conditions. The threshold was determined for each study site since available water, a factor used to calculate threshold, is affected by specific soil characteristics.

“The soil water contents differ across sites and depths depending on the sand, silt, and clay contents,” says Ochsner. “Readily available water is a site- and depth-specific parameter.”

Upon calculating soil water deficits and stress thresholds for the study sites, the research team compared their assessment of drought probability to assessments made using atmospheric data. They found that a previously developed method using atmospheric data often underestimated drought conditions, while soil water deficits measurements more accurately and consistently assessed drought probabilities. Therefore, the researchers suggest that soil water data be used whenever it is available to create a picture of the days on which drought conditions are likely.

If soil measurements are not available, however, the researchers recommend that the calculations used for atmospheric assessments be reconfigured to be more accurate. The authors made two such changes in their study. First, they decreased the threshold at which plants were deemed stressed, thus allowing a smaller deficit to be considered a drought condition. They also increased the number of days over which atmospheric deficits were summed. Those two changes provided estimates that better agreed with soil water deficit probabilities.

Further research is needed, says Ochsner, to optimize atmospheric calculations and provide accurate estimations for those without soil water data. “We are in a time of rapid increase in the availability of soil moisture data, but many users will still have to rely on the atmospheric water deficit method for locations where soil moisture data are insufficient.”

Regardless of the method used, Ochsner and his team hope that their research will help farmers better plan the cultivation of their crops and avoid costly losses to drought conditions.

Journal Reference:

  1. Guilherme M. Torres, Romulo P. Lollato, Tyson E. Ochsner.Comparison of Drought Probability Assessments Based on Atmospheric Water Deficit and Soil Water Deficit.Agronomy Journal, 2013; DOI: 10.2134/agronj2012.0295

The Storm That Never Was: Why Meteorologists Are Often Wrong (Science Daily)

Jan. 24, 2013 — Have you ever woken up to a sunny forecast only to get soaked on your way to the office? On days like that it’s easy to blame the weatherman.

BYU engineering professor Julie Crockett studies waves in the ocean and the atmosphere. (Credit: Image courtesy of Brigham Young University)

But BYU mechanical engineering professor Julie Crockett doesn’t get mad at meteorologists. She understands something that very few people know: it’s not the weatherman’s fault he’s wrong so often.

According to Crockett, forecasters make mistakes because the models they use for predicting weather can’t accurately track highly influential elements called internal waves.

Atmospheric internal waves are waves that propagate between layers of low-density and high-density air. Although hard to describe, almost everyone has seen or felt these waves. Cloud patterns made up of repeating lines are the result of internal waves, and airplane turbulence happens when internal waves run into each other and break.

“Internal waves are difficult to capture and quantify as they propagate, deposit energy and move energy around,” Crockett said. “When forecasters don’t account for them on a small scale, then the large scale picture becomes a little bit off, and sometimes being just a bit off is enough to be completely wrong about the weather.”

One such example may have happened in 2011, when Utah meteorologists predicted an enormous winter storm prior to Thanksgiving. Schools across the state cancelled classes and sent people home early to avoid the storm. Though it’s impossible to say for sure, internal waves may have been driving stronger circulations, breaking up the storm and causing it to never materialize.

“When internal waves deposit their energy it can force the wind faster or slow the wind down such that it can enhance large scale weather patterns or extreme kinds of events,” Crockett said. “We are trying to get a better feel for where that wave energy is going.”

Internal waves also exist in oceans between layers of low-density and high-density water. These waves, often visible from space, affect the general circulation of the ocean and phenomena like the Gulf Stream and Jet Stream.

Both oceanic and atmospheric internal waves carry a significant amount of energy that can alter climates.

Crockett’s latest wave research, which appears in a recent issue of the International Journal of Geophysics, details how the relationship between large-scale and small-scale internal waves influences the altitude where wave energy is ultimately deposited.

To track wave energy, Crockett and her students generate waves in a tank in her lab and study every aspect of their behavior. She and her colleagues are trying to pinpoint exactly how climate changes affect waves and how those waves then affect weather.

Based on this, Crockett can then develop a better linear wave model with both 3D and 2D modeling that will allow forecasters to improve their weather forecasting.

“Understanding how waves move energy around is very important to large scale climate events,” Crockett said. “Our research is very important to this problem, but it hasn’t solved it completely.”

Journal Reference:

  1. B. Casaday, J. Crockett. Investigation of High-Frequency Internal Wave Interactions with an Enveloped Inertia Wave. International Journal of Geophysics, 2012; 2012: 1 DOI: 10.1155/2012/863792

Physicist Happens Upon Rain Data Breakthrough (Science Daily)

John Lane looks over data recorded from his laser system as he refines his process and formula to calibrate measurements of raindrops. (Credit: NASA/Jim Grossmann)

Dec. 3, 2012 — A physicist and researcher who set out to develop a formula to protect Apollo sites on the moon from rocket exhaust may have happened upon a way to improve weather forecasting on Earth.

Working in his backyard during rain showers and storms, John Lane, a physicist at NASA’s Kennedy Space Center in Florida, found that the laser and reflector he was developing to track lunar dust also could determine accurately the size of raindrops, something weather radar and other meteorological systems estimate, but don’t measure.

The special quantity measured by the laser system is called the “second moment of the size distribution,” which results in the average cross-section area of raindrops passing through the laser beam.

“It’s not often that you’re studying lunar dust and it ends up producing benefits in weather forecasting,” said Phil Metzger, a physicist who leads the Granular Mechanics and Regolith Operations Lab, part of the Surface Systems Office at Kennedy.

Lane said the additional piece of information would be useful in filling out the complex computer calculations used to determine the current conditions and forecast the weather.

“We may be able to refine (computer weather) models to make them more accurate,” Lane said. “Weather radar data analysis makes assumptions about raindrop size, so I think this could improve the overall drop size distribution estimates.”

The breakthrough came because Metzger and Lane were looking for a way to calibrate a laser sensor to pick up the fine particles of blowing lunar dust and soil. It turns out that rain is a good stand-in for flying lunar soil.

“I was pretty skeptical in the beginning that the numbers would come out anywhere close,” Lane said. “Anytime you do something new, it’s a risk that you’re just wasting your time.”

The genesis of the research was the need to find out how much damage would be done by robotic landers getting too close to the six places on the moon where Apollo astronauts landed, lived and worked.

NASA fears that dust and soil particles thrown up by the rocket exhaust of a lander will scour and perhaps puncture the metal skin of the lunar module descent stages and experiment hardware left behind by the astronauts from 1969 to 1972.

“It’s like sandblasting, if you have something coming down like a rocket engine, and it lifts up this dust, there’s not air, so it just keeps going fast,” Lane said. “Some of the stuff can actually reach escape velocity and go into orbit.”

Such impacts to those materials could ruin their scientific value to researchers on Earth who want to know what happens to human-made materials left on another world for more than 40 years.

“The Apollo sites have value scientifically and from an engineering perspective because they are a record of how these materials on the moon have interacted with the solar system over 40 years,” Metzger said. “They are witness plates to the environment.”

There also are numerous bags of waste from the astronauts laying up there that biologists want to examine simply to see if living organisms can survive on the moon for almost five decades where there is no air and there is a constant bombardment of cosmic radiation.

“If anybody goes back and sprays stuff on the bags or touches the bags, they ruin the experiment,” Metzger said. “It’s not just the scientific and engineering value. They believe the Apollo sites are the most important archaeological sites in the human sphere, more important than the pyramids because it’s the first place humans stepped off the planet. And from a national point of view, these are symbols of our country and we don’t want them to be damaged by wanton ransacking.”

Current thinking anticipates placing a laser sensor on the bottom of one of the landers taking part in the Google X-Prize competition. The sensor should be able to pick up the blowing dust and soil and give researchers a clear set of results so they can formulate restrictions for other landers, such as how far away from the Apollo sites new landers can touch down.

As research continues into the laser sensor, Lane expects the work to continue on the weather forecasting side of the equation, too. Lane already presented some of his findings at a meteorological conference and is working on a research paper to detail the work. “This is one of those topics that span a lot of areas of science,” Lane said.

When data prediction is a game, the experts lose out (New Scientist)

Specialist Knowledge Is Useless and Unhelpful

By |Posted Saturday, Dec. 8, 2012, at 7:45 AM ET

 Airplanes at an airport.Airplanes at an airport. iStockphoto/Thinkstock.

Jeremy Howard founded email company FastMail and the Optimal Decisions Group, which helps insurance companies set premiums. He is now president and chief scientist of Kaggle, which has turned data prediction into sport.

Peter Aldhous: Kaggle has been described as “an online marketplace for brains.” Tell me about it.
Jeremy Howard: It’s a website that hosts competitions for data prediction. We’ve run a whole bunch of amazing competitions. One asked competitors to develop algorithms to mark students’ essays. One that finished recently challenged competitors to develop a gesture-learning system for the Microsoft Kinect. The idea was to show the controller a gesture just once, and the algorithm would recognize it in future. Another competition predicted the biological properties of small molecules being screened as potential drugs.

PA: How exactly do these competitions work?
JH: They rely on techniques like data mining and machine learning to predict future trends from current data. Companies, governments, and researchers present data sets and problems, and offer prize money for the best solutions. Anyone can enter: We have nearly 64,000 registered users. We’ve discovered that creative-data scientists can solve problems in every field better than experts in those fields can.

PA: These competitions deal with very specialized subjects. Do experts enter?
JH: Oh yes. Every time a new competition comes out, the experts say: “We’ve built a whole industry around this. We know the answers.” And after a couple of weeks, they get blown out of the water.

PA: So who does well in the competitions?
JH: People who can just see what the data is actually telling them without being distracted by industry assumptions or specialist knowledge. Jason Tigg, who runs a pretty big hedge fund in London, has done well again and again. So has Xavier Conort, who runs a predictive analytics consultancy in Singapore.

PA: You were once on the leader board yourself. How did you get involved?
JH: It was a long and strange path. I majored in philosophy in Australia, worked in management consultancy for eight years, and then in 1999 I founded two start-ups—one an email company, the other helping insurers optimize risks and profits. By 2010, I had sold them both. I started learning Chinese and building amplifiers and speakers because I hadn’t made anything with my hands. I travelled. But it wasn’t intellectually challenging enough. Then, at a meeting of statistics users in Melbourne, somebody told me about Kaggle. I thought: “That looks intimidating and really interesting.”

PA: How did your first competition go?
JH: Setting my expectations low, my goal was to not come last. But I actually won it. It was on forecasting tourist arrivals and departures at different destinations. By the time I went to the next statistics meeting I had won two out of the three competitions I entered. Anthony Goldbloom, the founder of Kaggle, was there. He said: “You’re not Jeremy Howard, are you? We’ve never had anybody win two out of three competitions before.”

PA: How did you become Kaggle’s chief scientist?
JH: I offered to become an angel investor. But I just couldn’t keep my hands off the business. I told Anthony that the site was running slowly and rewrote all the code from scratch. Then Anthony and I spent three months in America last year, trying to raise money. That was where things got really serious, because we raised $11 million. I had to move to San Francisco and commit to doing this full-time.

PA: Do you still compete?
JH: I am allowed to compete, but I can’t win prizes. In practice, I’ve been too busy.

PA: What explains Kaggle’s success in solving problems in predictive analytics?
JH: The competitive aspect is important. The more people who take part in these competitions, the better they get at predictive modeling. There is no other place in the world I’m aware of, outside professional sport, where you get such raw, harsh, unfettered feedback about how well you’re doing. It’s clear what’s working and what’s not. It’s a kind of evolutionary process, accelerating the survival of the fittest, and we’re watching it happen right in front of us. More and more, our top competitors are also teaming up with each other.

PA: Which statistical methods work best?
JH: One that crops up again and again is called the random forest. This takes multiple small random samples of the data and makes a “decision tree” for each one, which branches according to the questions asked about the data. Each tree, by itself, has little predictive power. But take an “average” of all of them and you end up with a powerful model. It’s a totally black-box, brainless approach. You don’t have to think—it just works.

PA: What separates the winners from the also-rans?
JH: The difference between the good participants and the bad is the information they feed to the algorithms. You have to decide what to abstract from the data. Winners of Kaggle competitions tend to be curious and creative people. They come up with a dozen totally new ways to think about the problem. The nice thing about algorithms like the random forest is that you can chuck as many crazy ideas at them as you like, and the algorithms figure out which ones work.

PA: That sounds very different from the traditional approach to building predictive models. How have experts reacted?
JH: The messages are uncomfortable for a lot of people. It’s controversial because we’re telling them: “Your decades of specialist knowledge are not only useless, they’re actually unhelpful; your sophisticated techniques are worse than generic methods.” It’s difficult for people who are used to that old type of science. They spend so much time discussing whether an idea makes sense. They check the visualizations and noodle over it. That is all actively unhelpful.

PA: Is there any role for expert knowledge?
JH: Some kinds of experts are required early on, for when you’re trying to work out what problem you’re trying to solve. The expertise you need is strategy expertise in answering these questions.

PA: Can you see any downsides to the data-driven, black-box approach that dominates on Kaggle?
JH: Some people take the view that you don’t end up with a richer understanding of the problem. But that’s just not true: The algorithms tell you what’s important and what’s not. You might ask why those things are important, but I think that’s less interesting. You end up with a predictive model that works. There’s not too much to argue about there.

Mathematical Counseling for All Who Wonder Why Their Relationship Is Like a Sinus Wave (Science Daily)

ScienceDaily (Nov. 15, 2012) — Neuroinformaticians from Radboud University Nijmegen provide a mathematical model for efficient communication in relationships. Love affair dynamics can look like a sinus wave: a smooth repetitive oscillation of highs and lows. For some couples these waves grow out of control, leading to breakup, while for others they smooth into a state of peace and quietness. Natalia Bielczyk and her colleagues show that the ‘relationship-sinus’ depends on the time partners take to form their emotional reactions towards each other.

The publication in Applied Mathematics and Computation is now available online.

An example of a modeled relationship, in this case between Romeo (solid lines) and Juliet (dashed lines). The tau (τ) above the individual figures indicates the delay in reactivity. Delays that are too short (<0,83) cause instability, just like delays that are too long (>2,364). Delays in the range of 0,83-2,364 cause stability in Romeo and Juliet’s relationship. (Credit: Image courtesy of Radboud University Nijmegen)

In 1988, Steven Strogatz was the first to describe romantic relationships with mathematical dynamical systems. He constructed a two-dimensional model describing two hypothetical partners that interact emotionally. He used a well known example: the changes of Romeo’s and Juliet’s love (and hate) over time. His model became famous and inspired others to analyze (fictional) relationship case studies like Jack and Rose in the Titanic movie. However, the Strogatz model does not include delays in the partner’s responses to one another. Therefore it is only a good start for fruitful studies on human emotions and relationships.

That is why Natalia Bielczyk adjusted Strogatz to a more life-like model by considering the time necessary for processing and forming the complex emotions in relationships. The reactivity in the relationship model is based on four parameters: both partners have a personal history (their ‘past’), and a certain reactivity to their partner and his/her history. Depending on these parameters, different classes of relationships can be found: some seem doomed to break regardless of the partners promptness to one another while others are solid enough to always be stable. In the calculated models, stability occurs when both partners reach a stable level of satisfaction and the sinus wave disappears. The paper concludes that for a broad class of relationships, delays in reactivity can bring stability to couples that are originally unstable.

These results are pretty intuitive: too prompt or too delayed responses evoke trouble. Below a certain value, delays caused instability and above this value they caused stability, showing that some minimum level of sloth can be beneficial for a relationship. The fact that too fast emotional reactivity can lead to destabilization, shows that reflecting each other’s moods is not enough for a stable relationship: a certain time range is necessary for compound emotions to form. Summarized, the publication offers mathematical justification for intuitive phenomena in social psychology. Working on good communication, studying each other’s emotions and working out the right timing can improve your relationship, even without trying to change your partners traits (which is harder and takes more time).

Journal Reference:

  1. Natalia Bielczyk, Marek Bodnar, Urszula Foryś. Delay can stabilize: Love affairs dynamics. Applied Mathematics and Computation, 2012; DOI: 10.1016/j.amc.2012.10.028

Nate Silver’s ‘Signal and the Noise’ Examines Predictions (N.Y.Times)

Mining Truth From Data Babel

By LEONARD MLODINOW

Published: October 23, 2012

A friend who was a pioneer in the computer games business used to marvel at how her company handled its projections of costs and revenue. “We performed exhaustive calculations, analyses and revisions,” she would tell me. “And we somehow always ended with numbers that justified our hiring the people and producing the games we had wanted to all along.” Those forecasts rarely proved accurate, but as long as the games were reasonably profitable, she said, you’d keep your job and get to create more unfounded projections for the next endeavor.

Alessandra Montalto/The New York Times

THE SIGNAL AND THE NOISE

Why So Many Predictions Fail — but Some Don’t

By Nate Silver

Illustrated. 534 pages. The Penguin Press. $27.95.

This doesn’t seem like any way to run a business — or a country. Yet, as Nate Silver, a blogger for The New York Times, points out in his book, “The Signal and the Noise,” studies show that from the stock pickers on Wall Street to the political pundits on our news channels, predictions offered with great certainty and voluminous justification prove, when evaluated later, to have had no predictive power at all. They are the equivalent of monkeys tossing darts.

As one who has both taught and written about such phenomena, I have long felt like leaning out my window to shout, “Network”-style, “I’m as mad as hell and I’m not going to take this anymore!” Judging by Mr. Silver’s lively prose — from energetic to outraged — I think he feels the same way.

Nate Silver. Robert Gauldin

The book’s title comes from electrical engineering, where a signal is something that conveys information, while noise is an unwanted, unmeaningful or random addition to the signal. Problems arise when the noise is as strong as, or stronger than, the signal. How do you recognize which is which?

Today the data we have available to make predictions has grown almost unimaginably large: it represents 2.5 quintillion bytes of data each day, Mr. Silver tells us, enough zeros and ones to fill a billion books of 10 million pages each. Our ability to tease the signal from the noise has not grown nearly as fast. As a result, we have plenty of data but lack the ability to extract truth from it and to build models that accurately predict the future that data portends.

Mr. Silver, just 34, is an expert at finding signal in noise. He is modest about his accomplishments, but he achieved a high profile when he created a brilliant and innovative computer program for forecasting the performance of baseball players, and later a system for predicting the outcome of political races. His political work had such success in the 2008 presidential election that it brought him extensive media coverage as well as a home at The Times for his blog, FiveThiryEight.com, though some conservatives have been critical of his methods during this election cycle.

His knack wasn’t lost on book publishers, who, as he puts it, approached him “to capitalize on the success of books such as ‘Moneyball’ and ‘Freakonomics.’ ” Publishers are notorious for pronouncing that Book A will sell just a thousand copies, while Book B will sell a million, and then proving to have gotten everything right except for which was A and which was B. In this case, to judge by early sales, they forecast Mr. Silver’s potential correctly, and to judge by the friendly tone of the book, it couldn’t have happened to a nicer guy.

Healthily peppered throughout the book are answers to its subtitle, “Why So Many Predictions Fail — but Some Don’t”: we are fooled into thinking that random patterns are meaningful; we build models that are far more sensitive to our initial assumptions than we realize; we make approximations that are cruder than we realize; we focus on what is easiest to measure rather than on what is important; we are overconfident; we build models that rely too heavily on statistics, without enough theoretical understanding; and we unconsciously let biases based on expectation or self-interest affect our analysis.

Regarding why models do succeed, Mr. Silver provides just bits of advice (other than to avoid the failings listed above). Mostly he stresses an approach to statistics named after the British mathematician Thomas Bayes, who created a theory of how to adjust a subjective degree of belief rationally when new evidence presents itself.

Suppose that after reading a review, you initially believe that there is a 75 percent chance that you will like a certain book. Then, in a bookstore, you read the book’s first 10 pages. What, then, are the chances that you will like the book, given the additional information that you liked (or did not like) what you read? Bayes’s theory tells you how to update your initial guess in light of that new data. This may sound like an exercise that only a character in “The Big Bang Theory” would engage in, but neuroscientists have found that, on an unconscious level, our brains do naturally use Bayesian prediction.

Mr. Silver illustrates his dos and don’ts through a series of interesting essays that examine how predictions are made in fields including chess, baseball, weather forecasting, earthquake analysis and politics. A chapter on poker reveals a strange world in which a small number of inept but big-spending “fish” feed a much larger community of highly skilled sharks competing to make their living off the fish; a chapter on global warming is one of the most objective and honest analyses I’ve seen. (Mr. Silver concludes that the greenhouse effect almost certainly exists and will be exacerbated by man-made CO2 emissions.)

So with all this going for the book, as my mother would say, what’s not to like?

The main problem emerges immediately, in the introduction, where I found my innately Bayesian brain wondering: Where is this going? The same question came to mind in later essays: I wondered how what I was reading related to the larger thesis. At times Mr. Silver reports in depth on a topic of lesser importance, or he skates over an important topic only to return to it in a later chapter, where it is again discussed only briefly.

As a result, I found myself losing the signal for the noise. Fortunately, you will not be tested on whether you have properly grasped the signal, and even the noise makes for a good read.

Leonard Mlodinow is the author of “Subliminal: How Your Unconscious Mind Rules Your Behavior” and “The Drunkard’s Walk: How Randomness Rules Our Lives.”

ONU quer garantir que temperatura global não se eleve mais que 2ºC (Globo Natureza)

JC e-mail 4582, de 13 de Setembro de 2012

As negociações climáticas da Organização das Nações Unidas (ONU) devem continuar pressionando por atitudes mais ambiciosas para garantir que o aquecimento global não ultrapasse os 2 graus, disse um negociador da União Europeia nesta semana, um mês depois de os EUA terem sido acusados de apresentar um retrocesso na meta.

Quase 200 países concordaram em 2010 em limitar o aumento das temperaturas para abaixo de 2 graus Celsius, acima da era pré-industrial para evitar os impactos perigos da mudança climática, como enchentes, secas e elevação do nível das marés.

Para desacelerar o ritmo do aquecimento global, as conversações climáticas da ONU na África do Sul concordaram em desenvolver um acordo climático legalmente vinculante até 2015, que poderia entrar em vigor no máximo até 2020.

Entretanto, especialistas advertem que a chance de limitar o aumento da temperatura global para menos de 2 graus está ficando cada vez menor, à medida que aumenta a emissão dos gases de efeito estufa por causa da queima de combustíveis fósseis.

“Está muito claro que devemos pressionar nas negociações de que a meta de 2 graus não é suficiente. A razão pela qual não estamos fazendo o bastante se deve à situação política em algumas partes do mundo”, disse Peter Betts, o diretor para mudança climática internacional da Grã-Bretanha e negociador sênior da UE, a um grupo de mudança climática no Parlamento britânico.

Na última semana, cientistas e diplomatas se reuniram em Bangcoc para a reunião da Convenção da ONU sobre Mudança Climática (UNFCCC, na sigla em inglês), a última antes do encontro anual que será realizado entre novembro e dezembro em Doha, no Qatar.

Flexibilidade nas metas – No mês passado, os EUA foram criticados por dizer que apoiavam uma abordagem mais flexível para um novo acordo climático – que não necessariamente manteria o limite de 2 graus -, mas depois acrescentaram que a flexibilidade daria ao mundo uma chance maior de chegar a um novo acordo.

Diversos países, incluindo alguns dos mais vulneráveis à mudança climática, dizem que o limite de 2 graus não é suficiente e que um limite de 1,5 graus seria mais seguro. As emissões do principal gás de efeito estufa, o dióxido de carbono, subiram 3,1% em 2011, em um recorde de alta. A China foi a maior emissora do mundo, seguida pelos EUA.

As negociações para a criação de um novo acordo global para o clima, nos mesmos moldes de Kyoto, já iniciaram. Na última conferência climática foi aprovada uma série de medidas que estabelece metas para países desenvolvidos e em desenvolvimento.

O documento denominado “Plataforma de Durban para Ação Aumentada” aponta uma série de medidas que deverão ser implementadas, mas na prática, não há medidas efetivas urgentes para conter em todo o planeta o aumento dos níveis de poluição nos próximos oito anos.

Obrigação para todos no futuro – Ele prevê a criação de um acordo global climático que vai compreender todos os países integrantes da UNFCCC e irá substituir o Protocolo de Kyoto. Será desenhado pelos países “um protocolo, outro instrumento legal ou um resultado acordado com força legal” para combater as mudanças climáticas.

Isso quer dizer que metas de redução de gases serão definidas para todas as nações, incluindo Estados Unidos e China, que não aceitavam qualquer tipo de negociação se uma das partes não fosse incluída nas obrigações de redução.

O delineamento deste novo plano começará a ser feito a partir das próximas negociações da ONU, o que inclui a COP 18, que vai acontecer em 2012 no Catar. O documento afirma que um grupo de trabalho será criado e que deve concluir o novo plano em 2015.

As medidas de contenção da poluição só deverão ser implementadas pelos países a partir de 2020, prazo estabelecido na Plataforma de Durban, e deverão levar em conta as recomendações do relatório do Painel Intergovernamental sobre Mudanças Climáticas (IPCC, na sigla em inglês), que será divulgado entre 2014 e 2015.

Em 2007, o organismo divulgou um documento que apontava para um aumento médio global das temperaturas entre 1,8 ºC e 4,0 ºC até 2100, com possibilidade de alta para 6,4 ºC se a população e a economia continuarem crescendo rapidamente e se for mantido o consumo intenso dos combustíveis fósseis.

Entretanto, a estimativa mais confiável fala em um aumento médio de 3ºC, assumindo que os níveis de dióxido de carbono se estabilizem em 45% acima da taxa atual. Aponta também, com mais de 90% de confiabilidade, que a maior parte do aumento de temperatura observado nos últimos 50 anos foi provocada por atividades humanas.

Bits of Mystery DNA, Far From ‘Junk,’ Play Crucial Role (N.Y.Times)

By GINA KOLATA

Published: September 5, 2012

Among the many mysteries of human biology is why complex diseases like diabetes, high blood pressure and psychiatric disorders are so difficult to predict and, often, to treat. An equally perplexing puzzle is why one individual gets a disease like cancer or depression, while an identical twin remains perfectly healthy.

Béatrice de Géa for The New York Times. “It is like opening a wiring closet and seeing a hairball of wires,” Mark Gerstein of Yale University said of the DNA intricacies.

Now scientists have discovered a vital clue to unraveling these riddles. The human genome is packed with at least four million gene switches that reside in bits of DNA that once were dismissed as “junk” but that turn out to play critical roles in controlling how cells, organs and other tissues behave. The discovery, considered a major medical and scientific breakthrough, has enormous implications for human health because many complex diseases appear to be caused by tiny changes in hundreds of gene switches.

The findings, which are the fruit of an immense federal project involving 440 scientists from 32 laboratories around the world, will have immediate applications for understanding how alterations in the non-gene parts of DNA contribute to human diseases, which may in turn lead to new drugs. They can also help explain how the environment can affect disease risk. In the case of identical twins, small changes in environmental exposure can slightly alter gene switches, with the result that one twin gets a disease and the other does not.

As scientists delved into the “junk” — parts of the DNA that are not actual genes containing instructions for proteins — they discovered a complex system that controls genes. At least 80 percent of this DNA is active and needed. The result of the work is an annotated road map of much of this DNA, noting what it is doing and how. It includes the system of switches that, acting like dimmer switches for lights, control which genes are used in a cell and when they are used, and determine, for instance, whether a cell becomes a liver cell or a neuron.

“It’s Google Maps,” said Eric Lander, president of the Broad Institute, a joint research endeavor of Harvard and the Massachusetts Institute of Technology. In contrast, the project’s predecessor, the Human Genome Project, which determined the entire sequence of human DNA, “was like getting a picture of Earth from space,” he said. “It doesn’t tell you where the roads are, it doesn’t tell you what traffic is like at what time of the day, it doesn’t tell you where the good restaurants are, or the hospitals or the cities or the rivers.”

The new result “is a stunning resource,” said Dr. Lander, who was not involved in the research that produced it but was a leader in the Human Genome Project. “My head explodes at the amount of data.”

The discoveries were published on Wednesday in six papers in the journal Nature and in 24 papers in Genome Research and Genome Biology. In addition, The Journal of Biological Chemistry is publishing six review articles, and Science is publishing yet another article.

Human DNA is “a lot more active than we expected, and there are a lot more things happening than we expected,” said Ewan Birney of the European Molecular Biology Laboratory-European Bioinformatics Institute, a lead researcher on the project.

In one of the Nature papers, researchers link the gene switches to a range of human diseases — multiple sclerosis, lupus, rheumatoid arthritis, Crohn’s disease, celiac disease — and even to traits like height. In large studies over the past decade, scientists found that minor changes in human DNA sequences increase the risk that a person will get those diseases. But those changes were in the junk, now often referred to as the dark matter — they were not changes in genes — and their significance was not clear. The new analysis reveals that a great many of those changes alter gene switches and are highly significant.

“Most of the changes that affect disease don’t lie in the genes themselves; they lie in the switches,” said Michael Snyder, a Stanford University researcher for the project, called Encode, for Encyclopedia of DNA Elements.

And that, said Dr. Bradley Bernstein, an Encode researcher at Massachusetts General Hospital, “is a really big deal.” He added, “I don’t think anyone predicted that would be the case.”

The discoveries also can reveal which genetic changes are important in cancer, and why. As they began determining the DNA sequences of cancer cells, researchers realized that most of the thousands of DNA changes in cancer cells were not in genes; they were in the dark matter. The challenge is to figure out which of those changes are driving the cancer’s growth.

“These papers are very significant,” said Dr. Mark A. Rubin, a prostate cancer genomics researcher at Weill Cornell Medical College. Dr. Rubin, who was not part of the Encode project, added, “They will definitely have an impact on our medical research on cancer.”

In prostate cancer, for example, his group found mutations in important genes that are not readily attacked by drugs. But Encode, by showing which regions of the dark matter control those genes, gives another way to attack them: target those controlling switches.

Dr. Rubin, who also used the Google Maps analogy, explained: “Now you can follow the roads and see the traffic circulation. That’s exactly the same way we will use these data in cancer research.” Encode provides a road map with traffic patterns for alternate ways to go after cancer genes, he said.

Dr. Bernstein said, “This is a resource, like the human genome, that will drive science forward.”

The system, though, is stunningly complex, with many redundancies. Just the idea of so many switches was almost incomprehensible, Dr. Bernstein said.

There also is a sort of DNA wiring system that is almost inconceivably intricate.

“It is like opening a wiring closet and seeing a hairball of wires,” said Mark Gerstein, an Encode researcher from Yale. “We tried to unravel this hairball and make it interpretable.”

There is another sort of hairball as well: the complex three-dimensional structure of DNA. Human DNA is such a long strand — about 10 feet of DNA stuffed into a microscopic nucleus of a cell — that it fits only because it is tightly wound and coiled around itself. When they looked at the three-dimensional structure — the hairball — Encode researchers discovered that small segments of dark-matter DNA are often quite close to genes they control. In the past, when they analyzed only the uncoiled length of DNA, those controlling regions appeared to be far from the genes they affect.

The project began in 2003, as researchers began to appreciate how little they knew about human DNA. In recent years, some began to find switches in the 99 percent of human DNA that is not genes, but they could not fully characterize or explain what a vast majority of it was doing.

The thought before the start of the project, said Thomas Gingeras, an Encode researcher from Cold Spring Harbor Laboratory, was that only 5 to 10 percent of the DNA in a human being was actually being used.

The big surprise was not only that almost all of the DNA is used but also that a large proportion of it is gene switches. Before Encode, said Dr. John Stamatoyannopoulos, a University of Washington scientist who was part of the project, “if you had said half of the genome and probably more has instructions for turning genes on and off, I don’t think people would have believed you.”

By the time the National Human Genome Research Institute, part of the National Institutes of Health, embarked on Encode, major advances in DNA sequencing and computational biology had made it conceivable to try to understand the dark matter of human DNA. Even so, the analysis was daunting — the researchers generated 15 trillion bytes of raw data. Analyzing the data required the equivalent of more than 300 years of computer time.

Just organizing the researchers and coordinating the work was a huge undertaking. Dr. Gerstein, one of the project’s leaders, has produced a diagram of the authors with their connections to one another. It looks nearly as complicated as the wiring diagram for the human DNA switches. Now that part of the work is done, and the hundreds of authors have written their papers.

“There is literally a flotilla of papers,” Dr. Gerstein said. But, he added, more work has yet to be done — there are still parts of the genome that have not been figured out.

That, though, is for the next stage of Encode.

*   *   *

Published: September 5, 2012

Rethinking ‘Junk’ DNA

A large group of scientists has found that so-called junk DNA, which makes up most of the human genome, does much more than previously thought.

GENES: Each human cell contains about 10 feet of DNA, coiled into a dense tangle. But only a very small percentage of DNA encodes genes, which control inherited traits like eye color, blood type and so on.

JUNK DNA: Stretches of DNA around and between genes seemed to do nothing, and were called junk DNA. But now researchers think that the junk DNA contains a large number of tiny genetic switches, controlling how genes function within the cell.

REGULATION: The many genetic regulators seem to be arranged in a complex and redundant hierarchy. Scientists are only beginning to map and understand this network, which regulates how cells, organs and tissues behave.

DISEASE: Errors or mutations in genetic switches can disrupt the network and lead to a range of diseases. The new findings will spur further research and may lead to new drugs and treatments.

 

Evolution could explain the placebo effect (New Scientist)

06 September 2012 by Colin Barras

Magazine issue 2881

ON THE face of it, the placebo effect makes no sense. Someone suffering from a low-level infection will recover just as nicely whether they take an active drug or a simple sugar pill. This suggests people are able to heal themselves unaided – so why wait for a sugar pill to prompt recovery?

New evidence from a computer model offers a possible evolutionary explanation, and suggests that the immune system has an on-off switch controlled by the mind.

It all starts with the observation that something similar to the placebo effect occurs in many animals, says Peter Trimmer, a biologist at the University of Bristol, UK. For instance, Siberian hamsters do little to fight an infection if the lights above their lab cage mimic the short days and long nights of winter. But changing the lighting pattern to give the impression of summer causes them to mount a full immune response.

Likewise, those people who think they are taking a drug but are really receiving a placebo can have a response which is twice that of those who receive no pills (Annals of Family Medicine, doi.org/cckm8b). In Siberian hamsters and people, intervention creates a mental cue that kick-starts the immune response.

There is a simple explanation, says Trimmer: the immune system is costly to run – so costly that a strong and sustained response could dangerously drain an animal’s energy reserves. In other words, as long as the infection is not lethal, it pays to wait for a sign that fighting it will not endanger the animal in other ways.

Nicholas Humphrey, a retired psychologist formerly at the London School of Economics, first proposed this idea a decade ago, but only now has evidence to support it emerged from a computer model designed by Trimmer and his colleagues.

According to Humphrey’s picture, the Siberian hamster subconsciously acts on a cue that it is summer because food supplies to sustain an immune response are plentiful at that time of year. We subconsciously respond to treatment – even a sham one – because it comes with assurances that it will weaken the infection, allowing our immune response to succeed rapidly without straining the body’s resources.

Trimmer’s simulation is built on this assumption – that animals need to spend vital resources on fighting low-level infections. The model revealed that, in challenging environments, animals lived longer and sired more offspring if they endured infections without mounting an immune response. In more favourable environments, it was best for animals to mount an immune response and return to health as quickly as possible (Evolution and Human Behavior, doi.org/h8p). The results show a clear evolutionary benefit to switching the immune system on and off depending on environmental conditions.

“I’m pleased to see that my theory stands up to computational modelling,” says Humphrey. If the idea is right, he adds, it means we have misunderstood the nature of placebos. Farming and other innovations in the past 10,000 years mean that many people have a stable food supply and can safely mount a full immune response at any time – but our subconscious switch has not yet adapted to this. A placebo tricks the mind into thinking it is an ideal time to switch on an immune response, says Humphrey.

Paul Enck at the University of Tübingen in Germany says it is an intriguing idea, but points out that there are many different placebo responses, depending on the disease. It is unlikely that a single mechanism explains them all, he says.

First Holistic View of How Human Genome Actually Works: ENCODE Study Produces Massive Data Set (Science Daily)

ScienceDaily (Sep. 5, 2012) — The Human Genome Project produced an almost complete order of the 3 billion pairs of chemical letters in the DNA that embodies the human genetic code — but little about the way this blueprint works. Now, after a multi-year concerted effort by more than 440 researchers in 32 labs around the world, a more dynamic picture gives the first holistic view of how the human genome actually does its job.

William Noble, professor of genome sciences and computer science, in the data center at the William H. Foege Building. Noble, an expert on machine learning, and his team designed artificial intellience programs to analyze ENCODE data. These computer programs can learn from experience, recognize patterns, and organize information into categories understandable to scientists. The center houses systems for a wide variety of genetic research. The computer center has the capacity to store and analyze a tremendous amount of data, the equivalent of a 670-page autobiography of each person on earth, uncompressed.The computing resources analyze over 4 pentabytes of genomic data a year. (Credit: Clare McLean, Courtesy of University of Washington)

During the new study, researchers linked more than 80 percent of the human genome sequence to a specific biological function and mapped more than 4 million regulatory regions where proteins specifically interact with the DNA. These findings represent a significant advance in understanding the precise and complex controls over the expression of genetic information within a cell. The findings bring into much sharper focus the continually active genome in which proteins routinely turn genes on and off using sites that are sometimes at great distances from the genes themselves. They also identify where chemical modifications of DNA influence gene expression and where various functional forms of RNA, a form of nucleic acid related to DNA, help regulate the whole system.

“During the early debates about the Human Genome Project, researchers had predicted that only a few percent of the human genome sequence encoded proteins, the workhorses of the cell, and that the rest was junk. We now know that this conclusion was wrong,” said Eric D. Green, M.D., Ph.D., director of the National Human Genome Research Institute (NHGRI), a part of the National Institutes of Health. “ENCODE has revealed that most of the human genome is involved in the complex molecular choreography required for converting genetic information into living cells and organisms.”

NHGRI organized the research project producing these results; it is called the Encyclopedia of DNA Elements or ENCODE. Launched in 2003, ENCODE’s goal of identifying all of the genome’s functional elements seemed just as daunting as sequencing that first human genome. ENCODE was launched as a pilot project to develop the methods and strategies needed to produce results and did so by focusing on only 1 percent of the human genome. By 2007, NHGRI concluded that the technology had sufficiently evolved for a full-scale project, in which the institute invested approximately $123 million over five years. In addition, NHGRI devoted about $40 million to the ENCODE pilot project, plus approximately $125 million to ENCODE-related technology development and model organism research since 2003.

The scale of the effort has been remarkable. Hundreds of researchers across the United States, United Kingdom, Spain, Singapore and Japan performed more than 1,600 sets of experiments on 147 types of tissue with technologies standardized across the consortium. The experiments relied on innovative uses of next-generation DNA sequencing technologies, which had only become available around five years ago, due in large part to advances enabled by NHGRI’s DNA sequencing technology development program. In total, ENCODE generated more than 15 trillion bytes of raw data and consumed the equivalent of more than 300 years of computer time to analyze.

“We’ve come a long way,” said Ewan Birney, Ph.D., of the European Bioinformatics Institute, in the United Kingdom, and lead analysis coordinator for the ENCODE project. “By carefully piecing together a simply staggering variety of data, we’ve shown that the human genome is simply alive with switches, turning our genes on and off and controlling when and where proteins are produced. ENCODE has taken our knowledge of the genome to the next level, and all of that knowledge is being shared openly.”

The ENCODE Consortium placed the resulting data sets as soon as they were verified for accuracy, prior to publication, in several databases that can be freely accessed by anyone on the Internet. These data sets can be accessed through the ENCODE project portal (www.encodeproject.org) as well as at the University of California, Santa Cruz genome browser,http://genome.ucsc.edu/ENCODE/, the National Center for Biotechnology Information,http://www.ncbi.nlm.nih.gov/geo/info/ENCODE.html and the European Bioinformatics Institute,http://useast.ensembl.org/Homo_sapiens/encode.html?redirect=mirror;source=www.ensembl.org.

“The ENCODE catalog is like Google Maps for the human genome,” said Elise Feingold, Ph.D., an NHGRI program director who helped start the ENCODE Project. “Simply by selecting the magnification in Google Maps, you can see countries, states, cities, streets, even individual intersections, and by selecting different features, you can get directions, see street names and photos, and get information about traffic and even weather. The ENCODE maps allow researchers to inspect the chromosomes, genes, functional elements and individual nucleotides in the human genome in much the same way.”

The coordinated publication set includes one main integrative paper and five related papers in the journal Nature; 18 papers inGenome Research; and six papers in Genome Biology. The ENCODE data are so complex that the three journals have developed a pioneering way to present the information in an integrated form that they call threads.

“Because ENCODE has generated so much data, we, together with the ENCODE Consortium, have introduced a new way to enable researchers to navigate through the data,” said Magdalena Skipper, Ph.D., senior editor at Nature, which produced the freely available publishing platform on the Internet.

Since the same topics were addressed in different ways in different papers, the new website, www.nature.com/encode, will allow anyone to follow a topic through all of the papers in the ENCODE publication set by clicking on the relevant thread at the Nature ENCODE explorer page. For example, thread number one compiles figures, tables, and text relevant to genetic variation and disease from several papers and displays them all on one page. ENCODE scientists believe this will illuminate many biological themes emerging from the analyses.

In addition to the threaded papers, six review articles are being published in the Journal of Biological Chemistry and two related papers in Science and one in Cell.

The ENCODE data are rapidly becoming a fundamental resource for researchers to help understand human biology and disease. More than 100 papers using ENCODE data have been published by investigators who were not part of the ENCODE Project, but who have used the data in disease research. For example, many regions of the human genome that do not contain protein-coding genes have been associated with disease. Instead, the disease-linked genetic changes appear to occur in vast tracts of sequence between genes where ENCODE has identified many regulatory sites. Further study will be needed to understand how specific variants in these genomic areas contribute to disease.

“We were surprised that disease-linked genetic variants are not in protein-coding regions,” said Mike Pazin, Ph.D., an NHGRI program director working on ENCODE. “We expect to find that many genetic changes causing a disorder are within regulatory regions, or switches, that affect how much protein is produced or when the protein is produced, rather than affecting the structure of the protein itself. The medical condition will occur because the gene is aberrantly turned on or turned off or abnormal amounts of the protein are made. Far from being junk DNA, this regulatory DNA clearly makes important contributions to human health and disease.”

Identifying regulatory regions will also help researchers explain why different types of cells have different properties. For example why do muscle cells generate force while liver cells break down food? Scientists know that muscle cells turn on some genes that only work in muscle, but it has not been previously possible to examine the regulatory elements that control that process. ENCODE has laid a foundation for these kinds of studies by examining more than 140 of the hundreds of cell types found in the human body and identifying many of the cell type-specific control elements.

Despite the enormity of the dataset described in this historic collection of publications, it does not comprehensively describe all of the functional genomic elements in all of the different types of cells in the human body. NHGRI plans to invest in additional ENCODE-related research for at least another four years. During the next phase, ENCODE will increase the depth of the catalog with respect to the types of functional elements and cell types studied. It will also develop new tools for more sophisticated analyses of the data.

Journal References:

  1. Magdalena Skipper, Ritu Dhand, Philip Campbell.Presenting ENCODE. Nature, 2012; 489 (7414): 45 DOI:10.1038/489045a
  2. Joseph R. Ecker, Wendy A. Bickmore, Inês Barroso, Jonathan K. Pritchard, Yoav Gilad, Eran Segal. Genomics: ENCODE explained. Nature, 2012; 489 (7414): 52 DOI:10.1038/489052a
  3. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome.Nature, 2012; 489 (7414): 57 DOI: 10.1038/nature11247

Design Help for Drug Cocktails for HIV Patients: Mathematical Model Helps Design Efficient Multi-Drug Therapies (Science Daily)

ScienceDaily (Sep. 2, 2012) — For years, doctors treating those with HIV have recognized a relationship between how faithfully patients take the drugs they prescribe, and how likely the virus is to develop drug resistance. More recently, research has shown that the relationship between adherence to a drug regimen and resistance is different for each of the drugs that make up the “cocktail” used to control the disease.

HIV is shown attaching to and infecting a T4 cell. The virus then inserts its own genetic material into the T4 cell’s host DNA. The infected host cell then manufactures copies of the HIV. (Credit: iStockphoto/Medical Art Inc.)

New research conducted by Harvard scientists could help explain why those differences exist, and may help doctors quickly and cheaply design new combinations of drugs that are less likely to result in resistance.

As described in a September 2 paper in Nature Medicine, a team of researchers led by Martin Nowak, Professor of Mathematics and of Biology and Director of the Program for Evolutionary Dynamics, have developed a technique medical researchers can use to model the effects of various treatments, and predict whether they will cause the virus to develop resistance.

“What we demonstrate in this paper is a prototype for predicting, through modeling, whether a patient at a given adherence level is likely to develop resistance to treatment,” Alison Hill, a PhD student in Biophysics and co-first author of the paper, said. “Compared to the time and expense of a clinical trial, this method offers a relatively easy way to make these predictions. And, as we show in the paper, our results match with what doctors are seeing in clinical settings.”

The hope, said Nowak, is that the new technique will take some of the guesswork out of what is now largely a trial-and-error process.

“This is a mathematical tool that will help design clinical trials,” he said. “Right now, researchers are using trial and error to develop these combination therapies. Our approach uses the mathematical understanding of evolution to make the process more akin to engineering.”

Creating a model that can make such predictions accurately, however, requires huge amounts of data.

To get that data, Hill and Daniel Scholes Rosenbloom, a PhD student in Organismic and Evolutionary Biology and the paper’s other first author, turned to Johns Hopkins University Medical School, where Professor of Medicine and of Molecular Biology and Genetics Robert F. Siliciano was working with PhD student Alireza Rabi (also co-first author) to study how the HIV virus reacted to varying drug dosages.

Such data proved critical to the model that Hill, Rabi and Rosenbloom eventually designed, because the level of the drug in patients — even those that adhere to their treatment perfectly — naturally varies. When drug levels are low — as they are between doses, or if a dose is missed — the virus is better able to replicate and grow. Higher drug levels, by contrast, may keep the virus in check, but they also increase the risk of mutant strains of the virus emerging, leading to drug resistance.

Armed with the data from Johns Hopkins, Hill, Rabi and Rosenbloom created a computer model that could predict whether and how much the virus, or a drug-resistant strain, was growing based on how strictly patients stuck to their drug regimen.

“Our model is essentially a simulation of what goes on during treatment,” Rosenbloom said. “We created a number of simulated patients, each of whom had different characteristics, and then we said, ‘Let’s imagine these patients have 60 percent adherence to their treatment — they take 60 percent of the pills they’re supposed to.’ Our model can tell us what their drug concentration is over time, and based on that, we can say whether the virus is growing or shrinking, and whether they’re likely to develop resistance.”

The model’s predictions, Rosenbloom explained, can then serve as a guide to researchers as they work to design new drug cocktails to combat HIV.

While their model does hold out hope for simplifying the process of designing drug “cocktails,” Hill and Rosenbloom said they plan to continue to refine the model to take additional factors — such as multiple mutant-resistant strains of the virus and varying drug concentrations in other parts of the body — into effect.

“The prototype we have so far looks at concentrations of drugs in blood plasma,” Rosenbloom explained. “But a number of drugs don’t penetrate other parts of the body, like the brains or the gut, with the same efficiency, so it’s important to model these other areas where the concentrations of drugs might not be as high.”

Ultimately, though, both say their model can offer new hope to patients by helping doctors design better, cheaper and more efficient treatments.

“Over the past 10 years, the number of HIV-infected people receiving drug treatment has increased immensely,” Hill said. “Figuring out what the best ways are to treat people in terms of cost effectiveness, adherence and the chance of developing resistance is going to become even more important.”

Journal Reference:

  1. Daniel I S Rosenbloom, Alison L Hill, S Alireza Rabi, Robert F Siliciano, Martin A Nowak. Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 2012; DOI: 10.1038/nm.2892

*   *   *

Anti-HIV Drug Simulation Offers ‘Realistic’ Tool to Predict Drug Resistance and Viral Mutation

ScienceDaily (Sep. 2, 2012) — Pooling data from thousands of tests of the antiviral activity of more than 20 commonly used anti-HIV drugs, AIDS experts at Johns Hopkins and Harvard universities have developed what they say is the first accurate computer simulation to explain drug effects. Already, the model clarifies how and why some treatment regimens fail in some patients who lack evidence of drug resistance. Researchers say their model is based on specific drugs, precise doses prescribed, and on “real-world variation” in how well patients follow prescribing instructions.

Johns Hopkins co-senior study investigator and infectious disease specialist Robert Siliciano, M.D., Ph.D., says the mathematical model can also be used to predict how well a patient is likely to do on a specific regimen, based on their prescription adherence. In addition, the model factors in each drug’s ability to suppress viral replication and the likelihood that such suppression will spur development of drug-resistant, mutant HIV strains.

“With the help of our simulation, we can now tell with a fair degree of certainty what level of viral suppression is being achieved — how hard it is for the virus to grow and replicate — for a particular drug combination, at a specific dosage and drug concentration in the blood, even when a dose is missed,” says Siliciano, a professor at the Johns Hopkins University School of Medicine and a Howard Hughes Medical Institute investigator. This information, he predicts, will remove “a lot of the current trial and error, or guesswork, involved in testing new drug combination therapies.”

Siliciano says the study findings, to be reported in the journalNature Medicine online Sept. 2, should help scientists streamline development and clinical trials of future combination therapies, by ruling out combinations unlikely to work.

One application of the model could be further development of drug combinations that can be contained in a single pill taken once a day. That could lower the chance of resistance, even if adherence is not perfect. Such future drug regimens, he says, will ideally strike a balance between optimizing viral suppression and minimizing risk of drug resistance.

Researchers next plan to expand their modeling beyond blood levels of virus to other parts of the body, such as the brain, where antiretroviral drug concentrations can be different from those measured in the blood. They also plan to expand their analysis to include multiple-drug-resistant strains of HIV.

Besides Siliciano, Johns Hopkins joint medical-doctoral student Alireza Rabi was a co-investigator in this study. Other study investigators included doctoral candidates Daniel Rosenbloom, M.S.; Alison Hill, M.S.; and co-senior study investigator Martin Nowak, Ph.D. — all at Harvard University.

Funding support for this study, which took two years to complete, was provided by the National Institutes of Health, with corresponding grant numbers R01-MH54907, R01-AI081600, R01-GM078986; the Bill and Melinda Gates Foundation; the Cancer Research Institute; the National Science Foundation; the Howard Hughes Medical Institute; Natural Sciences and Engineering Research Council of Canada; the John Templeton Foundation; and J. Epstein.

Currently, an estimated 8 million of the more than 34 million people in the world living with HIV are taking antiretroviral therapy to keep their disease in check. An estimated 1,178,000 in the United States are infected, including 23,000 in the state of Maryland.

Journal Reference:

  1. Daniel I S Rosenbloom, Alison L Hill, S Alireza Rabi, Robert F Siliciano, Martin A Nowak. Antiretroviral dynamics determines HIV evolution and predicts therapy outcome. Nature Medicine, 2012; DOI: 10.1038/nm.2892

Mathematics or Memory? Study Charts Collision Course in Brain (Science Daily)

ScienceDaily (Sep. 3, 2012) — You already know it’s hard to balance your checkbook while simultaneously reflecting on your past. Now, investigators at the Stanford University School of Medicine — having done the equivalent of wire-tapping a hard-to-reach region of the brain — can tell us how this impasse arises.

The area in red is the posterior medial cortex, the portion of the brain that is most active when people recall details of their own pasts. (Credit: Courtesy of Josef Parvizi)

The researchers showed that groups of nerve cells in a structure called the posterior medial cortex, or PMC, are strongly activated during a recall task such as trying to remember whether you had coffee yesterday, but just as strongly suppressed when you’re engaged in solving a math problem.

The PMC, situated roughly where the brain’s two hemispheres meet, is of great interest to neuroscientists because of its central role in introspective activities.

“This brain region is famously well-connected with many other regions that are important for higher cognitive functions,” said Josef Parvizi, MD, PhD, associate professor of neurology and neurological sciences and director of Stanford’s Human Intracranial Cognitive Electrophysiology Program. “But it’s very hard to reach. It’s so deep in the brain that the most commonly used electrophysiological methods can’t access it.”

In a study published online Sept. 3 in Proceedings of the National Academy of Sciences, Parvizi and his Stanford colleagues found a way to directly and sensitively record the output from this ordinarily anatomically inaccessible site in human subjects. By doing so, the researchers learned that particular clusters of nerve cells in the PMC that are most active when you are recalling details of your own past are strongly suppressed when you are performing mathematical calculations. Parvizi is the study’s senior author. The first and second authors, respectively, are postdoctoral scholars Brett Foster, PhD, and Mohammed Dastjerdi, PhD.

Much of our understanding of what roles different parts of the brain play has been obtained by techniques such as functional magnetic resonance imaging, which measures the amount of blood flowing through various brain regions as a proxy for activity in those regions. But changes in blood flow are relatively slow, making fMRI a poor medium for listening in on the high-frequency electrical bursts (approximately 200 times per second) that best reflect nerve-cell firing. Moreover, fMRI typically requires pooling images from several subjects into one composite image. Each person’s brain physiognomy is somewhat different, so the blending blurs the observable anatomical coordinates of a region of interest.

Nonetheless, fMRI imaging has shown that the PMC is quite active in introspective processes such as autobiographical memory processing (“I ate breakfast this morning”) or daydreaming, and less so in external sensory processing (“How far away is that pedestrian?”). “Whenever you pay attention to the outside world, its activity decreases,” said Parvizi.

To learn what specific parts of this region are doing during, say, recall versus arithmetic requires more-individualized anatomical resolution than an fMRI provides. Otherwise, Parvizi said, “if some nerve-cell populations become less active and others more active, it all washes out, and you see no net change.” So you miss what’s really going on.

For this study, the Stanford scientists employed a highly sensitive technique to demonstrate that introspective and externally focused cognitive tasks directly interfere with one another, because they impose opposite requirements on the same brain circuitry.

The researchers took advantage of a procedure performed on patients who were being evaluated for brain surgery at the Stanford Epilepsy Monitoring Unit, associated with Stanford University Medical Center. These patients were unresponsive to drug therapy and, as a result, suffered continuing seizures. The procedure involves temporarily removing small sections of a patient’s skull, placing a thin plastic film containing electrodes onto the surface of the brain near the suspected point of origin of that patient’s seizure (the location is unique to each patient), and then monitoring electrical activity in that region for five to seven days — all of it spent in a hospital bed. Once the epilepsy team identifies the point of origin of any seizures that occurred during that time, surgeons can precisely excise a small piece of tissue at that position, effectively breaking the vicious cycle of brain-wave amplification that is a seizure.

Implanting these electrode packets doesn’t mean piercing the brain or individual cells within it. “Each electrode picks up activity from about a half-million nerve cells,” Parvizi said. “It’s more like dotting the ceiling of a big room, filled with a lot of people talking, with multiple microphones. We’re listening to the buzz in the room, not individual conversations. Each microphone picks up the buzz from a different bunch of partiers. Some groups are more excited and talking more loudly than others.”

The experimenters found eight patients whose seizures were believed to be originating somewhere near the brain’s midline and who, therefore, had had electrode packets placed in the crevasse dividing the hemispheres. (The brain’s two hemispheres are spaced far enough apart to slip an electrode packet between them without incurring damage.)

The researchers got permission from these eight patients to bring in laptop computers and put the volunteers through a battery of simple tasks requiring modest intellectual effort. “It can be boring to lie in bed waiting seven days for a seizure to come,” said Foster. “Our studies helped them pass the time.” The sessions lasted about an hour.

On the laptop would appear a series of true/false statements falling into one of four categories. Three categories were self-referential, albeit with varying degrees of specificity. Most specific was so-called “autobiographical episodic memory,” an example of which might be: “I drank coffee yesterday.” The next category of statements was more generic: “I eat a lot of fruit.” The most abstract category, “self-judgment,” comprised sentences along the lines of: “I am honest.”

A fourth category differed from the first three in that it consisted of arithmetical equations such as: 67 + 6 = 75. Evaluating such a statement’s truth required no introspection but, instead, an outward, more sensory orientation.

For each item, patients were instructed to press “1″ if a statement was true, “2″ if it was false.

Significant portions of the PMC that were “tapped” by electrodes became activated during self-episodic memory processing, confirming the PMC’s strong role in recall of one’s past experiences. Interestingly, true/false statements involving less specifically narrative recall — such as, “I eat a lot of fruit” — induced relatively little activity. “Self-judgment” statements — such as, “I am attractive” — elicited none at all. Moreover, whether a volunteer judged a statement to be true or false made no difference with respect to the intensity, location or duration of electrical activity in activated PMC circuits.

This suggests, both Parvizi and Foster said, that the PMC is not the brain’s “center of self-consciousness” as some have proposed, but is more specifically engaged in constructing autobiographical narrative scenes, as occurs in recall or imagination.

Foster, Dastjerdi and Parvizi also found that the PMC circuitry activated by a recall task took close to a half-second to fire up, ruling out the possibility that this circuitry’s true role was in reading or making sense of the sentence on the screen. (These two activities are typically completed within the first one-fifth of a second or so.) Once activated, these circuits remained active for a full second.

Yet all the electrodes that lit up during the self-episodic condition were conspicuously deactivated during arithmetic calculation. In fact, the circuits being monitored by these electrodes were not merely passively silent, but actively suppressed, said Parvizi. “The more a circuit is activated during autobiographical recall, the more it is suppressed during math. It’s essentially impossible to do both at once.”

The study was funded by the National Institutes of Health, with partial sponsorship from the Stanford Institute for NeuroInnovation and Translational Neuroscience.

Rooting out Rumors, Epidemics, and Crime — With Math (Science Daily)

ScienceDaily (Aug. 10, 2012) — A team of EPFL scientists has developed an algorithm that can identify the source of an epidemic or information circulating within a network, a method that could also be used to help with criminal investigations.

Investigators are well aware of how difficult it is to trace an unlawful act to its source. The job was arguably easier with old, Mafia-style criminal organizations, as their hierarchical structures more or less resembled predictable family trees.

In the Internet age, however, the networks used by organized criminals have changed. Innumerable nodes and connections escalate the complexity of these networks, making it ever more difficult to root out the guilty party. EPFL researcher Pedro Pinto of the Audiovisual Communications Laboratory and his colleagues have developed an algorithm that could become a valuable ally for investigators, criminal or otherwise, as long as a network is involved. The team’s research was published August 10, 2012, in the journal Physical Review Letters.

Finding the source of a Facebook rumor

“Using our method, we can find the source of all kinds of things circulating in a network just by ‘listening’ to a limited number of members of that network,” explains Pinto. Suppose you come across a rumor about yourself that has spread on Facebook and been sent to 500 people — your friends, or even friends of your friends. How do you find the person who started the rumor? “By looking at the messages received by just 15-20 of your friends, and taking into account the time factor, our algorithm can trace the path of that information back and find the source,” Pinto adds. This method can also be used to identify the origin of a spam message or a computer virus using only a limited number of sensors within the network.

Trace the propagation of an epidemic

Out in the real world, the algorithm can be employed to find the primary source of an infectious disease, such as cholera. “We tested our method with data on an epidemic in South Africa provided by EPFL professor Andrea Rinaldo’s Ecohydrology Laboratory,” says Pinto. “By modeling water networks, river networks, and human transport networks, we were able to find the spot where the first cases of infection appeared by monitoring only a small fraction of the villages.”

The method would also be useful in responding to terrorist attacks, such as the 1995 sarin gas attack in the Tokyo subway, in which poisonous gas released in the city’s subterranean tunnels killed 13 people and injured nearly 1,000 more. “Using this algorithm, it wouldn’t be necessary to equip every station with detectors. A sample would be sufficient to rapidly identify the origin of the attack, and action could be taken before it spreads too far,” says Pinto.

Identifying the brains behind a terrorist attack

Computer simulations of the telephone conversations that could have occurred during the terrorist attacks on September 11, 2001, were used to test Pinto’s system. “By reconstructing the message exchange inside the 9/11 terrorist network extracted from publicly released news, our system spit out the names of three potential suspects — one of whom was found to be the mastermind of the attacks, according to the official enquiry.”

The validity of this method thus has been proven a posteriori. But according to Pinto, it could also be used preventatively — for example, to understand an outbreak before it gets out of control. “By carefully selecting points in the network to test, we could more rapidly detect the spread of an epidemic,” he points out. It could also be a valuable tool for advertisers who use viral marketing strategies by leveraging the Internet and social networks to reach customers. For example, this algorithm would allow them to identify the specific Internet blogs that are the most influential for their target audience and to understand how in these articles spread throughout the online community.

How Computation Can Predict Group Conflict: Fighting Among Captive Pigtailed Macaques Provides Clues (Science Daily)

ScienceDaily (Aug. 13, 2012) — When conflict breaks out in social groups, individuals make strategic decisions about how to behave based on their understanding of alliances and feuds in the group.

Researchers studied fighting among captive pigtailed macaques for clues about behavior and group conflict. (Credit: iStockphoto/Natthaphong Phanthumchinda)

But it’s been challenging to quantify the underlying trends that dictate how individuals make predictions, given they may only have seen a small number of fights or have limited memory.

In a new study, scientists at the Wisconsin Institute for Discovery (WID) at UW-Madison develop a computational approach to determine whether individuals behave predictably. With data from previous fights, the team looked at how much memory individuals in the group would need to make predictions themselves. The analysis proposes a novel estimate of “cognitive burden,” or the minimal amount of information an organism needs to remember to make a prediction.

The research draws from a concept called “sparse coding,” or the brain’s tendency to use fewer visual details and a small number of neurons to stow an image or scene. Previous studies support the idea that neurons in the brain react to a few large details such as the lines, edges and orientations within images rather than many smaller details.

“So what you get is a model where you have to remember fewer things but you still get very high predictive power — that’s what we’re interested in,” says Bryan Daniels, a WID researcher who led the study. “What is the trade-off? What’s the minimum amount of ‘stuff’ an individual has to remember to make good inferences about future events?”

To find out, Daniels — along with WID co-authors Jessica Flack and David Krakauer — drew comparisons from how brains and computers encode information. The results contribute to ongoing discussions about conflict in biological systems and how cognitive organisms understand their environments.

The study, published in the Aug. 13 edition of the Proceedings of the National Academy of Sciences, examined observed bouts of natural fighting in a group of 84 captive pigtailed macaques at the Yerkes National Primate Research Center. By recording individuals’ involvement — or lack thereof — in fights, the group created models that mapped the likelihood any number of individuals would engage in conflict in hypothetical situations.

To confirm the predictive power of the models, the group plugged in other data from the monkey group that was not used to create the models. Then, researchers compared these simulations with what actually happened in the group. One model looked at conflict as combinations of pairs, while another represented fights as sparse combinations of clusters, which proved to be a better tool for predicting fights. From there, by removing information until predictions became worse, Daniels and colleagues calculated the amount of information each individual needed to remember to make the most informed decision whether to fight or flee.

“We know the monkeys are making predictions, but we don’t know how good they are,” says Daniels. “But given this data, we found that the most memory it would take to figure out the regularities is about 1,000 bits of information.”

Sparse coding appears to be a strong candidate for explaining the mechanism at play in the monkey group, but the team points out that it is only one possible way to encode conflict.

Because the statistical modeling and computation frameworks can be applied to different natural datasets, the research has the potential to influence other fields of study, including behavioral science, cognition, computation, game theory and machine learning. Such models might also be useful in studying collective behaviors in other complex systems, ranging from neurons to bird flocks.

Future research will seek to find out how individuals’ knowledge of alliances and feuds fine tunes their own decisions and changes the groups’ collective pattern of conflict.

The research was supported by the National Science Foundation, the John Templeton Foundation through the Santa Fe Institute, and UW-Madison.

A Century Of Weather Control (POP SCI)

Posted 7.19.12 at 6:20 pm - http://www.popsci.com

 

Keeping Pilots Updated, November 1930

It’s 1930 and, for obvious reasons, pilots want regular reports on the weather. What to do? Congress’s solution was to give the U.S. Weather Bureau cash to send them what they needed. It was a lot of cash, too: $1.4 million, or “more than one third the sum it spend annually for all of its work.”

About 13,000 miles of airway were monitored for activity, and reports were regularly sent via the now quaintly named “teletype”–an early fax machine, basically, that let a typed message be reproduced. Pilots were then radioed with the information.

From the article “Weather Man Makes the Air Safe.”

 

Battling Hail, July 1947

We weren’t shy about laying on the drama in this piece on hail–it was causing millions in damage across the country and we were sick of it. Our writer says, “The war against hail has been declared.” (Remember: this was only two years after World War II, which was a little more serious. Maybe our patriotism just wouldn’t wane.)

The idea was to scatter silver iodide as a form of “cloud seeding”–turning the moisture to snow before it hails. It’s a process that’s still toyed with today.

From the article “The War Against Hail.”

 

Hunting for a Tornado “Cure,” March 1958

1957 was a record-breaking year for tornadoes, and PopSci was forecasting even rougher skies for 1958. As described by an official tornado watcher: ‘”They’re coming so fast and thick … that we’ve lost count.’”

To try to stop it, researchers wanted to learn more. Meteorologists asked for $5 million more a year from Congress to be able to study tornadoes whirling through the Midwest’s Tornado Alley, then, hopefully, learn what they needed to do to stop them.

From the article “What We’re Learning About Tornadoes.”

 

Spotting Clouds With Nimbus, November 1963

Weather satellites were a boon to both forecasters and anyone affected by extreme weather. The powerful Hurricane Esther was discovered two days before anything else spotted it, leaving space engineers “justifiably proud.” The next satellite in line was the Nimbus, which Popular Science devoted multiple pages to covering, highlighting its ability to photograph cloud cover 24 hours a day and give us better insight into extreme weather.

Spoiler: the results really did turn out great, with Nimbus satellites paving the way for modern GPS devices.

From the article “The Weather Eye That Never Blinks.”

 

Saving Money Globally With Forecasts, November 1970

Optimism for weather satellites seemed to be reaching a high by the ’70s, with Popular Science recounting all the disasters predicted–how they “saved countless lives through early hurricane warnings”–and now even saying they’d save your vacation.

What they were hoping for then was an accurate five-day forecast for the world, which they predicted would save billions and make early warnings even better.

From the article “How New Weather Satellites Will Give You More Reliable Forecasts.”

 

Extreme Weather Alerts on the Radio, July 1979

Those weather alerts that come on your television during a storm–or at least one radio version of those–were documented byPopular Science in 1979. But rather than being something that anyone could tune in to, they were specialized radios you had to purchase, which seems like a less-than-great solution to the problem. But at this point the government had plans to set up weather monitoring stations near 90 percent of the country’s population, opening the door for people to find out fast what the weather situation was.

From the article “Weather-Alert Radios–They Could Save Your Life.”

 

Stopping “Bolts From the Blue,” May 1990

Here Popular Science let loose a whooper for anyone with a fear of extreme weather: lightning kills a lot more people every year than you think, and sometimes a lightning bolt will come and hit you even when there’s not a storm. So-called “bolts from the blue” were a part of the story on better predicting lightning, a phenomenon more manic than most types of weather. Improved sensors played a major part in better preparing people before a storm.

From the article “Predicting Deadly Lightning.”

 

Infrared Views of Weather, August 1983

Early access to computers let weather scientists get a 3-D, radar-based view of weather across the country. The system culled information from multiple sources and placed it in one viewable display. (The man pictured looks slightly bored for how revolutionary it is.) The system was an attempt to take global information and make it into “real-time local predictions.”

From the article “Nowcasting: New Weather Computers Pinpoint Deadly Storms.”

 

Modernizing the National Weather Service, August 1997

A year’s worth of weather detection for every American was coming at the price of “a Big Mac, fries, and a Coke,” the deputy director of the National Weather Service said in 1997. The computer age better tied together the individual parts of weather forecasting for the NWS, leaving a unified whole that could grab complicated meteorological information and interpret it in just a few seconds.

From the article “Weather’s New Outlook.”

 

Modeling Weather With Computers, September 2001

Computer simulations, we wrote, would help us predict future storms more accurately. But it took (at the time) the largest supercomputer around to give us the kinds of models we wanted. Judging by the image, we might’ve already made significant progress on the weather modeling front.

Researchers Produce First Complete Computer Model of an Organism (Science Daily)

ScienceDaily (July 21, 2012) — In a breakthrough effort for computational biology, the world’s first complete computer model of an organism has been completed, Stanford researchers reported last week in the journal Cell.

The Covert Lab incorporated more than 1,900 experimentally observed parameters into their model of the tiny parasite Mycoplasma genitalium. () (Credit: Illustration by Erik Jacobsen / Covert Lab)

A team led by Markus Covert, assistant professor of bioengineering, used data from more than 900 scientific papers to account for every molecular interaction that takes place in the life cycle of Mycoplasma genitalium, the world’s smallest free-living bacterium.

By encompassing the entirety of an organism in silico, the paper fulfills a longstanding goal for the field. Not only does the model allow researchers to address questions that aren’t practical to examine otherwise, it represents a stepping-stone toward the use of computer-aided design in bioengineering and medicine.

“This achievement demonstrates a transforming approach to answering questions about fundamental biological processes,” said James M. Anderson, director of the National Institutes of Health Division of Program Coordination, Planning and Strategic Initiatives. “Comprehensive computer models of entire cells have the potential to advance our understanding of cellular function and, ultimately, to inform new approaches for the diagnosis and treatment of disease.”

The research was partially funded by an NIH Director’s Pioneer Award from the National Institutes of Health Common Fund.

From information to understanding

Biology over the past two decades has been marked by the rise of high-throughput studies producing enormous troves of cellular information. A lack of experimental data is no longer the primary limiting factor for researchers. Instead, it’s how to make sense of what they already know.

Most biological experiments, however, still take a reductionist approach to this vast array of data: knocking out a single gene and seeing what happens.

“Many of the issues we’re interested in aren’t single-gene problems,” said Covert. “They’re the complex result of hundreds or thousands of genes interacting.”

This situation has resulted in a yawning gap between information and understanding that can only be addressed by “bringing all of that data into one place and seeing how it fits together,” according to Stanford bioengineering graduate student and co-first author Jayodita Sanghvi.

Integrative computational models clarify data sets whose sheer size would otherwise place them outside human ken.

“You don’t really understand how something works until you can reproduce it yourself,” Sanghvi said.

Small is beautiful

Mycoplasma genitalium is a humble parasitic bacterium known mainly for showing up uninvited in human urogenital and respiratory tracts. But the pathogen also has the distinction of containing the smallest genome of any free-living organism — only 525 genes, as opposed to the 4,288 of E. coli, a more traditional laboratory bacterium.

Despite the difficulty of working with this sexually transmitted parasite, the minimalism of its genome has made it the focus of several recent bioengineering efforts. Notably, these include the J. Craig Venter Institute’s 2008 synthesis of the first artificial chromosome.

“The goal hasn’t only been to understand M. genitalium better,” said co-first author and Stanford biophysics graduate student Jonathan Karr. “It’s to understand biology generally.”

Even at this small scale, the quantity of data that the Stanford researchers incorporated into the virtual cell’s code was enormous. The final model made use of more than 1,900 experimentally determined parameters.

To integrate these disparate data points into a unified machine, the researchers modeled individual biological processes as 28 separate “modules,” each governed by its own algorithm. These modules then communicated to each other after every time step, making for a unified whole that closely matched M. genitalium‘s real-world behavior.

Probing the silicon cell

The purely computational cell opens up procedures that would be difficult to perform in an actual organism, as well as opportunities to reexamine experimental data.

In the paper, the model is used to demonstrate a number of these approaches, including detailed investigations of DNA-binding protein dynamics and the identification of new gene functions.

The program also allowed the researchers to address aspects of cell behavior that emerge from vast numbers of interacting factors.

The researchers had noticed, for instance, that the length of individual stages in the cell cycle varied from cell to cell, while the length of the overall cycle was much more consistent. Consulting the model, the researchers hypothesized that the overall cell cycle’s lack of variation was the result of a built-in negative feedback mechanism.

Cells that took longer to begin DNA replication had time to amass a large pool of free nucleotides. The actual replication step, which uses these nucleotides to form new DNA strands, then passed relatively quickly. Cells that went through the initial step quicker, on the other hand, had no nucleotide surplus. Replication ended up slowing to the rate of nucleotide production.

These kinds of findings remain hypotheses until they’re confirmed by real-world experiments, but they promise to accelerate the process of scientific inquiry.

“If you use a model to guide your experiments, you’re going to discover things faster. We’ve shown that time and time again,” said Covert.

Bio-CAD

Much of the model’s future promise lies in more applied fields.

CAD — computer-aided design — has revolutionized fields from aeronautics to civil engineering by drastically reducing the trial-and-error involved in design. But our incomplete understanding of even the simplest biological systems has meant that CAD hasn’t yet found a place in bioengineering.

Computational models like that of M. genitalium could bring rational design to biology — allowing not only for computer-guided experimental regimes, but also for the wholesale creation of new microorganisms.

Once similar models have been devised for more experimentally tractable organisms, Karr envisions bacteria or yeast specifically designed to mass-produce pharmaceuticals.

Bio-CAD could also lead to enticing medical advances — especially in the field of personalized medicine. But these applications are a long way off, the researchers said.

“This is potentially the new Human Genome Project,” Karr said. “It’s going to take a really large community effort to get close to a human model.”

Stanford’s Department of Bioengineering is jointly operated by the School of Engineering and the School of Medicine.