dataesr/scientific-paragraphs-categorization huggingface dataset:
Mammalia release 10 dataset (mammalia. odb10) was used here. Assemblies made from reads with low error rates, such as HiFi hifiasm and 10X supernova assemblies had, as expected, a much higher BUSCO score (>90% complete sequences). Assemblies produced from long error-rich reads as CLR or ONT have a lower BUSCO score (< = 90%), mainly because read sequencing errors had poorly been corrected in the consensus. Assemblies were compared using inspector  31  , which produces metrics including the number of misassemblies, the duplication ratio and the k-mer-based completeness. Inspector metrics in Table  2  shows assembly quality values (QV) ranging from 22. 29 for the ONT wtdbg2 to 47. 25 for the Hifi hifiasm. The alignment of these assemblies against the reference gave a coverage greater than 94.
In contrast, long read technologies exhibit no clear biais suggesting that they are able to correctly detect insertions and deletions in this large spectra. The Bionano technology exhibits a very different pattern with a difficulty to identify small variants, a behavior that was expected. to misphased contigs creating duplication in haplotype2. Haplotype2 also had 9 Mb larger N50 but BUSCO scores were similar between both haplotypes (95. 7% and 95. 8%). For HiFi hifiasm parent assemblies, both haplotypes metrics were close (3. 16 Gb vs 3. 11 Gb and 71 Mb vs 69 Mb N50), but BUSCO scores were slightly different (95. 8% for haplotype1 and 95. 3% for haplotype2). Having no phased reference to evaluate these assemblies, we tested an in-house protocol (see code availability section) to estimate phasing quality as the proportion of misplaced haplotypes in each assembly.
The lower HiFi hifiasm Hi-C assembly phasing quality comes from the fact that Hi-C data is not optimal to separate contigs with distinct parental origins. Therefore the same calculation was performed on the contig level, which produced intra-contig separations are 84. 6% and 85. 6% showing that the Hi-C separation is less efficient than parental separation, but works fine on a contig level even if parental data is not available. To conclude, contig haplotyping worked with both approaches, HiFi hifiasm parent method being more efficient than HiFi hifiasm Hi-C. HiFi hifiasm Hi-C required additional processing, such as manual reorganization of the contigs in the haplotypes, in order to obtain good quality assemblies.
HiFi hifiasm parent hap2 HiFi hifiasm Hi-C hap1   Structural variation. The sequencing data made available here allows first to comprehensively analyze the structural variability for a typical bovine, and second to benchmark the different sequencing technologies and associated detection software for structural variation discovery. The size distribution of the structural variants detected on these datasets (Fig. 6 ) illustrates these two points. First, it illustrates the specific bovine structural variability with peaks corresponding to SINEs, LTRs, and LINEs. Second, it illustrates the behavior of the different technologies with respect to variant detection, such as the size spectrum of the different technologies. For example, the obvious size limitation for insertion detection using Illumina data.
acknowledgements We would like to thank the anonymous reviewers for their helpful feedback during the peer-review process. We thank members of  SeqOccIn Consortium  for their feedback before and during this study. We thank " La Région Occitanie " and  European Union  for  financing project in the call for projects "Regional Platforms of Research and Innovation " of the Occitanie region on the  Operational Program FEDER-FSE MIDI-PYRENEES ET GARONNE 2014-2020. APIS-GENE  and also industrial organisations of the ruminant sector  CNIEL, Interbev, Eliance, CNE and Institut de l'Elevage  who contributed to the financing of these analyses. The  INRAE experimental unit of Le Pin  produced the samples (https://doi. org/10. 15454/1. 5483257052131956E12 ). The SIGENAE team carried out part of the analyses.
, 2015). To quantify the proportion of variance in oviposition preference explained by inorganic phosphate content, we computed the likelihood-based coefficient of determination using the rr2 package  (Ives, 2019). In addition, we separately performed the same analysis on the data from the 20 fruit arenas using the phosphorus content of the 12 fruit media as estimated in  Olazcuaga et al. , 2019.
Confocal Laser-Scanning Microscopy and redox ratio analysis Confocal microscopic observations were carried out using the Axio observer Z1 microscope with the LSM 700 scanning module and the ZEN 2010 software (Zeiss). Excitation of roGFP2 was performed at 488 and 405 nm and a bandpass (BP 490-555 nm) emission filter was used to collect roGFP2 signal. For background subtraction, signal was recovered using a BP 420-480 nm emission filter during excitation at 405 nm. Image analyses and quantifications were performed as previously described  (Gutscher et al. , 2008) , using the public domain image analysis program ImageJ 1. 52i (https://imagej. nih. gov/ij/).
12. 4  (Anders et al. , 2015)  in 'union' mode and normalized by total of mapped reads (reads per millions, rpm). Differential analysis was performed using Bioconductor R v4. 1. 2 package DESeq2  (Love et al. , 2014)  with a false discovery rate of 0. 05. P values were corrected for multiple tests by the Benjamini-Hochberg rule (adjusted P value). The upregulated genes were defined with a fold change minimum to 2 and the downregulated genes with a fold change inferior to 1. The heat map was obtained using heatmap. 2 function of gplots R package (https://CRAN. R-project. org/package=gplots) using Pearson distance and an average method for the hierarchical clustering. To identify DE lncRNAs, Col-0 and nas124 Illumina reads were aligned to the Arabidopsis Transcriptome ReconstructoR pipeline (https://doi.
org/10. 1101/2020. 12. 10. 418897). All WT and nas124 sequences were submitted to the Sequence Read Archive (SRA): PRJNA782822. https://dataview. ncbi. nlm. nih. gov/object/PRJNA782822?reviewer=ee044uijk32bst5p1v52bogou9. Gene Onthology (GO) analysis was performed using PANTHER (http://www. pantherdb. org/).
Acknowledgments The authors thank  Michel Delseny  and  Ortrun Mittelsten Scheid  for critical reading of the manuscript and  Hervé Moreau  for facilitating access to the BioPIC platform. We also thanks  Tom Roscoe  for comments and correction of the English;  Karl Ravet  for antibodies against Arabidopsis ferritin and  S. Brando  and  M. Laudie  for technical assistance.
Funding This work was supported by the  CNRS , the  UPVD  (fellowships to CM), by grants  ANR SUBCELIF 087217  and  MetRibo 210877  and  RoxRNase 210880  and  Labex AGRO  (under  I-Site Muse framework ) coordinated by the  Agropolis Fondation  (grant no. Flagship  Project  1802-002 -CalClim ). This study is set within the framework of the " Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ) and of the  "Ecole Universitaire de Recherche (EUR) "  TULP-GS  ( ANR-18-EURE-00019 ).
Extracellular electrophysiology A small craniotomy (diameter, 1-1. 5 mm) was made over the recording sites a day before the first recording session. Extracellular spikes were recorded using 64 ch two-shank silicon probes (H-2, Cambridge Neurotech). Voltage signals were multiplexed, recorded on a PCI6133 board (National instrument), and digitized at 400 kHz (14-bit). The signals were demultiplexed into 64 voltage traces sampled at 25 kHz and stored for offline analysis. All recordings were made with the open-source software SpikeGLX (http://billkarsh. github. io/SpikeGLX/). During recordings, the craniotomy was immersed in a cortex buffer (125 mM NaCl, 5 mM KCl, 10 mM glucose, 10 mM HEPES, 2 mM MgSO4, 2 mM CaCl2; adjust pH to 7. 4). Brain tissue was allowed to settle for at least five minutes before recordings.
Histology Mice were perfused transcardially with PBS, followed by 4% PFA / 0. 1 M PBS. Brains were post-fixed overnight and transferred to 20 or 30 % sucrose PB before sectioning on a freezing microtome. Coronal 50 µm free-floating sections were processed using standard fluorescent immunohistochemical techniques. For images in Extended Data Fig. 4 , all sections were stained with NeuroTrace® 435/455 Blue Fluorescent Nissl Stain (Thermo Fisher Scientific, N21479). The fluorescent label was amplified with chicken anti-GFP (Thermo Fisher Scientific, A10262) and goat anti-chicken 488 (Thermo Fisher Scientific, A11039). Slidemounted sections were imaged on a Zeiss microscope with a Ludl motorized stage controlled with Neurolucida software (MBF Bioscience, Williston VT). Imaging was done with a 10× objective and a Hamamatsu Orca Flash 4 camera.
Every section from the frontal pole through the brainstem were imaged. For the CaMKII⍺ staining (Extended Data Fig. 3 ), GFP signal was amplified with rabbit anti-GFP (Thermo Fisher Scientific, A11122, RRID: AB_221569, 1:500) and goat anti-rabbit 488 secondary antibodies (Thermo Fisher Scientific, A11008, RRID: AB_143165, 1:500). The CaMKII⍺ was labeled with anti-CaMKII-α (6G9) (Cell Signaling Technology, #50049, RRID: AB_2721906, 1:250) and goat anti-rabbit 647 secondary antibodies (Thermo Fisher Scientific, A21236, RRID: AB_2535805, 1:500). Sections were imaged with a confocal laser scanning microscope (ZEISS, LSM 980) using a 20× objective.
Acute slice recording AAV was injected into P28 mice to label PTupper neurons in ALM bilaterally (n = 5; see Extended Data Table  1  for injection coordinate). Two to three weeks later, blue light (0. 5mW 0. 2Hz for one hour) illumination was given to one hemisphere under isoflurane sedation. Immediately after this, a slice was prepared and recorded (we also performed ex vivo illumination of brain slices, which yielded similar results; data not shown). Mice were perfused intracardially with a chilled choline chloride solution (124 mM Choline Chloride, 2. 5 mM KCl, 26 mM NaHCO3, 3. 3 mM MgCl2, 1. 2 mM NaH2PO4, 10 mM Glucose, and 0. 5 mM CaCl2, pH 7. 4 equilibrated with 95 % O2 / 5 % CO2). The brain was removed and placed in the choline chloride solution. Transverse slices (300 μm) from both hemispheres containing the ALM were cut using a vibratome (Leica) and maintained in a submerged chamber at 32 °C for 1 h and then at room temperature in oxygenated artificial cerebrospinal fluid (ACSF: 127 mM NaCl, 2.
AAV2/9 based on these plasmids were packaged by UNC vector core. For validation of efficacy of gRNA (Extended Data Fig. 3a  and b ), we transfected Neuro-2A cells in 6 well plates with pCAG-cre, pLenti-Cas9-GFP (addgene: 86145), and pAAV-U6CaMKIIgRNA-hsyn-mScarlet using Lipofectamine TM 2000 (Invitrogen). Two days after lipofection, GFP+ mScarlet+ cells were sorted (BD FACSAria™ Fusion), and genome DNA was extracted using DNeasy Blood and Tissue Kit (QIAGEN). Index was added using PCR with Nextera XT Index Kit (Illumina). We used MiSeq 300 cycle ver. 2 (Illumina) for the sequencing.
Videography analysis High-speed (300 Hz) videography of orofacial movement (side view) was acquired using a CMOS camera (Chameleon3 CM3-U3-13Y3M-CS, FLIR) with IR illumination (940nm LED). We used DeepLabCut 87 to track the movement of the tongue and jaw. Movements along the dorsoventral direction were analyzed and plotted in Extended Data Fig 1. Trajectories were normalized: the mean position before the cue was subtracted from trajectories and divided by the minimum value (thus, the downward movement of the tongue and jaw is upward in the plot). The onset of jaw movement in each trial is the first time point after the cue when the normalized movement trajectory exceeds 0. 15. The onset of tongue movement is when DeepLabCut first detects the tongue after the cue.
Extracellular recording analysis JRClust 88 (https://github. com/JaneliaSciComp/JRCLUST) with manual curations was used for spike sorting. We used a combination of quality metrics to rigorously select single units for analysis (Extended Data Fig. 6 ). First, a false positive rate was estimated according to the inter-spike interval violation  89  , and units with a false positive rate < 0. 15 were selected. Second, we extracted spike features (2 principal components of spike shape recorded in 3 neighboring recording sites) of all spikes recorded at the same or adjacent recording sites. Then, we calculated the Mahalanobis distance of spike features of each spike from the center of the spike cluster (unit) of interest. We performed the receiver operating characteristic (ROC) analysis of Mahalanobis distance to distinguish spikes within vs.
Mean speed (Extended Data Fig. 7 ) is the mean of absolute change in spike rate between cue to lick (here, spike rate was calculated in 50 ms time bin). We performed hierarchical bootstrap for statistics and SEM (1000 iterations). For the correlation analysis (Fig. 3d  and h , and Extended Data Fig. 7g ), we looked for an n × 1 unit vector rT, trial type, representing the spike rate of all n neurons at time point T of a trial type of interest. Then, we calculated Pearson's correlation of r between the two trial types indicated in the plot across time points (in the case of Extended Data Fig. 7g , autocorrelation was calculated). For recording in the expert mice (Extended Data Fig. 2 ), we recorded 716 neurons that passed the quality metrics in ALM in 20 sessions from 4 expert mice. 647 putative pyramidal neurons were analyzed.
Histology Analysis For Extended Data Fig. 3e , cell counting and signal analysis were conducted using MATLAB. Cells were binary classified into GFP and/or RFP positive/negative based on the fluorescence signal intensity. The CaMKII⍺ signal intensity within the cell was normalized by the signal in the surrounding areas (median of areas within 12-60 pixels (0. 6 µm/pixel) from the cell). For Extended Data Fig. 4 , imaged sections were processed with NeuroInfo software (MBF Bioscience, Williston, VT) to align the serial sections into a whole brain volume. Neurons throughout the ALM cortex were detected and marked using the automatic cell detection function in NeuroInfo software, which uses a modified Laplacian of Gaussian (LOG) algorithm that detects labeled neuron perikarya based on size and fluorescence intensity with a neural network that eliminates signal artifacts.
Acknowledgment We thank  N. Li ,  L. Colgan ,  T. Wang ,  D. Fitzpatrick , and  Inagaki  lab members for comments on the manuscript,  M. Inagaki  and  L. Walendy  for animal training,  E. Wu ,  V. Grimaldi  and  R. DiCicco  for histology,  P. Scarpinato  for DeepLabCut analysis,  J. Yu  for molecular biology, and  H. Shearin  and other MPFI ARC members for animal care. This work was funded by  ZIA MH002497-34  (C. R. G. ),  Howard Hughes Medical Institute  (S. R. ),  Max Planck Florida Institute for Neuroscience  (H. K. I. and R. Y. ),  Max Planck Free Floater Program  (H. K. I. ),  NIH   New Innovator Award  ( NINDS  and OD;  1DP2NS132108 ; H. K. I. ),  Searle Scholars Program  (H. K. I. ),  Klingenstein-Simons Fellowship  (H. K. I. ), and  McKnight Scholar Award  (H. K. I. ).
e. Same as b for PTlower paAIP2 manipulation. N = 140 preparatory neurons in ALM (neurons with >=10 trials for all lick times were analyzed). Similar ALM activity across experimental conditions when mice licked around the same time a. Comparisons of the grand average PSTHs of positively-modulated ALM preparatory neurons on day 1 early trials vs. day 3 late trials (regardless of lick time; the same dataset is quantified in Fig. 4c-d). PSTHs are aligned to cue (top) or lick (bottom). Control shows a clear change in cue response and ramping activity (red arrows). Early and late trials, first 75 and last 75 trials in the session. See Extended Table 3 for number of neurons analyzed. b. When mice lick around the same time (regardless of the day of training), the grand average PSTHs of ALM are similar regardless of paAIP2 manipulation.
f. We have repeated this correlation analysis shown in d-e 1000 times (sampling random subsets of neurons without replacement; we have tested different numbers of clusters: 5, 10, or 20, all of which yielded similar results) to plot the mean and SEM. C, control: the correlation between WT vs. WT2, showing the upper bound of the correlation coefficient with this procedure and sample size (as neurons are sampled from the same dataset). Both PTupper and PTlower paAIP2 manipulation conditions have similar correlation coefficients to control (p > 0. 05), indicating that activity patterns in ALM are indistinguishable across conditions when mice lick around the same time, i. e. , paAIP2 manipulations in PT neurons do not change task-related activity patterns in ALM.
Meteorological data Meteorological data were extracted from the ERA5-Land reanalysis dataset  [34] , obtained from the Copernicus Climate Change Service (C3S) Climate Data Store, for the whole zone of interest, between 01/01/1990 and 31/12/2021. The ERA5-Land dataset contains hourly estimates of temperature and precipitations, with a 0. 1° x 0. 1° spatial resolution and covers the period from 1981 to present. The selection of the data for the study area resulted in a 35,789-pixel grid, for which we computed the daily mean temperature ('2m temperature' variable in ERA5-Land dataset) and extracted the total daily precipitations by selecting the last cumulative value of the day ('total precipitation' variable).
Hereafter, as there was several trapping methods used for some sources, data collected using a given trapping method in a given source will be referred to as a 'dataset', and longitudinal data from a given trap as a 'time series'. Overall, the entomological data included 15 datasets for Ae. caspius, 14 for Ae. detritus, 13 for Ae. vexans, 16 for Cx. pipiens and 15 for Cx. theileri, representing 9900, 9226, 8528, 9504 and 8835 time series, respectively (Supplementary Table  S1 ). All these data were geolocated in 570 pixels, with at least 505 different pixels per species.
Constant parameters and meteorological-dependent functions All constant parameters and meteorological-dependent functions were obtained from literature review. In the absence of information for one particular species, parameters or functions were extrapolated from another species of the same genus. Functions describing the relationship between transition rates and temperature were calibrated from experimental data when available, using the Logan model  [40, 41]. This model is described by Equation  (3). 𝑟(𝜃) = 𝐴 (e 𝐵(𝜃-𝜃 𝑚𝑖𝑛 ) -𝑒 𝐵(𝜃 𝑚𝑎𝑥 -𝜃 𝑚𝑖𝑛 )-𝜃 𝑚𝑎𝑥 -𝜃 𝐶 ) ( 3 ) where 𝑟 is the temperature-dependent rate, 𝜃 the temperature and 𝜃 𝑚𝑖𝑛 -𝜃 𝑚𝑎𝑥 the range of temperature of possible development. 𝐴, 𝐵 and 𝐶 were estimated in each case using a nonlinear leastsquares method (Supplementary Table  S2 ).
e. , rescaled between zero and the maximal observed value) at the same dates and locations (see the 'Parametrization of the carrying capacities' section in Supplementary Information). Because the biology of Aedes and Culex mosquitoes are different, we hypothesized that the relationship between the carrying capacities and the probability of presence was different between these genera. We thus computed one value of 𝛼 per genus. M1, M2 and M3 were evaluated using SP1 and IT1 datasets, because in both cases, data from several pixels were available for each of the five species, with a high diversity of presence probabilities. For instance, regarding Ae. caspius, time series were available for 42 pixels in IT1 data, where the probability of presence of this species varied between 0. 07 to 0. 69, and in 506 pixels in SP1 data, with a probability of presence between 0.
With M2 (Fig. 2-M2 ), the model again overpredicted mosquito population sizes in all pixels, but to a lesser extent in pixels having a low probability of presence. Finally, using M3 and the calibrated 𝛼 values, the differences between the rescaled predicted and observed values according to the probability of presence were low for all pixels, except for the pixel with the highest probability of presence in IT1 data, for which the model under-predicted mosquito abundance (Fig. 2-M3 ). The under-prediction in this pixel was due to the high value of 𝛼 for Aedes inhibiting the simulation of large mosquito populations with a probability of presence of 0. 68, and abundance peaks not being predicted by the model. Overall, based on the IT1 and SP1 datasets, the method using the probability of presence data, with a power link and a calibrated 𝛼 value (M3) was the best method to define the pixel-specific carrying capacities for the five mosquito species.
Discussion This study aimed to simulate the regional-scale spatial and temporal dynamics of RVFV vector populations in the western Mediterranean Basin. The model developed here was adapted from  [12]  and based on a generic framework that had already been used with satisfying results in Botswana  [14] , France (mainland and Reunion Island)  [23, 47, 48] , Mauritius  [49]  and Senegal  [13]  for several species belonging to Aedes, Anopheles and Culex mosquito genera. To spatialize the model at the scale of the western Mediterranean Basin, we used the probabilities of presence computed by Wint et al. [33]  for each mosquito species. Based on entomological datasets from four different countries, we calibrated the model and assessed its outputs. Datasets from Spain (SP1) and Italy (IT1) provided time series observed in a large number of pixels, with a large range of associated probabilities of presence for the five mosquito species.
Introduction of stochasticity is another way to reduce the dependence of outputs on parameter values, but this approach was discarded here due to computational time needed to perform multiple simulations. Predictions of the model were able to reproduce most of the periods of mosquito activity (Fig. 4 ). However, some discrepancies were noted. Ae. detritus field populations often decrease during summer months, as observed in most of the datasets used here and in the literature  [50, 51]. However, this phenomenon was not well reproduced by the model, at least for some areas such as the Gulf of Lion region, the coast of the Balearic Sea in Spain or the Naples region in Italy (Fig. 4  and Supplementary videos). In addition, our assumption of an absence of diapause in Maghreb may be subject to local variation, as such a period of inactivity has been observed for Ae.
In Senegal, the availability of larval developmental sites has been assessed using satellite images  [13] , but this approach would have been difficult to achieve, considering the size of the region of interest and the diversity of biotopes of the five mosquito species included. Spatial predictions of abundance levels were satisfying for Aedes species. However, the model tended to over-predict the abundance of Culex mosquitoes, in particular Cx. theileri, as in Tunisia (Bouattour, unpublished data), or in Italy where a few individuals were caught in the IT1 datasets  [57] , although our model predicted high abundance at the same time and locations. The time series with the highest observed abundance of Culex mosquitoes were located in pixels with low to medium probabilities of presence, leading to the computation of a relatively low value of 𝛼.
Acknowledgement Muñoz Sabater , J. , (2019) [34] was downloaded from the  Copernicus Climate Change Service (C3S) Climate Data Store. The results contain modified Copernicus Climate Change Service information 2020. Neither the  European Commission  nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. We are thankful to the  Spanish Ministry of Agriculture, Fishery and Food (MAPA)  for allowing access to data from the  Spanish National Surveillance  control and eradication of bluetongue program and to the  French Ministry of Agriculture , which funded this research. We are grateful to  Pauline Ezanno ,  Annelise Tran ,  William Wint  and  François Rebaudo  for their help in parametrizing the model.