dataesr/scientific-paragraphs-categorization huggingface dataset:
Post Run was set to 325 °C for 5 min. Helium served as carrier gas with a constant flow of 1. 2 ml per min and a pressure of 10. 42 psi. Initially CHC peaks were identified and the chromatogram was generated using the Qualitative Analysis Navigator of the MassHunter Workstation Software (vB. 08. 00 / Build 8. 0. 8208. 0, Agilent). CHC quantification was performed using the Quantitative Analysis MassHunter Workstation Software (vB. 09. 00 / Build 9. 0. 647. 0, Agilent). Peaks were quantified using their diagnostic (or the neighboring most abundant) ion as quantifier and several characteristic ions in their mass spectra as qualifiers to allow for unambiguous detection by the quantification software. The pre-defined integrator Agile 2 was used for the peak integration algorithm to allow for maximum flexibility.
Immune genes The list of immune genes to be searched against the A. ervi and L. fabarum genomes was established based on Drosophila melanogaster lists from the Lemaitre laboratory (lemaitrelab. epfl. ch/fr/ressources) [224, 225]  and from the interactive fly web site  [84]  (www. sdbonline. org/sites/fly/aignfam/immune. htm). Each D. melanogaster protein sequence was used in BLAST similarity searches against the two predicted wasp proteomes (BLASTp) and against the entire assembly (tBLASTn) when no direct match was obtained. The best match was retained, and its protein sequence was used to perform a new BLAST search using the NCBI non-redundant protein sequence database to confirm the similarity with the D. melanogaster sequence. When both results were concordant, the retained sequence was then searched for in Nasonia vitripennis and Apis mellifera proteomes to identify homologous genes in these species.
Olfactory genes Odorant-binding proteins (OBPs) and chemosensory proteins (CSPs) To identify OBPs based on homology to known sequences, we retrieved 60 OBP amino acid sequences from other Braconidae (namely Fopius arisanus bw Microplitis demolitor) from GenBank. To this, we added seven OBPs found in a previous transcriptome of A. ervi (Patrizia Falabella, unpublished, EBI SRI Accessions: ERS3933807-ERS3933809). To identify CSPs, we used CSP amino acid sequences from more Hymenoptera species (Apis mellifera, Nasonia vitripennis, Fopius arisanus and Microplitis demolitor). These sets were used as query against A. ervi and L. fabarum genomes using tBLASTn (e-value cutoff 10e-3 for OBPs and 10e-2 for CSPs). Genomic scaffolds that presented a hit with at least one of the query sequences were selected.
Odorant receptors (ORs) ORs were annotated using available OR gene models from Diachasma alloeum, Fopius arisanus, and Microplitis demolitor retrieved from NCBIs protein database (retrieved May 2017). Preliminary OR genes models for A. ervi and L. fabarum were predicted with exonerate (v2. 4. 0), GeMoMa v1. 4  [223] , and combined with EVidence Modeler v1. 1. 1  [236]. These preliminary models were subsequently screened for the 7tm_6 protein domain (with PfamScan v1. 5) and manually curated in WebApollo2. In an iterative approach, we annotated the IRs using known IR sequences from Apis melifera, Drosophila melanogaster, Microplitis demolitor and Nasonia vitripennis as queries to identify IRs in the genomes of A. ervi and L. fabarum. The hymenopteran IR sequences served as input for the prediction of initial gene model with Exonerate  [235]  and GeMoMa  [223].
Acknowledgements Thanks to the many people who helped with collections and insect rearing, especially  Paula Rodriguez  (EAWAG),  Laury Arthaud  and  Christian Rebuf  ( ESIM, INRAE-ISA ),  Francisca Zepeda-Paulo (UACH) ,  Sebastian Ortiz-Martinez ,  Cinthya Villegas , and  Daniela Sepúlveda  (UTALCA). Lucia Briones  (UTALCA) and  Dominique Cazes  ( ESIM, INRAE-ISA ) provided help in with A. ervi DNA extractions. Paul Saffert  ( Uni Potsdam ) provided valuable discussion leading to the codon usage analysis. David Pratella  ( ESIM, INRAE-ISA ) provided help on the annotation of immune genes. Lysiphlebus fabarum data were generated in collaboration with the Genetic Diversity Centre (GDC, with particular thanks to  Stefan Zoller  and  Jean-Claude Walser ), the  FGCZ at ETH Zurich , and utilized the ETH Scientific Computing Cluster (Euler).
Orthologs were computed on the  University of Potsdam 's High Performance Computing Cluster Orson2, managed by the ZIM. The ESIM team is supported by the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference #  ANR-11-LABX-0028-01. Mark Lammers  would like to thank  Panagiotis Provataris  ( ZFMK, Bonn, Germany ) for bringing to our attention the existent small whole genome bisulfite sequencing data set for Aphidius ervi. DNA sequences from the Myzus genomes used in comparative analysis of codon usage were downloaded from AphidBase. Funding for Myzus persicae clone G006 genomic sequencing was provided by  USDA-NIFA  award  2010-65105-20558. Funding for M. persicae clone O genomic sequencing was provided by  The Genome Analyses Centre (TGAC) Capacity and Capability Challenge program  (project  CCC-15  and  BB/J004553/1 ), from the  Biotechnology and Biological Sciences Research Council (BBSRC) , and the  John Innes Foundation.
Availability of data and materials Both genomes are available from the NCBI Genome database (PRJNA587428, A. ervi: SAMN13190903, L. fabarum: SAMN13190904). The bipaa webpage (https://bipaa. genouest. org) hosts the assemblies (https://bipaa. genouest. org/ sp/aphidius_ervi/download/genome/v3. 1/ and https://bipaa. genouest. org/ sp/lysiphlebus_fabarum/download/genome/v1. 0/), predicted genes, and annotations (https://bipaa. genouest. org/sp/aphidius_ervi/download/ annotation/ and https://bipaa. genouest. org/sp/lysiphlebus_fabarum/ download/annotation/v1. 0/). Raw Illumina and PacBio sequence data used to construct genomes are available in NCBI SRA for both A. ervi (SAMN12878248) and L. fabarum (accessions SAMN10617865, SAMN10617866, SAMN10617867), and further detailed in Supplementary Tables  1  and 2.
4 PRJNA258104 [34] Macrocentrus cingulum MCINOGS1. 0 132. 36 5696 (192. 4) 13,289 (64. 9) 11,993 35. 66 PRJNA361069 [35] Microplitis demolitor Mdem 2 241. 2 1794 (1140) 27,508 (14. 12) 18,586 33. 1 PRJNA251518 [36] Diadromus collaris ASM939471v1 399. 17 2731 (1030. 3) 20,676 (25,941) 15,328 37. 37 PRJNA307299 [32] Nasonia vitripennis Nvit_2. 1 295. 7 6169 (709) 26,605 (18. 5) 24,891 40. 6 PRJNA13660 [37].
Administrative customs data From Madagascar's customs administration, we obtained highly disaggregated administrative data tracking imports at the transaction level spanning the period January 2016-February 2019 (extracted through the customs management system (ASYCUDA)). For each import declaration, ASYCUDA data cover several standard variables: the port of entry, the HS 8-digit products included (designated as items) and for each item the import value (in Madagascar Ariary), taxes paid (tariff and value added tax as well as exemptions, all in Madagascar ariary) and weight (in kilograms), the country of origin (transit). The data also include more unique and novel variables such as the time stamps for date of registration, date of assessment, and other relevant dates in the customs clearance process (day/month/year/hour), the import value initially declared by the broker on the behalf of the importer, the inspection channel initially recommended by: (i) the risk management system; and (ii) by the RMU (documentary control/yellow channel, physical frontline inspection/red channel or no inspection/blue channel), and unique numeric identifiers for the broker, the importing firm and, importantly, the customs inspector assigned to handle the declaration, which allows us to study inspector performance.
Internal control and fraud data We merge these ASYCUDA data with additional data sets from the customs administration's internal control systems. First, we combine these data with information on modifications made by the customs inspector during the customs clearance process to the inspection channel (upgrade in the channel the import declaration was initially allocated by the RMU, as discussed in Section 2), the import value and the import weight. Second, we obtained information on fraud records from the Legal Department (Service des Affaires Juridiques et du Contentieux), namely which declarations were deemed fraudulent and the amount of taxes recovered (if any). These variables are unique and crucial for our definition of inspector actions that may potentially respond to the provision of information and the monitoring.
Risk scores, valuation advices and scanning results We obtained from the service provider GasyNet additional information that we merge to the ASYCUDA data on the risk score assigned to each declaration (related to the risk of noncompliance with customs regulations ranging from 1 to 9), the recommendation of an inspection channel (documentary control, physical frontline inspection, or no inspection), and whether a declaration was subject to a scanner exam. Most importantly, and as discussed in Section 2, the service provider also issues valuation advices. Since this service is costly given that it requires additional research, valuation advices are carried out for only a subset of declarations, about 6 percent of the total per year, with the selection made by the service provider and the customs administration based on risk criteria.
To our knowledge, ours is the first study with access to this type of risk and third-party valuation information. Further discussion on these valuation advices is provided in Section 4. Madagascar's raw customs data cover the universe of Madagascar's formal import transactions, that is, imports made under five regimes: final imports for consumption (imports for home use), re-imports, temporary admissions, inward processing, warehouse, and other. Our sample for the econometric analysis includes only import declarations that are subject to taxation and to a physical or documentary control by customs frontline inspectors. This implies focusing only on imports for home use (customs regime IM4) and excluding declarations from importers that are members of the "Procédure Accélérée de Dédouanement" (PAD), a trusted trader program that allows member firms to benefit from expedited clearance procedures with minimal controls at the border.
Recall that, prior to the start of the experiment, the RMU was considered to be dysfunctional. At the launch of the experiment, the head of the RMU decided to divide the unit's work into three teams: one inspector and three analysts per team to increase internal accountability and optimize workflow. A standardized reporting framework was adopted for all teams. In less than two weeks, the RMU's work dramatically changed. The new organizational structure incentivized the inspectors who were appointed head of their respective teams to share their knowledge with newly recruited analysts on potential fraud cases and give them advice on what to look for, e. g. , historical cases of fraud for some economic operators, abnormal price valuation for some imports. Moreover, they created a database of historical fraud cases and improved documentation of their risk analysis to facilitate internal knowledge transfer.
Robert Marty  provided excellent research assistance on text mining analysis. Hibret Maemir  and  George Schaur  helped with the differentiated and time-sensitive product classifications. This paper has been partly supported by the  World Bank's Multidonor Trust Fund for Trade and Development and the Strategic Research Partnership on Economic Development. We also acknowledge the generous financial support from the  World Bank  research support budget and the  Knowledge for Change Program (KCP) , a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the  World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the  International Bank of Reconstruction and Development/World Bank  and.
52, P = 0. 474), a fluctuating asymmetry was observed (χ 2 1 = 4. 50, P = 0. 033, Fig. 1A , Fig. S2A ). Fluctuating asymmetry explained 2. 4% of the total variance in gametocyte density. Although the asymmetry analysis considers the measurement variations, this result may be largely influenced by the individuals showing extremely low intensities of infection. However, when individuals with one or less than one gametocyte on average per 1000 leucocytes per hand, and then individuals with 5 or less than 5 gametocytes, were removed from the dataset, fluctuating symmetry was still detected (χ 2 1 = 5. 88, P = 0. 015, χ 2 1 = 4. 23, P = 0. 039, respectively). The individual asymmetry index corrected for size-dependence averaged 0. 67 ± 0. 11 for the complete dataset, 0. 36 ± 0. 05 for the dataset containing only individuals with more than one detected gametocytes and 0.
25 ± 0. 05 for the dataset containing only individuals with more than 5 detected gametocytes. Avian malaria. For each leg of each bird, three independent measurements of parasitaemia were made from three different drops of blood. The measurements repeatability between the three blood smears was highly significant (R = 0. 987 CI = 0. 980, 0. 992, p < 0. 0001). Variation in parasite densities within each leg of each bird represents only 0. 03% of the variance. As for human malaria, heterogeneity between birds explains a large part of the variance (97%, Fig. 1B ). While we showed absence of directional asymmetry (F = 2. 45, P = 0. 135), a fluctuating asymmetry of parasitaemia between the two bird sides was observed (1. 7% of the variance, χ 2 1 = 10. 81, P = 0. 001, Fig. 1B , Fig. S2B ). When individuals with a parasitaemia of less than 0.
1% where removed from the dataset, the fluctuating asymmetry of parasite density was still detected (χ 2 1 = 9. 052, P = 0. 002). However, when individuals with a parasitaemia of less than 0. 5% where removed from the dataset, the fluctuating asymmetry was no longer detected (χ 2 1 = 1. 014, P = 0. 314). The individual asymmetry index corrected for size-dependence averaged 0. 21 ± 0. 04 for the full dataset, 0. 17 ± 0. 03 for the dataset containing only birds with a parasitaemia of more than 0. 1% and 0. 13 ± 0. 02 for the dataset containing only birds with a parasitaemia of more than 0. 5%.
Repeatability estimation. The repeatability estimation of the parasite load between observers, in the case of samples from humans, and between slides within the same extremities, in the case of birds, were obtained using the intra-class correlation coefficient (package rptR  75  ). Asymmetry of parasite densities. The presence of directional and/or fluctuating asymmetry (DA and FA, respectively), while taking sampling or measurement variation into account, for avian and human malaria system respectively, was tested by the restricted maximum-likelihood (REML) estimation of a mixed regression model where we fitted the models to the repeated measurements of right and left parasite density sides  76. Side (right or left) was included as fixed effects in the models and log-transformed trait values (gametocyte number or parasitaemia) as response variables, individual identity and side were fitted as a random effects.
As our results indicated the existence of fluctuating asymmetry in human and avian malaria system (see Results), we calculated an individual asymmetry index (AI) as the unsigned right-left difference (|R-L | ) between the average of trait values across the two (human) or three (bird) replicate counts of each individual  53, 79. Trait size dependence was examined with a non-parametric test of association (Spearman test) between unsigned AI values (|R -L | ) and (R + L)/2. These analyses revealed positive trait size dependence for both human and avian malaria system (human: S = 2956, p = 0. 0009, Rho = 0. 55; bird: 384, p < 0. 0001, Rho = 0. 83). We therefore calculated a new asymmetry index with a correction for size-dependence (AIcor = |R -L | /(R + L)/2 [63,65]). The part of variance explained by difference in counts between observers or repeated slides, for humans and birds respectively, was estimated from a mixed model with observers or repeated slides as a fixed factor, and side (left, right), nested in individual, as a random factor (MuMin package  80  ).
Parasite transmission to mosquito vector. Mixed effects models were used to analyze the effect of bird leg infection class (LIL or HIL) on the mosquito blood meal rate (proportion of females that had taken a blood meal), blood meal size and Plasmodium transmission to the vector. The explanatory variables leg class (LIL or HIL) and haematin (when it was appropriate) were fitted as fixed factors. Individual, nested within experimental block, was fitted as a random factor. Blood meal rate and infection prevalence (oocyst presence/absence) were analyzed using GLMM with a binomial error distribution (lme4 package  81  ). Blood meal size and mosquito infection intensity (number of oocysts) were analyzed using lmer with normal error distribution. For the analysis of infection intensity, only individuals that developed ≥ 1 oocyst were included.
Acknowledgements We would like to thank all children and their parents for participating in this study, as well as the local authorities for their support. We are very grateful to  Raymond Hien ,  Jean Bazié ,  Ousmane Traoré , and  Adama Sankara  for technical assistance. We would thank two anonymous reviewers for very helpful and constructive comments. We would like to thank  Olivier Glaizot  and  Camille-Sophie Cozzarolo  for comments on the manuscript and  Frederic Schütz  for his advice on statistical analysis. We thank  Octopus Editing  for editing service and comments. The research was supported by grants from the  Swiss National Science Foundation  (grant number  31003A-138187 ,  31003A-159600 ) and by the  ANR  Grant no. " 16-CE35-0007 ",  JEAI-IRD "PALUNEC ". This project is part of the EDCTP2 programme supported by the  European Union  (grant number  RIA2016V-1649-MMVC ).
(B) Number of differentially expressed genes (DE, y-axis) versus the number of genes with differentially spliced exons (DS, x-axis), between pairs of samples of the same cell type (within, blue), or different cell types (between, red). DS genes have been obtained using IPSA (https://github. com/pervouchine/ipsa-full). See also Supplemental Figure  S19. (C) Fraction of 1 to 1 orthologs between each species and human for major cell-type-specific genes and for protein-coding genes overall. Species are sorted by increasing evolutionary distance from human. The black line is given as a reference, and it indicates the proportion of six-way orthologs (chimpanzee, rhesus, mouse, opossum, platypus, and chicken) that are present in each species. The proportion is not 100% in these species because different versions of the GENCODE gene set reference were used.
Estimation of the cellular composition of complex organs from the expression of cell-type-specific genes We used the patterns of expression of cell-type-specific genes to estimate the cellular composition of human tissues and organs from GTEx bulk tissue transcriptome data (version 6, 8555 samples, 31 tissues, 544 individuals) (The GTEx Consortium 2017). We used xCell  (Aran et al. 2017 ), using the sets of genes specific to epithelial, endothelial, and mesenchymal major cell types derived from EN-CODE, and specific to brain (neural) and blood derived from GTEx  (Yang et al. 2018)  as signatures, and computed the enrichments of these cell types in each GTEx tissue sample (Supplemental Methods 8). The xCell enrichments (Fig. 4A ; Supplemental Table  S12 ) are largely consistent with the histology of the tissues.
We analyzed single-cell RNA-seq data produced during skeletal myoblast differentiation  (Trapnell et al. 2014 ) and found that differentiating skeletal muscle cells retain the mesenchymal signature through most of the differentiation pathway, acquiring only the GTEx muscle specific signature when fully differentiated (Supplemental Fig. S25A-C ). Further supporting that muscle is indeed of mesenchymal type, potentially forming a well-defined subtype, gene expression profiles cluster together myoblast differentiating single cells with ENCODE mesenchymal cells, rather than with epithelial or endothelial cells, or forming a separate cluster (Supplemental Fig. S25D ). To independently assess the xCell enrichments, we analyzed the histological images of the few tissues in which samples were obtained from different subregions.
Thus, the limited passage has an unlikely effect on the gene expression program. We rely on the providers' standards for quality assurance. Quality sheets are available through the ENCODE portal (https://www. encode project. org/search/?type=Biosample&organism. scientific_name= Homo+sapiens&biosample_ontology. classification=primary+cell& lab. title=Thomas+Gingeras%2C+CSHL&source. title=PromoCell& award. rfa=ENCODE3). We ordered three vials per cell type per donor for a total of 3 million cells. The three vials were combined, and we isolated total RNA from them using the Ambion mirVana miRNA Isolation kit (AM1561). The rRNA was removed using the Gold Protocol (RZG1224). The libraries are made using a homebrew "dUTP" protocol  (Parkhomchuk et al. 2009) , which generates stranded libraries. They were sequenced on the Illumina platform in mate-pair fashion and processed through the data processing pipeline at the ENCODE DCC.
RAMPAGE sample preparation Isolation of RNA is described in the preceding section. The RAM-PAGE protocol  (Batut and Gingeras 2013)  was used to make libraries. Each library was sequenced in mate-pair fashion on the Illumina platform. Detailed protocol and quality-control images and metrics on a per library basis can be found in the "Production Documents" appended to each RAMPAGE assay at the ENCODE Data Coordination Center (https://www. encodeproject. org/). Small RNA isolation, library construction, and sequencing Isolation of RNA is described in the preceding section. The Illumina TruSeq protocol was used to make libraries. Each library was sequenced in single end fashion on the Illumina platform. Detailed protocol and quality-control images and metrics on a per library basis can be found in the "Production Documents" appended to each Small RNA assay at the ENCODE Data Coordination Center (https://www.
RNA-seq processing pipeline Raw reads from the 106 RNA-seq libraries (for a list of ENCODE library IDs, see Supplemental Table  S1 ; for submitted FASTQ files, see https://www. encodeproject. org/) were aligned with STAR v2. 3. 1z (Dobin et al. 2013)  to the human genome assembly hg19. Reads mapping to more than 20 multiple positions were discarded. Read counts for all long genes annotated in GENCODE v19  (Harrow et al. 2012)  were computed with RSEM 1. 2. 19 (expected read counts)  (Li and Dewey 2011). Statistics on the number of reads and mapping are available on Supplemental Table  S14. Furthermore, we verified using liftOver that the cell-type-specific genes are consistent between GRCh37/hg19 and GRCh38/hg38, with a successful conversion of 2855 of the 2871 genes. For most of the analyses, we average expression values for a given pair of replicates and sometimes the two biological replicates are from donors of the opposite sex; therefore, we remove genes on Chromosome Y.