dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Caroline Mailhes  and  Geeske M. van Woerden  for initial help with in utero electroporation. We thank  Michael Graupner  and  Christoph Schmidt-Hieber  for advice regarding establishing the in vivo whole-cell recording technique. This work was supported by the  European Research Council (ERC)  under the European Community  7th Framework Program  ( FP7/2007-2013/ERC grant  agreement  200808 ), '' Investissements d'avenir' '  ANR-10-IAIHU-06 ,  Agence Nationale de la Recherche  ( ANR-13-BSV4-0015-01 ,  ANR-16-CE16-0007-02 ,  ANR-17-CE16-0026-01 , and  ANR-18-CE16-0001-01 ),  Fondation Recherche Medicale (Equipe FRM   DEQ20150331684 ), a  NARSAD independent investigator grant , and a grant from the  Institut du Cerveau et de la Moelle E ´piniere (Paris)  (to A. B. ).
In addition to temperature, we find evidence for an association between viral genetic diversity dynamics and the number of human cases, but only when a lag period of at least one month is added to the model (having only monthly case counts available, it was not possible to test shorter lag periods). Such lag could, at least in part, be explained by the time needed for mosquitos to become infectious and bite humans. As human case counts are in theory backdated to the date of onset of illness, incubation time in humans should not contribute to this lag. Our study illustrates and details the utility of landscape phylogeographic and phylodynamic hypothesis tests when applied to a comprehensive data set of viral genomes sampled during an epidemic. Such spatially explicit investigations are only possible when viral genomes (whether recently collected or available on public databases such as GenBank) are associated with sufficiently precise metadata, in particular the collection date and the sampling location.
Methods Selection of viral sequences. We started by gathering all WNV sequences available on GenBank on the 20 th November 2017. We only selected sequences (i) of at least 10 kb, i. e. , covering almost the entire viral genome (~11 kb), and (ii) associated with a sufficiently precise sampling location, i. e. , at least an administrative area of level 2. Administrative areas of level 2 are hereafter abbreviated "admin-2" and correspond to US counties. Finding the most precise sampling location (admin-2, city, village, or geographic coordinates), as well as the most precise sampling date available for each sequence, required a bibliographic screening because such metadata are often missing on GenBank. The resulting alignment of 993 geo-referenced genomic sequences of at least 10 kb was made using MAFFT  62  and manually edited in AliView  63.
S1-S2 ). These genotypes were identified on the basis of the WNV database published on the platform Nextstrain  32, 73. For the purpose of comparison, we performed all the subsequent landscape phylogeographic approaches on the overall data set but also on these five different subsets of WNV lineages. Estimating and comparing lineage dispersal statistics. Phylogenetic branches, or "lineages", from spatially and temporally referenced trees can be treated as conditionally independent movement vectors 2. We used the R package "seraphim"  74  to extract the spatio-temporal information embedded within each tree and to summarise lineages as movement vectors. We further used the package "seraphim" to estimate two dispersal statistics based on the collection of such vectors: the mean lineage dispersal velocity (v mean ) and the weighted lineage dispersal velocity (v weighted )  74.
Finally, we also estimated the evolution of the maximal wavefront distance from the epidemic origin, as well as the evolution of the mean lineage dispersal velocity through time. Generating a null dispersal model of viral lineages dispersal. To generate a null dispersal model we simulated a forward-in-time RRW diffusion process along each tree topology used for the phylogeographic analyses. These RRW simulations were performed with the "simulatorRRW1" function of the R package "seraphim" and based on the sampled precision matrix parameters estimated by the phylogeographic analyses  61. For each tree, the RRW simulation started from the root node position inferred by the phylogeographic analysis. Furthermore, these simulations were constrained such that the simulated node positions remain within the study area, which is here defined by the minimum convex hull built around all node positions, minus non-accessible sea areas.
The resolution of the other original rasters of fixed-in-time environmental factors (elevation, mean annual temperature, and annual precipitation) was also decreased to 10 arcmin for tractability, which was mostly important in the context of the second landscape phylogeographic approach detailed below. To obtain the time-series collection of temperature and precipitation rasters analysed in these first tests dedicated to the impact of environmental factors on lineage dispersal locations, we used the thin plate spline method implemented in the R package "fields" to interpolate measures obtained from the database of the US National Oceanic and Atmospheric Administration (NOAA; https://data. nodc. noaa. gov). Testing the impact of environmental factors on the dispersal velocity of viral lineages.
Preliminary skygrid-GLM analyses were used to determine from what lag period we obtained a significant association between viral effective population size and the number of human cases. We then used this lag period (of 1 month) in subsequent analyses. Data used to estimate the average temperature and precipitation time-series were obtained from the same database mentioned above and managed by the NOAA. For each successive month, meteorological stations were selected based on their geographic location. To estimate the average temperature/precipitation value for a specific month, we only considered meteorological stations included in the corresponding monthly minimum convex polygon obtained from the continuous phylogeographic inference. For a given month, the corresponding minimum convex hull polygon was simply defined around all the tree node positions occurring before or during that month.
In order to take the uncertainty related to the phylogeographic inference into account, the construction of these minimum convex hull polygons was based on the 100 posterior trees used in the phylogeographic inference (see above). The rationale behind this approach was to base the analysis on covariate values averaged only over measures originating from areas already reached by the epidemic. Finally, the greenness index values were based on bimonthly Normalised Difference Vegetation Index (NDVI) raster files obtained from the NASA Earth Observation database (NEO; https://neo. sci. gsfc. nasa. gov). To obtain the same level of precision and allow the co-analysis of NDVI data with human cases and climatic variables, we aggregated NDVI rasters by month. The visual comparison between covariate and skygrid curves shown in Fig.
Acknowledgements We are grateful to  Frank La Sorte  for sharing their estimated flyway grids. The research leading to these results has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 725422-ReservoirDOCS ), from the  Welcome Trust  ( Artic Network , project  206298/Z/17/Z ), and from the  European Union 's  Horizon 2020  project  MOOD  (grant agreement no. 874850 ). S. D. is supported by the  Fonds National de la Recherche Scientifique  ( FNRS, Belgium ) and was previously funded by the  Fonds Wetenschappelijk Onderzoek  ( FWO, Belgium ). S. L. and P. B. were funded by the  Fonds Wetenschappelijk Onderzoek  ( FWO, Belgium ). B. V. was supported by a postdoctoral grant ( 12U7118N ) of the  Research Foundation -Flanders  ( Fonds voor Wetenschappelijk Onderzoek ).
Data availability BEAST XML files of the continuous phylogeographic and skygrid-GLM analyses are available at https://github. com/sdellicour/wnv_north_america. WNV sequences analysed in the present study were available on GenBank and deposited before November 21, 2017. Accession numbers of selected genomic sequences are listed in the file "WNV_GenBank_accessions_numbers. txt" available on the GitHub repository referenced above. The source of the different raster files used in this study is provided in Supplementary Table  S1. The administrative flyways were obtained from the US Fish and Wildlife Service (USFWS; https://www. fws. gov/birds/management/flyways. php).
They come from cooperative game theory. While these values have many advantages, including their theoretical guarantees, they are however really hard to calculate. Indeed, the complexity increases exponentially with the dimension (the number of variables). In this article, we propose two novel methods to approximate these Shapley Values. The first one is an optimization of an already existing Monte Carlo scheme. It reduces the number of prediction function calls. The second method is based on a projected gradient stochastic algorithm. We prove for the second approach some probability bounds and convergence rates for the approximation errors according to the learning rate type used. Finally, we carry out experiments on simulated datasets for a classification and a regression task. We empirically show that these approaches outperform the classical Monte Carlo estimator in terms of convergence rate and number of prediction function calls, which is the major bottleneck in Shapley Value estimation for our application.
g. gradient boosting trees, deep learning, kernel methods). In this study, the method that we improve is agnostic and local. This approach makes it possible to create a reusable and generic module for different use cases (this is the agnostic aspect). On the other hand, the complexity of such approaches is more important and therefore hinders applications where transparent decision making is done in real time. The objective of this work is precisely to speed up calculation of these explanations. Fig. 1 : Illustration of how are used Attribute Importance on a particular prediction. Here is the COMPAS dataset [?]. A machine learning has been trained to predict whether an individual will re-offend (giving the estimated probability associated). The importance of individual attributes give an interpretation of the difference between the probability of that individual (70% here) and the mean probability of a reference group (14%).
g. images, sentences, etc. ). This relationship can look like chains or grids for example. The authors propose two approaches they call L-Shapley and C-Shapley. Because the features can be represented by a graph, we can use the notion of neighbor to limit the number of coalitions to be considered for the calculation of a Shapley Value per player. In L-Shapley, we consider all coalitions with entities whose distance is less than k from the given feature, k being a parameter chosen. With C-Shapley, coalitions are taken into account if their distance is less than k but also if they have a direct link with the player. ). In the context of Federated Learning, [?] give another Shapley Value approximation technique. We use Federated Learning when several entities collaborate together to train a model on distributed datasets, but do not want to share their data.
In [?], they focus on a process where each member of the coalition train a model, and then a meta-model is learned. They are also dealing with Vertical Federated Learning, where there are many overlapped instances but few overlapped features (e. g. insurer and online retailers). Their objective is twofold: find the importance of each feature and preserve the confidential information of each partner. Their article propose an adaptation of [?] in this context. [?] formalize the use of Shapley Value for Machine Learning, in particular individual prediction explanation. All these previous approximation approaches have been used in specific contexts (Federated Learning, structured dataset) or models (Deep Learning, Tree-based model). We are interested in more generic cases. [?] propose an approach based on a Monte Carlo estimator which has a solid theoretical background.
Shapley Values as contrastive local attribute importance in Machine Learning In this study we consider the context described in [?] but all the estimation methods presented hereafter can be extended. Let be X * ⊂ d a dataset of individuals where a Machine Learning model f is trained and/or tested and d the dimension of X *. d > 1 else we do not need to compute Shapley Value. We consider the attribute importance of an individual x * = {x * 1 ,. , x * d } ∈ X * according to a given reference r = {r 1 ,. , r d } ∈ X *. We're looking for φ = (φ j ) j∈{1,. ,d} ∈ d such that: d j=1 φ j = f (x * ) -f (r), where φ j is the attribute contribution of feature indexed j. We loosely identify each feature by its column number. Here the set of players M = {1,. , d} is the feature set. The local explanation is then contrastive because attribute relevance depends on a reference.
Simulated dataset Classification We will use the simulated dataset introduced [?], page 339. The features X 1 ,. , X d are standard independent Gaussian, and the deterministic target Y is defined by: Y = 1 if d j=1 X 2 j > χ 2 d (0. 5) 0 otherwise , where χ 2 d (0. 5) is the median of a chi-squared random variable with d degrees of freedom (the sum of d standard Gaussian squared follows a χ 2 probability law). We denote by f class a classification function that returns 1 if the predicted score is greater than 0. 5 and 0 otherwise. For a reference r * and a target x * , we define the reward function v r * ,x * c such that for each coalition S, v r * ,x * c (S) = 1 f class (z(x * ,r * ,S))=f class (x * ). This reward function is one choice among others. Here, all the contributions sum to 1. Moreover, using an uncommon reward function in Machine Learning illustrates that the techniques used are agnostic of that reward function.
Regression We will also use a simulated dataset from the same book ([?], page 401, the Radial example). X 1 ,. , X d are standard independent Gaussian. The model is determined by: Y = d j=1 ρ(X j ), where ρ: t → (0. 5π) exp(-t 2 /2). The regression function f regr is deterministic and simply defined by f r : x → d j=1 ρ(x j ). For a reference r * and a target x * , we define the reward function v r * ,x * r such as for each coalition S, v r * ,x * r (S) = f regr (z(x * , r * , S)) -f regr (r * ). Procedure We select at random 50 couple of instances x * and r *. For each one, the true Shapley Values are calculated and estimation errors of the methods under study are stored for several iterations. Some graphs will sum up the experiments by displaying the mean estimation errors per iteration.
datasets. Due to computational constraints we will only study the classification problem. Indeed Shapley Values collapse towards zero in the regression settings. Because the true Shapley Values are intractable, we estimate it with the optimized Monte Carlo technique and a large number of iterations. We performed 5000 iterations which represent 5000 × d reward evaluations. The mean squared error norms can be observed on Figure ??. The constant step size is 0. 001 and the decreasing step size follows γ t = 0. 01 √ t. By its design, Monte Carlo techniques update each Shapley Value component sequentially during one iteration of d reward evaluations. In the meantime gradient based methods could update several Shapley Values components at each iteration.
Results Electron diffusion region encounter by MMS. The EDR event was encountered near Earth's subsolar magnetopause on December 24, 2016. MMS were located at [9. 4, 3. 7, 1. 6] R E (Earth radii) in geocentric solar ecliptic (GSE) coordinates. We use B L B M B N V L V M V N V L T ⎟⎟ T ⊥ V M V N E L E M E N ⏐B⏐ 20 W i [eV] B [nT] V i [km s -1 ] N [cm magnetic field B data from the fluxgate magnetometer  21  and the search-coil magnetometer  22. The electric field E data are from the electric field double probes  23, 24. The particle data are from the fast plasma investigation (FPI)  25. All data presented are in high-resolution burst mode. The burst-mode electron data are sampled at 30 ms resolution. The vectors are shown in boundarynormal (LMN) coordinates based on minimum variance analysis (MVA)  26  of B over the magnetopause crossing, unless otherwise stated.
Acknowledgements We thank the  MMS  team and instrument principal investigators for data access and support. W. Y. L. appreciates the helpful discussion with  Dr. B. Lembège  from  LATMOS Laboratoire, France  and  Dr. Lynn B. Wilson III  from  Goddard Space Flight Center, USA. This work was supported by the  NNSFC  Grant Nos. 41504114 ,  41731070 ,  41574159  and  41974170 ,  Swedish National Space Board , Grant Nos. 164/14 ,  128/17  and  176/15 ,  Swedish Research Council   2016-05507 ,  Strategic Pioneer Program on Space Science of Chinese Academy of Sciences , Grant No. XDA15052500 , and in part by the  Specialized Research Fund for State Key Laboratories of China. W. Y. L. is also supported by the  Youth Innovation Promotion Association  ( 2018177 ) and opening fund of  State Key Laboratory of Lunar and Planetary Sciences (Macau FDCT  Grand No.
Phylogenetic analyses and comparative genomics For phylogenetic tree construction and genome comparison, 3156 single-copy orthologues were selected among 13 published fungal genomes belonging to the Pezizomycetes class (Table  S1 ). Single-copy orthologues are well suited for phylogenetic analyses since they result from speciation events and go beyond the issues of conventional markers  (Debray et al. , 2019). Orthologous genes among the selected fungi were identified using FASTORTHO program with the parameters set to 50% identity, 50% coverage, inflation 3. 0  (Wattam et al. , 2014). Protein sequences were downloaded from the JGI MycoCosm database (https://genome. jgi. doe. gov/mycocosm/home). Clusters with single-copy genes were identified and aligned with MAFFT 7. 221  (Katoh & Standley, 2013) , ambiguous regions eliminated, and single-gene alignments concatenated with GBLOCKS 0.
91b  (Cruickshank, 2000). A phylogeny tree was constructed with RAXML 7. 7. 2 (Stamatakis, 2006)  using the standard algorithm, the PROTGAMMAWAG model of sequence evolution, 1000 bootstrap replicates, and random seed number (12345). Statistics of JGI genome assemblies (i. e. N50, number of genes and scaffolds, genome size) were obtained from JGI Mycocosm (https://genome. jgi. doe. gov/programs/fungi/index. jsf ). Genome completeness with single-copy orthologues was calculated using BUSCO v. 3. 0. 2 with default parameters  (Sima et al. , 2015). The coverage of TEs in genomes was calculated and visualized using our set of custom R scripts TINGO  (Morin et al. , 2019). Secreted proteins were predicted as described previously by  Pellegrin et al. (2015). The count and ratio of total (occurrences in genomes) and secreted CAZymes, lipases, proteases, and SSPs (< 300 amino acids) and the predicted plant cell-wall-degrading enzymes (PCWDEs) and microbe cell-walldegrading enzymes (MCWDEs) were calculated, visualized, and compared (note that some enzymes may belong to both PCWDE and MCWDE categories).
Global trends of ecological groups were evaluated using nonmetric multidimensional scaling with the count of total and secreted CAZymes using metaMDS in R package VEGAN  (Oksanen et al. , 2016). The output files generated were combined and visualized with the set of custom R scripts PRINGO  (Miyauchi et al. , 2020a)  incorporating R packages GGPLOT2, GGTREE and EGG  (Wickham, 2009; Auguie, 2017; Yu et al. , 2017). Gene family evolution ORTHOFINDER v. 2. 4. 0  (Emms & Kelly, 2019) and CAFE v. 4. 2 (Han et al. , 2013)  were used for the computational analysis of gene family evolution. The proteomes from the 13 fungi (Table  S1 ), plus Cenoccocum geophilum and Oidiodendron maius as external group, were aligned to each other with BLASTP, and the orthogroups were inferred by the MCL algorithm  (Enright et al.
, 2002)  within the ORTHOFINDER pipeline. The previously built phylogenetic tree was rooted and made ultrametric with R package APE  (Paradis & Schliep, 2019). We estimated the species divergence times using the mcmctree method implemented in PAML v. 4. 8. (Yang, 2007). The independent-rates clock model, a WAG substitution model and approximate likelihood calculation were used. The substitution rates of each gene were estimated by CODEML under a global clock model. We set the time unit to 100 Myr and applied uniform priors on two calibration points with lower and upper hard bounds: the Tuberaceae divergence time estimation between 134 and 179 Ma  (Bonito et al. , 2013)  and the Pezizomycetes-Leotiomyceta divergence time estimation between 400 and 487 Ma  (Prieto & Wedin, 2013). The orthogroups, together with the dated ultrametric tree, were used by CAFE to estimate an error model accounting for genome assembly errors (caferror command) and the birthdeath parameter k.
Pfam domain enrichment/depletion analysis The top 100 Pfam domains (https://pfam. xfam. org/, v. 32. 0) identified in the gene repertoires of the 13 fungal species were selected. The occurrence frequency values were transformed into z-scores, which are a measure of relative enrichment and depletion. The hierarchical clustering was done with a Euclidian distance metric and average linkage clustering method. Data were visualized and clustered using MULTIEXPERIMENT VIEWER (http:// mev. tm4. org). The Mann-Whitney-Wilcoxon test was performed to compare each Pfam family between the four desert truffles (T. claveryi, T. boudieri, T. nivea, and Kalaharituber pfeilii) and the rest of Pezizomycetes. MAT and sexual-related genes identification MAT 1-1-1 genes were identified by the JGI annotation pipeline in the genomes of T.
Transcriptome analysis of Terfezia claveryi Total RNA was extracted from three biological replicates of T. claveryi free-living mycelium (FLM) and from mycorrhizal roots of WWMPs, DSMPs, and NMPs with the same methodology as reported earlier herein. RNA sequencing (RNA-seq) was performed using 2 9 75 bp Illumina HiSeq2500 sequencing after messenger RNA (mRNA) library construction by Lifesequencing SL (Valencia, Spain). Raw reads were analysed using FASTQC for quality (https://www. bioinformatics. babraham. ac. uk/projects/fa stqc/), paired, trimmed, and mapped against T. claveryi reference transcripts (https://mycocosm. jgi. doe. gov/Tercla1/Tercla1. home. html) using CLC Genomics WORKBENCH 11 (Qiagen, Courtaboeuf, France). For mapping, the minimum length fraction was 0. 9, the minimum similarity fraction 0.
De novo assembly of the plant transcriptome A de novo transcriptome assembly for H. almeriense was performed with sequencing reads from NMP, WWMP, and DSMP samples using MEGAHIT v. 1. 1. 3 (Li et al. , 2015). Transcripts from each condition (NMP, WWMP, and DSMP) were mapped onto the assembled contigs with BOWTIE2 v. 2. 3. 0  (Langmead & Salzberg, 2012) , and reads mapped onto the contigs were counted with SAMTOOLS v. 1. 7  (Li et al. , 2009). Only the contigs with > 200 bases were selected for further steps, and then they were annotated using BLAST on the nonredundant National Center for Biotechnology Information (NCBI) sequence database (March 2018 version) and JGI MycoCosm annotation data (July 2018 version) using DIAMOND v. 0. 9. 19  (Buchfink et al. , 2014)  with the parameters more-sensitive max-target-seqs 1 max-hsps 1 evalue 0.