Evolutionary Modeling – E JEMED http://e-jemed.org/ Fri, 11 Jun 2021 17:29:44 +0000 en-US hourly 1 https://wordpress.org/?v=5.7.2 https://e-jemed.org/wp-content/uploads/2021/05/default1-150x150.png Evolutionary Modeling – E JEMED http://e-jemed.org/ 32 32 How to prevent the next pandemic: Monitor populations where the disease is spreading https://e-jemed.org/how-to-prevent-the-next-pandemic-monitor-populations-where-the-disease-is-spreading/ https://e-jemed.org/how-to-prevent-the-next-pandemic-monitor-populations-where-the-disease-is-spreading/#respond Fri, 11 Jun 2021 15:59:22 +0000 https://e-jemed.org/how-to-prevent-the-next-pandemic-monitor-populations-where-the-disease-is-spreading/ As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population. My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance […]]]>


As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population.

My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance in settings where disease spread from animals to humans is greatest. likely to occur.

In other words, don’t wait for sick people to show up to the hospital. Instead, watch for populations where the disease is actually spreading.

The current pandemic prevention strategy

Global health professionals have long known that pandemics fueled by spread of zoonotic diseases, or the transmission of disease from animals to humans, was a problem. In 1947, the World Health Organization established a worldwide network of hospitals for detect pandemic threats by a process called syndromic surveillance. The process relies on standardized symptom checklists to look for signals of emerging or re-emerging diseases with pandemic potential among patient populations with symptoms that are difficult to diagnose.

This clinical strategy is based both on the arrival of infected individuals sentinel hospitals and medical authorities who influential and persistent enough to sound the alarm.

There is only one problem: by the time a sick person comes to the hospital, an epidemic has already occurred. In the case of SARS-CoV-2, the virus that causes COVID-19, it was probably widespread long before it was detected. This time, the clinical strategy alone failed us.

The overflow of zoonotic disease is not a fact

A more proactive approach is now gaining in importance in the world of pandemic prevention: the theory of viral evolution. This theory suggests that animal viruses become dangerous human viruses gradually over time through frequent zoonotic outbursts.

This is not a one-size-fits-all deal: an “intermediate” animal such as a civet cat, a pangolin, or a pig can be made to mutate the virus so that it can make initial jumps to humans. But the final host that allows a variant to fully adapt to humans may be humans themselves.

The theory of viral evolution is played out in real time with the rapid development of COVID-19 variants. In fact, an international team of scientists have proposed that undetected human-to-human transmission after animal-to-human jump is the likely cause. origin of SARS-CoV-2.

When new outbreaks of zoonotic viral diseases like Ebola first gained global attention in the 1970s, research into the extent of disease transmission drew on antibody assays, blood tests to identify people who have already been infected. Antibody monitoring, also called serological surveys, test blood samples from target populations to identify the number of people infected. Serological surveys help determine if diseases like Ebola are circulating undetected.

Turns out they were: Ebola antibodies have been found in more than 5% of people tested in Liberia in 1982, decades before the West African epidemic of 2014. These findings support the theory of viral evolution: It takes time – sometimes a long time – to make an animal virus dangerous and transmissible between humans.

It also means that scientists have the opportunity to intervene.

Measuring the impact of zoonoses

One way to take advantage of the delay in adaptation of animal viruses to humans is repeated long-term monitoring. Establishment of a pandemic threat alert system with this strategy in mind could help detect pre-pandemic viruses before they become harmful to humans. And the best place to start is right at the source.

My team worked with virologist Shi Zhengli from the Wuhan Institute of Virology to develop a human antibody test to test a very distant cousin of SARS-CoV-2 found in bats. We established evidence for zoonotic overflow in a 2015 small serological survey in Yunnan, China: 3% of study participants living near bats carrier of this SARS-like coronavirus tested positive for antibodies. But there was an unexpected result: None of the previously infected study participants reported harmful health effects. Earlier fallout from SARS coronaviruses – like the first SARS outbreak in 2003 and Middle East Respiratory Syndrome (MERS) in 2012 – had caused high levels of illness and death. This one did no such thing.

Researchers conducted a larger study in southern China between 2015 and 2017. It is an area home to bats known to carry SARS-like coronaviruses, including the one that caused the 2003 original SARS pandemic and that most closely linked to SARS-CoV-2.

Less than 1% of participants in this study tested positive for antibodies, meaning they had previously been infected with the SARS-like coronavirus. Again, none of them reported negative health effects. But syndromic surveillance – the same strategy used by sentinel hospitals – revealed something even more unexpected: a 5% of community participants reported symptoms consistent with SARS in the past year.

This study did more than just provide the biological evidence needed to establish a proof of concept to measure zoonotic fallout. The Pandemic Threat Alert System also detected a signal of a SARS-like infection that could not yet be detected by blood tests. He may even have detected early variants of SARS-CoV-2.

If surveillance protocols had been in place, these findings would have triggered a search for community members who may have been part of an undetected outbreak. But without a plan in place, the signal was missed.

From prediction to monitoring via genetic sequencing

The lion’s share of funding and pandemic prevention efforts over the past two decades has focused on finding pathogens in wildlife and predicting pandemics before animal viruses can infect humans. But this approach did not predict major outbreaks of zoonotic diseases – including H1N1 influenza in 2009, MERS in 2012, the Ebola outbreak in West Africa in 2014, or the current COVID pandemic – 19.

Predictive modeling has, however, provided robust heat maps of the Global “hot spots” where zoonotic overflow is most likely to occur.

Regular long-term monitoring of these “hot spots” could detect overflow signals, as well as any changes that occur over time. These could include an increase in the number of antibody positive individuals, increased levels of disease, and demographic changes in those infected. As with any proactive disease surveillance, if a signal is detected, an outbreak investigation would follow. People identified with symptoms that are difficult to diagnose can then be screened by genetic sequencing to characterize and identify new viruses.

This is exactly what Greg Gray and his team at Duke University did in their search for undiscovered coronavirus in rural Sarawak, Malaysia, a known “hotspot” for zoonotic fallout. Eight of the 301 samples taken from pneumonia patients hospitalized in 2017-18 were found to have a canine coronavirus never before seen in humans. Complete sequencing of the viral genome not only suggested that it had recently jumped from an animal host, but also harbored the same mutation that made SARS and SARS-CoV-2 so deadly.

Let’s not miss the next pandemic warning signal

The good news is that the surveillance infrastructure in global “hot spots” already exists. the Linking organizations for regional disease surveillance The program links six regional disease surveillance networks in 28 countries. They pioneered ‘participatory surveillance’, partnering with communities at high risk for both initial zoonotic fallout and most severe health outcomes to aid in prevention efforts.

For example, Cambodia, a country at risk for the spread of pandemic bird flu, has set up a free national hotline for community members to report animal diseases directly to the Ministry of Health in real time. On-the-ground approaches like these are essential to a timely and coordinated public health response to stop epidemics before they turn into pandemics.

Warning signs are easy to miss when global and local priorities are tentative. The same error must not happen again.


Maureen miller, Assistant Associate Professor of Epidemiology, Columbia university

This article is republished from The conversation under a Creative Commons license. Read it original article.



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Engineers apply physics-based machine learning to solar cell production https://e-jemed.org/engineers-apply-physics-based-machine-learning-to-solar-cell-production/ https://e-jemed.org/engineers-apply-physics-based-machine-learning-to-solar-cell-production/#respond Wed, 09 Jun 2021 20:07:35 +0000 https://e-jemed.org/engineers-apply-physics-based-machine-learning-to-solar-cell-production/ PICTURE: Despite recent advances in the power conversion efficiency of organic solar cells, knowledge about the thermomechanical stability induced by processing of bulk heterojunction active layers is helping to advance the field …. see After Credit: Department of Mechanical and Mechanical Engineering / Lehigh University Today, solar power provides 2% of America’s electricity. However, by […]]]>


PICTURE: Despite recent advances in the power conversion efficiency of organic solar cells, knowledge about the thermomechanical stability induced by processing of bulk heterojunction active layers is helping to advance the field …. see After

Credit: Department of Mechanical and Mechanical Engineering / Lehigh University

Today, solar power provides 2% of America’s electricity. However, by 2050 renewables are expected to be the most widely used source of energy (overtaking oil and other liquids, natural gas and coal) and solar will overtake wind as the main source of energy. ‘renewable energy. To achieve this and make solar energy more affordable, solar technologies still require a number of breakthroughs. One is the ability to more efficiently convert photons from sunlight into usable energy.

Organic PV achieves a maximum efficiency of 15-20%, which is substantial, but limits the potential of solar energy. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient?

Balasubramanian, associate professor of mechanical engineering and mechanics, studies the basic physics of materials at the heart of solar energy conversion – organic polymers passing electrons from one molecule to another so that they can be stored and operated – as well as the manufacturing processes that produce commercial solar cells.

Architecture of the OPV bulk-heterojunction structure and design scope. [Credit: Ganesh Balasubramanian, Joydeep Munshi, Lehigh University]

Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi executed molecular models of organic solar cell production processes and designed a framework for determining optimal engineering choices. They described the computational effort and the associated results in the May issue of IEEE Computing in Science and Engineering.

“When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture that helps convert excitons and transport electrons,” Balasubramanian said. “We mimicked the way these cells are created, specifically the massive heterojunction – the absorption layer of a solar cell. Basically we are trying to understand how structural changes correlate with the efficiency of the cell. solar conversion? “

Balasubramanian uses what he calls “physics-based machine learning”. His research combines coarse-grained simulation – using approximate molecular models that represent organic materials – and machine learning. Balasubramanian believes this combination helps prevent artificial intelligence from coming up with unrealistic solutions.

“A lot of research uses machine learning on raw data,” Balasubramanian said. “But more and more there is an interest in using machine learning trained in physics. This is where I think the most benefits lie. Machine learning in itself is just math. there’s not a lot of real physics involved in this. “

Write in Computational Materials Science In February 2021, Balasubramanian and Munshi along with Wei Chen (Northwestern University) and TeYu Chien (University of Wyoming) described the results of a set of virtual experiments on Frontera testing the effects of various design changes. These included changing the proportion of donor and receptor molecules in bulk heterojunctions, as well as the temperature and time spent on annealing – a cooling and hardening process that contributes to product stability.

They exploited the data to train a class of machine learning algorithms called support vector machines to identify the material and production process parameters that would generate the most energy conversion efficiency, while also maintaining structural strength and stability. By coupling these methods together, the Balasubramanian team was able to reduce the time required to achieve an optimal process by 40%.

“Ultimately, molecular dynamics is the physical engine. It’s what captures fundamental physics,” he said. “Machine learning examines numbers and patterns, and evolutionary algorithms facilitate simulations.”

Tradeoffs and limitations

Like many industrial processes, compromises are necessary to fine-tune any facet of the manufacturing process. Faster cooling can help increase energy efficiency, but it can make the material brittle and prone to shattering, for example. Balasubramanian and his team used a multi-objective optimization algorithm that balances the pros and cons of each change to derive the overall optimal manufacturing process.

Flowchart describing the steps of a typical coupled Cuckoo Search-Coarse Grained Molecular Dynamics (CS-CGMD) algorithm. The dotted box represents the augmented machine learned exploration of regions of interest to supplement poorly executed nests with newer alternatives during each generation of CS optimization. [Credit: Ganesh Balasubramanian, Joydeep Munshi, Lehigh University]

“When you try to optimize a particular variable, you look at the problem in a linear fashion,” he said. “But most of these efforts have multi-faceted challenges that you are trying to solve simultaneously. There are trade-offs you have to make and synergistic roles that you have to grasp, to get to the right design.”

The Balasubramanian simulations corresponded to the experimental results. They determined that the composition of the heterojunction and the temperature / time of annealing have the greatest effects on overall efficiency. They also found what proportion of the materials in the heterojunction is best for efficiency.

“There are certain conditions identified in the literature which people believe are the best conditions for efficacy for these selected molecules and processing behavior,” he said. “Our simulation was able to validate these and show that other possible criteria would not give you the same performance. We were able to realize the truth, but from the virtual world.”

With more time allocation on Frontera in 2021-2022, Balasubramanian will add additional layers to the machine learning system to make it more robust. It plans to add experimental data, as well as other modalities of computer models, such as electronic structure calculations.

“The heterogeneity of the data will improve the results,” he said. “We plan to do material principle simulations first, and then feed that data into the machine learning model, along with coarse-grained simulation data.”

Balasubramanian believes that today’s organic photovoltaic may be reaching the limits of its efficiency. “There is a hard wall to penetrate and that is the material,” he said. “These molecules that we have used cannot go any further. The next thing to try is to use our framework with other advanced molecules and materials.”

His team tapped into the literature to understand the characteristics that increase solar efficiency, then trained a machine learning model to identify potential new molecules with ideal charge transport behaviors. They published their research in the Journal of Chemical Information and Modeling. Future work on Frontera will use the Balasubramanian framework to explore and computer test these alternative materials, assuming they can be produced.

“Once established, we can take realistic molecules made in the lab and put them into the framework that we created,” he said. “If we find new materials that work well, it will lower the cost of solar power generation devices and help Mother Earth.”

Balasubramanian’s research exploits the two things for which computer simulations are essential, he says. “One is to understand the science that we can’t study with the tools we have in the real world. And the other is to speed up science – streamline what we really need to do, which reduces our cost. and our time to make things and physically test them. “

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The more social the shrimp, the larger its genome https://e-jemed.org/the-more-social-the-shrimp-the-larger-its-genome/ https://e-jemed.org/the-more-social-the-shrimp-the-larger-its-genome/#respond Tue, 08 Jun 2021 13:56:46 +0000 https://e-jemed.org/the-more-social-the-shrimp-the-larger-its-genome/ A team of researchers from Columbia university discovered that eusocial shrimp have larger genomes than their less social parents. The larger genome includes an accumulation of transposable elements – moving DNA sequences – providing new information on the relationship between social and genomic evolution. The research is published in PNAS.1 Eusociality in sea creatures Professor […]]]>


A team of researchers from Columbia university discovered that eusocial shrimp have larger genomes than their less social parents. The larger genome includes an accumulation of transposable elements – moving DNA sequences – providing new information on the relationship between social and genomic evolution. The research is published in PNAS.1

Eusociality in sea creatures


Professor Dustin Rubenstein from Columbia University has been studying serpentine shrimp for many years. Crustaceans are so called because of the distinct “snap” sound their claws make when they close quickly. This is a defense mechanism that can deter predators and stun prey. Sometimes large gatherings of serpentine shrimp are therefore strong that they are known to disrupt underwater research and communications.

Rubenstein, professor of ecology, evolution and environmental biology, is particularly interested in the Synalpheus kind of serpentine shrimp, because it contains the only eusocial species.

What is a genre?


The genus is a biological classification classification that falls between family and species.

If an organism is eusocial, it demonstrates advanced social behaviors including: living together in groups, cooperating to care for offspring, division of labor, and overlapping of generations so that younger populations can help older generations. It is a phenomenon well recognized and studied in certain species of insects, such as ants.

Rubenstein and his team accidentally discovered that shrimp possess another interesting characteristic: the size of their genome varies considerably and seems to be linked to their social organization. This contradicts what we know about eusociality in the insect world. Genomic studies have shown that eusocial insects generally have smaller genomes than their less social parents.

Bigger genome, greater accumulation of transposable elements

The researchers – including collaborators from the University of Seattle and the Bedford Institute of Oceanography – decided to dig deeper to understand the larger genome of the social shrimp. Applying new genomic research techniques, the group analyzed the genomes of Synalpheus crunchy shrimp and found that the largest genome size identified in the eusocial shrimp contained an accumulation of transposable elements.

Transposable elements are sometimes called “jumping genes” – these are DNA sequences that travel through the genome. This movement can create and / or reverse mutations that alter the genetic identity of a cell, thus contributing to evolution.

“We have developed a method to extract information on transposable elements from low coverage sequencing data. This approach allowed us to compare a large number of shrimp species efficiently and cost effectively without first having to sequence and assemble the genomes of each species. »Rubenstein said Technological networks.

“We found that eusocial species had more transposable elements in their genome than non-eusocial species,” he added. “We used evolutionary modeling to explore why such a relationship exists and found that they seem to accumulate in the genomes of eusocial species.”

The researchers hypothesize that the increase in transposable elements in the genome is due to the unique social organization of the shrimp. Being an eusocial species, the majority of shrimp will never reproduce in their lifetime. The small effective size of the population means that it is difficult to “purge” transposable elements from the genome; therefore, they accumulate over time. “Interestingly, each eusocial species appears to accumulate a different type of transposable element in its genome,” Rubenstein added.

The relationship between the genome and social evolution


In the publication, the authors say their research highlights a fluid relationship between the genome and social evolution, demonstrating how social organization might influence genome architecture. “This is one of the first studies to link transposable elements to different forms of social life, opening up a whole series of questions about how the unique demography of the population of social species influences the evolution of the genome,” said commented Rubenstein.

The incredible size of shrimp genomes posed a challenge for researchers – it prevented them from applying next-generation sequencing techniques to the entire shrimp genome, a method known as whole genome sequencing, or WGS. “We have developed a new way to study transposable elements from limited amounts of sequence data,” explained Rubenstein. In a next step, the team hopes to go back and sequence the entire genome to collect even more data and learn as much as possible about the relationship between social evolution and genome evolution. They also intend to analyze – in more detail – where transposons occur to determine whether they play a role in the evolution of eusociality. “We also hope to examine elements transposable in other social organisms, such as birds and mammals, to see if these same relationships exist in vertebrates,” Rubenstein concluded.

Dustin Rubenstein was speaking to Molly Campbell, science writer for Technology Networks.

Reference: Chak STC, Harris SE, Hultgren KM, Jeffery NW, Rubenstein DR. Eusociality in crunchy shrimp is associated with larger genomes and an accumulation of transposable elements. Proc Natl Acad Sci United States. 2021; 118 (24): e2025051118. doi: 10.1073 / pnas.2025051118.



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Being Social Generates Bigger Genomes in Crunchy Shrimp https://e-jemed.org/being-social-generates-bigger-genomes-in-crunchy-shrimp/ https://e-jemed.org/being-social-generates-bigger-genomes-in-crunchy-shrimp/#respond Mon, 07 Jun 2021 19:03:11 +0000 https://e-jemed.org/being-social-generates-bigger-genomes-in-crunchy-shrimp/ In an article to appear in PNAS, on June 7, 2021, at 3 p.m. ET, a team of researchers led by Dustin R. Rubenstein of Columbia University, professor of ecology, evolution and environmental biology, discovered that within the same kind of Marine shrimp, Synalpheus, genome size and social behaviors not only vary greatly, but they […]]]>


In an article to appear in PNAS, on June 7, 2021, at 3 p.m. ET, a team of researchers led by Dustin R. Rubenstein of Columbia University, professor of ecology, evolution and environmental biology, discovered that within the same kind of Marine shrimp, Synalpheus, genome size and social behaviors not only vary greatly, but they also change over time.

Researchers have studied this group of serpentine shrimp for years because they contain the only known marine animals that evolved to live in eusocial societies similar to ants and bees, where some individuals in a colony forgo their own reproduction. to help raise the offspring of others. But it wasn’t until a few years ago that the research team discovered that serpentine shrimp exhibited extreme variation in genome size, with some species having very large genomes that are more than four to five times the size. size of the human genome.

“We also noticed,” said Rubenstein, “that the eusocial species seemed to have the largest genomes.” This is exactly the opposite of what is found in certain lines of insects. This scheme led the research team to delve deeper into the genomes of these sponge shrimp, many of which are the size of a grain of rice, to understand why eusocial species might have such large genomes.

The authors – who, in addition to Rubenstein, include former Columbia postdocs Solomon TC Chak and Stephen E. Harris, both now assistant professors at SUNY; Kristin M. Hultgren of the University of Seattle; and Nicholas W. Jeffery of the Bedford Institute of Oceanography in Toronto – not only did they confirm that eusocial serpentine shrimp species have larger genomes than their less social relatives, they also found that this increase in genome size is due to to an accumulation of transposable elements which have proliferated over the course of evolutionary time. Other less social serpentine shrimp species have retained small genomes with fewer transposable elements.

The research team also explored why eusocial shrimp species had more transposable elements in their genome than non-eusocial species. They speculated that “the buildup of transposable elements in the eusocial shrimp is likely the result of a strong reproductive division of labor where the queen is often the only breeding individual within a colony,” said Chak. Evolutionary modeling has confirmed that transposable elements proliferate in the genomes of eusocial species due to their unique form of social organization. However, since transposable elements are DNA sequences that can “jump” from one place in the genome to another, they are also a source of mutation and can lead to genomic rearrangement. Since scientists have long recognized that transposable elements can fuel adaptive genomic changes, the moderate abundances of transposable elements in ancestral Synalpheus species may have contributed to the initial transition to eusociality, although the researchers note that testing this idea will require additional work.

According to the authors, there is a powerful relationship between genome evolution and social evolution in shrimp capture in which social traits can influence genome architecture. “Understanding how living in complex societies can provide information about the architecture of the genome represents an intriguing new area of ​​study that has implications for all types of social animals, possibly even humans,” said Rubenstein. After all, transposable elements make up almost half of the human genome, and like breaker shrimp, we too live in complex societies that share many of the same characteristics.

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Contact information:

Dr Solomon Chak, SUNY Old Westbury, solomonchak@gmail.com

Dr Dustin Rubenstein, Columbia University, dr2497@columbia.edu

Warning: AAAS and EurekAlert! are not responsible for the accuracy of any press releases posted on EurekAlert! by contributing institutions or for the use of any information via the EurekAlert system.



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The next pandemic is already underway – targeted disease surveillance can help prevent it https://e-jemed.org/the-next-pandemic-is-already-underway-targeted-disease-surveillance-can-help-prevent-it/ https://e-jemed.org/the-next-pandemic-is-already-underway-targeted-disease-surveillance-can-help-prevent-it/#respond Mon, 07 Jun 2021 08:45:08 +0000 https://e-jemed.org/the-next-pandemic-is-already-underway-targeted-disease-surveillance-can-help-prevent-it/ As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population. My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance […]]]>


As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population.

My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance in settings where disease spread from animals to humans is greatest. likely.

In other words, don’t wait for sick people to show up to the hospital. Instead, watch for populations where the disease is actually spreading.

The current pandemic prevention strategy

Global health professionals have long known that pandemics fueled by spread of zoonotic diseases, or the transmission of disease from animals to humans, was a problem. In 1947, the World Health Organization established a worldwide network of hospitals for detect pandemic threats by a process called syndromic surveillance.

The process relies on standardized symptom checklists to look for signals of emerging or re-emerging diseases with pandemic potential among patient populations with symptoms that are difficult to diagnose.

This clinical strategy is based both on the arrival of infected individuals sentinel hospitals and the medical authorities who are influential and persistent enough to sound the alarm.

There is only one problem: by the time a sick person comes to the hospital, an epidemic has already occurred. In the case of SARS-CoV-2, the virus that causes COVID-19, it was probably widespread long before it was detected. This time, the clinical strategy alone failed us.

The overflow of zoonotic disease is not a fact

A more proactive approach is now gaining importance in the world of pandemic prevention: the theory of viral evolution. This theory suggests that animal viruses become dangerous human viruses gradually over time through frequent zoonotic outbursts.

This is not a one-size-fits-all deal: an “intermediate” animal such as a civet cat, pangolin, or pig can be made to mutate the virus so that it can make initial jumps to humans. But the final host that allows a variant to fully adapt to humans may be humans themselves.

The theory of viral evolution is played out in real time with the rapid development of Variants of Covid-19. In fact, an international team of scientists have proposed that undetected human-to-human transmission after animal-to-human jump is the likely cause. origin of SARS-CoV-2.

When new outbreaks of zoonotic viral diseases like Ebola first gained global attention in the 1970s, research into the extent of disease transmission drew on antibody assays, blood tests to identify people who have already been infected. Antibody monitoring, also called serological surveys, test blood samples from target populations to identify the number of people infected.

Serological surveys help determine if diseases like Ebola are circulating undetected.

Turns out they were: Ebola antibodies have been found in more than 5% of people tested in Liberia in 1982, decades before the West African epidemic of 2014. These findings support the theory of viral evolution: It takes time – sometimes a long time – to make an animal virus dangerous and transmissible between humans.

It also means that scientists have the opportunity to intervene.

Measuring the impact of zoonoses

One way to take advantage of the delay in adaptation of animal viruses to humans is repeated long-term monitoring. Establishment of a pandemic threat alert system with this strategy in mind could help detect pre-pandemic viruses before they become harmful to humans. And the best place to start is right at the source.

My team worked with virologist Shi Zhengli from the Wuhan Institute of Virology to develop a human antibody test to test a very distant cousin of SARS-CoV-2 found in bats. We established evidence of a zoonotic overflow in a 2015 small serological survey in Yunnan, China: 3% of study participants living near bats carrier of this SARS-like coronavirus tested positive for antibodies.

But there was an unexpected result: None of the previously infected study participants reported harmful health effects. Earlier fallout from SARS coronaviruses – like the first SARS outbreak in 2003 and Middle East Respiratory Syndrome (MERS) in 2012 – had caused high levels of illness and death. This one did no such thing.

Researchers conducted a larger study in southern China between 2015 and 2017. It is an area that is home to bats known to carry SARS-like coronaviruses, including the one that caused the original SARS pandemic of 2003 and that most closely linked to SARS-CoV-2.

Less than 1% of participants in this study tested positive for antibodies, meaning they had previously been infected with the SARS-like coronavirus. Again, none of them reported negative health effects. But syndromic surveillance – the same strategy used by sentinel hospitals – revealed something even more unexpected: a 5% of community participants reported symptoms consistent with SARS in the past year.

This study did more than just provide the biological evidence needed to establish a proof of concept to measure zoonotic fallout. The Pandemic Threat Alert System also detected a signal of a SARS-like infection that could not yet be detected by blood tests. He may even have detected early variants of SARS-CoV-2.

If surveillance protocols had been in place, these findings would have triggered a search for community members who may have been part of an undetected outbreak. But without a plan in place, the signal was missed.

From prediction to monitoring via genetic sequencing

The lion’s share of funding and pandemic prevention efforts over the past two decades has focused on discovering pathogens in wildlife and predicting pandemics before animal viruses can infect humans. But this approach did not predict major zoonotic outbreaks – including H1N1 influenza in 2009, MERS in 2012, the Ebola outbreak in West Africa in 2014, or the current COVID-19 pandemic. .

Predictive modeling has, however, provided robust heat maps of the Global “hot spots” where zoonotic overflow is most likely to occur.

Regular long-term monitoring of these “hot spots” could detect overflow signals, as well as any changes that occur over time. These could include a slight increase in the number of antibody positive individuals, increased levels of disease, and demographic changes in those infected. As with any proactive disease surveillance, if a signal is detected, an outbreak investigation would follow.

People identified with symptoms that are difficult to diagnose can then be screened by genetic sequencing to characterize and identify new viruses.

This is exactly what Greg Gray and his team at Duke University did in their search for undiscovered coronavirus in rural Sarawak, Malaysia, a known “hotspot” for zoonotic fallout. Eight of 301 samples taken from pneumonia patients hospitalized in 2017-18 were found to have a canine coronavirus never before seen in humans.

Complete sequencing of the viral genome not only suggested that it had recently jumped from an animal host, but also harbored the same mutation that made SARS and SARS-CoV-2 so deadly.

Let’s not miss the next pandemic warning signal

The good news is that the surveillance infrastructure in global “hot spots” already exists. the Linking organizations for regional disease surveillance The program links six regional disease surveillance networks in 28 countries. They pioneered “participatory surveillance”, partnering with communities at high risk for both initial zoonotic fallout and most severe health outcomes to aid in prevention efforts.

For example, Cambodia, a country at risk for the spread of pandemic bird flu, has set up a free national hotline for community members to report animal diseases directly to the Ministry of Health in real time.

On-the-ground approaches like these are essential to a timely and coordinated public health response to stop epidemics before they turn into pandemics.

Warning signs are easy to miss when global and local priorities are tentative. The same error must not happen again.

Maureen miller, Assistant Associate Professor of Epidemiology, Columbia university

This article is republished from The conversation under a Creative Commons license. Read it original article.



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Response to comment on “Individual heterozygosity predicts translocation success in endangered desert turtles” https://e-jemed.org/response-to-comment-on-individual-heterozygosity-predicts-translocation-success-in-endangered-desert-turtles/ https://e-jemed.org/response-to-comment-on-individual-heterozygosity-predicts-translocation-success-in-endangered-desert-turtles/#respond Thu, 03 Jun 2021 17:54:15 +0000 https://e-jemed.org/response-to-comment-on-individual-heterozygosity-predicts-translocation-success-in-endangered-desert-turtles/ Abstract Hansson et al. argue that our main finding could provide a metric that is too simplistic to maximize genetic rescue. They agree that translocation of the most genetically diverse individuals has led to a large increase in the survival of translocated turtles, but instead recommend relocating individuals that have a low genetic load and […]]]>


Abstract

Hansson et al. argue that our main finding could provide a metric that is too simplistic to maximize genetic rescue. They agree that translocation of the most genetically diverse individuals has led to a large increase in the survival of translocated turtles, but instead recommend relocating individuals that have a low genetic load and the greatest representation of metapopulation diversity. Their recommendation is based on specific model assumptions and fitness effects which are often unknown and not generalizable to many endangered species applications.

Scott et al. (1) showed that individual heterozygosity, but not the original genetic clade or geographic distance, correlated with post-translocation survival in Mojave Desert turtles. With this main finding, we argued that individual heterozygosity is an important tool that conservation biologists could use to determine which individuals to transfer. Hansson et al. (2) argue that this recommendation could be wrong as it ignores the potential increase in gene load that could follow post-translocation reproduction. They argue that translocation from less heterozygous individuals who have been purged of the deleterious variation would be a safer approach, and they provide simulation data to support their argument. Hansson et alCriticism of. may be reasonable for some specific cases of genetic rescue, but they are unlikely to be generalizable, nor do they negate our results. Here, we argue that our conclusion is always important when planning transfers, and we reiterate Scott’s conclusion. et al. that many ecological and genetic factors must be taken into account in transfer programs. We maintain our main conclusion: for translocations to be successful, individuals must survive, and more heterozygous individuals have higher survival. The additional benefits to the population in later generations are important, but the survival of transferred individuals must take place first, and for most species, survival after transfer is the most lethal part of the transfer process (36).

Hansson et al. imply that we recommend individual heterozygosity as the sole or primary consideration when translocating individuals, which we do not. They then build on the sometimes controversial but long-standing tradition of modeling that uses simulations and population genetics theory to determine the optimal source population (s) to use to facilitate genetic rescue (7). In a specific set of conditions for population differentiation, Hansson et al. show that translocation of the 20% of individuals with the greatest genetic diversity can have two main effects: (i) If there is a lot of genetic structure in a species, then one can end up transferring only a subset of the total genetics at the level of the species diversity, because these 20% can come exclusively from one or a few demes; and (ii) the choice of this set of individuals may increase the genetic load in subsequent generations compared to other translocated selection scenarios. These results can be true in an idealized situation. However, they do not reflect the reality or the purposes of the turtle translocations we studied. Hansson et al. model a specific partition of genetic variation within and between demes, and they suggest that by selecting only the most diverse individuals overall, some demes will not have enough heterozygous individuals to be translocated, thus resulting in a loss of diversity between demes. If this diversity among demes is related to fitness, either of the translocated individuals or their offspring, then an important component of the genetic variation is potentially lost. All of this may or may not be true, and it almost certainly varies across case studies. In the Mojave Desert tortoise, there are two major genetic groups (north and south) and a strong component of distance isolation within each. We show that there is no difference in the translocated geographic distance or the genetic population of origin between the individuals who lived or perished after the translocation. [figures 2 and 3 of (1)]. There is some uncertainty (about 36 km on average) on the site of origin of the displaced turtles, but this should not be biased by the status of the turtle; therefore, there is no reason why Hansson et alThe suggestion of. that there is “substantial noise” in our alleged turtle origins would alter our main conclusion. Only individual heterozygosity made the difference.

As we originally pointed out (1), there are many potential goals motivating translocations, including repatriation of displaced individuals, population supplementation / recovery, facilitation of the flow of adaptive variation, and genetic rescue. The potential for increased gene load may or may not be first-rate given these goals and situations. Given the near impossibility of accurately estimating the fitness consequences of individual substitutions across the genome, we doubt that empirical studies are able to simultaneously model the joint positive effects of heterozygosity on survival and the effects of heterozygosity. negatives of different allelic combinations after breeding in systems most important for conservation. Although models such as Genomic Evolution Rate Profiling (GERP) (8) can be used to estimate the potential genomic load from genomic signatures, these models are at best an approximation of the load. In addition, there appears to be little correlation between neutral genetic variation at the genome level and inbreeding depression, genetic load, or adaptive potential (9); thus, you really have to understand the individual loci for the load argument to be effective. Therefore, in situations where reducing gene load during gene rescue is a priority for translocation-based conservation measures, we recommend that the specific genetic condition of donor and source populations be investigated (ten) rather than following more generalized rules based on maximizing genetic diversity or minimizing the genetic load of the source population (s).

Hansson et al. also argue that we have oversimplified the basic operational practice of avoiding moving individuals long distances or across population boundaries. We do not agree. When other detailed information is lacking, minimizing distance or interclade translocation is literally the guiding principle of the manual (11). For Mojave Desert turtles, explicit recommendations based on geographic distance (12) or genetic clade (13) were the guiding principles (14). However, at least at the large-scale translocation site, where the main goals were to re-invade displaced or abandoned turtles and gain a better understanding of the factors underlying translocation survival, the end result was that most turtles died. Anything that can reverse this potentially devastating outcome is significant. When undertaking translocations for management, there are a variety of considerations, including environmental mismatch, phenology, behavior, season and genetics, all of which come into play. Our work indicates that individual genetic diversity should be included as a measure that can be used to improve post-translocation survival. It certainly appears to be important in desert turtles.

We applaud Hansson et alThe concern and the simulation work of. aimed to understand and minimize the potential future genetic load that could arise from translocation events. Further, we agree that if the fitness consequences of each mutation were known, individuals purged of deleterious mutations existed, and it could be ensured that they would live to reproduce, we might be able to increase the overall efficiency of translocations and genetic rescue. However, these models simplify the very complex real-world conservation needs and are probably not currently applicable to most non-model systems, especially given the difficulty of accurately estimating gene load. We stand by our initial conclusion that high individual heterozygosity improved translocation survival, and hope this becomes one more measure conservation biologists continue to test and consider in the ever-growing toolbox. available to conservation managers.

The references

  1. R. Frankham et al., Genetic management of fragmented animal and plant populations (Oxford Univ. Press, 2017).

  2. United States Fish and Wildlife Service, Translocation of Mojave Desert Turtles from Project Sites: Guidelines for Plan Development (2020).



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The next pandemic is already underway https://e-jemed.org/the-next-pandemic-is-already-underway/ https://e-jemed.org/the-next-pandemic-is-already-underway/#respond Thu, 03 Jun 2021 12:49:13 +0000 https://e-jemed.org/the-next-pandemic-is-already-underway/ As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population. My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance […]]]>


As more and more people around the world get vaccinated, you can almost hear the collective sigh of relief. But the next pandemic threat is probably already making its way into the population.

My research as an infectious disease epidemiologist has revealed that there is a simple strategy to mitigate emerging epidemics: proactive, real-time surveillance in settings where disease spread from animals to humans is greatest. likely to occur.

In other words, don’t wait for sick people to show up to the hospital. Instead, watch for populations where the disease is actually spreading.

The current pandemic prevention strategy

Global health professionals have long known that pandemics fueled by spread of zoonotic diseases, or the transmission of disease from animals to humans, was a problem. In 1947, the World Health Organization established a worldwide network of hospitals for detect pandemic threats by a process called syndromic surveillance. The process relies on standardized symptom checklists to look for signals of emerging or re-emerging diseases with pandemic potential among patient populations with symptoms that are difficult to diagnose.

This clinical strategy is based both on the arrival of infected individuals sentinel hospitals and medical authorities who influential and persistent enough to sound the alarm.


There is only one problem: by the time a sick person comes to the hospital, an epidemic has already occurred. In the case of SARS-CoV-2, the virus that causes Covid-19, it was probably widespread long before it was detected. This time, the clinical strategy alone failed us.

The overflow of zoonotic disease is not a fact

A more proactive approach is now gaining importance in the world of pandemic prevention: the theory of viral evolution. This theory suggests that animal viruses become dangerous human viruses gradually over time through frequent zoonotic fallout.

This is not a one-size-fits-all deal: an “intermediate” animal such as a civet cat, a pangolin, or a pig can be made to mutate the virus so that it can make initial jumps to humans. But the final host that allows a variant to fully adapt to humans may be humans themselves.

The theory of viral evolution is played out in real time with the rapid development of Variants of Covid-19. In fact, an international team of scientists have proposed that undetected human-to-human transmission after animal-to-human jump is the likely cause. origin of SARS-CoV-2.

When new outbreaks of zoonotic viral diseases like Ebola first gained global attention in the 1970s, research into the extent of disease transmission drew on antibody assays, blood tests to identify people who have already been infected. Antibody monitoring, also called serological surveys, test blood samples from target populations to identify the number of people infected. Serological surveys help determine whether diseases like Ebola are circulating undetected.

Turns out they were: Ebola antibodies have been found in more than 5% of people tested in Liberia in 1982, decades before the West African epidemic of 2014. These findings support the theory of viral evolution: It takes time – sometimes a long time – to make an animal virus dangerous and transmissible between humans.

It also means that scientists have a chance to step in.

Measuring the impact of zoonoses

One way to take advantage of the delay in adaptation of animal viruses to humans is repeated long-term monitoring. Establishment of a pandemic threat alert system with this strategy in mind could help detect pre-pandemic viruses before they become harmful to humans. And the best place to start is right at the source.

My team worked with virologist Shi Zhengli from the Wuhan Institute of Virology to develop a human antibody test to test a very distant cousin of SARS-CoV-2 found in bats. We established evidence for zoonotic overflow in a 2015 small serological survey in Yunnan, China: 3% of study participants living near bats carrier of this SARS-type coronavirus tested positive for antibodies. But there was an unexpected result: None of the previously infected study participants reported harmful health effects. Earlier fallout from SARS coronaviruses – like the first SARS outbreak in 2003 and Middle East Respiratory Syndrome (MERS) in 2012 – had caused high levels of illness and death. This one did no such thing.

Researchers conducted a larger study in southern China between 2015 and 2017. It is an area home to bats known to carry SARS-like coronaviruses, including the one that caused the original SARS pandemic of 2003 and that most closely linked to SARS-CoV-2.

Less than 1% of participants in this study tested positive for antibodies, meaning they had previously been infected with the SARS-like coronavirus. Again, none of them reported negative health effects. But syndromic surveillance – the same strategy used by sentinel hospitals – revealed something even more unexpected: a 5% of community participants reported symptoms consistent with SARS in the past year.

This study did more than just provide the biological evidence needed to establish a proof of concept to measure zoonotic fallout. The Pandemic Threat Alert System also detected a signal of a SARS-like infection that could not yet be detected by blood tests. He may even have detected early variants of SARS-CoV-2.

If surveillance protocols had been in place, these findings would have triggered a search for community members who may have been part of an undetected outbreak. But without a plan in place, the signal was missed.

From prediction to monitoring via genetic sequencing

The lion’s share of funding and pandemic prevention efforts over the past two decades has focused on finding pathogens in wildlife and predicting pandemics before animal viruses can infect humans. But this approach did not predict major outbreaks of zoonotic diseases – including H1N1 influenza in 2009, MERS in 2012, the Ebola outbreak in West Africa in 2014, or the current Covid pandemic – 19.


Predictive modeling has, however, provided robust heat maps of the Global “hot spots” where zoonotic overflow is most likely to occur.

Regular long-term monitoring of these “hot spots” could detect overflow signals, as well as any changes that occur over time. These could include an increase in the number of antibody positive individuals, increased levels of disease, and demographic changes in those infected. As with any proactive disease surveillance, if a signal is detected, an outbreak investigation would follow. People identified with symptoms that cannot be easily diagnosed can then be screened by genetic sequencing to characterize and identify new viruses.

This is exactly what Greg Gray and his team at Duke University did in their search for undiscovered coronavirus in rural Sarawak, Malaysia, a known “hotspot” for zoonotic fallout. Eight of 301 samples taken from pneumonia patients hospitalized in 2017-18 were found to have a canine coronavirus never before seen in humans. Complete sequencing of the viral genome not only suggested that it had recently jumped out of an animal host, but also harbored the same mutation that made SARS and SARS-CoV-2 so deadly.

[The Conversation’s most important coronavirus headlines, weekly in a science newsletter]

Let’s not miss the next pandemic warning signal

The good news is that the surveillance infrastructure in global “hot spots” already exists. the Linking organizations for regional disease surveillance The program links six regional disease surveillance networks in 28 countries. They pioneered ‘participatory surveillance’, partnering with communities at high risk for both initial zoonotic fallout and most severe health outcomes to aid in prevention efforts.

For example, Cambodia, a country at risk for the spread of pandemic bird flu, has set up a free national hotline for community members to report animal diseases directly to the Ministry of Health in real time. On-the-ground approaches like these are essential to a timely and coordinated public health response to stop epidemics before they turn into pandemics.

Warning signs are easy to miss when global and local priorities are tentative. The same error must not happen again.

This article is republished from The conversation under a Creative Commons license. Read it original article.



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Culture drives human evolution more than genetics https://e-jemed.org/culture-drives-human-evolution-more-than-genetics/ https://e-jemed.org/culture-drives-human-evolution-more-than-genetics/#respond Thu, 03 Jun 2021 07:27:07 +0000 https://e-jemed.org/culture-drives-human-evolution-more-than-genetics/ Credit: CC0 Public domain In a new study, researchers at the University of Maine have found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics. After carrying out an extensive review of the literature and the evidence for long-term human evolution, scientists Tim Waring and Zach Wood concluded […]]]>


Credit: CC0 Public domain

In a new study, researchers at the University of Maine have found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics.

After carrying out an extensive review of the literature and the evidence for long-term human evolution, scientists Tim Waring and Zach Wood concluded that humans are going through a “special evolutionary transition” in which the importance of culture, as knowledge, practices and skills acquired, exceed the value of genes as the main engine of human evolution.

Culture is an underestimated factor in human evolution, says Waring. Like genes, culture helps people adapt to their environment and meet the challenges of survival and reproduction. Culture, however, does this more efficiently than genes, because knowledge transfer is faster and more flexible than gene inheritance, according to Waring and Wood.

Culture is a more powerful coping mechanism for several reasons, says Waring. It’s faster: gene transfer only takes place once per generation, while cultural practices can be learned quickly and frequently updated. Culture is also more flexible than genes: gene transfer is rigid and limited to genetic information from both parents, while cultural transmission is based on flexible and effectively unlimited human learning with the ability to use peer information. and experts far beyond parents. As a result, cultural evolution is a stronger type of adaptation than old genetics.

Waring, associate professor of socio-ecological systems modeling, and Wood, postdoctoral associate researcher at the School of Biology and Ecology, have just published their findings in a review of the literature in the Proceedings of the Royal Society B, the flagship journal of biological research of the Royal Society in London.

“This research explains why humans are such a unique species. We evolve both genetically and culturally over time, but we are slowly becoming more and more cultural and less and less genetic,” Waring said.

Culture has influenced the way humans survive and evolve for millennia. According to Waring and Wood, the combination of culture and genes has fueled several key adaptations in humans, such as reduced aggression, cooperative inclinations, collaborative abilities, and the capacity for social learning. Increasingly, the researchers suggest, human adaptations are culture-driven and require genes to adapt.

Waring and Wood say the culture is also special in an important way: it is strongly group-oriented. Factors such as conformity, social identity, and shared norms and institutions – factors that have no genetic equivalent – make cultural evolution very group-oriented, the researchers say. Therefore, competition between culturally organized groups propels adaptations such as new cooperative norms and new social systems that help groups survive better together.

According to the researchers, “culturally organized groups seem to solve adaptation problems more easily than individuals, thanks to the added value of social learning and cultural transmission in groups”. Cultural adaptations can also occur more quickly in large groups than in small ones.

With groups that primarily run culture and culture now fueling human evolution more than genetics, Waring and Wood found that evolution itself has become more group-driven.

“In the very long term, we suggest that humans evolve from individual genetic organisms to cultural groups that function like superorganisms, similar to ant colonies and beehives,” says Waring. “The metaphor of ‘society as an organism’ is not that metaphorical after all. This idea can help society better understand how individuals can fit into a well organized and mutually beneficial system. Take the coronavirus pandemic, for example. An effective national epidemic response program is truly a national immune system, and therefore we can learn directly from how immune systems work to enhance our COVID response. ”


Social groups are essential for the preservation of natural resources


More information:
Timothy M. Waring et al, Long-term gene-culture coevolution and human evolutionary transition, Proceedings of the Royal Society B: Biological Sciences (2021). DOI: 10.1098 / rspb.2021.0538

Provided by the University of Maine


Quote: Researchers: Culture drives human evolution more than genetics (2021, June 3) retrieved June 3, 2021 from https://phys.org/news/2021-06-culture-human-evolution-genetics.html

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Culture drives human evolution more than genetics – sciencedaily https://e-jemed.org/culture-drives-human-evolution-more-than-genetics-sciencedaily/ https://e-jemed.org/culture-drives-human-evolution-more-than-genetics-sciencedaily/#respond Wed, 02 Jun 2021 19:18:45 +0000 https://e-jemed.org/culture-drives-human-evolution-more-than-genetics-sciencedaily/ In a new study, researchers at the University of Maine have found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics. After carrying out an extensive review of the literature and the evidence for long-term human evolution, scientists Tim Waring and Zach Wood concluded that humans are going […]]]>


In a new study, researchers at the University of Maine have found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics.

After carrying out an extensive review of the literature and the evidence for long-term human evolution, scientists Tim Waring and Zach Wood concluded that humans are going through a “special evolutionary transition” in which the importance of culture, as knowledge, practices and surpasses the value of genes as a primary driver of human evolution.

Culture is an underestimated factor in human evolution, says Waring. Like genes, culture helps people adapt to their environment and meet the challenges of survival and reproduction. Culture, however, does this more efficiently than genes, because knowledge transfer is faster and more flexible than gene inheritance, according to Waring and Wood.

Culture is a more powerful coping mechanism for several reasons, says Waring. It’s faster: gene transfer only takes place once per generation, while cultural practices can be learned quickly and frequently updated. Culture is also more flexible than genes: gene transfer is rigid and limited to genetic information from both parents, while cultural transmission is based on flexible and effectively unlimited human learning with the ability to use peer information. and experts far beyond parents. As a result, cultural evolution is a stronger type of adaptation than old genetics.

Waring, associate professor of socio-ecological systems modeling, and Wood, postdoctoral associate researcher at the School of Biology and Ecology, have just published their findings in a review of the literature in the Proceedings of the Royal Society B, the flagship journal of biological research of the Royal Society in London.

“This research explains why humans are such a unique species. We evolve both genetically and culturally over time, but we are slowly becoming more and more cultural and less and less genetic,” Waring said.

Culture has influenced the way humans survive and evolve for millennia. According to Waring and Wood, the combination of culture and genes has fueled several key adaptations in humans, such as reduced aggressiveness, cooperative inclinations, collaborative abilities, and the capacity for social learning. Increasingly, the researchers suggest, human adaptations are culture-driven and require genes to adapt.

Waring and Wood say the culture is also special in an important way: it is strongly group-oriented. Factors like conformity, social identity, and shared norms and institutions – factors that have no genetic equivalent – make cultural evolution very group-oriented, the researchers say. Therefore, competition between culturally organized groups propels adaptations such as new cooperative norms and new social systems that help groups survive better together.

According to the researchers, “culturally organized groups seem to solve adaptation problems more easily than individuals, thanks to the added value of social learning and cultural transmission in groups”. Cultural adaptations can also occur more quickly in large groups than in small ones.

With groups that primarily run culture and culture now fueling human evolution more than genetics, Waring and Wood found that evolution itself has become more group-driven.

“In the very long term, we suggest that humans evolve from individual genetic organisms to cultural groups that function like superorganisms, similar to ant colonies and beehives,” says Waring. “The metaphor of ‘society as an organism’ is not that metaphorical after all. This idea can help society better understand how individuals can fit into a well organized and mutually beneficial system. Take the coronavirus pandemic, for example. An effective national epidemic response program is truly a national immune system, and therefore we can learn directly from how immune systems work to enhance our COVID response. “

Source of the story:

Materials provided by University of Maine. Note: Content can be changed for style and length.



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Live imaging of chromatin distribution reveals new principles of nuclear architecture and chromatin compartmentalization https://e-jemed.org/live-imaging-of-chromatin-distribution-reveals-new-principles-of-nuclear-architecture-and-chromatin-compartmentalization/ https://e-jemed.org/live-imaging-of-chromatin-distribution-reveals-new-principles-of-nuclear-architecture-and-chromatin-compartmentalization/#respond Wed, 02 Jun 2021 18:04:19 +0000 https://e-jemed.org/live-imaging-of-chromatin-distribution-reveals-new-principles-of-nuclear-architecture-and-chromatin-compartmentalization/ Abstract The three-dimensional organization of chromatin contributes to transcriptional control, but information on the distribution of native chromatin is limited. Live chromatin imaging Drosophila larvae, with preserved nuclear volume, revealed that active and repressed chromatin separates from inside the nucleus and forms a peripheral layer under the nuclear lamina. This contrasts with the current view […]]]>


Abstract

The three-dimensional organization of chromatin contributes to transcriptional control, but information on the distribution of native chromatin is limited. Live chromatin imaging Drosophila larvae, with preserved nuclear volume, revealed that active and repressed chromatin separates from inside the nucleus and forms a peripheral layer under the nuclear lamina. This contrasts with the current view that chromatin is distributed throughout the nucleus. In addition, the peripheral organization of chromatin has been observed in Drosophila tissues, as well as in effector T lymphocytes and living human neutrophils. Upregulation of Lamin A / C resulted in collapse of chromatin to the nuclear center and was correlated with a significant reduction in active chromatin levels. Physical modeling suggests that binding of slide-associated domains combined with self-attracting chromatin interactions recapitulates experimental chromatin distribution profiles. Taken together, our results reveal a novel mesoscale mode of organization of peripheral chromatin sensitive to limbus composition, which is conserved throughout evolution.



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