Macroporous Photonic Crystal Membrane, Methods of Making, and Methods of Use

In accordance with the purpose(s) of the present disclosure, as embodied and broadly described herein, embodiments of the present disclosure, in one aspect, relate to macroporous photonic crystal membranes, structures including macroporous photonic crystal membranes, devices including macroporous photonic crystal membranes, methods of using macroporous photonic crystal membranes, methods of making macroporous photonic crystal membranes, and the like

Pharmacological Blockade of the Calcium Plateau Provides Neuroprotection Following Organophosphate Paraoxon Induced Status Epilepticus in Rats

Organophosphate (OP) compounds which include nerve agents and pesticides are considered chemical threat agents. Currently approved antidotes are crucial in limiting OP mediated acute mortality. However, survivors of lethal OP exposure exhibit delayed neuronal injury and chronic behavioral morbidities. In this study, we investigated neuroprotective capabilities of dantrolene and carisbamate in a rat survival model of paraoxon (POX) induced status epilepticus (SE). Significant elevations in hippocampal calcium levels were observed 48-h post POX SE survival, and treatment with dantrolene (10 mg/kg, i.m.) and carisbamate (90 mg/kg, i.m.) lowered these protracted calcium elevations. POX SE induced delayed neuronal injury as characterized by Fluoro Jade C labeling was observed in critical brain areas including the dentate gyrus, parietal cortex, amygdala, and thalamus. Dantrolene and carisbamate treatment provided significant neuroprotection against delayed neuronal damage in these brain regions when administered one-hour after POX-SE. These results indicate that dantrolene or carisbamate could be effective adjuvant therapies to the existing countermeasures to reduce neuronal injury and behavioral morbidities post OP SE survival

Housing Arrangement and Location Determine the Likelihood of Housing Loss Due to Wildfire

Surging wildfires across the globe are contributing to escalating residential losses and have major social, economic, and ecological consequences. The highest losses in the U.S. occur in southern California, where nearly 1000 homes per year have been destroyed by wildfires since 2000. Wildfire risk reduction efforts focus primarily on fuel reduction and, to a lesser degree, on house characteristics and homeowner responsibility. However, the extent to which land use planning could alleviate wildfire risk has been largely missing from the debate despite large numbers of homes being placed in the most hazardous parts of the landscape. Our goal was to examine how housing location and arrangement affects the likelihood that a home will be lost when a wildfire occurs. We developed an extensive geographic dataset of structure locations, including more than 5500 structures that were destroyed or damaged by wildfire since 2001, and identified the main contributors to property loss in two extensive, fire-prone regions in southern California. The arrangement and location of structures strongly affected their susceptibility to wildfire, with property loss most likely at low to intermediate structure densities and in areas with a history of frequent fire. Rates of structure loss were higher when structures were surrounded by wildland vegetation, but were generally higher in herbaceous fuel types than in higher fuel-volume woody types. Empirically based maps developed using housing pattern and location performed better in distinguishing hazardous from non-hazardous areas than maps based on fuel distribution. The strong importance of housing arrangement and location indicate that land use planning may be a critical tool for reducing fire risk, but it will require reliable delineations of the most hazardous locations

Diverse Splicing Patterns of Exonized Alu Elements in Human Tissues

Exonization of Alu elements is a major mechanism for birth of new exons in primate genomes. Prior analyses of expressed sequence tags show that almost all Alu-derived exons are alternatively spliced, and the vast majority of these exons have low transcript inclusion levels. In this work, we provide genomic and experimental evidence for diverse splicing patterns of exonized Alu elements in human tissues. Using Exon array data of 330 Alu-derived exons in 11 human tissues and detailed RT-PCR analyses of 38 exons, we show that some Alu-derived exons are constitutively spliced in a broad range of human tissues, and some display strong tissue-specific switch in their transcript inclusion levels. Most of such exons are derived from ancient Alu elements in the genome. In SEPN1, mutations of which are linked to a form of congenital muscular dystrophy, the muscle-specific inclusion of an Alu-derived exon may be important for regulating SEPN1 activity in muscle. Realtime qPCR analysis of this SEPN1 exon in macaque and chimpanzee tissues indicates human-specific increase in its transcript inclusion level and muscle specificity after the divergence of humans and chimpanzees. Our results imply that some Alu exonization events may have acquired adaptive benefits during the evolution of primate transcriptomes

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ε[subscript max] (LUE), V[subscript cmax] (unstressed Rubisco catalytic capacity) or J[subscript max] (the maximum electron transport rate)

Palmer Lab High Coverage WGS DeepVariant Genotyping Pipeline

<p>The very first version of Palmer Lab's High Coverage WGS DeepVariant Genotyping Pipeline v1.0.0. Acceptable sequencing data: High Coverage WGS</p>If you use this pipeline or use data generated by this pipeline, please cite it as below

Palmer Lab Heterogeneous Stock Rats Genotyping Pipeline

<p>The third version of Palmer Lab's HS Rats Genotyping Pipeline v3.0.0. Acceptable sequencing data: Riptide library prep lc-WGS, and ddGBS sequences Genotype calling and imputation: STITCH niteration=2 with a high quality consensus reference panel Variants filtering: Keep high confident SNPs based on INFO score, and discard some identified BAD SNPs positions</p>If you use this pipeline or use data generated by this pipeline, please cite it as below

Machine learning for cell type classification from single nucleus RNA sequencing data

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines