dataesr/scientific-paragraphs-categorization huggingface dataset:
This data set, referred to as Inventaire National du Patrimoine Naturel (INPN; https://www. gbif. org/dataset/75956ee6-1a2b-4fa3-b3e8-ccda64ce6c2d; Dutrève and Robert, 2016) , comes from the Global Biodiversity Information Facility (https://www. gbif. org/). The underlying occurrences were collected in various contexts, including floras and regional catalogs, specific inventories, field notebooks, and surveys carried out by botanical conservatories. We kept only a subset of these data corresponding to the five invasive species considered in our study. The resulting data set contains 20,810 occurrences (see Table  1  for the detailed numbers per species) aggregated in 3242 quadrat cells of 100 km 2 distributed on a regular grid of 5175 quadrat cells covering the French territory. Species distribution models were computed via MAXENT  (Phillips et al.
, 2004 (Phillips et al. , , 2006)) , a popular environmental niche modeling method. In particular, we used the implementation of the maxnet  (Phillips et al. , 2017 ) R package that expands the input environmental variables with several functions (including linear, quadratic, threshold, hinge, and first-order interactions). Because we used presence-only SDM, we used pseudo-absence localities for model parameterization (see Appendix 2 for more details). MAXENT was computed on a set of 29 input environmental variables, including bioclimatic, pedological, topological, hydrographical, and land cover variables from CHELSA Climate data 1. 1  (Karger et al. , 2017) , Consultative Group on International Agricultural Research-Consortium for Spatial Information (CGIAR-CSI) potential evapo-transpiration (ETP) data  (Zomer et al.
In the predicted abundance function, we then kept these variables constant across space to cancel the effect of the sampling effort (see Appendix 2 for more details). This approach has already been proposed and successfully used in the literature of SDMs  (Warton et al. , 2013; Stolar and Nielsen, 2015). The predictive effectiveness of the models was then assessed using the INPN count data as a validation set. We used two evaluation metrics: (1) the true skills statistics (TSS) equal to the sum of the sensitivity and the specificity minus one (as described in  Allouche et al. , 2006) , and (2) the accuracy on 10% densest quadrats (A10DQ; see Appendix 2 for more details). The TSS is the sum of sensibility and specificity minus one when comparing the SDM predicted presences/absences of a species with the references (the INPN data set).
3%). Consequently, the model could capture information on the distribution of Reynoutria from the Pl@ntNet data. Conversely, very good results were obtained for both metrics for Opuntia ficus-indica and Carpobrotus edulis. the models. Accordingly, sensitivity values were generally accurate for most species. Nevertheless, there were also regions for which the Pl@ntNet model and INPN data disagreed; in these regions the Pl@ntNet model predicted high abundances but there were none or very few occurrences in the INPN data. The strongest disagreement occurred for Reynoutria japonica, i. e. , the taxon for which the specificity was the lowest. Other false-positive prediction regions included the west coast for Opuntia ficus-indica and Carpobrotus edulis and the "Golfe du Lion" (arc on the southeast coast) for O.
Typically, specimens occurring outside of presence areas identified by experts and not categorized as cultivated or casual invasive should be prioritized for expert validation. In this study, our sampling effort correction approach was based on prior knowledge of sampling intensity in the Pl@ntNet data. We could not evaluate the errors related to the sampling effort bias without complementary systematic survey data. Nevertheless, the INPN data have their own heterogeneity in the spatial distribution of the sampling effort. These data were collected by independent regional conservatories, and variations in sampling by different workforces may have introduced regional heterogeneity. Furthermore, some zones are not surveyed by conservatories, typically cities in most cases, which tends to bias the Pl@ntNet model error in urban areas.
CONCLUSIONS This study is the first to evaluate the potential of automated identification of opportunistic plant observations for modeling species distributions. The described methodology allowed us to analyze the potential usefulness of the Pl@ntNet data. By comparing SDMs trained on Pl@ntNet unvalidated observations with validated independent count data on a large spatial scale, we found that the data are rich enough to be used for SDM with only a single year of data collection. However, we also showed that distributions reported from Pl@ntNet data do not precisely match those of expert data. The main reasons for these deviations appear to be the presence of cultivated or casual invasive specimens in the data set, the detection of real presence in new areas, and the limits of the sampling bias correction method.
Because the GoogleNet model we used was already pre-trained on this generalist data set, we used the following methodology for finetuning it on our data set of 11,000 species (using the Caffe framework  [Jia et al. , 2014] ): 1. The linear classification layer was replaced by a new one aimed at classifying the new classes (i. e. , the 11,000 species). It was initialized with random weights and the learning rate was multiplied by 10 for this layer. 2. The other layers were kept unchanged to initialize the network with the weights learned from ImageNet. 3. The network was trained on the 332,000 plant images of our training set. A batch size of 16 images was used for each iteration, with a learning rate of 0. 0075 with images of 224 × 224 resolution. Simple crop and resize data augmentation was used with the default settings of the Caffe framework.
Calculation of N c (i): We order the cells by decreasing the count of i and note C k the count of the k-th cell in this order. As we are interested in the quadrat cells ranked in the highest 10%, if C 518 > C 519 , we set N c (i) = 518. Otherwise, C 518 = C 519 (ex aequo exists for 518th position), then we note sup the position of the last cell with count C 519 and inf the position of the first cell with count C 519. The chosen rule is to take N c (i) such that N c (i) = Min(|sup-518|, |inf-518|). APPENDIX 3. Detailed methodology of how environmental variables were collected and formatted in our study. We used data covering the French metropolitan territory, freely available on the web. The environmental descriptors are listed in using the rgdal package in R, which was the reference coordinate system for our observations, quadrature points, and quadrat cells.
We then created the maximum of the proximity raster derived from COURS_D_ EAU. shp and SURFACES_HYDROGRAPHIQUES. shp (so the value of 1 corresponds to an approximate distance of less than 50 m to a watercourse or hydrographic surface of fresh water). For "dmer, " i. e. , the distance to the ocean, we calculated, using QGIS, the distance raster at a resolution of 12. 5 m to polygons with attribute TYPE="Pleine mer" in the shapefile SURFACES_ HYDROGRAPHIQUES. shp of BD Carthage up to a distance of 32,767 m for storage format convenience. • ROUTE500 1. 1: This database register classifies road linkages between cities (highways, national roads, and departmental roads) in France in shapefile format, representing approximately 500,000 km of roads. It is produced under free license (all uses) by the IGN.
3%). Consequently, the model could capture information on the distribution of Reynoutria from the Pl@ntNet data. Conversely, very good results were obtained for both metrics for Opuntia ficus-indica and Carpobrotus edulis. Figure4further shows the distributions predicted for each species using p min (k max |x) = 70%. For comparison, we also displayed the expert count data of INPN, as well as the specificity and sensitivity of our model measured with that data (at TSS max). Most regions with high INPN counts were reasonably well predicted by.
-resolution satellite images. We set this variable as categorical in MAXENT with only 30 relevant categories for our purposes. This database of the European Union is freely accessible online at: http://land. copernicus. eu/pan-european/ corine-land-cover/clc-2012. • CGIAR-CSI ETP data: The Consultative Group on International Agricultural Research-Consortium for Spatial Information (CGIAR-CSI) distributes this worldwide monthly potential evapo-transpiration raster data. It is pulled from a model developed by Antonio Trabucco (Zomer et al. , 2007, 2008). Rasters are estimated by the Hargreaves formula using mean monthly surface temperatures and standard deviation from WorldClim 1:4 (http:// www. worldclim. org/version1), and radiation on top of atmosphere. The raster is at a 1-km resolution and is freely downloadable for a nonprofit use at http://www.
cgiar-csi. org/ data/global-aridity-and-pet-database#description. • U. S. Geological Survey Digital Elevation data: The Shuttle Radar Topography Mission achieved in 2010 by the Endeavour shuttle measured digital elevation at 3 arcs per second resolution over most of the earth's surface. Raw measures have been post-processed by the National Aeronautics and Space Administration and the National Geospatial-Intelligence Agency to correct detection anomalies. This gives a precision measurement of approximately 90 m for this variable. The data are available from the U. S. Geological Survey and are downloadable on the EarthExplorer (https://earthexplorer. usgs. gov/). See https://lta. cr. usgs. gov/SRTMVF for more information. • BD Carthage v3: BD Carthage is a spatial database holding information on the structure and nature of the French Metropolitan hydrological network.
We focus on the geometric segments representing watercourses, polygons representing hydrographic fresh surfaces, and the ocean. The data have been produced by the Institut National de l'information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. The database is maintained by SANDRE under free license for non-profit use and is downloadable at: http://services. sandre. eaufrance. fr/ telechargement/geo/ETH/BDCarthage/FXX/2014/arcgis/.
We focus on the geometric segments representing watercourses, polygons representing hydrographic fresh surfaces, and the ocean. The data have been produced by the Institut National de l'information Géographique et forestière (IGN) from an interpretation of the BD Ortho IGN. The database is maintained by SANDRE under free license for non-profit use and is downloadable at: http://services. sandre. eaufrance. fr/ telechargement/geo/ETH/BDCarthage/FXX/2014/arcgis/. The data are under Creative Commons Attribution 4. 0 International License (available at http://chelsa- climate. org/downloads/). • The ESDB v2, 1kmx1km Raster Library (Panagos, 2006; Van Liedekerke et al. , 2006; Panagos et al. , 2012): The library contains multiple soil pedological descriptor raster layers covering Eurasia at a resolution of 1 km.
We selected 10 de- scriptors from the library. They represent quantitative phys- ico-chemical quantities of the soil (from the PedoTransfer Rules Database [PTRDB attributes, available at https://esdac. jrc. ec. europa. eu/content/ptrdb-attributes]) that have been deduced from soil classification with expert rules, and their values are aggregated in intervals. As there are few possi- ble intervals by variables (2-6), we integrated them as cat- egorical variables in MAXENT. The data are maintained and distributed freely for scientific use by the European Soil Data Centre at http://eusoils. jrc. ec. europa. eu/content/ european-soil-database-v2-raster-library-1kmx1km. • CORINE Land Cover 2012, version 18. 5. 1, 12/2016: This is a raster layer describing soil occupation with 48 categories across Europe (25 countries) at a resolution of 100 m.
Gene sequence analysis The gene synthesis product in this study is that of the 1862 bp human AG1 gene (GenBank accession no: AF380580. 1) which encodes the 615 amino acid DUF1220 AG1 protein fragment (http:// www. uniprot. org/uniprot/Q8IX72). The AG1 gene and amino acid sequences are highly repetitive (Fig. 1 ). In order to increase the speed and efficiency of gene synthesis, we modified the AG1 nucleotide sequence. However, these changes did not affect the amino acid sequence. This codon optimization allows us to exploit the frequently used codons in Escherichia coli to obtain high level gene expression. Moreover, in order to improve the efficiency of gene transcription and RNA stability, the GC content of the synthetic gene was held at 52. 9%. (Tables  2  and 3 ).
Oligonucleotide design and purification The amino acid sequences of two human AG1 fragments (1-1581 bp, 1-527 aa and 1-843 bp, 1-281 aa) were obtained from GenBank (GenBank accession nos: AAO15403. 1 and AAX85105. 1). The amino acid and nucleotide sequences of these proteins were analyzed and codon-optimized using ClustalW 2. 1, Vector NTI Viewer 4. 0. 1, Sequencher_v4. 1. , ExPASy Bioinformatics Resource Portal (http://www. expasy. org/), and NCBI Blast (https://blast. ncbi. nlm. nih. gov/Blast. cgi) software packages. The design of the synthetic assembly oligonucleotides was similar to that of Xiong et al. [1], whereby each optimized DNA sequence was divided into economically sized oligonucleotides approximately 57-59 bases long that had 17-19 overlapping bases at both the 5 0 and 3 0 ends, leaving a 21 base gap between the overlapping regions.
We recorded neurons from 9 rats trained in a pellet-chasing task in a circular arena. The animals were either implanted above the CA3 region (in order to record CA3 place cells and then dLGN neurons; n = 5) or just above the dLGN (n = 4) in order to further characterize dLGN place cell properties. Overall, we recorded 228 CA3 and 196 dLGN place cells (see Fig. 1a -e for recording locations and Extended Data Tab. 1 for detailed results per animal). We first confirmed our histology-based identification with an analysis of electrophysiological properties extracted from neurons waveforms (Extended Data Fig. 1a ). To this end we used the coefficient of variation and the peak-to-trough parameters that allowed us to isolate two main clusters (Extended Data Fig. 1b ). Using k-means clustering, we identified a first cluster that comprised around 75% of the CA3 place cells and a second cluster that comprised around 93% of dLGN place cells (insert in Extended Data Fig.
1b ). To rule out the possibility of fibers origin to our dLGN recordings we looked at the types of waveforms recorded in this region. We therefore computed a normalized phase plot (which shows the rate of change of the membrane potential against the membrane potential itself) for each waveform and computed the barycentre of the resulting polygon (Extended Data Fig. 1c top ). We then reported the x and y coordinates of the barycentre on a two-dimensional plot (Extended Data Fig. 1c bottom ). According to this representation, triphasic waveforms, usually considered as compatible with action potentials emitted by fibers  11  , should have their barycentre coordinates clustered around the plot origin (Extended Data Fig. 1d ). Here, we found scarce recordings displaying such triphasic waveforms (Extended Data Fig.
//github. com/jrclimer/Pass_Index) and developed by Climer et al. 10. To compute the field index we first calculated occupancy-normalized firing rate for 1cm square bins of position data. Data were then smoothed with a pseudo-Gaussian kernel with a five pixel (5 cm) standard deviation. Each bin of the field index map was then attributed a value percentile normalized between 0 and 1. The omnidirectional pass index was computed on the raw rate maps. Quantification of phase precession was achieved by using the circular-linear correlation coefficient11  (rho), the statistical significance of the Pearson correlation (p value) and the slope (s expressed in degrees per pass). A place cell was considered omnidirectionally precessing if the significance level of the correlation was below 0. 01. 1 Hz power modulation.
Support for this work was provided by the  Centre National de la Recherche Scientifique (CNRS)  and the  Agence Nationale de la Recherche  ( ANR-10-BLAN-1413 ). The authors wish to thank  Frédéric Chavane  and  Matteo Carandini  for useful discussions,  Francesca Sargolini  for her careful reading and suggestions,  Laure Spieser  for her help in programming and  Didier Louber  for his precious technical assistance. P-Y. J. salary was financed by the  Fondation pour la Recherche Médicale  ( N° ARF20170938640 ).
Objective: Trimethylation of histone 3 (H3) at 4th lysine N-termini (H3K4me3) in gene promoter region was the universal marker of active genes specific to cell lineage. On the contrary, coexistence of trimethylation at 27th lysine (H3K27me3) in the same loci-the bivalent H3K4m3/H3K27me3 was known to suspend the gene transcription in germ cells, and could also be inherited to the developed stem cell. In galline species, throughout example of H3K4m3 and H3K27me3 ChIP-seq analysis was still not provided. We therefore designed and demonstrated such procedures using ChIP-seq and mRNA-seq data of chicken follicular mesenchymal cells and male germ cells. Methods: Analytical workflow was designed and provided in this study. ChIP-seq and RNAseq datasets of follicular mesenchymal cells and male germ cells were acquired and properly preprocessed.
In ChIP-Seq, the chromatin regions presented with modified histones of interest are immunoprecipitated and sequenced to study epigenetic control of gene expressions in several cell types  [2, 5, 9, 10]. Not only mammals, ChIP-Seq of H3 trimethylation were also conducted in chicken (Gallus gallus) as the representative of galline species  [2, [11] [12] [13]. These studies provided biologists and veterinarians the valuable insights into evolution and cell development of galline species. Of note, an increase of H3K4me3 and H3K27me3 ChIP-seq databases in chicken also allowed both biologists and veterinarians a new opportunity to observe and meta-analyze ChIP-seq data of various cell types acquired from different experiments. Analytical guidelines are generally concerned in human and mouse ChIP-Seq studies  [10].
On the contrary, the issue was limitedly described in H3 trimethylation ChIP-Seq data analysis in galline species  [2, 12, 13]. Since the procedures could affect the analytical results differently, a practical demonstration of such approach should prove beneficial, especially for those who are not acquainted with ChIP-Seq data analysis. Due to such necessity, we therefore demonstrated a chicken ChIP-seq meta-analysis in this study. The H3K4me3 ChIP-Seq data of chicken follicular mesenchymal cells and male germ cells were acquired, and we also included H3K27me3 ChIP-Seq data of chicken germ cells to demonstrate the effect of bivalent H3K4me3/H3K27me3 on gene transcription.
Sample datasets As far as we knew, the follicular mesenchymal cell was the only lineage-committed cell type with H3 trimethylation ChIP-seq data available in public database. A list of follicular mesenchymal cell and germ cell datasets used in this study is provided (Table  1 ). Follicular mesenchymal cells used in this study consisted of two populations isolated from median (Med) and lateral mesenchyme (Lat) of feather follicles, accordingly. Male germ cells used in this study derived from two time points during development: pachytene spermatocyte (PS) acquired during prophase of meiosis I, and round spermatid (RS) acquired after completing meiosis. Both germ cell types were representatives of male germ cells to illustrate the poised genes-where their poised states were already known to be retained throughout spermatogenic development  [2].
Analytical workflow In this study, we categorized our analytical processes into 3 steps-data preprocessing, peak calling with visualization, and integrative genomics viewer (IGV) exploration (Figure  1 ). In brief, the sample datasets would be aligned to chicken genome in order to acquire ChIP files and RNA-seq files in the data preprocessing step (Figure  1A ). The ChIP files were then applied in peak calling step to identify genome regions enriched with H3 trimethylation-the significant peaks (Figure  1B ). Only peaks overlapped with gene promoter region would be considered in IGV exploration of which, the enriched peaks along with the mapped sequences ChIP and RNA-seq files were integrated and visualized together in IGV platform (Figure  1C ). Data preprocessing: The sequence read archive (SRA) files of germ cells and follicular mensenchymal ChIP-Seq and RNA-Seq datasets were retrieved from SRA database (https://www.
ncbi. nlm. nih. gov/sra) (Table  1 ), subsequently extracted, and preprocessed as previously described  [14]. The ChIP files were aligned to chicken genome (Gallus gallus 5. 0) by Bowtie2 aligner  [15] , while STAR aligner was applied with the RNA-seq files  [14, 16]. The aligned files were converted into "bam" format, indexed by gene annotation, removed for duplicated sequences  [17] , and bias corrected for guanine-cytosine base balance  [18]  to acquire ChIP files and RNA-seq files. The qualities of both raw and preprocessed datasets were then determined by FastQC as previously described  [14]. For ChIP quality determination, normalized strand cross-correlation coefficient (NSC), relative strand cross-correlation coefficient (RSC) and DNA fragment length were additionally included. In more details, NSC is genome-wide correlation between positive and negative strand read counts when shifted by half of the fragment length relative to background.
NSC value thus represents enrichment of clustered ChIP fragments around target sites. RSC is the relative enrichment of fragment-length cross-correla-  Peak calling with visualization: Peak calling was, in case of this study, the computational method to identify genomewide chromatin regions with targeted H3 trimethylation significant to the other regions. The peaks therefore represented the genome area with significant presences of H3K4me3 or H3K27me3 when compared to the same region presented in the corresponding control datasets (Table  1 ). Model-based analysis of ChIP-seq 2 (MACS2) algorithm  [19]  was used in this study. Since the broad peak regions were suggested in the previous quality analysis, we applied broad peak calling func-tion in MACS2 with all ChIP files. Further details for such function calling was already described  [19].
All sample datasets were successfully preprocessed and aligned to chicken genome The raw datasets of all cell types were retrieved and preprocessed according to the methodology (Figure  1A ). All preprocessed datasets (both ChIP-seq and RNA-seq data in Table  1 ) acquired quality scores across all bases ≥30 without presence of contaminated adapters. Alignment percentages of ChIP-seq and RNA-seq datasets were ≥85% and ≥70%, accordingly. NSC, RSC, and DNA fragment length of ChIP-seq samples are provided in Table  2.
Since the methodology required no specific process, ChIP-seq data of other histone modifications could also be analyzed using similar approach. There were, however some important limitations that should be noted in this study. These included the lack of H3K27me3 ChIP-seq data of follicular mesenchymal cells to confirm abortion of poised genes, and limited samples per cell type available for valid differential analysis  [21]. Hopefully, the rapid growth of galline ChIP-seq database would allow us to demonstrate such analysis in our future study.