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News Article | May 24, 2017
Site: phys.org

Texas A&M University researchers have developed an intelligent transportation system prototype designed to avoid collisions and prevent hacking of autonomous vehicles. Modern vehicles are increasingly autonomous, relying on sensors to provide information to automatically control them. They are also equipped with internet access for safety or infotainment applications making them vulnerable to cyberattacks. This will only multiply as society transitions to self-driving autonomous vehicles in which hackers could gain control of the sensors, causing confusion, chaos and collisions. Although autonomous vehicles are essentially large computers on wheels, securing them is not the same as securing a communication network that connects desktop computers and smartphones to large geographical areas due to the roles that the sensors and actuators play in the physical layer of the network. Working in the Texas A&M's Cyberphysical Systems Laboratory, Dr. P.R.Kumar, University Distinguished Professor in the Department of Electrical and Computer Engineering, along with graduate students Bharadwaj Satchidanandan and Woo-Hyun Ko, have applied the theory of dynamic watermarking of sensors in autonomous vehicles to prevent malicious attacks. In their research demonstrations, 10 cameras recorded the movement of the self-driving prototype vehicles. The vision sensors in the system received the images and accurately calculated the exact location and orientation of the vehicles. Then they transmitted this information to a server, which in turn controlled the vehicles. "Sensors are like GPS navigation in the network that gather information about the environment," said Satchidanandan. "Actuators such as motors, or controls such as the steering wheel, interact with them. If the sensors are corrupted or hijacked by malicious agents through the internet, they can provide false information on vehicle locations resulting in collisions." To fix this, Kumar and his team added a random private signal called a 'watermark' to the actuators. The presence of this watermark and its statistical properties were known to every node in the system, but its actual random values were not revealed. When the measurements reported by the sensors did not have the right properties of this watermark, the actuators assumed that the sensors or their measurements had been tampered with somewhere along the line. With this new information, the researchers could predict a collision. The researchers showed that their technology could work in the lab. The actuators in the autonomous vehicles halted themselves when the sensors were tampered with. "This is an instance of the broader concern of security of cyberphysical systems. The increasing integration of critical physical infrastructures, such as the smart grid or automated transportation, with the cyber system of the internet has led to such vulnerabilities," said Kumar. "If these technologies are to be adopted by society, they will need to be protected against malicious attacks on sensors." Explore further: Honda, Google in talks on self-driving vehicle partnership More information: Theory and implementation of dynamic watermarking for cybersecurity of advanced transportation systems. DOI: 10.1109/CNS.2016.7860529


News Article | May 28, 2017
Site: www.sciencedaily.com

Providing answers -- or at least more information -- to the most difficult medical questions is the aim of medical scientists. And how research findings are translated and made available can be as important as the discoveries themselves. In recent years, one area of medical research receiving increased attention is mitochondrial disease -- a group of disorders caused by dysfunctional mitochondria. DNA polymerase gamma is the enzyme responsible for duplicating and maintaining mitochondrial DNA. Disorders related to its loss of function are a major cause of mitochondrial disease. Michigan State University biochemist Laurie Kaguni and her team have created a new tool -- the POLG Pathogenicity Prediction Server -- to help clinicians and scientists better diagnose POLG disorders and more accurately predict their outcomes. The tool is featured in BBA Clinical. Because of their central role in cellular energy production and multiple metabolic processes, mitochondrial diseases can affect organs, motor function and the nervous system. The wide spectrum of symptoms presented by these disorders poses significant challenges to their diagnosis. The database contains 681 anonymous POLG patient entries gathered from publicly available case reports. Each patient entry includes data on age of diagnosis and symptoms present. "POLG disorders, largely neurological and muscular, range from prenatally fatal conditions and severe infantile onset disorders, to milder, late onset conditions," said Kaguni, University Distinguished Professor at MSU and director of MSU's Center for Mitochondrial Science and Medicine. To date, 176 unique POLG missense mutations in mitochondrial patients have been reported in the literature. "POLG syndromes are largely multi-system, so it is often difficult to identify them as such," said Kaguni, who has also held a joint appointment at the Institute of Biosciences and Medical Technology at the University of Tampere in Finland while pursuing this study. "And most of these syndromes are complicated by what is called compound heterozygosity, which means there is a different mutation in each of the two chromosomes in a pair. That presents a huge problem for pathogenicity prediction, and one that we decided to tackle." Kaguni and her team approached this problem initially as a collaboration between her two labs and the group of Professor Anu Suomalainen at the University of Helsinki by studying Alpers syndrome -- a severe form of POLG syndrome that has an onset of infantile to two years of age and, frequently, leads to death by age two. Because the symptoms (epilepsy, loss of brain function, liver failure) are clinically nearly unmistakable, they decided to use the 67 known Alpers mutations instead of all 176 POLG mutations to initiate the work. What they discovered when they mapped the variants on a crystal structure of POLG modeled with the DNA substrate was that the mutations fall into five distinct clusters. This led to the conclusion that if an individual is identified as having a mutation in a given cluster and a second in another cluster, one can predict what their combination would do. Building on this finding, Kaguni's team, which included graduate students from MSU and Tampere -- Greg Farnum and Anssi Nurminen -- then added the rest of the known POLG mutations to their study and found that all but two of them fell within the same clusters they made for Alpers syndrome. "These findings show us that we can predict -- for any given mutation -- what impact it will have on the biochemistry of the enzyme," Kaguni said. "When we consider pairs of mutations in the context of all of the collected patient data, we can now predict with reasonable confidence whether the disease is going to be early, mid-life or later-life onset -- and what the symptoms are likely to be." The server features a mutation query interface so that the user can enter the POLG mutations identified in a patient. Based on this information, the server displays the cluster mapping of the input mutations and shows any existing patient cases. Using existing cases with similar cluster-mapping mutations, the server displays an indicator of the most probable age of onset, which can be used as the basis for a diagnosis/prognosis for a patient. "If someone has been diagnosed with a particular mutant pair and there is published data on it, you can find out quite accurately what is likely to happen," Kaguni said. "You can also look at what the symptoms are for other patients with that pairing. Notably, there are a number of common mutations in the global population, so that we have substantial data that will allow us to predict the outcome of new mutations within those clusters, or of new pairs of mutations. "Our aim is to extend the use of the server and database to enable early diagnosis, because there are many deleterious combinations that we would expect to be developmentally lethal," Kaguni continued. "On the other end of the spectrum, for late-onset disorders, early diagnosis will aid in intervention with dietary and physical therapy regimes."


News Article | May 24, 2017
Site: www.eurekalert.org

EAST LANSING, Mich. - Providing answers -- or at least more information -- to the most difficult medical questions is the aim of medical scientists. And how research findings are translated and made available can be as important as the discoveries themselves. In recent years, one area of medical research receiving increased attention is mitochondrial disease -- a group of disorders caused by dysfunctional mitochondria. DNA polymerase gamma is the enzyme responsible for duplicating and maintaining mitochondrial DNA. Disorders related to its loss of function are a major cause of mitochondrial disease. Michigan State University biochemist Laurie Kaguni and her team have created a new tool -- the POLG Pathogenicity Prediction Server - to help clinicians and scientists better diagnose POLG disorders and more accurately predict their outcomes. The tool is featured in BBA Clinical. Because of their central role in cellular energy production and multiple metabolic processes, mitochondrial diseases can affect organs, motor function and the nervous system. The wide spectrum of symptoms presented by these disorders poses significant challenges to their diagnosis. The database contains 681 anonymous POLG patient entries gathered from publicly available case reports. Each patient entry includes data on age of diagnosis and symptoms present. "POLG disorders, largely neurological and muscular, range from prenatally fatal conditions and severe infantile onset disorders, to milder, late onset conditions," said Kaguni, University Distinguished Professor at MSU and director of MSU's Center for Mitochondrial Science and Medicine. To date, 176 unique POLG missense mutations in mitochondrial patients have been reported in the literature. "POLG syndromes are largely multi-system, so it is often difficult to identify them as such," said Kaguni, who has also held a joint appointment at the Institute of Biosciences and Medical Technology at the University of Tampere in Finland while pursuing this study. "And most of these syndromes are complicated by what is called compound heterozygosity, which means there is a different mutation in each of the two chromosomes in a pair. That presents a huge problem for pathogenicity prediction, and one that we decided to tackle." Kaguni and her team approached this problem initially as a collaboration between her two labs and the group of Professor Anu Suomalainen at the University of Helsinki by studying Alpers syndrome -- a severe form of POLG syndrome that has an onset of infantile to two years of age and, frequently, leads to death by age two. Because the symptoms (epilepsy, loss of brain function, liver failure) are clinically nearly unmistakable, they decided to use the 67 known Alpers mutations instead of all 176 POLG mutations to initiate the work. What they discovered when they mapped the variants on a crystal structure of POLG modeled with the DNA substrate was that the mutations fall into five distinct clusters. This led to the conclusion that if an individual is identified as having a mutation in a given cluster and a second in another cluster, one can predict what their combination would do. Building on this finding, Kaguni's team, which included graduate students from MSU and Tampere -- Greg Farnum and Anssi Nurminen -- then added the rest of the known POLG mutations to their study and found that all but two of them fell within the same clusters they made for Alpers syndrome. "These findings show us that we can predict -- for any given mutation -- what impact it will have on the biochemistry of the enzyme," Kaguni said. "When we consider pairs of mutations in the context of all of the collected patient data, we can now predict with reasonable confidence whether the disease is going to be early, mid-life or later-life onset -- and what the symptoms are likely to be." The server features a mutation query interface so that the user can enter the POLG mutations identified in a patient. Based on this information, the server displays the cluster mapping of the input mutations and shows any existing patient cases. Using existing cases with similar cluster-mapping mutations, the server displays an indicator of the most probable age of onset, which can be used as the basis for a diagnosis/prognosis for a patient. "If someone has been diagnosed with a particular mutant pair and there is published data on it, you can find out quite accurately what is likely to happen," Kaguni said. "You can also look at what the symptoms are for other patients with that pairing. Notably, there are a number of common mutations in the global population, so that we have substantial data that will allow us to predict the outcome of new mutations within those clusters, or of new pairs of mutations. "Our aim is to extend the use of the server and database to enable early diagnosis, because there are many deleterious combinations that we would expect to be developmentally lethal," Kaguni continued. "On the other end of the spectrum, for late-onset disorders, early diagnosis will aid in intervention with dietary and physical therapy regimes." Michigan State University has been working to advance the common good in uncommon ways for more than 150 years. One of the top research universities in the world, MSU focuses its vast resources on creating solutions to some of the world's most pressing challenges, while providing life-changing opportunities to a diverse and inclusive academic community through more than 200 programs of study in 17 degree-granting colleges. For MSU news on the Web, go to MSUToday. Follow MSU News on Twitter at twitter.com/MSUnews.


News Article | May 25, 2017
Site: www.biosciencetechnology.com

Providing answers -- or at least more information -- to the most difficult medical questions is the aim of medical scientists. And how research findings are translated and made available can be as important as the discoveries themselves. In recent years, one area of medical research receiving increased attention is mitochondrial disease -- a group of disorders caused by dysfunctional mitochondria. DNA polymerase gamma is the enzyme responsible for duplicating and maintaining mitochondrial DNA. Disorders related to its loss of function are a major cause of mitochondrial disease. Michigan State University biochemist Laurie Kaguni and her team have created a new tool -- the POLG Pathogenicity Prediction Server - to help clinicians and scientists better diagnose POLG disorders and more accurately predict their outcomes. The tool is featured in BBA Clinical. Because of their central role in cellular energy production and multiple metabolic processes, mitochondrial diseases can affect organs, motor function and the nervous system. The wide spectrum of symptoms presented by these disorders poses significant challenges to their diagnosis. The database contains 681 anonymous POLG patient entries gathered from publicly available case reports. Each patient entry includes data on age of diagnosis and symptoms present. "POLG disorders, largely neurological and muscular, range from prenatally fatal conditions and severe infantile onset disorders, to milder, late onset conditions," said Kaguni, University Distinguished Professor at MSU and director of MSU's Center for Mitochondrial Science and Medicine. To date, 176 unique POLG missense mutations in mitochondrial patients have been reported in the literature. "POLG syndromes are largely multi-system, so it is often difficult to identify them as such," said Kaguni, who has also held a joint appointment at the Institute of Biosciences and Medical Technology at the University of Tampere in Finland while pursuing this study. "And most of these syndromes are complicated by what is called compound heterozygosity, which means there is a different mutation in each of the two chromosomes in a pair. That presents a huge problem for pathogenicity prediction, and one that we decided to tackle." Kaguni and her team approached this problem initially as a collaboration between her two labs and the group of Professor Anu Suomalainen at the University of Helsinki by studying Alpers syndrome -- a severe form of POLG syndrome that has an onset of infantile to two years of age and, frequently, leads to death by age two. Because the symptoms (epilepsy, loss of brain function, liver failure) are clinically nearly unmistakable, they decided to use the 67 known Alpers mutations instead of all 176 POLG mutations to initiate the work. What they discovered when they mapped the variants on a crystal structure of POLG modeled with the DNA substrate was that the mutations fall into five distinct clusters. This led to the conclusion that if an individual is identified as having a mutation in a given cluster and a second in another cluster, one can predict what their combination would do. Building on this finding, Kaguni's team, which included graduate students from MSU and Tampere -- Greg Farnum and Anssi Nurminen -- then added the rest of the known POLG mutations to their study and found that all but two of them fell within the same clusters they made for Alpers syndrome. "These findings show us that we can predict -- for any given mutation -- what impact it will have on the biochemistry of the enzyme," Kaguni said. "When we consider pairs of mutations in the context of all of the collected patient data, we can now predict with reasonable confidence whether the disease is going to be early, mid-life or later-life onset -- and what the symptoms are likely to be." The server features a mutation query interface so that the user can enter the POLG mutations identified in a patient. Based on this information, the server displays the cluster mapping of the input mutations and shows any existing patient cases. Using existing cases with similar cluster-mapping mutations, the server displays an indicator of the most probable age of onset, which can be used as the basis for a diagnosis/prognosis for a patient. "If someone has been diagnosed with a particular mutant pair and there is published data on it, you can find out quite accurately what is likely to happen," Kaguni said. "You can also look at what the symptoms are for other patients with that pairing. Notably, there are a number of common mutations in the global population, so that we have substantial data that will allow us to predict the outcome of new mutations within those clusters, or of new pairs of mutations. "Our aim is to extend the use of the server and database to enable early diagnosis, because there are many deleterious combinations that we would expect to be developmentally lethal," Kaguni continued. "On the other end of the spectrum, for late-onset disorders, early diagnosis will aid in intervention with dietary and physical therapy regimes."


News Article | May 25, 2017
Site: www.biosciencetechnology.com

Providing answers -- or at least more information -- to the most difficult medical questions is the aim of medical scientists. And how research findings are translated and made available can be as important as the discoveries themselves. In recent years, one area of medical research receiving increased attention is mitochondrial disease -- a group of disorders caused by dysfunctional mitochondria. DNA polymerase gamma is the enzyme responsible for duplicating and maintaining mitochondrial DNA. Disorders related to its loss of function are a major cause of mitochondrial disease. Michigan State University biochemist Laurie Kaguni and her team have created a new tool -- the POLG Pathogenicity Prediction Server - to help clinicians and scientists better diagnose POLG disorders and more accurately predict their outcomes. The tool is featured in BBA Clinical. Because of their central role in cellular energy production and multiple metabolic processes, mitochondrial diseases can affect organs, motor function and the nervous system. The wide spectrum of symptoms presented by these disorders poses significant challenges to their diagnosis. The database contains 681 anonymous POLG patient entries gathered from publicly available case reports. Each patient entry includes data on age of diagnosis and symptoms present. "POLG disorders, largely neurological and muscular, range from prenatally fatal conditions and severe infantile onset disorders, to milder, late onset conditions," said Kaguni, University Distinguished Professor at MSU and director of MSU's Center for Mitochondrial Science and Medicine. To date, 176 unique POLG missense mutations in mitochondrial patients have been reported in the literature. "POLG syndromes are largely multi-system, so it is often difficult to identify them as such," said Kaguni, who has also held a joint appointment at the Institute of Biosciences and Medical Technology at the University of Tampere in Finland while pursuing this study. "And most of these syndromes are complicated by what is called compound heterozygosity, which means there is a different mutation in each of the two chromosomes in a pair. That presents a huge problem for pathogenicity prediction, and one that we decided to tackle." Kaguni and her team approached this problem initially as a collaboration between her two labs and the group of Professor Anu Suomalainen at the University of Helsinki by studying Alpers syndrome -- a severe form of POLG syndrome that has an onset of infantile to two years of age and, frequently, leads to death by age two. Because the symptoms (epilepsy, loss of brain function, liver failure) are clinically nearly unmistakable, they decided to use the 67 known Alpers mutations instead of all 176 POLG mutations to initiate the work. What they discovered when they mapped the variants on a crystal structure of POLG modeled with the DNA substrate was that the mutations fall into five distinct clusters. This led to the conclusion that if an individual is identified as having a mutation in a given cluster and a second in another cluster, one can predict what their combination would do. Building on this finding, Kaguni's team, which included graduate students from MSU and Tampere -- Greg Farnum and Anssi Nurminen -- then added the rest of the known POLG mutations to their study and found that all but two of them fell within the same clusters they made for Alpers syndrome. "These findings show us that we can predict -- for any given mutation -- what impact it will have on the biochemistry of the enzyme," Kaguni said. "When we consider pairs of mutations in the context of all of the collected patient data, we can now predict with reasonable confidence whether the disease is going to be early, mid-life or later-life onset -- and what the symptoms are likely to be." The server features a mutation query interface so that the user can enter the POLG mutations identified in a patient. Based on this information, the server displays the cluster mapping of the input mutations and shows any existing patient cases. Using existing cases with similar cluster-mapping mutations, the server displays an indicator of the most probable age of onset, which can be used as the basis for a diagnosis/prognosis for a patient. "If someone has been diagnosed with a particular mutant pair and there is published data on it, you can find out quite accurately what is likely to happen," Kaguni said. "You can also look at what the symptoms are for other patients with that pairing. Notably, there are a number of common mutations in the global population, so that we have substantial data that will allow us to predict the outcome of new mutations within those clusters, or of new pairs of mutations. "Our aim is to extend the use of the server and database to enable early diagnosis, because there are many deleterious combinations that we would expect to be developmentally lethal," Kaguni continued. "On the other end of the spectrum, for late-onset disorders, early diagnosis will aid in intervention with dietary and physical therapy regimes."


News Article | March 2, 2017
Site: www.eurekalert.org

Images of our faces exist in numerous important databases - driver's license, passport, law enforcement, employment -- all to accurately identify us. But can these images continue to identify us as we age? Michigan State University biometrics expert Anil Jain and team set out to investigate what extent facial aging affects the performance of automatic facial recognition systems and what implications it could have on successfully identifying criminals or determining when identification documents need to be renewed. "We wanted to determine if state-of-the-art facial recognition systems could recognize the same face imaged multiple years apart, such as at age 20 and again at age 30," said Jain, University Distinguished Professor of computer science and engineering. "This is the first study of automatic facial recognition using a statistical model and large longitudinal face database." Jain and doctoral student Lacey Best-Rowden found that 99 percent of the face images can still be recognized up to six years later. However, the results also showed that due to natural changes that occur to a face over time as a person ages, recognition accuracy begins to drop if the images of a person were taken more than six years apart. This decrease in face recognition accuracy is person-dependent; some people age faster than others due to lifestyle, health conditions, environment or genetics. "This research shows the importance of capturing new images every four to five years to reduce the number of false positives or chance of not finding a candidate in a facial recognition search due to length of time between captures," said Pete Langenfeld, manager in the Biometrics and Identification Division at the Michigan State Police. "Criminal acquisition is dependent on the number of times a person is arrested, as the majority are not required to update their image. However, civil applications that require updated facial images should look at reducing the time between captures if it is greater than every four years." Jain's team studied two police mugshot databases of repeat criminal offenders with each offender having a minimum of four images acquired over at least a five-year period. The total number of repeat offenders studied was 23,600. Mugshot databases are the largest source of facial aging photos available with well-controlled standards to ensure the photos are uniform. These are the largest facial-aging databases studied to date in terms of number of subjects, images per subject and elapsed times. Academic research has enabled automated face recognition to play an increasingly large role in the criminal justice system. However, there has been a lack of research about the proper usage of these systems, said Brendan Klare, CEO of Rank One Computing, a major supplier of face recognition software. "This comprehensive study by Jain and Best-Rowden provides for the first time an unprecedented body of knowledge regarding the limits of automated face recognition." The paper will appear in the IEEE Transactions on Pattern Analysis & Machine Intelligence journal. The study was conducted in collaboration with the National Institute of Standards and Technology.


News Article | February 17, 2017
Site: www.eurekalert.org

In new research published Monday in the journal Proceedings of the National Academy of Sciences, Northeastern University psychology professor Lisa Feldman Barrett found, for the first time, that the neurotransmitter dopamine is involved in human bonding, bringing the brain's reward system into our understanding of how we form human attachments. The results, based on a study with 19 mother-infant pairs, have important implications for therapies addressing postpartum depression as well as disorders of the dopamine system such as Parkinson's disease, addiction, and social dysfunction. "The infant brain is very different from the mature adult brain--it is not fully formed," says Barrett, University Distinguished Professor of Psychology and author of the forthcoming book How Emotions Are Made: The Secret Life of the Brain. "Infants are completely dependent on their caregivers. Whether they get enough to eat, the right kind of nutrients, whether they're kept warm or cool enough, whether they're hugged enough and get enough social attention, all these things are important to normal brain development. Our study shows clearly that a biological process in one person's brain, the mother's, is linked to behavior that gives the child the social input that will help wire his or her brain normally. That means parents' ability to keep their infants cared for leads to optimal brain development, which over the years results in better adult health and greater productivity." To conduct the study, the researchers turned to a novel technology: a machine capable of performing two types of brain scans simultaneously--functional magnetic resonance imaging, or fMRI, and positron emission tomography, or PET. fMRI looks at the brain in slices, front to back, like a loaf of bread, and tracks blood flow to its various parts. It is especially useful in revealing which neurons are firing frequently as well as how different brain regions connect in networks. PET uses a small amount of radioactive chemical plus dye (called a tracer) injected into the bloodstream along with a camera and a computer to produce multidimensional images to show the distribution of a specific neurotransmitter, such as dopamine or opioids. Barrett's team focused on the neurotransmitter dopamine, a chemical that acts in various brain systems to spark the motivation necessary to work for a reward. They tied the mothers' level of dopamine to her degree of synchrony with her infant as well as to the strength of the connection within a brain network called the medial amygdala network that, within the social realm, supports social affiliation. "We found that social affiliation is a potent stimulator of dopamine," says Barrett. "This link implies that strong social relationships have the potential to improve your outcome if you have a disease, such as depression, where dopamine is compromised. We already know that people deal with illness better when they have a strong social network. What our study suggests is that caring for others, not just receiving caring, may have the ability to increase your dopamine levels." Before performing the scans, the researchers videotaped the mothers at home interacting with their babies and applied measurements to the behaviors of both to ascertain their degree of synchrony. They also videotaped the infants playing on their own. Once in the brain scanner, each mother viewed footage of her own baby at solitary play as well as an unfamiliar baby at play while the researchers measured dopamine levels, with PET, and tracked the strength of the medial amygdala network, with fMRI. The mothers who were more synchronous with their own infants showed both an increased dopamine response when viewing their child at play and stronger connectivity within the medial amygdala network. "Animal studies have shown the role of dopamine in bonding but this was the first scientific evidence that it is involved in human bonding," says Barrett. "That suggests that other animal research in this area could be directly applied to humans as well." The findings, says Barrett, are "cautionary." "They have the potential to reveal how the social environment impacts the developing brain," she says. "People's future health, mental and physical, is affected by the kind of care they receive when they are babies. If we want to invest wisely in the health of our country, we should concentrate on infants and children, eradicating the adverse conditions that interfere with brain development."


News Article | November 15, 2016
Site: www.sciencedaily.com

Few Americans may be aware of it, but in 1952 a killer fog that contained pollutants covered London for five days, causing breathing problems and killing thousands of residents. The exact cause and nature of the fog has remained mostly unknown for decades, but an international team of scientists that includes several Texas A&M University-affiliated researchers believes that the mystery has been solved and that the same air chemistry also happens in China and other locales. Texas A&M researcher Renyi Zhang, University Distinguished Professor and the Harold J. Haynes Chair of Atmospheric Sciences and Professor of Chemistry, along with graduate students Yun Lin, Wilmarie Marrero-Ortiz, Jeremiah Secrest, Yixin Li, Jiaxi Hu and Bowen Pan and researchers from China, Florida, California Israel and the UK have had their work published in the current issue of Proceedings of the National Academy of Sciences (PNAS). In December of 1952, the fog enveloped all of London and residents at first gave it little notice because it appeared to be no different from the familiar natural fogs that have swept over Great Britain for thousands of years. But over the next few days, conditions deteriorated, and the sky literally became dark. Visibility was reduced to only three feet in many parts of the city, all transportation was shut down and tens of thousands of people had trouble breathing. By the time the fog had lifted on Dec. 9, at least 4,000 people had died and more than 150,000 had been hospitalized. Thousands of animals in the area were also killed. Recent British studies now say that the death count was likely far higher -- more than 12,000 people of all ages died from the killer fog. It has long been known that many of those deaths were likely caused by emissions from coal burning, but the exact chemical processes that led to the deadly mix of fog and pollution have not been fully understood over the past 60 years. The 1952 killer fog led to the passage of the Clean Air Act in 1956 by the British Parliament and is still considered the worst air pollution event in the European history. Through laboratory experiments and atmospheric measurements in China, the team has come up with the answers. "People have known that sulfate was a big contributor to the fog, and sulfuric acid particles were formed from sulfur dioxide released by coal burning for residential use and power plants, and other means," Zhang says. "But how sulfur dioxide was turned into sulfuric acid was unclear. Our results showed that this process was facilitated by nitrogen dioxide, another co-product of coal burning, and occurred initially on natural fog. Another key aspect in the conversion of sulfur dioxide to sulfate is that it produces acidic particles, which subsequently inhibits this process. Natural fog contained larger particles of several tens of micrometers in size, and the acid formed was sufficiently diluted. Evaporation of those fog particles then left smaller acidic haze particles that covered the city." The study shows that similar chemistry occurs frequently in China, which has battled air pollution for decades. Of the 20 most polluted cities in the world, China is home to 16 of them, and Beijing often exceeds by many times the acceptable air standards set by the U.S. Environmental Protection Agency. "The difference in China is that the haze starts from much smaller nanoparticles, and the sulfate formation process is only possible with ammonia to neutralize the particles," Zhang adds. "In China, sulfur dioxide is mainly emitted by power plants, nitrogen dioxide is from power plants and automobiles, and ammonia comes from fertilizer use and automobiles. Again, the right chemical processes have to interplay for the deadly haze to occur in China. Interestingly, while the London fog was highly acidic, contemporary Chinese haze is basically neutral." Zhang says China has been working diligently over the past decade to lessen its air pollution problems, but persistent poor air quality often requires people to wear breathing masks during much of the day. China's explosive industrial and manufacturing growth and urbanization over the past 25 years have contributed to the problem. "A better understanding of the air chemistry holds the key for development of effective regulatory actions in China," he adds. "The government has pledged to do all it can to reduce emissions going forward, but it will take time," he notes. "We think we have helped solve the 1952 London fog mystery and also have given China some ideas of how to improve its air quality. Reduction in emissions for nitrogen oxides and ammonia is likely effective in disrupting this sulfate formation process."


News Article | November 8, 2016
Site: phys.org

The findings, featured in the journal Current Zoology, fully describe for the first time, cooperative behavior during fights between two apex predators—spotted hyenas and lions. Understanding the factors involved in the emergence of cooperation among organisms is central to the study of social evolution, said Kenna Lehmann, MSU doctoral candidate of integrative biology and study co-author. "When hyenas mob during hyena-lion interactions, there is significant risk of injury by participating in this cooperative behavior," Lehmann said. "However, when they gang-up like this, they are more likely to win control of the food. This suggests that cooperative behavior increases fitness in hyenas." Interestingly enough, hyenas will even mob lions when no food is present. The research team, part of University Distinguished Professor of integrative biology Kay Holekamp's lab, found that hyenas are more likely to mob lions when there are more hyenas present, regardless of food presence, fight location and how many lions are involved. As this video shows, the interactions are intense. The mob sometimes starts small, but more hyenas enter the fray as the battle intensifies. Even against three lions, the smaller hyenas group as a single unit, giggling, growling and snapping like a hyena-headed hydra. Then, resembling a well-drilled military unit, they creep forward, drive the larger predators off a carcass and claim a feast for themselves and their clan. Lions and hyenas have long competed directly and indirectly for resources. Even though cooperative mobbing behavior has been documented in birds and other mammals, this is the first research to fully describe this interaction. Analysis of these complex interactions requires a large sample size, which can be obtained only from detailed long-term observational data, Lehmann said. Having access to MSU's Mara Hyena Project, the team was able to evaluate 27 years of data, covering the territories of seven hyena clans at two study sites in Kenya. "This work would not have been possible without the long-term database," said Tracy Montgomery, MSU doctoral candidate of integrative biology and study co-author. "Not only did it allow us to demonstrate that mobbing likely increases fitness in hyenas, but it also will help us identify factors that will help predict whether this cooperative behavior will occur. It also has set the stage for additional studies." Future research will dissect the elements of the mob, such as identifying the participants and their sex and rank. The researchers also will try to determine if all animals are sharing the workload or if there are regular cheaters, interlopers that skip the fight but share in the feast. Additional studies also will hone in on the signaling involved, from vocalizations to changes in hormones before and during the events to glean insights on communication, cognition and cooperation, Lehmann said. Explore further: Hyenas' ability to count helps them decide to fight or flee


News Article | November 8, 2016
Site: www.eurekalert.org

EAST LANSING, Mich. -- Submitting to mob mentality is always a risky endeavor, for humans or hyenas. A new Michigan State University study focusing on the latter, though, shows that when it comes to battling for food, mobbing can be beneficial. The findings, featured in the journal Current Zoology, fully describe for the first time, cooperative behavior during fights between two apex predators ¬- spotted hyenas and lions. Understanding the factors involved in the emergence of cooperation among organisms is central to the study of social evolution, said Kenna Lehmann, MSU doctoral candidate of integrative biology and study co-author. "When hyenas mob during hyena-lion interactions, there is significant risk of injury by participating in this cooperative behavior," Lehmann said. "However, when they gang-up like this, they are more likely to win control of the food. This suggests that cooperative behavior increases fitness in hyenas." Interestingly enough, hyenas will even mob lions when no food is present. The research team, part of University Distinguished Professor of integrative biology Kay Holekamp's lab, found that hyenas are more likely to mob lions when there are more hyenas present, regardless of food presence, fight location and how many lions are involved. As this video shows, the interactions are intense. The mob sometimes starts small, but more hyenas enter the fray as the battle intensifies. Even against three lions, the smaller hyenas group as a single unit, giggling, growling and snapping like a hyena-headed hydra. Then, resembling a well-drilled military unit, they creep forward, drive the larger predators off a carcass and claim a feast for themselves and their clan. Lions and hyenas have long competed directly and indirectly for resources. Even though cooperative mobbing behavior has been documented in birds and other mammals, this is the first research to fully describe this interaction. Analysis of these complex interactions requires a large sample size, which can be obtained only from detailed long-term observational data, Lehmann said. Having access to MSU's Mara Hyena Project, the team was able to evaluate 27 years of data, covering the territories of seven hyena clans at two study sites in Kenya. "This work would not have been possible without the long-term database," said Tracy Montgomery, MSU doctoral candidate of integrative biology and study co-author. "Not only did it allow us to demonstrate that mobbing likely increases fitness in hyenas, but it also will help us identify factors that will help predict whether this cooperative behavior will occur. It also has set the stage for additional studies." Future research will dissect the elements of the mob, such as identifying the participants and their sex and rank. The researchers also will try to determine if all animals are sharing the workload or if there are regular cheaters, interlopers that skip the fight but share in the feast. Additional studies also will hone in on the signaling involved, from vocalizations to changes in hormones before and during the events to glean insights on communication, cognition and cooperation, Lehmann said. This research was funded by the National Institutes of Health and the National Science Foundation. Michigan State University has been working to advance the common good in uncommon ways for more than 150 years. One of the top research universities in the world, MSU focuses its vast resources on creating solutions to some of the world's most pressing challenges, while providing life-changing opportunities to a diverse and inclusive academic community through more than 200 programs of study in 17 degree-granting colleges. For MSU news on the Web, go to MSUToday. Follow MSU News on Twitter at twitter.com/MSUnews.

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