dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, the model built for our study is shared publicly in the same Zenodo repository as the dataset (doi: 10. 5281/zenodo. 4972593). The new S2 image time-series as input can be used to predict species orchids composition anywhere on earth or to build highresolution maps of specific orchid species at a global scale. It may also be used for other ecological tasks via transfer learning approaches (i. e. , keeping unchanged all the weights of the model except those of the last layer dedicated to species classification,  Torrey and Shavlik, 2010).
On Temporal and Spatial Biases In the context of species and habitats distribution modeling in general, a recurrent challenge is a possible mismatch, both in time and space, between the occurrences and the environmental variables  (Phillips et al. , 2006). As shown in Figure  2B , in particular, a fraction of the occurrences in our dataset date from several decades ago, while the satellite data is from March 2020 to February 2021. If the environment changed since the observation, e. g. , because of a housing project or deforestation, the model may learn incorrect relationships. Figure  11A  focuses on this particular issue and acknowledges the influence of occurrence observation date on model performances. The top-30 test accuracy is gradually higher on more recent occurrences than older ones.
Besides, performance on both common and rare species remains almost constant when uncertainty is inferior to the median equal to 707 meters. Thereby, when the maximum uncertainty is of the order of the patch size, the model performs as well as on very precise occurrences. Finally, the Q 3 marked difference of evolution between micro/macro top-30 accuracy could be explained by the following hypothesis: rare species predictions are more affected by a growing coordinates uncertainty than common species because of more locally specific habitat preferences. In machine learning, such mismatch between labels and predictive data is called label noise  (Frénay and Verleysen, 2013)  and is actively studied  (Ghosh et al. , 2017; Lee et al. , 2018). The strength of our dataset in counteracting this noise is its very large size, as demonstrated by  Rolnick et al.
(2017). Their work showed that deep learning models can learn correct generalizations even with massively noisy datasets. At last, the strong spatial bias present in the DeepOrchidSeries dataset influences SDMs predictions  (Beck et al. , 2014). Such bias results from a very uneven sampling effort (Shown in Figure  4C  map) and not from orchids distribution. The use of methods to mitigate spatial bias at the cost of occurrence number is a promising direction to exploit DeepOrchidSeries (see abovementioned publication). Nonetheless, true understanding of orchids distribution and health will only be reached with significant and uniform observation effort. Having access to constructive and global predictive data is remarkably valuable but not sufficient. Biodiversity hotspots  (Myers et al. , 2000)  urgently need to be sampled with high standards of care to limit human disturbance.
CONCLUSION In this paper, we studied for the first time a worldwide SDM based on high-resolution remote sensing image time series. Therefore, we built and shared a substantial dataset (called DeepOrchidSeries) aimed at modeling the distribution of orchids on a global scale from Sentinel-2 data. The spatial structure and phenology of species habitat are captured over a whole year for 999,258 occurrences. We then trained deep-SDMs resting on an Inception v3 architecture whose input was modified to deal with 12 months time-series of RGB+IR images. The analysis of the resulting model reveals that the temporal information contained in the time series enables a strong improvement of the predictive performance compared to a purely spatial model. Thanks to interpretability experiments, we did show that seasonal patterns, in particular, are well captured, resulting in better discrimination of habitats all over the world.
ACKNOWLEDGMENTS We warmly thank  Alexander Zizka  for providing us with the filtered set of orchid occurrences. Our dataset contains modified Copernicus Sentinel data and Copernicus Service information (2020, 2021). Sentinel-2 MSI data used were available at no cost from  ESA Sentinels Scientific Data Hub. This work was granted access to the  HPC resources of IDRIS  under the allocation 20XX-AD011011389R1 made by  GENCI. Finally, we would like to thank the reviewers for their insightful comments that helped us improve our manuscript.
DATA AVAILABILITY STATEMENT -Code is available on the sen2patch gitlab: https://gitlab. inria. fr/ jestopin/sen2patch. -Occurrences initial GBIF query is https://doi. org/10. 15468/dl. 4bijtu (accessed August 2019). -The dataset and models generated for this study can be found in Zenodo at: https://doi. org/10. 5281/zenodo. 4972593.
Model simulations The epileptor network model (Eqs 1-5) were implemented as a system of stochastic differential equations in the Itocalculus sense, where ξ i (t) and η i (t) thus denote Wiener processes. For numerical integration we used the Heun's method, with the Wiener process increments implemented as i. i. d. Gaussian random variables with zero mean and variance σ 2 � Δt. The above five-dimensional node system was implemented as a 6-dimensional system by rewriting Eq 6 as corresponding differential equation. We wrote Python code to implement the numerical simulations and used an integration step of 0. 05. To match typical oscillatory properties of focal epileptical seizures and for plotting purposes, we assumed a time unit in the model to correspond to 0. 02 s. Given the simulation step size of 0.
Local linear stability analysis Fixed points for the epileptor network model were computed by solving for the zeros of the deterministic system of differential equations (Eqs 1-6) with the Matlab function fsolve (Mathworks, Inc). We first determined the fixed points for a single-node epileptor. Given our choice of parameters and initial conditions for the numerical search, we found three fixed points: one typically stable, and the other two unstable. When solving for fixed points of the epileptor network, we started with initial conditions near to the stable fixed point found for the single-node epileptor model. set to σ = 0. 05. (A) The active EZ node (in red) was "inhibited" by the surrounding nodes in the network and, as a consequence, did not transition into a seizure. Global connectivity strength and excitability in the surrounding nodes were set to w = 1.
As an example of currently available data based on SEEG recordings of focal seizures, all cases in the 15-patient dataset in  [31]  show partial spread confined to a few areas. Cases of secondary generalization would require a much broader SEEG coverage for spread assessment than commonly done. Explosive/threshold spreading dynamics, which would lead to corresponding bimodal distributions with modes near small and almost full spread, have been suggested in previous theoretical studies involving for example networks of Kuramoto oscillators (e. g. [40]. We conjecture that a more continuous range of spread size (in particular power law distributions) might be possible in this class of models. That would be enabled by critical points (second-order phase transitions) in the excitability and global connectivity strength parameter space.
Bioinformatic analysis: Data processing, alignment, gene quanti cation and QA/QC The sequencing data was analyzed using the Cell Ranger Pipeline (version 6. valid barcodes and UMIs were used to form an un ltered data matrix. The barcodes with total UMI counts exceeding 10% of the 99th percentile of the expected recovery cells (default = 3000) was considered to contain cells and selected to produce a ltered gene-barcode matrix for further analysis. "gene and transcripts (UMI counts) per cell" was used to compare the sensitivity of scRNA-seq. "Fraction Reads in Cells" was determined by the fraction of cell barcoded, con dently mapped reads with cell-associated barcodes to check the background of cell-free (ambient) RNA in cell suspension.
PCA and tSNE analysis for cell clustering and classi cation, and data visualization The Cell Ranger count and aggr pipelines were used to run secondary analysis. Before clustering the cells, Principal Component Analysis (PCA) was run on the normalized, log-transformed, centered, and scaled gene-barcodes matrix to reduce the number of feature (gene) dimensions. The pipeline adopts a python implementation of the IRLBA algorithm. This produced a projection of each cell onto the rst N principal components. It did not lter out any "low-quality" genes and cells as described above and previously and was used by Seurat package before PCA analysis. After running PCA, t-distributed Stochastic Neighbor Embedding (t-SNE) was run to visualize cells in 2-D space. Clustering is then run to group cells together that have similar expression pro les, based on their projection into PCA space.
Two clustering methods were performed: graph-based and k-means. Cell Ranger also produced a table indicating which genes were differentially expressed in each cluster relative to all other clusters. Classi cation of PBMCs was inferred from the annotation of cluster-speci c genes and was based on expression of some well-known markers of immune cell types (marker-based classi cation). Loupe Cell Browser (v5. 0) was used to view the entire dataset and interactively nd signi cant genes, cell types, and substructure within cell clusters  [30]. For multiplexed samples, HTO data are processed using the "cite-seq count, v1. 4. 3" tool ( https://hoohm. github. io/CITE-seq-Count/ & https://zenodo. org/record/2590196). The results are read using the Seurat "Read10X" tool (https://satijalab. org/seurat/articles/hashing_vignette.
html ). Only common cell barcodes from the HTO (from cite-seq count) and mRNA (from cellranger) data are retained. These barcodes represent the cells detected by cellranger and for which an HTO was assigned. Finally, the HTO arrays are normalized using the Log-Ratio Centered (CLR) method. This method is recommended by the developers of Seurat. Next, the "MULTIseqDemux" function is used to assign an HTO name to the cells. 1e ).
Cryptic Human Leukocyte Antigen (HLA)-presented peptide identification from unannotated genome sources is a priority for target antigen discovery for development of next generation immunotherapies in cancer. Current immunopeptidomic approaches utilize the integration of transcriptomics data to inform spectral interpretation, however, recent observations that tumour-associated antigen-encoding RNA levels are often low highlights limitations of such proteogenomic approaches 1. We here employ a de novo sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical HLA-associated peptide sequences (HLAp) in cancer without integration of transcript sequence information. Our strategy integrates HLA binding prediction, peptide retention time prediction, and average local confidence scores culminating in the machine learning model MARS (MHC binding prediction, Average Local Confidence Score, and Retention time integration for improved de novo candidate Selection).
We demonstrate increased HLA-I peptide identification sensitivity by benchmarking our model against de novo sequencing alone with a large synthetic HLA-I peptide library dataset. We further define the sensitivity of MARS by reanalysis of a published dataset of high-quality non-canonical HLAp identifications in human cancer cell line and tissue datasets and achieve almost 2-fold improvement of the full sequence recall (FSR) for high quality spectral assignments in comparison to de novo sequencing alone 2. We minimize the false discovery rate (FDR) through a step-wise peptide sequence mapping strategy and are able to expand the reported non-canonical peptide space with an assignment accuracy above 85. 7%. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, non-canonical peptide sequences in primary tumour tissue at reduced FDR, in the absence of transcriptomic sequencing data.
Software tools to interrogate LC-MS spectral data have widely been adopted from the field of proteomics, and extensive differences in performance have been observed for HLA-derived peptide data  2, 10, 11. Spectra interpretation software is based on three main strategies. The most common approach, the "database search" approach  [12] [13] [14]  , relies on matching the masses of experimentally obtained peptide fragment spectra to all theoretically possible peptide fragments of sequences in a protein sequence list or "database". This approach is limited to identifications in the sequence database, and can therefore not be used for discovery of non-canonical peptides unless such information is added to known canonical protein databases. For increased sensitivity, this approach has been extended by integration of spectral matching assignment, which uses advanced deep learning and peptide fragmentation prediction to annotate the experimentally obtained peptide fragment spectra with high accuracy  [15] [16] [17] [18] [19].
Thirdly, de novo algorithms interpret the MS spectra without comparison to a protein sequence database, annotating the amino acid sequence directly from the spectral information available  [20] [21] [22]. Integration of de novo approaches and database searching (de novo-assisted database approach) has been shown to be particularly advantageous for HLA-peptide datasets  10, 11, 23. For the interrogation of immunopeptidome datasets with a focus on cancer-specific peptide ligands, proteogenomics approaches have been used to refine the database-search approach in order to identify peptide sequences that contain tumour-specific mutations or other non-canonical sources like functional RNA genes, intronic sequences, and/or reading frames in unannotated genomic origins that are generally not present in universal protein databases  2, 7, [23] [24] [25].
Here, genomics and/or transcriptomics data is integrated to achieve a highly accurate, sample-specific database that can be used as template for a "database search" analysis approach. The incorporation of "personalised" binding prediction (MSRescue  26  ), or difference between observed and predicted retention time (DeepRTplus  27  , Deep Rescore  19  ) to database-search based peptide identification workflows can also improve performance. Recently, machine learning has been applied in a personalized fashion in order to identify patient neoantigens  28. The purpose of this study is to increase the power of the de novo approach for identification of noncanonical HLA-I-associated peptides (HLA-Ip) without the use of a personalized template protein database. We report that by integration of (i) HLA binding affinity predictions and (ii) differences between observed and predicted retention time (RT) of candidate peptide sequences as additional factors, HLA-I peptide identification sensitivity can be improved over the baseline de novo sequencing annotation.
Training Data Selection and Processing Our aim was to construct and validate our computational model for improved de novo (templateindependent) identification of HLA-I-associated peptides. We selected a panel of in-house single allele We analyzed these datasets using Peaks DeNovo and also the Peaks DeNovo-assisted Database search (DB) using the SwissProt human canonical proteome  29. From the Peaks DeNovo search, we obtained up to 100 sequence candidates per spectrum (independent from the provided sequence database), with associated average local confidence (ALC) scores for each candidate sequence as a measure for the confidence of each sequence assignment. The Peaks DB search results were returned as a list of sequence assignments that matched to the SwissProt database. This latter search results in one sequence assignment per spectrum (with exception of L/I isomers) with an associated score (-10lgP), which reflects the probability of a correct peptide spectrum match.
The results are controlled by a simultaneous decoy database search which provides an accurate estimation of the false discovery rate to be expected in the result  30. In order to define a subset of "true sequences" to use as a training dataset, we defined spectra as confidently assigned if they were identified with Peaks DB with high confidence -10logP ≥ 20 resulting in an FDR of less than 1% on peptide-spectrum match (PSM) level. In addition, we only included peptides with a length of 8-13 amino acids, and those that were assigned a low NetMHCpan 4. 1 Eluted Ligand Rank (ELR) score (ELR < 10), aiming to remove peptides that are likely originating from co-purification during biochemical enrichment. All spectra that fulfilled these criteria were defined as the training dataset. Next, within the Peaks DeNovo search results, the "true" peptide was considered the Peaks DB assigned sequence identification with all constituent isoleucine (I) replaced by leucine (L), because Peaks DeNovo algorithm does not distinguish I from L.
MARS improves HLAp identification sensitivity across HLA-A and HLA-B alleles To substantiate our findings, we applied MARS to synthetic HLAp standard data for sensitivity evaluation. We hypothesized that the improvement of the re-ranking of de novo HLAp identification by the MARS model is robust in the sense that it can be applied to different datasets with fixed factor coefficients. The only requirement is a transfer learning scheme needed to calibrate the DeepRTplus model in order to accommodate the discrepancy introduced by the LC system setups and gradient conditions between different datasets. We utilized a large dataset published by Wilhelm, Zolg et al. , of ~169,000 synthetic HLA class I peptides acquired by liquid chromatography mass spectrometry (LC-MS)  17. Out of this dataset, we selected the 116 most frequent HLA alleles  31  , comprising 35 HLA-A, 59 HLA-B, and 22 HLA-C alleles (Supplementary Table  2 ).
MARS identifies previously identified HLAp of non-canonical origins without integration of samplespecific RNA sequencing data To further assess the identification performance of the MARS model, and in particular the performance in the non-canonical HLA peptide space, we utilized a published dataset from Chong et al. comprising nine LC-MS datasets for five melanoma cell lines and two sets of lung cancer and healthy control tissues spanning a wide range of HLA alleles 2. The authors identify and report 508 non-canonical, HLA-presented peptides mostly sourced from lncRNA genes, which are not present in the canonical human proteome. The authors achieved identification of these peptides sequences using an immunopeptidomics approach which carefully integrates RNA sequencing and ribosome profiling data for generation of personalized, sample-specific transcript assemblies.
These were then used for in-silico translation into protein databases and mapping of non-canonical, HLA-presented sequences in the immunopeptidomics data using a standard database-search. Here, we wanted to understand whether MARS could recover these sequences without the integration of any of the additional RNA sequencing datasets used in the original publication. Firstly, we searched the Chong et al. datasets against the SwissProt human protein database using Peaks software in order to exclude spectra that were assigned to the canonical human proteome from our search. N-terminal acetylation and oxidation were included in the search parameters in order to match the analysis settings used in the original publication. We identified a total of 83,295 canonical peptides using the PEAKS database search then applied the MARS model to the remaining spectra, returning an additional 333,011 unique peptide identifications.
7), and region 3: >45% FDR (MARS score >80) (Figure  3A ). With a stringent MARS score threshold of >94. 7, and an estimated 30% FDR, we were able to recover 175/272 (64%) of sequences, while ALC alone could recover only 101/272 (37%) peptides (Figure  3B , Table  S2 , Supplementary Table  3 ), resembling an almost 2-fold improvement in FSR. Integration of a sequence mapping strategy for MARS peptide candidates confirms non-canonical HLAp origin. We next aimed to assign the 333,011 MARS identified sequences in the interrogated dataset to their respective likely gene origin. We therefore developed a stratified search approach to associate MARS identified HLAp to a wide range of possible origins across the human genome. We defined peptides mapping to (i) canonical origins mapping to known protein isoforms as "Human Protein", (ii) single amino acid substitutions as "1AA substitution", and (iii) other locations in the human genome as "non-canonical" (Figure  3C , and Methods section).
Further to the 83,295 peptides that had initially been identified by the PEAKS database search, 19,619 sequences were additionally identified as isoforms of the known proteome space as assigned and reported by SwissProt, Tremble and Ensemble by MARS. A further 22,934 MARS sequences mapped to canonical human proteins with exactly 1 aa substitution, which we considered as likely products of germline or tumour-specific mutations (neoantigens). A total of 11,339 peptides were linearly mapped to non-canonical sequences in the human genome. For 279,119 MARS peptides we could not find an accurate match within the human genome with less than 1 aa distance, and we therefore considered these sequences as distant from the human proteome ("unmatched", Figure  3D , left panel, see Discussion). 193/272 of the Chong et al.
published de novo-identifiable peptides were as expected part of the "noncanonical" peptide category as defined here. In addition, with our search strategy, we found 16 peptides reported in our DB database search, an additional 23 peptides matched to known human isoforms reported by SwissProt, Trembl and/or Ensemble, and 30 peptides to human proteins with 1 AA substitution (Figure  3D , right panel). The 194 peptides identified by both Chong et al. and 194 MARS had an overall higher MARS score (mainly falling into regions 1 and 2) in comparison to the 13,724 additional non-canonical peptide candidates MARS recovered from the published datasets (Figure  3D, E ), indicating that these peptides were recovered with high confidence and that a higher MARS score cut-off can likely limit the FDR significantly.
Out of the 508 peptides originally published by Chong et al. , 399 peptides are defined as "non-canonical" according to our categorization, which excludes peptides with single amino acid substitutions (57 peptides, "1 AA Substitutions" category) and those that map to canonical sequence databases in our workflow (52 peptides, "Human Protein" category). MARS was able to expand the originally published, in our definition "non-canonical" peptide identifications from 399 originally published peptides in this category to 1870 peptides from non-canonical origins at a an FDR between 0<FDR<14. 3% (Figure  3H ).
MARS uniquely identifies novel non-canonical lncRNA-derived HLA peptide sequences in primary tumour tissue In order to establish whether MARS could identify novel non-canonical, lncRNA-derived peptides in primary tumour tissue, we applied MARS to a set of in-house immunopeptidomics data from ten primary cervical tumour resections spanning 35 HLA alleles (Supplementary Figure  2 , Supplementary Table  5 ). Apart from 32,366 Peaks database search identifications using the Peaks DeNovo-assisted database search, we could expand identifications using MARS by a further 77,129 assigned sequences. We mapped 6,551 further peptides to human source proteins, 5,828 peptides with a single amino-acid substitution, and 3,980 peptides to non-canonical origins (Figure  4A ). 60,770 peptides remained unmatched.