dataesr/scientific-paragraphs-categorization huggingface dataset:
The main limitation of our study is related to the validation dataset, as we have a small sampling size, sometimes Figure  5. Left-hand plot (a) , relationship between the scale of effect for the modelled bees and their average foraging distances (α). Righthand plot (b), relationship between the scale of effect for the observed bee species and the prediction of their typical homing distance based on the intertegular distance according to  Greenleaf et al. (2007). The numbers represent the species identity (Table  1 ). In both plots, the black circles represent the scale of effect based on the complete sample size, and the blue dots represent the scale of effect values obtained with jackknife subsampling. The size of the dots represents the occurrence frequency of the scale of effect values.
Acknowledgements -This study was supported by a grant from Occitanie region. We thank  Fabien Laroche ,  Marc Deconchat ,  Emilie Andrieu ,  Francois Calatayud  and  Yousra Hamrouni  for their help and insightful comments. We are grateful to the farmers for allowing us to work in their fields and to everyone involved in the field surveys and species identification. Funding -Field work was part of the  Sebioref  project funded by the  Occitanie region  and the  French National Research Institute for Agriculture, Food and Environment (INRAE).
Sample and Data Collection On both sites, non-invasive sampling of bat feces was performed from August 2020 to July 2021 at monthly intervals. Two-square-meter plastic sheets were placed under the bat roosts overnight (5 sheets per site). Feces were collected in the mid-morning from each plastic sheet. Individual sampling was carried out by assuming that a space of 20 by 20 cm on the plastic sheet was representative of the defecation area of one individual: One dropping of fresh feces was selected and placed in 2 mL tubes with 0. 5 mL of home-made RNA stabilization solution (http://www. protocol-online. org/prot/Protocols/RNAlater-3999. html; accessed on 1 May 2020). Samples were then stored at -80 • C before further laboratory analyses. During sampling, other observations were recorded such as females showing pregnancy, puppies attached to mothers and puppies in specific roost nurseries.
Genetic Analyses 2. 8. 1. Viruses Sequence Identification and Phylogenetic Analysis Generated Sanger viral sequence assemblage was performed using Geneious software package V. 2021. 2. 2 (Biomatters Ltd. , Auckland, New Zealand). The generated contigs Viruses 2022, 14, 781 5 of 18 were compared to a database of sequences online using Basic Local Alignment Search Tool (BLAST) (https://blast. ncbi. nlm. nih. gov/Blast. cgi) to identify the amplified sequence. Mega 7  [38]  was used to align the CoV sequences obtained in this study with references obtained from GenBank. A maximum likelihood phylogenetic tree of aligned CoV sequences was implemented by using IQ-Tree  [39]. The reliability of branching orders was tested using the bootstrap approach (1000 replicates), and the GTR + F+ I substitution model was determined as the best suited evolution model  [39].
Sampling, Data Collection and Reproductive Phenology Due to travel restrictions linked to COVID-19 in Zimbabwe, no samples were collected in January, April or June 2021 at Chirundu farm and in Magweto none were collected from December 2020 to February 2021 or in May and July 2021. We analyzed 1866 fecal samples from different insectivorous bat species including 308 fecal samples from Macronycteris gigas species collected at Magweto cave site and a total of 1732 fecal samples from insectivorous bats collected at the Chirundu farm site (Table  1 ). At both sites, the reproductive season, i. e. , from gestation to weaning, was observed from September to February for the predominant insectivorous bat species. At both sites, the different insectivorous bat families observed were found to be synchronous regarding their reproductive cycles, and consensus reproduction periods were determined based on the literature and observations (Tables  1  and S1 ).
Bat Species By using BLAST to compare the newly obtained 800 bp Cytochrome B sequences of the CoV positive samples to the CytB sequences available in GenBank we identified different bat species at the two sampling sites. We identified at least seven species; Hipposideros caffer and Miniopterus mossambicus were present at both sites. At Chirundu farm, we also identified Rhinolophus landeri and Nycteris thebaica, whereas at Magweto cave we also characterized Macronycteris gigas, Cleotis percivali and Rhinolophus simulator. The presence of these species was confirmed by capture sessions. We also characterized bats from the Rhinolophus genus, close to Rhinolophus eloquens from the captured animals. Results obtained by BLAST analysis were confirmed by maximum likelihood phylogenetic analysis (Figure  S1 ).
Acknowledgments: We thank  Billy Butete  for his field assistance. The authors gratefully acknowledge and thank the  International Atomic Energy Agency  for making available the sequencing services. We thank the  Research Council of Zimbabwe  for approving this study (research registration certificate no. 03006 ) and the Hurungwe Rural District council for their assistance and facilitation. We thank the  Animal Research Ethics Committee of Zimbabwe  for their approval (ref number  002/2017 ). This work was conducted within the framework of the  Research Platform "Production and Conservation in Partnership" (RP-PCP ).
We, therefore, pooled the two groups of Ctrl mice for further analysis and data representation. Food intake and body weight were recorded throughout the different protocols. Unloading in control mice resulted in only a small body weight loss (<10%) that occurred within the first 3 days concomitantly with a decrease in food intake, whereas HF treatment did not modify food intake or body weight ( see Supplementary Figures  S1  and S2 ). Tissue collection. At the end of the experiments, mice were euthanised by cervical dislocation. The soleus, gastrocnemius, tibialis anterior, and extensor digitorum longus (EDL) muscles were carefully collected and weighed prior to immediate freezing in liquid nitrogen and storage at -80 • C until analyses. Measurement of protein synthesis in gastrocnemius. At the end of protocol 2, mice received an intraperitoneal injection of 0.
Both were subsequently hybridised with a corresponding secondary antibody conjugated to Alexa-Fluor (Invitrogen, Cergy-Pontoise, France). Image acquisitions were performed with a high-resolution ORCA-Flash4. 0 LT+ Digital CMOS camera coupled to a IX-73 microscope (Olympus, Münster, Germany) and Cell-Sens dimension software (Olympus Soft Imaging Solutions, Münster, Germany). The CSA was determined for 1000-1500 fibres per animal, using ImageJ software 1. 53f51 (http://rsb. info. nih. gov/ij/, accessed on 3 April 2018). Protein isolation. Gastrocnemius muscles were pulverised in liquid nitrogen. (  1 ) For all targets, ~30 mg of the resulting powders were homogenised using a polytron in 1 mL of an ice-cold buffer (10 mM Tris pH 7. 5, 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, and 0.
For anti-puromycin antibody, an anti-mouse IgG2Ak (115-035-206, Jackson ImmunoResearch Laboratories, West Grove, PA, USA) was used. Signals were detected after incubation with Luminata Crescendo Western HRP substrate (Millipore, Burlington, MA, USA) and visualised using G: BOX ChemiXT4 (XL1) imaging system (Syngene, Frederick, MD, USA). Signals were then quantified using ImageJ 1. 53f51 software. Two samples from each group were loaded on each gel. The signal recorded within each lane of one Western blot was normalised to the overall signal of that blot, and then signals were normalised to the total amount of proteins determined by the Biorad's stain-free system or ponceau S to correct for uneven loading. The normalised values were then averaged by group and expressed as the fold change from the mean of all H 2 O-ctrl samples.
PCR reactions were performed using the IQ SYBR Green Supermix (Biorad, Marnes-la-Coquette, France), in accordance with the instructions of the manufacturer. The comparative threshold cycle (2∆∆CT) method was used to compare the relative mRNA expression between each group, using TBP (TATA binding protein) as a reference gene for muscle. The relative mRNA abundance was arbitrarily set to 1 for the H 2 O-Ctrl group. Statistics. All data are means ± SEM and were analysed for normality of residuals using the Shapiro-Wilk test. No set of data was transformed for non-normality distribution. For protocol 1 (n = 6/group), we performed a multiple Welch t-test within each week. For protocol 2 (n = 8-19/group), we performed a two-way ANOVA with the factors "Hindlimb suspension" and "Halofuginone" and corrected the data for multiple comparisons using Tukey's test.
These analyses were performed using Prism 9 (GraphPad Prism 9, San Diego, CA, USA). Transcriptomic Data. We used transcriptomic data from already published studies  [46]. The transcriptomic bear data supporting Figure  6  of this study are openly available in the GEO repository database (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi, reference no. GSE144856, accessed on 1 September 2021). To identify the differentially expressed genes (DEGs) from this list, we selected a winter/summer (FC) > 1. 0 with an adjusted p-value < 0. 05 as cut-off for the up-regulated genes. Supplementary Materials: The supporting information can be downloaded at: https://www. mdpi. com/article/10. 3390/ijms24010621/s1.
Contributions: Conceptualisation, L. C. (Laura Cussonneau), A. -C. M. and L. C. (Lydie Combaret); methodology, L. C. (Laura Cussonneau), L. C. (Lydie Combaret), C. D. , C. C. -G. , G. C. , J. H. , M. D. -M. and Y. D. ; analysis, L. C. (Laura Cussonneau), L. C. (Lydie Combaret), C. D. and C. C. -G. ; writing-original draft preparation, L. C. (Laura Cussonneau) and L. C. (Laura Cussonneau); writingreview and editing, all authors; project administration, L. C. (Laura Cussonneau); funding acquisition, E. L. , F. B. and L. C. (Laura Cussonneau). All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Center National d'Etudes Spatiales (CNES, #865, #974, #1006, and #1905), the iSITE Challenge 3 Mobility program (Université Clermont Auvergne), and the Agence Nationale de la Recherche (B-STRONG ANR-22-CE14-0018).
Author Contributions: Conceptualisation, L. C. (Laura Cussonneau), A. -C. M. and L. C. (Lydie Combaret); methodology, L. C. (Laura Cussonneau), L. C. (Lydie Combaret), C. D. , C. C. -G. , G. C. , J. H. , M. D. -M. and Y. D. ; analysis, L. C. (Laura Cussonneau), L. C. (Lydie Combaret), C. D. and C. C. -G. ; writing-original draft preparation, L. C. (Laura Cussonneau) and L. C. (Laura Cussonneau); writingreview and editing, all authors; project administration, L. C. (Laura Cussonneau); funding acquisition, E. L. , F. B. and L. C. (Laura Cussonneau). All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Center National d'Etudes Spatiales (CNES, #865, #974, #1006, and #1905), the iSITE Challenge 3 Mobility program (Université Clermont Auvergne), and the Agence Nationale de la Recherche (B-STRONG ANR-22-CE14-0018).
This assumption does not hold for cyclists  [24]. Recently, cities have deployed bicycle counters data request can be sent to Mathieu Cunche, Associate Professor at INSA-Lyon / Inria, member of the Privatics team hosted by the CITI laboratory, at [mathieu. cunche@inria. fr]. The second dataset (Monresovelo) is accessible here: https://donnees. montreal. ca/ville-de-montreal/trajets-individuelsvelo-enregistre-mon-resovelo. The other data used are publicly available through OpenStreeetMap. and made count data publicly available. They help to study cyclists' behavior in more detail than by using only origin/destination pairs. Some studies use these data to check the representativity of non public crowd-sourced GPS data  [25] , to identify environment factors influencing the bicycle volume  [25, 26]  or to create models that fit the pattern of seasonal bicycle demand  [27].
Experienced tracks Despite the increasing number of GPS sensors and the number of studies using these kinds of data, few data-sets of biking GPS tracks are publicly available, and those are of small or medium size. Two data-sets are used in our study: • An open data-set provided by Montreal, Canada, collected by their MonResoVelo application (public). MonResoVelo is a smartphone application that cyclists could use while biking to be tracked during their trips. Data collected by the city were then used to improve the development of cycling facilities. 4881 GPS tracks from this application are public. They have been generated by cyclists from June 2013 to October 2015 and are contained in a JSON file. Each track t is a sequence of N ordered points t = {p 1 ,. , p N } with each point p t = (lat t , lon t ) being made up of a latitude value and a longitude value.
Other information such as the departure and arrival time are available but we only used the GPS points. The tracks are already mapmatched and anonymized. We decided to ignore the temporal properties of the tracks and to only focus on the spatial ones, because it makes the setting of the learning algorithms used later easier. We thus removed the timestamps from the Veleval project tracks. We used the Ramer-Douglas-Peucker algorithm on both datasets to decrease the number of GPS points per tracks. Fewer GPS points means shorter processing time. For the sake of brevity, only the Veleval data are presented in the paper. We preferred the Veleval dataset to the MonResoVelo one because it contains almost exclusively commuting trips. As our model has to create paths using an O/D pair, using a dataset of cyclists with a specific destination for each of their trips seemed more relevant.
Preferred road segments identification Cyclists in our dataset tend to make detours to use preferred road segments. Paths created by our model must therefore make the same detours to simulate cyclists' behavior. We need to first identify the preferred road segments. Using the entire data-set allows to identify the preferred road segments used by most cyclists, but not to find those used by only a part of them. We assume that decomposing the data-set will allow us to access more detailed information and identify all preferred road segments, regardless of the number of cyclists using them. Therefore we divide our data-set of experienced tracks into clusters (see the Classification block on  Fig 1). Each cluster is grouping experienced tracks that share one or several road segments. Road segments identified in the clusters are then used as preferred road segments to simulate detours made by cyclists.
Given a set of points and a distance, DBSCAN will group points closely packed together and mark as outliers points that have not a sufficient number of close neighbors. It does not require a predefined number of clusters, and has two parameters. We explain their meanings and how we set them up in Section 3. 3. DBSCAN's properties make it a clustering algorithm adapted to our case. It generates groups of tracks that all share a certain amount of road segments which can then be used as preferred road segments. It also discards tracks that do not share enough road segments with others, allowing our model to use only tracks that represent minimally widespread behavior in our dataset. To confirm our intuition, we compared DBSCAN with k-means, the most known clustering algorithm. K-means is not density-based, and requires a number of clusters.
3 to create the testing data-set. During the training phase, only tracks belonging to the training set are sent. We used tracks belonging to the testing set to see how successful our NN is at classifying tracks that were not used during its training. Our trained LSTM has a success rate of 97% on the training set and 79. 1% on the testing set. Fig  10  shows the good and bad predictions of the LSTM in each cluster in the testing dataset. There are few clusters for which the LSTM makes only false predictions, and these clusters are all very small. It is possible that these clusters being small, they are not represented during the training phase of the LSTM. To overcome this issue, we tried to create our testing data-set by taking 20% of the experienced tracks of each cluster instead of taking 20% of the whole data-set, without success.
Discussion A first limitation of our work is the dataset we used. We were unable to find a massive public dataset of GPS tracks generated by cyclists. Therefore, we used a private one that we already owned. It gathers GPS tracks of a small group of cyclists who mainly made commuting trips. It is thus made up of tracks that are similar to each other, facilitating the clustering work. In future research, it would be meaningful to use a set of GPS tracks with more data and generated by more cyclists. In the S1 Appendix, we used the data-set MonResoVelo which contains almost 5000 crowd-sourced GPS tracks, and created a model using the same methodology. The silhouette score of the clusters dropped to almost 0, and the performances of the LSTM dropped to 55%. However, even with a silhouette score at 0, the tracks created with the clusters are still way more similar to the GPS tracks than the three other types of tracks used as a comparison.
4B, C ) did not reflect these changes. Overall, these results suggest that along the activation/differentiation program of anti-tumor T cells, Suv39h1-dependent silencing of the IFN-I response and of pathways of the exhaustion program, imposes an epigenetic barrier that prevents re-programing of  The anti-PD-1 response signature of Suv39h1-deficient CD8+ T cells discriminates response to anti-PD-1 in melanoma patients. We then asked whether the signature of Suv39h1-KO CD8+ T cells that increase after anti-PD-1 in the mice is relevant to human cancer. To address this question, we first interrogated whether our data correlated with available published signatures density from scRNAseq of CD8+ T cells from melanoma patients responding or not to immune checkpoint blockade  33. We first confirmed that the "responder" and "non-responder" signatures from the original paper correspond to distinct subpopulations of cells in the human dataset (Supplementary Fig.
B Heatmap of proportion and numeric value of the downsampled 4097 cells for each of the four conditions within the eight clusters. C Feature plots of human melanoma CD8+ TILs showing responder and not responder signature score from scRNAseq published dataset  32. D Feature plots of human melanoma CD8+ TILs showing distribution of signature scores from littermate WT and Suv39h1-KO treated or not with anti-PD-1 dataset conditions. E Correlation between transcriptomic signatures from Suv39h1-KO mice treated with anti-PD-1 and the responder and not responder melanoma patients 32 calculated with Pearson correlation coefficient. F Feature plots of murine CD8+ TILs (Fig. 3B ) highlighting distribution of signature scores of littermate WT and Suv39h1-KO treated or not with anti-PD-1 dataset conditions.
Cell surface labelling was done according to the experiment at 4 °C and intracellular staining was done using a fixation/permeabilization kit (eBioscience/Thermo Fischer) according to the manufacturer's instructions. Annexin V APC Apoptosis Detection Kit with 7-AAD (Ozyme) staining was performed after cell surface staining per the manufacturer's instructions. All data acquisition was done using an LSRFortessa instrument with FACSDiva software v8. 0. 1 (BD) and analyzed using FlowJo software (TreeStar). Representative flow cytometry gating strategy for CD8+ T cells are shown in Supplementary Fig. 7A. Adoptive transfer. Total splenocytes and lymph nodes suspension cells of WT littermate and Suv39h1-KO CD45. 1 OT-1 male mice were cultured for 6 days in vitro with anti-CD3 (10 μg/mL), anti-CD28 (1 μg/mL) mAbs (BD) and OVA peptide (SIINFEKL, 1 mg/ml) supplemented with rhIL-2 (100 U/mL Proleukin, Novartis) in RPMI medium (GIBCO), with 2-mercaptoethanol, Pen-Strep, L-Glu- tamine (LifeTechnologies), and 10% FCS (Biosera).
Tumor infiltrating cells, 20 days after tumor implantation, were stained with a mix of PEconjugated H-2Kb/SIINFEKL and APC-conjugated H-2Kb/SIINFEKL tetramer, the biotiniylated monomer H-2Kb/SIINFEKL was produced by the recombinant protein facility P2R (Inserm-U1232, SFR Santé, Nantes, France) and analyzed using FlowJo software. The tetramer+ cells were gated on CD45+ TCRb+ CD8+ cells. Sample preparation for single-cell RNA-seq and ATAC-seq. Suv39h1-KO and littermate WT male mice were injected with B16-OVA tumor cells as previously described. On day 20, tumors were processed with Liberase and DNase I, stained with DAPI, CD45. 2, TCRb, CD8, CD19, NK1. 1, F4/80 and sorted on an FACSAria III (BD) by gating on live, CD45. 2+, TCRb+, (CD19, NK1. 1, F4/80-negative cells) and CD8+ on PBS 0. 04% BSA.
Indexed libraries were tested for quality, equimolarly pooled and sequenced on an Illumina HiSeq2500 using paired-end 26 × 98bp as sequencing mode. By using a full Rapid flow cell, coverage was around 100M reads per sample corresponding to 100,000 reads per cell. Single-cell RNA-seq analysis. Single-cell expression was analyzed using the Cell Ranger Single Cell Software Suite (v2. 0. 1) to perform quality control, sample demultiplexing, barcode processing, and single-cell 3′ gene counting. Sequencing reads were aligned to the mm10 v1. 20 transcriptome using the Cell Ranger suite with default parameters. Downstream analyses were performed using Seurat (v3. 2. 1)  50. Starting from the unfiltered matrices, empty droplets were discarded using the EmptyDrops package 51 using a FDR threshold of 0.
01. The gene-cellbarcode matrix of individual samples was log-transformed and filtered based on the number of genes detected per cell (any cell with <500 genes or more than 5000 genes per cell was filtered out). Based on inspection of the UMI distribution over mitochondrial content, any cell with more than 9% of mitochondrial UMI counts. Contaminating cells were removed using the expression of Tyrobp, H2-Eb1, Apoe and Hbb-b. After filtering, a total of 21,646 single cells were analyzed. To allow comparison between genotypes, treatments and replicates, integration was performed using Seurat v3 anchors procedure, selecting for the 2000 most variable genes using the 'vst' method and computing anchors using the first 30 principal components. Clusters were then identified using the "Find_Clusters" function in Seurat with a resolution parameter of 0.
5. Clustree analysis was performed using Clustree R package (Version 0. 2. 2)  52. 16 clusters were initially defined, and are visualized using UMAP in Supplementary Fig. 4A. To simplify the analysis, some clusters which shared defined signatures and which did not evidently change among the different conditions were grouped as follow: original clusters 1, 3, 6, 7 and 11, corresponded to cycling cells in the G1/S and G2/M phase (Supplementary Fig. 4B, C ), were merged into one cluster (cluster 8 "cycling cells", Fig. 3B ); and original clusters 0, 8, 12, 13, and 14 (Supplementary in Fig. 4A ) which shared a "late exhaustion" signature, were merged together (as cluster 7 "late exhausted", Fig. 3B ). Unique cluster-specific genes were identified by running the Seurat "Find_All_Markers" function using the wilcoxon test.