dataesr/scientific-paragraphs-categorization huggingface dataset:
Ultrasonography. SMAs were scanned using a Vevo 3100 ultrasound system with an MS550 transducer (FUJIFILM VisualSonics Inc. ). Mice were placed on a heated platform (37°C) to avoid hypothermia and anesthetized with isoflurane (1-2% vol/vol) to adjust the heart rate between 400 and 550 beats/minute. Color Doppler was used to confirm the pulsatile flow of the abdominal aorta. Then the probe was moved from the diaphragm caudally to visualize the SMA. A cine loop of the SMA was captured to define the maximum dilation of the SMA. Maximal luminal diameters were measured on the captured images using Vevo LAB 3. 1. 1 software (FUJIFILM VisualSonics Inc. ). Microfil injection. Mice were euthanized by an overdose of ketamine and xylazine cocktail (90 and 10 mg/kg, respectively). The thoracic cavity was cut open, and the right atrium was nicked to allow the exit of blood flow.
Saline (10 mL) was perfused through the left ventricle using a pressure-controlled peristatic pump (PS/200, Living Systems Instrumentation) at physiological pressure. Directly after perfusion, the right atrium was sealed, and Microfil (Flow Tech, Inc. ) was injected through the same catheter at physiological pressure. Once Microfil was visualized in the arterioles surrounding the small intestine, the pump was stopped, the catheter was clamped shut to prevent backflow of Microfil into the thoracic cavity, and the animal was set aside to allow the compound to harden (~90 minutes). Micro-CT scanning and 3D reconstruction. After Microfil perfusion, animals were scanned using a Skyscan 1276 micro-CT (Bruker), and images were acquired with a pixel size of 20 μm at 2,016 × 1,344 resolution. CT scans were reconstructed using the NRecon program (Bruker) to adjust for beam hardening and ring artifacts.
Image sets were saved as DICOM or BMP files (~1,200-1,500 images/animal). Reconstruction in 3D was performed using the 3D Slicer program. All bone and vasculatures not of interest were removed using the scissors tool within the program to display the aorta and its major branches. To visualize SMAs, all the other vasculatures were removed using the scissors tool. AGT ASO experiments. AGT ASOs were provided by Ionis Pharmaceuticals. PBS alone (control) or AGT ASO (80 mg/kg) was injected subcutaneously on days 1 and 4 in male LRP1 +/+ and smLRP1 -/-littermates when they were 6 weeks of age. Subsequently, either PBS or AGT ASO (40 mg/kg) was injected once every week for 11 weeks. At termination (18 weeks of age), plasma was collected to measure AGT concentrations. Plasma AGT and renin measurements.
The tissue was then fixed again in 4% paraformaldehyde overnight, then placed in 70% ethanol solution in preparation for decalcification, sectioning, and staining. Tissue cross sections of 5 μm thickness were sliced and stained by H&E or EVG. Morphometric measurements were performed using EVOS FL Auto Imaging System software (Invitrogen, Thermo Fisher Scientific) and ImageJ (NIH). All measurements were performed while blinded to the sample identification. Carotid artery ligation. Ligation of the left common carotid artery was performed on male LRP1 +/+ and smLRP1 -/-mice at 12-16 weeks of age. Mice were placed in an induction chamber and anesthetized with 3% vaporized isoflurane (Fluriso; VetOne 502017) in oxygen flowing at 1 L/min. Sedated mice were laid supine on a heating pad and maintained on 2.
MS1 spectra were acquired at a resolution of 120,000, and the 15 most abundant precursor ions were selected for fragmentation by higher energy collision dissociation. MS2 spectra were acquired at a resolution of 15,000. Dynamic exclusion parameters were list size of 500, mass window JCI Insight 2023;8(2):e164751 https://doi. org/10. 1172/jci. insight. 164751 of ±7 ppm, and duration of 1 minute. Automatic gain control settings were MS1 target 1 × 10 6 , maximum inject time 100 ms; MS2 target 4 × 10 4 , maximum inject time 100 ms. Mass spectrometry data analysis. Spectrum matching and protein identification and validation were performed with MSFragger (90), and quantification of protein intensities with matching between runs was performed with IonQuant (91) as components of the FragPipe analysis pipeline using the default settings of each module.
The protein database used for the search was the Mus musculus reviewed sequence database downloaded from UniProt on March 8, 2022. The results were subsequently processed to filter out common contaminants, decoy hits from the reverse database, and protein groups identified by a single peptide. The data were filtered as follows: (a) binary expression of a protein (i. e. , protein exclusively identified in either LRP1 +/+ or smLRP1 -/-) was only considered relevant if all LRP1 +/+ samples or all smLRP1 -/-samples expressed the protein. The missing values were imputed with the minimum intensity value for each specific data set; (b) for samples expressed in both LRP1 +/+ and smLRP1 -/-tissue, the filtering process required 2 or more proteins to be detected in both the LRP1 +/+ and smLRP1 -/-samples.
False discovery analysis was performed using the Benjamini, Krieger, and Yekutieli method (92) using GraphPad Prism 9. 0 software. Causal analysis of proteomic data was performed  (52)  in IPA upstream analysis software (QIAGEN). For IPA, the binary values were imputed using local minimum intensities. Enrichment analyses for gene ontology (biological process) were performed using clusterProfiler 4. 2. 2 R package on R 4. 1. 0. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (93) partner repository with the data set identifier PXD038236. Statistics. Prism 9. 0 (GraphPad Software) was used for statistical analysis. Normality was determined on data sets using the Shapiro-Wilk test. To compare variables between 2 groups, an unpaired 2-tailed Student's t test was used for normally distributed variables.
Acknowledgments This study was supported by  NIH  grants  R35HL135743  (to DKS),  R35HL155649  (to AD),  F32HL131293  (to DTA), and  1K08HL146893-01  (to AAU); the  American Heart Association  Grant  AHA 15SGF24470170  (to SCM); and the  Strategically Focused Research Network in Vascular Disease   18SFRN33960163  (to AD). MMW and JMZ were supported by  NIH   T32HL007698. AAU was supported by  SVS Foundation/American College of Surgeons   Mentored Clinical Scientist Research Career Development Award. PW was supported by the  Undergraduate Summer Training in Cardiovascular Research program  sponsored by the  Cardiovascular Research Priority Area at the University of Kentucky. SCM contributed to this article as an employee of the  University of Maryland, Baltimore. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
Participants Twenty males volunteered to participate (age: 30 ± 7 years, height: 177. 9 ± 7. 0 cm, body mass: 76. 5 ± 12. 2 kg). Participants were asked to avoid caffeine and alcohol consumption as well as to abstain from strenuous exercise 24 h prior to the testing session. Participants provided informed consent and this study conformed to the ethical standards set by the Declaration of Helsinki, except for registration in a database. The study was approved by the local research ethics committees (CPP SudEst I; 1 408 208-2015-A00036-43).
Design Data collection was performed in a single session. Participants were seated on the custom-built chair of a dynamometer, and all the measurements were performed on the right leg. After placement of the high-density electromyography (HD-EMG) grid on the TA, participants initially performed dorsiflexion, triangular contractions for familiarisation and warm-up purposes. They performed ramp contractions to low and moderate torque targets until they were able to smoothly follow the torque path to the best of their abilities. Subsequently, participants were asked to perform at least two 3-s maximal isometric voluntary dorsiflexion contractions (MVCs) with a 60-s inter-trial passive rest. Additional MVCs were performed until the difference between the two best trials was less than 5%. Visual feedback of the torque trace was continuously provided on a screen in front of the participant, with standardised verbal encouragement.
Data analysis Motor unit identification and tracking. HD-EMG signals and torque recordings were converted from the OT BioLab+ format into MATLAB-compatible data files (Version R2021B, The MathWorks, Natick, MA, USA). These files were then processed offline with the DEMUSE software tool (v5. 01; The University of Maribor, Slovenia) that relies on the convolutive blind source separation method  (Holobar & Zazula, 2007). Band-pass zero-phase (20-500 Hz), zero-phase second order finite impulse response high-pass differential (230 Hz) and notch (50 Hz and their higher harmonics) filters were applied in DEMUSE. For each subject, the three channels with the lowest signal-to-noise ratio were automatically removed to optimise decomposition, and 50 sequential decomposition runs were conducted in each ramp contraction independently.
, 2014). Extraction of motor unit firing characteristics. After carefully editing the spike trains, firing events were converted into instantaneous firing rates and smoothed using a fifth order polynomial function, with additional MATLAB scripts and functions. All polynomials were visually inspected and if edge effects were observed at MU recruitment or derecruitment (i. e. a clear mismatch between the change in the smoothed and instantaneous firing rate), the MU from that specific trial was not included in further analyses. MU maximal firing rate was considered as the maximal value obtained from the polynomial curve, i. e. smoothed peak firing rate. Recruitment and derecruitment thresholds were also computed (i. e. torque level (% MVC) at the time when the MU started and stopped firing action potentials, respectively).
Statistical analysis Analyses of MU variables were conducted in R (version 4. 0. 5; R Foundation for Statistical Computing, Vienna, Austria), using RStudio environment (version 1. 4. 1106). Separate repeated-measures nested linear mixed-effects models were used to examine the effect of ongoing LV and prolonged LV on F, MU recruitment threshold, MU derecruitment threshold, and peak smoothed firing  rates of the whole sample of test units or MUs  (Boccia et al. , 2019). The lmerTest package was used  (Kuznetsov et al. , 2017). Variables were analysed with a random intercept (parallel slopes) model using 'time point' as a fixed effect, and 'participant' and 'test unit' (or 'MU') as random effects. After running the models, residuals were plotted against fitted values to assess whether variance was consistent across the fitted range and Q-Q plot inspection was used to assess the assumption of normality of residuals.
In post hoc tests, estimated marginal means (with 95% confidence intervals (CI)) were quantified using the emmeans package  (Lenth & Lenth, 2018). Given the non-independence nature of the observations, repeated measures correlations were computed using the rmcorr package  (Bakdash & Marusich, 2017)  to investigate an association between both the changes in F and MU recruitment threshold during the control time points. This allowed us to measure repeated measures correlation coefficients (r rm ). Correlation magnitude was interpreted based on Cohen's criteria  (Cohen, 2013) : trivial, r rm < 0. 1; weak, r rm = 0. 1-0. 3; moderate, r rm = 0. 3-0. 5; large, r rm = 0. 5-0. 7; very large, r rm = 0. 7-0. 9; and nearly perfect, r rm > 0. 9.
Small RNA sequencing and analysis Library construction and sequencing was performed at BGI Genomics (Hong Kong). Single-end 50-nt reads were pre-processed by ltering collapsed reads according to length and quality. Filtered reads were mapped to the Arabidopsis TAIR10 genome with bowtie  45  , reporting all multi-mappers. Only perfectmatch reads were used for all downstream analysis. Reads were normalized by dividing non-redundant read counts by the number of genomic hits, and subsequently calculating the number of reads per million of ltered (18-30 nt) and perfectly mapped reads. Differential expression analysis was performed with the R package DESeq2 46 using an FDR adjusted p-value of 0. 05. Additional downstream analyses and plots were done with custom R scripts. Graphical outputs were produced using the R package "ggplot2".
A summary of all small-RNA sequencing data generated in this study is presented in Supplementary Data 5, and is publicly available in the NCBI's Gene Expression Omnibus under the accession number GSE231668. Whole-genome bisul te sequencing and DNA methylation analysis Genomic DNA was puri ed from total pollen, and library preparation and sequencing were performed by BGI Genomics (Hong Kong) as paired-end 100bp reads using DNBSEQ technology. Pre-processed and high-quality reads were mapped to the TAIR10 genome using bismark with default settings for paired-end libraries  47  , and all gures and downstream analysis were performed using custom R scripts. Graphical outputs were produced using the R package "ggplot2" and "ComplexHeatmap". Methylome data from nrpd1a-3 seedlings was already available  48.
DMRs were de ned as 100-bp bins containing at least four, ve or six differentially methylated CGs, CHGs or CHH and with an absolute methylation difference of at least 0. 4, 0. 35 or 0. 25, respectively. Bins localizing within 200 bp of each other were merged and considered as DMRs. A summary of all WGBS sequencing data generated in this study is presented in Supplementary Data 5, and is publicly available in the NCBI's Gene Expression Omnibus under the accession number GSE231667.
RNA sequencing and analysis Sequencing of messenger RNA was performed by BGI Genomics (Hong Kong) as paired-end 100bp reads using DNBSEQ technology. High-quality raw reads were aligned to the TAIR10 genome using STAR  49. Normalization and analysis of differential gene expression was performed using the R package DESeq2 46 , with an FDR adjusted p-value of 0. 05. Graphical outputs were produced using the R packages "ggplot2", "pheatmap", and "ComplexHeatmap". A summary of all RNA sequencing data generated in this study is presented in Supplementary Data 5, and is publicly available in the NCBI's Gene Expression Omnibus under the accession number GSE231669.
Acknowledgments This work was supported by the grant EpiHYBRIDS from the  French National Agency of Research  ( ANR-19-CE120008 ). We thank all members of the EPIREP team for daily discussions,  Hervé Vaucheret  for critical reading of the manuscript and sharing the p35S:: RTL1-Myc  plasmid and seeds, and  Gregory Copenhaver  for the FTL1230 line. The authors acknowledge technical support from  Mickael Bourge  and  Nicolas Valentin  from the cytometry facility of the I2BC. This work has bene ted from the support of IJPB's Plant Observatory technological platforms. The IJPB bene ts from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
The average rainfall in the area is very high, with an annual value of more than 2,500 mm/year. The highly monthly rainfall is recorded in December, while July has the lowest. Rainfall data were collected from the Meteorological, Climatological, and Geophysical Agency of Pulau Baai Bengkulu, Indonesia. The elevation of the area spread out from 500 to 2,500 masl. The area is dominated by land with elevation 500-1,000 masl (62%), followed by 1,000-1,500 masl (30. 42%), 1. 500 masl (5. 5%) and >2. 000 masl (1. 14%). Most of the area is dominated by land with a slope of 25-45% (30. 34%), followed by 15-25% (22. 32%), 8-15% (18. 19%), 0-8% (16. 47%), and >45% (12. 67%). The elevation and slope data are derived from DEM that can be downloaded from http://tanahair. indonesia. go. id.
LULC change analysis Satellite image taken by  Landsat-5 (1993 and 2006)  and  Landsat-8 (2019)  was classified into eight types of land use and land cover, namely built-up area, forest cover, water bodies, paddy fields, dry land agriculture, mixed dry land agriculture, bare soil/bare ground, and dry shrub. The satellite image was downloaded from www. earthexplorer. usgs. gov. Land use and land cover type are classified with the supervised classification  (Ma et al. , 2017)  method in Arcgis 10. 8. 2. The accuracy classification was validated by verifying a fixed number of locations on the map and identifying those areas on the fields. The prediction of LULCC used Land Change Modeler (LCM) model on IDRISI Terrset. The LCM approach integrates the Multilayer-Perceptron (MLP) with the Markov-Chain Method to model land transition probabilities using historical land use and land cover data and other geospatial datasets  (Dangula et al.
Both two strains studied herein had a MALDI-TOF score lower than 2. 0, which did not allow their correct identification. Their spectra were then added to the local MALDI-TOF MS database (https:// www. medit erran ee-infec tion. com/ urms-data-base). 16S rRNA gene sequencing and identification. The 16S rRNA sequences from both strains were directly extracted from their whole genomes sequences and then, compared by Basic Local Alignment Search Tool nucleotide (BLASTn) to the non-redundant (nr) databases  13. The obtained sequence similarity percentages allowed identification of the closest species to each strain, and to predict if it was new species (< 98. 65% of similarity). Then, the phylogenetic tree was constructed based on these 16S rRNA gene sequences in comparison to the closest related species of each studied strain.
Fatty acid methyl ester (FAME) analysis was explored by Gas Chromatography/Mass Spectrometry, as previously reported  17, 18. FAMEs were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer®, Courtaboeuf, France). Obtained spectra were compared with those contained in the repertory databases using MS Search 2. 0 operated with the Standard Reference Database 1A (National Institute of Standards and Technology-NIST, Gaithersburg, USA), and FAMEs mass spectral database (Wiley, Chichester, UK). Whole genomic sequencing and bioinformatic analyses. First, bacterial DNA was extracted using the EZ1 DNeasy Blood Tissue Kit (Qiagen® GmbH, Hilden, Germany) in line with the manufacturer's protocol  19. Whole-genome sequencing was performed using an Illumina® MiSeq sequencer (Illumina®, San Diego, CA, USA)  20.
Then, sequenced genomes were assembled using SPAdes 3. 5. 0 software  21  , which reduces short indels and the huge number of mismatches. Raw reads in contigs less than 700-bp-long were removed. Finally, the quality of the sequenced genome was checked using BLAST against the nr/nt database. This method allowed us to better explore the relationship between a submitted assembly of our new species to the International Nucleotide Sequence Database Collaboration (INSDC), i. e. , DDBJ, ENA, or GenBank, and the assembly represented in the NCBI reference sequence (RefSeq) project. The global statistics section reported general statistics information including Gaps between scaffolds, number of scaffolds, number of contigs, total sequence length, and total ungapped length. Furthermore, taxonomic data were checked according to the best-matching-type strain with the declared new species repertory in NCBI  22.
During annotation, genomic parameters were evaluated including transfer-messenger RNAs (tmRNAs) and transfer RNAs (tRNAs) using ARAGORN version 1. 2 and ribosomal RNAs (rRNAs) using Barrnap version 0. 9  23, 24. Generated file (. faa) was used for BLAST-P analyses against the Clusters of Orthologous Genes (COGs) database, and used for CRISPR-Cas identification  25. Resistance genes were screened using ResFinder  26. Other bioinformatic tools were also used such as AntiSMASH to search polyketide synthases (PKS) and non-ribosomal peptide synthetases (NRPS)  27. Circular maps of the two genomes were generated using CGView (Circular Genome Viewer) software. This Java application converts XML or tab-delimited input into a Vector Graphics format  28. Besides, phylogenetic trees of interest were generated with the FastME 2.
Antibiotic resistance genes and defense mechanisms. Using the ResFinder software, antibiotic resistance genes (ARG) erm (F) and tet (Q) were detected within the genome of strain Marseille-P2698 T , with identity percentages of 100% and 99. 8%, respectively. For strain Marseille-P2260 T , ARG included erm (B), tet (M), and tet (O), with identity percentage ranging from 99. 7% to 100% (Table  5 ). The genomes of strains Marseille-P2698 T and Marseille-P2260 T contained CRISPR-Cas modules (Table  6 ). Strain Marseille-P2698 T contains one defense mechanism composed of polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS) enzymes. NRPS-PKS was previously demonstrated to have a role in the biosynthesis of pharmaceutically-important natural products  31. Using ANTI SMASH software, NRPS-PKS-like genes cluster had been detected in genome of strain Marseille-P2698 T , and β-lactone; containing potent medicinal properties  32, 33  , had been detected in strain Marseille-P2698 T genome.
Funding This work was supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was also supported by  Région Provence Alpes Côte d' Azur and European funding FEDER PRIMI.
Patients and Samples The study cohort included patients with Ph-negative B-ALL ages 18 to 59 years enrolled in the GRAALL-2014 trial between 2016 and 2020 and French patients ages ≥55 years enrolled in the EWALL-INO trial between 2017 and 2022 (ClinicalTrials. gov number NCT02617004 and NCT03249870, respectively). The GRAALL-2014 protocol was an intensive pediatric-inspired treatment similar to the GRAALL-2005 (  5 ), but with age adaptation of doses for patients 45 to 59 years old. The standard induction comprised prednisone, daunorubicin, vincristine, cyclophosphamide, L-asparaginase, and intrathecal prophylaxis. The EWALL-INO protocol was a treatment based on a low-intensity chemotherapy backbone and intrathecal prophylaxis with the addition of inotuzumab ozogamicin (INO) during the two-phase induction.
Targeted DNA Sequencing A custom panel of genes previously known to be targeted by recurrent mutations or CNA in B-ALL was analyzed by pan-exon capture-based target enrichment (SureSelectXT Low Input Target Enrichment System, Agilent) followed by library sequencing on the Illumina NextSeq500 platform (Illumina) as previously described (gene list in Supplementary Table  S7 ; ref. 36). Data were analyzed for variant calling using Varscan  (37)  and Pindel  (38) , and CNA analysis was conducted using Viscap  (39)  and Facets  (40). Lollipop plots were drawn with ProteinPaint (41).
Bioinformatics Analysis Raw data were processed using Mission Bio's Tapestri Pipeline (DNA pipeline version 2. 0. 2 and Protein pipeline version 2. 0. 1) for preprocessing, alignment (reference genome hg19), cell barcode correction, cell identification, variant calling of DNA amplicons, and ADT-seq reads counting. Multiomics h5 files were then analyzed on R version 4. 1. 2 (R Foundation for Statistical Computing). TapestriR package was used to annotate variants: a genotype was considered informative if the singlecell sequencing depth was ≥5 reads. Variants were categorized as wildtype (WT) if VAF <10%, heterozygous (het) if VAF comprised between 10% and 90%, and homozygous (hom) if VAF ≥90%. Variants covered by <5 reads were considered noninformative and labeled as "missing genotype. " No genotype filtering was performed before integration with ADT-seq data, in order to avoid data attrition, as all genotypes (i.