Virginia beach

The following is a collection of short stories about a mid-Atlantic beach community, the people that leave it, and those that stay. The stories are loosely linked in that all the characters are neighbors, whether they know it or not

Some Aspects of the Mechanical Response of BMI 5250-4 Neat Resin at 191°C: Experiment and Modeling

The mechanical response of BMI 5250-4 neat resin at 191 degrees C was studied using both creep and recovery tests where several variables were allowed to change. In these tests, the effect of stress rate, prior history, and panel variability were all taken into account. During the creep test, the material showed both primary and secondary creep over 20 h. The recovery tests showed full recovery after it was subjected to 80% UTS. The higher stress rate caused a much greater response in both creep and recovery tests. The prior history was studied by allowing the specimens to go through a stepwise creep test. They behaved similar to the single step creep test when preceded by a loading segment. During creep tests preceded by unloading, the material showed a decrease in creep strain. This decrease grew as the creep stress approached zero. The only difference that could be seen with panel variability was that the UTS dropped dramatically between the panels. A nonlinear viscoelastic model was created that was based on the work by Schapery. This model included four constants that were material specific and stress dependent. These constants were obtained by viewing the response during a two- step program including creep and recovery. The model was verified by comparing the predictions to previous tests including the creep and stepwise creep tests. The model predicted the creep strain preceded by load up well. It was capable of showing the response of the negative creep but the error grew as more steps were introduced. The model could not take into account stress rate. Therefore it could only predict the results at the higher stress rate

Fluorescent Nanoparticles for the Measurement of Ion Concentration in Biological Systems

Tightly regulated ion homeostasis throughout the body is necessary for the prevention of such debilitating states as dehydration.1 In contrast, rapid ion fluxes at the cellular level are required for initiating action potentials in excitable cells.2 Sodium regulation plays an important role in both of these cases; however, no method currently exists for continuously monitoring sodium levels in vivo3 and intracellular sodium probes 4 do not provide similar detailed results as calcium probes. In an effort to fill both of these voids, fluorescent nanosensors have been developed that can monitor sodium concentrations in vitro and in vivo.5,6 These sensors are based on ion-selective optode technology and consist of plasticized polymeric particles in which sodium specific recognition elements, pH-sensitive fluorophores, and additives are embedded.7-9 Mechanistically, the sodium recognition element extracts sodium into the sensor. 10 This extraction causes the pH-sensitive fluorophore to release a hydrogen ion to maintain charge neutrality within the sensor which causes a change in fluorescence. The sodium sensors are reversible and selective for sodium over potassium even at high intracellular concentrations.6 They are approximately 120 nm in diameter and are coated with polyethylene glycol to impart biocompatibility. Using microinjection techniques, the sensors can be delivered into the cytoplasm of cells where they have been shown to monitor the temporal and spatial sodium dynamics of beating cardiac myocytes.11 Additionally, they have also tracked real-time changes in sodium concentrations in vivo when injected subcutaneously into mice.3 Herein, we explain in detail and demonstrate the methodology for fabricating fluorescent sodium nanosensors and briefly demonstrate the biological applications our lab uses the nanosensors for: the microinjection of the sensors into cells; and the subcutaneous injection of the sensors into mice

Colorimetric nanofibers as optical sensors

Sensors play a major role in many applications today, ranging from biomedicine to safety equipment, where they detect and warn us about changes in the environment. Nanofibers, characterized by high porosity, flexibility, and a large specific surface area, are the ideal material for ultrasensitive, fastresponding, and user-friendly sensor design. Indeed, a large specific surface area increases the sensitivity and response time of the sensor as the contact area with the analyte is enlarged. Thanks to the flexibility of membranes, nanofibrous sensors cannot only be applied in high-end analyte detection, but also in personal, daily use. Many different nanofibrous sensors have already been designed; albeit, the most straightforward and easiest-to-interpret sensor response is a visual change in color, which is of particular interest in the case of warning signals. Recently, many researchers have focused on the design of so-called colorimetric nanofibers, which typically involve the incorporation of a colorimetric functionality into the nanofibrous matrix. Many different strategies have been used and explored for colorimetric nanofibrous sensor design, which are outlined in this feature article. The many examples and applications demonstrate the value of colorimetric nanofibers for advanced optical sensor design, and could provide directions for future research in this area

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

A second update on mapping the human genetic architecture of COVID-19

Matters Arising From: COVID-19 Host Genetics Initiative. Nature https://doi.org/10.1038/s41586-021-03767-x (2021)Data availability: Summary statistics generated by the COVID-19 HGI are available online, including per-ancestry summary statistics for African, admixed American, East Asian, European and South Asian ancestries (https://www.covid19hg.org/results/r7/). The analyses described here used the data release 7. If available, individual-level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale laboratory (http://www.nealelab.is/uk-biobank/), the Finucane laboratory (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (http://www.ebi.ac.uk/eqtl/).Code availability: The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses (https://github.com/covid19-hg/); for heritability estimation (https://github.com/AndrewsLabUCSF/COVID19_heritability); for Mendelian randomization and genetic correlation (https://github.com/marcoralab/MRcovid); and subtype analyses (https://github.com/mjpirinen/covid19-hgi_subtypes) are available at GitHub.Reporting summary: Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article online at: https://www.nature.com/articles/s41586-023-06355-3#MOESM2 .Supplementary information is available online at: https://www.nature.com/articles/s41586-023-06355-3#Sec4 .Copyright © The Author(s) 2023. Investigating the role of host genetic factors in COVID-19 severity and susceptibility can inform our understanding of the underlying biological mechanisms that influence adverse outcomes and drug development1,2. Here we present a second updated genome-wide association study (GWAS) on COVID-19 severity and infection susceptibility to SARS-CoV-2 from the COVID-19 Host Genetic Initiative (data release 7). We performed a meta-analysis of up to 219,692 cases and over 3 million controls, identifying 51 distinct genome-wide significant loci—adding 28 loci from the previous data release2. The increased number of candidate genes at the identified loci helped to map three major biological pathways that are involved in susceptibility and severity: viral entry, airway defence in mucus and type I interferon

Mapping the human genetic architecture of COVID-19

Matters Arising to this article was published on 03 August 2022, available online at: https://doi.org/10.1038/s41586-022-04826-7 . A second Matters Arising to this article was published on 06 September 2023, available online at: https://doi.org/10.1038/s41586-023-06355-3 .Data availability: Summary statistics generated by the COVID-19 HGI are available at https://www.covid19hg.org/results/r5/ and are available in the GWAS Catalog (study code GCST011074). The analyses described here include the freeze-5 data. COVID-19 HGI continues to regularly release new data freezes. Summary statistics for non-European ancestry samples are not currently available due to the small individual sample sizes of these groups, but results for lead variants of 13 loci are reported in Supplementary Table 3. Individual level data can be requested directly from contributing studies, listed in Supplementary Table 1. We used publicly available data from GTEx (https://gtexportal.org/home/), the Neale lab (https://www.nealelab.is/uk-biobank/), Finucane lab (https://www.finucanelab.org), the FinnGen Freeze 4 cohort (https://www.finngen.fi/en/access_results) and the eQTL catalogue release 3 (https://www.ebi.ac.uk/eqtl/).Code availability: The code for summary statistics lift-over, the projection PCA pipeline including precomputed loadings and meta-analyses are available on GitHub (https://github.com/covid19-hg/) and the code for the Mendelian randomization and genetic correlation pipeline is available on GitHub at https://github.com/marcoralab/MRcovid.Reporting summary: Further information on research design is available in the Nature Research Reporting Summary linked to this paper online at: https://www.nature.com/articles/s41586-021-03767-x#MOESM2 .Supplementary information is available onlne at: https://www.nature.com/articles/s41586-021-03767-x#Sec24 .Extended data figures and tables are available online at: https://www.nature.com/articles/s41586-021-03767-x#Sec23 .Copyright © The Author(s) 2021. The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease