dataesr/scientific-paragraphs-categorization huggingface dataset:
During each run, the importance of each odor rating was measured as the mean decrease in the Gini impurity if the respective feature was excluded from random forest learning. To assess whether the random forests were able to detect differences between normosmic and hyposmic subjects, as a prerequisite for using feature importance as a quantitative measure of the significance of each rating, the trained classifiers were applied to the one third of the data not used for training during each run. The classification performance was evaluated by calculating the area under the ROC curve (AUC-ROC) using the R-library "pROC" (https:// cran. r-proje ct. org/ Figure 1. Heat plot of the means of the ratings of odors (rows) for seven different properties (columns). The 40 odors used in the study were split into four sets of 10 odors each (indicated at the left).
org/ packa ges/ relea se/ bioc/ html/ Compl exHea tmap. html  68  ). packa ge= pROC)  50. The classification performance was again assessed using permuted data for training with the expectation that then the classification will be not better than guessing, else overfitting was likely. In addition, during each run the values of -log(p) resulting from Wilcoxon-Mann-Whitney-U tests  51, 52  for group differences between the olfactory diagnoses were kept, for which the actual training data subsets were used. Both measures were averaged for the 1,000 runs and provided finally two 40 × 7 sized matrices (40 odors, seven rated perceptions). These performance measures were rank transformed. A combination under independence assumption is calculated by a multiplication of the two measures. That is, we merged the importance in random forests, measured as the decrease in Gini impurity when the particular feature was omitted when training the forest, with the degree of statistical difference, measured as Wilcoxon W.
These pharmacophoric atom types reflect the interaction types that are possible for a ligand with a protein counterpart. The necessary molecular descriptors of chemical properties  54  were obtained using the Dragon software (version 6, Talete s. r. l. , Milan, Italy; http:// www. talete. mi. it, accessed on May 22, 2020). This provided n = 90 features of the CATS class. The maximum distance considered for counting the internal binding distance was eight, due to the size of the molecules of current interest. The total number of possible symmetrical combinations is (n*(n -1)/2) i. e. , 5*4/2 = 10 and the distance = 8 (9 distances), summed up to 10 × 9 columns, which formed the chemical feature matrix used. A statistical model was derived that could help to classify the two odorant quantities and predict future ones.
The figure has been created using the R software package (version 4. 0. 3 for Linux; https:// CRAN. R-proje ct. org/  40  ) and the R libraries RAM" (https:// cran. r-proje ct. org/ packa ge= RAM  70  ), "ggplot2" (https:// cran. r-proje ct. org/ packa ge= ggplo t2  71  ) and "treemapify" (https:// cran. r-proje ct. org/ packa ge= treem apify  72  ). lows: "CATS2D_", "distance2D_", "type atom pair". Thus, "CATS2D_06_AL" means: the count of all molecular graph distances = 6 between atom pairs acceptor-lipophilic (AL) (Fig. 4 ). The machine-learning algorithms trained with the remaining 20 descriptors, i. e. , random forests (RF) and bagged CART achieved high performances with leave-one-out cross-validation of median AUC-ROC = 100%, which substantially exceeded the values obtained when the training data were permuted (61 and 48% respectively).
3 for Linux; https:// CRAN. R-proje ct. org/ (R Development Core Team, 2008)) and the libraries "ggplot2" (https:// cran. r-proje ct. org/ packa ge= ggplo t2(Wickham, 2009)) and "FactoMineR" (https:// cran. r-proje ct. org/ packa ge= Facto MineR 69  ).
The silkworm Bombyx mori is a domestic insect for silk production and a lepidopteran model. The currently available genomes limit a full understanding of its genetic and phenotypic diversity. Here we assembled long-read genomes of 545 domestic and wild silkworms and constructed a high-resolution pan-genome dataset. We found that the silkworm population harbors extremely variable genomes containing 7,308 new gene families, 4,260 (22%) core gene families, and 3,432,266 non-redundant SVs. We deciphered a series of causal genes and variants associated with domestication, breeding, and ecological adaptation traits, and experimentally validated two of those genes using CRISPR-Cas9 or RNA interference. This unprecedented large-scale genomic resource allows for high-throughput screening of interesting traits for functional genomic research and breeding improvement of silkworms and may serve as a guideline for traits decoding in other species.
Long-read sequencing For long-read DNA sequencing, 545 samples, containing 39 wild, 162 local, 117 improved, and 227 genetic stock silkworms, were selected to build Oxford Nanopore Technology (ONT) sequencing libraries. According to the standard procedure of the library construction of Oxford Nanopore Technologies Company, 20 kb de novo regular library of each sample was used to sequence on PromethION platform of BGI-Shenzhen. For the pooling library of two samples, reads of each sample have to be split before filter, guppy_barcoder v3. 1. 5 and qcat v1. 0. 1 were used to split reads and the intersection of two results was used as split data. The porechop v0. 2. 4 program was used to find and remove adapters. We also removed the reads with length < 5 kb and average quality less than 7. The sequencing depth ranged from 48× to 277×.
Phylogenetic analysis VCF2Dis v1. 42 (https://github. com/BGI-shenzhen/VCF2Dis) was used to estimate p distances between every two samples based on VCF file containing SNPs. PHYLIPNEW v3. 69 fneighbor (http://emboss. sourceforge. net/apps/cvs/embassy/phylipnew/) was used to build the Neighbor-Joining Tree. For Bootstrap Tree, SNPs were randomly selected to calculate the p distances 200 times in parallel by VCF2Dis (-Rand 0. 25) and PHYLIPNEW fconsense was used to merge all the results into a consensus tree. The number of bootstrap times was changed into percentage format by percentageboostrapTree. pl in VCF2Dis package.
The final correction was conducted using the medaka program. (d) The pilon v1. 23  48  software was used to polish the corrected contigs. In this step, NGS reads were mapped to corrected contigs using BWA mem, and pilon was used to polish with the following parameters: --fix bases --mindepth 20 --verbose --diploid. We only used the reads with mapping quality more than 20 for this step. (e) If the assembled genome exceeded the estimated size of genome survey by more than 5%, we performed de-redundancy using Purge Haplotigs  49. (f) To obtain chromosome-level genomes, we used RagTag v1. 0. 0 50 to anchor the contigs to chromosomes of the reference genome. Finally, we mapped NGS reads to the assembled genome, and assessed the genome coverage and mapping ratio. We also used Benchmarking Universal Single-copy Orthologues (BUSCO v 5.
Gene annotation Three approaches including ab initio prediction, homology-based prediction, and reference-guided transcriptome-based assembly were used to predict genes structure in 100 genomes, including 14 wild, 41 local, 15 improved, and 30 genetic stock. For homology-based prediction, GeMoMa 59 was used to align the protein sequences of the reference silkworm, Drosophila melanogaster, Apis mellifera, and Danaus plexippus to the newly assembled silkworm genomes. For the reference-guided transcriptomebased assembly, STAR  60  was used to map the mRNA sequencing reads (NCBI project ID: PRJNA262539, PRJNA264587, PRJNA407019) to the newly assembled genomes, stringtie  61  was used to perform RNA assembly, and PASA 62 was used to predict open reading frames (ORFs). For the ab initio prediction, augustus  63  with default parameters was used to perform ab initio gene prediction based on the training set that obtained by PASA.
Finally, EVidenceModeler (EVM)  64  was used to produce an integrated gene set. Gene function information, motifs, and domains of each genome were annotated through homology searching against the public databases including Swiss-Prot, NCBI non-redundant (NR) proteins, Kyoto Encyclopedia of Genes and Genomes (KEGG), EuKaryotic Orthologous Groups (KOG), and Gene Ontology (GO). The GO items of genes were identified using the InterProScan  65  program with default parameters. For the other four annotations, the proteins of each genome were used as query in BLASTP 66 (e-value < 1e-5) search against Swiss-Prot, NR, KEGG, and KOG databases, respectively.
ncRNA annotation For non-coding RNA (ncRNA) annotation, tRNAscan-SE  67  was used to identify transfer RNAs (tRNAs), Infernal cmscan  68  was used to identify microRNA, rRNA and small nuclear RNA (snRNA) by searching against Rfam database (http://rfam. xfam. org/). Then, RNAmmer 69 was used to identify rRNA and their subunits. Finally, an average of 691 (638-871) transfer RNAs (tRNAs), 126 (54-280)   ribosome RNAs (rRNAs), 13 (10-18) small nuclear RNAs (snRNAs), and 6,432 (6,177-8,911) microRNAs (miRNAs) per genome were identified (Supplementary Table  4 ).
GWAS analysis based on SVs Genome wide association studies (GWAS) were performed using GAPIT3  78. Before GWAS analysis, SVs with minor allele frequency (MAF) < 0. 01 were filtered. SVs phasing and imputation were performed using SHAPEIT v2. r837  79. Population structure analysis was performed using Admixture v1. 3. 0  80  , the K value with the lowest cross-validation error was chosen as the best K value. The tassel5. 0 81 software was used to calculate kinship matrix. Finally, kinship matrix was used to perform GWAS analysis using multiple models containing general linear model (GLM), mixed linear model (MLM), compressed mixed linear model (CMLM), fixed and random model circulating probability unification (FarmCPU), and Bayesianinformation and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) implemented in GAPIT3.
Identification of Y gene duplication To identify the copy number of Y gene (carotenoid-binding protein, CBP) in each genome, the reported CBP transcript was used as query to perform BLASTN 66 (e-value < 1E-5) against each newly assembled genome. The precise copy number and positions of CBP genes in each strain were obtained. Next, we performed pairwise comparison for genomic sequences of CBP gene in these strains with different CBP copy number using BLASTN (e-value < 1E-150) to observe their genomic structure variation. According to the results of pairwise comparison, the genome collinearity of Y gene region was drew using RectChr (https://github. com/BGI-shenzhen/RectChr/).
Acknowledgments We thank  Chao Su  at  Northwest A&F University, Bing Li at Soochow University, Keping Chen at Jiangsu University, Yuyin Chen at Zhejiang University, Weizheng Cui at Shandong Agriculture University ,  Chaobin Luo  at  Sericulture Institute of Guizhou ,  Yongqiang Wang  at  Zhejiang Academy of Agriculture Science ,  Wenfu Xiao ,  Gang Liu , and  Yian Chen  at  Sericulture Research Institute of Sichuan, Zhanpeng Dong at Sericulture and Apicultural Research institute of Yunnan, Anjie Wang at Shandong Institute of Sericulture, Tao Fan at Sericulture Institute of Anhui, Lihui Bi at Sericulture Institute of Guangxi Zhuang Autonomous Region (GZAR ),  Fan Wu  at  Economy Crops Institute of Hubei ,  Yuxia Wang  at  Shanxi Research Institute of Sericulture , and  Junwen Ai  at the  Sericultural Research Institute of Hunan  for helping in the collection of silkworm strains.
Data and materials availability Raw data of the long-read sequencing and short-read sequencing (including RNA-seq and whole-genome resequencing) used in this study were deposited into the CNGB Nucleotide Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb, https://db. cngb. org), and are available through BioProject ID CNP0001815. This study also analyzes four wild silkworm genome data which available in Sequence Read Archive (SRA) database according to accession number of DRX054041, DRX054040, ERS402904, ERS402902.
g. ,  Ling et al. , 2010; Wei et al. , 2018). However,  Camporeale (2019)  notes that although many approaches have been tested, it remains difficult to predict these fluxes due to certain physical phenomena that are difficult to consider for a "black-box" type model. Thus, many more recent models based on machine learning methods do not perform better than older models. In addition, using data-driven approaches to predict radiation belt dynamics with in-situ data is challenging since it is important to have large databases that are properly calibrated (which is more complicated when using space-borne instruments rather than ground-based ones). Recently,  Bernoux & Maget (2020)  have proposed a new time-integrated geomagnetic index that aims to represent the state of filling the Earth's radiation belts.
Data sets It is now well known that the geomagnetic indices representing the state of the magnetosphere are predominantly driven by solar wind dynamics  (Akasofu, 1981; Baker et al. , 1981). That is why, as in many other studies (e. g. ,  Lundstedt & Wintoft, 1994; Wu & Lundstedt, 1997; Wing et al. , 2005; Chandorkar et al. , 2017; Chakraborty & Morley, 2020) , we use solar wind parameters available in the OMNIweb database  (King & Papitashvili, 2005)  as inputs to our geomagnetic index forecast models. The OMNIweb database (https://omniweb. gsfc. nasa. gov/) grants access to hourly spacecraft-interspersed near-Earth measurements of solar wind parameters. The earliest solar wind parameters have been available since late 1963. In particular, we select the plasma bulk velocity V sw , the ion density q, the southward component of the interplanetary magnetic field (IMF) B z , and the plasma temperature T as the inputs to our models.
In such a context, the Ca index would act as a proxy for relativistic electron fluxes, monitored from ground-based magnetometers. As stated in Section 1, using a data set that already has decades of cross-calibrated samples is also a great asset when dealing with data-driven approaches that require lots of data to be efficient. Besides, it may also be more reliable in terms of continuity of service to rely on ground-based instruments rather than onboard instruments that are subject to the risks associated with their being in space, at least as a backup. Thus, the prediction of the Ca index is of immediate interest to the operators of spaceborne systems.
Splitting the data sets In this subsection, we briefly analyse the time series supplied by the OMNIweb database to detect any important data gaps (that would be prejudicial for the training of a machine learning algorithm) and carefully choose the time periods used to train, validate and evaluate our models. Dividing a data set into training, validation, and test sets is a very common practice in machine learning applications. If needed, the reader is referred to  Carè & Camporeale (2018)  for more details. Before the availability of the Wind/Solar Wind Experiment (Wind/SWE) and the Advanced Composition Explorer magnetometer and Solar Wind Electron, Proton, and Alpha Monitor G. Bernoux et al. : J. Space Weather Space Clim. 2021, 11, 60  (ACE/MAG and ACE/SWEPAM) data starting in 1995 and 1998, the OMNIweb database has a high percentage of missing data.
Therefore in our study, we only use data from 1995 onward. For the 1995-2019 period, there was on average 2. 41% of missing data per year. Even if most of the gaps are very short ones, some gaps larger than three or four days require proper handling. That is why we decided to fill the data gaps with the method introduced in  Kondrashov et al. (2010). This method is based on Singular Spectrum Analysis, a data-adaptive spectral estimation method designed to provide information on the underlying dynamics of a (multivariate) time-series  (Ghil et al. , 2002). In the context of space physics, SSA has already been used to fill the gaps in the OMNIweb database, which improved the accuracy of empirical magnetic field models compared to another simpler method based on linear interpolations  (Kondrashov et al.
For this purpose, we use the ICME database provided by  Chi et al. (2016)  and the SIR database provided by  Chi et al. (2018). These databases include the time of beginning and time of ending for several ICME-and SIR-induced geomagnetic disturbances between 1995 and 2015 (2016 for SIRs). According to these databases, 212 SIRs and 204 ICMEs were observed in the near-Earth environment between 1997 and 2002 included. In our study, we define an ICME-(respectively SIR-) induced disturbance period as the time period during which an ICME-(respectively SIR-) induced geomagnetic disturbance has an influence on the dynamics of the Ca index. The beginning of the disturbance period is given by the beginning of the storm, as indicated in the database. The ending of the disturbance period is given by adding s = 4 days to the ending of the storm as indicated in the database.
-We use L2-regularisation with weight 1 Â 10 À5. L2-regularisation consists of adding the squared sum of the network's weights (with a multiplicative constant) to the loss function to avoid overfitting. -Size of each mini-batch: 256. -The training is done with 120 epochs and with early stopping. Early stopping consists in stopping the training of the network as soon as clear signs of overfitting are observed. The model was developed using the PyTorch (v1. 9) library for Python  (Paszke et al. , 2019).
Acknowledgements. The authors would like to thank the anonymous reviewers for their insightful suggestions and comments, which helped improve the overall quality of the paper. The authors are thankful to the  NOAA-POES  for online data access available on the CDAweb (at http://cdaweb. gsfc. nasa. gov/). The results presented in this paper rely on geomagnetic indices calculated and made available by ISGI Collaborating Institutes from data collected at magnetic observatories. We thank the involved national institutes, the  INTERMAGNET network and ISGI  (http://isgi. unistra. fr). The OMNI data were obtained from the  GSFC/SPDF OMNIWeb  interface (at https://omniweb. gsfc. nasa. gov). Sunspot data from the  World Data Center SILSO, Royal Observatory of Belgium, Brussels. G. Bernoux  is thankful for funding from  Région Occitanie and ONERA , under Grant Agreements 19008721/ALDOCT and  30196.
Prioritisation procedure A prioritisation procedure based on the sum of KTAbased or proxy-based distances was applied to our dataset. This procedure involved a k-mean clustering approach aiming at dividing all population pairs into k clusters (i. e. population pairs belonging to different clusters should be considered as different in terms of KTA or proxy; see Methods for details). The results obtained for the prioritisation strategy are presented in Table  2. When looking at proxies independently, the highest proxy-distance value corresponds to different population pairs depending on the proxy considered (Table  2 ). The prioritisation procedure indicates three clusters with the A cluster grouping populations presenting the highest values for the sum of proxy-based distances (Table  2 ). Populations belonging to cluster A mostly belong to clusters 1 and 2 when considering the sum of KTA-based distances and present high numbers of KTA statistically differentiated (Table  2 ).
An example of prioritisation procedure exemplified with our test-case species We recommend evaluating population/population group pairs according to both their degree of divergences in each proxy and the degree of congruence in divergence of all proxies. We applied here a prioritisation procedure on our dataset to validate this procedure. Firstly, in this example, depending on the proxy considered, the conclusion regarding which population pairs are the most interesting would differ (Table  2 ). For instance, considering the genetic proxy, the highest distance is seen between VAL and BAL while with hydrologic and habitat proxies, the comparisons VAL-HOH and GEN-HOH would be promoted, respectively. This confirms the need for a multi-proxy approach and a prioritisation procedure to make a consensus between proxies.
Genetic distance proxy The genetic assessment of the seven populations was performed on 10 individuals per population collected randomly at the end of the experiment. Larvae were stored in 99% ethanol at -20 °C until analyses. Samples were sent to Genoscreen (Lille, France) for DNA isolation, marker amplification, and Sanger sequencing. Three mitochondrial regions were studied (D-loop of control region, cytochrome b, and 16S rRNA; see references for primers in  [76] ) following the protocol available in Toomey et al. [76]. Both strands of each PCR product were sequenced. Consensus sequences of mitochondrial regions were computed and edited using CodonCode Aligner 7. 1. 2 (CodonCode Corporation, Dedham, Massachusetts, USA). There was no uncertainty in the consensus sequences. The P. fluviatilis origin of each sequence was verified using BLAST  [77].
Sequence alignment was performed in MAFFT (default parameters;  [78] ). Translation to proteins for Cytb was performed in Mesquite 3. 20  [79]. A tandemly repeated array was identified in D-loop (previously reported in previous P. fluviatilis studies;  [80] ) and mutations in the repeated array were coded as a single mutational step for further analyses. Mitochondrial markers were concatenated within a single alignment for further analyses using Mesquite. Haplotype sequences were deposited in GenBank (GenBank accession numbers: MN939382 to MN939395). Genetic distances between populations were calculated in R using ɸ ST distance  [81]  in SPADS  [82].
Habitat divergence proxy We used 19 bioclimatic data (BIO1 to BIO19; https:// www. world clim. org/ data/ biocl im. html) as well as annual mean solar radiation (kJ m -2 day -1 ), annual mean wind speed (m s -1 ), and annual mean water vapor pressure (kPa) from WorldClim Version2 to assess sampling place environments. We considered these abiotic environmental parameters (at the sampling location) recorded between 1970 and 2000 at a resolution of 10 arc-minutes  [83]. We also used four additional variables: lake area (km 2 ), catchment area (km 2 ), average and maximum depths (m), and altitude (m). All variables were standardised to zero mean and unit variance due to their different scales of measurement (package "vegan"  [84] ). A principal component analysis was performed (Additional file 3: Table  S1 ; packages "MASS"  [85]  and "factoextra"  [86] ) using the Kaiser-Guttman rule to determine the number of axes to retain (here the first four axes representing 98.
Geographic and hydrologic distance proxies Geographic distance matrix was set through the calculation of the great-circle distances (i. e. shortest distance over the earth's surface) according to the "Vincenty" (ellipsoid) method  [87]  implemented in the R package "geosphere"  [88]. Because this geographic distance is a priori not relevant for fish species, we also estimated a hydrologic distance using an algorithm based on circuit theory and implemented in the program Circuitscape  [89]. Rivers and lakes shapefiles were respectively extracted from the Catchment Characterisation and Modelling river and catchment database v2  [90]  and HydroSHEDS  [91]  and from Natural Earth (www. natur alear thdata. com) databases, and jointly rasterised on a geo-referenced grid with a resolution of 0. 5 arcmin.