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Of ever-increasing concern for operating a tactical communications network is the possibility that a sophisticated adversary may detect friendly transmissions. Army researchers developed an analysis framework that enables the rigorous study of the detectability of ultraviolet communication systems, providing the insights needed to deliver the requirements of future, more secure Army networks.

In particular, ultraviolet communication has unique propagation characteristics that not only allow for a novel non-line-of-sight optical link, but also imply that the transmissions may be harder for an adversary to detect.

Building off of experimentally validated channel modeling, channel simulations, and detection and estimation theory, the developed framework enables the evaluation of tradeoffs associated with different design choices and the manner of operation of ultraviolet communication systems, said Dr. Robert Drost of the U.S. Army Combat Capabilities Development Command's Army Research Laboratory.

"While many techniques have been proposed to decrease the detectability of conventional radio-frequency, or RF, communications, the increased atmospheric absorption of deep-ultraviolet wavelengths implies that ultraviolet communication, or UVC, has a natural low-probability-of-detection, or LPD, characteristic," Drost said.

"In order to fully take advantage of this characteristic, a rigorous understanding of the LPD properties of UVC is needed."

In particular, Drost said, such understanding is essential for optimizing the design and operation of UVC systems and networks and for predicting the quality of the LPD property in a given scenario, such as using UVC to securely network a command post that has an estimate of the direction and distance to the adversary.

Without such a predictive capability, he said, users would lack the guidance needed to know the extent and limit of their detectability, and this lack of awareness would substantially limit the usefulness of the LPD capability.

The researchers, including Drs. Mike Weisman, Fikadu Dagefu, Terrence Moore and Drost from CCDC ARL and Dr. Hakan Arlsan, Oak Ridge Associated Universities postdoctoral fellow at the lab, demonstrated this by applying their framework to produce a number of key insights regarding the LPD characteristics of UVC, including:

LPD capability is relatively insensitive to a number of system and channel properties, which is important for the robustness of the LPD property
Adversarial line-of-sight detection of a non-line-of-sight communication link is not as significant of a concern as one might fear
Perhaps counter to intuition, steering of a UVC transmitter does not appear to be an effective detection-mitigation strategy in many cases
Line-of-sight UVC link provides non-line-of-sight standoff distances that are commensurate with the communication range

Prior modeling and experimental research has demonstrated that UVC signals attenuate dramatically at long distance, leading to the hypothesis that UVC has a fundamental LPD property, Drost said. However, there has been little effort on rigorously and precisely quantifying this property in terms of the detectability of a communication signal.

"Our work provides a framework enabling the study of the fundamental limits of detectability for an ultraviolet communication system meeting desired communication performance requirements," Drost said.

Although this research is focused on longer-term applications, he said, it is addressing the Army Modernization Priority on Networks by developing the fundamental understanding of a novel communications capability, with a goal of providing the Soldier with network connectivity despite challenging environments that include adversarial activity.

"The future communications and networking challenges that the Army faces are immense, and it is essential that we explore all possible means to overcoming those challenges," Drost said. "Our research is ensuring that the community has the fundamental understanding of the potential for and limitations of using ultraviolet wavelengths for communications, and I am confident that this understanding will inform the development of future Army networking capabilities. Conducting fundamental research that impacts decision making and Army technologies is why we work for the Army, and it is very satisfying to know that our work will ultimately support the warfighter in his or her mission."

The researchers are currently continuing to develop refined understanding of how best to design and operate ultraviolet communications, and an important next step is the application of this framework to understand the detectability of a network of ultraviolet communications systems.

Another key effort involves the experimental characterization, exploration and demonstration of this technology in a practical network using ARL's Common Sensor Radio, a sophisticated mesh-networking radio designed to provide robust and energy-efficient networking.

This research supports the laboratory's FREEDOM (Foundational Research for Electronic Warfare in Multi-Domain Operations) Essential Research Program goal of studying the integration of low-signature communications technologies with advanced camouflage and decoy techniques.

According to Drost, the work is also an on-ramp to studying how ultraviolet communications and other communications modalities, including conventional radio-frequency communications, can operate together in a seamless and autonomous extremely heterogeneous network, which the researchers believe is needed in order to fully realize the benefits of individual novel communication technologies.

As they make continued progress on these fundamental research questions, the researchers will continue to work closely with their transition partner at the CCDC C5ISR (Command, Control, Computers, Communications, Cyber, Intelligence, Surveillance and Reconnaissance) Center to push ultraviolet communications toward nearer term transition to the warfighter.

A new animation of night-time imagery from NASA-NOAA's Suomi NPP satellite revealed how the Eastern Pacific Ocean's Elida transformed into a hurricane over a three-day period.

NASA's Night-Time View of Elida's Intensification

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard Suomi NPP provided a night-time image of Hurricane Elida during the early morning hours of Aug. 11 (8 p.m. EDT on Aug. 10). The storm had intensified into a hurricane and an eye was clearly apparent, surrounded by powerful thunderstorms around it.

At NASA's Goddard Space Flight Center in Greenbelt, Md. an animation of night-time imagery from NASA-NOAA's Suomi NPP satellite shows the development and intensification of Hurricane Elida in the Eastern Pacific Ocean from Aug. 9 to 11, 2020 at 0000 UTC (which is 8 p.m. EDT Aug. 8 to 10). On Aug. 9, Elida appeared somewhat shapeless, and by the night-time hours of Aug.10, the storm took on a general tropical cyclone shape with bands of thunderstorms wrapping into the low-level center. Elida became a hurricane by 5 p.m. EDT (2100 UTC) on Aug. 10. By Aug. 11, Elida had a tight circulation of powerful thunderstorms around the center and an eye was apparent on the night-time imagery. The animation was created using the NASA Worldview application.

Hurricane Elida's Status on Aug. 11

At 11 a.m. EDT (1500 UTC) on Aug. 11, the National Hurricane Center (NHC) noted the center of Hurricane Elida was located near latitude 21.3 degrees north and longitude 113.8 degrees west. That is about 275 miles (440 km) west-southwest of the southern tip of Baja California, Mexico.

Elida was moving toward the northwest near 14 mph (22 kph). A west-northwestward to northwestward motion with a decrease in forward seed is expected during the next several days. Maximum sustained winds have increased to near 100 mph (155 kph) with higher gusts. Elida is now a Category 2 hurricane on the Saffir-Simpson Hurricane Wind Scale. Hurricane-force winds extend outward up to 15 miles (30 km) from the center and tropical-storm-force winds extend outward up to 70 miles (110 km). The estimated minimum central pressure is 975 millibars.

Rapid weakening is expected to begin tonight as Elida moves over colder water, and the cyclone is expected to weaken to a tropical storm on Wednesday and degenerate to a remnant low pressure area on Thursday, Aug. 13.

BROOKLYN, New York, Tuesday, August 11, 2020 - Surges in firearm acquisition after mass shootings is a well-documented phenomenon, but analytic research into the causes of this behavior -- be it driven by a desire for self-protection, or a fear that access to firearms will be curtailed -- is sparse.

A new study applying a data science methodology of state-by-state data to infer causal relationships finds that the decision to purchase a gun is driven by the latter concern -- stricter regulations on gun purchase and ownership -- more than by a desire to protect oneself after a mass shooting. The study, led by Maurizio Porfiri, Institute professor at NYU Tandon, is his second in a year to examine causative factors driving consumer firearm-purchase behavior.

It also presages a much more comprehensive effort backed by a $2 million grant from the National Science Foundation (NSF). The project, funded under the NSF's LEAP HI program (Leading Engineering for America's Prosperity, Health and Infrastructure), will examine causal relationships between potentially contributing factors as firearm prevalence, state legislation, media exposure, and people's opinion on firearm-related harms, at the individual, state, and nation levels.

This will be the first research of its kind to unfold the firearm ecosystem simultaneously on three levels:

Macroscale: causality between firearm prevalence and firearm-related harms at the national level

Mesoscale: policy diffusion across states

Microscale: individual opinions about firearm safety

"By cogently linking these scales, we will lay the foundation for analysis, diagnostics, and prediction of firearm-related harms," said Porfiri.

The research effort, 'Understanding and Engineering the Ecosystem of Firearms: Prevalence, Safety, and Firearm-Related Harms, will be orchestrated by a multidisciplinary team comprising co-principal investigators Oded Nov, a professor of Technology Management and Innovation at NYU Tandon; Igor Belykh, a professor of Mathematics and Statistics at Georgia State University; James Macinko, a professor of Health Policy and Management at the University of California Los Angeles; and Rifat Sipahi, a professor of Mechanical and Industrial Engineering at Northeastern University. Also involved are Shinnosuke Nakayama, formerly a post-doctoral associate in Porfiri's lab at NYU Tandon and now a data research scientist at the Center for Ocean Solutions at Stanford University; and Maria Grillo, a project associate in the Institute for Invention, Innovation, and Entrepreneurship at NYU Tandon.

New research comprises three studies focused on state-level data

The newly-published research, "Self-protection versus fear of stricter firearm regulations: examining the drivers of firearm acquisitions in the aftermath of a mass shooting," appears in the Cell Press journal Patterns. It comprises three studies based on data the team collected on mass shootings, federal background checks related to firearm purchases, media output on firearm control and shootings from several media outlets in the country, and firearm safety laws from 1999 to 2017.

The authors of the new study, including Roni Barak-Ventura, research assistant in Porfiri's Dynamical Systems Laboratory and Manuel Ruiz Marín of the Technical University of Cartagena, Spain, puts forward a data science framework, based on the mathematical construct of transfer entropy to discover causal links between multiple variables by examining the degree to which one variable influences another. In these analyses, influence is defined as an improved ability to make predictions about the future status of a variable (in this case, background checks) based on present knowledge of another variable (for example, media stories about gun control policy).

The team first conducted a cluster analysis to partition states according to the restrictiveness of their firearm-related legal environment. That was followed by a transfer entropy analysis to unveil causal relationships at the state-level between mass shootings, media coverage of gun violence, media coverage of firearm regulations, and background checks.

The first study examined how the occurrence of mass shootings in the nation, media reports about shootings, and media reports on firearm control influence the number of background checks in firearm restrictive and firearm permissive states. The researchers found that increased media coverage of firearm control influenced background checks in permissive states.

The second study tested whether the location of a mass shooting had a potential influence on the number of background checks across the country. The researchers found that the number of background checks in one state was not significantly affected by mass shootings in another, irrespective of their location or the state's restrictiveness.

The third study looked at the influence on the number of background checks in a given state of background checks in geographically neighboring states. The team found a strong interaction among states, whether they are permissive or restrictive, so that firearm purchases in a state determine purchases in neighboring state.

"The analysis suggests that fear of stricter firearm regulations is a stronger driver than the desire of self-protection for firearm acquisitions," said Porfiri, who is on a research sabbatical at the Technical University of Cartagena, Spain. "This fear is likely to cross states' borders, thereby shaping a collective pattern of firearm acquisition throughout the nation."

Ruiz Marín added that "This research brings forward an alternative data science methodology to examine causal links in spatio-temporal data, with potential application to the study of a number of problems in economics and social sciences."

Porfiri's first study of this kind, published in Nature Human Behavior in September, 2019, similarly applied entropy transfer techniques to mass shootings and the publicity around them, and demonstrated the potential of quantitative methods, grounded in engineering principles, to elucidate key aspects of the firearm ecosystem.

"Engineering, by definition, applies mathematical tools and scientific principles to real-world challenges. Porfiri's work -- the importance of which is reflected in the generosity of the NSF LEAP HI award -- is proof positive that engineering offers solutions beyond hardware, software, chemical innovations, and physical structures," said Jelena Kovačevi?, Dean of the NYU Tandon School of Engineering. "Indeed, Porfiri's research brings hard data and rigorous analytics to bear on the sometimes amorphous patterns and influences that drive our society and, ultimately, shine a light on the machinery of our democracy."

ITHACA, N.Y. - Machine learning can assess the effectiveness of mathematical tools used to predict the movements of financial markets, according to new Cornell research based on the largest dataset ever used in this area.

The researchers' model could also predict future market movements, an extraordinarily difficult task because of markets' massive amounts of information and high volatility.

"What we were trying to do is bring the power of machine learning techniques to not only evaluate how well our current methods and models work, but also to help us extend these in a way that we never could do without machine learning," said Maureen O'Hara, the Robert W. Purcell Professor of Management at the SC Johnson College of Business.

O'Hara is co-author of "Microstructure in the Machine Age," published July 7 in The Review of Financial Studies.

"Trying to estimate these sorts of things using standard techniques gets very tricky, because the databases are so big. The beauty of machine learning is that it's a different way to analyze the data," O'Hara said. "The key thing we show in this paper is that in some cases, these microstructure features that attach to one contract are so powerful, they can predict the movements of other contracts. So we can pick up the patterns of how markets affect other markets, which is very difficult to do using standard tools."

Markets generate vast amounts of data, and billions of dollars are at stake in mining that data for patterns to shed light on future market behavior. Companies on Wall Street and elsewhere employ various algorithms, examining different variables and factors, to find such patterns and predict the future.

In the study, the researchers used what's known as a random forest machine learning algorithm to better understand the effectiveness of some of these models. They assessed the tools using a dataset of 87 futures contracts - agreements to buy or sell assets in the future at predetermined prices.

"Our sample is basically all active futures contracts around the world for five years, and we use every single trade - tens of millions of them - in our analysis," O'Hara said. "What we did is use machine learning to try to understand how well microstructure tools developed for less complex market settings work to predict the future price process both within a contract and then collectively across contracts. We find that some of the variables work very, very well - and some of them not so great."

Machine learning has long been used in finance, but typically as a so-called "black box" - in which an artificial intelligence algorithm uses reams of data to predict future patterns but without revealing how it makes its determinations. This method can be effective in the short term, O'Hara said, but sheds little light on what actually causes market patterns.

"Our use for machine learning is: I have a theory about what moves markets, so how can I test it?" she said. "How can I really understand whether my theories are any good? And how can I use what I learned from this machine learning approach to help me build better models and understand things that I can't model because it's too complex?"

Huge amounts of historical market data are available - every trade has been recorded since the 1980's - and vast volumes of information are generated every day. Increased computing power and greater availability of data have made it possible to perform more fine-grained and comprehensive analyses, but these datasets, and the computing power needed to analyze them, can be prohibitively expensive for scholars.

In this research, finance industry practitioners partnered with the academic researchers to provide the data and the computers for the study as well as expertise in machine learning algorithms used in practice.

"This partnership brings benefits to both," said O'Hara, adding that the paper is one in a line of research she, Easley and Lopez de Prado have completed over the last decade. "It allows us to do research in ways generally unavailable to academic researchers."

BYRON BAY, Australia (August 11, 2020) - A study by WCS and partners produced new analyses to pinpoint the most important forests for biodiversity conservation remaining in Central Africa. The results highlight the importance of the Democratic Republic of Congo (DRC), northern Republic of Congo, and much of Gabon as the most important countries in Central Africa for safeguarding biodiversity and intact forests.

The study combines new datasets on forests to identify where the most intact forests remain across this vast area with previous work that identified strongholds for bonobos, forest elephants, gorillas, and chimpanzees across the region. The results reveal that the Democratic Republic of Congo has the largest amount of priority areas in the region, containing more than half, followed by Gabon, the Republic of Congo, and Cameroon. Specific regions include: the Salonga area and East-central DRC; northern Republic of Congo; extensive areas in Gabon including Crystal Mountains (Monts de Cristal) and Chaillu Mountains (Monts de Chaillu), areas along the coast, and the north-east.

The authors compared their approach to one that solely prioritizes forest intactness based on a forest fragmentation and degradation model, and models of human pressures on the forest, to one that aims to achieve only biodiversity representation objectives, and one that combines them all. They found that when priorities are only based on forest intactness without considering biodiversity representation, there are significantly fewer biodiversity benefits and vice versa.

The study's lead author, Dr. Hedley Grantham, WCS Director of Conservation Planning, said: "This study shows that just prioritizing forests based on their condition will trade-off biodiversity representation benefits, and vice versa will miss locations for preserving the remaining intact forests important for many species in an increasingly human-dominated world. Our approach can inform various types of conservation strategies, including land-use planning, carbon payments, protected area expansion, community forest management, and forest concession plans."

The forests of Central Africa contain some of Earth's few remaining intact forests. These forests are increasingly threatened by infrastructure development, agriculture, and unsustainable extraction of natural resources (e.g., minerals, bushmeat, and timber), all of which is leading to deforestation and forest degradation, particularly defaunation, and hence causing declines in biodiversity and a significant increase in carbon emissions.

Co-author WCS Conservation Scientist Fiona "Boo" Maisels said: "By highlighting areas of high importance for biodiversity and forest intactness, our analysis can guide national infrastructure and agricultural development plans to the areas of low conservation value, thus simultaneously enabling sustainable development and sound conservation stewardship"

Olivia Rickenbach led the development of guidelines on High Conservation Value (HCV) identification and management for Forest Stewardship Council (FSC) certified forest management in the Congo Basin. She notes: "FSC initiated and coordinated the collaboration that produced this analysis. It was sparked by the lack of readily available data and decision-making tools which could identify the most important zones for biodiversity conservation at a landscape level. The analysis was also needed due to the regional critique of IFLs (Intact Forest Landscapes) as indicators to define such areas. Draft HCV guidelines were approved in November 2019 by the regional working group supervising this task; they now propose this method and data to identify HCV 2 areas."

CLEVELAND - Cleveland Clinic researchers have developed and validated a risk prediction model (called a nomogram) that can help physicians predict which patients who have recently tested positive for SARS-CoV-2, the virus that causes COVID-19, are at greatest risk for hospitalization.

This new model, published in PLOS One, is the second COVID-19-related nomogram that the research team--led by Lara Jehi, M.D., chief research information officer at Cleveland Clinic, and Michael Kattan, Ph.D., chair of Lerner Research Institute's Department of Quantitative Health Sciences--has developed. Their earlier model forecasts an individual patient's likelihood of testing positive for the virus.

"Ultimately, we want to create a suite of tools that physicians can use to help inform personalized care and resource allocation at many time points throughout a patient's experience with COVID-19," said Dr. Jehi, corresponding author on the study.

The team's newest model was developed and validated using retrospective patient data from more than 4,500 patients who tested positive for COVID-19 at Cleveland Clinic locations in Northeast Ohio and Florida during a three-month time period (early March to early June). Data scientists used statistical algorithms to transform data from registry patients' electronic medical records into the risk prediction model.

Comparing characteristics between those patients who were and were not hospitalized due to COVID-19 revealed several previously undefined hospitalization risk factors, including:

Smoking. Former smokers were more likely to be hospitalized than current smokers.

Taking certain medications. Using univariable analysis, patients taking Angiotensin Converting Enzyme (ACE) inhibitors or angiotensin II type-I receptor blockers (ARBs) were more likely to be hospitalized than patients not taking those drugs.

Race. African American patients were more likely to be hospitalized than patients of other races.

Dr. Kattan, an expert in developing and validating prediction models for medical decision making, cautions that additional studies will be necessary to further explore the association between ACE inhibitors and ARBs. "In our study, taking these drugs was only found to confer increased risk for hospitalization when run through univariable analysis, which means the observed association could be the result of other, confounding variables, like a preexisting condition."

The team's findings also revealed that patients presenting with a symptom complex including fever, shortness of breath, vomiting and fatigue were more likely to be hospitalized than those who did not experience this quadrumvirate of symptoms.

The study confirmed other associations previously well-reported in the literature, including higher risk of hospitalization among older people; men; and those with co-morbidities, like diabetes and hypertension, or from lower socioeconomic backgrounds (as measured by zip code).

"Hospitalization can be used as an indicator of disease severity," said Dr. Jehi. "Understanding which patients are most likely to be admitted to the hospital for COVID-19-related symptoms and complications can help physicians decide not only how to best manage a patient's care from the time of testing, but also how to allocate beds and other resources, like ventilators."

The nomogram, which is freely available as an online risk calculator, was shown to be well calibrated and perform well, offering substantially better predictions than using no model at all. The model was also shown to perform well in different geographic regions as data from Ohio and Florida were used in its development.

In addition to further interrogating the association between taking ACE inhibitors and ARBs, it will be important to study on a pathogenic level how these risk factors confer increased hospitalization risk. It is also important to note that the team's findings only offer associations and do not suggest that these factors are causative.

Feeling green might not be a bad thing when it comes to friendships, especially during a pandemic.

Having friends is healthy. Not having friends is associated with a greater risk of dying from heart disease and with becoming sick from viruses.

A new study from Arizona State University, Oklahoma State University, and Hamilton College has found feelings of jealousy can be a useful tool in maintaining friendships. Feelings of jealousy were related to the value of the friendship and also motivated behaviors that maintain friendships. The work was published online in the Journal of Personality and Social Psychology on August 11.

"Friends aren't just fun. They are an important resource, especially in our current situation with ongoing COVID-19 outbreaks. Friends give support during conflict, buffer against loneliness, and can even provide life sustaining resources when we need them," said Jaimie Arona Krems, who earned her doctorate at Arizona State University and is now an assistant professor of psychology at Oklahoma State University. "We wanted to understand how we keep friendships, and we found feelings of jealousy can act like a tool for maintaining friendships."

The third wheel

Not all threats to friendships evoked jealousy. If a best friend moved away, people felt sadness and anger more than jealousy. But when friendships were threatened by another person - such as a new romantic partner or new friend at work - jealousy was the dominant feeling.

The intensity of jealous feelings varied by how likely the third-party threat was to replace someone in the friendship. A best friend gaining a romantic partner elicited less jealous feelings than them gaining a potential new friend.

"The third party threats to a friendship were not just related to a best friend spending time away from us: It mattered whether the person they were spending time with could replace us as a friend. We found people felt less jealous about their best friend spending the same amount of time with a new romantic partner than a new acquaintance, which means what makes us most jealous of is the possibility that we might be replaced," said Douglas Kenrick, who is a President's Professor of psychology at ASU and author on the paper.

Guarding friendships

Feelings of jealousy over being replaced were associated with behaviors that could overcome the third-party threats, like trying to monopolize a best friend's time and manipulate their emotions.

"Together, these behaviors are called 'friend guarding', and they occur across cultures and also in non-human animals. Female wild horses are known to bite and kick other female horses," said Keelah Williams, assistant professor of psychology at Hamilton College who earned her doctorate and law degree at ASU.

Not all friend guarding behaviors focus on trying to control a best friend; jealousy also led people to commit to being a better friend.

"Getting jealous can sometimes be a signal that a friendship is threatened, and this signal can help us jump into action to invest in a friendship that we might have been neglecting," said Athena Aktipis, assistant professor of psychology at ASU and author on the paper.

Making decisions in the face of uncertainty has never been easy. But the global pandemic has raised the stakes for many previously mundane choices: how to travel, where to get food, when to send kids back to school.

Understanding how humans have made high-stakes decisions over evolutionary time may help to explain our choices in the present day – including our tendency to veer from the preferences predicted by economic models, according to a new study from scholars at Stanford University and the Santa Fe Institute.

“Rather than starting with utility – the happiness or value I get out of making my decision now – let’s think about how the brain was constructed over evolutionary history,” said study co-author James Holland Jones, a biological anthropologist at Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth). The research was published in the journal Evolutionary Human Sciences.

The pair’s proposal adds a new perspective to long-running scholarly debates over why practices designed to improve the standard of living among subsistence populations don’t take hold, such as the seemingly slow adoption of new farming technologies among poor, small-scale farmers, and more recently, the unwillingness of the poorest poor to adopt microfinance and other development schemes.

“There is an inclination to think of the poorest people as being ‘natural entrepreneurs’ because they have nothing to lose economically,” Jones explained. “However, the evolutionary logic we employ suggests that the poorest poor have everything to lose and are, in fact, closer to losing it than better-off people. Our model predicts that very poor people would be especially risk-averse.”

It also points to the weakness of lean systems in the face of uncommon but severe threats, such as the coronavirus. “One of the things we’re seeing right now is a world that has been optimized for efficiency and is extremely vulnerable to risk,” he said. “If you scale back organizations to keep them running at a mean level that’s high, and you don’t have a lot of slack, when a crisis hits you’re in trouble.”

Rational choices in evolutionary systems

According to the theory of expected utility, a staple of modern economics, people should always carefully weigh the likelihood of an event along with the prizes or consequences that would accrue from our decision – and then choose the option with the highest average payoff. Of course, we rarely calculate these averages in practice, as behavioral economists have long recognized. Yet an assumption that our brains will behave as if we made decisions this way – maximizing personal gain at every turn – is still baked into many public and economic policies.

“We might expect evolutionary systems to mirror markets, with organisms that act rationally out-competing those not behaving rationally,” said Jones, an associate professor of Earth system science at Stanford Earth and a senior fellow at the Stanford Woods Institute for the Environment. “The catch is that you can’t outcompete something if you’re extinct.”

In addition to influencing policy, business and financial markets, theories of how we make decisions have filtered into popular culture through books like Nudge and Thinking Fast and Slow. However, they tend to deal poorly with choices that humans have faced for the vast majority of their history on Earth – namely, those shaped not by market forces, but by environmental variables like temperature or rainfall. In this context, boom times can’t compensate for a single lethal bust. Just one bad heat wave, drought, cold snap or flood can leave a household hungry or worse. “Variance is what drives you to extinction,” Jones said.

As a result, when it comes to preferences that evolve by natural selection, he said, we should expect to see people undervalue long shots that could be profitable, play it safe when things look risky and generally overestimate the likelihood of rare bad outcomes.

Pessimism pays

On the timescale of evolution, the salient outcome of a decision is how it contributes to fitness, meaning the proportion of the population through time that carries your DNA. Unlike utility, fitness is a measure that multiplies over time. “If any generation in your lineage has zero offspring in it, it’s game over,” Jones said. “It is a general aversion to zeros that leads to pessimism.”

At the same time, fitness plays out over such long timescales that it can’t directly influence our behavior. The things that do shape our choices day to day are more like utility in that they can rise and fall without bringing catastrophe. “Psychological mechanisms, like satiety or sexual gratification, or something like love of your children, can motivate you in the immediate. They promote fitness in the long run, but they are not the thing actually being maximized over time,” he said.

Maximizing fitness leads us to be more pessimistic in our economic decisions than utility models predict. The optimal level of pessimism to promote survival depends “on the exact universe the organism occupies,” the authors write. For example, hunters targeting rare, big game may stand to bring home more calories if they succeed, but their household could go hungry if they fail. Herders have to weigh not only the productivity of their animals, but also their susceptibility to drought and disease.

“Any time where you have to avoid zero, pessimism will pay off, because you’d rather leave money on the table than run the risk of going extinct,” Jones said.

Theory into practice

When social distancing restrictions loosen enough to conduct group experiments, Jones and co-author Michael Price, PhD ’15, who studies complex systems as a fellow at the Santa Fe Institute, plan to test their theory with games challenging participants to maximize payoffs that multiply over time or that are hidden but associated with some tangible proxy. By formalizing and eventually testing the theory, the researchers write, they “hope to stimulate more work on the possible evolutionary foundations of key results from behavioral economics.”

DALLAS, August 11, 2020 -- Interstage Home Monitoring (IHM) programs for infants with single ventricle heart defects help families recognize potential complications early and improve infant survival rates and growth prior to the second of multiple surgeries, according to a new Scientific Statement from the American Heart Association, "Interstage Home Monitoring for Infants With Single Ventricle Heart Disease: Education and Management," published today in the Journal of the American Heart Association.

The National Pediatric Cardiology Quality Improvement Collaborative (NPC-QIC), a network of pediatric cardiology care centers across the U.S., reported an average 40% decrease (9.5% to 5.3%) in infant mortality and a 28% improvement in infant weight gain (18.6% to 13.1%) across 50 cardiac centers using IHM programs from 2008 to 2016.

Treatment for the single ventricle heart defect hypoplastic left heart syndrome - in which the heart's left side is underdeveloped - involves one surgery shortly after birth with a second surgery planned four to six months later, and a third procedure a year or so after that. IHM programs concentrate on the high-risk time between the first two surgeries, known as the interstage period. The primary focus of IHM programs is to help family caregivers carefully monitor several important health parameters including an infant's oxygen saturation levels, caloric intake and weight gain. Weight gain is an important marker for an infant to successfully undergo the second surgery.

IHM programs also train caregivers to recognize early "red flag" symptoms such as respiratory changes, sweating, fussiness, diarrhea, fever or changes in skin color that warrant immediate notification of the infant's health care team.

The AHA scientific statement outlines plans for health care professionals when training home caregivers while the infant is still hospitalized, and also addresses caregiver support and education, health care teams and resources, surveillance strategies and practices, national quality improvement efforts, interstage outcomes and future areas for research.

"This is a comprehensive resource for cardiology care professionals and family caregivers, and it also provides a framework and roadmap for cardiac centers looking to establish an IHM program or possibly expand or strengthen one already in place," said chair of the statement writing group Nancy Rudd, M.S., C.P.N.P.-P.C./A.C., FAHA, nurse coordinator for the Interstage Home Monitoring Program at Herma Heart Institute, Children's Wisconsin, and a nurse practitioner in the division of pediatric cardiology at the Medical College of Wisconsin, both in Milwaukee. "The statement is also a much-needed document validating the need for cost coverage for the various parts of IHM programs that lead to improved patient outcomes."

The first IHM program was initiated in 2000 at Children's Wisconsin due to trends indicating mortality rates were as high as 16% during the interstage period. The pediatric quality improvement cooperative was formed in 2008 and has advanced knowledge and best practice guidelines to improve the outcomes and quality of life for children with hypoplastic left heart syndrome during the interstage period.

"Prior to IHM programs, the outpatient management of interstage infants was the same as that of much less complex patients. Unfortunately, the tenuous nature of these infants means they can get very sick very quickly from even minor childhood illness like the common cold," said Rudd.

Statement authors noted other practice and program changes associated with caring for these pediatric patients also contributed to their improved survival and weight gain.

Sarah Robinson's daughter, now two years old, was born with hypoplastic left heart syndrome. As part of an IHM program, Robinson learned how to care for her infant through a rooming-in session during the baby's first post-surgery hospital stay. "Before discharge, we had to provide 24 hours of care, which meant doing everything for our daughter by ourselves with no machines on--all the feedings, administering medications and more--while having medical staff available if needed or if a problem arose," Robinson said in a perspective published with the statement. "Having a program like this in place gave us comfort, knowing we would not be completely alone during a very stressful and anxious time before the second surgery. Interstage home monitoring was our life preserver and safety net."

Many IHM programs have evolved to include telehealth platforms, and expanding technology enables optimized data collection and real-time video visual assessments of patients at home. The authors conclude that together with improved care coordination, discharge planning, and nutritional management bundles, IHM is a key component in optimizing outcomes in these high-risk infants.

The study, conducted by an international team and led by RMIT University's Professor Christian Doerig, outlines a strategy that could save years of drug discovery research and millions of dollars in drug development by repurposing existing treatments designed for other diseases such as cancer.

The approach shows so much promise it has received government funding for its potential application in the fight against COVID-19.

The study, published in Nature Communications, demonstrated that the parasites that cause malaria are heavily dependent on enzymes in red blood cells where the parasites hide and proliferate.

It also revealed that drugs developed for cancer, and which inactivate these human enzymes, known as protein kinases, are highly effective in killing the parasite and represent an alternative to drugs that target the parasite itself.

Lead author, RMIT's Dr Jack Adderley, said the analysis revealed which of the host cell enzymes were activated during infection, revealing novel points of reliance of the parasite on its human host.

"This approach has the potential to considerably reduce the cost and accelerate the deployment of new and urgently needed antimalarials," he said.

"These host enzymes are in many instances the same as those activated in cancer cells, so we can now jump on the back of existing cancer drug discovery and look to repurpose a drug that is already available or close to completion of the drug development process."

As well as enabling the repurposing of drugs, the approach is likely to reduce the emergence of drug resistance, as the pathogen cannot escape by simply mutating the target of the drug, as is the case for most currently available antimalarials.

Doerig, Associate Dean for the Biomedical Sciences Cluster at RMIT and senior author of the paper, said the findings were exciting, as drug resistance is one of the biggest challenges in modern healthcare, not only in the case of malaria, but with most infectious agents, including a large number of highly pathogenic bacterial species.

"We are at risk of returning to the pre-antibiotic era if we don't solve this resistance problem, which constitutes a clear and present danger for global public health. We need innovative ways to address this issue," he said.

"By targeting the host and not the pathogen itself, we remove the possibility for the pathogen to rapidly become resistant by mutating the target of the drug, as the target is made by the human host, not the pathogen."

Doerig's team will now collaborate with the Peter Doherty Institute for Infection and Immunity (Doherty Institute) to investigate potential COVID-19 treatments using this approach, supported by funding from the Victorian Medical Research Acceleration Fund in partnership with the Bio Capital Impact Fund (BCIF).

Co-investigator on the grant, Royal Melbourne Hospital's Dr Julian Druce, from the Victorian Infectious Diseases Reference Laboratory (VIDRL) at the Doherty Institute, was part of the team that were first to grow and share the virus that causes COVID-19, and said the research was an important contribution to efforts to defeat the pandemic.

Royal Melbourne Hospital's Professor Peter Revill, Senior Medical Scientist at the Doherty Institute and a leader on Hepatitis B research, said the approach developed by the RMIT team was truly exciting.

"This has proven successful for other human pathogens including malaria and Hepatitis C virus, and there are now very real prospects to use it to discover novel drug targets for Hepatitis B and COVID-19," he said.

Enzymes used by bacteria to break down mucus in the gut could provide a useful biomarker for intestinal diseases, according to new research published in Nature Communications.

Researchers at the University of Birmingham and Newcastle University have successfully identified and characterised one of the key enzymes involved in this process. They demonstrated how the enzyme enables bacteria to break down and feed off sugars in the layers of mucus lining the gut.

The research offers a significant step forward in our understanding of the complex co-dependent relationships at work in the gut, about which little is currently known. Because the mechanism used by the enzyme is particularly distinctive, the researchers anticipate it can be used in the development of new diagnostics for intestinal diseases.

The molecules in mucus, called mucin, are constantly produced by the body to generate the layer of mucus in the gut that provides a barrier between the gut's complex populations of bacteria and the rest of the body. Mucin contain chains of sugar molecules called glycans, and these also provide an essential source of nutrients for bacteria.

The team investigated how this enzyme sits on the outside of the bacterial cell and clips away parts of the mucin molecule, taking them inside the bacterial cell to be consumed.

Because glycans are known to change when certain diseases are present in the body, the researchers anticipate it will be possible to use the enzymes to take a snapshot of the glycans within a biopsy and use that as a biomarker for early detection of the disease.

Lead researcher, Dr Lucy Crouch, of the University of Birmingham's School of Biosciences, explains: "Mucus is structured a bit like a tree, with lots of different branches and leaves. Lots of the enzymes discovered so far might clip away some of the leaves to eat, but the enzyme we studied will clip away a whole branch - that's quite a distinctive mechanism and it gives us a useful biomarker for studying disease."

The team have investigated this process in three different diseases. They examined tissue from adults suffering from ulcerative colitis and colorectal cancer, and from preterm infants with necrotising enterocolitis, a serious illness in which the gut becomes inflamed and can start to die. They found that by adding the enzyme to the samples and labelling the glycans with a fluorescent dye, they were able to get useful information about the glycan structure.

Dr Crouch adds: "Although we still don't fully understand what the glycan structures are made from and how these vary between different tissue types, we can see that the differences in structure between health and non-healthy tissue is quite distinctive. We hope to be able to use these enzymes to start producing better diagnostics for the very early stages of these diseases."

ITASCA, Illinois - A new study published in the Journal of Food Science suggests monosodium glutamate (MSG) can be used to significantly reduce sodium while also promoting the enjoyment of better-for-you foods like grains and vegetables. In the study, supported by Ajinomoto Co., Inc., participants evaluated four different recipes in which sodium was reduced by 31 to 61 percent through the addition of MSG, and described the dishes as "flavorful," "delicious," and "balanced."

Ninety percent of Americans consume too much sodium and often have misperceptions about the taste of nutritious foods creating a barrier to healthy eating. MSG (or umami seasoning) can be one tool to encourage healthier dietary patterns.

"Just as the substitution of butter with olive oil can help to reduce saturated fat intake, MSG can be used as a partial replacement for salt to reduce sodium intake," says Dr. Jean-Xavier Guinard, Professor of Sensory Science, Co-Director of the Coffee Center at the University of California, Davis, and a lead investigator in this study. "MSG has two-thirds less sodium than table salt and imparts umami - a savory taste. Taste is a key factor in what people decide to eat. Using MSG as a replacement for some salt in the diet and to increase the appeal of nutritious foods can help make healthy eating easier, likely leading to a positive impact on health."

Culinary scientists from Pilot R&D, a food innovation and development company, developed four dishes - roasted vegetables, a quinoa bowl, a savory yogurt dip, and cauliflower fried rice with pork. Study participants (163 total, aged 18-62 years) evaluated three different versions of each dish - a standard recipe with typical salt content, a reduced salt recipe with significant sodium reduction, and the same reduced salt recipe with significant sodium reduction plus MSG added. For each dish, participants rated overall liking, appearance, flavor, texture, saltiness, aftertaste, and how likely they would be to order the dish at a restaurant. The reduced salt recipes with added MSG were liked as much as or better than (in the case of the quinoa bowl and savory yogurt dip) the standard recipes, suggesting that MSG can be used as a way to reduce sodium without compromising taste. Whereas the reduced salt recipes were commonly described as "bland" and the standard recipes described as "salty" and "sour" in some cases, the MSG recipes were associated with "delicious," "flavorful," "balanced," and "savory" in some instances.

Previous research has shown that MSG can be used to reduce sodium by 30 percent, and in some cases up to 50 percent, in packaged foods and snacks such as soups, broths, chips, and sausage, without compromising taste and consumer preference for the products. For the first time, this study shows promise for using MSG in better-for-you foods, or those with a desirable nutritional profile that consumers should be eating more of.

"Extensive scientific research confirms MSG's safety, and now we see a benefit of using it to improve the flavor of nutritious foods," says Guinard. "Survey results from our study show that many people are not aware of how to use MSG in their own cooking. The easiest place to start is to replace half of the salt in your salt shaker with MSG, or if a recipe calls for 1 teaspoon of salt, try ½ teaspoon of salt and ½ teaspoon of MSG, instead - and of course, savor the flavor."

As with any study, limitations should be considered. The study could have included many more versions of the recipes with varying levels of salt and MSG to optimize results. However, this is a promising starting point for using MSG in better-for-you foods.

Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future.

A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification. The AI enabled the team to perform the processing without human intervention.

Automated processing techniques for extraction and judgment of features with deep-learning algorithms have been rapidly developed since 2012. Now they usually surpass humans in terms of accuracy and are used for autonomous vehicles, security cameras, and many other applications. Dr. Ken-ichi Tadaki, a Project Assistant Professor at NAOJ, came up with the idea that if AI can classify images of cats and dogs, it should be able to distinguish "galaxies with spiral patterns" from "galaxies without spiral patterns." Indeed, using training data prepared by humans, the AI successfully classified the galaxy morphologies with an accuracy of 97.5%. Then applying the trained AI to the full data set, it identified spirals in about 80,000 galaxies.

Now that this technique has been proven effective, it can be extended to classify galaxies into more detailed classes, by training the AI on the basis of a substantial number of galaxies classified by humans. NAOJ is now running a citizen-science project "GALAXY CRUISE," where citizens examine galaxy images taken with the Subaru Telescope to search for features suggesting that the galaxy is colliding or merging with another galaxy. The advisor of "GALAXY CRUISE," Associate Professor Masayuki Tanaka has high hopes for the study of galaxies using artificial intelligence and says, "The Subaru Strategic Program is serious Big Data containing an almost countless number of galaxies. Scientifically, it is very interesting to tackle such big data with a collaboration of citizen astronomers and machines. By employing deep-learning on top of the classifications made by citizen scientists in GALAXY CRUISE, chances are, we can find a great number of colliding and merging galaxies."

TAMPA, Fla. (Aug. 10, 2020) – In certain patients suffering a severe ischemic stroke, a mechanical device can be used to remove a clot blocking blood flow to the brain. The minimally invasive procedure either suctions the clot directly out of a large artery to the brain (known as aspiration thrombectomy), or by grabbing and extracting the blockage with a stent (stent-retrieval thrombectomy). Last year, a major trial known as COMPASS found both catheter-guided techniques to be equally safe and effective as first-line approaches for treating emergent large vessel occlusions (ELVO), the most destructive type of ischemic stroke.

Now, a multicenter study led by the University of South Florida Health (USF Health) Department of Neurosurgery and Tampa General Hospital, reports that clot perviousness, or permeability – the ability for contrast used during the initial imaging workup to seep through a clot, as estimated by CT imaging – is associated with “first-pass success” in ELVO patients initially treated with the aspiration thrombectomy approach.

Findings from this posthoc analysis of 165 eligible patients enrolled in the COMPASS trial were published July 17 in the Journal of Neurointerventional Surgery.

First-pass success means achieving complete reopening of a blocked artery in the first attempt with a thrombectomy device. The treatment success of the stent retriever-first approach for a large vessel occlusion stroke was less dependent on clot perviousness, the study found. Time-sensitive, successful removal of the clot restores blood flow (and therefore oxygen) to the brain, improving the likelihood of faster stroke recovery and reduced complications and disability.

The data is the first to indicate that highly pervious clots may result in better treatment success after first attempt clot removal using the aspiration thrombectomy technique. However, clots with low perviousness are more resistant to either thrombectomy approach, the researchers report, and more research is needed to determine the most effective way to treat ELVO in this population of patients.

“Currently, physicians who treat strokes with thrombectomy are ‘in the dark.’ We have a variety of tools available but, frankly, we often don’t know which particular device will be most effective in a particular patient,” said the paper’s lead author Max Mokin, MD, PhD, associate professor of neurosurgery and neurology at the USF Health Morsani College of Medicine and Tampa General Hospital. “This study provides the first set of clues to guide us in selecting the devices (aspiration and/or stent retrievers) that may provide the most advantage, making the thrombectomy procedure safer, faster and ultimately more effective.”

Dr. Mokin specializes in neurointerventional surgery for the USF Health and Tampa General Hospital, one of the largest academic medical centers in Florida. He leads a three-year National Institutes of Health grant investigating how patient brain vessel anatomy interacts with clot removal devices. The goal is to optimize endovascular approaches for treating acute ischemic stroke, a leading cause of death and long-term disability worldwide.

Journal

Journal of NeuroInterventional Surgery

DOI

10.1136/neurintsurg-2020-016434

Aurora, Colo. (Aug. 11, 2020) - Greater rates of Colorado's children are going to the pediatric emergency department as a result of dog bites during the COVID-19 pandemic, according to a recently published commentary article in the Journal of Pediatrics. The article's authors, Cinnamon Dixon, DO, MPH/MSPH and Rakesh Mistry, MD/MS, who are attending physicians at Children's Hospital Colorado (Children's Colorado) and University of Colorado School of Medicine faculty, share data revealing significant increases in dog bite rates presenting to Children's Colorado since the initiation of statewide stay-at-home orders in March. Moreover, high rates of dog bite injuries have continued even as these orders have relaxed over time.

"It is well known that the number of dog bites tends to increase during the spring and summer months," said Dr. Dixon. "However this year's rates of emergency department visits due to dog bites have been startling." The incidence of visits for dog bites to Children's Colorado's emergency department in spring 2020 was nearly triple that of last year's rates at the same time.

"These findings are likely not unique to Colorado nor this institution," said Dr. Dixon. "There are approximately 82 million children and 77 million pet dogs in the U.S. who are all living in some variation of restriction. Families across the country are living under extreme stress and angst during the pandemic, and our canine friends are not immune to their human caregivers' increased anxiety. Not to mention, parents have competing priorities now more than ever, which may make them less focused on supervising their child when they are near a dog."

Factors that could be contributing to the increased rates of dog bites during the pandemic include:

Increased child-dog exposure earlier in the year because of shelter-in-place regulations

Heightened stress for dogs as they intuitively pick up on amplified household stress

Decreased adult supervision around dogs and children as adults juggle increased responsibilities at home

According to the CDC's National Center for Injury Prevention and Control, more than 40% of dog bite injuries resulting in emergency department visits are to children and adolescents. Children ages five to nine have the highest risk of dog bites, with infants and children at greater risk of bites to the head and neck. Most dog bites are by the family dog or another known dog.

"Dogs can be amazing companions and enrich our lives in so many ways; however it's important to remember that any dog can bite given the right circumstance," continued Dr. Dixon. "Recognizing the intense pressures and responsibilities that families are under, it is critical that parents and caregivers of children prioritize the best way to prevent dog bites - which is to always, always supervise infants and children whenever they are near a dog."

A number of additional strategies can help prevent dog bites as well, including:

Teaching children to:

o Never disturb a dog who is caring for puppies, eating or sleeping

o Never reach through a fence to pet a dog

o Never run from a dog

Encouraging dog owners to:

o Keep their dog healthy and ensure routine veterinary care

o Properly train and socialize their dog