dataesr/scientific-paragraphs-categorization huggingface dataset:
First, we perform spectral analysis. Figure  4  shows the background-subtracted gamma-ray energy spectra, extracted from -69 s < t < -39 s and -30 s < t < -10 s for detectors A and B, respectively. The detector response function is calculated with the GEANT4 Monte Carlo simulation framework  47  , and is convolved with a model spectrum in spectral fitting using the XSPEC package  48. The observed spectra, of which instrumental responses are corrected, are found to be well explained by an empirical power-law function with an exponential cutoff, ε -Γ exp[(-ε/ε cut ) α ], where ε, Γ, ε cut , and α are the photon energy (MeV), power-law photon index, cutoff energy (MeV), and cutoff index, respectively. The best-fitting parameters are Γ ¼ 0:90 þ0:06 À0:08 and 1:02 þ0:04 À0:05 , ε cut ¼ 6:4 þ1:0 À1:1 and 8:5 þ0:8 À0:9 MeV,  Fig.
Wind estimation with X-band radar. We utilised data of eXtended RAdar Information Network (XRAIN). XRAIN is a polarimetric weather radar network in the X band and has a spatial resolution of 280 m (east-west) × 230 m (north-south) mesh. It records two-dimensional precipitation maps with a 1-min interval. XRAIN also obtains three-dimensional maps of radar echoes and particle types with a 5min interval by the constant-altitude plan position indicator technique. However, the three-dimensional data are not utilised in the present paper because the XRAIN observations have a moderate spatial resolution of altitude (≥1 km), which is insufficient to discuss charge structures in the thundercloud. Wind velocity and direction are estimated by overlaying and shifting precipitation maps at different time.
We found that both models can reproduce the obtained count-rate histories and spectra; The estimated parameters are h = 600 m and σ = 200 m for the Gaussian model, and h = 400 m and L = 150 m for the exponential model. Comparing these two best models, we found that the exponential model explains the observation better, particularly for the count-rate histories of detector B (Supplementary Fig. 4b ). Therefore, we employ the exponential model as a working hypothesis to interpret the observation. Seed electrons. We assume that the seed electrons of the RREA processes are mainly produced by cosmic rays. To calculate the electron fluxes of secondary cosmic rays, we employed Excel-based Program for calculating Atmospheric Cosmic-ray Spectrum (EXPACS)  55, 56  , which calculates the flux and spectrum of cosmic-ray particles as a function of an altitude, latitude, longitude, and solar modulation.
Acknowledgements We thank  K. Watarai ,  K. Yoneguchi ,  K. Kimura ,  K. Kitano , and  K. Kono  for detector deployment;  H. Sakurai ,  M. Niikura , and the Sakurai group members at  RIKEN Nishina Center  for providing Bi 4 Ge 3 O 12 scintillation crystals;  T. Nagao ,  T. Suzuki , and  H. Fujiwara  for providing lightning and weather data;  T. Nakano ,  T. Tamagawa ,  A. Bamba ,  H. Odaka , and  P. Laurent  for project supports and discussions; and  S. Otsuka  and  H. Kato  for supporting detector developments. Deployment of LF stations is supported by  Uozu City, Himi City , and  Nyuzen Town  in Toyama Prefecture. The ELF observation system at Kuju is maintained by  International Center for Space Weather Science and Education (ICSWSE) in Kyushu University. This research is supported by  JSPS/MEXT KAKENHI  grants  15K05115 ,  15H03653 ,  16H04055 ,  16H06006 ,  16K05555 ,  17K05659 ,  18J13355 ,  18H01236 ,  19H00683 , by  Hakubi  project and SPIRITS 2017 of  Kyoto University , and by the  joint research program  of the  Institute for Cosmic Ray Research (ICRR), the  University of Tokyo.
); snow depth is measured with a SR50A sonic sensor (Campbell Scientific, Inc. ). Albedo is measured with a Kipp & Zonen CNR4 net radiometer. The snow water equivalent (SWE) is measured with a gamma monitor (GMON; Campbell Scientific, Inc. ) sensor. Solid and liquid precipitations were measured with a Pluvio2 OTT instrument. Wind speed and direction were measured with a CSAT3 three-dimensional sonic anemometer (Campbell Scientific, Inc. ). Data are available at hourly time resolution.
8 •. The camera was fixed at 2. 5 m above the ground, and the same scene was repeatedly photographed. Digital images were collected in the Joint Photographic Experts Group (JPEG) format with a resolution of 12 megapixels and threecolour channels (namely red, green, and blue) featuring eight bits of radiometric resolution. The images were collected from 10:00 to 17:00 LT (local time: UTC+1), with an hourly temporal resolution. Exposure mode and white balance were set to automatic. A region of interest (ROI) was firstly identified in an approximately flat area to analyse snow evolution. Images were acquired during the hydrological years 2013-2016. Red, green, and blue chromatic coordinates were extracted from the selected ROI using the Phenopix R package  (Filippa et al. , 2016). Then, the snow darkening index (SDI; Di  Mauro et al.
The model was forced using meteorological data from the station in Torgnon for the seasons 2013 and 2016 at an hourly time step. All variables were measured at the station of Torgnon, except for diffuse shortwave incoming radiation, which was measured (with a BF3 sensor; Delta-T Devices Ltd. , Cambridge, UK) in another station located 2 km from Torgnon. The instrument used for precipitation measurements (Pluvio2 OTT) does not feature a windshield. This can be problematic, since underestimations of snowfall can occur during intense wind events. For this reason, we corrected the data following the prescriptions proposed in  Kochendorfer et al. (2017). Some manual adjustments to solid precipitations were needed in the case of strong wind events. In addition to the abovementioned meteorological data, the Crocus version of  Tuzet et al.
To obtain dust mass from particle volume, a crustal density of 2. 5 g cm -3 was adopted. Total dust concentration was calculated considering the integral of the concentration between 2 and 60 µm. Details about the technique can be found in  Ruth et al. (2008). In addition, dust samples collected in the Alps at 150 km from Torgnon (Artavaggio, Lecco, Italy, 1650 m a. s. l. ) in March 2014 are used here to characterize the bulk composition of dust events and the elemental input to alpine ecosystems. Snow samples were transported before melting in a cold facility, where they were stored until the preparation for the successive analyses. At first, they were melted, and an aliquot (5-10 mL) was measured through Coulter counter technique (CC) for the determination of dust size distribution. These data were already published  (Di Mauro et al.
, 2006)  and in the Caucasus (1. 98-4. 16 µm;  Kutuzov et al. , 2013). Differences between our samples and these studies may be ascribed to the different elevation of the samplings. Samples shown in Fig. 5  feature a significant noise in the tail of the distribution. This can be ascribed to the aggregation of fine particles or to an input of local particles with larger diameters. A contribution of large particles of local origin cannot be excluded, and it may have a strong influence on snowmelt. At the moment, we do not have enough data to decouple the effect of large and small particles on the snow albedo. Total concentration of dust in Torgnon was estimated by adding up different channels from the size distributions. Among the six different snow profiles measured, surface concentrations reached a maximum of 65 µg dust g -1 snow , with a mean of 45.
Two late snowfalls occurred in May, and this probably increased the difference between the simulations. Since air temperatures were still close to 0 • (data not shown), snow was preserved at the ground in the simulations without impurities, and this further prolonged the snow season duration. Considering that the first significant snowfalls occurred in January, the snow season was shortened by about 20 % of the total because of impurities. In Fig. 6 , we plot the SDI calculated from the radiative transfer model (TARTES) included in Crocus (SDI-Crocus hereafter) and from the RGB camera (SDI-Phenocam hereafter). Regarding the digital camera data, days with SDI > 0 are represented as shaded green bands. We observed an agreement between the two datasets. SDI-Crocus increased during April. In particular, at the beginning of April two peaks in SDI-Crocus are also seen by SDI-Phenocam.
In this paper, we estimate an advance in snow melt-out days of 18, 11, and 38 d for the three seasons considered. The estimation for the season 2015-2016 is very high, also considering that snow cover normally lasts about 7 months at this elevation (2160 m a. s. l. ). In the future, impurity concentration estimated with the atmospheric model should be evaluated using ground observations. In this sense, data from in situ spectrometers (e. g. Dumont et al. , 2017; Picard et al. , 2016)  and repeated digital images can be very helpful. In fact, the concentration of different impurities may be retrieved from spectral reflectance using both inversion of radiative transfer models and spectral indices. Dust transport is a natural phenomenon, but it can be intensified by anthropogenic activities  (Neff et al.
, 2011). For this reason, the dataset presented in this study can be considered representative for the main composition of long-range dust deposition on snow in the Alps. Saharan dust events are regional episodes that move large quantities of mineral dust from arid regions to different latitudes and longitudes. There are two main pathways for the transport of dust: it can reach Europe overpassing the Mediterranean and also by looping back over the Atlantic  (Israelevich et al. , 2012; Sodemann et al. , 2006). For this reason, we can assume that the bulk geochemical composition of dust events that occurred in different locations in the Alps and at different times is comparable. Between 18 and 20 February 2014 a relevant event was observed, involving not only southern Europe and the Alps but also a large fraction of Europe.
Acknowledgements. We acknowledge the  ARPA (Environmental Protection Agency) of the Aosta Valley region  for maintaining the  AWS in Torgnon  and for providing the dataset. RGB images were analysed using the Phenopix R package (https://r-forge. r-project. org/projects/phenopix/,last access: 2 April 2019). The complete set of Phenocam images is available at the following website: https://phenocam. sr. unh. edu/webcam/sites/torgnon-nd/(last access: 2 April 2019). Edoardo Cremonese acknowledges the support of the NextData Data-LTER- Mountain  project  CNRM. CEN and IGE are part of  Labex  OSUG@2020. The modelling work was funded by the  ANRJCJC EBONI  grant no. 16-CE01-0006. We thank the editor and the two reviewers for the constructive comments on a previous version of the paper. Review statement.
2. 1,  [10] ) being closely similar to those of enzootic H1 av N1 strains circulating in the French pig population (Figure  1A ). By contrast, phylogenetic analyses showed that NA genes did not originate from enzootic swIAVs of the European H1 hu N2 or H3N2 lineages, but were closely related (97. 09% identity) to N2 genes of seasonal human H3N2 viruses isolated in 2003 (Figure  1B ). The time of the most recent common ancestor (TMRCA) was estimated to  2003. 528 [2003. 094; 2003. 801] using BEAST software, leading to the hypothesis that these reassortants originated from a co-infection with a H1 av N1 swIAV and a 2003 human H3N2. All internal genes of the three strains were shown to be very similar with each other and closely related to internal genes from H1 av N1 and H1 hu N2 viruses circulating in France, except the M-encoding gene of A/Sw/ France/37-160178/2016 that belonged to the H1N1pdm lineage (Figures  1C  and D  and Additional file 1).
Looking for the occurrence of these three mutations in swine and human IAV N2 sequences retrieved from the Influenza Research Database, it appeared they were rarely observed in human strains from 2000 to 2005 regardless their genogroup (Table  1 ). By contrast, mutation S372L was observed in the majority of both recent human and swine strains, i. e. , isolated from 2011 to 2016 (Table  1 ). Changing in NA-S372 residue was suspected to reduce the hemadsorption activity of human H3N2 and H2N2 strains  [12, 13]. Interestingly, NA-K221N and two other mutations, i. e. , NA-Y40H, and NA-V263I, were almost exclusively associated to swine but not human strains in the recent years 2011-2016 (Table  1 ) and could reflect divergent host-specific evolutions and/or swine host adaptation following inter-species transmission.
Discussion In this study, we evidenced two successive reassortment events that probably occurred 10 years apart, i. e. , around 2003 and 2013, respectively. Thus, a swine H1 av N1 virus first acquired a N2 segment from a contemporary human H3N2 virus, and then a M segment from a H1N1pdm strain. The human N2 incorporation into a French swine H1 av N1 strain certainly resulted from a human-to-swine transmission of a human H3N2 virus around 2003, as similar N2 sequences were never described in European swine strains before. However, the origin of the parental human strain remains uncertain, as the few N2 sequences of 2000-2005 French H3N2 strains available in public databases do not belong to the A/New York/55/01(H3N2) lineage. The closest N2 genes that were retrieved belong to 2003 human H3N2 viruses isolated in England and Denmark, the latest originating from a reassortment between A/New York/55/01(H3N2) and A/Fujan/411/2002(H3N2)  [17].
Funding This study was partially funded by European  FP7  project  ESNIP3  (no. 259949 ), by the  CoVetLab joint research project 2016 Genetic Characterization of Swine Influenza A Viruses in European Pigs by Next-Generation Sequencing  (no. CF0010 ), and by the  European Union 's  Horizon 2020 program  COMPARE (no. 643476 ).
Results: The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of proteomic data revealed that biofilm cells expressed higher levels of proteins which are associated with glycolysis, amino acid biosynthesis, biosynthesis of secondary metabolites, microbial metabolism in diverse environments and stress response factors. Besides these basic survival pathways, LuxS-mediated quorum sensing, arginine metabolism, rhamnose biosynthesis, pheromone and adhesion associated proteins were found to be upregulated during the biofilm transit from planktonic stages. The selected subsets were validated by quantitative real-time PCR. In silico functional interaction analysis revealed that the genes involved in upregulated pathways pose a close molecular interaction thereby coordinating the regulatory network to thrive as a biofilm community.
Methods Bacterial strain and growth condition E. faecalis SK460, isolated from chronic diabetic ulcer patient was used for quantitative proteomics approach to identify biofilm-associated proteins. The study was approved by the Institutional Human Ethics Committee (Reference number RGCB-IEC No. IHEC/01/2013/11) of Rajiv Gandhi Centre for Biotechnology, Kerala, India. The draft genome sequence of this strain was deposited at NCBI Genbank with accession no. NIXL00000000  [41]. The strain was cultured in Luria Bertani broth (Becton Dickinson, New Jersey, United States) at 37 °C with 150 rpm. The media used for culturing biofilm was Tryptic soy broth (Himedia, Mumbai, India) with 0. 75% Glucose.
Biofilm quantification and imaging For confocal imaging, an overnight culture of E. faecalis SK460 was diluted to OD600 = 0. 1 and 4 ml of diluted broth was distributed to 6 well microtitre plate (Nunc, Roskilde, Denmark). A sterile square glass coverslip was inserted horizontally in each well and incubated at 37 °C in a static condition for two-time intervals (12 h, 24 h). Biofilm staining was carried out with Syto9 (Invitrogen, California, United States) as described previously  [42]. The slides were then observed using a Nikon Eclipse Ti Confocal Laser scanning inverted microscope (Nikon Instruments Corporation, New York, United States). The measurement of biofilm thickness was performed using NIS-Element AR software, version 4. 00. 04.
LC-MS/MS analysis Peptide samples were analyzed by nano-LC/MS E (at elevated energy) using a nanoACQUITY UPLC® system (Waters, Altrincham, UK) coupled to a Quadrupole-Time of Flight (Q/TOF) mass spectrometer (SYNAPT-G2, Waters, Altrincham, UK). Instrument operation and control for both the system was done employing Mas-sLynx4. 1 SCN781 software. The peptides were separated by reverse-phase column chromatography in the nanoACQUITY UPLC®. Peptides eluted from the nano-LC were subjected to mass spectrometric analysis on a SYNAPT® G2 High Definition MS™ System (Waters, Altrincham, UK). All analyses were performed using positive mode ESI using a NanoLockSpray™source. The detailed operational set up was followed as in previous publications  [44].
Data analysis ProteinLynx Global SERVER™ (PLGS) v2. 5. 3 (Waters, Altrincham, UK) was used to analyze the acquired ion mobility enhanced MS E spectra for protein identification as well as for the label-free relative protein quantification. Data processing and parameter setups were carried out as followed by  [44]. The sequence database of the reference strain E. faecalis V583 (NCBI Reference Sequence: NC_004668. 1) was used for database search. The false positive rate (FPR) was set to 4% with a randomized database, appended to the original one. The parameters for protein identification were made in such a way that a peptide was required to have at least one fragment ion match, a protein was required to have at least three fragment ion matches, and two peptide matches for identification. The peptides with 50% or more probability to be present in the mixture and detected with a score above 20, as calculated by the software were selected for proteomic analysis  [45].
Gene ontology analysis The total proteins from all the stages were analyzed using the Venny tool 2. 1. 0 to identify proteins specific to the planktonic and biofilm stages. The total proteome of the planktonic stage was obtained by compiling the total proteins identified in the triplicates of two different time points of planktonic stage viz. , 12 h and 24 h. Similarly, the total proteome of biofilm stages was compiled. The identified proteins were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) functional Annotation Bioinformatic analyzer  [47, 48]. DA-VID generates Expression Analysis Systematic Explorer (EASE) score, a modified Fisher's Exact P-value for each term and the GO and KEGG pathway terms with a P value ≤0.
Acknowledgments The authors would like to thank  Prof. M. Radhakrishna Pillai , Director,  Rajiv Gandhi Centre for Biotechnology , for providing the necessary facilities. We also acknowledge  Arun Surendran  and  Mahesh Chandran  at the  Mass Spectrometry and Proteomics Core Facility  for technical assistance in LC-MS/ MS analysis and also members of  Confocal Imaging Core Facility  for assistance in imaging. KS acknowledges  Kerala State Council for Science, Technology & Environment, Government of Kerala  for the fellowship provided.
Methods and Results: We combined a comprehensive analytical pipeline of mRNAseq dataset and systems biology analysis to characterize the acute phase response of mouse myocardium at 0 min, 45 min, and 24 h after surgery to better characterize the molecular processes inadvertently induced in sham animals. Our analysis showed that the surgical intervention induced 1209 differentially expressed transcripts (DETs). The clustering of positively co-regulated transcript modules at 45 min fingerprinted the activation of signalization pathways, while positively co-regulated genes at 24 h identified the recruitment of neutrophils and the differentiation of macrophages. In addition, we combined the prediction of transcription factors (TF) regulating DETs with proteinprotein interaction networks built from these TFs to predict the molecular network which have induced the DETs.
, 2010). More recently, a study by  Hoffmann et al. (2014)  showed that sham-operated and MI animals display a similar monocyte and granulocyte circulation pattern over time, resulting in a background inflammatory response which prohibited the assessment of the MI-induced inflammatory response. Unfortunately, myocardial transcriptomic studies seldom analyzed the gene modifications induced by the surgery procedure itself  (Harpster et al. , 2006; Roy et al. , 2006)  and to the best of our knowledge the influence of the "cytokine storm" on the modification of genes expression in cardiac cells has not been deciphered. Therefore, we combined freely available R packages and databases in order to analyze a kinetic dataset of sham animals in order (1) to identify immune cells recruitment and differentiation in the acute phase (within first 24 h after surgery), and, (2) to identify the main cytokine/signalization pathways/transcription factors controlling the modifications in DETs.
Our pipeline includes sorting out differential expressed transcripts, gene ontology analysis, time-dependent clustering of co-regulated transcripts, cross-identification of transcription factors (TFs) involved in the expression of transcript clusters and finally prediction of TFs-based protein-protein interaction (PPI) networks. In one hand, we took advantage of the gene clustering achieved by weighted gene co-expression network analysis WGCNA  (Langfelder and Horvath, 2008)  to isolate the different temporal groups of transcripts prior to analyzing their GO terms and comparing them to the ones predicted from the whole list of DETs. We identified inflammation and immune responses as major biological processes that involved neutrophil, monocyte and macrophage cell markers. In the other hand, we figured out the history of DETs by retrieving the TFs most likely involved in DETs regulation prior to predicting the most probable PPI networks that could rule the activity of the highly enriched TFs.
RNA Sequencing Purified RNA samples were provided to ProfileXpert, Inc. , for library construction and sequencing. Quality and quantity checks were performed by means of Fragment Analyzer (Agilent) and QuantiFluor RNA dye (Promega). Library construction was carried out using NextFlex Rapid Directional mRNA-Seq (Bioo-Scientific, PerkinElmer Company) following the manufacturer protocol. Libraries were applied to an Illumina flow cell High and run on the Illumina Nextseq 500 as a single end read for 76 pb. On average, 12 samples were loaded to each flow cell. Image analysis and base calling was carried out using the NCS 2. 0. 2 and RTA 2. 4. 11 Illumina software suite implemented on the Illumina sequencing machine. Final file formatting, demultiplexing, and fastq generation were carried out using Bcl2fastq v2.
Bioinformatic Analysis Trimming of reads was performed using cutadapt v1. 9. 1 software  (Martin, 2011). Then the reads were mapped to the mm10 genome using TopHat v2. 1. 0 (Kim et al. , 2013)  software with default parameters (bowtie 2. 2. 9; Langmead and Salzberg, 2012). Reads were counted using htseq-count v0. 6. 0 software to generate raw counts. Several pipelines and software packages have been developed to aid in the management and analysis of the high throughput data. These packages differ considerably in their analytical pipeline, statistical model  (Robinson and Smyth, 2007; Tarazona et al. , 2015)  and normalization tool  (Bullard et al. , 2010; Robinson et al. , 2010; Trapnell et al. , 2012; Rapaport et al. , 2013; Love et al. , 2014; Evans et al. , 2017). The choice between the normalization methods strongly influences the differential expression analysis  (Burden et al.