Culture

While many people think of lead poisoning as a problem of the past, chronic exposure still occurs in some communities that may be missed in limited screening programs for children's blood lead levels. Now researchers at Emory University have developed a more precise screening index, illustrated with a map, which provides a fine-grain view of areas where children are most at risk for low-level lead exposure in the city of Atlanta and throughout the state of Georgia.

Scientific Reports published their new method, including analyses that tested and showed its efficacy, using historical data.

The new screening index is based on established risk factors for lead exposure, including poverty and housing built before 1950. The index pinpointed 18 highest-priority census tracts in metro Atlanta, encompassing 2,715 children under the age of six -- or 1.7 percent of all children that age in greater Atlanta.

These highest-priority areas include the historically black neighborhoods of English Avenue and Vine City, where Emory researchers had previously identified elevated levels of lead in the soil of some yards and vacant lots.

"As we move forward into an age when acute lead poisoning is rare, we need better tools to monitor for chronic, long-term exposure to lead," says Emory graduate Samantha Distler, first author of the paper. "We developed an interactive map that can be used by physicians and other health officials, and even by individuals who want to check their own children's risk levels. You can easily zoom in to find an exact location, so there's less guess work involved in assessing what is a high-risk area."

The method could be applied to any area in the United States, she adds.

Distler led the work as an Emory undergraduate majoring in quantitative sciences on the neuroscience and behavioral biology track. She is now a graduate student of epidemiology at the University of Michigan School of Public Health.

"Lead is a toxicant that is particularly dangerous to children and their developing brains," Distler says. "Even low blood lead levels are associated with neurological deficits in children."

"One of the biggest problems concerning lead is that many people don't know if their children are being exposed," says Eri Saikawa, senior author of the study and associate professor in Emory's Department of Environmental Sciences and Rollins School of Public Health. "Detecting lead exposure as early as possible is very important so preventative measures can be taken. The easiest way to do that is to screen the blood."

In 2018, the Saikawa lab collaborated with members of Atlanta's Historic Westside Gardens to test urban soil on Atlanta's Westside for contaminants. That project uncovered high levels of heavy metal and metalloids in some yards, and even some industrial waste known as slag. The project led to an investigation by the U.S. Environmental Protection Agency, which in 2019 began decontaminating properties in the area by removing and replacing soil.

In addition to neurological deficits, lead exposure is associated with immunological and endocrine effects and cardiovascular disease. Decades ago, federal regulations reduced lead in paint and gasoline and other common exposure sources. The resulting drop in children's blood lead levels in the United States is considered one of the greatest public health achievements in the country's history.

Many people remain unaware, however, that lead persists in the environment. "It can linger for a really long time in everything from soil to water," Distler says. "That puts some people at risk for chronic exposures to low levels over a long time."

The Centers for Disease Control and Prevention (CDC) estimates that at least four million households in the United States have children living in them who are being exposed to high levels of lead. And about half a million of those children aged one to five years have blood lead levels above five micrograms per deciliter, the level at which the CDC recommends initiating public health action.

Despite this alarming statistic, many children in higher-risk areas are not screened for blood lead levels. In Georgia, data from the period 2011 to 2018 show that the proportion in various ZIP code tabulation areas who have been tested range from 1 percent to 67 percent, with a median of 13 percent.

The Emory researchers realized that one problem may be that health officials focus screening efforts on a county-wide basis, rather than zeroing in on the highest-risk neighborhoods within those counties.

In 2009, a team led by researchers at the CDC developed and published a priority screen index for Atlanta neighborhoods based on housing age and percentage of residents enrolled in Georgia's Special Supplemental Nutrition Program for Women, Infants and Children (WIC), a proxy for poverty.

For the current paper, the Emory researchers built on the efforts of the 2009 paper, drilling down from neighborhoods to more precise U.S. Census Bureau tracts. Data from the American Community Survey was used to assess the relative level of poverty and proportion of homes built before 1950.

A priority screening index, ranging from two to eight, was applied to the census tracts. The areas of highest relative poverty and proportion of homes built before 1950 received the highest score. The researchers applied this index to census tracts across the state of Georgia and to the entire United States to identify tracts that consistently have the highest priority screening index values.

"The visualizations of our priority screening index that we've created using interactive maps can empower physicians and health officials to better target children at high risk for lead exposure," Distler says. "We hope our work will help lead to improved policies and actions to reach children who are most at risk for lead exposure and to improve their lives -- not just in Georgia but throughout the United States."

One of the best sources of information on the evolution of terrestrial ecosystems and plant diversity over millions of years is fossil pollen. For palynologists--the scientists who study ancient pollen--a common challenge in the field is the identification of plant species based on these fossil grains. By integrating machine-learning technology with high-resolution imaging, a team from the Smithsonian Tropical Research Institute (STRI), the University of Illinois at Urbana-Champaign, the University of California, Irvine and collaborating institutions was able to advance this goal.

Pollen is resistant to disintegration, even when exposed to elevated temperatures or strong acids. This allows it to preserve in sediments for millions of years, making it an invaluable record of how the different groups of plants have evolved and the environmental factors that have played a role in this evolution. In order to achieve this, researchers must first be able to identify what they are observing under the microscope.

To help improve the efficiency and accuracy of fossil pollen identifications, scientists developed and trained three machine-learning models to differentiate among several existing Amherstieae legume genera and tested them against fossil specimens from western Africa and northern South America dating back to the Paleocene (66-56 million years ago), Eocene (56-34 million years ago) and Miocene (23-5.3 million years ago).

The models classified existing pollen accurately over 80% of the time and also showed high consensus on the identification of fossil pollen specimens. These results support previous hypotheses suggesting that Amherstieae originated in Africa and later dispersed to South America, revealing an evolutionary history of nearly 65 million years.

"We do not know the biological affinity of the majority of types of deep-time fossil pollen," said STRI paleontologist Carlos Jaramillo, co-author of the study. "This study shows that with the right tools, we are able to taxonomically classify fossil pollen beyond what has been previously possible."

However, over a third of the fossil specimens did not present biological affinity with any existing genera, suggesting that part of this ancient diversity may have went extinct at some point during the evolutionary process.

"These new tools reveal the vast amount of taxonomic information that pollen can offer and that has been hidden from researchers until now," said Ingrid Romero, doctoral student at the University of Illinois-Urbana Champaign and lead author of the study.

This new approach improves the taxonomic resolution of fossil pollen identifications and greatly enhances the use of pollen data in ecological and evolutionary research. It also narrows down the range of options for experts in fossil pollen identification, allowing them to save time and invest their energy on the most challenging specimens.

"Fossil pollen analysis is a very visual science and computer vision algorithms augment expert identifications and can assist palynologists with challenging visual classification problems," said Surangi Punyasena, associate professor at the University of Illinois-Urbana Champaign and senior author of the study. "The machine-learning models that our colleagues Shu Kong and Charless Fowlkes developed are truly remarkable. Our hope is that other researchers apply these techniques and that as a community we expand the ecological and evolutionary questions that are addressed by the fossil pollen record."

Access to more localized data on childhood vaccination coverage, such as at the school or neighborhood levels, could help better predict and prevent measles outbreaks in the United States, according to a new University of Michigan study.

The research also shows that when people who are not vaccinated are geographically clustered, the probability and size of an outbreak increases. The study is scheduled for publication Oct. 26 in the journal Proceedings of the National Academy of Sciences.

"We found that even at 99% overall vaccination coverage, clustering of non-vaccinators allowed outbreaks to occur. This means that we really must rethink whether or not herd immunity is meaningful when applied at large spatial scales like the state or country level," said lead author Nina Masters, a doctoral student in epidemiology at the U-M School of Public Health.

Because measles is a highly contagious disease, vaccination coverage of at least 95% is required to maintain herd immunity. But despite having reached that level nationally, the U.S. saw 1,282 cases of measles in 31 states last year--the most cases since 1992.

Masters and colleagues sought to present a simple, easy-to-understand model that shows what happens when people who are not vaccinated become clustered within neighborhoods and other small communities, even within a population that has achieved 95% vaccination coverage overall against measles.

They also analyzed how the aggregation of vaccination data to the levels it is typically reported at, such as cities and states, may inadvertently obscure important fine-scale clustering, making outbreaks appear less likely than they truly are in many cases.

Fine-scale vaccination data at the level the researchers would like to be able to analyze is not widely accessible or available in the U.S., so the researchers instead built a computational model representing a mid-sized city: a 16x16 square grid with 256 cells of 1,000 people each. The model had four levels: blocks (cells) of 1,000 people that approximate census block groups; tracts of 4,000 people (four cells, approximating census tracts); neighborhoods of 16,000 people (16 cells); and quadrants of 64,000 people (64 cells, approximating towns or districts).

Holding the overall vaccination rate constant at the typical herd immunity threshold of 95%, the researchers used an array of different clustering patterns to distribute the 5% of non-vaccinators across the environment, and examined the impact of different clustering motifs on outbreak size and frequency.

The researchers also looked at how aggregating the data up to these four coarser levels would impact predicted outbreak size. At the block level, across all the clustering scenarios, the average predicted outbreak size was 3,886 cases, however, as they 'zoomed out,' the details of the outbreak were lost. Aggregating data to the tract-level predicted 45% fewer cases, to the neighborhood-level predicted 76.5% fewer cases, and to the quadrant level predicted a 99% reduction.

"Holding everything else constant, we saw that the more you increase fine-scale clustering, the higher the outbreak potential," Masters said. "And then as you aggregate up, you lose a lot of the ability to predict those outbreaks. This shows that if we can't measure that fine-scale clustering, we don't know what landscape of susceptibility we're dealing with, and then can't effectively assess outbreak risk."

In the U.S., public health surveillance systems typically report vaccination coverage at the county and state level, obscuring fine-scale clustering and susceptibility to potential outbreaks.

For example, in Michigan, only 4.5% of kindergarteners statewide had vaccination waivers for the 2018-19 school year--above the herd immunity threshold of 95%. However, that same year, a large measles outbreak occurred in Oakland County, where the waiver rate was about 7%. In that county, school-district waiver rates ranged from 0 to about 23%, and two schools reported more than half of kindergarteners with vaccine waivers.

"Vaccination data is collected at the school level, and in an ideal world, it would be great to have that data available at the school level so local and state health departments can be aware of regions that are very susceptible to an outbreak and respond accordingly," Masters said, adding this is especially important now, as the number of children receiving vaccinations has decreased due to the pandemic.

"I think it's safe to say that the existing clusters of non-vaccinators have gotten worse, because more people who normally would get their vaccines are not getting vaccines right now for their children."

Masters said it's important for policymakers to think critically about how assumptions in science are made and how they should be applied at the population level. For example, while the critical vaccination fraction to reach herd immunity is often used as a tool for setting disease elimination targets--the World Health Organization uses a target of 95% vaccination against measles at the national level--this calculation assumes that populations are homogeneous: both in terms of vaccination distribution and human contact.

However, societies are actually very heterogeneous and humans are very clustered in terms of their vaccination behavior, which calls into question how useful thresholds like these really are, Masters said.

"Transmission happens on city blocks, not in countries," she said. "We have to think more about how these definitions make sense in terms of matching the scale (of the policies) to the scale of actual transmission."

The researchers said that globally, there were 791,143 suspected cases in 2019, compared to 484,077 in 2018, a 63% increase. In all, 187 out of 194 WHO member states reported measles cases in 2019. To meet global measles elimination goals and prevent a worldwide resurgence, finer-scale measles data should be used to better predict and prevent outbreaks.

Roots are essential for reaching water and nutrients, for anchorage to the ground, but also for interacting and communicating with microorganisms in the soil. A long root enables the plant to reach deeper, more humid layers of soil, for example during drought. A shallower root with many root hairs is good for phosphate uptake, as phosphate is mostly found in the upper soil layers.

Caroline Gutjahr, Professor of Plant Genetics at the TUM School of Life Sciences in Weihenstephan, and her team discovered new hormone interactions which influence the growth of plant roots.

Why some plant roots have long and others have short hairs

"We found that the protein SMAX1 acts as molecular break for ethylene production", says Caroline Gutjahr. Ethylene is a plant hormone that is considered to trigger or accelerate the ripening of many fruits and vegetables, but it can also trigger other processes in plants. If less of the gaseous hormone is produced by the plant, the plant is stimulated to grow long roots and short root hairs.

The suppressor "SMAX1" can be removed by activating the so-called karrikin signaling pathway, which is triggered by another hormone. This switches on the production of ethylene, resulting in short primary roots and elongated root hairs.

This is the first time that scientists have succeeded in identifying and understanding a molecular process that is switched on by the karrikin signaling pathway and in showing a molecular mechanism, by which this signaling pathway regulates a developmental process in plants.

Plant diversity is also reflected in molecular mechanisms

"Surprisingly this mechanism has a significant impact on the roots of the legume Lotus japonicus, the model plant for peas, beans and lentils, on which we conducted our research," says Gutjahr.

In contrast, the research team observed a much weaker influence in the roots of another model plant, Arabidopsis thaliana or thale cress, which is related to cabbage plants.

"This shows that the diversity of plants is not only reflected in their appearance, but also in the effect of their molecular triggers on growth," the researcher concludes.

The relevance of improving root growth for plant breeding

"If we understand more precisely how root growth is regulated at the molecular level and in coordination with environmental stimuli, we can cultivate crops that are better able to cope with unfavorable environmental conditions and thus produce yield even under stress," explains the scientist.

This is why her research group is now investigating how the identified hormone signaling pathways (karrikin and ethylene signaling) react to different environmental conditions. They hope to discover how these two signaling pathways collaborate with the sensors that allow plants to perceive various environmental influences to adjust root growth to benefit plant survival and yield.

Columbia University Mailman School of Public Health researchers find that strong state firearm laws are associated with fewer firearm homicides--both within the state where the laws are enacted and across state lines. Conversely, weak firearm laws in one state are linked to higher rates of homicides in neighboring states.

Results of the study appear in the journal Epidemiology, and are based on an analysis of county-level data on firearm homicides as they relate to state gun laws between 2000 and 2014.

Around 14,000 Americans civilians die and over 75,000 people are treated in emergency departments because of gun violence every year. This study adds evidence that the count and composition of states' gun laws affect within-state homicide incidence. The associations are strongest for background checks compared to other gun laws, including child access prevention laws, dealer registration laws, and licensing laws. As well as examining within-state effects, the research is one of the few studies to explore the potential effects of gun laws on homicides in neighboring states, with the results emphasizing that firearm laws spill over state lines.

The study found that homicide incidence was greatest in counties with weak within-state laws and where the largest nearby population centers were in other states that also weak laws. As an example, the researchers contrast New Hampshire and Alabama, which both had 10 gun laws in 2014. The most populous urban center near New Hampshire is Boston, which had 100 gun laws, whereas the major city nearest to Alabama is Atlanta, where there were 6 laws. The weak gun laws in Alabama and Georgia both contribute to higher homicide incidence in Alabama, but the stronger gun laws in Massachusetts temper the effect of the weak laws in New Hampshire. To explain these results, the researchers suggest it may be easier for guns to flow undetected into places where laws are already weak.

"Gun violence is a public health crisis in the United States," says first author Christopher Morrison, PhD, Assistant Professor of epidemiology at the Columbia Mailman School. "Research has demonstrated that strong gun laws can reduce this burden. It's now becoming clear that weak gun laws don't only drive up gun violence within their own borders, they also affect gun violence in neighboring states."

URBANA, Ill. - Since COVID-19 began its menacing march across Wuhan, China, in December 2019, and then across the world, the SARS-CoV-2 virus has taken a "whatever works" strategy to ensure its replication and spread. But in a new study published in Evolutionary Bioinformatics, University of Illinois researchers and students show the virus is honing the tactics that may make it more successful and more stable.

A group of graduate students in a spring-semester Bioinformatics and Systems Biology class at Illinois tracked the mutation rate in the virus's proteome - the collection of proteins encoded by genetic material - through time, starting with the first SARS-CoV-2 genome published in January and ending more than 15,300 genomes later in May.

The team found some regions still actively spinning off new mutations, indicating continuing adaptation to the host environment. But the mutation rate in other regions showed signs of slowing, coalescing around single versions of key proteins.

"That is bad news. The virus is changing and changing, but it is keeping the things that are most useful or interesting for itself," says Gustavo Caetano-Anolles, professor of bioinformatics in the Department of Crop Sciences at Illinois and senior author on the study.

Importantly, however, the stabilization of certain proteins could be good news for the treatment of COVID-19.

According to first author Tre Tomaszewski, a doctoral student in the School of Information Sciences at Illinois, "In vaccine development, for example, you need to know what the antibodies are attaching to. New mutations could change everything, including the way proteins are constructed, their shape. An antibody target could go from the surface of a protein to being folded inside of it, and you can't get to it anymore. Knowing which proteins and structures are sticking around will provide important insights for vaccines and other therapies."

The research team documented a general slowdown in the virus's mutation rate starting in April, after an initial period of rapid change. This included stabilization within the spike protein, those pokey appendages that give coronaviruses their crowned appearance.

Within the spike, the researchers found that an amino acid at site 614 was replaced with another (aspartic acid to glycine), a mutation that took over the entire virus population during March and April.

"The spike was a completely different protein at the very beginning than it is now. You can barely find that initial version now," Tomaszewski says.

The spike protein, which is organized into two main domains, is responsible for attaching to human cells and helping inject the virus's genetic material, RNA, inside to be replicated. The 614 mutation breaks an important bond between distinct domains and protein subunits in the spike.

"For some reason, this must help the virus increase its spread and infectivity in entering the host. Or else the mutation wouldn't be kept," Caetano-Anolles says.

The 614 mutation was associated with increased viral loads and higher infectivity in a previous study, with no effect on disease severity. Yet, in another study, the mutation was linked with higher case fatality rates. Tomaszewski says although its role in virulence needs confirmation, the mutation clearly mediates entry into host cells and therefore is critical for understanding virus transmission and spread.

Remarkably, sites within two other notable proteins also became more stable starting in April, including the NSP12 polymerase protein, which duplicates RNA, and the NSP13 helicase protein, which proofreads the duplicated RNA strands.

"All three mutations seem to be coordinated with each other," Caetano-Anolles says. "They are in different molecules, but they are following the same evolutionary process."

The researchers also noted regions of the virus proteome becoming more variable through time, which they say may give us an indication of what to expect next with COVID-19. Specifically, they found increasing mutations in the nucleocapsid protein, which packages the virus's RNA after entering a host cell, and the 3a viroporin protein, which creates pores in host cells to facilitate viral release, replication, and virulence.

The research team says these are regions to watch, because increasing non-random variability in these proteins suggests the virus is actively seeking ways to improve its spread. Caetano-Anolles explains these two proteins interfere with how our bodies combat the virus. They are the main blockers of the beta-interferon pathway that make up our antiviral defenses. Their mutation could explain the uncontrolled immune responses responsible for so many COVID-19 deaths.

"Considering this virus will be in our midst for some time, we hope the exploration of mutational pathways can anticipate moving targets for speedy therapeutics and vaccine development as we prepare for the next wave," Tomaszewski says. "We, along with thousands of other researchers sequencing, uploading, and curating genome samples through the GISAID Initiative, will continue to keep track of this virus."

North and South, rural and urban--the United States is a complex mix of cultures, mindsets, and life experiences. And, as a new report by researchers at Stanford University, Resources for the Future, and ReconMR illustrates, those state-by-state differences affect climate attitudes and opinions.

The report is the latest installment of Climate Insights 2020, a seven-part series that reveals American beliefs, attitudes, and opinions on climate change and mitigation policies. The latest installment combines data from 27,661 respondents into a single dataset, then separates those data by state. Through this unique formulation, the report reveals state and regional opinions on climate change and their potential impacts on voting just days ahead of the presidential election.

As well as assessing where each state stands on a variety of climate-related issues, the report explains variation between states using indicators ranging from state-level politics to typical temperatures to residential energy prices.

Top Findings

The majority of residents in all analyzed states hold "green" opinions--for example, more than 70% of residents in all states believe that climate change has occurred.

At least 60% of people in all analyzed states believe that climate change will be a serious problem for the United States and world.

The "issue public"--the people who consider climate change extremely personally important and vote based on the issue--varies from state to state. In Rhode Island, 33% of people care deeply about climate change, while in South Dakota only 9% do so.

People in states that conferred more votes to President Trump in the 2016 election demonstrated a lower level of acceptance of the fundamentals of climate change and reduced support for specific policies to mitigate it.

The larger the majority expressing "green" opinions on climate change, the more likely their US congressional representatives were to vote for "green" policies. The more of a state's population is passionate about the issue, the more likely representatives are to vote for those policies.

"These data provide strong signals to many policymakers about how their constituents would like them to vote on legislation related to global warming," report coauthor Jon Krosnick said. "With just over a week until the presidential election, these findings document the likely role that climate will play in voting decisions from coast to coast."

To learn more about these findings, read Climate Insights 2020: Opinion in the States by Jared McDonald, postdoctoral research fellow at Stanford University; Bo MacInnis, lecturer at Stanford University and PhD economist; and Jon Krosnick, social psychologist, professor at Stanford University, and RFF university fellow. The Climate Insights 2020 interactive data tool also allows users to explore the data in greater depth.

The final report installment in this series will be a synthesis of the six installments. Previous ones have focused on overall trends, natural disasters, climate policies, partisan breakdowns of those policies, and electric vehicles.

An analysis of the phase III NRG Oncology clinical trial RTOG 9601 on men receiving salvage radiotherapy (SRT) following prostatectomy for recurrent prostate cancer indicated that, while biochemical failure (BF) was not a strong surrogate endpoint to determine overall survival (OS), metastasis-free survival (MFS) was in this patient population. These findings are consistent with data in the intact prostate primary radiation treatment setting. The analysis results were presented at the virtual edition of the American Society for Radiation Oncology's (ASTRO) Annual Meeting in October 2020.

Previously, the Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) working group identified MFS as a valid surrogate for OS in men treated with localized prostate cancer, however, only 8% of the men in the ICECaP analysis were treated with prostatectomy and this included no trials that employed SRT. Prior to the analysis of NRG-RTOG 9601, the performance of intermediate clinical endpoints (ICEs) to serve as surrogate endpoints were unknown in a SRT setting. The NRG-RTOG 9601 analysis surveyed two types of BF including PSA nadir +0.3-0.5 ng/mL or initiation of salvage hormone therapy as defined by the NRG-RTOG 9601 study and PSA nadir + 2ng/mL as defined by the NRG-RTOG 0534 study. Researchers also assessed DM and MFS endpoints. Endpoints were all assessed for surrogacy using two approaches; the Prentice Criteria, and a two-stage meta analytic approach in which two conditions had to be met for surrogacy including that the ICE had to be correlated to OS and the treatment effect on the ICE and OS had to be correlated.

Although BF, MFS, and distant metastasis (DM) each satisfied the four Prentice Criteria for OS, there were differences with the two-condition approach that was used to determine surrogacy. In the two-stage, meta-analytic approach, MFS was strongly correlated with OS (τ = 0.86). Other endpoints were not significantly associated with OS. DM was only moderately correlated with OS (τ = 0.66) and BF weakly correlated to OS on both the RTOG 9601-defined BF (τ = 0.25) and RTOG 0534-defined BF (τ = 0.40) endpoints. Biochemical failure was considered prognostic in this analysis; however, PSA-based ICEs as well as the treatment effect of antiandrogen therapy were only weakly correlated with OS and, therefore, would be considered a poor surrogate endpoint.

"From this analysis, we believe that researchers should be cautious when inferring clinical benefit from studies utilizing biochemical failure as a surrogate for overall survival," stated William Jackson, MD, of the University of Michigan and lead author of the NRG-RTOG 9601 analysis abstract. "These findings highlight that the two-stage meta analytic approach should be the preferred method when assessing surrogacy."

These findings can be further validated as data is collected from current, ongoing salvage radiotherapy trials in this patient population.

With a training technique commonly used to teach dogs to sit and stay, Johns Hopkins University computer scientists showed a robot how to teach itself several new tricks, including stacking blocks. With the method, the robot, named Spot, was able to learn in days what typically takes a month.

By using positive reinforcement, an approach familiar to anyone who's used treats to change a dog's behavior, the team dramatically improved the robot's skills and did it quickly enough to make training robots for real-world work a more feasible enterprise. The findings are newly published in a paper called, "Good Robot!"

"The question here was how do we get the robot to learn a skill?" said lead author Andrew Hundt, a PhD student working in Johns Hopkins' Computational Interaction and Robotics Laboratory. "I've had dogs so I know rewards work and that was the inspiration for how I designed the learning algorithm."

Unlike humans and animals that are born with highly intuitive brains, computers are blank slates and must learn everything from scratch. But true learning is often accomplished with trial and error, and roboticists are still figuring out how robots can learn efficiently from their mistakes.

The team accomplished that here by devising a reward system that works for a robot the way treats work for a dog. Where a dog might get a cookie for a job well done, the robot earned numeric points.

Hundt recalled how he once taught his terrier mix puppy named Leah the command "leave it," so she could ignore squirrels on walks. He used two types of treats, ordinary trainer treats and something even better, like cheese. When Leah was excited and sniffing around the treats, she got nothing. But when she calmed down and looked away, she got the good stuff. "That's when I gave her the cheese and said, 'Leave it! Good Leah!'"

Similarly, to stack blocks, Spot the robot needed to learn how to focus on constructive actions. As the robot explored the blocks, it quickly learned that correct behaviors for stacking earned high points, but incorrect ones earned nothing. Reach out but don't grasp a block? No points. Knock over a stack? Definitely no points. Spot earned the most by placing the last block on top of a four-block stack.

The training tactic not only worked, it took just days to teach the robot what used to take weeks. The team was able to reduce the practice time by first training a simulated robot, which is a lot like a video game, then running tests with Spot.

"The robot wants the higher score," Hundt said. "It quickly learns the right behavior to get the best reward. In fact, it used to take a month of practice for the robot to achieve 100% accuracy. We were able to do it in two days."

Positive reinforcement not only worked to help the robot teach itself to stack blocks, with the point system the robot just as quickly learned several other tasks - even how to play a simulated navigation game. The ability to learn from mistakes in all types of situations is critical for designing a robot that could adapt to new environments.

"At the start the robot has no idea what it's doing but it will get better and better with each practice. It never gives up and keeps trying to stack and is able to finish the task 100% of the time," Hundt said.

The team imagines these findings could help train household robots to do laundry and wash dishes - tasks that could be popular on the open market and help seniors live independently. It could also help design improved self-driving cars.

"Our goal is to eventually develop robots that can do complex tasks in the real world -- like product assembly, caring for the elderly and surgery," Hager said. "We don't currently know how to program tasks like that -- the world is too complex. But work like this shows us that there is promise to the idea that robots can learn how to accomplish such real-world tasks in a safe and efficient way."

New genetic sensors, developed by scientists at University of Warwick and Keele University, could function as a lab test device and even as a live monitoring system inside living cells

The innovative system can detect when a specific gene in a cell is active - instead of only detecting its presence

The technology is based on the widely utilised CRISPR system and could be applied to a variety of biotech applications, including therapeutics and diagnostics

These genetic devices could also respond to pathogen-specific sequences and activate genetic functions in cells uniquely when these are introduced in the cell

Genetic sensors that can detect the activity from genes, rather than just the genes themselves, have been developed by a team led by University of Warwick scientists.

Based on the CRISPR gene editing system, the scientists from Warwick and Keele universities have developed microscopic machines that use these sensors to detect when genes are 'on' or 'off' inside a cell, and react to those changes dynamically - making them a potentially ideal monitoring system.

These genetic sensors are detailed in a new paper published in The CRISPR Journal, where the scientists demonstrate a genetic device based on the CRISPR system inside a bacterial cell. The work is the first step in scientists developing genetic devices that can make changes to gene expression after sensing the existing gene activity within a cell.

Lead author Professor Alfonso Jaramillo from the School of Life Sciences at the University of Warwick said: "Currently, we don't know how to design novel genetic systems to see which genes are on or off inside a cell. In nature, there are proteins that do that, they can sense the status of the cell, and the best we can do is to take those from one organism and put them in another one.

"We wanted to approach a new way of doing this, from scratch, to ask how we can program a system to listen to whatever we want inside a cell.

"Cells contain a number of genes that are expressed to perform numerous functions, from sensing their environment and processing food. By having a sensor that can detect when those genes are active, scientists could program a machine to react to a specific process, such as when the cell digests its food."

The researchers based their genetic device on the CRISPR system which is now broadly used for a variety of gene editing applications, including gene therapies. CRISPR molecules allow scientists to target and modify specific genomic sequences within cells. The advantage of the CRISPR system is its programmability, which allows it to be redirected to virtually any genetic targets, such as genetic mutations causing diseases.

To generate these novel genetic devices, the scientists used as a scaffold the programmable part of CRISPR which is also responsible for sequence recognition and binding, called guide RNA sequence (gRNA). They were able to redesign the gRNA sequence by introducing in it a sensor so that the CRISPR complex would be able to bind the DNA target only after being activated by a trigger signal, such as short segments of viral RNA sequences. The sensor can be triggered by any chosen RNA sequence and in this way it activates a CRISPR system at any point of the life cycle of a cell or virus.

The authors tested the genetic devices also in living Escherichia coli bacteria, by introducing a fluorescent gene that they could switch on or off only after interaction between the sensing device and the triggering molecule. They further validated their system to detect an RNA molecule deriving from the HIV virus, exemplifying its potential usability in medicine.

The scientists believe their system will be useful for many researchers looking to program cells with greater sophistication, for example to generate new synthetic circuits.

Dr Jaramillo adds: "This is quite different from gene editing, where you simply modify the genome. This is about watching the behaviour of the genome. If you have a monitor of the cell's behaviour then you can make the cell correct that behaviour if you don't like it, you can suppress it, or you can exploit that to switch on other genes.

"The drive is to have a genetic device able to monitor the behaviour of a cell. Monitoring the behaviour allows us to reprogram the cell to respond to specific signals, this is the first step towards so many other things."

Co-lead author Dr Roberto Galizi, from the School of Life Sciences at Keele University said:
"Coupling a genetic sensor with CRISPR tools offers an unprecedented opportunity for researchers to take genetic editing technologies to a completely new dimension. Eukaryotic cells could be programmed to detect deleterious mutations that may arise within its own genes, or to respond when invaded by pathogens like bacteria naturally do against phages.

"One interesting feature is that we can program these molecular tools to sense any predesigned RNA molecule in a sequence-specific manner and, at the same time, target any desirable gene or genetic sequence to stimulate various genetic actions, all within the same cell.

"Even genetic technologies aimed to control vector-borne diseases could benefit of such innovation. For example, we could engineer mosquitoes to sense and counteract pathogen transmission, or even mutations that makes vector or pest insects resistant to insecticides."

In a new study published in Politics & Policy, Deserai Crow, PhD, associate professor at the University of Colorado Denver in the School of Public Affairs, found significant differences in rhetoric between both party affiliation and gender. Narratives from both Republican and Democratic candidates in 48 U.S. House campaigns from the 2018 midterm election were analyzed in this study.

While both Republican and Democratic candidates spoke mostly about the same issues, the frequency at which they were discussed varied. Democrats were more likely to discuss women's issues/abortion, campaign finance reform, education, environment, civil rights, infrastructure/transportation, and workers/wages. Republicans, on the other hand, were more likely to focus on immigration and spending/taxes.

After analyzing the data, Crow found that the issues campaigns focused on not only varied based on political affiliation, but by candidate gender. Female candidates were significantly more likely to talk about the economy, education, environment, and gun violence issues on their campaign websites than male candidates. On social media, female candidates were more likely to discuss taxes, the economy, and worker/wage issues than their male counterparts.

According to researchers, females also were found to regard the purpose of conversation different than males. Female candidates, for example, place higher value on the communication process rather than the outcome, while males tend to use more assertive or outcome-oriented communication tactics. Creating a social connection and enhancing relationships using affiliative language is more common in females.

"The data show that despite all of the media/social media conversation around issues like immigration, civil rights, and similar issues that capture media attention, candidates who are more likely to win talk about the kitchen table issues that affect voters' lives like healthcare, education, the economy, and similar topics," said Crow.

One other set of differences researchers saw that varied by gender was in the type of people, or narrative characters, that candidates used in their communications about policy issues. Some of the characters most referenced by female candidates included families, their home states ("Coloradans"), and citizens, suggesting that relational or familiar language was more frequently used by female candidates. In fact, female candidates were 20% more likely to discuss family issues on social media than males.

"It will be interesting to assess what candidates this election cycle focus on and who prevails, given the major upheaval during 2020 with protests against masks, Black Lives Matter protests, and similar concerns across the country," said Crow. "It's possible that we could see a major change this year and we will be exploring that as well."

HOUSTON - (Oct. 26, 2020) - Nanomechanics at Rice University and the University of Houston are getting ready to rev their engines for the second international Nanocar Race.

While they'll have to pump the brakes for a bit longer than expected, as the race has been bumped a year to 2022, the Rice-based team is pushing forward with new designs introduced in the American Chemical Society's Journal of Organic Chemistry.

The work led by chemists James Tour of Rice and Anton Dubrovskiy of the University of Houston-Clear Lake upgrades the cars with wheels of tert-butyl that should help them navigate the course laid out on a surface of gold, with pylons consisting of a few well-placed atoms.

Like their winning entry in 2017's first international Nanocar Race, these nanocars have permanent dipole moments to increase their speed and drivability on the surface.

"The permanent dipoles make the cars more susceptible to being influenced by electric field gradients, which are used to propel and maneuver them," Tour said. "It is a feature we introduced for the first competition, and I'm sure many of the entries will now have this advanced design element built into their nanocars."

This year's models are also lighter, a little more than the minimum 100 atoms required by new regulations. "The car we used in the first race had only 50 atoms," Tour said. "So this is a substantial increase in the molecular weight, as required by the updated standards.
"It's likely the race organizers wanted to slow us down since the last time, when we finished the 30-hour race in only 1.5 hours," he said.

To bring the cars past 100 atoms while streamlining their syntheses, the researchers used a modular process to make five new cars with either all tert-butyl, all adamantyl wheels (as in previous nanocars) or combinations of the two.

At 90 atoms, cars with only butyl wheels, which minimize interactions with the track, and shorter chassis were too small. By using wheel combinations, the Rice lab made nanocars with 114 atoms. "This keeps the weight at a minimum while meeting the race requirements," Tour said.

The nanocars will again be driven by a team from University of Graz in Austria led by Professor Leonhard Grill. The team brought the Rice vehicle across the finish line in 2017 and has enormous expertise in scanning tunneling microscope-directed manipulations, Tour said. The Grill and Tour groups will meet again in France for the race.

The overarching goal of the competition is to advance the development of nanomachines capable of real work, like carrying molecular-scale cargo and facilitating nano-fabrication.

"This race pushes the limits of molecular nanocar design and methods to control them," Tour said. "So through this competitive process, worldwide expertise is elevated and the entire field of nanomanipulation is encouraged to progress all the faster."

The race, originally scheduled for next summer, has been delayed by the pandemic. The racers will still need to gather in France to be overseen by the judges, but all of the teams will control their cars via the internet on tracks under scanning tunneling microscopes in their home labs.

"So the drivers will be together, and the cars and tracks will be dispersed around the world," Tour said. "But the distance of each track will be identical, to within a few nanometers."

The Rice-Graz entry won the 2017 race with an asterisk, as its car moved so quickly on the gold surface that it was impossible to capture images for judging. The team was then allowed to race on a silver surface that offered sufficient resistance and finished the 150-nanometer course in 90 minutes.

"The course was supposed to have been only 100 nanometers, but the team was penalized to add an extra 50 nanometers," Tour said. "Eventually, it was no barrier anyway." First prize on the gold track went to a Swiss team that finished a 100-nanometer course in six-and-a-half hours.

Tour's lab built the world's first single-molecule car in 2005 and it has gone through many iterations since, with the related development of molecular motors that drill through cells to deliver drugs.

Rice graduate student Alexis van Venrooy is lead author of the paper. Co-authors are Rice alumnus Victor García-López and undergraduate John Tianci Li. Dubrovskiy is an assistant professor of chemistry at the University of Houston-Clear Lake and an academic visitor at Rice. Tour is the T.T. and W.F. Chao Chair in Chemistry as well as a professor of computer science and of materials science and nanoengineering at Rice.

CHAPEL HILL, NC - University of North Carolina School of Medicine scientists have made a curious discovery about a well-known human protein that helps the immune system fight viral infections. The lab of Stan Lemon MD, and colleagues found that one class of viruses actually requires this protein to infect cells and replicate.

Published in the Proceedings of the National Academy of Sciences, the research reveals an Achilles heel of rhinoviruses, which account for as much as 70% of common colds and acute wheezing episodes, and likely account for tens of billions of dollars in health-related costs each year in the United States. There is no effective anti-viral treatment.

"We found that a large proportion of these rhinoviruses, particularly the ones that cause severe disease, need a human protein called STING to make copies of its RNA," said Lemon, professor of medicine and microbiology and immunology at the UNC School of Medicine. "We don't know how or why; we'll have to study this further. But our work opens the door to a new strategy for controlling infection of these pesky and at times very dangerous pathogens."

Viruses are relatively simple bugs able to infect human cells and then replicate to cause diseases from the common cold to COVID-19 and more dangerous pathogens, such as HIV and Ebola. Humans have developed some defenses against these invasions, and one part of the defense is called the 'stimulator of interferon gene' protein, or STING, so named for its ability to sense invaders and enhance our immune response to many viruses, including herpes viruses and cytomegalovirus, a common bug that infects half of adults by age 40 and causes symptoms similar to many other viral infections. Rhinoviruses turn STING against us, and use it to promote their own growth. The genomes of many viruses are made up of DNA, whereas the genomes of rhinoviruses are composed of RNA, a similar kind of genetic code at the foundation of all living things. STING helps us defend against DNA viruses, but is instead helping this RNA virus.

Human rhinoviruses comprise a large group of common respiratory tract pathogens - dozens of different viruses - that are associated with asthma, pneumonia and exacerbations of chronic lung disease in both children and adults. There are no vaccines available to prevent these common infections because the viruses are very diverse and different from each other in terms of how they are 'seen' by the human immune system.

"If you are immune to one, you can easily catch another," Lemon said. "There is also no effective antiviral therapy for any of them."

Lemon's lab studies how viruses like rhinoviruses interact with human proteins with the hope of finding a way to block or limit the interaction and, as a result, limit symptoms and disease progression. While studying the human proteins involved in this interaction, they found something completely unexpected. Using cell cultures, they employed experimental techniques to disable STING to see what happened. To their surprise, the virus could not infect cells and replication was stopped.

"It could be possible to target this protein with a small molecule in way that would benefit people with rhinoviruses, especially children and others who can become severely ill," Lemon said.

Philadelphia, October 26, 2020 - A dual therapy treatment regimen of expanded-spectrum cephalosporins (ESCs) plus azithromycin (AZM) is the recommended standard of care for gonorrhea. A strain of the Neisseria (N.) gonorrhoeae that is resistant to the ESC and AZM combination has emerged around the world with the potential to make gonorrhea untreatable. The currently used screening methods for antimicrobial resistant (AMR) determinants are slow, expensive, and not widely available. In an article in The Journal of Molecular Diagnostics, published by Elsevier, researchers report a rapid and cheap method that can provide real-time surveillance to help control the spread of AMR strains of N. gonorrhoeae.

"N. gonorrhoeae has developed resistance to almost all classes of antibiotics that were previously recommended for treatment," explained lead investigator Junping Peng, MD, NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. "Timely determination and monitoring of AMR profiles are crucial for appropriately personalized treatment and maintenance of treatment effectiveness."

Current surveillance systems primarily depend on culture-based methods, which have high sensitivity but long turnaround times. Several nucleic acid amplification testing methods have been developed, but they are expensive and not widely available. Dr. Peng and his team have spent the past several years assessing the status of gonococcal AMR and the development of AMR screening technologies. They designed and developed a multiplex assay based on high-resolution melting (HRM) technology. Compared with other molecular methods that detect a single genetic mutation, HRM technology can detect the most frequent mutations associated with ESCs and AZM resistance in a single test. It uses a real time PCR system, equipment commonly found in most microbiological laboratories and clinical settings.

Forty-eight well-characterized N.gonorrhoeae clinical specimens and 15 non-gonococcal strains were selected for the initial assay establishment. The multiplex HRM assays were able to accurately identify different nucleotide variations of the AMR determinants related to ceftriaxone and azithromycin resistance. Then, results from 556 multiplex HRM tests of clinical isolates and samples were compared with results from whole gene sequencing and PCR sequencing of the same samples. Compared with whole genome sequencing, the sensitivity, specificity, positive predictive value, and negative predictive value of the multiplex HRM assays for detection of AMR determinants were 98.6 percent, 99.2 percent, 98.6 percent, and 99.2 precent, respectively. The results were available within 90 minutes at a cost of less than $1.00 per sample.

This sequencing-free HRM assay may be applied to large-scale epidemiological programs for increasing surveillance of ESCs and AZM resistance and supporting identification and investigation of antimicrobial-resistant N. gonorrhoeae outbreaks in real-time. By application of this assay, gonococcal AMR surveillance could be enhanced significantly, resulting in improved management programs aimed at controlling the further spread of antimicrobial-resistant N. gonorrhoeae strains and pathogen eradication.

"Our team has been committed to the assessment of the current status of gonococcal AMR and the development of AMR screening technologies to provide scientific and technological support for the effective prevention and treatment of gonorrhea," observed Dr. Peng. This fast, simple, and cheap method could have significant implications in resource-limited countries with a high burden of disease,"

Bladder preservation with trimodality therapy can be a safe and effective alternative to cystectomy for selected patients with muscle-invasive bladder cancer. The phase II NRG Oncology NRG-RTOG 0712 trial evaluated two regimens. One was the prior RTOG standard using 5-flourouracil and cisplatin with twice daily radiation (FCT), and the other a regimen of gemcitabine and daily radiation (GD) which had demonstrated efficacy in single institution clinical trials. This trial sought to determine if either or both of these regimens demonstrated sufficient efficacy to be included in future larger studies. The previously published results demonstrated high efficacy rates and low rates of distant metastasis in both arms. The updated results were presented at the virtual edition of the American Society for Radiation Oncology's (ASTRO) Annual Meeting in October 2020.

NRG-RTOG 0712 accrued 66 eligible patients with muscle invasive bladder cancer for analysis and randomly assigned them to either receive the FCT or (GD) regimen. The trial was designed to determine if either or both treatments exceed a 3-year distant metastasis free (DMF3) rate of 75% defined as the benchmark for efficacy. Researchers on the trial also assessed 5-year distant metastasis free rates (DMF5), toxicity, complete response, and bladder intact distant free survival for each treatment arm. This trial was not statistically powered to compare the two treatment regimens.

With a median follow up of 7.3 years, the FCT treatment arm yielded a 79% DMF3 rate and the GD treatment arm had a DMF3 rate of 85%. Secondary objectives and efficiency endpoints data demonstrated that: DMF5 was 70% in the FCT arm and 77% in the GD arm; 3-year bladder-intact distant metastasis free (BI-DMF3) rates were 67% for the FCT treatment and 72.5% for GD and 5-year rates (BI-DMF5) were 65.1% for FCT and 72.5% for GD; and, post-induction complete response rates were 88% for FCT treatment and 76% for GD treatment. 58% of patients in the FCT treatment arm had treatment related-grade 3 or 4 hematologic, gastrointestinal, or genitourinary toxicities and 52% of patients in the GD arm exhibited these toxicities. Both regimens demonstrated a DMF3 greater than 75%, thus confirming gemcitabine and daily radiation is an acceptable alternative treatment option.

"Not only did this trial conclude that gemcitabine and daily radiation is an acceptable regimen with favorable distant metastasis free rates at 3 years, the results also indicate that both treatments maintain their high rates of distant metastasis free at 5 years and both treatments had low cystectomy rates with high overall survival rates. The efficacy of the GD regimen may facilitate wider adoption of bladder preservation as many patients have comorbidities that preclude the use of cisplatin. The daily radiation schedule is also easier for patients and treatment centers to administer than twice daily treatment which will also likely increase interest in this strategy. These findings are important to future trials that may assess a systemic therapy for this muscle-invasive bladder cancer," stated John J. Coen, MD, of GenesisCare ( formerly 21st Century Oncology) and the Principal Investigator of the NRG-RTOG 0712 study.