Notes

Anatomical analysis regarding Cut family genetics pertaining to early-onset Parkinson's illness within China human population.
This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https//github.com/SIAT-code/AIMEE.Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications of this type of networks to high-dimensional omics data and, most importantly, meaningful interpretation of the results returned from such models in a biomedical context remains an open problem. Here we present, an approach applying a CNN to nonimage data for feature selection. Our pipeline, DeepFeature, can both successfully transform omics data into a form that is optimal for fitting a CNN model and can also return sets of the most important genes used internally for computing predictions. Within the framework, the Snowfall compression algorithm is introduced to enable more elements in the fixed pixel framework, and region accumulation and element decoder is developed to find elements or genes from the class activation maps. In comparative tests for cancer type prediction task, DeepFeature simultaneously achieved superior predictive performance and better ability to discover key pathways and biological processes meaningful for this context. Capabilities offered by the proposed framework can enable the effective use of powerful deep learning methods to facilitate the discovery of causal mechanisms in high-dimensional biomedical data.Epithelia migrate as physically coherent populations of cells. Previous studies have revealed that mechanical stress accumulates in these cellular layers as they move. These stresses are characteristically tensile in nature and have often been inferred to arise when moving cells pull upon the cell-cell adhesions that hold them together. We now report that epithelial tension at adherens junctions between migrating cells also increases due to an increase in RhoA-mediated junctional contractility. We found that active RhoA levels were stimulated by p114 RhoGEF (also known as ARHGEF18) at the junctions between migrating MCF-7 monolayers, and this was accompanied by increased levels of actomyosin and mechanical tension. Applying a strategy to restore active RhoA specifically at adherens junctions by manipulating its scaffold, anillin, we found that this junctional RhoA signal was necessary to stabilize junctional E-cadherin (CDH1) during epithelial migration and promoted orderly collective movement. We suggest that stabilization of E-cadherin by RhoA serves to increase cell-cell adhesion to protect against the mechanical stresses of migration. This article has an associated First Person interview with the first author of the paper.
A survey was performed to evaluate the methods used for reduction or elimination of the aortic impulse (REAI) to facilitate precise stent graft placement and balloon moulding during thoracic endovascular aortic repair (TEVAR).

A total of 127 physicians (1 per hospital) were contacted and asked to fill out a short, comprehensive questionnaire on an internet-based platform.

Fifty physicians (39.4%) responded and completed the survey. Routine use of REAI for stent graft deployment is most frequently used in the ascending aorta and less frequently in the aortic arch and the descending aorta (86.4% vs 69.4% vs 56%). Some physicians based the decision of whether to use REAI on the type of stent graft in the respective location (13.6% vs 24.5% vs 24.0%). momordin-Ic Stent-graft deployment without REAI, irrespective of the type of stent graft used, was never done in the ascending aorta (0.0%), in 3 centres in the aortic arch (6.1%) and in 10 centres in the descending aorta (20%). The REAI method most frequently used was dewith the patient under general anaesthesia. The types of stent grafts and moulding balloons used have an impact on the use or non-use of REAI.The computational identification of long non-coding RNAs (lncRNAs) is important to study lncRNAs and their functions. Despite the existence of many computation tools for lncRNA identification, to our knowledge, there is no systematic evaluation of these tools on common datasets and no consensus regarding their performance and the importance of the features used. To fill this gap, in this study, we assessed the performance of 17 tools on several common datasets. We also investigated the importance of the features used by the tools. We found that the deep learning-based tools have the best performance in terms of identifying lncRNAs, and the peptide features do not contribute much to the tool accuracy. Moreover, when the transcripts in a cell type were considered, the performance of all tools significantly dropped, and the deep learning-based tools were no longer as good as other tools. Our study will serve as an excellent starting point for selecting tools and features for lncRNA identification.Drug combination therapy is a promising strategy to treat complex diseases such as cancer and infectious diseases. However, current knowledge of drug combination therapies, especially in cancer patients, is limited because of adverse drug effects, toxicity and cell line heterogeneity. Screening new drug combinations requires substantial efforts since considering all possible combinations between drugs is infeasible and expensive. Therefore, building computational approaches, particularly machine learning methods, could provide an effective strategy to overcome drug resistance and improve therapeutic efficacy. In this review, we group the state-of-the-art machine learning approaches to analyze personalized drug combination therapies into three categories and discuss each method in each category. We also present a short description of relevant databases used as a benchmark in drug combination therapies and provide a list of well-known, publicly available interactive data analysis portals. We highlight the importance of data integration on the identification of drug combinations. Finally, we address the advantages of combining multiple data sources on drug combination analysis by showing an experimental comparison.Following the Deepwater Horizon oil spill disaster, thousands of workers and volunteers cleaned the shoreline across four coastal states of the Gulf of Mexico. For the GuLF STUDY, we developed quantitative estimates of oil-related chemical exposures [total petroleum hydrocarbons (THC), benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H)] from personal measurements on workers performing various spill clean-up operations on land. These operations included decontamination of vessels, equipment, booms, and personnel; handling of oily booms; hazardous waste management; beach, marsh, and jetty clean-up; aerial missions; wildlife rescue and rehabilitation; and administrative support activities. Exposure estimates were developed for unique groups of workers by (i) activity, (ii) state, and (iii) time period. Estimates of the arithmetic means (AMs) for THC ranged from 0.04 to 3.67 ppm. BTEX-H estimates were substantially lower than THC (in the parts per billion range). Both THC and BTEX-H estimates were substantially lower than their respective occupational exposure limits. The work group, 'Fueled engines' consistently was one of the higher exposed groups to THC and BTEX-H. Notable differences in the AM exposures were observed by activity, time and, to a lesser degree, by state. These exposure estimates were used to develop job-exposure matrices for the GuLF STUDY.In genome-wide mixed model association analysis, we stratified the genomic mixed model into two hierarchies to estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and statistically infer the association of GBVs with each SNP using the generalized least square. The hierarchical mixed model (Hi-LMM) can correct confounders effectively with polygenic effects as residuals for association tests, preventing potential false-negative errors produced with genome-wide rapid association using mixed model and regression or an efficient mixed-model association expedited (EMMAX). Meanwhile, the Hi-LMM performs the same statistical power as the exact mixed model association and the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, the Hi-LMM can detect more quantitative trait nucleotides (QTNs) than existing methods. Especially under the Hi-LMM framework, joint association analysis can be made straightforward to improve the statistical power of detecting QTNs.A system for controlled generation of peracetic acid (PAA) atmospheres used to test and evaluate sampling and measurement devices was developed and characterized. Stable atmospheric conditions were maintained in a dynamic flow system for hours while multiple sensors were simultaneously exposed to equivalent atmospheres of PAA vapors. Atmospheres characterized by a range of PAA concentrations at a controlled flow rate, temperature, and humidity were generated. Presented herein is a system for vaporization of PAA solutions to generate controlled atmospheres with less than 3% relative standard deviation (RSD) of the PAA concentrations over time.Polyvinyl chloride (PVC) is one of the most widely used thermoplastics but is also a material of concern because of the generation and release of harmful chemicals during its life cycle. Amongst the chemicals added to PVC are metal-based stabilisers and Sb-based halogenated flame retardant synergists. However, very little quantitative information exists on these additives, and in particular in PVC lost to the environment. In this study, the distribution of PVC amongst consumer plastics in societal circulation and plastics retrieved from marine and lacustrine beaches and agricultural soils are compared, along with the presence and concentrations of Ba, Cd, Pb, Sb, Sn and Zn as proxies for common metal-based additives and determined by X-ray fluorescence spectrometry. About 10% of consumer plastics and 2% of environmental plastics were constructed of PVC, with the discrepancy attributed to the long service lives and managed disposal of PVC used in the construction sector and the propensity of the plastic to sink in aquatic systems and evade detection. Metal-based additives, defined as having a metal concentration >1000 mg kg-1, were present in about 75% of consumer and environmental PVC, with Ba and Pb most abundant and Cd and Zn least abundant in both types of sample, and median concentrations statistically different only for Ba. Metals also appeared to be present as contaminants (defined as concentrations less then 1000 mg kg-1) arising from manufacturing or recycling. Metals in PVC are believed to pose little risk when the material is in use, but experimental evidence in the literature suggests that significant mobilisation and exposure may occur from PVC microplastics when ingested by wildlife.
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