Implementing the discontinuous-Galerkin finite element method using graph neural networks with application to diffusion equations.

Machine learning (ML) has benefited from both software and hardware advancements, leading to increasing interest in capitalising on ML throughout academia and industry. There have been efforts in the scientific computing community to leverage this development via implementing conventional partial differential equation (PDE) solvers with machine learning packages, most of which rely on structured spatial discretisation and fast convolution algorithms. However, unstructured meshes are favoured in problems with complex geometries.

Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes.

Background: Critically ill hospitalized patients with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized.

Methods: In a cohort study of 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and more than 20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms.

Inter-laboratory harmonization of microsphere immunoassays for SARS-CoV-2 antibody detection in dried blood spots and oral fluids.

Dried blood spots (DBS) and oral fluids (OF) are easily attainable biospecimen types that have enabled population scale antibody monitoring for SARS-CoV-2 exposure and vaccination. However, the degree to which the two different biospecimen types can be used interchangeably remains unclear. To begin to address this question, we generated contrived DBS (cDBS) and OF (cOF) from serum panels from SARS-CoV-2 infected, vaccinated, and uninfected individuals.

Early, robust mucosal secretory IgA but not IgG response to SARS-CoV-2 spike in oral fluid is associated with faster viral clearance and COVID-19 symptom resolution.

Background: High priority efforts are underway to support the development of novel mucosal COVID-19 vaccines, such as the US Government's Project NextGen and the Center for Epidemic Preparedness Innovations' goal to respond to the next pandemic with a new vaccine in 100 days. However, there is limited consensus about the complementary role of mucosal immunity in disease progression and how to evaluate immunogenicity of mucosal vaccines. This study investigated the role of oral mucosal antibody responses in viral clearance and COVID-19 symptom duration.

Challenges and Approaches to Establishing Multi-Pathogen Serosurveillance: Findings from the 2023 Serosurveillance Summit.

Multiplex-based serological surveillance is a valuable but underutilized tool to understand gaps in population-level exposure, susceptibility, and immunity to infectious diseases. Assays for which blood samples can be tested for antibodies against several pathogens simultaneously, such as multiplex bead immunoassays, can more efficiently integrate public health surveillance in low- and middle-income countries. On March 7-8, 2023 a group of experts representing research institutions, multilateral organizations, private industry, and country partners met to discuss experiences, identify challenges and solutions, and create a community of practice for integrated, multi-pathogen serosurveillance using multiplex bead assay technologies.

Mobile laboratory measurements of air pollutants in Baltimore, MD elucidate issues of environmental justice.

The City of Baltimore, MD has a history of problems with environmental justice (EJ), air pollution, and the urban heat island (UHI) effect. Current chemical transport models lack the resolution to simulate concentrations on the scale needed, about 100 m, to identify the neighborhoods with anomalously high air pollution levels. In this paper we introduce the capabilities of a mobile laboratory and an initial survey of several pollutants in Baltimore to identify which communities are exposed to disproportionate concentrations of air pollution and to which species.

SARS-CoV-2 antibody prevalence by industry, workplace characteristics, and workplace infection prevention and control measures, North Carolina, USA, 2021 to 2022.

The COVID-19 pandemic has disproportionately affected workers in certain industries and occupations, and the workplace can be a high-risk setting for SARS-CoV-2 transmission. In this study, we measured SARS-CoV-2 antibody prevalence and identified work-related risk factors in a population primarily working at industrial livestock operations. We used a multiplex salivary SARS-CoV-2 IgG assay to determine infection-induced antibody prevalence among 236 adult (≥18 yr) North Carolina residents between February 2021 and August 2022.

COVID-19 point-of-care tests can identify low-antibody individuals: In-depth immunoanalysis of boosting benefits in a healthy cohort.

The recommended COVID-19 booster vaccine uptake is low. At-home lateral flow assay (LFA) antigen tests are widely accepted for detecting infection during the pandemic. Here, we present the feasibility and potential benefits of using LFA-based antibody tests as a means for individuals to detect inadequate immunity and make informed decisions about COVID-19 booster immunization.

A cluster-randomized trial of water, sanitation, handwashing and nutritional interventions on stress and epigenetic programming.

A regulated stress response is essential for healthy child growth and development trajectories. We conducted a cluster-randomized trial in rural Bangladesh (funded by the Bill & Melinda Gates Foundation, ClinicalTrials.gov NCT01590095) to assess the effects of an integrated nutritional, water, sanitation, and handwashing intervention on child health.

Applying Convolutional Neural Networks to data on unstructured meshes with space-filling curves.

This paper presents the first classical Convolutional Neural Network (CNN) that can be applied directly to data from unstructured finite element meshes or control volume grids. CNNs have been hugely influential in the areas of image classification and image compression, both of which typically deal with data on structured grids. Unstructured meshes are frequently used to solve partial differential equations and are particularly suitable for problems that require the mesh to conform to complex geometries or for problems that require variable mesh resolution.

SARS-CoV-2 antibody prevalence by industry, workplace characteristics, and workplace infection prevention and control measures, North Carolina, 2021 to 2022.

Background: The COVID-19 pandemic has disproportionately affected workers in certain industries and occupations, and the workplace can be a high risk setting for SARS-CoV-2 transmission. In this study, we measured SARS-CoV-2 antibody prevalence and identified work-related risk factors in a population primarily working at industrial livestock operations.

Methods: We used a multiplex salivary SARS-CoV-2 IgG antibody assay to determine infection-induced antibody prevalence among 236 adult (≥18 years) North Carolina residents between February 2021 and August 2022.

: the implications of excitatory GABAergic signaling in neurological disorders.

Gamma-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the central nervous system. In the mature brain, inhibitory GABAergic signaling is critical in maintaining neuronal homeostasis and vital human behaviors such as cognition, emotion, and motivation. While classically known to inhibit neuronal function under physiological conditions, previous research indicates a paradoxical switch from inhibitory to excitatory GABAergic signaling that is implicated in several neurological disorders.

Community-driven research and capacity building to address environmental justice concerns with industrial air pollution in Curtis Bay, South Baltimore.

Introduction: Curtis Bay (CB) is an environmental justice (EJ) community in South Baltimore. With a high concentration of industrial polluters and compounding non-chemical stressors, CB has experienced socioeconomic, quality of life, and health burdens for over 100 years. Today, these polluters include the open-air CSX Coal Terminal, waste-to-energy incinerators, and heavy diesel traffic through residential areas.

Clinical Assessment of SARS-CoV-2 Antibodies in Oral Fluids Following Infection and Vaccination.

Background: SARS-CoV-2 variants continue to circulate globally, even within highly vaccinated populations. The first-generation SARS-CoV-2 vaccines elicit neutralizing immunoglobin G (IgG) antibodies that prevent severe COVID-19 but induce only weak antibody responses in mucosal tissues. There is increasing recognition that secretory immunoglobin A (SIgA) antibodies in the upper respiratory tract and oral cavity are critical in interrupting virus shedding, transmission, and progression of disease.

Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.

Critically ill people with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. In 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and >20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms.

Aberrant DJ-1 expression underlies L-type calcium channel hypoactivity in dendrites in tuberous sclerosis complex and Alzheimer's disease.

L-type voltage-gated calcium (Ca) channels (L-VGCC) dysfunction is implicated in several neurological and psychiatric diseases. While a popular therapeutic target, it is unknown whether molecular mechanisms leading to disrupted L-VGCC across neurodegenerative disorders are conserved. Importantly, L-VGCC integrate synaptic signals to facilitate a plethora of cellular mechanisms; however, mechanisms that regulate L-VGCC channel density and subcellular compartmentalization are understudied.

Urinary IL-6 and IL-8 as predictive markers in bladder urothelial carcinoma: A pilot study.

Background: Cytokines are known to be a key a factor in numerous malignancies and to exert an important regulatory role in the tumor microenvironment. Interest has grown in understanding how cytokines modulate the tumor microenvironment and which cytokines may serve as markers of the tumor process; however, a complete picture of the cytokine landscape in bladder cancer remains unclear.

Methods: Fresh urine specimens with sufficient volume were collected at random intervals.

The SeroNet Clinical and Translational Serology Task Force (CTTF) SARS-CoV-2 mucosal immunity methodological considerations and best practices workshop.

SARS-CoV-2 persists in certain populations, even with vaccination and boosters. Emerging evidence suggests that reductions in virus transmission and infection will likely require involvement of the mucosal immune system, especially secretory antibodies in the upper respiratory tract. The Clinical and Translational Serology Task Force (CTTF) within The National Cancer Institute (NCI)'s Serological Sciences Network for COVID-19 (SeroNet) hosted a workshop to review the status of development and standardization of mucosal sample collection methods and assays, identify challenges, and develop action plans to bridge gaps.

Evaluating SARS-CoV-2 Saliva and Dried Blood Spot Surveillance Strategies in a Congregate Population.

The optimal approach to COVID-19 surveillance in congregate populations remains unclear. Our study at the US Naval Academy in Annapolis, Maryland, USA, assessed the concordance of antibody prevalence in longitudinally collected dried blood spots and saliva in a setting of frequent PCR-based testing. Our findings highlight the utility of salivary-based surveillance.

Benefit finding and well-being over the course of the COVID-19 pandemic.

This study focuses on understanding benefit finding, the process of deriving growth from adversity, and its relationship to well-being amidst the COVID-19 pandemic. Participants (n = 701) completed online surveys at 1, 3, 6, and 12 months after a shelter-in-place mandate was announced in California, USA. Identifying as female or of Asian descent, having a supportive social network, and reporting more distress were associated with higher levels of general benefit finding at all data collection points, while other demographics were not.