Culture

DALLAS - Nov. 19, 2020 - Pregnant women who test positive for COVID-19 and their newborn babies have a low risk of developing severe symptoms, according to a new study from UT Southwestern.

The study, published today in JAMA Network Open, shows that 95 percent of women who tested positive for COVID-19 during pregnancy had no adverse outcomes. Additionally, the study found that the virus was transmitted to the fetus in just 3 percent of the cases.

"Our findings are that approximately 5 percent of all delivered women with COVID-19 infection develop severe or critical illness. Five percent is a major concern when a pandemic is making its way through a population; however, it's lower than previous reports from the Centers for Disease Control and Prevention (CDC)," says Emily Adhikari, M.D., an obstetrician, gynecologist, and first author of the study. "Most women with asymptomatic or mild infection will be relieved to know that their babies are unlikely to be affected by the virus."

The researchers set out to measure how COVID-19 infection impacts pregnancy outcomes, how severely ill a pregnant woman gets, placental pathology, and neonatal infections by studying women at Parkland Health and Hospital System - a high-volume prenatal clinic system and public hospital affiliated with UT Southwestern. The team followed 3,374 mothers, 252 of whom tested positive for the virus during pregnancy, from March through August. The group was predominantly Hispanic (75 percent), followed by Black (18 percent) and white (4 percent). There were no significant differences between the expectant mothers in age, number of previous births, BMI, or diabetes.

The pandemic has hit the Hispanic population in Dallas particularly hard. "While they make up 75 percent of the population of over 12,000 women delivering annually at our institution, women of Hispanic ethnicity made up over 90 percent of COVID-19-positive women. The higher frequency among Hispanic women in our study is consistent with data on racial and ethnic disparities in COVID-19 cases and deaths reported nationwide," says Adhikari, medical director of perinatal infectious diseases at Parkland Memorial Hospital and an assistant professor of obstetrics and gynecology.

Among the 252 women who tested positive, 239, or 95 percent, were asymptomatic or had mild symptoms at first. Six of those women subsequently developed severe or critical COVID-19 pneumonia. Comparing mothers with and without COVID-19 diagnosed any time during pregnancy, the COVID-19 virus did not increase the risk of adverse outcomes, including preterm birth, preeclampsia with severe features, or cesarean delivery for abnormal fetal heart rate. However, preterm birth was increased among mothers who developed severe or critical illness before reaching 37 weeks in their pregnancy, and it's hard to predict who that will be. The study found that diabetes may be one factor that increases the risk for severe or critical maternal illness.

Pathologists who examined placentas - the organ that functions as the source of oxygen and nourishment for unborn babies - found that the majority were unaffected by the virus.

COVID-19 mothers who were outpatients were followed using telemedicine with a scripted evaluation of symptoms and protocol-based management, including instructions for referral to the emergency department for worsening respiratory symptoms or obstetric concerns. Telemedicine has been a vital tool used by many UT Southwestern and Parkland physicians during the pandemic.

Further study is needed to understand whether maternal infection with COVID-19 impacts long-term maternal or infant health.

"Our goal is to develop evidence-based guidelines for the majority of pregnant women who are recovering at home," Adhikari says. "It's difficult to predict who will become severely ill, which is why prevention strategies such as hand-washing, masking, and social distancing are still extremely important."

Dr Zi Yang MENG from Division of Physics and Astronomy, Faculty of Science, the University of Hong Kong (HKU), is pursuing a new paradigm of quantum material research that combines theory, computation and experiment in a coherent manner. Recently, he teamed up with Dr Wei LI from Beihang University, Professor Yang QI from Fudan University, Professor Weiqiang YU from Renmin University and Professor Jinsheng WEN from Nanjing University to untangle the puzzle of Nobel Prize-winning theory Kosterlitz-Thouless (KT) phase.

Not long ago, Dr Meng, Dr Li and Dr Qi achieved accurate model calculations of a topological KT phase for a rare-earth magnet TmMgGaO4 (TMGO), by performing computation on the Supercomputers Tianhe 1 and Tianhe 2 (see supplementary information); this time, the team overcame several conceptual and experimental difficulties, and succeeded in discovering a topological KT phase and its transitions in the same rare-earth magnet via highly sensitive nuclear magnetic resonance (NMR) and magnetic susceptibility measurements, means of detecting magnetic responses of material. The former one is more sensitive in detecting small magnetic moments while the latter one can facilitate easy implementation of the experiment.

These experimental results, further explained the quantum Monte Carlo computations of the team, have completed the half-a-century pursuit of the topological KT phase in quantum magnetic material, which eventually leads to the Nobel Physics Prize of 2016. The research findings are recently published in renowned academic journal Nature Communications.

KT phase of TMGO is detected

Quantum materials are becoming the cornerstone for the continuous prosperity of human society, including the next-generation AI computing chips that go beyond Moore's law, the high-speed Maglev train, and the topological unit for quantum computers, etc. However, these complicated systems require modern computational techniques and advanced analysis to reveal their microscopic mechanism. Thanks to the fast development of the supercomputing platforms all over the world, scientists and engineers are now making great use of these facilities to discover better materials that benefit our society. Nevertheless, computation cannot stand alone.

In the present investigation, experimental techniques for handling extreme conditions such as low temperature, high sensitivity and strong magnetic field, are required to verify the predictions and make discoveries. These equipments and technologies are acquired and organised by the team members coherently.

The research is inspired by the KT phase theory discovered by V Berezinskii, J Michael Kosterlitz and David J Thouless, of which the latter two are laureates of the Nobel Prize in Physics 2016 (together with F Duncan M Haldane) for their theoretical discoveries of topological phase, and phase transitions of matter. Topology is a new way of classifying and predicting the properties of materials, and now becoming the mainstream of quantum material research and industry, with broad potential applications in quantum computer, lossless transmission of signals for information technology, etc. Back to 1970s, Kosterlitz and Thouless had predicted the existence of topological phase, hence named after them as the KT phase in quantum magnetic materials. Although such phenomena have been found in superfluids and superconductors, KT phase has yet been realised in bulk magnetic material, and is eventually discovered in the present work.

To detect such interesting KT phase in a magnetic material is not easy, as usually the 3-dimensional coupling would render magnetic material to develop ordered phase but not topological phase at low temperature, and even if there exists a temperature window for the KT phase, highly sensitive measurement technique is required to be able to pick up the unique fluctuation pattern of the topological phase, and that is the reason why such phase has been enthusiastically perused, but its experimental discovery has defied many previous attempts. After some initial failures, the team member discovered that the NMR method under in-plane magnetic fields, do not disturb the low-energy electronic states as the in-plane moment in TMGO is mostly multipolar with little interference on magnetic field and intrinsic magnetic moments of the material, which consequently allows the intricated topological KT fluctuations in the phase to be detected sensitively.

As shown in Fig.1, NMR spin-lattice relaxation rate measurements indeed revealed a KT phase sandwiched between a paramagnetic phase at temperature T > T_u and an antiferromagnetic phase at temperature T

This finding indicates a stable phase (KT phase) of TMGO, which serves as a concrete example of topological state of matter in crystalline material, might have potential applications in future information technologies. With its unique properties of topological excitations and strong magnetic fluctuations, many interesting research and potential applications with topological quantum materials can be pursued from here.

Dr Meng said: "It will eventually bring benefits to the society, such that quantum computers, lossless transmission of signals for information technology, faster and more energy-saving high-speed trains, all these dreams could gradually come true from quantum material research."

"Our approach, combining the state-of-art experimental techniques with unbiased quantum many-body computation schemes, enables us to directly compare experimental data to accurate numerical results with key theoretical predictions quantitatively, providing a bridge way to connect theoretical, numerical and experimental studies, the new paradigm set up by the joint team will certainly lead to more profound and impactful discoveries in quantum materials." He added.

The supercomputers used in computations and simulations

The powerful supercomputers Tianhe-1 and Tianhe-2 in China used in the computations are among the world's fastest supercomputers and ranked No.1 in 2010 and 2014 respectively in the TOP500 list (https://www.top500.org/). Their next-generation Tianhe-3 is expected to be in usage in 2021 and will be world first exaFLOPS scale supercomputer. The quantum Monte Carlo and tensor network simulations performed by the joint team make use of the Tianhe supercomputers and requires the parallel simulations for thousands of hours on thousands of CPUs, it will take more than 20 years to finish if performed in common PC.

How do people experience time alone and time with others? Findings from a new Bar-Ilan University study reveal the intricacies of people's experiences in these basic social conditions.

The study used a unique approach of analyzing self-generated text from more than 1,700 participants who performed a sentence-completion task regarding their experience alone and their social experience when in the company of others. This approach shed light on people's perceptions when free to express themselves without being bound to specific questions.

The results of the study, just published in the journal Social Psychology, showed that when people think about themselves with others, they are more focused on the present, and less focused on the past or the future than when they think about themselves alone. Furthermore, when with others, more anxiety and anger, but less sadness, are expressed than when alone.

Time alone is reflected in people's thoughts as an opportunity to think about past experiences and future plans, to relax from the stress of social interactions, and to engage in self-selected leisure activities.

"Being alone and being with others are represented in people's minds as qualitatively different experiences, each contributing to the formation of an integrated self," says Dr. Liad Uziel, of the Department of Psychology at Bar-Ilan University, who conducted the study. "One needs a combination of constructive alone and social experiences, as each type of social setting contributes much-needed, unique advantages" he adds.

For those facing current lockdowns alone, Uziel says the present findings, which highlight potential constructive effects of time alone, indicate that this could also be an opportunity for personal growth.

It is something quite common in physics: electrons leave a certain material, they fly away and then they are measured. Some materials emit electrons, when they are irradiated with light. These electrons are then called "photoelectrons". In materials research, so-called "Auger electrons" also play an important role - they can be emitted by atoms if an electron is first removed from one of the inner electron shells. But now scientists at TU Wien (Vienna) have succeeded in explaining a completely different type of electron emission, which can occur in carbon materials such as graphite. This electron emission had been known for about 50 years, but its cause was still unclear.

Strange electrons without explanation

"Many researchers have already wondered about this," says Prof. Wolfgang Werner from the Institute of Applied Physics. "There are materials that consist of atomic layers that are held together only by weak Van der Waals forces, for example graphite. And it was discovered that this type of graphite emits very specific electrons, which all have exactly the same energy, namely 3.7 electron volts."

No known physical mechanism could explain this electron emission. But at least the measured energy gave an indication of where to look: "If these atomically thin layers lie on top of each other, a certain electron state can form in between," says Wolfgang Werner. "You can imagine it as an electron that is continuously reflected back and forth between the two layers until at some point it penetrates the layer and escapes to the outside."

The energy of these states actually fits well with the observed data - so people assumed that there is some connection, but that alone was no explanation. "The electrons in these states should not actually reach the detector," says Dr. Alessandra Bellissimo, one of the authors of the current publication. "In the language of quantum physics one would say: The transition probability is just too low."

Skipping cords and symmetry

To change this, the internal symmetry of the electron states must be broken. "You can imagine this like rope skipping," says Wolfgang Werner. "Two children hold a long rope and move the end points. Actually, both create a wave that would normally propagate from one side of the rope to the other. But if the system is symmetrical and both children behave the same way, then the rope just moves up and down. The wave maximum always remains at the same place. We don't see any wave movement to the left or right, this is called a standing wave". But if the symmetry is broken because, for example, one of the children moves backwards, the situation is different - then the dynamics of the rope changes and the maximum position of the oscillation moves.

Such symmetry breaks can also occur in the material. Electrons leave their place and start moving, leaving a "hole" behind. Such electron-hole pairs disturb the symmetry of the material, and thus it can happen that the electrons suddenly have the properties of two different states simultaneously. In this way, two advantages can be combined: On the one hand, there is a large number of such electrons, and on the other hand, their probability of reaching the detector is sufficiently high. In a perfectly symmetrical system, only one or the other would be possible. According to quantum mechanics, they can do both at the same time, because the symmetry refraction causes the two states to "merge" (hybridize).

"In a sense, it is teamwork between the electrons reflected back and forth between two layers of the material and the symmetry-breaking electrons," says Prof. Florian Libisch from the Institute of Theoretical Physics. "Only when you look at them together can you explain that the material emits electrons of exactly this energy of 3.7 electron volts."

Carbon materials such as the type of graphite analyzed in this research work play a major role today - for example, the 2D material graphene, but also carbon nanotubes of tiny diameter, which also have remarkable properties. "The effect should occur in very different materials - wherever thin layers are held together by weak Van der Waals forces," says Wolfgang Werner. "In all these materials, this very special type of electron emission, which we can now explain for the first time, should play an important role".

The largest freshwater bacterium, Achromatium oxaliferum, is highly flexible in its requirements, as researchers led by the IGB have now discovered: It lives in places that differ extremely in environmental conditions such as hot springs and ice water. The bacterial strains from the different ecosystems do not differ in their gene content, but rather chose what to express. The adaptation is probably achieved by a process which is unique to these bacteria: only relevant genes are enriched in the genomes and transcribed, while others are archived in cell compartments.

Achromatium is special in many respects: It is 30,000 times larger than its "normal" counterparts that live in water and owing to its calcite deposits it is visible to the naked eye. It has several hundred chromosomes, which are most likely not identical. This makes Achromatium the only known bacterium with several different genomes.

The researchers have analyzed sequence data bases of sediments and show that Achromatium is universal. It is found in a broad range of environments: in shallow waters as well as in the ocean at a depth of 4000 metres. It can be found in hot springs and ice-cold water; in acidic and alkaline environments as well as in hypersaline waters.

Typically, such a wide range of environmental conditions would result in the establishment of new species, well-adapted to their specific environment. However, Achromatium defies this expectation. Though, equipped with equal functionality, the bacteria in the various ecosystems differ in their gene expression patterns by transcribing only relevant genes.

Compartments formed by folded cell membrane might serve as gene archives

"We suggest environmental adaptation in Achromatium occurs by increasing the copy number of relevant genes across the cell's hundreds of chromosomes. This is in stark contrast to other bacteria which eventually lose irrelevant genes. So the high number of genomes makes the versatility possible", explains Dr. Danny Ionescu, leader of the study from IGB.

Achromatium is full of calcium carbonate crystals that are located between the outer and cytoplasmic membranes. These crystals fold the cytoplasmic membrane forming pockets of cytoplasm which the researchers suggest to hold clusters of chromosomes. They hypothesize that these clusters enable Achromatium to "archive" genes of no immediate use.

No copy of the mother cell: each bacterium is unique

"The functional versatility of Achromatium and its genomic features contradict what we know for other bacteria, for example the concept of bacterial species and the driving forces of bacterial speciation. In Achromatium, mother and daughter cells are likely not identical and each cell is unique holding a multitude of genes, some of which are not essential for life in a particular habitat. Therefore, each cell keeps the potential to rapidly adapt to changing or new environmental conditions", concludes Professor Hans-Peter Grossart, co-author of the study and head of the aquatic microbial ecology group at IGB.

In normal circumstances, particles will follow well-established random motions as they diffuse through liquids and gases. Yet in some types of system, this behaviour can be disrupted - meaning the diffusion motions of particles are no longer influenced by the outcomes of chains of previous events. Through research published in EPJ E, Bernhard Mitterwallner, a Ph.D. student in the team of Roland Netz at the Free University of Berlin, Germany, has developed new theories detailing how these unusual dynamics can be reproduced in generalised mathematical models.

The team's approach could enable researchers to learn more about behaviours including the transport of biological cells, and the motions of 'active' materials - whose particles harvest energy in their surrounding environments to propel themselves forwards. Typically, these diffusion characteristics only appear briefly as systems transition between stable states - but under the right conditions, they can persist over far longer timescales. Researchers can study this effect by introducing a 'memory term' into their calculations, which can account for the influences of past events on different timescales. Several studies have now used this principle to explore how this 'transient persistent motion' can be captured in models of viscoelastic media - which can resist deformation when stress is applied.

The authors took a more general approach in their study; basing their calculations around an equation of motion which offered a useful framework for describing unconventional diffusion behaviours. When adding a memory term into the equation, their models give rise to transient persistent motion in a range of different systems, which had not been explored in previous studies. The team's results could now enable researchers to accurately model diffusion behaviours in a broader range of situations - and could be particularly useful for studies of advanced materials which respond to their surrounding environments.

Pairs of antibodies may be more effective than single antibodies at preventing and treating COVID-19, according to a new study by researchers at the University of North Carolina at Chapel Hill and The Rockefeller University in New York. The study, published November 19 in the Journal of Experimental Medicine (JEM), also suggests that in addition to blocking SARS-CoV-2’s entry into cells, the antibodies may combat the virus by enlisting various types of white blood cells to fight the infection.

Human antibodies that neutralize SARS-CoV-2 hold great potential for preventing and treating COVID-19, and researchers have identified several potent antibodies that bind to the spike protein on the virus’s surface, thereby preventing it from mediating the virus’s entry into cells.

“However, the neutralizing activity of antibodies to SARS-CoV-2 has primarily been tested using cells cultured in the laboratory, and how these in vitro results translate to protection in animals or humans has not been determined,” says Timothy P. Sheahan, an assistant professor at the Gillings School of Public Health at the University of North Carolina at Chapel Hill.

Research teams led by Sheahan and co-lead author Michel C. Nussenzweig, who is a professor, investigator, and senior physician at The Rockefeller University and Howard Hughes Medical Institute, tested the ability of several human antibodies to prevent SARS-CoV-2 infection in mice or hamsters. Surprisingly, the researchers found that certain antibodies were more potent than expected. Some antibodies that were relatively poor at blocking viral entry into cultured cells were much more effective at preventing SARS-CoV-2 infection in rodents. The researchers determined that this is partly because, in addition to blocking viral entry, the antibodies can activate various types of white blood cells. These “antibody effector functions” help the immune system target the virus and/or virally infected cells.

Sheahan and Nussenzweig’s team also found that antibodies may be even more effective when used in combination with each other. Pairs of antibodies that target slightly different parts of the viral spike protein could successfully prevent or treat SARS-CoV-2 infection in mice and hamsters at much lower doses than single-antibody treatments. This is a particularly attractive approach because targeting multiple parts of the spike protein reduces the chance of the virus mutating and becoming resistant to antibody treatments.

“Some antibody combinations can be effective for prevention and early therapy of SARS-CoV-2 even at relatively low doses,” Sheahan says. “Overall, our data support the idea that specific combinations of antibodies with the ability to activate immune cells should be developed for optimal protection and therapy against SARS-CoV-2.”

A study published today in JAMA Network Open/Emergency Medicine supports evidence that older persons admitted to emergency departments (ED), and subsequently diagnosed positive for COVID-19, often present with delirium when they show no other typical COVID-19 symptoms, such as fever and cough. Sharon K. Inouye, M.D., M.P.H., Director of the Aging Brain Center in the Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife and Professor of Medicine at Harvard Medical School, is senior author on the study. Co-first authors include Maura Kennedy, M.D., Assistant Professor of Emergency Medicine, Massachusetts General Hospital and Benjamin K. Helfand, M.D., Ph.D. (Cand.), University of Massachusetts Medical School and Warren Alpert Medical School, Brown University.

Although COVID-19 poses a risk at all ages, adults aged 65 years and older are at greatest risk of severe disease, hospitalization, intensive care use, and death. Persons older than 65 years comprise 16 percent of the United States population yet have accounted for more than 80 percent of deaths in the U.S.

Delirium is an acute state of confusion, characterized by an altered level of consciousness, disorientation, inattention, and other cognitive disturbances. Beyond COVID-19, delirium is known to be a common symptom in older adults with severe disease in the ED and is associated with extended hospitalization, and increased morbidity and mortality. Despite the threat delirium poses to older ED patients, it is undetected in two-thirds of cases.

Researchers involved in the study examined 817 patients 65 or older admitted to the ED and who were diagnosed with COVID-19. They found almost a third had delirium at the time they were seen in the ED. A delirium diagnosis was the main presenting symptom for 16 percent of those patients, and 37 percent had no typical COVID-19 symptoms; delirium was the sixth most common presenting symptoms in all patients. These findings suggest the clinical importance of including delirium on checklists of presenting signs and symptoms of COVID-19 that guide screening, testing, and evaluation.

According to Dr. Inouye, "This study demonstrates that delirium is not only a common symptom of COVID-19, but also may be the leading and possibly sole symptom in older persons. Thus, delirium should be considered an important presenting symptom of COVID-19."

Additional collaborating institutions on the study include:

St. Mary Mercy Livonia Hospital

Beth Israel Deaconess Medical Center

Maine Medical Center

Brigham and Women's Hospital

Yale School of Medicine

University of North Carolina Chapel Hill School of Medicine.

A collaborative research team from the University of Oklahoma, the Memorial Sloan Kettering Cancer Center
and Merck & Co. published an opinion article in the journal, Nature Chemical Biology, that addresses the gap in the discovery of new antibiotics.

"The rapid spread of antibiotic-resistant bacteria in clinics challenges our modern medicine and the traditional approaches to antibiotic discovery fail to generate new drugs needed for treatment of antibiotic resistant infections," Zgurskaya said. "The current COVID-19 pandemic further magnifies this problem because patients in intensive care units are particularly vulnerable to such infections ... (our) team is working on developing new tools to guide the discovery and optimization of new antibacterial agents."

Zgurskaya adds that the increasing frequency of antibiotic resistance has created a significant health care challenge and will progressively worsen without innovative solutions.

"In particular, Gram-negative pathogens present both biological and chemical challenges that hinder the discovery of new antibacterial drugs," Zgurskaya said. "As a result of these challenges, intensive screening campaigns have led to few successes, highlighting the need for new approaches to identify regions of chemical space that are specifically relevant to antibacterial drug discovery."

In the article, the research team provides an overview of emerging insights into this problem and outline a general approach for researchers and scientists to address it.

"The overall goal is to develop robust cheminformatic tools to predict Gram-negative permeation and efflux, which can then be used to guide medicinal chemistry campaigns and the design of antibacterial discovery libraries," Zgurskaya said.

Peer-reviewed / Systematic review and meta-analysis / People

Systematic review and meta-analysis of three human coronaviruses suggests that people infected with SARS-CoV-2 are most likely to be highly infectious in the first week after symptom onset, highlighting the need to identify and isolate cases early.

SARS-CoV-2 viral load appears to peak in the upper respiratory tract (which is thought to be the main source of transmission [1]) early in the disease course (from symptom onset to day five) while SARS-CoV and MERS-CoV viral load peak later, providing the likely explanation for why the COVID-19 pandemic spreads more rapidly in the community.

The evidence so far on SARS-CoV-2 points to a pattern of a nine-day period of infectiousness. As the study only looks at confirmed cases and not those who may have been exposed, it is unable to provide insight into the recommended duration of quarantine.

Although viral loads appear to be similar between people infected with SARS-CoV-2 who develop symptoms and those who do not, most studies indicate that asymptomatic individuals may clear the virus faster from their body and might be infectious for a shorter amount of time.

Although SARS-CoV-2 genetic material may still be detected in respiratory or stool samples for several weeks, no live virus (that can cause infection) was found in any type of sample collected beyond nine days of symptoms starting and people with SARS-CoV-2 are mostly likely to be highly infectious from symptom onset and the following five days, according to a systematic review and meta-analysis of three human coronaviruses published in The Lancet Microbe journal.

"This is the first systematic review and meta-analysis that has comprehensively examined and compared viral load and shedding for these three human coronaviruses. It provides a clear explanation for why SARS-CoV-2 spreads more efficiently than SARS-CoV and MERS-CoV and is so much more difficult to contain," says lead author Dr Muge Cevik of the University of St Andrews, UK.

"Our findings are in line with contact tracing studies which suggest the majority of viral transmission events occur very early, and especially within the first 5 days after symptom onset, indicating the importance of self-isolation immediately after symptoms start. We also need to raise public awareness about the range of symptoms linked with the disease, including mild symptoms that may occur earlier on in the course of the infection than those that are more prominent like cough or fever." [2]

This study specifically looked at people infected with SARS-CoV-2 and mainly those who were hospitalised, so the results are only relevant for the period of self-isolation for people with confirmed COVID-19, and do not apply to people quarantining who may or may not have been exposed after contact with someone infected. Many countries currently recommend that people with a SARS-CoV-2 infection should self-isolate for 10 days, which the authors say is in line with their findings, cautiously covering the period of infectiousness.

Understanding when patients are most likely to be infectious is of critical importance for informing effective public health measures to control the spread of SARS-CoV-2. This study looked at key factors involved in this: viral load (how the amount of the virus in the body changes throughout infection), viral RNA shedding (the length of time someone sheds viral genetic material (RNA), which does not necessarily indicate a person is infectious, as this is not necessarily able to replicate), and isolation of the live virus (a stronger indicator of a person's infectiousness, as the live virus is isolated and tested to see if it can successfully replicate in the laboratory).

The researchers included 98 studies that had five or more participants, cohort studies and randomised controlled trials; 79 focussed on SARS-CoV-2, 73 of which included hospitalised patients only; eight on SARS-CoV; and 11 on MERS-CoV infection. From these studies, the authors calculated the average length of viral RNA shedding and examined the changes in viral load and the success of isolating the live virus from different samples collected throughout an infection.

Analysing the results from the SARS-CoV-2 studies showed that the average length of time of viral RNA shedding into the upper respiratory tract, lower respiratory tract, stool and serum were 17 days, 14.6 days, 17.2 days and 16.6 days, respectively. The longest length of time that RNA shedding lasted was 83, 59, 35 and 60 days, respectively.

Of the eleven studies that attempted to isolate the live virus, all eight studies included that used respiratory samples successfully managed to culture viable virus within the first week of illness. Of the studies that also measured RNA viral load, these demonstrated a link between the success of isolating the live virus with viral load levels. No study included in this systematic review managed to successfully isolate live virus beyond day nine of symptoms in any type of sample, despite persistently high viral RNA loads. So far, only a few studies successfully isolated the live virus from stool samples despite prolonged RNA shedding, and the role of oral-faecal transmission for SARS-CoV-2 remains unclear.

"These findings suggest that in clinical practice, repeat PCR testing may not be needed to deem that a patient is no longer infectious, as this could remain positive for much longer and does not necessarily indicate they could pass on the virus to others. In patients with non-severe symptoms, their period of infectiousness could instead be counted as 10 days from symptom onset," says Dr Cevik. [2]

The highest viral load of SARS-CoV-2 RNA were detected early in the course of the disease - at the time symptoms begin, or before day five of symptoms. In contrast, the viral loads 8 and MERS-CoV peaked at 10-14 days and 7-10 days after symptom onset, respectively - explaining why transmission of these viruses can be effectively reduced by immediate identification, isolation and quarantine of people who show symptoms of the disease.

Only twelve studies reported on asymptomatic individuals infected with SARS-CoV-2 and of those, six also looked at how quickly people cleared the viral material out of their body.

"Although viral RNA loads appear to be largely similar between those with and without symptoms, a few studies suggest that asymptomatic individuals might clear the viral material from their bodies faster," says Dr Cevik. "Several studies have found that individuals with asymptomatic infection may clear the virus faster, suggesting that those without symptoms may be as infectious as those with symptoms at the beginning of infection, but may be infectious for a shorter period. However, at this stage, there are limited data available on the shedding of infectious virus in asymptomatic individuals to inform any policy change on quarantine duration in the absence of testing." [2]

This is the most comprehensive study of these three respiratory coronaviruses to date and is larger than the previous one meta-analysis on SARS-CoV-2, but the authors note some limitations. Many of the patients across the different studies included in the systematic review and meta-analysis were hospitalised and received a range of treatments that may affect the course of their infection, the studies included different populations who were followed up and managed differently, and in the interpretation of statistics used to measure the length of viral shedding. The period of infectiousness may also not exactly align with the successful culturing of the live virus from samples, although these are likely to broadly overlap.

"The majority of studies included in our review were performed in patients who were admitted to hospital. Therefore, our findings may not apply to people with milder infection although these results suggest those with milder cases may clear the virus faster from their body. Additionally, the increasing deployment of treatments, such as dexamethasone, remdesivir as well as other antivirals and immunomodulators in clinical trials are likely to influence viral shedding in hospitalised patients. Further studies on viral shedding in this context are needed" says senior author, Dr Antonia Ho of MRC-University of Glasgow Centre for Virus Research, UK. [2]

Announcing a new article publication for BIO Integration journal. In this article the authors Zhong, Cuiyu; Zhang, Lei; Huang, Jiandong; Huang, Songyin; Yao, Yandan from Sun Yat-sen University, Guangzhou, China; Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China and Shenzhen Institutes of Advanced Technology, Guangdong, China review antimicrobial peptides with anticancer properties.

There is considerable interest in a new class of anticancer molecules that is currently still under investigation termed the cationic antimicrobial peptides (AMPs). AMPs are a group of pervasive components of the innate immunity which can be found throughout all classes of life. The small innate peptides cover a broad spectrum of antibacterial activities due to their electrostatic interactions with the negatively charged bacterial membrane.

Compared with normal cells, cancer cells have increased proportions of negatively charged molecules, including phosphatidylserine, glycoproteins, and glycolipids, on the outer plasma membrane. This provides an opportunity for exploiting the interaction between AMPs and negatively charged cell membranes in developing unconventional anticancer strategies. Some AMPs may also be categorized into a group of potential anticancer agents called cationic anticancer peptides (ACPs) due to their relative selectivity in cell membrane penetration and lysis, which is similar to their interaction with bacterial membranes.

The authors review several examples of ACPs that are used in tumor therapy for their ability in penetrating or lysing tumor cell membrane and discuss recent advances and challenges in the application of ACPs.

The African crested rat (Lophiomys imhausi) is hardly the continent's most fearsome-looking creature--the rabbit-sized rodent resembles a gray puffball crossed with a skunk--yet its fur is packed with a poison so lethal it can fell an elephant and just a few milligrams can kill a human. In a Journal of Mammology paper published today, the University of Utah, Smithsonian Conservation Biology Institute, and National Museums of Kenya researchers found the African crested rat is the only mammal known to sequester plant toxins for chemical defense and uncovered an unexpected social life--the rats appear to be monogamous and may even form small family units with their offspring.

"It's considered a 'black box' of a rodent," said Sara Weinstein, lead author and Smithsonian-Mpala postdoctoral fellow and postdoctoral researcher at the University of Utah. "We initially wanted to confirm the toxin sequestration behavior was real and along the way discovered something completely unknown about social behavior. Our findings have conservation implications for this mysterious and elusive rat."

People in East Africa have long suspected the rat to be poisonous. A 2011 paper proposed these large rodents sequester toxins from the poison arrow tree (Acokanthera schimperi). A source of traditional arrow poisons, Acokanthera contains cardenolides, compounds similar to those found in monarch butterflies, cane toads and some human heart medications. Cardenolides, particularly the ones in Acokanthera, are highly toxic to most animals.

"The initial 2011 study observed this behavior in only a single individual. A main goal of our study was to determine how common this exceptional behavior was," said co-author Denise Dearing from the University of Utah.

When threatened, the African crested rat lives up to its name and erects a crest of hair on its back to reveal a warning on its flanks--black and white stripes running from neck-to-tail on each side of its body. The 2011 study hypothesized that the rats chew the Acokanthera bark and lick the plant toxins into specialized hairs at the center of these stripes.

In the new study, researchers trapped 25 African crested rats, the largest sample size of the species ever trapped. Using motion-activated cameras, they documented nearly 1,000 hours of rat behavior. For the first time, they recorded multiple rats sequestering Acokanthera toxins and discovered many traits that suggest the are social, and likely monogamous.

"Everyone thought it was a solitary animal. I've been researching this rat for more than ten years, so you would expect there to be fewer surprises," said Bernard Agwanda, curator of Mammals at the Museums of Kenya, co-author of this study and the 2011 paper. "This can carry over into conservation policy."

A rich social life

As a postdoctoral fellow at the Mpala Research Centre, Weinstein first searched for the rats with camera traps, but found that they rarely triggered the cameras. Weinstein was then joined by Katrina Nyawira, the paper's second author and now a graduate student at Oxford Brookes University. Together, they spent months experimenting with live traps to capture the elusive rodents.

"We talked to rangers and ranchers to ask whether they'd seen anything." said Nyawira. Eventually they figured out that loading the traps with smelly foods like fish, peanut butter and vanilla, did the trick. "Out of 30 traps, we finally got two animals. That was a win. This thing is really rare."

Those two animals changed the course of the study. They first caught an individual female, then caught a male at the same site two days later.

"We put these two rats together in the enclosure and they started purring and grooming each other. Which was a big surprise, since everyone we talked to thought that they were solitary," Weinstein said. "I realized that we had a chance to study their social interactions."

Weinstein and Nyawira transformed an abandoned cow shed into a research station, constructing stalls equipped with ladders and nest boxes to simulate their habitat in tree cavities. They placed cameras in strategic spots of each pen and then analyzed every second of their footage, tracking the total activity, movement and feeding behavior. The aim was to build a baseline of normal behavior before testing whether behavior changed after the rats chewed the toxin cardenolides from the poison arrow tree.

"They're herbivores, essentially rat-shaped little cows," Weinstein said. "They spend a lot of time eating, but they walk around, mate, groom, climb up the walls, sleep in the nest box."

The footage and behavioral observations strongly support a monogamous lifestyle. They share many of the traits common among monogamous animals: large size, a long life span and a slow reproductive rate. Additionally, the researchers trapped a few large juveniles in the same location as adult pairs, suggesting that offspring spend an extended period of time with their parents. In the pens, the paired rats spent more than half of their time touching each other, and frequently followed each other around. The researchers also recorded special squeaks, purrs and other communicative noises making up a wide vocal repertoire. Further behavioral studies and field observation would uncover more insights into their reproductive and family life.

After the researchers established a baseline of behavior, they offered rats branches from the poison arrow tree. Although rats did not sequester every time the plant was offered, 10 rats did at least once. They chewed it, mixed it with spit, and licked and chewed it into their specialized hairs. Exposure to the Acokanthera toxins did not alter rat behavior, and neither did eating milkweed, the same cardenolide-enriched plant used as chemical defense by monarch butterflies. Combined, these observations suggest that crested rats are uniquely resistant to these toxins.

"Most people think that it was a myth because of the potency of the tree," said Nyawira. "But we caught it on video! It was very crazy."

The rats were selective about using Acokanthera cardenolides, suggesting that rats may be picky about their toxin source, or that anointed toxins remain potent on the fur a long time, just like traditional arrow poisons from the same source.

African crested rat conservation

The African crested rat is listed as IUCN species of least concern, but there's little actual data on the animals. Agwanda has studied African crested rats for more than a decade--and sees indications that they're in trouble.

"We don't have accurate numbers, but we have inferences. There was a time in Nairobi when cars would hit them and there was roadkill everywhere," said Agwanda, who continues to monitor the populations. "Now encountering them is difficult. Our trapping rate is low. Their population is declining."

The research team is planning future studies to better understand their physiology and behavior. "We are particularly interested in exploring the genetic mechanisms that allow the crested rats and their parasites to withstand the toxic cardenolides" said co-author Jesús Maldonado of the Smithsonian Conservation Biology Institute and Weinstein's Smithsonian-Mpala Postdoctoral fellowship co-advisor.

"We are looking at a broad range of questions influenced by habitat change. Humans have cleared forests to make farms and roads. We need to understand how that impacts their survival," Agwanda said. Additionally, Agwanda is building an exhibit at the Museums of Kenya to raise awareness about this unique poisonous animal.

DALLAS (SMU) - A new study suggests that the Texas Research Incentive Program (TRIP) has succeeded in boosting the amount of private donations to public universities, indicating that policymakers can effectively leverage public investment to spur private donations.

The authors of the analysis, published in the American Educational Research Journal, note that further questions warrant study - such as whether the TRIP model increases institutional inequity over time.

Under the 2009 law that created TRIP, seven public "emerging research universities" were made eligible to receive matching funds from the state for gifts or endowment donations received from private sources: the University of Houston, University of North Texas, Texas Tech University and four of the University of Texas branch campuses. Texas State University was added to that list in 2013. TRIP-eligible colleges were selected based on a set of metrics such as the number of doctoral degrees awarded and faculty quality.

Researchers used data collected from 2006 to 2017 from the government's Integrated Postsecondary Education Data System, state-level data from the Census Bureau and the Texas Higher Education Coordinating Board to determine TRIP's effect on the eligible universities. The universities that adopted TRIP saw a 37.2 percent annual increase ($6.7 million more per year) in private gifts made by donors when compared to the amount similar non-TRIP institutions received. State funding for grants and contracts, which includes matching money for these gifts, also increased by $14 million among TRIP-eligible universities.

The program did not lead to a significant increase in long-term endowment assets, which indicates the donations are likely used for short-term funding.

"Our study provides new evidence that increased government support could stimulate monetary donations and help build universities' research capacity," said Denisa Gándara, co-author and assistant professor in SMU's Department of Education Policy and Leadership in the Simmons School of Education and Human Development. "Government support appears to signal two things to potential donors: one, that their dollar will be worth more given the matching funds and two, that their research for the eligible institutions is a valuable one."

The research team, which included Frank Fernandez at the University of Mississippi, stopped short of saying whether other states should adopt a similar program as Texas to boost research in public universities.

"One perspective is that TRIP is reducing inequality between the emerging research universities and two of the largest public universities for research in Texas--University of Texas at Austin and Texas A&M University," said lead author Xiaodan Hu, assistant professor in higher education at Northern Illinois University. "Another perspective is that the ascendance of emerging research universities could allow other public institutions in Texas, including minority-serving institutions, to fall further behind in terms of public funding and research capacity."

Finding the balance between efficiency and equity is therefore important to consider when adopting a research incentive policy statewide, Hu said.

Researchers were not able to isolate the amount of private donations specifically earmarked for research due to data limitations. They did, however, take multiple steps to ensure their findings were the result of TRIP and not some other reason. One of those measures was accounting for institution-specific trends that occurred before and after the program took effect in 2009 to ensure those trends were not a factor in their results.

The researchers also compared several non-TRIP colleges around the country to the eight universities that were studied.

Astronomers using data from the ongoing VLA Sky Survey (VLASS) have found a number of distant galaxies with supermassive black holes at their cores that have launched powerful, radio-emitting jets of material within the past two decades or so. The scientists compared data from VLASS with data from an earlier survey that also used the National Science Foundation's Karl G. Jansky Very Large Array (VLA) to reach their conclusion.

"We found galaxies that showed no evidence of jets before but now show clear indications of having young, compact jets," said Dr. Kristina Nyland, who is an NRC postdoctoral fellow in residence at the Naval Research Laboratory.

"Jets like these can strongly affect the growth and evolution of their galaxies, but we still don't understand all of the details. Catching newborn jets with surveys like VLASS provides a measure of the role of powerful radio jets in shaping the lives of the galaxies over billions of years," Nyland said.

VLASS is a project that will survey the sky visible from the VLA -- about 80 percent of the entire sky -- three times over seven years. The observations began in 2017 and the first of the three scans now is complete. Nyland and her colleagues compared data from this scan with data from the FIRST survey that used the VLA to observe a smaller portion of the sky between 1993 and 2011.

They found about 2,000 objects that appear in the VLASS images, but were not detected in the earlier FIRST survey. From these, they selected 26 objects that previously were categorized as galaxies with active nuclei -- powered by supermassive black holes -- by optical and infrared observations. The FIRST observations of the 26 objects had been made between 1994 and 2001. The VLASS observations were made in 2019. The intervals between observations of the objects thus ranged from 18 to 25 years.

They chose 14 of these galaxies for more detailed observations with the VLA. These observations provided higher-resolution images and also were done at multiple radio frequencies to get a more complete understanding of the objects' characteristics.

"The data from these detailed observations tell us that the most likely cause of the difference in radio brightness between the FIRST and the VLASS observations is that the 'engines' at the cores of these galaxies have launched new jets since the FIRST observations were made," explained Dillon Dong, from Caltech.

The black holes at the cores of galaxies are known to interact with the galaxies themselves, and the two evolve together. The jets launched from the regions near the black holes can affect the amount of star formation within the galaxy.

"Radio jets provide natural laboratories for learning about the extreme physics of supermassive black holes, whose formation and growth are believed to be intrinsically linked to that of the galaxy centers in which they reside," said Pallavi Patil, of the University of Virginia.

"Jets as young as the ones discovered in our study can provide us with a rare opportunity to gain new insights on how these interactions between the jets and their surroundings work," Nyland said.

"VLASS has proven to be a key tool for discovering such jets, and we eagerly await the results of its next two observing epochs," said Mark Lacy, of the National Radio Astronomy Observatory.

Nyland and her colleagues plan further studies of the galaxies using the Very Long Baseline Array (VLBA), the Chandra X-Ray Observatory, and visible-light and infrared telescopes. The paper has been accepted into publication by the Astrophysical Journal.

Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they're correct? Alexander Amini and his colleagues at MIT and Harvard University wanted to find out.

They've developed a quick way for a neural network to crunch data, and output not just a prediction but also the model's confidence level based on the quality of the available data. The advance might save lives, as deep learning is already being deployed in the real world today. A network's level of certainty can be the difference between an autonomous vehicle determining that "it's all clear to proceed through the intersection" and "it's probably clear, so stop just in case."

Current methods of uncertainty estimation for neural networks tend to be computationally expensive and relatively slow for split-second decisions. But Amini's approach, dubbed "deep evidential regression," accelerates the process and could lead to safer outcomes. "We need the ability to not only have high-performance models, but also to understand when we cannot trust those models," says Amini, a PhD student in Professor Daniela Rus' group at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

"This idea is important and applicable broadly. It can be used to assess products that rely on learned models. By estimating the uncertainty of a learned model, we also learn how much error to expect from the model, and what missing data could improve the model," says Rus.

Amini will present the research at next month's NeurIPS conference, along with Rus, who is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, director of CSAIL, and deputy dean of research for the MIT Stephen A. Schwarzman College of Computing; and graduate students Wilko Schwarting of MIT and Ava Soleimany of MIT and Harvard.

Efficient uncertainty

After an up-and-down history, deep learning has demonstrated remarkable performance on a variety of tasks, in some cases even surpassing human accuracy. And nowadays, deep learning seems to go wherever computers go. It fuels search engine results, social media feeds, and facial recognition. "We've had huge successes using deep learning," says Amini. "Neural networks are really good at knowing the right answer 99 percent of the time." But 99 percent won't cut it when lives are on the line.

"One thing that has eluded researchers is the ability of these models to know and tell us when they might be wrong," says Amini. "We really care about that 1 percent of the time, and how we can detect those situations reliably and efficiently."

Neural networks can be massive, sometimes brimming with billions of parameters. So it can be a heavy computational lift just to get an answer, let alone a confidence level. Uncertainty analysis in neural networks isn't new. But previous approaches, stemming from Bayesian deep learning, have relied on running, or sampling, a neural network many times over to understand its confidence. That process takes time and memory, a luxury that might not exist in high-speed traffic.

The researchers devised a way to estimate uncertainty from only a single run of the neural network. They designed the network with bulked up output, producing not only a decision but also a new probabilistic distribution capturing the evidence in support of that decision. These distributions, termed evidential distributions, directly capture the model's confidence in its prediction. This includes any uncertainty present in the underlying input data, as well as in the model's final decision. This distinction can signal whether uncertainty can be reduced by tweaking the neural network itself, or whether the input data are just noisy.

Confidence check

To put their approach to the test, the researchers started with a challenging computer vision task. They trained their neural network to analyze a monocular color image and estimate a depth value (i.e. distance from the camera lens) for each pixel. An autonomous vehicle might use similar calculations to estimate its proximity to a pedestrian or to another vehicle, which is no simple task.

Their network's performance was on par with previous state-of-the-art models, but it also gained the ability to estimate its own uncertainty. As the researchers had hoped, the network projected high uncertainty for pixels where it predicted the wrong depth. "It was very calibrated to the errors that the network makes, which we believe was one of the most important things in judging the quality of a new uncertainty estimator," Amini says.

To stress-test their calibration, the team also showed that the network projected higher uncertainty for "out-of-distribution" data -- completely new types of images never encountered during training. After they trained the network on indoor home scenes, they fed it a batch of outdoor driving scenes. The network consistently warned that its responses to the novel outdoor scenes were uncertain. The test highlighted the network's ability to flag when users should not place full trust in its decisions. In these cases, "if this is a health care application, maybe we don't trust the diagnosis that the model is giving, and instead seek a second opinion," says Amini.

The network even knew when photos had been doctored, potentially hedging against data-manipulation attacks. In another trial, the researchers boosted adversarial noise levels in a batch of images they fed to the network. The effect was subtle -- barely perceptible to the human eye -- but the network sniffed out those images, tagging its output with high levels of uncertainty. This ability to sound the alarm on falsified data could help detect and deter adversarial attacks, a growing concern in the age of deepfakes.

Deep evidential regression is "a simple and elegant approach that advances the field of uncertainty estimation, which is important for robotics and other real-world control systems," says Raia Hadsell, an artificial intelligence researcher at DeepMind who was not involved with the work. "This is done in a novel way that avoids some of the messy aspects of other approaches -- e.g. sampling or ensembles -- which makes it not only elegant but also computationally more efficient -- a winning combination."

Deep evidential regression could enhance safety in AI-assisted decision making. "We're starting to see a lot more of these [neural network] models trickle out of the research lab and into the real world, into situations that are touching humans with potentially life-threatening consequences," says Amini. "Any user of the method, whether it's a doctor or a person in the passenger seat of a vehicle, needs to be aware of any risk or uncertainty associated with that decision." He envisions the system not only quickly flagging uncertainty, but also using it to make more conservative decision making in risky scenarios like an autonomous vehicle approaching an intersection.

"Any field that is going to have deployable machine learning ultimately needs to have reliable uncertainty awareness," he says.