dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion In this study, we performed a large-scale serological investigation in cats and dogs during the emergence of the COVID-19 pandemic in Seoul and Hong Kong, regions geographically adjacent to Mainland China, from where SARS-CoV-2 originated. Our results showed that the seroprevalence of SARS-CoV-2 exposure in cats and dogs in Korea was 4. 51% and 4. 94%, respectively, whereas SARS-CoV-2 seropositive cats and dogs in Hong Kong were 2. 54% and 6. 46%, respectively, as determined by RBD-ELISA. For cats, our data indicated that the seroprevalence in Hong Kong was relatively lower than in Korea, which likely reflects the difference in human incidence of SARS-CoV-2 between the two regions during the sample collection period. In Korea, pet cat samples were collected from three veterinary clinics located in different districts of Seoul from April 2020 to July 2020, where the human incidence rate was 1150 cases in Seoul during this period (total cumulative human incidence of 5,707,477 cases in the city by the end of July 2020), according to the data released by the Ministry of Health and Welfare of South Korea (available at https://ncov.
The data presented in this study supports previous studies showing that dogs and cats can develop neutralizing antibodies against SARS-CoV-2  [31, 32, 34, 40, 54]. However, this study has some limitations; the owners' health status in association with COVID-19 was not known at the time of sampling of the dogs and cats presented to veterinary clinics. Additionally, except for samples collected from cats in an animal shelter in Hong Kong that were tested by RT-PCR, the status of SARS-CoV-2 shedding in pet animals was not determined. Therefore, it was not possible to determine associations between antibody levels and possible exposure to SARS-CoV-2 from infected humans, nor any perceived association of COVID-19 disease in these patients. In summary, our data confirmed that cats and dogs in close contact with people can develop an antibody response against SARS-CoV-2 infection, and similar to other studies, the seroprevalence of SARS-CoV-2 among cats and dogs belonging to owners of unknown COVID-19 status was low  [28] [29] [30] [31] [32].
org/protocols-and-standards/16s) to detect both Archaea and Bacteria and because this region is widely used to study soil microbiomes  [37]. In addition, the prokaryotic primer pair 515 F-806R was reported to provide a great depth and taxa coverage for frameworks emphasizing ecological relationships between soil, plant, animal and human health  [37]. The sequencing was done with an average of 20 k raw reads per sample. The raw 16 S RNA data are available in the NCBI SRA repository under the Bio-Project ID PRJNA925577: HALOREMED (submission: SUB12696467, SRA accession numbers from SAMN32802582 to SAMN32802610).
Amplicon analysis The raw reads were processed using the DADA2  [38]  package available in the Rstudio software, where all the sequences with a quality score above 30 were kept. The sequences were filtered and trimmed with the following parameters: a maximum excepted error (maxEE) at 2, a maximum N (maxN) at 0, a truncation based on quality scores (truncQ) at 2, trimLeft at 19 pb and 20 pb to remove the primers and truncLen set at 240 pb for the forward reads and 180 pb for the reverse reads to remove low quality tails. The chimeras were removed using the consensus method, and the taxonomy was assigned using the Silva 138 SSU Ref NR99 database  [39]. Prior to further analysis, sequences with no affiliation or affiliated to the Eukaryota, Mitochondria or Chloroplasts were removed from the ASV table.
Alpha diversity The alpha diversity was estimated using the richness indices of ACE (Abundance-based coverage estimator) and Observed ASV calculated on RStudio software with the microeco package. Definition of this different indices can be found in Hughert and Anderson 2017  [40]. The observed ASV represent the number of different ASVs in a sample. Whereas, the ACE index (Abundance-based Coverage Estimator) estimate the richness of a sample by taking account the number of singletons, doubleton and rare OTUs (generally less than 10 sequences)  [40, 41]. We also used the Inverse Simpson and Exponential Shannon indices from Hill numbers (q = 1 and q = 2) with function renyi from vegan package. The Hill numbers was recommended to a reliable estimation of the microbial richness and diversity  [42, 43].
Then a non-parametric test of Kruskal-Wallis followed by a Dunn test was performed with RStudio (dunn. test package) to show significant differences between experimental conditions (dry, wet, halophyte species) and sampling time (D0 and D150 only). Prior to downstream microbial analysis, data were then normalized with the Counts Per Million (CPM) method using the cpm function available in the edgeR package under RSudio, allowing to normalized all the libraries to 1. 000. 000 reads as described in  Giraud et al. , (2021; 2022)  and  Callac et al. , (2022; 2023)   [30, 35, 44]. We used the CPM normalization, method to normalize gene expression, as we have extracted RNA and then used cDNA to sequence the V4 region of the 16 S RNA gene to investigate the active microbiota of the sediments.
Sediment functional community profiling Relevant biomarkers of sediment specific microbiomes were used to make functional annotation of 16 S ribosomal RNA bacterial gene sequences from the SILVA database. The microeco R package was used to assign putative ecological function of the previously evidenced taxa from the LEfSe. We used Tax4fun R package  [48]  to determine the metabolic pathway prediction of the microbial communities and FAPTROAX v1. 2. 4 database  [49]  to make a Functional Annotation of Prokaryotic Taxa. Tax4Fun predict microbial communities function as phenotypes of gene families or enzymes activities based on gene content, whereas FAPROTAX predict metabolic phenotypes and ecologically relevant functions based on the literature of cultured taxa  [49, 50]. FAPROTAX was reported as a helpful tool to highlight functions related to biogeochemical dynamics especially on N and C cycles  [50].
The LefSe analysis (Fig. 4B ) shown the significantly enriched functional categories in the sediment according to the experimental conditions. Based on a minimum LDA score of 3, the highest number of significant functional categories was found in wet sediment  (7) , followed by SuaA (5), SarQ (3), then AtrJ (2) and dry sediments  (2). Putative functional activities in the sediment with SuaA were significantly enriched in amino acid metabolism, xenobiotic biodegradation and lipid metabolism (Fig. 4B ). All functions found in sediment with SuaA belonged to the "Metabolism" category (Fig. 4B  and the KO database). Putative microbial functions in the sediment with SarQ were enriched in carbohydrate and energy metabolism as well as in cell motility. Putative microbial functions in the sediment hosting AtrJ were enriched in nucleotide metabolism and membrane transport (Fig.
4B ). In the wet sediment, the putative microbial functions were linked to signal transduction, replication and repair, folding, sorting and degradation. Those related functions belong to the Environmental and Genetic Information Processing categories (Following Fig. 4B  and the KO database). In the wet sediment, only one function was related to Metabolism category; the glycan biosynthesis and metabolism. The putative microbial functions found in dry sediment were related to cellular processes (cell growth and death) and genetic information processing (translation). However, there was no biomarkers related to the Metabolism category (Fig. 4B ).
Acknowledgements The authors thank  M. BULL Michel  from  Aigue Marine aquaculture  farm for his support for the project. Thanks go to  LAMA Plateform (IRD), Noumea, New-Caledonia (France)  for sediment nitrogen and organic carbon analysis and  MrDNA lab ,  Shallowater   (Texas) , for sequencing analysis. Authors would also thank  Ifremer  unit from the  Department of Biological Resources and Environment (RBE) and ISEA laboratory (Institute of Exact and Applied Sciences)  for their financial contributions. Thanks also to  Valérie Perez  to her help with bioinformatics data. To end, we gratefully acknowledge the referees for their constructive advices to improve this paper.
The CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3. 8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass JHEP05(2023)227   30 ) µm in the transverse (longitudinal) impact parameter  [21]. Events of interest are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors, uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of about 4 µs  [22]. The second level, known as the high-level trigger (HLT), consists of a farm of processors running a version of the full event reconstruction software optimized for fast processing, and reduces the event rate to around 1 kHz before data storage  [23].
JHEP05(2023)227 3 Collision data and simulated events The data sample used for this analysis was collected during 2016-2018 pp operation at a center-of-mass energy of 13 TeV. After applying data quality requirements, such as requiring minimum fractions of active detector channels, the total integrated luminosity is 138 fb -1. Simulated samples of signal and background events are produced with several event generators. The RPV SUSY ν τ , Z , and QBH signal events are generated at leading order (LO) precision, using the CalcHEP 3. 6  [28] , pythia 8. 203  [29] , and qbh 3. 0  [12]  Monte Carlo (MC) generators, respectively. The width of the Z signal is taken as 3% of its mass, similar to that of the Z boson. The RPV ν τ and QBH signals are generated using the CTEQ6L  [30]  parton distribution functions (PDF), while the Z boson signals are simulated using the NNPDF 3.
Background estimation The SM background in the LFV dilepton search includes several processes that produce a final state with two different-flavor charged leptons. For all channels, the dominant background contributions originate from tt production. Other less significant backgrounds originate from diboson (WW, WZ, and ZZ), Z → + -( = e, µ, τ), Wγ, and single top quark production processes. All these backgrounds are estimated from MC simulation. For the Z → ττ process, all decay modes of τ lepton have been considered. Multijet and W+jets processes also contribute due to the misidentification of jets as leptons. These backgrounds are estimated from data. For the eµ channel, to determine the contributions from W+jets and multijet processes to the m eµ distribution, a control sample in data is defined using jet-to-electron misidentification rate (F e ).
The effects of uncertainties in the energy scale of the unclustered particles and jet energy resolution are evaluated in a similar way. These systematic uncertainties affect the shapes as well as the normalizations of the collinear mass distributions. Uncertainties of the second type directly influence only the normalization of the mass distribution. A systematic uncertainty of 1. 6% in the integrated luminosity  [54] [55] [56]  is taken for the backgrounds and signals. Among the uncertainties in the cross sections used for the normalization of various simulated backgrounds, the 5% uncertainty in the tt background is the most important. A systematic uncertainty of 50% is applied to the estimate of the misidentified jet background derived from data in all three channels. For τ h final states, this uncertainty is obtained by using the misidentification probability F τ derived from an independent control sample of Z (→ µµ)+jets events for the estimation of misidentified jet background.
Uncertainties in the shape of the distributions are modeled via "template morphing" techniques  [60]. A Markov Chain MC method  [61]  is used for the integration. All limits presented here are at 95% confidence level (CL). Model-specific limits for the product of the cross section and the branching fraction were obtained for the RPV SUSY, Z , and QBH signals and are shown in figures 3, 4, and 5, respectively. These are full Run 2 results based on 2016-2018 data sets. The observed and expected lower mass limits obtained in all three channels are summarized in table 1. A ν τ in RPV SUSY is excluded up to a mass of 4. 2 TeV in the eµ channel, up to 3. 7 TeV in the eτ channel, and up to 3. 6 TeV for the µτ channel, in each case for the coupling hypothesis λ = λ = 0. 1. If λ = λ = 0. 01 is assumed, the observed limits drop to 2.
6, 5. 2, and 5. 0 TeV are excluded in the eµ, eτ, and µτ channels, respectively. In addition, model-independent limits are provided allowing the results to be interpreted in other models with the same final states and similar kinematic distributions. Limits in the eτ and µτ final states, as well as model-independent limits, are reported for the first time in the context of a highmass lepton flavor violation search. These are the first results of a high-mass lepton flavor violation search using the full Run 2 data set, and they are currently the most stringent limits from any collider experiment.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid and other centers for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NK-FIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MES  and  NSC (Poland) ;  FCT (Portugal) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  MHESI  and  NSTDA (Thailand) ;  TUBITAK  and  TENMAK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Behavioral setup and training Behavioral experiments were performed using a data acquisition interface (USB-6008, National Instruments) and custom-written MATLAB software to control devices required for the task and to record trial-related neural activity and licking data. Behavior training for the recording experiments started ∼2 weeks after the surgery (∼5 weeks for inhibition and miniscope experiments). First, mice were given 2 days of habituation to the T-maze. Habituation sessions consisted of 5-10 min of free exploration in the maze with all doors open and freely accessible water rewards. On the subsequent 2-5 days, mice underwent behavioral shaping, which consisted of 10 trials per day running to baited goal arms in alternating directions. A drop of water (10 μl) was used as a reward.
The photocurrent was amplified in the custom-built pre-amplifier and transmitted to the data acquisition board (DAQ USB 6211, National Instruments). The fluorescence signals were recorded at 2 kHz continuously until the end of each session and demodulated with the digital lock-in detection (MATLAB R2019b). Fluorescence contributions excited by the 425-nm and 488-nm lasers were demixed by demodulation at the respective modulation frequencies in 50-ms time bins, resulting in an effective 20-Hz rate for the fluorescence recording. At the beginning of each recording session, the mouse was placed into the start box before the first trial. Mice were simultaneously video tracked with the IR illuminated camera at 15 Hz frame rate (DMK 33UX178, Imaging Source). All continuous behavioral parameters such as the licking response were also simultaneously recorded on the same data acquisition board (DAQ USB 6211, National Instruments).
We used a custom-written MATLAB program for simultaneous data acquisition and DNMTP T-maze task control. For synchronization, several parallel routines were triggered simultaneously: (1) PMT signal recording for fiber photometry (via USB-interface on NI DAQ 6211); (2) Video recording for mouse tracking; (3) An online video analysis routine, which used image intensity changes in specific regions of interest (ROIs) to detect when the animal reached key positions in the maze (start box; exit of start box; end of corridor before T-junction; entry positions in the two T-maze arms; reward zone). These online detected events were timestamped and, together with the lick sensor, they were used to control the opening/closing of the maze doors and the waterspout valve opening according to the task structure.
After insertion of the silicon probe, we positioned an optical fiber (0. 2-mm diameter) directly next to the probe, directed downwards towards the brain. We waited 30 min to allow the recording to stabilise after implantation of the electrode array. After stabilisation, the broadband voltage was amplified and digitally sampled at a rate of 30 kHz using a commercial extracellular recording system (RHD2000, Intan Technologies). The raw voltage traces were filtered offline to separate the multi-unit activity (MUA; bandpass filter 0. 46-6 kHz) using a fourthorder Butterworth filter. Subsequently, the high-pass data were thresholded at 5. 5 times the standard deviation across the recording session and the numbers of spikes in windows of interest were counted. Once the recording was stable, we performed light stimulation through the optical fiber to drive the expressed opsin.
Behavioral analysis All behavioral analysis was performed using custom-written MATLAB scripts. First, based on the behavior videos and licking actions, we identified 11 salient events in each of the 1085 trials collected from six mice. For correct trials the events were the following (Fig. 1a ): 1-start of the sample run, 2-turning at the T-junction of the main maze arm, 3-first water reward, 4-end of licking period, 5-turn to run towards the start box, 6-reaching the start box, 7-start of the choice run, 8-turning at the T-junction, 9-second water reward, 10-end of licking period, 11-end of choice run. Because mice did not receive a reward in incorrect trials, events 9 and 10 were omitted for these trials. These 11 salient trial-related events defined 10 trial phases and were used for the analysis of optical recordings and video analysis of mouse behavior.
In addition, we estimated (3) the position in the maze, (4) the movement (estimated as the frame-to-frame change of the nose coordinates divided by the time between frames); when mice moved along the maze this variable represented speed, and (5) the time of the first re-orienting turn towards the door after arrival in the start-box. Potentially, all these behavioral variables could carry some explanatory power of the future behavioral choice and/or the measured prefrontal calcium signals. For the dimensionality reduction of all behavioral motor variables, we used UMAP embedding with the MATLAB library from Connor Meehan, Jonathan Ebrahimian, Wayne Moore, and Stephen Meehan (Uniform Manifold Approximation and Projection (UMAP), 2022, https://www. mathworks. com/matlabcentral/fileexchange/71902).
The median duration was used for plotting. This registration of the recorded signals by segment-wise temporal resampling permitted us to average and compare the activity patterns in the separate task phases across trials and mice. All mice contributed 4 expert sessions to the dataset, which in total comprised 1085 trials (544 rightward sample runs: 407 correct, 137 mistakes; 541 leftward sample runs: 405 correct, 136 mistake). The resampled traces were used for detailed analysis of the maintenance period (between events 5 and 7) in Figs. 2  and 4. To control for potential motion artefacts and hemodynamic signal components in our fiber photometry data of mPFC→dmStr pathway activity, we used two experimental strategies: First, we recorded task-related photometric fluorescence signals in a separate cohort of GFP-expressing control mice (4 mice, 5 sessions each).
Miniscope imaging and data analysis Four weeks after GRIN lens implantations, mice with GCaMP expression were taken into behavioral experiments. We used a head-mounted miniaturized microscope (nVista, Inscopix) for calcium imaging with cellular resolution. All mice were recorded at 20 Hz with a gain of 3-4 and 10-25% LED illumination (0. 2-0. 5 mW/mm 2 ). Simultaneously, we video tracked the mouse and recorded behavioral parameters such as water reward delivery and licking response. After mice completed several expert sessions, calcium imaging data were pre-processed using Inscopix Data Processing Software (version 1. 2. 1), allowing for motion correction and extraction of cellular calcium signal traces. For efficient data processing, movies were spatially downsampled by a factor of 4 and temporally downsampled by a factor of 2.
After applying motion correction in the Inscopix software, ΔF/F was calculated pixelwise using the mean fluorescence across all frames as F 0. Then, we applied the Inscopix PCA-ICA algorithm to automatically identify the spatial location of cells and their activity profile throughout the recorded session. Finally, in order to match cells recorded over several sessions, we employed the longitudinal registration algorithm. For further analysis, we used only those cells that were successfully recorded longitudinally in all expert sessions. For comparison with the fiber-photometry data, we summated the pre-processed activity of all longitudinally tracked neurons (28-88 neurons per mouse) for each mouse (n = 6 mice, 3-5 sessions per mouse) and treated these population signals in the same way as the bulk fluorescence signals measured by fiber photometry.
Analysis of single-cell activity across task periods and task phases We evaluated the degree to which neurons were specifically active in defined time windows (either the 3 task periods or the 10 task phases defined as the intervals between the 11 salient T-maze events). To optimize temporal accuracy and reduce the amount of non-spike fluctuations in the signal, we temporally deconvolved the preprocessed miniscope calcium imaging data using a supervised algorithm based on deep neural networks  43. Next, we calculated for all neurons and each trial the mean activity in each time window considered (either periods or phases). Based on a rank sum test of mean activity values for each time window across all correct trials against the pool of all other time windows, we identified the significance level of a given neuron being active in a particular time window.