dataesr/scientific-paragraphs-categorization huggingface dataset:
The sensitivity of a double beta decay experiment depends on the number of potential decaying nuclei, the background index in the region of interest, the energy resolution and the experiment duration. For this reasons CUORE has been designed to contain more than 200 kg of isotope, and a data taking of 5 effective years has been scheduled. The low background has been achieved by placing the experiment underground, at Laboratori Nazionali del Gran Sasso, by surrounding the detector with passive lead shields and by selecting radiopure materials for the detectors. The target values are 0. 01 counts/yr/keV/kg in the region of interest for the background index and the energy resolution of 5 keV FWHM at 2615 keV. The last requirement drove the choice of the bolometric technique as detection method.
Systematic SMB monitoring programmes began in the late 1940s and early 1950s in most of the European countries (e. g. France, Norway, Sweden, Switzerland). Gradually, more glaciers have become monitored, reaching the present worldwide figure of 440. However, this represents only a small sample of the nearly 250 000 inventoried glaciers worldwide  (Pfeffer et al. , 2014). Among the existing methods to quantify changes in glacier SMB, the well-established glaciological method has become a standard, widely used worldwide, Published by Copernicus Publications on behalf of the European Geosciences Union. yielding most of the reference datasets (World Glacier Monitoring Service, WGMS;  Zemp et al. , 2015). Based on repeated in situ measurements, this method requires intensive fieldwork. However, this method is unable to reconstruct the SMB of unmonitored glaciers.
In situ albedo measurements Albedo measurements acquired punctually using an AWS on Saint-Sorlin Glacier have been used to evaluate the MODISretrieved albedo. In situ albedo measurements were available for three periods in the ablation zone (July-August 2006; June-August 2008; June-September 2009) and for one period in the accumulation zone  (June-September 2008). Albedo data from these AWS have been calculated as the ratio of the reflected to incident shortwave radiation (0. 3-2. 8 µm) using two Kipp and Zonen pyranometers. With a potential tilt of the instrument with respect to surface melting and the intrinsic sensor accuracy (±3 %,  Six et al. , 2009) , the calculated albedo at the AWS shows a ±10 % accuracy  (Kipp and Zonen, 2009; Dumont et al. , 2012). Table  1. List of studied glaciers, characteristics and albedo/mass balance correlations over 2000-2015, except for summer coefficients  (over 2000-2010).
, 2009). The algorithm implemented in MODImLab is fully described by  Sirguey et al. (2009)  and was inspired by  Dozier et al. (1981)  and  Dozier and Frew (1990)  for the sky obstruction factor processing (Horizon and Vsky in  Sirguey et al. , 2016) , and  Richter (1998)  for the correction of shadows. It is first computed at 125 m resolution, providing Booleantype products of self and cast shadows per pixel. Results are then averaged and aggregated to 250 m resolution, producing a sub-pixel fraction of shadow (further detailed in  Sirguey et al. , 2009). Finally, MODIS data processed with MOD-ImLab provide, among others, near-daily maps of white-sky albedo at 250 m resolution together with cloud masks and cast and projected shadows. Albedo maps have been processed for 5068 images for the Ecrins range, 4973 for Mont Blanc and 5082 for Vanoise over the period 2000-2015.
α min a and annual SMB The summer minimum average albedo for each year and each glacier has been linearly correlated with the glacier-wide annual SMB. Figure  6  illustrates the relationship between α min a and b a for Blanc Glacier. Error bars show the dispersion of the SMB dataset for each year, and from the glacier intrinsic variability of the albedo signal on the day of α min a acquisition. For the glaciers where the glacier-wide annual SMB is available from the SLA method, the uncertainty is about ±0. 22 m w. e. on average (ranging from 0. 19 to 0. 40 m w. e. depending on the glacier;  Rabatel et al. , 2016). Twenty-seven glaciers show significant correlations (see Table  1  for full results) if considering a risk of error of 5 % (according to a Student's t test), which confirms the robust correlation between α min a and b a.
However, the linear correlation has no statistical significance for three glaciers with r 2 < 0. 25. A possible explanation is the high number of removed images in summer due to manually checked thin overlying clouds not detected by the MODImLab cloud algorithm. Looking at the 27 glaciers for which significant relationships have been found, 2001 is regularly identified as an outlier. According to existing SMB datasets, 2001 is the only year of the period 2000-2015 for which the annual SMB has been positive for all the studied glaciers (+0. 80 m w. e. yr -1 on average). To predict correctly the SMB values for the year 2001 using the albedo method, monitored minimum glacier-wide average albedo would need to be extremely high (often greater than 0. 7, i. e. 0. 83 and 0. 95 for Rochemelon and Vallonnet glaciers, respectively), to match the regression line derived from other years of the time series (Table  1 ).
Limits of the albedo method In agreement with  Dumont et al. (2012)  and  Brun et al. (2015) , retrieving the glacier annual SMB from albedo summer minima proves to be an efficient method. Low correlations often result from high and persistent cloud coverage during summer, reducing the chance of spotting the albedo summer minimum. For SMB reconstruction purpose, a future line of research could rest upon linking morpho-topographic features of the glacier, such as glacier surface area, mean altitude or slope, to the regression coefficients of both annual and seasonal SMB vs. albedo relationships, giving the opportunity to establish analogy between monitored and unmonitored glaciers. Tests have been carried out, but no significant and satisfying results have been obtained, due to a presumably too heterogeneous dataset, where large glaciers (> 10 km 2 ) and/or south-facing glaciers are largely under-represented.
For future works, the MODIS archive together with albedo maps, cloud and shadow masks processed with MODImLab, together with validation data from AWS, offer a unique dataset to monitor the temporal and spatial evolution of the surface albedo of glaciers at a regional scale. For instance, computing the absorbed solar radiation  (Bair et al. , 2016)  by date and for each glacier would be an appropriate protocol to estimate the impact of a changing glacier surface albedo in terms of snow or ice melt. Quantifying albedo changes and resulting mass losses with such an approach would be of major interest to better understand the potential effects of possibly increasing dust content, glacier orientation or snow grain growth on glacier surface melt processes. To conclude, the use of optical satellite images to estimate glacier surface processes and quantify annual and summer SMB from the albedo cycle is very promising and should be expanded to further regions.
Acknowledgements. This study was conducted within the  Service National d'Observation GLACIOCLIM  and supported by the  French National Research Institute for Sustainable Development (IRD)  and the  French National Research Agency (ANR)  through  ANR-13-SENV-0005-04-PRESHINE. Equipex GEOSUD (Investissements d'avenir - ANR-10-EQPX-20 ) is acknowledged for providing the 2014 SPOT-6 images. The MODIS Level-1B data were processed by the  MODIS Adaptive Processing System (MODAPS)  and the  Goddard Distributed Active Archive Center (DAAC)  and are archived and distributed by the  Goddard DAAC. In situ mass balance data for the Glacier Blanc were kindly provided by the  Parc National des Ecrins. The authors acknowledge the contribution the  Labex OSUG@2020 (Investissements d'avenir - ANR10 LABX56 ).
Pilot Scale Passive Treatment System Description and Sample Collection A field-scale pilot, made with five shallow trays (1. 5 × 1 × 0. 11 m) stacked head to tail on a shelf, was implemented at the Carnoulès mine (France) for the treatment of AMD, as described in  Fernandez-Rojo et al. (in press ) (Figure  1A ). The bioreactor was settled on June 2016 10th and was monitored during 194 days. Nine sampling campaigns were conducted and inlet water and biogenic precipitates were characterized for their physico-chemical properties  (Fernandez-Rojo et al. , in press). From the whole dataset and samples available, we selected seven dates for microbial characterization: 21st of June (D11); 29th of June (D19); 28th of July (D48); 3rd of October (D115); 28th of November (D171), 6th of December (D179), 13th of December (D186).
Selection was based on the temporal variations of the As(V)/Fe molar ratio in the biogenic precipitate (Figure  1B ). Arsenic removal rates from water and Fe(II) oxidation rates are also given. Biogenic precipitates were collected from tray 1 (T1) and tray 5 (T5) for all the campaigns and from tray 1, 2, 3, 4, and 5 for the D48 and D171 campaigns. Each tray was divided into three sections (Figure  1A ): "a" refers to the location close to the inlet of the tray, "b" corresponds to the middle section, and "c" refers to the location close to the outlet of the tray. Based on ARISA (Automated Ribosomal Intergenic Spacer Analysis), bacterial community structures in the three sections (a, b, c) of a given tray exhibited similar genetic structure (data not shown). Then, for each tray, samples collected in sections a, b and c were used as triplicates for 16S rRNA metabarcoding analyses.
Physico-Chemical Characterization of Inlet and Outlet Waters The chemical characterization of the water (inlet and outlet) and the bioprecipitate samples was performed in our previous study  (Fernandez-Rojo et al. , in press). Briefly, the concentrations of dissolved Fe(II) were determined using spectrophotometry after filtration through 0. 22 µm. Total dissolved concentrations of Fe, As, and other elements [S (SO 4 2-), Al, Zn, and Pb] were determined using ICP-MS. Arsenic redox speciation was determined using HPLC-ICP-MS. The data used in the present study are summarized in Figure  1B  and Supplementary Table  S1.
Bioinformatic Analyses of 16S rRNA Gene Sequences Pair-end sequences were merged by flash software version 1. 2. 6  (Magoè and Salzberg, 2011) , with maximum 10% of mismatch into the overlap region. The raw datasets are available on the EBI database system under project accession number [PRJEB27907]. The average Phred score (Q-score) of these joined reads was superior to 30 for every base. Bioinformatic analyses were conducted using the software program Mothur version 1. 39. 5  (Schloss et al. , 2009). First step was the selection of high quality sequences based on the following criteria: length between 330 and 460 bp, a homopolymer length inferior to 7 nt and no ambiguous bases. Singletons, chimeric and unaligned reads were removed using UCHIME  (Edgar et al. , 2011)  and the SILVA reference database (Release 128).
The pre-cluster command served to reduce sequencing noise by clustering reads differing by only one base every 100 bases. The high quality sequences were taxonomically assigned using the SILVA reference database, by the Bayesian method with a bootstrap confidence score of 80  (Wang et al. , 2007). After the removal of sequences that were not assigned to the Bacteria kingdom, samples contained between 2227 and 34928 reads. A subsampling selected a random set of 6295 reads in each sample in order to efficiently compare the datasets. Samples containing less than 6295 reads were removed to maintain a sequencing effort that adequately covers the bacterial diversity. A distance matrix was generated with the remaining sequences and used to cluster these sequences into Operational Taxonomic Units (OTUs) defined at 97% cutoff, using the average neighbor algorithm.
Statistical Analyses All statistical analyses were performed with R version 3. 4. 3 using mainly vegan package 1. The statistically significant differences between CARD-FISH data were assessed with the non-parametric Kruskal-Wallis test followed by Dunn's post hoc test using Bonferroni p-value adjustment. Non-metric multidimensional scaling (NMDS), based on the weighted pairwise Unifrac distances, was used to illustrate the dynamics of the bacterial communities structure during the monitoring. The significance of the differences observed between groups of samples (phase I vs. phase II and tray 1 vs. tray 5) was assessed with an ADONIS test (999 permutations). Redundancy analysis (RDA) was performed to highlight the main environmental drivers shaping the bacterial community structure inside the bioreactor.
ACKNOWLEDGMENTS We thank the  IngECOST-DMA  project ( ANR-13-ECOT-0009 ), the  OSU OREME (SO POLLUMINE Observatory , funded since 2009), the  BRGM  and  ADEME  (Ph. D. fellowship of EL,  2016-2019 ) and the  ADEME COMPAs project  ( GESIPOL 2017/34/003 ) for the financial support. We thank  Jennifer Hellal  for helpful discussion about statistical analyses and the Montpellier RIO Imaging microscopy platform for their kind assistance in microscopy.
Adhesion • tail-like protein STM2699 (Fels-2) in lysogenic conversion and for which no obvious function in metabolic or pathogenic pathways have been identified. In order to find more of these genes whose expression remains significant under lysogenic conditions, one can look at the impressive amount of transcriptomic data available in the public databases and have a search in prophage-related regions. One such very helpful database dedicated to S. enterica is the SalCom compendium developed by and hosted in Jay Hinton's lab  (Kröger et al. , 2013; Colgan et al. , 2016)  (http://bioinf. gen. tcd. ie/cgi-bin/salcom. pl?_HL).
Contributions to phage resistance and physiology Being extremely abundant on earth while presenting a gene content very different from that currently available in the genomic databases, bacteriophage genomes are believed to contribute extensively to the genetic "dark matter"  (Rinke et al. , 2013; Roux et al. , 2015b; Nadeem and Wahl, 2017). Indeed, even when focusing on a single bacterial host the gene repertoire carried by its infecting phages is highly diverse  (Hatfull et al. , 2010; Cresawn et al. , 2011; Hatfull, 2015) , and a large proportion of these genes remain annotated with unknown functions. A number of contributions to the host physiology might also beneficiate to the prophages themselves; among them are those preventing phage superinfection and/or recognition by the immune system from mammals.
Acknowledgements Work in our group is supported by the  French Research National Agency  grants  ANR-16-CE12-0029-01  and  ANR-17-CE11-0038-02 , and institutional funding from the  Centre National de la Recherche Scientifique  and the  Aix-Marseille Université. We are grateful to  S. Champ ,  N. Ginet  and the whole phage group at  LCB  for stimulating discussions and suggestions. We thank  M. Touchon  and  E. Rocha for Salmonella  genome analysis and to  J. R. Fantino  for his help in figure design. M. A. also thanks  J. Casadesús  who provided an inspiring environment while part of this review was written. Although literature citations have not been limited, we may have omitted some pieces of the abundant literature to be more focused, we apologize to those authors whose research is not included.
Metabolite Extraction, Identification, and GC-TOF Analyses The metabolites were extracted from lyophilized samples using a methanol/water mixture (80/20 v/v). Metabolite derivatization, GC (gas chromatography)-TOF (time-offlight)-MS, data processing, and profile analyses were performed, as described in Su et al. [54]. Each metabolite was identified by comparison of the spectra with those that were obtained using the corresponding pure metabolite and its relative amount was calculated on the basis of the corresponding peak area compared with an internal standard (ribitol). The analysis was performed using a LECO Pegasus III with an Agilent (Massy, France) 6890N GC system and an Agilent 7683 automatic liquid sampler. The column was an RTX-5 w/integra-Guard (30 m (0. 25 mm internal diameter) + 10 m integrated guard column) (Restek, Evry, France).
RNA Extraction, Microarray Hybridization, and Analysis The total RNA was extracted as previously described by Oracz et al. [5]. The RNA hybridization (two biological and two technical repetitions) was performed using Affymetrix Sunflower Gene WT Chip at the Affymetrix platform (INRA-URGV) and the protocol developed on sunflower seeds  [6, 55, 56]. The statistical analyses were based on the t-test adjusted with the Bonferroni method  [55, 56]. A Bonferroni p-value less than 0. 05 was required for a significant variation in the gene expression. The data are available on the gene expression omnibus (GEO) repository of the national center for biotechnology information (NCBI) at the accession number of GSE44165.
Microarrays The microarray of strain 630 genome (GEO database accession number GPL10556) was used as previously described  (Saujet et al. , 2011). RNA quality was checked using Agilent RNA 6000 Nano kit and 2100 Bioanalyser apparatus as recommended. For each condition, four independent RNA samples (10 µg) were used. First-strand cDNA synthesis and labeling were performed using a SuperScript Indirect cDNA labeling kit (Invitrogen) and Cy3 and Cy5 fluorescent dyes (GE Healthcare). Cy3and Cy5-labeled cDNAs (200 pmol each) were mixed, and hybridized to microarrays for 17 h at 65 • C. After array scanning, the data were analyzed using R and limma software (Linear Model for Microarray Data) from the Bioconductor project 2. For each slide, background correction was performed using the normexp method, leading to strictly positive values and to reduced variability in the log ratios for genes with low levels of hybridization signal.
Each slide was then normalized by the loess method. The significance of a variation in gene expression was evaluated using the Bayesian adjusted t statistics and a Benjamini-Hochberg multiple testing correction based on the false discovery rate. A gene was considered to be differentially expressed when the p-value was <0. 05. The complete data sets are available from the GEO database with the following accession numbers: (i) GSE85980 3  and (ii) GSE100946 (Token: wxitckyehfgnjyz). For result analysis, gene organization as operons was inferred from genome-wide mapping of Transcriptional Start Sites in strain 630 erm  (Soutourina et al. , 2013).
ClosTron Mutants ClosTron mutagenesis was performed as previously described  (Saujet et al. , 2011). Briefly, to retarget the group II intron for specific insertion into a gene of interest, primers (Supplementary Table  S1C ) were designed with Targetron design software (Sigma-Aldrich). Each specific overlap PCR product was sequenced and cloned into either pMTL007 or pMTL007C-E5 (Supplementary Table  S1B ). After intron sequencing, each resulting plasmid was transformed into E. coli HB101(RP4). The resulting donor strain and strain 630 erm were mated, and transconjugants were submitted to erythromycin selection to obtain mutant candidates. After chromosomal DNA extraction, the correct insertion of the retargeted group II intron into the gene of interest leading to its inactivation was verified by PCR as soon as candidate mutants were obtained.
Acquisitions were performed with the LAS X High Content Screening A Matrix Screener module. Single 2D xy sections were acquired at a scan speed of 600 Hz in bidirectional mode, with a definition of 512 pixels × 512 pixels (corresponding to a field size of 184. 52 µm × 184. 52 µm) and a z-step of 1 µm between xy sections leading to a z-stack. Images were obtained using IMARIS 7. 7. 2 software (Bitplane, Switzerland): three-dimensional projections were reconstructed and orthogonal views were extracted using, respectively, the blend mode of Easy 3D function and the section mode. Several parameters describing the architecture of each biofilm: biovolume (× 1000 µm 3 ), mean thickness (µm), maximum coverage (%) and biovolume/surface ratio (µm) were extracted using a home-made ICY routine software  (Bridier et al.
For simplicity, the number of molecules is not indicated in metabolic reactions (e. g. , for glycolysis, each molecule of glucose leads to two molecules of glyceraldehyde 3-phosphate and all subsequent products). PEP, Phospho-Enol-Pyruvate; THF, TetraHydroFolate; CoA, Co-enzyme A; P, phosphate. rather alternative pathways (see below). Down-regulated genes encode: (i) Ldh, responsible for lactate production from pyruvate, (ii) AdhE and CD3105, both responsible for ethanol and butanol production from acetyl-CoA and butyryl-CoA, respectively, and (iii) enzymes needed for most steps of butyrate production from acetyl-CoA, ThlA1-HbD-Crt2-Bcd2-EtfAB (CD1054-CD1059) and Ptb (Figure  2 ). These data suggest that fermentation-end products could be reduced in biofilm. The supernatants of biofilms and planktonic cultures were therefore compared by gas phase chromatography.
S5 ). After live dead staining of intact biofilms directly in the micro-titer plates, their microscopic architecture was observed in situ by CLSM. Images representative of three independent experiments (each using three clones) are shown. Raw confocal z-stacks were treated using IMARIS software. This allowed obtaining both a 3D projection upside view, with its shadow on the right (A,E), and a section view close to the surface (B,F), in which the white bar indicates the scale (50 µm). For the section view of each strain, magnifications of micro-aggregated forms (C,G) and rods (D,H) are also provided, with the corresponding scale bar in white (5, 10, or 15 µm as indicated). After data recovery, biovolume, maximum coverage, mean thickness and biovolume/surface ratio were quantified and a statistical analysis was performed (see Supplementary Figure  S6 ).
ACKNOWLEDGMENTS We would like to deeply thank all our colleagues from  LPBA, Institut Pasteur, and more specifically Pierre Boudry ,  Nicolas Kint , and  Johann Peltier , for sharing results prior to publication, and  Emilie Camiade  (now at  Tours University ) for her kind help at the beginning of this study. We are grateful to  Jean Sautureau  and  Philippe Bouvet  from  Centre National de Référence des Clostridies, Institut Pasteur , for gas phase chromatography. We thank  Julien Deschamps  and  Ludovic Bridoux  from  Micalis Institute, INRA , for their help in image analysis.
The Two-Higgs-doublet model (2HDM)  [4] [5] [6]  is one of such extensions of the Standard Model. Alike other popular NP models, it gets more and more constrained by recent experimental progress, especially by the LHC data. As the name suggests the 2HDM has two Higgs doublets in contrast to the single Higgs doublet in the SM. This extension of the Higgs sector leads to the existence of five scalar bosons, namely a heavy and light CP-even Higgs boson, H and h, a CP-odd Higgs boson, A, and a pair of charged Higgs bosons, H ±. Whether the scalar boson observed in the Run 1 of LHC is a part of an extended Higgs sector is an outstanding question and is at the cynosure of attention of the current Run 2 (13 TeV) phase of the LHC. The questions we ask is: Which parts of the 2HDM parameter space are favoured after imposing the latest experimental data from the LHC? Compared to the Run 1 phase of the LHC, in Run 2 the situation has changed in several respects: Alongside the latest Higgs signal strength data by the ATLAS and CMS collaborations also many of the recent results of searches for additional heavy Higgs bosons are more constraining than the Run 1 data.