dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics and graphical visualization Statistical tests were performed as unpaired Student's t-test with Welch's correction using GraphPad Prism software version 7. 02. Figures display the mean and standard error of the mean (SEM) values. Significance was assumed at p<0. 05 and highlighted with asterisks: *p<0. 05, **p<0. 01, ***p<0. 001, ****p<0. 0001.
org/) documented Ataxin-2 transcript expression to be similar in neurons and astrocytes, while Ataxin-2 transcript expression in oligodendrocytic, microglial and endothelial cells ranges at 30-50% in comparison to neurons. To assess the presence of ATXN2 within microglia at the protein level, the microglial cell line BV2 was immunocytochemically stained for ATXN2 and its direct interactor protein PABPC1, while exposure to oxidative stress via NaArs administration was used to trigger the formation of characteristic stress granules, with the relocalization of ATXN2 and PABPC1 there ensuring staining specificity. The immunofluorescent signals confirmed the typical diffuse cytosolic pattern for both proteins that changes to multiple granules with the expected delay (Fig. 4B ). These data indicate that microglia would not only phagocytose ATXN2 aggregates that were produced by neurons, but may also suffer in cell-autonomous manner from the RNA toxicity triggered by the ATXN2 mutation.
Acknowledgements For their technical assistance, we are grateful to  Birgitt Meseck-Selchow  and  Gabriele Köpf  in Frankfurt, to  Jérome Sinniger  in Strasbourg, to  Michaela Neuenkirch in Langen , to  Anja Kerksiek  for technical assistance to perform sterol and oxysterol analysis, and to the staff of the ZFE animal facility at the  Goethe University in Frankfurt. For financial support, we thank the  Deutsche Forschungsgemeinschaft  (grants  AU96/ 11-1  and  11-3  to GA). GA receives financial support from  Roche Pharma  and  Takeda Pharma  for further projects on ATXN2.
It is also worth mentioning that the elemental analyses of complex 3 agrees with the chemical formula [Fe(py3C-OEt)2][Fe(py3C-OEt)(NCSe)3]2•2CH3CN, expected for a complex isomorph to 1 and 2. Therefore, all the data strongly support that the crystal structure of complex 3 is isomorph to those of complexes 1 and 2  [34] ; and therefore its crystal structure consists of a low spin (LS) [Fe(py3C-OEt)2] 2+ cationic complex (Figure  2b ), an anionic [Fe(py3C-OEt)(NCE)3] -(E = S (1), BH3 (2) Se (  3 )) complex (Figure  2a ) and two CH3CN solvent molecules. As previously described, complex 1 exhibits an incomplete HS to LS transition at T1/2 = 205 K, while 2 displays a complete HS to LS two-step like transition centred at around 245 K and 380 K (Figure  3 ). For both complexes, the magnetic properties were measured in both cooling and warming modes (300-2-400-2 K for 1; 300-2-500-2 K for 2) but no significant hysteretic effects or any change due to possible desolvation were detected.
Funding: This research was funded by the  French CNRS  ( MITI  Project),  Mol-CoSM ANR  Project  N° ANR-20-CE07-0028-01  the  "Université de Brest" (IBSAM institute ), the  Région Bretagne (EC) , the  Generalidad Valenciana  ( Prometeo2019/076  project) and the  Spanish MINECO  (Project  CTQ2017-87201-P AEI/FEDER, EU ).
LRZ9 Pseudomonas entomophila L48 826, 826, 92%, 99. 8%, 0. 0 NR_102854. 1  [37] [38] [39]  The maximum identities of all V3/V4 16S rRNA gene sequences were searched by using the GenBank DNA sequence database and the BLASTN. The results showed that all three bacteria have α-Gal on its surface (Figure  2A, B ) with significantly highest relative levels (>10 3 FSC-H) in A. veronii (55% cells) and P. entomophila (26% cells) when compared to S. xiamenensis (7%) (Figure  2C ). As a reference, published data for M. marinum showed 3. 2% cells with highest α-Gal content  [24]. These results correlated with the reported carbohydrate structure in these bacteria, in which A. veronii but not P. entomophila contain galactose in addition to α-Gal  2D ). in compound ID 12335 [-4)-a- D-Quip3NAc-(1-3)-a-L-Rhap-(1-4)-b-D-Galp-(1-3_-a-D-GalpNAc-(1-] [40] (Figure Among fish-associated bacteria, A.
For further analysis, ASVs with low counts and those with prevalence lower than 0. 01% were filtered to remove spurious ASVs in the bacterial dataset. The results showed that the zebrafish gut microbiota in all experimental groups and at each time point (pre-challenge and post-challenge) is dominated by members of the phylum Proteobacteria (genera Aeromonas, Acinetobacter, Gemmobacter and Plesiomonas) followed by Bacteroidota (genera Cloacibacterium), Firmicutes, Actinobacteria and Planctomycetota phyla (Supplementary Materials File S1: Figures  S2-S4 ). These microbial composition trends have been previously reported in the zebrafish gut microbiota  [37, 63]. To examine the dissimilarities in community composition between experimental groups at each time point (pre-challenge and post-challenge), beta diversity metric was assayed by using Bray-Curtis dissimilarity in univariable PERMANOVA models.
All PCR products were purified by using the ExoSap-IT PCR Product Clean-Up kit (Applied Biosystems, Foster City, CA, USA) following the manufacturer's instructions and Sanger sequenced, using the ABI PRISM ® 3730 platform (Applied Biosystems) at the Genomic Unit (Campus Moncloa, University Complutense of Madrid, Madrid, Spain; https://www. ucm. es/english/genomics-and-proteomics). All the V3/V4 16S rRNA gene sequences were edited by using SnapGene software (https://www. snapgene. com, accessed on 30 June 2020) and the maximum identities were searched by using the GenBank DNA sequence database and the Nucleotide Basic Local Alignment Search Tool (BLASTN; https://blast. ncbi. nlm. nih. gov/Blast. cgi, accessed on 30 June 2020) (Table  2 ).
Then, cells were incubated for 14 h at 4 • C with the α-Gal epitope monoclonal antibody (M86, Enzo Life Sciences, Farmingdale, NY, USA) diluted 1:50 in 3% HSA/PBS. FITC-goat anti-mouse IgM (Abcam, Cambridge, UK) labelled antibody (diluted 1/200 in 3% HSA/PBS) was used as a secondary antibody and incubated for 1 h, at RT. Samples were analyzed on a FAC-Scalibur flow cytometer equipped with CellQuest Pro software v. 4 (BD Bio-Sciences, Madrid, Spain). The viable cell population was gated according to forward-scatter (FSC-H) and side-scatter (SSC-H) parameters. The viable cell population was gated according to forward-scatter (FSC-H) and side-scatter (SSC-H) parameters. The percentage of viable cell population with highest α-Gal content (mean fluorescence intensity >10 3 FSC-H) was compared between different bacteria by one-way ANOVA test (p < 0.
Bacterial Carbohydrate Structure The bacterial carbohydrate structure for bacteria identified in the zebrafish microbiota with highest α-Gal content, namely A. veronii and P. entomophila, was characterized by using the Bacterial Carbohydrate Structure Database (http://csdb. glycoscience. ru/bacterial/mai n. html)  [83] [84] [85] [86]. Symbol nomenclature for glycans is disclosed at the database (http://csdb. glycoscience. ru/database/index. html?help=eog). The International Union of Pure and Applied Chemistry (IUPAC; https://iupac. org) condensed terms for the glycan structure are ndHex (Deoxy-hexose; http://www. monosaccharidedb. org/display_monosaccharide. action?name=deoxy-HEX), Rha (L-Rhamnose; http://www. monosaccharidedb. org/displ ay_monosaccharide. action?name=?LRhap), FucNAc (N-acetyl-L-fucosamine; http://ww w.
monosaccharidedb. org/display_monosaccharide. action?name=LFucpNAc), QuiNAc (N-acetyl-D-quinovasomine; http://www. monosaccharidedb. org/display_monosacchar ide. action?name=DQuipNAc), Gal (D-galactose; http://www. monosaccharidedb. org/d isplay_monosaccharide. action?name=?DGalp), GalNAc (N-acetyl-D-galactosamine; http: //www. monosaccharidedb. org/display_monosaccharide. action?name=DGalpNAc) and GlcNAc (N-acetylglucosamine; https://pubchem. ncbi. nlm. nih. gov/compound/N-Acetyl-D-Glucosamine). All databases were accessed in 28 February 2021.
Characterization of M. marinum RNA Levels by RT-qPCR Total RNA was extracted from zebrafish gut samples by using the AllPrep DNA/RNA/ Protein kit (Qiagen, Hilden, Germany). The M. marinum RNA levels were determined by real-time reverse transcription polymerase chain reaction (RT-qPCR), using the iTaq TM Universal SYBR Green One-Step Kit (BioRad, CA, USA) in the CFX96 TM Real-Time System (Bio-Rad) thermocycler following manufacturer's recommendations with specific primers and conditions for M. marinum heat-shock protein 65 gene (hsp65; Genebank accession number: AF547855. 1) [88]  (hsp65Forward-F: 5 -CAACCCGCTCGGTCTGAA-3 , hsp65Reverse-R: 5 -CGACCTCTTTGGCCGACTT-3 , annealing at 59 • C for 30 s). A dissociation curve was run at the end of the reactions to ensure that only one amplicon was formed and that the amplicon denatured consistently in the same temperature range for every sample  [89].
Characterization of mRNA Levels of Selected Zebrafish Immune Response and Nutrient Metabolism Genes by RT-qPCR Total gut RNA extracted as descried above (Section 3. 10) was used for analysis. Selected zebrafish genes included immune response chemokine receptor 6a (ccr6a; NM_001099991. 1), toll-like receptor 2 (tlr2; NM_212812. 1), interleukin 1-beta (IL-1ß; NM_212844. 2), akirin 2 (akr2; NM_213294. 2), complement component 3 (C3; NM_131243. 1), interleukin-6 (IL-6; JN698962), tumor necrosis factor-alpha (tnf-α; BC165066), nuclear factor-kB (NF-kB; AY163838) and nutrient metabolism hexokinase 1 (hk-1; BC067330. 1) [16, 24]. Sequences were obtained from NCBI nucletotide database (https://www. ncbi. nlm. nih. gov/nucleotide/, accessed on 30 November 2020) and the UCSC Genome Browser on Zebrafish May 2017 (GRCz11/danRer11) Assembly (http://genome.
ucsc. edu/cgi-bin/hgTracks?db=danRer11&lastVirtModeType=defaul t&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&pos ition=chr19%3A27019529%2D27023771&hgsid=1072595985_aaRkNS7FkPbTrWiA6ZHMU kLZ1fRT, accessed on 30 November 2020). To characterize the expression of selected genes, an RT-qPCR was performed for the analysis of D. rerio mRNA levels. The RT-qPCR was performed, and data were normalized as described above for mycobacterial RNA levels, using specific primers and conditions following manufacturer's recommendations (Table  4 ). The RNA normalized Ct values were compared between groups at T4 by one-way ANOVA test followed by post hoc Holm multiple comparisons (https://astatsa. com/OneWay_Anova_with_TukeyHSD/; p < 0. 05, n = 3-20/group) and between T1 and T3/T4 by Student's t-test with unequal variance (p < 0.
Downstream Data Analysis for 16S rRNA Sequencing Processing and ASVs Workflow A total of 12,799,596 MiSeq reads passing filter were pair-end demultiplexed and fastq file generated, using the Illumina MiSeq Reporter software. The raw 16S rRNA sequences were uploaded to the Sequence Read Archive (SRA) repository (https://www. ncbi. nlm. nih. gov/sra; BioProject ID PRJNA728442, accession numbers SAMN19079379-SAMN19079417). Sequence analysis was performed by using DADA2 inference algorithm on primer-free reads to correct sequencing errors and create the ASVs for the zebrafish gut microbial communities (v. 1. 12) in R (v. 4. 0. 1)  [92]. The reads were quality filtered by using the filterAndTrim (https://rdrr. io/bioc/dada2/man/filterAndTrim. html) function that truncated the forward and reverse reads at 280 bp and 255 bp for the zebrafish gut microbiota dataset.
Then, reads with more than 2 errors in the forward and 2 errors in the reverse reads were removed. Reads were merged after inference of sequence variation with learnErrors (https://rdrr. io/bioc/dada2/man/learnErrors. html) and denoised by using dada (https://rdrr. io/bioc/dada2/man/dada. html) functions. Chimeric sequences were eliminated with removeBimeraDenovo (https://rdrr. io/bioc/dada2/man/remov eBimeraDenovo. html), and taxonomy was assigned to ASVs by using the classify-learn naïve Bayes taxonomic classifier assignTaxonomy (https://rdrr. io/bioc/dada2/man/a ssignTaxonomy. html) based on the SILVA database (https://www. arb-silva. de; v. 138) database  [93]. Taxa count abundances were extracted from original outputs for each taxonomic level (Supplementary Materials File S1: Data S1).
Statistical Analysis of Gut Zebrafish Microbial Communities For the zebrafish gut microbiota dataset, the ASVs count table was generated. A total of 8922 ASVs were assigned to the 39 samples (Table  5 ) and at 6 taxonomic ranks (kingdom, phylum, class, order, family and genus). All the subsequent biological analyses were performed by using the phyloseq (https://bioconductor. org/packages/release/bioc/h tml/phyloseq. html; v. 3. 10) [94]  package and ggplot2 (https://ggplot2. tidyverse. org) was used for visualizations in R (v. 4. 0. 1). All ASVs with low counts and those with prevalence lower than 0. 01% were filtered to remove spurious ASVs in the bacterial dataset. Then the microbial community composition was represented in terms of relative abundance at phylum, family and genus levels, keeping the most abundant five featured taxa at each level by using the tax-glom function (https://rdrr.
io/bioc/phyloseq/man/tax_glom. html) in the phyloseq package (v. 3. 10). For estimating microbial community dissimilarities, Bray-Curtis distances were calculated by phyloseq (v. 3. 10) and vegan (https://cran. r-project. org/web/packages/vegan/index. html; v. 2. 5. 7)  [94]  package implemented in R (v. 4. 0. 3). Data were normalized by rarefaction, with no replacement, using the phyloseq function rarefy_even_depht (https://rdrr. io/bioc/phyloseq/man/rarefy_even_depth. html) and clr transform, using the microbiome package (https://microbiome. github. io/tutorials /; v. 1. 12. 0) prior to diversity measures. Further, principal component analysis (PCA) plots were constructed to visualize the categorical partition of the samples explained by Bray-Curtis dissimilarity. Adonis from vegan package (https://rdrr.
005) followed by post hoc Holm multiple comparisons (* p = 0. 002, ** p = 0. 02, *** p = 0. 04, n = 5 biological replicates). (D) The bacterial carbohydrate structure for bacteria identified in the zebrafish microbiota with highest α-Gal content, namely A. veronii and P. entomophila, was characterized by using the Bacterial Carbohydrate Structure Database. The α-Gal was included as reported here in both bacteria. Compound IDs are shown. IUPAC condensed terms are disclosed in Materials and Methods.
Acknowledgments: We thank  Pilar Alberdi  ( IREC, Spain ) for technical assistance with flow cytometry and  David Fernandez Castellanos  ( IREC, Spain ) for assistance with the zebrafish experimental unit. Juan Galcerán Sáez  ( IN-CSIC-UMH, Spain ) is acknowledged for providing zebrafish. MV was supported by  UCLM  and  EU-FEDER.
Overall, these results show previously unappreciated locusspecific variation in chromatin diffusive properties, which in some cases correlate with underlying histone modifications or CTCF binding. GP-Tool allows user-friendly application of GP-FBM. To facilitate use of GP-FBM by the community, we developed a freely available graphical user interface called GP-Tool (Fig. 5 ; github. com/guilmont). GP-Tool contains 4 plugins: movie, alignment, trajectories and g-process. The movie plugin allows the user to open TIFF files, display basic ImageJ and OME metadata, define colormaps for each channel and manually correct for contrast. The alignment plugin runs the algorithm described in Methods to digitally correct chromatin aberration and possible camera alignment issues. Alternatively, the user can manually modify each of the parameters.
Finally, the g-process plugin allows to infer optimal values for the apparent diffusion and anomalous coefficients for several cells in the same movie whilst correcting for substrate movement if two or more particles are selected. It is also possible to use a Metropolis-Hastings sampler to obtain the posterior probability distribution associated with each of these parameters. Once the analysis is complete, the tool provides the possibility to save the results into JSON files. It also provides export functions to save tables in CSV format. All these formats are easily parsed in all major computing languages, such as C/C++, Python and R. Finally, GP-Tool provides shared libraries and C/C++ examples for batching multiple movies. A complete documentation of the software can be found in the aforementioned Github account and in Supplementary Materials.
Data availability The data that support this study are available from the corresponding authors upon reasonable request. Microscopy data with analysis files that support the findings of this study have been deposited in Zenodo and can be accessed with https://doi. org/10. 5281/ zenodo. 5359893 59  , https://doi. org/10. 5281/zenodo. 5360028 60  and https://doi. org/ 10. 5281/zenodo. 5361054 61. The source data are provided with this paper. ES Hi-C sequence data from  42  were taken from Gene Expression Omnibus (GSE96107 [https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE96107]), and mapped to mm9 and normalized with FAN-C  62. The normalized submatrix (chr6:51500000-53000000) was then extracted for visualization. ES and neuronal precursor cell H3K27ac and CTCF ChIP-seq data were taken as bigWig files from Gene Expression Omnibus; GSE96107 [https://www.
Study Inclusion and Periods The study included three procurement categories of public hospitals in Myanmar for whom drug procurement data was available for the entire study period. These were hospitals with less than 200 beds in the same eight regions/states, hospitals with 200 or more beds, and central/specialist hospitals. For the estimation of consumption, we included antibiotics for systemic use, i. e. , including all antibiotics categorized under the ATC group J01. Anti-tuberculosis drugs and antibiotics used for local therapy (e. g. , topical creams, eye/ear drops) are not included in the WHO surveillance methodology for consumption and were thus excluded  [15]. The study period included data for the period 2014 to 2017.
Data Variables, Sources of Data, and Statistical Analysis Data on public hospitals and coverage populations were obtained from annual hospital reports. Details of the antibiotics procured were obtained from the drug procurement reports available from the Procurement Division, Department of Medical Services, Naypyidaw. For DDD calculation, variables collected included International Non-proprietary Name (INN), the strength of the active ingredient(s) in the unit it is specified, route of administration, and the number of units procured of the product. Details on the procured antibiotic products were manually entered into a formatted Microsoft excel template provided by WHO with embedded macros to generate DDD per product and year. Annually aggregated DDDs were then stratified according to ATC level 3 and level 5, route of administration, and AWaRe categories.
Acknowledgments: This research was conducted through the  Structured Operational Research and Training Initiative (SORT IT ), a global partnership coordinated by  TDR , the  Special Program for Research and Training in Tropical Diseases  at the  World Health Organization (TDR). The specific SORT IT program that led to these publications included a partnership of TDR with WHO Country offices of Nepal and Myanmar and was implemented along with The Tuberculosis Research and  Prevention Center Non-Governmental Organization , Armenia; The  International Union Against Tuberculosis and Lung Diseases , Paris and South East Asia offices;  Institute of Tropical Medicine, Antwerp, Belgium; Sustainable Health Systems, Freetown ,  Sierra Leone ;  Department of Medical Research, Ministry of Health and Sports, Myanmar; School of Public Health, Kathmandu, Nepal; Bahir Dar University Bahir Dar, Ethiopia ;  Centre National de Formation et de Recherche en Santé Rurale de Maferinyah , Guinea; Department of antibiotics and infection control of the public health agency oy Sweden;  University of Toronto ,  Canada , and the  University of Washington.
MCT1-dependent lactate fluxes are required for β3-adrenergic activation of glycolysis in beige adipocytes We next investigated the role of MCT1 in beige adipocytes metabolism. The effect of AZD on lactate metabolism was first studied by monitoring lactate content in the supernatant of primary adipocytes. Time course experiments highlighted a bi-phasic curve where adipocytes exhibited a first phase of net lactate release and a second phase of net lactate consumption after 32 hours in culture (Fig. 5A ). Lactate release was significantly reduced by AZD (Fig. 5A, 5B ), showing the primary role of MCT1 in lactate export. The residual export is probably due to other isoforms such as MCT4  (20, 38)  although we could not detect its protein expression in adipocytes in vivo (data not shown). Strikingly, inhibiting MCT1 abrogated lactate consumption by primary differentiated adipocytes (Fig.
C lactate uptake and oxidation assay Differentiated adipocytes were incubated with L-[U-14 C]-lactate (1µCi/ml) and nonlabelled (cold) lactate (1mM) for 3 hours. Following incubation, 14 C lactate in the sample and 14 C CO2 released in the medium was measured as previously described  (53). Briefly, assayed medium was transferred into a custom-made Teflon 48-well trapping plate. The plate was clamped, sealed and perchloric acid was injected through perforations in the lid into the medium which drives CO2 thought a tunnel to an adjacent well where it was trapped in 1N NaOH. Following trapping, aliquots of NaOH were transferred in scintillation vials and radioactivity was measured with a multipurpose scintillation counter (LS 6500; Beckman Coulter). Data are normalized to cell protein content.
NMR spectroscopy experiments The labeling patterns of medium metabolites in 13 C-labeling experiments were measured by 1D 1 H-and 2D-heteronuclear J-RES NMR spectroscopy  (54). All NMR spectra were recorded on a Bruker Avance III 800 MHz spectrometer equipped with a QPCI 5mm cryogenic probe head. Spectra were acquired and processed using the Bruker Topspin 4. 0 software. Analyses were performed at 280 K. Acquisition conditions for 1D 1 H-NMR spectra were as follows: 30° flip angle, 5000 Hz spectral width, 32 K memory size, and 30 sec total recycle time. The 2D ZQF-H-JRES spectra were recorded with quadrature phase detection in both dimensions, using TPPI in the indirect dimension, 128 increments in the F1 dimension, and 8 eight transients per increment, and were accumulated with the same sweep width and acquisition times as in 1D experiments.
Immunofluorescence and quantification Inguinal, interscapular brown adipose tissue and perigonadal white adipose tissues were fixed in paraformaldehyde 4% overnight before being cut into 300 µm sections using a vibratome (Campden). Sections were incubated in blocking solution (2% normal horse serum and 0. 2% triton X-100 in PBS) at room temperature 6 hours before being incubated for 24 hours at room temperature with primary antibodies (sheep anti-UCP1 1:1000 generous gift from Pr. Daniel Ricquier, rabbit anti-MCT1 1:200 EMD Millipore AB3538P, chicken anti-MCT1 1:500 EMD Millipore AB1286I and rabbit TOM20 1:400, Santa Cruz, sc-11415). After overnight incubation at 4 °C with Alexa488 or Alexa555-conjugated secondary antibodies (Life Technology) and 30 minutes with DAPI (1:10000) imaging was performed using a confocal Laser Scanning microscope (LSM880, Carl Zeiss) and image analysis using Fiji software (NIH).