Hawkinsmccain2280

The MetaTX R package is freely available at GitHub https//github.com/yue-wang-biomath/MetaTX.1.0.

The MetaTX R package is freely available at GitHub https//github.com/yue-wang-biomath/MetaTX.1.0.The neocortex is composed of layers. Whether layers constitute an essential framework for the formation of functional circuits is not well understood. We investigated the brain-wide input connectivity of vasoactive intestinal polypeptide (VIP) expressing neurons in the reeler mouse. This mutant is characterized by a migration deficit of cortical neurons so that no layers are formed. Still, neurons retain their properties and reeler mice show little cognitive impairment. We focused on VIP neurons because they are known to receive strong long-range inputs and have a typical laminar bias toward upper layers. In reeler, these neurons are more dispersed across the cortex. We mapped the brain-wide inputs of VIP neurons in barrel cortex of wild-type and reeler mice with rabies virus tracing. Innervation by subcortical inputs was not altered in reeler, in contrast to the cortical circuitry. Numbers of long-range ipsilateral cortical inputs were reduced in reeler, while contralateral inputs were strongly increased. Reeler mice had more callosal projection neurons. Hence, the corpus callosum was larger in reeler as shown by structural imaging. We argue that, in the absence of cortical layers, circuits with subcortical structures are maintained but cortical neurons establish a different network that largely preserves cognitive functions.

Applied research in machine learning progresses faster when a clean dataset is available and ready to use. Several datasets have been proposed and released over the years for specific tasks such as image classification, speech-recognition, and more recently for protein structure prediction. However, for the fundamental problem of RNA structure prediction, information is spread between several databases depending on the level we are interested in sequence, secondary structure, 3 D structure, or interactions with other macromolecules. In order to speed-up advances in machine-learning based approaches for RNA secondary and/or 3 D structure prediction, a dataset integrating all this information is required, to avoid spending time on data gathering and cleaning.

Here we propose the first attempt of a standardized and automatically generated dataset dedicated to RNA combining together RNA sequences, homology information (under the form of position-specific scoring matrices), and information derived by annotation of available 3 D structures (including secondary structure, canonical and non-canonical interactions, and backbone torsion angles). The data is retrieved from public databases PDB, Rfam and SILVA. The paper describes the procedure to build such dataset and the RNA structure descriptors we provide. Some statistical descriptions of the resulting dataset are also provided.

The dataset is updated every month and available online (in flat-text file format) on the EvryRNA software platform (https//evryrna.ibisc.univ-evry.fr/evryrna/rnanet). An efficient parallel pipeline to build the dataset is also provided for easy reproduction or modification.

louis.becquey@univ-evry.fr, fariza.tahi@univ-evry.fr.

louis.becquey@univ-evry.fr, fariza.tahi@univ-evry.fr.

Data analysis is requisite on reliable data. In genetics this includes verifying that the sample is not contaminated with another, a problem ubiquitous in biology.

In human, and other diploid species, DNA contamination from the same species can be found by the presence of three haplotypes between polymorphic SNPs. read_haps is a tool that detects sample contamination from short read whole genome sequencing data.

github.com/DecodeGenetics/read_haps.

github.com/DecodeGenetics/read_haps.Recently, the combination of radical fluoroalkylation of alkenyl or alkynyl moieties and 1,4-functional group migration (1,4-FGM) has emerged as a powerful strategy for the synthesis of fluorine-containing compounds. In this article, some representative reactions of 1,4-FGM-mediated radical fluoroalkylation of N-(arylsulfonyl)acrylamides, tertiary alcohol-containing alkynes, tertiary alcohol-containing alkenes and intermolecular 1,4-FGM-type substrates have been discussed based on the types of substrates.In this work, we have systematically investigated the HER activity of the RE2Co17 (RE = Y, Pr, Gd, Tb, Ho and Er) series and revealed that their HER activities are highly correlated with the averaged Co-Co bond length of each compound. The HER performance follows the order of Gd2Co17 > Tb2Co17 > Pr2Co17 > Y2Co17 > Ho2Co17 > Er2Co17. This suggests that the unique feature of rare-earth metals, lanthanide contraction, can effectively alter the interatomic spacing and impact the corresponding HER activity. Additionally, Gd2Fe17 and Gd2Ni17 with different d electron density in the system were synthesized and comparison of their HER efficiencies is also discussed. Gd2Ni17 demonstrates the highest HER efficiency among all samples, and it only requires an overpotential (η) of 44 mV to acquire a current density of 10 mA cm-2. The theoretical calculation offers a clue that the H adsorption energy (GHad) for H atoms on Ni is lower than that on Co and Fe due to the high electron population in the antibonding state of the Ni atom. This well explains the origin of the synergistic effect for the high electrocatalytic HER of these iron triad intermetallics.Luteolin (LU) is a flavonoid compound and metformin hydrochloride (MH) is a kind of drug. Studies have shown that both LU and MH have the function of hypoglycemic effect. However, there are few reports indicating that LU cooperated with MH (LU·MH) can relieve lipid metabolism disorders and optimize intestinal flora compositions of high-fat diet mice. In this research, we investigated the effects of LU, MH and LU·MH on lipid metabolism disorders and intestinal flora composition in high-fat diet mice. The study found that compared with high-fat diet (HFD) alone, LU, MH and LU·MH could significantly reduce the lipid metabolism disorder. Furthermore, compared with LU or MH alone, the biochemical indicators of LU·MH were significantly improved and the results of the histopathological section also showed that LU·MH has stronger liver repair ability. It revealed that the potential mechanisms of the LU·MH alleviating lipid metabolism disorders were involved in the simultaneous regulation of SREBP-1c/FAS and SREBP-1c/ACC/Cpt-1. In addition, LU·MH could regulate the intestinal flora compositions. This includes significantly reducing the ratio of Firmicutes and Bacteroidetes(F/B) and at the family level, increasing the relative abundance of Lachnospiraceae, Helicobacteraceae, Marinifilaceae and Peptococcaceae to relieve lipid metabolism disorders. In conclusion, the work found that LU·MH regulates the signal pathway of SREBP-1c/FAS and SREBP-1c/ACC/Cpt-1 simultaneously and decreases the ratio of F/B, as well as increases the relative abundance of certain microbiota to alleviate the lipid metabolism disorders of HFD-fed mice.Alcoholic beverages are a well-known risk factor for cancer. N2-Ethyl-2'-deoxyguanosine (N2-Et-dG) is a promising biomarker for alcohol-associated cancers. However, the lack of a convenient detection method for N2-Et-dG hinders the development of practical DNA damage markers. Herein, we develop a detection method for N2-Et-dG using a single-molecule quantum sequencing (SMQS) method and machine learning analysis. Our method succeeded in discriminating between N2-Et-dG and dG with an accuracy of 99%, using 20 signals. Our developed method quantified the mixing ratio of N2-Et-dG from a mixed solution of N2-Et-dG and dG. It is shown that our method has the potential to facilitate the development of DNA damage markers, and thus the early detection and prevention of cancers.Machine Learning (ML) has found several applications in spectroscopy, including recognizing minerals and estimating elemental composition. ML algorithms have been widely used on datasets from individual spectroscopy methods such as vibrational Raman scattering, reflective Visible-Near Infrared (VNIR), and Laser-Induced Breakdown Spectroscopy (LIBS). We firstly reviewed and tested several ML approaches to mineral classification from the existing literature, and identified a novel approach for using Deep Learning algorithms for mineral classification from Raman spectra, that outperform previous state-of-the-art methods. We then developed and evaluated a novel method for automatic mineral identification from combining measurements with two complementary spectroscopic methods using Convolutional Neural Networks (CNN) for Raman and VNIR, and cosine similarity for LIBS. Specifically, we evaluated fusing Raman + VNIR, Raman + LIBS or VNIR + LIBS spectra in order to classify minerals. ML methods applied to combined spectral methods presented here are shown to outperform the use of a single data source by a significant margin. Our approach was tested on both open access experimental Raman (RRUFF) and VNIR (USGS, RELAB, ECOSTRESS) libraries, as well as on synthetic LIBS (NIST) spectral libraries. Our cross-validation tests show that multi-method spectroscopy paired with ML paves the way towards rapid and accurate characterization of rocks and minerals. Future solutions combining Deep Learning Algorithms, together with data fusion from multi-method spectroscopy, could drastically increase the accuracy of automatic mineral recognition compared to existing approaches.A highly substituted phenol derivative, effphenol A (1), was isolated from the deep-sea-derived fungus Trichobotrys effuse FS524. Its complete structural assignment was established through a combination of spectroscopic analysis together with single-crystal X-ray diffraction experiments and further unequivocally confirmed by a biomimetic total synthesis. Structurally, effphenol A possesses a poly-substituted 6-5/6/6 tetracyclic ring system, which represents the first case of such a skeleton found in nature. Chk inhibitor Furthermore, the cytotoxic activity of effphenol A (1) toward four human cancer cell lines was assayed.Post-traumatic stress disorder (PTSD) is a widespread psychiatric injury that develops serious life-threatening symptoms like substance abuse, severe depression, cognitive impairments, and persistent anxiety. However, the mechanisms of post-traumatic stress injury in brain are poorly understood due to the lack of practical methods to reveal biochemical alterations in various brain regions affected by this type of injury. Here, we introduce a novel method that provides quantitative results from Raman maps in the paraventricular nucleus of the thalamus (PVT) region. By means of this approach, we have shown a lipidome comparison in PVT regions of control and PTSD rat brains. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry was also employed for validation of the Raman results. Lipid alterations can reveal invaluable information regarding the PTSD mechanisms in affected regions of brain. We have showed that the concentration of cholesterol, cholesteryl palmitate, phosphatidylinositol, phosphatidylserine, phosphatidylethanolamine, sphingomyelin, ganglioside, glyceryl tripalmitate and sulfatide changes in the PVT region of PTSD compared to control rats. A higher concentration of cholesterol suggests a higher level of corticosterone in the brain. Moreover, concentration changes of phospholipids and sphingolipids suggest the alteration of phospholipase A2 (PLA2) which is associated with inflammatory processes in the brain. Our results have broadened the understanding of biomolecular mechanisms for PTSD in the PVT region of the brain. This is the first report regarding the application of Raman spectroscopy for PTSD studies. This method has a wide spectrum of applications and can be applied to various other brain related disorders or other regions of the brain.