Element Load Data Processor (ELDAP) Users Manual

Often, the shear and tensile forces and moments are extracted from finite element analyses to be used in off-line calculations for evaluating the integrity of structural connections involving bolts, rivets, and welds. Usually the maximum forces and moments are desired for use in the calculations. In situations where there are numerous structural connections of interest for numerous load cases, the effort in finding the true maximum force and/or moment combinations among all fasteners and welds and load cases becomes difficult. The Element Load Data Processor (ELDAP) software described herein makes this effort manageable. This software eliminates the possibility of overlooking the worst-case forces and moments that could result in erroneous positive margins of safety and/or selecting inconsistent combinations of forces and moments resulting in false negative margins of safety. In addition to forces and moments, any scalar quantity output in a PATRAN report file may be evaluated with this software. This software was originally written to fill an urgent need during the structural analysis of the Ares I-X Interstage segment. As such, this software was coded in a straightforward manner with no effort made to optimize or minimize code or to develop a graphical user interface

People’s understanding of verbal risk descriptors in patient information leaflets : a cross-sectional national survey of 18- to 65-year-olds in England

Introduction Evidence suggests the current verbal risk descriptors used to communicate side effect risk in patient information leaflets (PILs) are overestimated. Objectives The aim was to establish how people understand the verbal risk descriptors recommended for use in PILs by the European Commission (EC), and alternative verbal risk descriptors, in the context of mild and severe side effects. Methods A cross-sectional online survey was carried out by a market research company recruiting participants aged between 18 and 65 years living in England. Data were collected between 18 March and 1 April 2016. Participants were given a hypothetical scenario regarding the risk of mild or severe medication side effects and asked to estimate how many out of 10,000 people would be affected for each of the verbal risk descriptors being tested. Results A total of 1003 participants were included in the final sample. The risks conveyed by the EC recommended verbal risk descriptors were greatly overestimated by participants. Two distinct distributions were apparent for participant estimates of side effect risks: those for ‘high risk’ verbal descriptors (e.g. ‘common’, ‘likely’, ‘high chance’) and those for ‘low risk’ verbal descriptors (e.g. ‘uncommon’, ‘unlikely’, ‘low chance’). Within these two groups, the distributions were near to identical regardless of what adverb (e.g. very, high, fair) or adjective (e.g. common, likely, chance) was used. The EC recommended verbal risk descriptors were more likely to be understood in accordance with their intended meanings when describing severe side effects. Very few demographic or psychological factors were consistently associated with how well participants understood the EC recommended verbal risk descriptors. Discussion The current verbal risk descriptors used in PILs are ineffective at best and misleading at worst. Discontinuing the use of verbal risk descriptors would limit the likelihood of people overestimating the risk of side effects

Prostaglandin D2/J2 signaling pathway in a rat model of neuroinflammation displaying progressive parkinsonian-like pathology: potential novel therapeutic targets

Abstract Background Prostaglandins are products of the cyclooxygenase pathway, which is implicated in Parkinson’s disease (PD). Limited knowledge is available on mechanisms by which prostaglandins contribute to PD neurodegeneration. To address this gap, we focused on the prostaglandin PGD2/J2 signaling pathway, because PGD2 is the most abundant prostaglandin in the brain, and the one that increases the most under pathological conditions. Moreover, PGJ2 is spontaneously derived from PGD2. Methods In this study, we determined in rats the impact of unilateral nigral PGJ2-microinfusions on COX-2, lipocalin-type PGD2 synthase (L-PGDS), PGD2/J2 receptor 2 (DP2), and 15 hydroxyprostaglandin dehydrogenase (15-PGDH). Nigral dopaminergic (DA) and microglial distribution and expression levels of these key factors of the prostaglandin D2/J2 pathway were evaluated by immunohistochemistry. PGJ2-induced motor deficits were assessed with the cylinder test. We also determined whether oral treatment with ibuprofen improved the PD-like pathology induced by PGJ2. Results PGJ2 treatment induced progressive PD-like pathology in the rats. Concomitant with DA neuronal loss in the substantia nigra pars compacta (SNpc), PGJ2-treated rats exhibited microglia and astrocyte activation and motor deficits. In DA neurons, COX-2, L-PGDS, and 15-PGDH levels increased significantly in PGJ2-treated rats compared to controls, while DP2 receptor levels were unchanged. In microglia, DP2 receptors were basically non-detectable, while COX-2 and L-PGDS levels increased upon PGJ2-treatment, and 15-PGDH remained unchanged. 15-PGDH was also detected in oligodendrocytes. Notably, ibuprofen prevented most PGJ2-induced PD-like pathology. Conclusions The PGJ2-induced rat model develops progressive PD pathology, which is a hard-to-mimic aspect of this disorder. Moreover, prevention of most PGJ2-induced PD-like pathology with ibuprofen suggests a positive feedback mechanism between PGJ2 and COX-2 that could lead to chronic neuroinflammation. Notably, this is the first study that analyzes the nigral dopaminergic and microglial distribution and levels of factors of the PGD2/J2 signaling pathway in rodents. Our findings support the notions that upregulation of COX-2 and L-PGDS may be important in the PGJ2 evoked PD-like pathology, and that neuronal DP2 receptor antagonists and L-PGDS inhibitors may be novel pharmacotherapeutics to relieve neuroinflammation-mediated neurodegeneration in PD, circumventing the adverse side effects of cyclooxygenase inhibitors

Admixture Mapping in Lupus Identifies Multiple Functional Variants within IFIH1 Associated with Apoptosis, Inflammation, and Autoantibody Production

Systemic lupus erythematosus (SLE) is an inflammatory autoimmune disease with a strong genetic component. African-Americans (AA) are at increased risk of SLE, but the genetic basis of this risk is largely unknown. To identify causal variants in SLE loci in AA, we performed admixture mapping followed by fine mapping in AA and European-Americans (EA). Through genome-wide admixture mapping in AA, we identified a strong SLE susceptibility locus at 2q22-24 (LOD=6.28), and the admixture signal is associated with the European ancestry (ancestry risk ratio ~1.5). Large-scale genotypic analysis on 19,726 individuals of African and European ancestry revealed three independently associated variants in the IFIH1 gene: an intronic variant, rs13023380 [P(meta) = 5.20×10(-14); odds ratio, 95% confidence interval = 0.82 (0.78-0.87)], and two missense variants, rs1990760 (Ala946Thr) [P(meta) = 3.08×10(-7); 0.88 (0.84-0.93)] and rs10930046 (Arg460His) [P(dom) = 1.16×10(-8); 0.70 (0.62-0.79)]. Both missense variants produced dramatic phenotypic changes in apoptosis and inflammation-related gene expression. We experimentally validated function of the intronic SNP by DNA electrophoresis, protein identification, and in vitro protein binding assays. DNA carrying the intronic risk allele rs13023380 showed reduced binding efficiency to a cellular protein complex including nucleolin and lupus autoantigen Ku70/80, and showed reduced transcriptional activity in vivo. Thus, in SLE patients, genetic susceptibility could create a biochemical imbalance that dysregulates nucleolin, Ku70/80, or other nucleic acid regulatory proteins. This could promote antibody hypermutation and auto-antibody generation, further destabilizing the cellular network. Together with molecular modeling, our results establish a distinct role for IFIH1 in apoptosis, inflammation, and autoantibody production, and explain the molecular basis of these three risk alleles for SLE pathogenesis