A new stochastic backscatter model for large-eddy simulation of neutral atmospheric flows

A stochastic backscatter (SB) approach to subgrid-scale (SGS) modelling for large-eddy simulation (LES) of the neutral atmospheric boundary layer (ABL) has previously been shown to reduce excessive velocity shear, as seen with the popular Smagorinsky SGS model, in the under-resolved surface layer. However, previous SB models exhibit unwanted grid-dependency issues, and the range of atmospheric flows tested remains limited. Here, a new SB model is proposed that uses a grid-adaptive filter to control the length-scale, anisotropy and momentum flux of the backscatter fluctuations, independently of the model grid. Model performance is confirmed to be grid-independent in simulations of the neutral ABL, in which an 80% reduction in excessive near-surface velocity shear is achieved. The model is also applied to street canyon flow, where the shear layer that separates the recirculating vortex within the canyon from the external flow is again typically under-resolved in most LES set-ups. The backscatter acts to increase momentum transfer across the shear layer, bringing the simulated vortex intensity significantly closer towards wind-tunnel observations. A passive tracer is also released to model traffic emissions, and the pollutant exchange velocity between the canyon and the external flow is again found in better agreement with wind-tunnel data. This information can be used to improve operational urban dispersion models

White-matter abnormalities in brain during early abstinence from methamphetamine abuse

Previous studies revealed microstructural abnormalities in prefrontal white matter and corpus callosum of long-term abstinent chronic methamphetamine abusers. In view of the importance of the early abstinence period in treatment retention, we compared 23 methamphetamine-dependent subjects abstinent from methamphetamine for 7–13 days with 18 healthy comparison subjects. As certain metabolic changes in the brain first manifest after early abstinence from methamphetamine, it is also possible that microstructural white-matter abnormalities are not yet present during early abstinence. Using diffusion tensor imaging at 1.5 T, fractional anisotropy (FA) was measured in prefrontal white matter at four inferior–superior levels parallel to the anterior commissure–posterior commissure (AC–PC) plane. We also sampled FA in the corpus callosum at the midline and at eight bilateral, fiber-tract sites in other regions implicated in effects of methamphetamine. The methamphetamine group exhibited lower FA in right prefrontal white matter above the AC–PC plane (11.9% lower; p = 0.007), in midline genu corpus callosum (3.9%; p = 0.019), in left and right midcaudal superior corona radiata (11.0% in both hemispheres, p’s = 0.020 and 0.016, respectively), and in right perforant fibers (7.3%; p = 0.025). FA in left midcaudal superior corona radiata was correlated with depressive and generalized psychiatric symptoms within the methamphetamine group. The findings support the idea that methamphetamine abuse produces microstructural abnormalities in white matter underlying and interconnecting prefrontal cortices and hippocampal formation. These effects are already present during the first weeks of abstinence from methamphetamine and are linked to psychiatric symptoms assessed during this period

Computational Design of Novel Insulin Degrading Enzyme Inhibitors

Human insulin degrading enzyme (IDE) plays a role in the proteolytic cleavage of insulin, glucagon, and other short, hydrophobic peptides with roles in glucose and cellular metabolism. Because of IDE’s role in insulin clearance, IDE inhibitors may hold promise as therapies for potentiating insulin signaling in patients suffering from type 2 diabetes mellitus. IDE is a large (~100 kDa) chambered protease of the conserved M16A subfamily of zinc metalloproteases. The enzyme adopts a structure that is analogous to a clamshell formed by the joining of the N terminal and C terminal domains. The characteristic zinc binding and catalytic motif (HXXEH) is positioned within the enzyme’s N terminus, while C terminal residues also play important roles in substrate binding and catalysis. Here, we describe the use of a computational work-flow for identifying novel IDE inhibitors. The work flow integrates mutation-based active site structural analysis, virtual screening, docking and fragment-based design. Initial computational results appear promising and should lead to assay testing in the near future

The application and use of chemical space mapping to interpret crystallization screening results

Mapping crystallization results in chemical space helps to correlate seemingly distant relationships between crystallization conditions, points to possible optimization strategies and reveals promising unsampled areas of crystallization space

Universal features of dimensional reduction schemes from general covariance breaking

Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. By investigating residual covariance we introduce lower dimensional tensors, that successfully generalize to one side Kaluza–Klein gauge fields and to the other side extrinsic curvature and torsion of embedded spaces, thus fully characterizing the geometry of dimensional reduction. We obtain general formulas for the reduction of the main tensors and operators of Riemannian geometry. In particular, we provide what is probably the maximal possible generalization of Gauss, Codazzi and Ricci equations and various other standard formulas in Kaluza–Klein and embedded spacetimes theories. After general covariance breaking, part of the residual covariance is perceived by effective lower dimensional observers as an infinite dimensional gauge group. This reduces to finite dimensions in Kaluza–Klein and other few remarkable backgrounds, all characterized by the vanishing of appropriate lower dimensional tensors

Low cost, low tech SNP genotyping tools for resource-limited areas: Plague in Madagascar as a model

Genetic analysis of pathogenic organisms is a useful tool for linking human cases together and/or to potential environmental sources. The resulting data can also provide information on evolutionary patterns within a targeted species and phenotypic traits. However, the instruments often used to generate genotyping data, such as single nucleotide polymorphisms (SNPs), can be expensive and sometimes require advanced technologies to implement. This places many genotyping tools out of reach for laboratories that do not specialize in genetic studies and/or lack the requisite financial and technological resources. To address this issue, we developed a low cost and low tech genotyping system, termed agarose-MAMA, which combines traditional PCR and agarose gel electrophoresis to target phylogenetically informative SNPs

Charcot-Marie-Tooth gene, SBF2, associated with taxaneinduced peripheral neuropathy in African Americans

PURPOSE: Taxane-induced peripheral neuropathy (TIPN) is one of the most important survivorship issues for cancer patients. African Americans (AA) have previously been shown to have an increased risk for this toxicity. Germline predictive biomarkers were evaluated to help identify a priori which patients might be at extraordinarily high risk for this toxicity. EXPERIMENTAL DESIGN: Whole exome sequencing was performed using germline DNA from 213 AA patients who received a standard dose and schedule of paclitaxel in the adjuvant, randomized phase III breast cancer trial, E5103. Cases were defined as those with either grade 3-4 (n=64) or grade 2-4 (n=151) TIPN and were compared to controls (n=62) that were not reported to have experienced TIPN. We retained for analysis rare variants with a minor allele frequency <3% and which were predicted to be deleterious by protein prediction programs. A gene-based, case-control analysis using SKAT was performed to identify genes that harbored an imbalance of deleterious variants associated with increased risk of TIPN. RESULTS: Five genes had a p-value < 10-4 for grade 3-4 TIPN analysis and three genes had a p-value < 10-4 for the grade 2-4 TIPN analysis. For the grade 3-4 TIPN analysis, SET binding factor 2 (SBF2) was significantly associated with TIPN (p-value=4.35 x10-6). Five variants were predicted to be deleterious in SBF2. Inherited mutations in SBF2 have previously been associated with autosomal recessive, Type 4B2 Charcot-Marie-Tooth (CMT) disease. CONCLUSION: Rare variants in SBF2, a CMT gene, predict an increased risk of TIPN in AA patients receiving paclitaxel

Age-related fertility decline: is there a role for elective ovarian tissue cryopreservation?

Age-related fertility decline (ARFD) is a prevalent concern amongst western cultures due to the increasing age of first-time motherhood. Elective oocyte and embryo cryopreservation remain the most established methods of fertility preservation, providing women the opportunity of reproductive autonomy to preserve their fertility and extend their childbearing years to prevent involuntary childlessness. Whilst ovarian cortex cryopreservation has been used to preserve reproductive potential in women for medical reasons, such as in pre- or peripubertal girls undergoing gonadotoxic chemotherapy, it has not yet been considered in the context of ARFD. As artificial reproductive technology (ART) and surgical methods of fertility preservation continue to evolve, it is a judicious time to review current evidence and consider alternative options for women wishing to delay their fertility. This article critically appraises elective oocyte cryopreservation as an option for women who use it to mitigate the risk of ARFD and introduces the prospect of elective ovarian cortex cryopreservation as an alternative

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health