dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018)  ), (Fig. 1G  and S2E ). Stress Index scored high for most cells of the muscle but only a fraction of the liver atlas (Fig. S2F ), in contrast to our data on dissociated liver (Fig. S2C ). We postulated that these variations were linked to the time of the cell-dissociation procedure. To corroborate this hypothesis, we inferred the digestion time for every cell from the Tabula Muris dataset (supplementary Table  2 ) and found a strong correlation between dissociation time and Stress Index (Fig. 1G , H; S2G, H). This correlation held across organs, with those dissociated for more than 60 min -such as the bladder, the tongue, the trachea, the limb muscle or the mammary gland -presenting with a high Stress Index, while tissues dissociated for less than 20 min -such as the liver, the marrow, the spleen, the pancreas or the brainpresenting with a low Stress Index (Fig.
2G ). Furthermore, to crosscorrelate in vivo activation and the stress response core genes, we visualized the Stress Index of each cell according to its pseudotime value. We noticed that MuSCs activated in vivo exhibited a strong and transient induction of the Stress-core genes (Fig. 2H ), indicating that the transcription of the early response genes is transient and dynamic. Finally, implementation of the iSiFi protocol during early activation, enabled us to provide an explanation for some puzzling features in high-quality single-cell datasets on quiescent MuSCs  (Camps et al. , 2020; Dell'Orso et al. , 2019; Giordani et al. , 2019; Schaum et al. , 2018) ; namely, the low detection of the MuSCs hallmark gene Pax7 and, paradoxically, the high abundance of several oncogene transcripts, such as Egr1, Jun, and Fos (Fig.
Time-course analysis of MuSCs quiescence exit reveals modules of co-regulated genes. To further define the kinetics of MuSCs quiescence-exit, we generated a comprehensive timecourse transcriptional map on iSiFi-processed muscle stem cells. In situ fixed MuSCs were isolated by FACS every 30 min for 2 h during the standard dissociation procedure and RNAsequenced in bulk. A time-point of 15 min (T15) was introduced to detect earlier transcriptional events (Fig. 3A ). PCA on the transcriptomes clustered samples by timepoints and formed a clear activation trajectory along the three main principal components (Fig. 3B ). Following 15 min, 42 genes were upregulated (FC > 2, p < 0. 05) and most differential expression events took place at T60, reaching a maximum at T120 (> 3,000 DEGs, FC > 2; Fig. 3C ).
Differences were observed in the relative abundance of the populations: Myonuclei: digested 6%, intact 64%; Endothelial muscle: digested 29%; intact 1%; Endothelial liver: digested 36%, intact 4%. (F) Gene ontology analysis of the Stress core using the biological process category. Selected terms with corrected p-value < 0. 05 are displayed and ranked by fold-change. (G) UMAP plot representing the Stress Index of every cell from the published Tabula Muris Smart-Seq2 dataset. (H) UMAP plot representing the calculated digestion time at 37 °C (see Methods) of every cell from the Tabula Muris Smart-Seq2 dataset  (53,760 cells). Note the correlation between high digestion time and high Stress Index (Spearman correlation coefficient: 0. 51). (I) Median Stress Index in different cell types. Median digestion time at 37 °C is color-coded (grey/blue gradient).
(A) Nuclei from intact and 4h-injured Tibialis Anterior (TA) muscle and liver were FACSisolated based on DAPI and analyzed using 10X Genomics. Intact muscle: 7,951 nuclei from 2 biological replicates. Injured muscle: 1,618 nuclei from 2 biological replicates. Each muscle biological replicate is a pool of TAs from 4 mice. Intact liver: 8,996 nuclei from 1 biological replicate. Injured liver: 6,271 nuclei from 1 biological replicate. Each liver biological replicate is a pool of livers from 3 mice. (B) UMAP plots displaying the different populations inferred from intact and injured liver (top) and muscle (bottom) single-nucleus RNA-seq after data integration using Seurat3. Abbreviations: Endo: endothelial cells; T: T cells; B: B cells; FAPs: fibro-adipogenic progenitors; Teno: tenocytes; SM: smooth-muscle cells; Macro: macrophages; MuSCs: skeletal muscle stem cells; Lymph: lymphatic cells; Adipo: adipocytes; Chol: cholangiocytes.
(C) UMAP plots displaying the different populations inferred from intact (blue) or injured (red) liver (top) and muscle (bottom) single-nucleus transcriptomes without data integration. Red box inset in muscle UMAP designates the MuSC and tenocyte populations (D) UMAP plots representing the Stress Index among liver (top) and muscle (bottom) intact and injured cells. (E) Graphical scheme of single-MuSC RNAseq post injury experiment: TA muscles of Tg:Pax7-nGFP mice were injected with BaCl2 and single MuSCs were isolated at 0, 2 and 4 h post-injury using the iSiFi methodology. 280 cells were obtained from 6 mice (2 biological replicates per time-point). (F) PCA of resting (T0) and in vivo activated MuSCs (T2 and T4). PC1 and PC2 represent 4% and 3% of variance explained, respectively. (G) Pseudotime-analysis (Slingshot) shows the differentiation trajectory of MuSCs during in vivo activation.
(H) Mean normalized counts per million of the genes contained in the 5 cluster modules of the ex vivo bulk RNA-seq dataset (Fig. 3D ) compared to the mean normalized counts per million of the same genes into the in vivo injury-activated MuSCs. (C) Immunostaining and quantification for MYOD and EdU on dissociated muscle cells after 45 h in culture in the presence of the ERK1/2 inhibitor SCH772984 or DMSO vehicle control. MuSCs were detected by GFP in control (Tg:Pax7-nGFP) and NICD-ires-GFP expression (Pax7 CreERT2 ; R26 stop-NICD-ires-nGFP ), (n=4 mice per genotype and per condition). (D) Immunostaining and quantification for PAX7, EdU (24 h chase) and NICD (GFP) on cultured muscle cells from Pax7 CreERT2 ; R26 stop-NICD-ires-nGFP mice with or without SCH772984 after plating for 72 h; * EdU -cell; (n=4 mice per genotype and per condition).
The homogenates were filtered with 70 μm and 40 μm cells strainers. Nuclei were pelleted by centrifugation for 5 min (500 g, 4 °C). Nuclei were washed in 1 ml of cold wash buffer, transferred in a 1. 5 ml tube and centrifuged 5 min (500 g, 4 °C). The pellets were resuspended in 250 μl of wash buffer and stained during 15 min in the dark at 4 °C with DAPI at 10 μg/ml. The nuclei were therefore washed and spun down 5 min (500 g, 4 °C), resuspended in 300 μl of wash buffer and filtered with 30 μm cell strainers. Nuclei were then FACS sorted for Hoechst to exclude debris with a BD FACSAria III and the BD FACSDIVA software.
Sequencing was performed with the Illumina HiSeq 2500 system and HiSeq SBS kit v4 reagents. Runs were carried out over 65 cycles, including seven indexing cycles, to obtain 65 bp single-end reads. Sequencing data were processed with Illumina Pipeline software (Casava version 1. 9).  For scRNA-seq: cDNA libraries were generated based on the Smart-seq2 protocol  (Picelli et al. , 2014). To increase RNA extraction from fixed cells, we sorted single cells into 96-well tubes containing 5 µL of PKD Buffer (Qiagen) with 1:16 Proteinase K Solution (Qiagen), as previously described  (Thomsen et al. , 2016). Briefly, mRNA was reverse transcribed and cDNA was amplified via PCR for 27 cycles. Amplification was done with KAPA HIFI Hot Start ReadyMix (Roche, 07958919001) and purification by magnetic beads (CleanNA CPCR).
Quantification and statistical analysis High-throughput datasets processing  snRNA-seq The sequencing data was processed into transcript count tables with the Cell Ranger Single Cell Software Suite 3. 0. 2 by 10X Genomics. Raw base call files from the Nextseq 500 were demultiplexed with the cellranger mkfastq pipeline into library-specific FASTQ files. The FASTQ files for each library were then processed independently with the cellranger count pipeline. This pipeline used STAR to align cDNA reads to the Mus musculus transcriptome (Sequence: GRCm38, Mouse reference provided by 10X. ). Once aligned, barcodes associated with these reads -cell identifiers and Unique Molecular Identifiers (UMIs), underwent filtering and correction. Reads associated with retained barcodes were quantified and used to build a transcript count table.
We re-run this same pipeline with the pre-mRNA reference, build with cellranger mkgtf and cellranger mkref. Quality control on aligned and counted reads was performed keeping cells with > 500 reads, > 250 detected genes and less than 15 % mitochondrial genes. The subsequent visualizations, clustering and differential expression tests were performed in R (v 3. 4. 3) using Seurat (v3. 0. 2)  (Stuart et al. , 2019) , Pooling of intact and dissociated nuclei was performed by merging the raw data per tissue followed by normalisation.  Bulk RNA-seq datasets processing Bioinformatics analysis was performed using the RNA-seq pipeline from Sequana  (Cokelaer et al. , 2017). Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version  1. 11 (Martin, 2011). Only sequences at least 25 nt in length were considered for further analysis.
STAR version 2. 5. 0a (Dobin et al. , 2013) , with default parameters, was used for alignment on the reference genome (Mus musculus GRCm38 assembly from Ensembl release 87). Genes were counted using featureCounts version 1. 4. 6-p3 (Liao et al. , 2013)  from Subreads package (parameters: -t exon -g gene_id -s 1) under gencode mm10 annotation version M11. Count data were analyzed using R version 3. 4. 3 and the Bioconductor package DESeq2 version 1. 18. 1  (Love et al. , 2014). Due to putative genomic DNA contamination, only genes with an average number of reads greater than 150 were used as input of the differential analysis (N=8059 genes). The normalization was performed using the "shorth" parameter and dispersion estimation was performed using the default parameters. A generalized linear model was set in order to test for the differential expression between the different time points.
For each gene, expression estimates per gene were expressed as log-transformed counts by taking the log 2 (counts + 1). We used the top 500 highest variable genes to obtain a 2D representation of the cells, while maintaining the similarity relationships between them using PCA as implemented in the package Scater  1. 8. 4 (McCarthy et al. , 2017). The trend line and IC-75 were calculated using the function geom_smooth() in R using the following parameters: method="glm", formula = y ~ poly(x, 2), level=0. 75, alpha=0. 04. All data analysis was conducted in Python v3. 6 (Python software foundation) or R v3. 5. 1 (CRAN). Differential expression was performed using MAST version 1. 10  (Finak et al. , 2015)  and Monocle2 version 2. 12. 0  (Qiu et al. , 2017 ) across all time-points.
Associations with Tabula-Muris scRNA-seq The Tabula Muris droplet and Smart-Seq2 datasets were loaded from the TabulaMuris R package (v1. 0) analyzed and visualized using Seurat. The Stress Index was calculated using the Seurat function "AddModuleScore" using the Stress response core from Figure  S4B  as a feature set. The Tabula Muris tissue digestion time were inferred from the original paper supplemental methods (supplemental Table  1 ). An arbitrary conversion factor of 0. 5 and 0. 2 were applied to correct kinetics of digestion steps at RT or on ice, respectively. The sum of all corrected sequential enzymatic digestion times for each cell was used to define the digestion time at 37 °C. The relative gene expression on muscle stem cells in each dataset shown in Figure  S4J  (iSiFi single-cell RNA-seq, snRNA-seq and Tabula Muris scRNAseq) was analyzed and visualized using Seurat.
(D) Heatmap displaying the expression of common cellular populations markers used to annotate the nuclei from integrated intact and injured liver. (E) Measurement of early MuSCs response to muscle injury (1. 2% BaCl2) by RT-qPCR at 0, 2, 4 and 6 h post-injury. Transcripts were measured in FACS-isolated, iSiFi-treated MuSCs. Statistical t-tests are T0/T6 for Myod1 and Pax7 and T0/T2 for Jun. (F) UMAP plot of intact (blue) and injured (red) muscle cells after data integration using Seurat3. (G) Heatmap displaying the expression of common cellular populations markers used to annotate the nuclei from integrated intact and injured muscle. Independent student t-test was performed; * p<0. 05, ** p<0. 01, *** p<0. 001. (K) Comparison of the T0/T120 fold changes (Fig. 3 ) and the T0/T3  (Machado et al.
, 2017)  fold changes for shared genes, showing strong reproducibility between the two RNA-seq experiments. Note that T3 refers to MuSCs digested for 120 min. (L) Gene ontology analysis using EnrichR for all of the genes within each of the five clusters. Color code indicates p-value and circle size the combined enrichment score (0 to 60). The ontology terms that emerged during MuSCs dissociation represented a sequence of coherent biological processes: the triggering events (cluster 1) were initiation of transcription and stimulation of the MAPK cascade. This was followed by mRNA processing, including splicing and initiation of translation (cluster 2), and in a later phase by ontologies related to ribosome assembly and biosynthesis (cluster 3). Of note, late cluster 3 (maximum at T120) also contained ontologies related to inactivation of MAPK signaling, which coincides with the attenuation of the corresponding genes of early cluster 1 (drop at T120).
Acknowledgements We would like to thank  Hanne Grosemans  for performing the muscle injuries. We thank  Brigitte Izac  and  Benjamin Saintpierre ,  Institut Cochin  ( Université de Paris, Institut Cochin, CNRS, INSERM, F-75014 Paris, France ) for single-nucleus sequencing libraries and data processing. We also thank  Erica Kimber  for her elaborative editing of the manuscript. FR laboratory receives funding from  Association Française contre les Myopathies (AFM)  via  TRANSLAMUSCLE  ( PROJECT 22946 ),  Labex REVIVE  ( ANR-10-LABX-73 ),  Agence Nationale pour la Recherche (ANR)  grant  Bone-muscle-repair  ( ANR-13-BSV1-0011-02 ),  Satnet  ( ANR-15-CE13-0011-01 ),  BMP-MyoStem  ( ANR-16-CE14-0002-03 ),  MyoStemVasc  ( ANR-17-CE14-0018-01 ),  muscleXTRA  ( ANR-19-CE13-0010 ),  FRM ECO201806006793 , and  RHU CARMMA  ( ANR-15-RHUS-0003 ).
The authors of  [18]  provided a CoMACbased spectrum sensing approach and proposed to solve the non-convex EE optimization problem by minimizing the average energy consumption. The sensing thresholds and the golden decision threshold were jointly analyzed in order to define the boundary conditions of sensing performances. More importantly, the authors pointed out that the sensing time optimization is the key issue for improving the sensing performance. They also mentioned that the SUs' detection accuracy is subject to the average sustainable transmission power of the PU. In  [19] , B. Soni et al. adopted a long short-term memory (LTSM)-based spectrum sensing model: a deep learning-based method to extract explicit features by constructing and training the spectrum dataset. Besides, the authors analyzed the PU statistics including active/sleep period duration and duty cycle, based on which the overall sensing performance is further improved.
The authors of  [7]  aimed to proportionally deduct the penalty of miss detection and the SUs' energy cost from the positive throughput gain in order to form a reasonable weighted utility function. Afterwards, they optimized this utility function and found the equilibrium by performing cooperative spectrum sensing. However, the authors did not clarify how to select the weights for the three parts of the unity function, and the definition of utility was purely empirical where persuasive mathematical demonstrations were still lacking. In a recent study  [14] , O. I. Khalaf et al. employed the look-up table with rapidly updated access as a sensing database for SUs to work in a cooperative way, where the reporting time can be shortened in comparison with traditional techniques. Moreover, the authors proposed a fuzzy selection scheme to switch SUs' harvested energy states between their main power source and residential energy, depending on the total energy cost during their sensing and transmission periods.
Simulation Results Our simulations are implemented using MATLAB. The initial parameters of the simulations are presented in Table  3. For the fusion rule, k is tested from 1 to N. The SNR at the transmit end of SUs is considered as 20 dB, based on which C_00 is calculated as 6. 65 bps/Hz. In Figure  3 , we take an example of N = 4, with different k values applied for the fusion rule. The individual probabilities of detection and false alarms depend on the values of k. In this figure, P d0 = 0. 9 and P f 0 = 0. 1 are defined as the threshold of detection/false alarm probabilities; since the fusion rule is more difficult to be satisfied for a larger k, the threshold of an SU's individual probability is also increased. For example, the AND rule (k = N = 4) requires a detection probability of 0.
For this purpose, we carried out a comprehensive literature search on PubMed and Google Scholar websites. We included papers published in English language up to April 2020 using the following search terms and/or phrases: 'Gram negative bacilli', 'Enterobacteriaceae', ' Pseudomonas aeruginosa ', ' Acinetobacter baumannii ', and 'carbapenemases', 'metallo-β-lactamases', 'KPC', 'GES', 'IMI', 'VIM', 'NDM', 'IMP', 'OXA-48', and 'water environments', 'aquatic environments', 'water', 'wastewater', 'sewage', 'hospital wastewater', 'hospital sewage', 'wastewater treatment plants', 'surface water', 'ground water'. Search terms were separated by the 'AND' Boolean operator.
Determining the mechanism by which high-mass stars are formed is essential for our understanding of the energy budget and chemical evolution of galaxies. By using the New IRAM KIDs Array 2 (NIKA2) camera on the Institut de Radio Astronomie Millimétrique (IRAM) 30-m telescope, we have conducted high-sensitivity and large-scale mapping of a fraction of the Galactic plane in order to search for signatures of the transition between the high-and low-mass star-forming modes. Here, we present the first results from the Galactic Star Formation with NIKA2 (GASTON) project, a Large Programme at the IRAM 30-m telescope which is mapping ≈ 2 deg 2 of the inner Galactic plane (GP), centred on ℓ = 23. • 9,  = 0. • 05, as well as targets in Taurus and Ophiuchus in 1. 15 and 2. 00 mm continuum wavebands. In this paper we present the first of the GASTON GP data taken, and present initial science results.
08 to 0. 44, and a mean value of 0. 19, as measured by the IRAM 30-m taumeter. The mean source elevation was 39. • 9, and the majority ( 80 per cent) of the observations were obtained between the hours of 08:00-14:00, indicating that the primary beams are likely to be relatively stable around their nominal values of 11. 1 (1. 15 mm) and 17. 6 (2. 00 mm) arcsec  (Perotto et al. 2020 , se their 2 Image generated using the package : https:// github. com/pjcigan/multicolorfits 25. 0 Fig. 12 ). The remainder were observed between 15:00-19:00, during which time the primary beams tend to degrade slightly to ∼ 12. 5 and 18. 0 arcsec, respectively. A detailed description of the operation, calibration and data reduction methods for NIKA2 can be found in  Adam et al. (2018)  and  Perotto et al. (2020).
Data reduction The data were reduced using the IDL pipeline developed by the NIKA Core Team, which converts the raw time-series data into calibrated maps. While a detailed description of this pipeline will be presented in  Ponthieu et al. (in prep) , we summarise the main steps here. First, individual KIDs that do not meet the performance criteria, are masked from the timelines, as is also done for samples associated with detected cosmic ray impacts. Next, the pointing information from the telescope control system is used in conjunction with a record of the positional offsets of each KID with respect to a reference position to determine the position of each KID on the sky as a function of time. The KIDs are then inter-calibrated, using coefficients determined from their relative gains, before applying a correction for the instantaneous atmospheric opacity, and finally applying an absolute calibration determined for the specific observing run (see  Sect.
5 arcsec pixels. Inverse-variance noise weighting is used to assign the data samples onto the pixel grid, using a nearest grid point method, and to combine the maps associated with each individual scan into a mosaic of the GP field. The correlated noise removal is the origin of the spatial filtering within the reduced maps, which is common to all submillimetre and millimetre-continuum imaging from ground-based telescopes. The level of spatial filtering applied here is typically on the order of the instantaneous FoV which, for NIKA2, is approximately 6. 5. We do not calculate the pipeline transfer function that describes the ratio of the power spectrum within the reduced image to an estimate of the absolute source power spectrum over the sky region in this paper. An example of the 2. 00 mm NIKA2 transfer function towards a galaxy cluster may be found in  Ruppin et al.
Source velocity assignments Determining the systemic velocity, and thereby a kinematic distance, for each extracted source is a necessary step in calculating many of its physical properties. However, due to both the high sensitivity of the GASTON GP maps at 1. 15 mm, and the particularly complicated line-of-sight for the survey region, source confusion presents a significant challenge in their determination. We therefore adopted a procedure with several stages to establish the most reliable possible velocities, which are summarised in this Section, and further details may be found in Appendix C. To determine a velocity for each GASTON source, first, an integrated 13 CO (1-0) spectrum is extracted from the FUGIN data of all pixels that lie within the source footprint. Many of the spectra exhibit multiple emission features, and so the velocity with the greatest intensity is assigned as the centroid velocity,  cen , of each source.
Observations of dense gas tracers would provide more suitable velocities for the GASTON GP sources than 13 CO (1-0), and so, as a second stage, we use data from the RAMPS pilot study  (Hogge et al. 2018)  of NH 3 (1,1), which has a critical density of  crit ∼ 3 × 10 3 cm -3 to determine dense-gas velocities. We adopt velocities for 517 (21 per cent) of the sources from the first component from the line-fitting procedure, and a further 513 (21 per cent) from the first-moment maps of  Hogge et al. (2018) , assigning a quality flag of 3 for all ammonia-derived velocities. The hierarchical structure of the 1. 15 mm emission is recorded within the dendrogram catalogue, and since each source now has an individual velocity assignment, along with a quality flag, the structure can be used to inform us about likelihood of the more dubious velocity assignments being the correct ones.
By comparison, 77 per cent of the ATLASGAL clumps within the catalogue of  Urquhart et al. (2018)  have distances less than the tangent distance, after reducing the sample to those within the GASTON field, and adjusting for the difference in tangent distance associated   (2017)  were assigned distances on the near-side of the tangent point. However, in the latter case, there is a bias towards far distances, as objects for which data (such as extinction data) that may allow a near determination to be made were unavailable are assumed to be located at the far distance. After removing clumps with this default assumption from the Hi-GAL catalogue, the number rises sharply to 89 per cent. We show the resulting distribution of distances in Fig. 3 , and compare the values to their counterparts in other catalogues in Appendix E.