dataesr/scientific-paragraphs-categorization huggingface dataset:
THE CONSEQUENCES IN TERMS OF METAGENOMIC STUDIES AND EVOLUTIONARY BIOLOGY AND TAXONOMY: Advances in Next Generation Sequencing (NGS) have made a significant contribution to advanced microbial ecology studies, hence the implementation of metagenomics. This is defined as a process of analysis of the DNA without the need to perform a culture. With the implementation of databases and tools, bioinformatics and statistical analysis have made it possible to better exploit metagenomic data  (Oulas et al. 2015). Together with marker genes, the use of metagenomics is an efficient approach to obtain a taxonomic profile of a given community using PCR amplification and sequencing of conserved marker genes such as 16S rRNA  (Tringe & Hugenholtz 2008). The latter was considered a fundamental fold for the characterization of a given microbial community, but as a result, this approach is overshadowed by metagenomics, which provides the overall functioning of a community rather than an inventory of these inhabitants  (Tringe & Hugenholtz 2008).
CONCLUSION AND DISCUSSION: The evaluation of the accuracy of sequence transfer detection for all the tools testifies to the great variety of algorithms. For example, BellerophonGG is able to identify chimeras mainly restricted to the most divergent sequence pairs, while Pintail is highly sensitive to the detection of chimeras in full-length sequences, not chimeras in shorter sequences. However, the perfect detection of chimeras is still an unresolved problem. More complex chimeras and sequence anomalies may escape detection. This underlines the importance of obtaining and validating the sequences that represent the new bacterial diversity and of continuing to develop the reference database and additional analytical tools. The problem of identifying rare species that correlate with diseases or other important functions of the microbial ecosystem remains difficult.
The constructs were verified by sequencing and then transformed into A. thaliana Col-0 ecotype by floral dip; homozygous lines containing single construct insertions lines were selected based on Hygromycin B resistance. Generation of amiRNA:PI4Kα1 lines: two artificial microRNA sequences specific to the the class III PI4Kα1 gene (AT1G49340) were designed using the software WMD3 Web MicroRNA Designer (ref. 62; see Supplementary Table  1 ). The amiRNA:pi4kIIIα1 precursor fragment sequences were PCR amplified from the pRS300 plasmid as previsouly described  62  using specific primers listed in Supplementary Table  1. PCR products were cloned into the pENTR/D-TOPO vector (pENTR Directional TOPO Cloning kit, Invitrogen). Clones were verified by sequencing and then transferred by gateway LR cloning into the destination vector PMDC7 which allows expression of the transgene under the control of β-estradiol  63.
Confocal microscope images were processed using the Zen Black Software (Zeiss) for intensity optimization. High resolution Airyscan imaging of GFP and mCherry were performed using the ZEISS LSM880 confocal microscope equipped with a Plan-Apochromat 63×/1. 4NA oil objective and a super-resolution AiryScan module with a band-pass filter 495-550 nm and a long pass filter 570 nm. Number of puncta, probe localization, cell number and surface areas were quantified using Zen Black Software (Zeiss) and Image J. Fluorescent puncta (ATG8, ATG18, ATG5, ATG4, and PI4P probes) were counted manually on a minimum of 12 images per condition and per independent experiment (representing over 80 cells total). At least three independent biological experiments were performed for each analysis. For propidium iodide assay, propidium iodide was added to the mounting medium at the working concentration of 10 µg/mL.
69. Image analysis was carried out using the ImageJ software (https://imagej. nih. gov/ij/download. html) with the Bio-Format plugin (https://www. openmicroscopy. org/bio-formats/) to open and process the Zeiss. czi files and to open the. mrc tilt series files and tomograms. Distance between autophagic structures and other organelles was measure with ImageJ at the closest point between structures. Mosaic images of TEM were assembled using Photomerge function of Adobe Photoshop. No image analysis and measurements were carried out on mosaic and correlated images, they were only used as a visualization tools.
Blots were imaged at different exposition times, long enough to observe clear distinct bands but prior to pixel saturation. Blot images were analyzed using the ImageJ software; band intensities were calculated using the plot surface areas functionality of the software. The intensities of the free-GFP band and the total GFP (GFP-ATG8 + free-GFP) were then used to calculate ratios representatives of the autophagic flux.
RT-qPCR analysis Total RNA was extracted using the RNeasy mini kit (Qiagen). To eliminate genomic DNA contamination, an additional DNase treatment was performed according to the RNeasy kit instruction with the DNA removal kit (Invitrogen). One microgram of total RNA was reversetranscribed into cDNA in a 20 μL reaction mixture using the Superscript II reverse transcriptase enzyme (Invitrogen). cDNA levels were then analyzed using the iQ™ Sybr Green supermix (BioRad) on the iQ iCycler thermocycler (BioRad) with the gene-specific primers listed in Table  S1. The thermocycling program consisted of one hold at 95 °C for 10 min, followed by 40 cycles of 15 s at 95 °C and 1 min at 60 °C. After completion of these cycles, melting-curve data were then collected to verify PCR specificity, contamination, and the absence of primer dimers.
Statistical analyses Statistical analyses were performed using GraphPad Prism 9. 3. 1 (GraphPad Software, La Jolla, CA, USA) or using Microsoft Excel with tests indicated in the figure legends. P-values are as follows: P-value > 0. 05 (non-significant, ns), *P < 0. 05, ** P < 0. 01 and *** P < 0. 001.
Acknowledgements We thank  Dr. Frédéric Domergue  for critical revisions of the manuscript,  Professor L. Jiang  ( The Chinese University of Hong Kong ) for YFP-ATG18 seeds,  Dr. Y. Dagdas  ( GMI, Vienna ) for the pUbi:: GFP-ATG8e  and pUbi:: GFP-ATG8f  seeds and  Dr. Satiat Jeunemaitre  for the ATG5-mCherry seeds. We also thank the  Bordeaux Imaging Center , part of the  France BioImaging national infrastructure grant  no. ANR-10-INBS-04. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  852136  to AB) and from  Idex Bordeaux  ( AutoLip Emergence Program  to AB).
Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of 74 CUD patients (9 female) and matched 64 healthy controls (6 female) that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. the repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform.
Alterations aforementioned areas have been shown to undergo functional connectivity changes that compromise processes such as reward signal, executive function, and impulsivity  [12] [13] [14]. Taken together, studies using MRI techniques could help to understand CUD pathology and contribute to the improvement of therapeutic approaches. Our dataset comes from a cross-sectional case-control study with the goal of understanding the clinical, cognitive, neuroanatomical and functional pathology that crack cocaine induces. It includes a sample of 75 (9 female) CUD patients and 62 (11 female) healthy controls (HC). The acquired MRI sequences comprise whole-brain: 1) structural (T1-weighted), 2) novel multishell high angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences.
Participants also underwent comprehensive clinical and cognitive evaluations. A participant checklist with the specific MRI sequence acquired can be found in Supplementary File 1. Four features from this dataset can be highlighted: (1) the use of the same scanner, protocol and operating procedure, (2) the CUD group shows greater movement, commonly found in psychiatric samples, (3) the novel multi-shell DWI protocol which allows for further study of brain structure and (4) the extensive clinical and cognitive evaluations the participants underwent. Previous publications have used this dataset using diffusion kurtosis imaging analysis  15  , neurite orientation dispersion and density imaging analysis  16  , and a machine learning approach for the identification of cognitive markers in CUD  17  , among others  [18] [19] [20] [21].
testing procedure. To be considered for the study, CUD patients had to be active cocaine consumers or with an abstinence period shorter than 60 days prior to the scan; frequency of use had to be at least 3 days per week with no more than 60 continued days of abstinence during the last 12 months. Participants were requested to remain without any drug consumption before and at the day of the study. At the time of acquisition, participants reported not having consumed any drugs. The instant view drug screening test was applied for the evaluation of drug use during the protocol (data are available in Zenodo). Additional exclusion criteria for all groups were: medical, neurological and other psychiatric disorders, severe suicidal risk, history of head trauma with loss of consciousness, and non-compliance with magnetic resonance imaging safety standards.
If the participants closed their eyes for more than 10 seconds, we would wake them up using the communication through the headphones, reminding them to try to not fall asleep for the 10 minutes the sequence lasted, and we re-started the sequence over. To create field maps intended for rs-fMRI use, we acquired opposite phase sequences, using the same GE-EPI sequence with the following parameters: TR/TE = 2000/30 ms, flip angle = 75°, matrix = 80 × 80, voxel size = 3 × 3 × 3 mm, number of slices = 36, phase encoding direction = PA. A total of 4 volumes were acquired. and is available and updated on the OpenNeuro Data sharing platform (https://Openneuro. org). BIDS organization are used to facilitate data sharing and unify the majority of projects in the field by using simple folder structure, standardized file names accordingly to acquisition modality in NIfTI format (converted from DICOM) with data descriptions and metadata in JavaScript Object Notation (JSON) files  24.
Anatomical and diffusion images. Quality control. Quality assessment of the MRI dataset was run using image Quality Measures (MRIQC v. 0. 15: an automatic prediction of quality with visual reports of each sequence) from structural T1w and rs-fMRI images, available in OpenNeuro platform. This tool helps to extract and compute different metrics such as signal-to-noise ratio, noise model fitting parameters, and spatial distribution of information. All of these can be exported to HTML and JSON reports  27. Please find further details in corresponding documentation (mriqc. readthedocs. io/). Mini International Neuropsychiatric Interview -Plus (M. I. N. I. -Plus). The M. I. N. I. is a comprehensive diagnostic psychiatric interview designed to assess the 17 most common psychiatric disorders of the Axis I in DSM-IV and the ICD-10 28.
In the same way, volumes (Fig. 1c ) were obtained as the count of all voxels that are in total of cortical gray matter and  in subcortical gray matter, these metrics are commonly associated with alteration according to demographic or genetic variables and in several pathologies such as CUD  71. Signal in MRI refers to the mean voxel intensity in the image, in contrast to the random differences in voxel intensity which is considered as noise  72, 73. The ratio of the mean signal intensity and noise is defined as Signal-to-Noise ratio. SNR was extracted from MRIQC output for T1w and rs-fMRI images. Moreover, for DWI, SNR within the Corpus Callosum was calculated using Diffusion Imaging in Python (Dipy; https://dipy. org/ documentation/) protocol estimation  74. We extracted the b0 (Fig. 3a ), the worst (Fig.
acknowledgements We thank the people who helped this project in one way or another:  Alan Dávalos ,  Adedamola Falana ,  Francisco J. Pellicer Graham ,  Margarita López-Titla ,  Aline Leduc ,  Erik Morelos-Santana ,  Lya Paas ,  Daniela Casillas ,  Sarael Alcauter ,  Luis Concha  and  Bernd Foerster. We also thank  Rocio Estrada Ordoñez  and  Isabel Lizarindari Espinosa Luna  at the  Unidad de Atención Toxicológica Xochimilco  for all their help and effort. Finally, we thank the study participants for their cooperation and patience. Victor Issa-Garcia  and  Eduardo A. Garza-Villarreal  would like to thank  Dirección General de Calidad y Educación en Salud, Secretaría de Salud, México  for the scholarship support provided to Victor. This project was funded by  CONACYT-FOSISS  project No. 0201493  and  CONACYT-Cátedras  project No.
13 t(111) = -4. 73 p < 0. 05 Alcohol Years consumption n. a 14. 86 ± 9. 96 n. a Cannabis Years consumption n. a 4. 01 ± 4. 31 n. a characteristics of dataset. Data are given as N(%) or mean (SD), Two samples t-test was performed for age, and χ² for categorical variables, n. a: no applica. HC, healthy control; CUD, cocaine use disorder.
Whole Exome Sequencing (WES) Molecular diagnostics were performed using whole exome sequencing (WES). Venous blood samples were collected from the proband and his parents. WES was prepared using proband's DNA sample with SureSelectXT Human kit All Exon v7 (Agilent, Agilent Technologies, Santa Clara, CA) and paired-end sequenced (2 × 100 bp) on HiSeq 1500 (Illumina, San Diego, CA, USA). The mean depth of coverage in the sequenced sample was 98×, with 98% of target covered ≥ 10×, and 94. 7%, which covered ≥ 20×. A bioinformatics analysis of the raw WES data and variants prioritization were performed as previously described  [7]. In brief, reads were aligned to the GRCh38 (hg38) reference genome with the Burrows-Wheeler Alignment Tool (http://bio-bwa. sourceforge. net/, accessed on 1 March 2022), and processed further by the Picard (http://broadinstitute.
github. io/picard/, accessed on 1 March 2022) and Genome Analysis Toolkit (https://software. broadinstitute. org/gatk/ accessed on 1 March 2022). The identified variants were annotated with functional infor-mation, frequency in population (including gnomAD (http://gnomad. broadinstitute. org/ accessed on 1 March 2022), and an in-house database of > 5000 Polish exomes), and the known association with clinical phenotypes, which was based on both the Clin-Var (https://www. ncbi. nlm. nih. gov/clinvar/, accessed on 1 March 2022) and HGMD (http://www. hgmd. cf. ac. uk, accessed on 1 March 2022) databases. An in-silico pathogenicity prediction was performed based on the pathogenicity and conservation scores available from the Varsome database (https://varsome. com/, accessed on 1 March 2022), including BayesDel addAF, BayesDel noAF, EIGEN, EIGEN PC, FATHMM-MKL, FATHMM-XF, LRT, M-CAP, MutPred, MutationTaster, PROVEAN, PrimateAI, SIFT, DEOGEN2, FATHMM, LIST-S2, MVP, Mutation assessor, and SIFT4G.
Results By applying WES in the proband we identified a novel hemizygous synonymous variant in the LAS1L gene (hg38; chrX: g. 65529212C > G, NM_031206. 7:c. 846G > C, p. (Thr282=)). The presence of p. (Thr282=) was further confirmed by ADS in the proband and in his unaffected mother in heterozygotic form (Figure  1A ). The p. (Thr282=) has 0 frequency in all tested databases, including gnomAD v3. 1. 2 and the in-house database of >5000 WES of Polish individuals. The c. 846G > C is located in exon 6 out of 14 (residue 282 out of 735) of LAS1L and the c. 846G position is highly conserved (PhastCons100way score 1. 0). According to ACMG classification, the p. (Thr282=) variant was predicted as "likely pathogenic" (PP3 strong, PM2-moderate)  [9]. Moreover. the p. (Thr282=) is suspected to impair the RNA splicing process.
COVID-19 can lead to heart failure (HF) and even cardiac death. The 2',5'-oligoadenylate synthetase (OAS) gene family is associated with the antiviral immune responses of COVID-19. While the potential association of OAS family with cardiac injury and failure in COVID-19 has not been determined. Hence, in our study, the expression levels and biological functions of OAS gene family in SARS-CoV-2 infected cardiomyocytes dataset (GSE150392) and HF dataset (GSE120852) were determined by comprehensive bioinformatic analysis and experimental validation. miRNAs targeting OAS gene family were explored from Targetscan and HF miRNA database. The potential OAS gene family-regulatory chemicals or ingredients were predicted using Comparative Toxicogenomics Database (CTD) and SymMap database. Results showed that OAS genes were highly expressed in both SARS-CoV-2 infected cardiomyocytes and failing hearts.
The differentially expression genes (DEGs) in two datasets were enriched in cardiovascular disease and COVID-19 related pathways, respectively. The miRNAs-target analysis indicated that 10 miRNAs increase OAS genes expression. A variety of chemicals or ingredients were predicted regulating the expression of OAS gene family, especially estradiol. In conclusion, OAS gene family is an important mediator of HF in COVID-19 and may serve as a potential therapeutic target for cardiac injury and HF in COVID-19.
These oligomers then activate RNaseL  (Rebouillat & Hovanessian, 1999; Rebouillat, Hovnanian, David, Hovanessian, & Williams, 2000). OAS proteins act as a sensor of dsRNA and block viral replication by activating RNaseL  (Schwartz S. L. & Conn G. L. , 2019; Eskildsen, Hartmann, Kjeldgaard, & Justesen, 2002). In this process, NOD-like proteins can interact with the OAS family to enhance the activity of RNase L. The degraded RNA activates retinoic acid-inducible gene-I (RIG-1)  (Schwartz Samantha L. & Conn Graeme L. , 2019; Chakrabarti, Jha, & Silverman, 2011). RIG-1 amplify production of IFN-α/β and GSE120852 is a mRNA expression dataset of HF (COVID-19 free), including 5 non-failing (NF) left ventricles (LV), 5 NF right ventricles (RV), 5 LV tissues from subjects of LV failure, 5 RV tissues from LV failure, 5 LV tissues from biventricular failure (Bi-HF), and 5 RV tissues from Bi-HF.
In the condition of LV failure, the mRNA expression profiles of LV and the RV were extracted and compared with the corresponding normal ventricular tissues (non-failing ventricles) to obtain the differential gene 1 (diff-1) and gene 2 (diff-2). In the condition of biventricular failure, the mRNA expression profiles of LV and the RV were derived and compared with that of the corresponding normal ventricular tissues to obtain the differential gene 3 (diff-3) and gene 4 (diff-4). The detailed information of GSE120852 dataset is shown in Table  2. GSE147507 and GSE179850 are mRNA expression databsets related with COVID-19. In GSE147507, we chose three groups of human lung epithelial (NHBE) cells treated with mock and three groups of NHBE cells treated with SARS-CoV-2, the detailed information is showed in Table  3.
Analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment GO and KEGG pathways of DEGs were analyzed using Metascape. Metascape  (Zhou et al. , 2019)  (http://metascape. org) is a public online database, it is designed to provide a comprehensive gene list annotation and analysis resource. GO is a gene function classification system to describe gene property. In the GO enrichment analysis, three aspects were analyzed, including biological processes (BP), cellular components (CC), and molecular functions (MF). KEGG pathway was used to analyze gene function, genomic information, and target relationship of pathways. P < 0. 01 was chosen as the cut-off criteria.
RNA isolation and qPCR Total mRNA was extracted from mice failing heart tissues and H9C2 cells using TRIzol (Invitrogen, Carlsbad, CA), then was reverse-transcribed into cDNA according to the instructions of TaKaRa PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa, Osaka, Japan), according to the manufacturer's instruction. PCR amplifications were quantified according to the instructions of TaKaRa TB Green Premix Ex Taq II (TaKaRa, Osaka, Japan). The primer sequences for qPCR were designed by Sangon Biotech Co. , Ltd (Shanghai, China). The expression data were normalized to the reference glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the mRNA levels were calculated using the 2 -[?][?]Ct method. Primer sequences for qPCR are shown in Table  S1.
Analysis of differentially expressed genes (DEGs) The "limma" package in the R language was used to analyze the data downloaded from NCBI-GEO. P < 0. 05 and |logFC| > 1 were chosen as the cut-off criteria in the analyses of GSE150392 and GSE104150; P < 0. 05 and |logFC| > 0. 8 were set as the cut-off criteria in GSE120852. The common DEGs in GSE120852 were generated and visualized using FunRich software (version 3. 1. 3. ).
Analysis of DEGs interactions and screening of hub genes The protein-protein interaction (PPI) network of DEGs were determined using STRING  (Szklarczyk et al. , 2014) (version 11. 5 , https://string-db. org/). STRING is an online database for searching protein-protein interactions including direct physical interactions and indirect functional correlations between proteins. In the PPI analysis, the minimally required interaction score was set as medium confident 0. 4. Then, results were used to screen hub genes using CytoHubba in Cytoscape (version 3. 9. 0. ). CytoHubba  (Chin et al. , 2014)  is a plugin in Cytoscape, it provides 12 topological analysis methods which can be used to explore important nodes in biological networks. We used Density of Maximum Neighborhood Component (DMNC), one of the 12 topological analysis methods, to search the top 30 hub genes.
Prediction of chemicals andingredients interacting with OAS gene family Comparative Toxicogenomics Database (CTD) and SymMap database were used to analyze the chemicals or drugs which interact with the OAS gene family. CTD  (Davis et al. , 2021)  (http://ctdbase. org/) is a powerful public database which integrates large amounts of data among chemicals, genes, functional phenotypes and diseases. It provides information on the associations of chemical-gene/protein, chemical-disease, and genedisease, and thus helps to predict mechanistic hypotheses about the influence of environment on disease. In the CTD database, we chose chemicals with "interaction" > 3 as the cut-off criteria to perform further analysis. SymMap  (Wu Y. et al. , 2019)  database (http://www. symmap. org/) is an integrative database of Traditional Chinese Medicine (TCM) enhanced by symptom mapping.
Docking analysis of affinity between chemicals/ingredients and OAS gene family Molecular docking approach was used to identify the affinities of chemicals/ingredients with OAS1, OAS2, OAS3, and OASL. The structures of the small molecules from PubChem database (https://pubchem. ncbi. nlm. nih. gov/) were downloaded, and Chem3D software was used to minimize the ligand molecular energy. The 3D structures of OAS genes were obtained from PDB database (https://www1. rcsb. org/) or UniProt database (https://www. uniprot. org/). AutoDockTools 1. 5. 6 software was used to find out the active pockets. Vina script was run to calculate the molecular binding energy, Vina [?] -7. 0 kcal*mol -1 indicated strong binding of "ligand" with "receptor". PyMOL software was used to display the results.