Effective method to mitigate impact of rain or snowmelt sewer flushing events on wastewater-based surveillance measurements.

Wastewater-based surveillance (WBS) is increasingly used for monitoring disease targets in wastewaters around the world. This study, performed in Ottawa, Canada, identifies a decrease in SARS-CoV-2 wastewater measurements during snowmelt-induced sewer flushing events. Observations first revealed a correlation between suppressed viral measurements and periods of increased sewage flowrates, air temperatures above 0 °C during winter months, and solids mass flux increases.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data.

Efficient Generation of Protein Pockets with PocketGen.

Designing protein-binding proteins is critical for drug discovery. However, the AI-based design of such proteins is challenging due to the complexity of ligand-protein interactions, the flexibility of ligand molecules and amino acid side chains, and sequence-structure dependencies. We introduce PocketGen, a deep generative model that simultaneously produces both the residue sequence and atomic structure of the protein regions where ligand interactions occur.

Corrigendum: Wastewater-based surveillance identifies start to the pediatric respiratory syncytial virus season in two cities in Ontario, Canada.

[This corrects the article DOI: 10.3389/fpubh.2023.

Impact of coagulation on SARS-CoV-2 and PMMoV viral signal in wastewater solids.

Wastewater surveillance (WWS) of SARS-CoV-2 has become a crucial tool for monitoring COVID-19 cases and outbreaks. Previous studies have indicated that SARS-CoV-2 RNA measurement from testing solid-rich primary sludge yields better sensitivity compared to testing wastewater influent. Furthermore, measurement of pepper mild mottle virus (PMMoV) signal in wastewater allows for precise normalization of SARS-CoV-2 viral signal based on solid content, enhancing disease prevalence tracking.

Modulatory effects of dietary saturated fatty acids on platelet mitochondrial function following short-term exposure to ambient Particulate Matter (PM).

Exposure to ambient fine particulate matter (PM) was found to produce vascular injury, possibly by activating platelets within days after exposure. The aim of this study was to investigate the modulatory effects of dietary saturated fatty acids on platelet mitochondrial respiratory parameters following short-term inhalational exposure to PM. A total of 22 healthy male volunteers were recruited from the Research Triangle area of North Carolina.

Wastewater-based surveillance identifies start to the pediatric respiratory syncytial virus season in two cities in Ontario, Canada.

Introduction: Detection of community respiratory syncytial virus (RSV) infections informs the timing of immunoprophylaxis programs and hospital preparedness for surging pediatric volumes. In many jurisdictions, this relies upon RSV clinical test positivity and hospitalization (RSVH) trends, which are lagging indicators. Wastewater-based surveillance (WBS) may be a novel strategy to accurately identify the start of the RSV season and guide immunoprophylaxis administration and hospital preparedness.

Rapidly developed, optimized, and applied wastewater surveillance system for real-time monitoring of low-incidence, high-impact MPOX outbreak.

Recent MPOX viral resurgences have mobilized public health agencies around the world. Recognizing the significant risk of MPOX outbreaks, large-scale human testing, and immunization campaigns have been initiated by local, national, and global public health authorities. Recently, traditional clinical surveillance campaigns for MPOX have been complemented with wastewater surveillance (WWS), building on the effectiveness of existing wastewater programs that were built to monitor SARS-CoV-2 and recently expanded to include influenza and respiratory syncytial virus surveillance in wastewaters.

Deep learning of 2D-Restructured gene expression representations for improved low-sample therapeutic response prediction.

Clinical outcome prediction is important for stratified therapeutics. Machine learning (ML) and deep learning (DL) methods facilitate therapeutic response prediction from transcriptomic profiles of cells and clinical samples. Clinical transcriptomic DL is challenged by the low-sample sizes (34-286 subjects), high-dimensionality (up to 21,653 genes) and unordered nature of clinical transcriptomic data.

Should nails be locked dynamically or statically in atypical femoral fractures? - A radiological analysis of time to union and reoperations in 236 displaced fractures with 4 years average follow-up.

Introduction: Atypical femoral fractures (AFFs) are associated with delayed union and higher reoperation rates. Axial dynamization of intramedullary nails is hypothesized to reduce time-to-union (TTU) and fixation failure as compared to static locking.

Methods: Consecutive acutely displaced AFFs fixed with long intramedullary nails across five centres between 2006 and 2021 with a minimum postoperative follow-up of three months were retrospectively reviewed.

Toward ordered -omics data science: Researchers on the magic of turning metagenomic chaos into image-like patterns.

Wan Xiang Shen, a postdoctoral researcher at National University of Singapore, and Yu Zong Chen, the PI of the Bioinformatics and Drug Design (BIDD) group, have developed an AI pipeline for enhanced deep learning of metagenomic data. Their paper highlights the advantages of unsupervised data restructuring in microbiome-based disease prediction and biomarker discovery. They talk about their view of data science and the backstory of the article published in .

Enhanced metagenomic deep learning for disease prediction and consistent signature recognition by restructured microbiome 2D representations.

Metagenomic analysis has been explored for disease diagnosis and biomarker discovery. Low sample sizes, high dimensionality, and sparsity of metagenomic data challenge metagenomic investigations. Here, an unsupervised microbial embedding, grouping, and mapping algorithm (MEGMA) was developed to transform metagenomic data into individualized multichannel microbiome 2D representation by manifold learning and clustering of microbial profiles (e.

Reply to Kuo et al. Comment on "Du et al. Gender Differences in the Relationships between Perceived Stress, Eating Behaviors, Sleep, Dietary Risk, and Body Mass Index. 2022, , 1045".

We thank Kuo et al. for their comments [..

Municipal and neighbourhood level wastewater surveillance and subtyping of an influenza virus outbreak.

Recurrent influenza epidemics and pandemic potential are significant risks to global health. Public health authorities use clinical surveillance to locate and monitor influenza and influenza-like cases and outbreaks to mitigate hospitalizations and deaths. Currently, global integration of clinical surveillance is the only reliable method for reporting influenza types and subtypes to warn of emergent pandemic strains.

Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic.

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul.

Wastewater to clinical case (WC) ratio of COVID-19 identifies insufficient clinical testing, onset of new variants of concern and population immunity in urban communities.

Clinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) will have greater value as an important diagnostic tool. An in-depth analysis and understanding of the metrics derived from WWS is required to interpret and utilize WWS-acquired data effectively (McClary-Gutierrez et al.

Environmental Uncertainty, Environmental Regulation and Enterprises' Green Technological Innovation.

This paper examines the impact of environmental uncertainty and environmental regulation on enterprises' green technological innovation, using a panel data of Chinese A-share listed companies in Shanghai and Shenzhen from 2005 to 2019 to conduct an empirical study using an OLS model and Poisson regression model. We employ environmental complexity and environmental dynamism to measure environmental uncertainty, and we have the following findings: first, both environmental uncertainty and environmental regulation promote enterprises' green technological innovation, while environmental regulation has positive moderating effects on the relationship between environmental uncertainty and enterprises' green technological innovation; second, environmental complexity positively affects enterprises' green technological innovation, while environmental dynamism has negative effects on enterprises' green technological innovation; third, environmental regulation accentuates the relationship between environmental complexity and green technological innovation, while it weakens the relationship between environmental dynamism and green technological innovation.

Fish oil blunts lung function decrements induced by acute exposure to ozone in young healthy adults: A randomized trial.

Background: Over one-third of the U.S. population is exposed to unsafe levels of ozone (O).

Gender Differences in the Relationships between Perceived Stress, Eating Behaviors, Sleep, Dietary Risk, and Body Mass Index.

Background: Obesity is a growing epidemic among university students, and the high levels of stress reported by this population could contribute to this issue. Singular relationships between perceived stress; engagement in restrained, uncontrolled, and emotional eating; sleep; dietary risk; and body mass index (BMI) have been reported in the current body of literature; however, these constructs interact with each other, and the complex relationships among them are infrequently examined. Therefore, the aim of the present study was to explore the complex relationships between these constructs using mediation and moderation analyses stratified by gender.

Omega-3 fatty acids attenuate cardiovascular effects of short-term exposure to ambient air pollution.

Background: Exposure to air pollution is associated with elevated cardiovascular risk. Evidence shows that omega-3 polyunsaturated fatty acids (omega-3 PUFA) may attenuate the adverse cardiovascular effects of exposure to fine particulate matter (PM). However, it is unclear whether habitual dietary intake of omega-3 PUFA protects against the cardiovascular effects of short-term exposure to low-level ambient air pollution in healthy participants.

AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.

Omics-based biomedical learning frequently relies on data of high-dimensions (up to thousands) and low-sample sizes (dozens to hundreds), which challenges efficient deep learning (DL) algorithms, particularly for low-sample omics investigations. Here, an unsupervised novel feature aggregation tool AggMap was developed to Aggregate and Map omics features into multi-channel 2D spatial-correlated image-like feature maps (Fmaps) based on their intrinsic correlations. AggMap exhibits strong feature reconstruction capabilities on a randomized benchmark dataset, outperforming existing methods.

The influence of dietary intake of omega-3 polyunsaturated fatty acids on the association between short-term exposure to ambient nitrogen dioxide and respiratory and cardiovascular outcomes among healthy adults.

Background: Short-term exposure to ambient nitrogen dioxide (NO) is associated with adverse respiratory and cardiovascular outcomes. Supplementation of omega-3 polyunsaturated fatty acids (PUFA) has shown protection against exposure to fine particulate matter. This study aims to investigate whether habitual omega-3 PUFA intake differentially modify the associations between respiratory and cardiovascular responses and short-term exposure to ambient NO.