Culture

People living in deprived, less affluent neighborhoods spent less time indoors at home during lockdown, according to a study that tracked data from millions of mobile phone users across the United States.

The study, published in the journal Annals of the American Association of Geographers, adds to growing evidence that low earners are less likely to comply with stay-at-home orders, either because they simply can't afford to, or because they work in professions in which working from home is not possible.

The finding is concerning given the fact that vulnerable groups are already at greater risk from COVID.

In March 2020, the US like many countries in the world entered a state of lockdown, with its citizens advised to stay at home to curb the spread of Coronavirus. Non-essential businesses closed, with people asked to work from home.

To investigate levels of compliance with these orders, researchers analyzed anonymous tracking data from 45 million mobile phone users across the United States. The authors calculated how much time residents in New York, Los Angeles, Chicago, Dallas, Houston, Washington D.C., Miami, Philadelphia, Atlanta, Phoenix, Boston, and San Francisco spent at home in the period between 1 January 2020 to 31 August 2020.

They then compared this with demographic information about the neighborhoods in which people lived, collected through The American Community Survey (ACS), a demographics survey program conducted by the U.S. Census Bureau.

The findings revealed that people living in areas with a higher percentage of wealthy residents, and with a higher average household income level tended to spend more time at home under the stay-at-home orders than people living in poor communities. This finding was valid across all cities that the researchers looked at.

The study also showed that education was correlated with compliance, as people who lived in neighborhoods with a high percentage of postgraduates tended to spend longer at home.

"Our study reveals the luxury nature of stay-at-home orders, which lower-income groups cannot afford to comply with," says author Xiao Huang, Assistant Professor of Geosciences at the University of Arkansas.

"This disparity exacerbates long-standing social inequality issues present in the United States, potentially causing unequal exposure to a virus that disproportionately affects vulnerable populations."

In the UK, too, it has been well-documented that those in more deprived and ethnically diverse communities are at greater risk from the virus.

Data from the Office for National Statistics (ONS) shows that those living in the most deprived neighbourhoods have been more than twice as likely to die from COVID as those in the least deprived. One of the reasons for this is thought to be that low-income workers typically have jobs that cannot be done from home, placing them at greater risk of contracting COVID-19.

They are also more likely to have insecure 'zero hours' contracts, making them worry that if they do not go into work they might not have a job to return to.

Previous research by SAGE has also shown that people who earn less than £20,000, or who have savings of less than £100 are three times less likely to self-isolate.

The authors of the study argue that more needs to be done to protect vulnerable groups from the effects of COVID.

"We must confront systemic social inequality and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups," says Xiao Huang.

A new study exploits the characteristic epigenetic signatures of childhood tumors to detect, classify and monitor the disease. The scientists analyzed short fragments of tumor DNA that are circulating in the blood. These "liquid biopsy" analyses exploit the unique epigenetic landscape of bone tumors and do not depend on any genetic alterations, which are rare in childhood cancers. This approach promises to improve personalized diagnostics and, possibly, future therapies of childhood tumors such as Ewing sarcoma. The study has been published in Nature Communications.

A study led by scientists from St. Anna Children's Cancer Research Institute (St. Anna CCRI) in collaboration with CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences provides an innovative method for "liquid biopsy" analysis of childhood tumors. This method exploits the fragmentation patterns of the small DNA fragments that tumors leak into the blood stream, which reflect the unique epigenetic signature of many childhood cancers. Focusing on Ewing sarcoma, a bone tumor of children and young adults with unmet clinical need, the team led by Eleni Tomazou, PhD, St. Anna CCRI, demonstrates the method's utility for tumor classification and monitoring, which permits close surveillance of cancer therapy without highly invasive tumor biopsies.

In tumors, cancer cells constantly divide, with some of the cancer cells dying in the process. These cells often release their DNA into the blood stream, where it circulates and can be analyzed using genomic methods such as high-throughput DNA sequencing. Such "so-termed liquid biopsy" analyses provide a minimally invasive alternative to conventional tumor biopsies that often require surgery, holding great promise for personalized therapies. For example, it becomes possible to check frequently for molecular changes in the tumor. However, the use of liquid biopsy for childhood cancers has so far been hampered by the fact that many childhood tumors have few genetic alterations that are detectable in DNA isolated from blood plasma.

Exploiting tumor-specific epigenetic patterns

Cell-free DNA from dying tumor cells circulates in the blood in the form of small fragments. Their size is neither random nor determined solely by the DNA sequence. Rather, it reflects how the DNA is packaged inside the cancer cells, and it is influenced by the chromatin (i.e., complex of DNA, protein and RNA) structure and epigenetic profiles of these cells. This is very relevant because epigenetic patterns - sometimes referred to as the "second code" of the genome - are characteristically different for different cell types in the human body. Epigenetic mechanisms lead to changes in gene function that are not based on changes in the DNA sequence but are passed on to daughter cells. The analysis of cell-free DNA fragmentation patterns provides a unique opportunity to learn about the epigenetic regulation inside the tumor without having to surgically remove tumor cells or even know whether and where in the body a tumor exists.

"We previously identified unique epigenetic signatures of Ewing sarcoma. We reasoned that these characteristic epigenetic signatures should be preserved in the fragmentation patterns of tumor-derived DNA circulating in the blood. This would provide us with a much-needed marker for early diagnosis and tumor classification using the liquid biopsy concept", explains Dr. Tomazou, Principal Investigator of the Epigenome-based precision medicine group at St. Anna CCRI.

Machine learning increases sensitivity

The new study benchmarks various metrics for analyzing cell-free DNA fragmentation, and it introduces the LIQUORICE algorithm for detecting circulating tumor DNA based on cancer-specific chromatin signatures. The scientists used machine-learning classifiers to distinguish between patients with cancer and healthy individuals, and between different types of pediatric sarcomas. "By feeding these machine learning algorithms with our extensive whole genome sequencing data of tumor-derived DNA in the blood stream, the analysis becomes highly sensitive and in many instances outperforms conventional genetic analyses", says Dr. Tomazou.

When asked about potential applications, she explains: "Our assay works well, we are very excited. However, further validation will be needed before it can become part of routine clinical diagnostics." According to the scientists, their approach could be used for minimally invasive diagnosis and, but also as a prognostic marker, monitoring which patient responds to therapy. Additionally, it might serve as a predictive marker during neoadjuvant therapy (i.e., chemotherapy before surgery), potentially enabling dose adjustments according to treatment response. "Right now, most patients receive very high doses of chemotherapy, while some patients may be cured already with a less severe therapy, which would reduce their risk of getting other cancers later in life. There is a real medical need for adaptive clinical trials and personalized treatment of bone tumors in children."

Researchers from Tulane University and University of Maryland published a new paper in the Journal of Marketing that examines the dynamic interplay between free and paid versions of an app over its lifetime and suggests a possible remedy for the failure of apps.

The study, forthcoming in the Journal of Marketing, is titled "Managing the Versioning Decision over an App's Lifetime" and is authored by Seoungwoo Lee, Jie Zhang, and Michel Wedel.

Is it really over for paid mobile apps? The mobile app industry is unique because free apps are much more prevalent than paid apps in most app categories, contrary to many other product markets where free products primarily play a supportive role to the paid products. Apps have been trending toward the free version in the past decade, such that in July 2020, 96% of apps on the Google Play platform were free. However, 63% of the free apps had fewer than a thousand downloads per month and 60% of app publishers generated less than $500 per month in 2015.

Are there ways for paid apps to make free apps more profitable? And how can app publishers improve profitability by strategically deploying or eliminating the paid and free versions of an app over its lifetime? To answer these questions, the research team investigated app publisher's decisions to offer the free, paid, or both versions of an app by considering the dynamic interplays between the free and paid versions. The findings offer valuable insights for app publishers on how to manage the versioning decision over an app's lifetime.

First, the researchers demonstrate how the free and paid versions influence each other's current demand, future demand, and in-app revenues. They find that either version's cumulative user base stimulates future demand for both versions via social influence, yet simultaneously offering both versions hurts the demand for each other in the current period. Also, the presence of a paid version reduces the in-app purchase rate and active user base and, therefore, the in-app purchase and advertising revenues of a free app, but the presence of a free version appears to have little negative impact on the paid version of an app. Therefore, app publishers should be mindful of the paid version's negative impact on the free version. In general, simultaneously offering both versions helps a publisher achieve cost savings via economies of scale, but it reduces revenues from each version compared to when either version is offered alone.

Second, analyses show that the most common optimal launch strategy is to offer the paid version first. Paid apps can generate download revenues from the first day of sales, while in-app revenues from either version rely on a sizeable user base which takes time to build. So, publishers can rely on paid apps to generate operating capital and recuperate development and launch costs much more quickly. Nonetheless, there are variations across app categories, which are related to differences in apps' abilities to monetize from different revenue sources. For example, the percentage of utility apps that should launch a paid app is particularly high because they have a lower ability to monetize the free app through in-app purchase items and advertising. In contrast, entertainment apps should mostly launch a free version because they have high availability of in-app ad networks and in-app purchase items.

Third, the optimal versioning decisions and their evolutionary patterns change over an app's ages and vary by app category. The evolutionary patterns of optimal versioning decisions show that, for most apps, the relative profitability of the free version tends to increase with app age while that of the paid version tends to decline. Therefore, the profitability of simultaneously offering both versions tends to increase with app age until a certain point, after which the free-only version will take over as the most common optimal versioning decision, which occurs about 1.5 years after launch on average for the (relatively more successful) apps in the data. Also, there is substantial cross-category variations in the versioning evolution patterns. For example, unlike for the other categories examined, the optimal versioning decision for most utility apps in our data is to stay with the paid-only option throughout an app's lifetime.

This research reveals the dynamic interplay between free and paid versions of an app over its lifetime and suggests a possible remedy for the failure of apps. As the researchers explain, "Many apps that start out with a free version fail because they cannot generate enough revenue to sustain early-stage operations. We urge app publishers to pay close attention to the interplay between free and paid app versions and to improve the profitability of free apps by strategically deploying or eliminating their paid version counterparts over an app's lifetime."

Artificial intelligence (AI) technology developed by researchers at the University of Waterloo is capable of assessing the severity of COVID-19 cases with a promising degree of accuracy.

A study, which is part of the COVID-Net open-source initiative launched more than a year ago, involved researchers from Waterloo and spin-off start-up company DarwinAI, as well as radiologists at the Stony Brook School of Medicine and the Montefiore Medical Center in New York.

Deep-learning AI was trained to analyze the extent and opacity of infection in the lungs of COVID-19 patients based on chest x-rays. Its scores were then compared to assessments of the same x-rays by expert radiologists.

For both extent and opacity, important indicators of the severity of infections, predictions made by the AI software were in good alignment with scores provided by the human experts.

Alexander Wong, a systems design engineering professor and co-founder of DarwinAI, said the technology could give doctors an important tool to help them manage cases.

"Assessing the severity of a patient with COVID-19 is a critical step in the clinical workflow for determining the best course of action for treatment and care, be it admitting the patient to ICU, giving a patient oxygen therapy, or putting a patient on a mechanical ventilator," Wong said.

"The promising results in this study show that artificial intelligence has a strong potential to be an effective tool for supporting frontline healthcare workers in their decisions and improving clinical efficiency, which is especially important given how much stress the ongoing pandemic has placed on healthcare systems around the world."

A paper on the research, Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays, appears in the journal Scientific Reports.

The next generation of electric vehicle batteries, with greater range and improved safety, could be emerging in the form of lithium metal, solid-state technology.

But key questions about this promising power supply need to be answered before it can make the jump from the laboratory to manufacturing facilities, according to University of Michigan researchers. And with efforts to bring electric vehicles to a larger part of the population, they say, those questions need answering quickly.

Jeff Sakamoto and Neil Dasgupta, U-M associate professors of mechanical engineering, have been leading researchers on lithium metal, solid-state batteries over the past decade. In a perspective piece in the journal Joule, Sakamoto and Dasgupta lay out the main questions facing the technology. To develop the questions, they worked in close collaboration with leaders in the auto industry.

Major automakers are going all-in on electric vehicles this year, with many announcing plans to phase out internal-combustion engine cars in the coming years. Lithium-ion batteries enabled the earliest EVs and they remain the most common power supply for the latest models coming off assembly lines.

Those lithium-ion batteries are approaching their peak performance in terms of the EV range on a single charge. And they come with the need for a heavy and bulky battery management system--without which there is risk of onboard fires. By utilizing lithium metal for the battery anode along with a ceramic for the electrolyte, researchers have demonstrated the potential for doubling EV range for the same size battery while dramatically reducing the potential for fires.

"Tremendous progress in advancing lithium metal solid-state batteries was made over the last decade," Sakamoto said. "However, several challenges remain on the path to commercializing the technology, especially for EVs."

Questions that need to be answered to capitalize on that potential include:

How can we produce ceramics, which are brittle, in the massive, paper-thin sheets lithium metal batteries require?

Do lithium metal batteries' use of ceramics, which require energy to heat them up to more than 2,000 degrees Fahrenheit during manufacturing, offset their environmental benefits in electric vehicles?

Can both the ceramics and the process used to manufacture them be adapted to account for defects, such as cracking, in a way that does not force battery manufacturers and automakers to drastically revamp their operations?

A lithium metal solid-state battery would not require the heavy and bulky battery management system that lithium-ion batteries need to maintain durability and reduce the risk of fire. How will the reduction in mass and volume of the battery management system--or its removal altogether--affect performance and durability in a solid-state battery?

The lithium metal needs to be in constant contact with the ceramic electrolyte, meaning additional hardware is needed to apply pressure to maintain contact. What will the added hardware mean for battery pack performance?

Sakamoto, who has his own startup company focused on lithium metal solid-state batteries, says the technology is having a moment right now. But the enthusiasm driving the moment, he says, must not get ahead of itself.

Paper Title: Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19

Journal: The Clinical Journal of the American Society of Nephrology (published online May 24, 2021)

Authors: Girish Nadkarni, MD, Associate Professor in the Department of Medicine (Nephrology), Clinical Director of the Hasso Plattner Institute for Digital Health, and Co-Chair of the Mount Sinai Clinical Intelligence Center at the Icahn School of Medicine at Mount Sinai; Lili Chan, MD, Assistant Professor in the Department of Medicine (Nephrology) at the Icahn School of Medicine at Mount Sinai; Akhil Vaid, MD, postdoctoral fellow in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai, and member of the Mount Sinai Clinical Intelligence Center and the Hasso Plattner Institute for Digital Health at Mount Sinai; and other coauthors.

Bottom Line: SARS-CoV-2, the virus that causes COVID-19, has infected more than 103 million people worldwide. Acute kidney injury (AKI) treated with dialysis was a common complication in patients who were hospitalized with COVID-19. Acute kidney injury is associated with increased risks for morbidity and mortality. Early prediction of which patients will need dialysis or experience critical illness leading to mortality during hospital care can enhance appropriate monitoring, and better inform conversations with patients and their caretakers.

Results: The Mount Sinai team developed and tested five different algorithms to predict patients requiring treatment with dialysis or critical illness leading to death on day 1, 3, 5, and 7 of the hospital stay, using data from the first 12 hours of admission to the Mount Sinai Health System. Out of the five models, the XGBoost without imputation method, outperformed all others with higher precision and recall.

Why the Research Is Interesting: While the Mount Sinai model requires further external review, such machine learning models can potentially be deployed throughout healthcare systems to help determine which COVID-19 patients are most at risk for adverse outcomes of the coronavirus. Early recognition of at-risk patients can enhance closer monitoring of patients and prompt earlier discussions regarding goals of care.

Who: More than 6,000 adults with COVID-19 admitted to five hospitals within the Mount Sinai Health System.

When: COVID-19 patients admitted from March 10 to December 26, 2020.

What: The study uses a machine learning model to determine COVID-19 patients most at risk for treatment requiring dialysis or critical illness leading to death.

How: The team used data on adults hospitalized with COVID-19 throughout the Mount Sinai Health System to develop and validate five models for predicting treatment with dialysis or death at various time periods --1, 3, 5 and 7 days --following hospital admission. Patients admitted to Mount Sinai Hospital in Manhattan were used for internal validation, while the other four hospital locations were part of the external validation cohort. Assessed features included demographics, comorbidities, laboratory results, and vital signs within 12 hour of hospital admission.

The five models created and tested were: the logistic regression, LASSO, random forest, and XGBoost with and without imputation. Out of the total model approaches used, XGBoost without imputation had the highest area under the receiver curve and area under the precision recall curve on internal validation for all time points. This model also had the highest test parameters on external validation across all time windows. Features including red cell distribution width, creatinine, and blood urea nitrogen were major drivers of model prediction.

Study Conclusions: Mount Sinai researchers have developed and validated a machine learning model to identify hospitalized COVID-19 patients at risk of acute kidney injury and death. The XGBoost model without imputation had the best performance compared to standard and other machine learning models. Widespread use of electronic health records makes the deployment of prediction models, such as this one, possible.

Said Mount Sinai's Dr. Girish Nadkarni of the research:
The near universal use of electronic health records has created a tremendous amount of data, which has enabled us to generate prediction models that can directly aid in the care of patients. A version of this model is currently deployed at Mount Sinai Hospital in patients who are admitted with COVID-19.

Said Mount Sinai's Dr. Lili Chan of the research:
As a nephrologist, we were overwhelmed with the increase in patients who had AKI during the initial surge of the COVID-19 pandemic. Prediction models like this enable us to identify, early on in the hospital course, those at risk of severe AKI (those that required dialysis) and death. This information will facilitate clinical care of patients and inform discussions with patients and their families.

Said Mount Sinai's Dr. Akhil Vaid of the research:
Machine learning allows us to discern complex patterns in large amounts of data. For COVID-19 inpatients, this means being able to more easily identify incoming at-risk patients, while pinpointing the underlying factors that are making them better or worse. The underlying algorithm, XGBoost, excels in accuracy, speed, and other under-the-hood features that allow for easier deployment and understanding of model predictions.

The technology using DNA-based genealogy that solved a double murder in Linköping opens completely new possibilities in investigating serious crime. LiU researchers are now involved in spreading new knowledge about the technology, which brings hope to police forces and has aroused major international interest.

"We want to tell others about the problems that we faced when working with this pilot case, and how we dealt with them. We can prevent others reinventing the wheel, and make sure that the knowledge available is extended and improved", says Andreas Tillmar.

He is forensic geneticist at the National Board of Forensic Medicine, and adjunct senior lecturer in the Department of Biomedical and Clinical Sciences at Linköping University. His research is focussed on developing methods to obtain genetic information from low-quality DNA samples, such that they give sufficient information, for example, to allow searches in genealogical databases. His methods contributed to the murders in Linköping in 2004 being solved.

Together with colleagues from the Swedish Police Authority and the National Forensic Centre, among others, Andreas Tillmar has published an article in the prestigious scientific journal Forensic Science International: Genetics. The article is a case study of a double murder in October 2004, in which an eight-year-old boy and a 56-year-old woman were stabbed to death. The case was finally solved in June 2020.

The Police Authority conducted a legal inquiry early in 2019 which concluded that the double murder was to be used as a pilot case to test the DNA-based genealogy method. After this, it took 1.5 years of collaboration between researchers and authorities before the murderer could be arrested. The successful resolution was a result of the police being able to use commercial genealogy databases and in this way gain access to a significantly larger selection of people to search.

The article describes the technical, legal and ethical aspects that it was necessary to solve during the work, and how the joint work finally gave a solution.

When the Swedish pilot case started in 2019, the technology of DNA-based genealogical research had been used only to a very limited extent. The first known case, from 2018, had resulted in a serial killer known as the Golden State killer being arrested and convicted in the US.

"This case aroused much attention in the media, but the knowledge behind the arrest was never published, since the technology had been managed by a private company. It wanted to keep the knowledge to itself for commercial reasons. It's different in our case: we have knowledge that is in strong demand and we want to spread it", says Andreas Tillmar.

The article describes not only the painstaking work that resulted in improved DNA-based methods: it also gives examples of legal and ethical questions.

The legal questions concern such matters as the current legislation on personal privacy. It is not obvious that detectives are to be able to use genetic information from commercial DNA-based genealogy databases. "It's a grey area. Technology is often one step ahead of the law."

Ethical dilemmas that arise with this type of DNA analysis include the fact that the police obtain the DNA information of individuals and in this way insight into their private lives. This includes kinship relationships, and their risk of developing certain genetic diseases.

"Thus, there is a risk of a conflict between that two important principles: the right of the individual to privacy against the aspiration of society to solve serious crime", says Andreas Tillmar.

He points out that the solution to the double murder has aroused considerable international interest.

"As far as we know, we are the first outside the US to use the technology. We hope that others can benefit from our work, and that we can improve these DNA-based methods through, for example, international collaboration," says Andreas Tillmar.

University of Virginia School of Medicine researchers have shed new light on how our brains develop, revealing that the very last step in cell division is crucial for the brain to reach its proper size and function.

The new findings identify a potential contributor to microcephaly, a birth defect in which the head is underdeveloped and abnormally small. That's because the head grows as the brain grows. The federal Centers for Disease Control estimates that microcephaly affects from 1 in 800 children to 1 in 5,000 children in the United States each year. The condition is associated with learning disabilities, developmental delays, vision and hearing loss, movement impairment and other problems.

"By understanding the genetic causes of microcephaly, even though they are rare, we can also help to understand how some viral infections can cause of microcephaly, such as Zika virus or cytomegalovirus," said researcher Noelle D. Dwyer, PhD, of UVA's Department of Cell Biology.

Understanding Brain Development

Dwyer and her team aim to understand how small changes in individual cells can lead to dramatic changes in the brain. In this case, they have identified an important role for abscission, the final step in cell division. During abscission, a new, or "daughter," cell severs its connection to its "mother" cell. Think of it like cutting the cord when a new baby arrives in the world.

Scientists have suspected that a particular cellular protein, Cep55, is essential for proper abscission. Dwyer wanted to investigate that, to determine what would happen if the protein were absent. She and her colleagues were surprised to find that abscission could still occur in their lab mice. However, the process took longer than usual, and the failure rate went up substantially.

Notably, the neural stem cells that failed abscission signaled that they needed to be removed from the brain, the researchers report. That led to massive numbers of cells dying and being removed. That's in contrast to cells elsewhere in the body, which don't call for their own removal when abscission fails.

"Neural stem cells in the prenatal brain seem to have tighter 'quality control' than cells in other parts of the body. If their DNA or organelles are damaged, they have this hair-trigger response to sacrifice themselves, so that they don't make abnormal brain cells that might cause brain malfunction, or brain tumors," Dwyer said. "Brain can still function. Other tissues seem to have a higher tolerance for damaged cells and don't activate this cell-death response."

Blocking the neural stem cells' signal for removal helped the brains of lab mice grow larger, Dwyer found, but this restored only part of the brain's normal size. Further, normal brain organization and function remained disrupted. This shows the importance of proper abscission in healthy brain development, the researchers say.

Dwyer noted that blocking the cell death signal with drugs or gene therapy could help restore brain growth in certain types of microcephaly, but it also might make brain function worse. "That's why it's important to test these ideas in animal models and cell-culture models," she said.

UVA's new findings align with what scientists have known about the gene that makes the Cep55 protein. People who have mutations in the Cep55 gene suffer severe defects in their brain and central nervous system, while the rest of their bodies are relatively spared. Dwyer's new research helps explain why that is.

The new findings also benefit the battle against cancer. "Cep55 mutations are also found associated with many human cancers, so understanding the normal function of Cep55 in dividing cells in the brain helps inform cancer researchers how its altered function could lead to abnormal cell division that can initiate or fuel tumor growth," Dwyer said.

Dwyer noted the important contributions of Jessica Little and Katrina McNeely, who recently completed their PhDs in Dwyer's lab. Little is an MD-PhD student in UVA's Cell & Developmental Biology program who graduated this spring; McNeely was a Neuroscience graduate student who defended her dissertation last year.

Corona Virus Disease (COVID -19) patients primarily appear with respiratory issues and viral pneumonia. The patients may also present cardiovascular issues includes early signs of acute myocardial injury. The researchers from Sohag University, Egypt, found that cardiac troponin I (cTnI) can prove to be a gold-standard biomarker for necrosis and myocardial risk assessment in COVID-19 sufferers.

The researchers aimed to assess the prognostic value of cTnI in COVID-19 sufferers. The study included ninety-two COVID-19 patients admitted in the El Helal Hospital, Sohag, Egypt. The hospital, upon admission, did routine investigations including cTnI, chest Computed Tomography (CT), and Electrocardiogram (ECG). Additionally, the patients admitted to the intensive care unit (ICU) received echocardiography testing. The research found that the cTnI level was elevated in 30 patients admitted to the ICU, of whom 17 died. The researchers conclude that cardiac troponin I levels are a prognostic factor for ICU admission and mortality in COVID-19 patients.

To download and read the Full-text article, please visit: https://benthamopen.com/ABSTRACT/TOCMJ-15-18

Public use of parks and reserves increased only slightly during last year's COVID-19 national lockdown despite gyms and sports facilities shutting down, a University of Queensland study found.

UQ School of Biological Sciences PhD candidate Violeta Berdejo-Espinola surveyed 1000 people in Brisbane, measuring their use of urban green space and the benefits people associated with visiting the areas during lockdown.

"People all around Brisbane, myself included, noticed a boom in park use in 2020, but while more people ventured into local parks, many folks were left indoors," Ms Berdejo-Espinola said.

"Thirty-six per cent of people increased their use of city green spaces, yet at the same time, 26 per cent reduced it - there was a great deal of flux during this time.

"As an avid green space user and runner, I noticed plenty of people that I had to dodge - kids on bikes, people walking and running, doing yoga, or reading a book.

"If you were already using green spaces frequently, chances are you significantly reduced your use of them - maybe because there were too many people to dodge.

"It was the same story if you were older - you might not have wanted to go to parks as often as you once did, which is concerning because staying isolated at home can have negative health implications."

The research also found that lockdown periods greatly motivated those who had never really visited Brisbane's parks before.

"Forty-five per cent of people who weren't previous users of urban green spaces started to use them for the first time during the restrictions period," Ms Berdejo-Espinola said.

"It seemed these people were finally discovering that our city's parks are excellent for fostering good mental health during stressful times and realised they're a highly effective nature-based coping mechanism.

"Programs exist across the globe where GPs are prescribing nature to people, and Brisbanites were naturally discovering these anxiety-reducing effects."

Fellow author Professor Richard Fuller said careful planning meant Brisbane was well-endowed with high-quality green spaces.

"Brisbane's comparative abundance of usable green spaces has undoubtedly played a role in helping our population through the crisis," he said.

"Yet it is concerning that many people actually reduced their park use during the lockdowns.

"Programs to help everyone benefit from the health and wellbeing effects of natural spaces will help Brisbane achieve United Nations Sustainable Development Goal 11 - making inclusive, resilient, and sustainable cities.

"COVID-19 is changing the way we're using green spaces, and now is the perfect time to place the human health benefits of urban green spaces at the forefront of our urban planning policies."

MAY 26, 2021 -- Researchers from UTSA, the University of Central Florida (UCF), the Air Force Research Laboratory (AFRL) and SRI International have developed a new method that improves how artificial intelligence learns to see.

Led by Sumit Jha, professor in the Department of Computer Science at UTSA, the team has changed the conventional approach employed in explaining machine learning decisions that relies on a single injection of noise into the input layer of a neural network.

The team shows that adding noise--also known as pixilation--along multiple layers of a network provides a more robust representation of an image that's recognized by the AI and creates more robust explanations for AI decisions. This work aids in the development of what's been called "explainable AI" which seeks to enable high-assurance applications of AI such as medical imaging and autonomous driving.

"It's about injecting noise into every layer," Jha said. "The network is now forced to learn a more robust representation of the input in all of its internal layers. If every layer experiences more perturbations in every training, then the image representation will be more robust and you won't see the AI fail just because you change a few pixels of the input image."

Computer vision--the ability to recognize images--has many business applications. Computer vision can better identify areas of concern in the livers and brains of cancer patients. This type of machine learning can also be employed in many other industries. Manufacturers can use it to detect defection rates, drones can use it to help detect pipeline leaks, and agriculturists have begun using it to spot early signs of crop disease to improve their yields.

Through deep learning, a computer is trained to perform behaviors, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through set equations, deep learning works within basic parameters about a data set and trains the computer to learn on its own by recognizing patterns using many layers of processing.

The team's work, led by Jha, is a major advancement to previous work he's conducted in this field. In a 2019 paper presented at the AI Safety workshop co-located with that year's International Joint Conference on Artificial Intelligence (IJCAI), Jha, his students and colleagues from the Oak Ridge National Laboratory demonstrated how poor conditions in nature can lead to dangerous neural network performance. A computer vision system was asked to recognize a minivan on a road, and did so correctly. His team then added a small amount of fog and posed the same query again to the network: the AI identified the minivan as a fountain. As a result, their paper was a best paper candidate.

In most models that rely on neural ordinary differential equations (ODEs), a machine is trained with one input through one network, and then spreads through the hidden layers to create one response in the output layer. This team of UTSA, UCF, AFRL and SRI researchers use a more dynamic approach known as a stochastic differential equations (SDEs). Exploiting the connection between dynamical systems to show that neural SDEs lead to less noisy, visually sharper, and quantitatively robust attributions than those computed using neural ODEs.

The SDE approach learns not just from one image but from a set of nearby images due to the injection of the noise in multiple layers of the neural network. As more noise is injected, the machine will learn evolving approaches and find better ways to make explanations or attributions simply because the model created at the onset is based on evolving characteristics and/or the conditions of the image. It's an improvement on several other attribution approaches including saliency maps and integrated gradients.

Jha's new research is described in the paper "On Smoother Attributions using Neural Stochastic Differential Equations." Fellow contributors to this novel approach include UCF's Richard Ewetz, AFRL's Alvaro Velazquez and SRI's Sumit Jha. The lab is funded by the Defense Advanced Research Projects Agency, the Office of Naval Research and the National Science Foundation. Their research will be presented at the 2021 IJCAI, a conference with about a 14% acceptance rate for submissions. Past presenters at this highly selective conference have included Facebook and Google.

"I am delighted to share the fantastic news that our paper on explainable AI has just been accepted at IJCAI," Jha added. "This is a big opportunity for UTSA to be part of the global conversation on how a machine sees."

The Menetries' tiger moth (Arctia menetriesii) is one of the rarest and most poorly studied Palaearctic moth species. Even though its adult individuals are large and brightly coloured, they are difficult to spot, because they aren't attracted to light, they're not active at night, and they fly reluctantly. Currently, the species only inhabits two countries - Finland and the Russian Federation, and is included in the Red Lists of both, as Data Deficient in the former and Vulnerable in the latter.

For 13 years, researcher Evgeny Koshkin of the Institute of Water and Ecology Problems of the Far Eastern branch of the Russian Academy of Sciences kept searching for the elusive Menetries' tiger moth in its habitat in the Bureinsky Nature Reserve, 400 km north of Khabarovsk, Russia, but he only ever found it in 2018, in what was the first record of this species in 34 years in this region. That's how rare it is.

After collecting eggs from a female moth, Koshkin documented the species' biology under laboratory conditions and described its immature stages in the open-access, peer-reviewed scientific journal Nota Lepidopterologica. For the first time, detailed photographs of all developmental stages of this species have been published.

In laboratory conditions, the development cycle of the Menetries' tiger moth from egg laying to an adult individual lasts between 72 and 83 days. Out of the 105 eggs that the female moth laid in captivity, however, only 13 transformed into adults, and out of those, only four were able to spread their wings. In the last larval instar, about 75% of the larvae died immediately before pupation, and a number of metamorphosis anomalies were observed in the ones that survived.

This is the first time that such anomalies and morphological defects of pupae are documented in the Menetries' tiger moth, and it is possible that they occur in a similar way in nature. Some metamorphosis anomalies manifested as larva-pupa intermediates due to disrupted molting, and pupae with severe anomalies produced adults that were unable to inflate their wings.

It is possible that the diet of the laboratory-reared larvae might have had something to do with the high mortality rate before pupation and the metamorphosis anomalies during it. Some of the larvae were fed on Aconitum leaves and larch needles during certain periods of their lives, and it is possible that toxic compounds found in these plants might have impacted their health and development. More research on larval diet would be needed, however, to confirm or reject this hypothesis.

Men aged 65 and over should monitor their sleep patterns and seek medical advice after a warning from Flinders University experts that disrupted slumber can be linked to cognitive dysfunction.

In a new article published in the Journal of Sleep Research, the Adelaide Institute for Sleep Health research group studied a group of 477 middle-aged and older men's attention and processing speed in relation to their sleep.

The participants from the Florey Adelaide Male Ageing Study undertook cognitive testing and a successful sleep study.

"Less deep sleep and more light sleep is related to slower responses on cognitive function tests," says lead author Jesse Parker.

"While obstructive sleep apnoea itself is not directly related to cognitive function in all men studied, we did note that in men aged 65 and older, more light sleep was related to worse attention and processing speed."

Senior author of the study, Flinders Associate Professor Andrew Vakulin, says the results suggest that day-to-day activities that rely on optimal attention and cognitive speed such as driving, physical activities and walking might be affected by the encroachment of poor sleep.

Medical Director of the research group, Professor Robert Adams, says decreasing deep sleep as people age is associated with cognition. This emphasises the importance of ongoing research looking at ways to stimulate deep sleep as a means of slowing cognitive decline with age.

Further longitudinal investigation is needed to connect poor sleep and sleep apnoea with future changes in sleep patterns and cognitive decline as well as general microarchitectural changes in older people's sleep patterns.

Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed a new biomaterial made entirely from discarded bullfrog skin and fish scales that could help in bone repair.

The porous biomaterial, which contains the same compounds that are predominant in bones, acts as a scaffold for bone-forming cells to adhere to and multiply, leading to the formation of new bone.

Through laboratory experiments, the NTU Singapore team found that human bone-forming cells seeded onto the biomaterial scaffold successfully attached themselves and started multiplying - a sign of growth. They also found that the risk of the biomaterial triggering an inflammatory response is low.

Such a scaffold could be used to help with the regeneration of bone tissue lost to disease or injury, such as jaw defects from trauma or cancer surgery. It could also assist bone growth around surgical implants such as dental implants.

The scientists believe the biomaterial is a promising alternative to the current standard practice of using a patient's own tissues, which requires additional surgery for bone extraction. At the same time, the production of this biomaterial tackles the problem of aquaculture waste, said Assistant Professor Dalton Tay of the NTU School of Materials Science and Engineering (MSE), who led the multidisciplinary study.

More than 20 million tonnes of fishery by-products, such as fins, scales, and skins, are discarded every year. In Singapore, the combined annual consumption of frog flesh and fish is estimated to be around 100 million kilograms, making bullfrog skin and fish scales two of Singapore's largest aquaculture waste side streams.

Asst Prof Dalton Tay said: "We took the 'waste-to-resource' approach in our study and turned discards into a high-value material with biomedical applications, closing the waste loop in the process. Our lab studies showed that the biomaterial we have engineered could be a promising option that helps with bone repair. The potential for this biomaterial is very broad, ranging from repairing bone defects due to injury or ageing, to dental applications for aesthetics. Our research builds on NTU's body of work in the area of sustainability and is in line with Singapore's circular economy approach towards a zero-waste nation."

Professor Matthew Hu Xiao, the study's co-author and Director of the Environmental Chemistry and Materials Centre, Nanyang Environment and Water Research Institute (NEWRI), added: "These waste streams can also be converted into green chemicals and materials for environmental remediation and timely treatment can reduce wastewater contamination."

Clinical Associate Professor Goh Bee Tin, Director for Research at the National Dental Centre Singapore, who was not involved in the study, said: "The National Dental Centre Singapore is excited about the use of bullfrog skin as a natural biomaterial for tissue regeneration. We see many potential dental applications ranging from the regeneration of gum tissues in periodontal disease, to bone for placement of dental implants, to jawbone following tumour surgery. Obviating the need for additional bone harvesting surgery also translates to time and cost savings, and less pain for patients."

The research findings were published online in Materials Science and Engineering C in April and will be published in Volume 126 of the journal in July.

The research team has filed patents for the biomaterial's wound healing and bone tissue engineering applications. The team is now further evaluating the long-term safety and efficacy of the biomaterial as dental products under a grant from the China-Singapore International Joint Research Institute and aims to bring the waste-to-resource technological pipeline closer to commercialisation.

Turning waste to treasure

With the combined annual consumption of frog flesh and fish in Singapore estimated to be around 100 million kilograms, bullfrog skin and fish scales are two of Singapore's largest aquaculture waste side streams. The fishery waste used by the NTU team was collected from Khai Seng Fish Farm and Jurong Frog Farm.

To make the biomaterial, the team first extracted Type 1 tropocollagen (many molecules of which form collagen fibres) from the discarded skins of the American bullfrog, locally farmed and imported into Singapore in large numbers for consumption; and hydroxyapatite (a calcium-phosphate compound) from the scales of snakehead fish, commonly known as the Toman fish.

Collagen and hydroxyapatite (HA) are two predominant components found in bones, thus conferring on the biomaterial a structure, composition, and ability to promote cell attachment that are like the bone. These two components also make the biomaterial tough.

The scientists removed all impurities from the bullfrog skin, then blended it to form a thick collagenous paste that is diluted with water. Collagen was then extracted from this mix. "Using this approach, we were able to obtain the highest ever reported yield of collagen of approximately 70 per cent from frog skin, thus making this approach commercially viable," said Asst Prof Tay, who is also from the NTU School of Biological Sciences (SBS).

HA was harvested from discarded fish scales through calcination - a purification process that requires high heat - to remove the organic matter, and then air-dried.

The biomaterial was synthesised by adding HA powder to the extracted collagen, then cast into a mould to produce a 3D porous scaffold. This entire process took less than two weeks and the team believes it can be both further shortened and scaled-up.

Proof-of-concept experiment

To assess the biological performance of the porous biomaterial scaffold for bone repair, the scientists seeded bone-forming cells onto the scaffold.

In their laboratory experiments, they found that the number of cells increased significantly. After a week, the cells were uniformly distributed across the scaffold - an indicator that the scaffold could promote proper cellular activities and eventually lead to the formation of tissues. The scientists also found that the presence of HA in the biomaterial significantly enhanced bone formation.

The biomaterial was also tested for its tendency to cause an inflammatory response, which is common after a biomaterial is implanted in the body. Using real-time polymerase chain reaction, the scientists found that the expression level of pro-inflammatory genes in human immune cells exposed to the biomaterial remained "relatively modest" when compared to a control exposed to endotoxins, a compound known to stimulate immune response, said Asst Prof Tay.

For instance, the expression of the gene IL6 in the biomaterial group was negligible and at least 50 times lower than that of the endotoxins-exposed immune cells. This suggests that the risk of the NTU-developed biomaterial to trigger an excessive acute inflammatory response is low.

Taken together, these findings demonstrate the potential of the biomaterial scaffold, synthesised from discarded bullfrog skin and fish scales, as a promising waste-to-resource bone graft substitute material for bone repair and regeneration.

Ms Chelsea Wan, Director, Jurong Frog Farm said: "The aquaculture industry is an important avenue to meet the global growing demand for safe and quality seafood, but a big challenge we face is the huge wastage and downcycling of valuable aquatic resources. In Singapore, the combined annual consumption of frog flesh and fish is estimated to be around 100 million kilograms, making bullfrog skin and fish scales two of the largest aquaculture waste side streams here. The integration of multiple seafood waste streams into a single high value product is a leading example of sustainable innovation for the aquaculture industry."

Going forward, the research team hopes to work with clinical and industrial partners on animal studies to find out how tissues in the body would respond to this biomaterial in the long term, and the material's ability to repair bone defects and dermal wounds, as well as to bring the entire waste-to-resource technological pipeline closer to commercialisation.

This multidisciplinary research is carried out by MSE, SBS, NEWRI, and the Energy Research Institute at NTU.

International research led by Monash University and the Peter Doherty Institute for Infection and Immunity has achieved a proof of concept for a new, fast, portable saliva screening test that uses an infrared light technology to confirm infection with SARS-CoV-2.

The research is published today in Angewandte Chemie.

Professor Bayden Wood, from the Monash University School of Chemistry, Dr Phil Heraud formerly from the Monash Biomedicine Discovery Institute and collaborators Professors Dale Godfrey and Damian Purcell from the Doherty Institute, report on a new diagnostic approach, which involves the use of a portable infrared instrument to detect the SARS-CoV-2 virus in saliva.

The team identified a signature of the infectious agent in the infrared spectra of saliva from 27 out of the 29 SARS-CoV-2-infected human subjects who presented at The Royal Melbourne Hospital with COVID-19-like symptoms.

"The most significant advantages of using this infrared-based technology on saliva samples, include the speed and ease with which the test can be performed, its affordability and the reduced risk to both patients and healthcare workers," said Professor Wood.

The scientists say this is very encouraging preliminary research and are keen to see further testing with a larger patient cohort to better understand the specificity of this approach.

A portable infrared spectrometer was modified to enable high throughput screening enabling the samples to be rapidly scanned in a contactless mode without having to clean the instrument between measurements.

Professor Wood estimates that this technique could be capable of screening 5000 samples per day per instrument, with results for each sample being ready in five minutes.

Dr Heraud said because the infrared light interacted with the vibrations of molecules, it could be used to generate a spectrum that represented a unique chemical fingerprint of the sample that was then processed using machine learning algorithms.

"The approach has significant advantages over the standard Real Time Reverse Transcription Polymerase Chain Reaction (RT-PCR) that is the current gold standard for detection," said Professor Godfrey.

"As we know, this requires that samples are sent to a dedicated laboratory and results take a day or more".

According to Professor Purcell the proposed new test also avoids the discomfort associated with nasopharyngeal swabs, an advantage that could improve community participation in testing.

"A person can contribute the sample by simply dribbling into a sterile container," he said.

"The result can be derived in less than five minutes and a rapid result minimises the delay in determining if quarantine is required, therefore minimising the risk of further spread of infection."

A similar infrared technique known as Attenuated Total Reflectance (ATR) spectroscopy has previously been used by the Monash researchers to detect malaria and hepatitis.

The new transflection infrared based approach offers triple the absorbance and hence interrogates more saliva to detect pathogens compared to the traditional ATR technology.

The speed and versatility of the technique potentiates its use for point-of-care screening at airports, sporting venues, universities or schools, to triage patients for RT-PCR testing.

Although multiple techniques were used in validating the approach, Professor Bayden Wood carried out much of the initial work using the infrared microspectroscopy beamline at the Australian Synchrotron (AS), with support from the beamline team.

The measurements performed at the AS were recorded from highly purified clusters of virus, provided by the team at the Doherty Institute.

The unique infrared RNA signature from the SARS-CoV-2 virus was then detected in the infrared spectra of the saliva samples recorded using the portable instrument.