dataesr/scientific-paragraphs-categorization huggingface dataset:
DISCUSSION Methodological variation to quantify ABU is high  (18). There are multiple metrics, which are classified as (i) weight-or (ii) dose-based  (19, 38). Weight-based measurements are convenient based on accessibility, but dose-based measurements are more accurate  (19, 38). Dose-based measurements are calculated from prescription records. However, it is currently not feasible to only use prescription records as a data source in South Africa, as some antibiotics can be purchased over-the-counter  (6). Alternative data sources are therefore required to quantify ABU at farmlevel in South Africa. The ESVAC performed a pilot study on pigs to determine the most appropriate variables required for the reporting of national sales data on antibiotic consumption  (39). The pilot study included invoices as a data source, but ESVAC found that invoices are often unavailable and not frequently used  (39).
F7. 7 purity and chemical structure was further confirmed by nuclear magnetic resonance (NMR)(data not shown). To confirm that Pba was the active compound of the Mo extract, a dose-response experiment was performed with both the pure compound isolated in F7. 7 and commercial Pba against HCoV-229E-Luc in Huh-7 cells. As shown in Figure  1C , IC 50 values were comparable for both natural and commercial Pba (0. 51 µM and 0. 54 µM, respectively), confirming that Pba was indeed the active substance of Mo.
Trans-epithelial electrical resistance Mucilair TM were incubated with Pba and lysis solution as described above. Trans-epithelial electrical resistance was measured using a volt/ohm meter and electrode (Millicell ERS2, Millipore) at 24 h, 48 h and 72 h. 200 µl of prewarmed Mucilair TM culture medium was added at the apical surface prior resistance measurement. Data are expressed relative to control untreated cells.
Nuclei were stained with DAPI. Coverslips were mounted on microscope slides in Mowiol 4-88containing medium. Images were acquired on an Evos M5000 imaging system (Thermo Fisher Scientic) equipped with light cubes for DAPI, and RFP, and a 10× objective. For each coverslip, a series of six 8-bit images of randomly picked areas were recorded. Cells labelled with anti-virus mAbs were counted as infected cells. The total number of cells was obtained from DAPI-labelled nuclei. Infected cells and nuclei were automatically counted using macros written in ImageJ. Infections were scored as the ratio of infected over total cells. The data are presented as the percentage of infection relative to the control condition.
Statistical analysis and IC 50 and CC 50 calculation Values were graphed and IC 50 calculated by non-linear regression curve fitting with variable slopes constraining the top to 100% and the bottom to 0%, using GraphPad PRISM software. Kruskal Wallis nonparametric test followed by a Dunn's multicomparison post hoc test with a confidence interval of 95% was used to identify individual difference between treatments. P values < 0. 05 were considered as significantly different from the control. FIGURE LEGENDS.
Acknowledgments We thank  Volker Thiel  for providing HCoV-229E-RLuc,  Philippe Desprès  for YFV,  Luis Enjuanes  for  MERS-CoV  and  Didier Hober  for  CVB4. We are also grateful to  Robin Prath  and  Nicolas Vandenabele  for their technical help in the BSL3 facility. Authors are grateful to the LARMN platform ( University of Lille, France ) and wish to thank  N. Azaroual  and  V. Ultré  for their help on NMR analysis.
Dose equivalent rate calculations The prompt dose equivalent rates were calculated from the convolution of the fluence of neutrons, protons, charged pions, and muons with their respective energydependent fluence-to-effective dose conversion coefficients. The residual dose equivalent rates were calculated by the convolution of the fluence of photons, electrons, and positrons from γ-and β-decays with the respective energy-dependent fluence-to-effective dose conversion coefficients. The dose conversion coefficient EWT74 provided by the FLUKA database was used. Various cooling times: 1 s, 1 H, 1 day, 10 days, 1 and 3 months after turning off the beam were assumed for the residual dose equivalent rates calculations. The radiation categorisation of the specific zones of the target station complex relies on the Radiation Protection Ordinance (RPO) of ESS.
inlib is strongly inspired by the OpenInventor developed by Silicon Graphics Inc. in the 1980s  [236]. The web site for EsbRootView is at https:// gbarrand. github. io-the "softinex portalle"  [237] -under the section "EsbRootView" at left. The source code of stable releases is on github at https://github. com/gbarrand/EsbRootView. git. The license is a customisation of the FreeType license. For each release, binaries are provided at least for Linux, macOS, and Windows. A WebAssembly version is available through the web pages. It should be noted that inlib/exlib classes are delivered under the namespace "tools" in Geant4  [238]  as a base layer to the analysis "category"; these facilitate the creation of histograms and ntuples, I/O at various formats (ROOT, csv, HDF5), and offscreen plotting with our scene graph logic (see  [239]  and  [240] ).
Finally, more general bin-to-bin uncorrelated uncertainties will be implemented (one independent nuisance parameter per bin) which may encode more complex situations such as the unknown shape of the neutrino cross sections at the energies of interest. These sets of nuisance parameters are meant to capture all sources of uncorrelated uncertainties affecting the far detector expectation after the measurements at the near detector and beam-monitoring information have been taken into account. Thus, the near-detector events are not simulated explicitly, unlike some previous works such as  [271] , to avoid double counting its impact. For the estimation of the sensitivity, the Poisson loglikelihood will be used χ 2 stat = 2 n i=1 N test i -N true i -N true i log N test i N true i , (9. 3) where N test is the number of events expected for the values of the oscillation parameters tested for, N true is the number of events expected for the parameter values assumed to be realised in nature (Asimov dataset) and i is the number of energy bins.
Acknowledgements This project has been supported by the  COST Action EuroNuNet Combining  forces for a novel European facility for neutrino-antineutrino symmetryviolation discovery. It has also received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 777419. We acknowledge further support provided by the following research funding agencies:  Centre National de la Recherche Scientifique  and  Institut National de Physique Nucléaire et de Physique des Particules, France ,  Deutsche Forschungsgemeinschaft, Germany, Projektnummer 423761110 ,  Agencia Estatal de Investigacion  through the grants  IFT Centro de Excelencia Severo Ochoa, Spain , Contract No. CEX2020-001007-S  and  PID2019-108892RB  funded by.
Detection of intracellular CK1α UT7 cells were washed with PBS, labelled with the Fixable Viability Dye eFluor780 at a dilution of 1/1000 for 30 min at 4°, washed twice with PBS, and then fixed and permeabilized with the Foxp3/Transcription Factor Staining Buffer Set according to the manufacturer's recommendations. After two wash with PBS, cells were stained for 30 min with FITC-conjugate rabbit anti-CK1α at a concentration of 2 µg/10 6 cells (Assay Pro) or FITC conjugate rabbit IgG isotype control. Cell were washed and then run on a Beckman Coulter Gallios cytometer. Live cells were analyzed using FlowJo software (version 10. 0. 8, TreeStar).
Detection of activated BAX and BAK1 Every day of the CD36 culture, erythroid cells were washed with PBS, labelled with the Fixable Viability Dye eFluor780 at a dilution of 1/1000 for 30 min at 4°, washed twice with PBS, then fixed with PFA 4% and permeabilized with saponin 0. 1% BSA 0. 5%. Cells were labelled with anti-BAX 6A7 antibody (ab5714) or anti-BAK1 TC-100 antibody (Merck Millipore) for 30 min then stained with FITC conjugate anti-mouse Ab. Live cells were analyzed for activated BAX or BAK1 using FlowJo software.
2f , Supplementary Figs. S2b,  and S7 ). Moreover, caspase-10 knockdown inhibited the cleavage that generates the 13 kDa fragment of BID (Fig. 2g , Supplementary Fig. S7 ). These data indicate that the 13 kDa fragment likely corresponds to P13-tBID. BID sequence assessment revealed another potential caspase-10 cleavage site (RELD 38 ), which may generate the P18-tBID cleaved form (Fig. 2d ). To confirm that BID is cleaved by caspase-10, we performed mass spectrometry. A FLAG-tagged form of P22-BID was transduced into erythroid cells, and live P22-BID-FLAG-GFP + cells were sorted on day 5 and day 7 of the CD36 culture. BID-FLAG proteins were immunoprecipitated from lysates with an anti-FLAG antibody and separated on a polyacrylamide gel. Proteins with a molecular mass of ~13 kDa or 18 kDa were digested with trypsin and analyzed by mass spectrometry.
ACKNOWLEDGEMENTS ML was a recipient of grants from  INSERM  and  Assistance Publique-Hôpitaux de Paris (AP-HP ) (poste d'accueil APHP-INSERM). FG was a recipient of grants from the  labex-GR-Ex  funded by the program "Investments for the future" of the  French National Research Agency ,  ANR-11-IDEX-0005-02. We thank nurses of the  Obstetric unit (Hospital Necker-Enfants Malades ) and the  CRB, Hospital Saint-Louis, Paris  for providing us with cord blood samples. We also thank  Philippe Chafey  and the  3P5 proteomics facility at Institut Cochin  for proteomic studies,  Olivier Pellé  and the cell sorting facility of  Imagine Institute , the histology facility at  Institut Necker-Enfants Malades.
Evolution can repeat itself, resulting in parallel adaptations in independent lineages occupying similar environments. Moreover, parallel evolution sometimes, but not always, uses the same genes. Two main hypotheses have been put forth to explain the probability and extent of parallel evolution. First, parallel evolution is more likely when shared ecologies result in similar patterns of natural selection in different taxa. Second, parallelism is more likely when genomes are similar, because of shared standing variation and similar mutational effects in closely related genomes. Here we combine ecological, genomic, experimental and phenotypic data with randomization tests and Bayesian modeling to quantify the degree of parallelism and study its relationship with ecology and genetics. Our results show that the probability of parallel adaptation to climate among species of Timema stick insects is shaped collectively by shared ecology and genomic background.
Identifying climate-associated genomic regions within species We first identified the genomic regions most strongly associated with climatic variation within each of the eight species. To do so, we analyzed single nucleotide polymorphisms (SNPs) obtained through previous genotyping-by-sequencing of natural populations  (Riesch et al. 2017)  to quantify the number of genetic regions associated with climatic variation within each Timema species. Because individual SNPs and their numbers differ among species, we focus on 100 kilo base (Kb) SNP windows to allow eventual comparisons among species (n = 9487 windows in each species, across the eight study species). For each species, we quantified SNP-climate associations for each of the three climate PCs using BayPass (version 1. 2). This software controls for background population structure and generates Bayes Factors (BF), which indicate the evidence for an association of each SNP with an environmental variable (in our case, a PC axis).
Parallelism declines predictably with ecological and genomic divergence between species We next tested the shared ecology and shared genetics hypotheses for variation in the degree of parallelism. Shared ecology would cause a higher degree of parallelism due to similar selective pressures from similar environments. One the other hand, shared genetics would a higher degree of parallelism due to higher probability of gene reuse. Here, we quantified parallelism as the degree of excess overlap of climate-associated SNP windows relative to null expectations, for pairwise comparisons (Figure  2B ). We estimated ecological similarity between pairs of species using climatic data and genetic similarity based on a previously published phylogeny. We then fit Bayesian linear mixed models to explicitly compare models where the degree of parallelism is determined by ecological similarity, genetic similarity, and both.
CONCLUSION We used GBS data from 1420 individuals across eight species, combined with other forms of data, to show that adaptation to climate is occurs in parallel across species but as a function of the ecological and genomic divergence between species. Our results inform three fundamental issues in biology, namely the repeatability of evolution, the effect of ecology and genetics on parallelism, and the processes promoting parallelism. We treat these in turn. Firstly, we show that evolution in response to climate occurs in parallel among eight species and that parallelism likely involves multiple loci. These findings fill a gap in our knowledge of parallel evolution because many studies, including past work in Timema, have mostly focused on parallelism driven by single-genes or specific regions of the genome  (Colosimo et al.
Climate variables and identifying SNP by climate associations in nature We used 22 climate layers values associated with our 53 study localities (Table  S2 ), which were extracted from the WorldClim database version 1. 4 (https://www. worldclim. org/data/v1. 4/worldclim14. html; climate data for. Since the first three PC scores explained the overwhelming majority (92. 4%) of variation in the climate variables (Figure  2B , Table  S2 , Figure  S1 ), we used these three PCs to study genomic associations with climate in all further analyses. We used BayPass version 2. 1  (Gautier 2015)  to identify genomic regions associated with the three sets of PC scores for the climate variables. The BayPass software controls for background population structure and is based on the BAYENV method introduced by Gunther and Coop  (Günther and Coop 2013).
Our independent variables were ecological and genetic distances, estimated as follows. Climatic (i. e. , ecological) distance was calculated as pairwise absolute mean difference of PC scores of each species. We calculated genetic (i. e. , phylogenetic) distances based on the previously published phylogeny described in  Riesch et al. 2017. Briefly, we used the data from this previous phylogeny constructed using Bayesian phylogenetic inference with BEAST (version 2. 1. 387) for 11 Timema species based on GBS data of curated dataset of 19,556 singlenucleotide variants. For our current study, we used pairwise phylogenetic distances for the eight Timema species as our metrics of genetic distances for this analysis. All variables were standardized (given mean 0 and standard deviation of 1) before analysis.
Whole-genome sequence data, which we analyze here, was obtained from 491 of the 500 stick insects  (Nosil et al. 2018). For the current study, we estimated allele frequencies in the released and recaptured stick insects at the 6,175,495 bi-allelic SNPs identified by  Nosil et al. 2018. This was done using an expectation-maximization (EM) algorithm as implemented in the program estpEM (version 0. 1) with tolerance of 0. 001 and a maximum of 50 EM iterations  (Soria-Carrasco et al. 2014). We then used these estimates to compute allele-frequency change between the start and end of the experiment. Then, for each SNP we calculated the Pearson correlation between allele frequency change and the elevation at each of the ten transplant sites. Finally, we determined the average correlation between change and elevation for the 100 kb windows across the genome.
Testing for introgression and quantifying population structure We quantified both historical and contemporary gene flow patterns, respectively as follows. For identifying historical introgression, we used TREEMIX (version 1. 13)  (Pickrell and Pritchard 2012)  to construct a population-based phylogeny to identify historical admixture or gene flow among our 53 focal populations. This differed from previous TREEMIX analysis done for Timema species where we used the data only from the Mel-Stripe locus  (Villoutreix et al. 2020). For the analysis in our study here, we re-aligned the GBS sequences for 1420 individuals (across 53 populations) included in this study to the T. cristinae genome (draft version 0. 3). We did this by using the MEM algorithm from BWA (version 0. 7. 17-r1188). We ran BWA MEM with a minimum seed length of 15, internal seeds of longer than 20 bp, and only output alignments with a quality score >= 30.
We then used the prcomp function  (Kassambara 2019)  to perform a PCA in R (3. 4) on the centered, but unstandardized genotype matrix. associated DNA sequence data have been archived on NCBIs SRA (Accession: PRJNA356405 ID: 356405). Computer code are available on https://github. com/karwaan/Timema_climate_adaptation_genomics. Correspondence for materials (data, scripts, or samples) should be addressed to Samridhi Chaturvedi (samridhi. chaturvedi@gmail. com) or Zachariah  Gompert (zach. gompert@usu. edu) or Patrik  Nosil (patrik. nosil@cefe. crns. fr). Figure  3 : Manhattan plots showing the strength of evidence for association (measured here using the Bayes factor from the software BayPass) between a SNP window and climate (in this case, PC3, see Figures  S2  and S3  for analogous results for PC1 and PC2).
FIGURES 912 Figure  S3 : Manhattan plots showing the strength of evidence for association (measured here using the Bayes factor from the software BayPass) between a SNP window and climate for PC2. Results are shown along the 13 linkage groups. Red points denote the SNP windows in the top 10% quantile (i. e. , referred to as 'climate-associated SNP windows' throughout the main text). In each panel title, the two values in parentheses are the number of SNP windows in the top 10% quantile ( windows"), followed by the number of linkage groups with at least 1 SNP window in the top 10% quantile ( LG"). FIGURE S8, S9, S10: Plots show summaries of population structure based on principal component analysis for 8 species included in this study for PC1 vs. PC2 (Figure  19 ), PC1 vs. PC3 (Figure  20 ), and PC2 vs.
ACKNOWLEDGEMENTS SC was supported by the  Utah State University College of Science and School of Graduate Studies. MM was supported by  Swiss National Science Foundation  Postdoc Mobility grants  PBBSP3_141367  and  P300P3_147888. ZG was supported by the  US NSF  ( DEB 1844941 ). This study is part of a project that has received funding from the  European Research Council (ERC) , to PN, under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 770826  EE-Dynamics). The support and resources from the  Center for High Performance Computing at the University of Utah  are gratefully acknowledged.
The LHCb detector The upgraded LHCb detector, which will be operating during Run 3, detailed by  Bediaga et al. (2012) , is summarized below. The most important detector improvements as compared to the previous detector  (Alves et al. , 2008)  are the new tracking system and the readout architecture. The readout architecture uses a purely software-based trigger that enables data to be recorded at five times the previous rate, and increases the acceptance of purely hadronic b decays by up to a factor of 2. LHCb is comprised of a tracking system (Vertex Locator, Upstream Tracker, and SciFi tracker), as well as two Ring Imaging Cherenkov detectors, a hadronic calorimeter, and an electromagnetic and muon chambers. These are summarized below. The VErtex LOcator (VELO) is based on pixelated-silicon sensors and is critical to determine the decay vertices of b and c flavored hadrons.
The Upstream Tracker, UT, contains vertically-segmented silicon strips and continues the tracking upstream of the VELO. It is also used to determine the momentum of charged particles with p/p ≈ 10% and is useful for removing low-momentum tracks from being extrapolated downstream, thus speeding up the software trigger by about a factor of 3. Tracking after the magnet is handled by the new scintillating fiber-based Scintillating Fiber detector (SciFi). Two Ring Imaging CHerenkov (RICH) detectors supply particle identification. RICH1 is mainly for lower momentum particles and RICH2 is for higher momentum ones. The Electromagnetic Calorimeter (ECAL) identifies electrons and reconstructs photons and neutral pions. The Hadronic Calorimeter (HCAL) measures the energy deposits of hadrons, and the muon chambers M2-M5 are mostly used for muon identification.
The LHCb event-builder The main goal of the upgraded LHCb data-acquisition system is to enable the event filter to efficiently select events based on the full data readout from the detectors. The heart of the LHCb online system is the event builder, which is capable of collecting and aggregating data fragments coming from the FPGA front-ends of the various sub-detectors at the full collision rate of 30  MHz (LHCb, 2014b). The data size of a full event during the Run 3 is expected to be as high as 150 kB. Therefore, taking into account the data packed and the transfer protocol overheads, the new event builder has to sustain a throughput of up to 40 Tb/s. The event builder is essentially made up of three components: the readout units, the builder units, and the network interconnecting them. Each readout unit receives the event fragments from the detector point-to-point links and makes them available on the EB network.
Conclusion In summary, during the past years, the LHCb detector has undergone a major upgrade, which will allow the data for Run 3 to be taken at an averaged proton-proton collision rate of 30 MHz. The first trigger stage of the LHCb will be based for the first time entirely on GPU cards following the Allen framework, a complete high-throughput trigger algorithm design. This will largely increase the capabilities of the trigger system for selecting the data of interest. Tracking reconstruction at early stages of the data chain is becoming an arduous task due to the increase of the output data. This is even more challenging if the reconstruction involves particle trajectories that do not leave signals in the first tracker of the LHCb detector, hitting only the UT and/or the SciFi, which are bigger detectors with high occupancy.
Funding This work is supported and has been developed in the framework of the  RTA  project inside the LHCb collaboration. This work has been partially funded by the  CERN  and the  MICINN national agency in Spain. AH is supported by the  NSF  award  1904160. DM is supported by the  CONEXION AIHUB-CSIC. LC is funded by the  ERC Refs :  724777-RECEPT  and  714536-PRECISION , as well as the  PUNCH4NFDI  consortium supported by the  DFG  fund  NFDI 39/1.
a key role on fast event decisions, and the fact that they are inherently parallelizable processes suggests a way to increase the trigger performance. Thus, the HLT1 has been implemented on a number of GPUs through the Allen project(Aaij et al. , 2020), which manages 4 TB/s and reduces the data rate by a factor of 30. After this initial selection, data are passed to a buffer system that allows real-time calibration and alignment of the detector. This is used for the full and improved event reconstruction carried out by HLT2. Figure4shows the dataflow at LHCb in the first trigger level. Due to timing constraints, the current LHCb implementation in this stage is based on partial reconstruction and focuses solely on long tracks, i. e. , tracks that have hits in the VELO. This trigger thus significantly affects the identification of particles with long lifetimes, in particular LLP searches in LHCb, where some of the final-state particles are created further than roughly a meter away from the IP and thus outside of the VELO acceptance.