ELISA-R: an R-based method for robust ELISA data analysis.

Enzyme-linked immunosorbent assay (ELISA) is a technique to detect the presence of an antigen or antibody in a sample. ELISA is a simple and cost-effective method that has been used for evaluating vaccine efficacy by detecting the presence of antibodies against viral/bacterial antigens and diagnosis of disease stages. Traditional ELISA data analysis utilizes a standard curve of known analyte, and the concentration of the unknown sample is determined by comparing its observed optical density against the standard curve.

Heterogeneity in immune cell composition is associated with replication at the granuloma level.

The control of bacterial growth is key to the prevention and treatment of tuberculosis (TB). Granulomas represent independent foci of the host immune response that present heterogeneous capacity for control of bacterial growth. At the whole tissue level, B cells and CD4 or CD8 T cells have an established role in immune protection against TB.

Health implications of urban tree canopy policy scenarios in Denver and Phoenix: A quantitative health impact assessment.

Background: Urban tree canopy (UTC) goals are a popular policy to increase urban vegetation, support climate strategies, and encourage a healthy environment. Health studies related to UTC are needed across cities to support evidence-based decision-making.

Methods: We used a quantitative Health Impact Assessment (HIA) to model the annual number of premature deaths prevented, and the number of stroke and dementia cases, under UTC goals in Denver, Colorado, and Phoenix, Arizona, USA, using standing policy goals (20% and 25% UTC, respectively) and 50% ("half-way") attainment scenarios from current levels (16.

Short-term excess mortality following tropical cyclones in the United States.

Knowledge of excess deaths after tropical cyclones is critical to understanding their impacts, directly relevant to policies on preparedness and mitigation. We applied an ensemble of 16 Bayesian models to 40.7 million U.

Brief Report: Decreased Bone Health in Children with Autism Spectrum Disorder and Avoidant Restrictive Food Intake Disorder.

Purpose: Children with autism spectrum disorder (ASD) and food selectivity are at increased risk for nutritional deficiencies which could affect bone health.

Methods: We report on four male patients with ASD and avoidant restrictive food intake disorder (ARFID) with significant bone conditions including rickets, vertebral compression fractures, osteopenia, and slipped capital femoral epiphyses.

Results: Each patient was at risk for at least one nutritional deficiency.

Increased Mortality Risks from a Spectrum of Causes of Tropical Cyclone Exposure - China, 2013-2018.

What Is Already Known About This Topic?: Tropical cyclone (TC) has a substantial and adverse impact on non-accidental mortality. However, whether heterogeneity exists when examining deaths from sub-causes and how TC impacts non-accidental mortality in the short term remain unclear.

What Is Added By This Report?: This study found substantial associations at lag 0 between TC exposure and circulatory and respiratory mortality.

Simultaneous inhibition of endocytic recycling and lysosomal fusion sensitizes cells and tissues to oligonucleotide therapeutics.

Inefficient endosomal escape remains the primary barrier to the broad application of oligonucleotide therapeutics. Liver uptake after systemic administration is sufficiently robust that a therapeutic effect can be achieved but targeting extrahepatic tissues remains challenging. Prior attempts to improve oligonucleotide activity using small molecules that increase the leakiness of endosomes have failed due to unacceptable toxicity.

Structures of annexin A2-PS DNA complexes show dominance of hydrophobic interactions in phosphorothioate binding.

The introduction of phosphorothioate (PS) linkages to the backbone of therapeutic nucleic acids substantially increases their stability and potency. It also affects their interactions with cellular proteins, but the molecular mechanisms that underlie this effect are poorly understood. Here, we report structural and biochemical studies of interactions between annexin A2, a protein that does not possess any known canonical DNA binding domains, and phosphorothioate-modified antisense oligonucleotides.

Diving into the vertical dimension of elasmobranch movement ecology.

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species.

Hurricanes and Mortality among Patients Receiving Dialysis.

Background: Hurricanes are severe weather events that can disrupt power, water, and transportation systems. These disruptions may be deadly for patients requiring maintenance dialysis. We hypothesized that the mortality risk among patients requiring maintenance dialysis would be increased in the 30 days after a hurricane.

Projecting the Impacts of a Changing Climate: Tropical Cyclones and Flooding.

Purpose Of Review: There is clear evidence that the earth's climate is changing, largely from anthropogenic causes. Flooding and tropical cyclones have clear impacts on human health in the United States at present, and projections of their health impacts in the future will help inform climate policy, yet to date there have been few quantitative climate health impact projections.

Recent Findings: Despite a wealth of studies characterizing health impacts of floods and tropical cyclones, many are better suited for qualitative, rather than quantitative, projections of climate change health impacts.

Ten simple rules for finding and selecting R packages.

R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R's utility and ameliorate inefficient solutions to data science problems. We outline 10 simple rules for finding relevant packages and determining which package is best for your desired use.

Tracking the transmission dynamics of COVID-19 with a time-varying coefficient state-space model.

The spread of COVID-19 has been greatly impacted by regulatory policies and behavior patterns that vary across counties, states, and countries. Population-level dynamics of COVID-19 can generally be described using a set of ordinary differential equations, but these deterministic equations are insufficient for modeling the observed case rates, which can vary due to local testing and case reporting policies and nonhomogeneous behavior among individuals. To assess the impact of population mobility on the spread of COVID-19, we have developed a novel Bayesian time-varying coefficient state-space model for infectious disease transmission.

Association of Tropical Cyclones With County-Level Mortality in the US.

Importance: Tropical cyclones have a devastating effect on society, but a comprehensive assessment of their association with cause-specific mortality over multiple years of study is lacking.

Objective: To comprehensively evaluate the association of county-level tropical cyclone exposure and death rates from various causes in the US.

Design, Setting, And Participants: A retrospective observational study using a Bayesian conditional quasi-Poisson model to examine how tropical cyclones were associated with monthly death rates.

Exposure Assessment for Tropical Cyclone Epidemiology.

Purpose Of Review: Tropical cyclones impact human health, sometimes catastrophically. Epidemiological research characterizes these health impacts and uncovers pathways between storm hazards and health, helping to mitigate the health impacts of future storms. These studies, however, require researchers to identify people and areas exposed to tropical cyclones, which is often challenging.

Integrated causal-predictive machine learning models for tropical cyclone epidemiology.

Strategic preparedness reduces the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protective impact could be enhanced by a more comprehensive and rigorous characterization of TC epidemiology. To generate the insights and tools necessary for high-precision TC preparedness, we introduce a machine learning approach that standardizes estimation of historic TC health impacts, discovers common patterns and sources of heterogeneity in those health impacts, and enables identification of communities at highest health risk for future TCs. The model integrates (i) a causal inference component to quantify the immediate health impacts of recent historic TCs at high spatial resolution and (ii) a predictive component that captures how TC meteorological features and socioeconomic/demographic characteristics of impacted communities are associated with health impacts.

Tropical cyclone exposure is associated with increased hospitalization rates in older adults.

Hurricanes and other tropical cyclones have devastating effects on society. Previous case studies have quantified their impact on some health outcomes for particular tropical cyclones, but a comprehensive assessment over longer periods is currently missing. Here, we used data on 70 million Medicare hospitalizations and tropical cyclone exposures over 16 years (1999-2014).

Tropical Cyclone Exposures and Risks of Emergency Medicare Hospital Admission for Cardiorespiratory Diseases in 175 Urban United States Counties, 1999-2010.

Background: Although injuries experienced during hurricanes and other tropical cyclones have been relatively well-characterized through traditional surveillance, less is known about tropical cyclones' impacts on noninjury morbidity, which can be triggered through pathways that include psychosocial stress or interruption in medical treatment.

Methods: We investigated daily emergency Medicare hospitalizations (1999-2010) in 180 US counties, drawing on an existing cohort of high-population counties. We classified counties as exposed to tropical cyclones when storm-associated peak sustained winds were ≥21 m/s at the county center; secondary analyses considered other wind thresholds and hazards.

Heterologous Expression of Cryptomaldamide in a Cyanobacterial Host.

Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, nonribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drug leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers.

The Unexpected Base-Pairing Behavior of Cyanuric Acid in RNA and Ribose versus Cyanuric Acid Induced Helicene Assembly of Nucleic Acids: Implications for the Pre-RNA Paradigm.

The cyanuric acid (CA) heterocycle forms supramolecular structures with adenine nucleobases/nucleosides and oligonucleotides, leading to speculation that they can act as forerunners to RNA. Herein, the assembly behavior of RNA containing CA and CA-ribose nucleoside was studied. Contrary to previous reports, CA in RNA and the CA-ribonucleoside resulted in destabilization of supramolecular assemblies, which led to a reevaluation of the CA-adenine hexameric rosette structure.

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.

Assessing United States County-Level Exposure for Research on Tropical Cyclones and Human Health.

Background: Tropical cyclone epidemiology can be advanced through exposure assessment methods that are comprehensive and consistent across space and time, as these facilitate multiyear, multistorm studies. Further, an understanding of patterns in and between exposure metrics that are based on specific hazards of the storm can help in designing tropical cyclone epidemiological research.

Objectives: ) Provide an open-source data set for tropical cyclone exposure assessment for epidemiological research; and ) investigate patterns and agreement between county-level assessments of tropical cyclone exposure based on different storm hazards.

Tropical cyclones and risk of preterm birth: A retrospective analysis of 20 million births across 378 US counties.

Background: The public health impacts of tropical cyclones (TCs) are expected to increase due to the continued growth of coastal populations and the increasing severity of these events. However, the impact of TCs on pregnant women, a vulnerable population, remains largely unknown. We aimed to estimate the association between prenatal exposure to TCs and risk of preterm birth in the eastern United States (US) and to assess whether the association varies by individual- and area-level characteristics.

Acquisition of High-Quality Spectral Flow Cytometry Data.

Flow cytometry allows the visualization of physical, functional, and/or biological properties of cells including antigens, cytokines, size, and complexity. With increasingly large flow cytometry panels able to analyze up to 50 parameters, there is a need to standardize flow cytometry protocols to achieve high-quality data that can be input into analysis algorithms. Without this clean data, algorithms may incorrectly categorize the cell populations present in the samples.