dataesr/scientific-paragraphs-categorization huggingface dataset:
To this end, network diagrams were built and which countries are using a given country's DSI (i. e. , the countries to which country X is providing DSI. The "using" i. e. , network displays the countries whose DSI are being used by country X's scientists. These data are also helpful to show that both neighbouring and distant countries use DSI from many countries. In Fig. 4 , DSI provisioning and use for Malaysia, which is both a G77 member and a mega-biodiverse country, is shown as an example. In Fig. 4 , many LMICs (and not just HICs) use DSI from Malaysia, e. g. , Zambia, India, Peru, and Mexico, scientists in Malaysia use DSI from 68 countries. Again, here there is no evidence of a provider-use relationship in DSI usage. Rather, Malaysian scientists use (cite in publications) DSI from a wide variety of countries and economic settings including Germany, Norway, Costa Rica, and Ghana.
These data complement the data presented in Fig. 3 , which suggests that, although scientists use their own national DSI more frequently, when they use foreign DSI they do not seem to be primarily using DSI from biodiversity-rich countries but rather DSI from across the world without any clear geographical or economic clustering patterns. A final question that we addressed is what the overall providing-use relationship is for every country and whether there is a global trend to this relationship. Indeed, given the linear trend displayed in Fig. 5 , it seems that many countries provide DSI to and use DSI from a roughly equal number of countries. In other words, if scientists from a given country are providing DSI, they are often using DSI at a similar level. However, small countries, especially LMICS, with accordingly smaller datasets and scientific communities, tend to cluster in the bottom left of the graph, meaning that they have little provisioning of DSI (i.
Discussion Preconceptions in policymaking can be tested by looking at empirical data. Because there is a central repository for DSI, namely, the International Nucleotide Database Collaboration, global analyses can be conducted to inform the debate around benefit sharing from DSI and test hypotheses. The data presented here show that the concept of a user-provider dichotomy from provision by LMICs to use by HICs for DSI is rejected. This suggests that if an ABS policy mechanism for DSI incorrectly assumes a unidirectional provider-user relationship in which benefits (non-monetary and monetary) flow from HICs to LMICs, this will only occur in some instances and, indeed, based on Fig. 3 , is the least frequent type of DSI use. Given current political discussions, LMIC-sourced DSI would be the most likely to fall under an ABS regime, but it is LMIC researchers who are the predominant users of this DSI (Fig.
This would be a much different approach than a "lockit-up and control it" approach to DSI, which would negatively affect researchers everywhere on the globe and especially those in LMICs. Furthermore, provisioning, use, and reuse of DSI as interpreted through DSI citation in publications is not a one-way street but rather a multi-directional traffic circle of data flowing in many different directions amongst all countries of the world. DSI is used by neighbouring countries and distant countries, by LMICs and HICs alike without any clear patterns or regional or economic trends (Fig. 4 ). Additionally, the network diagram reminds us that scientists working in developing countries, as well as the biological diversity of developed countries, are often overlooked in political discussions that oversimplify provision and use of biological data.
The sole clear trend observed in this analysis is that providing and using tend to go hand in hand (Figs  2  and 5 ). Large countries tend to use and provide a relatively large amount of DSI and smaller countries use and provide less, but trends based on development status (or, indirectly, the presence of megabiodiversity) were not detected. In general, the relationship between use and provision seems to be relatively linear and not biased towards HICs but, instead, slightly biased towards LMICs (see G77 and BRICS trend lines, Fig. 5 ). The dataset presented here is not a comprehensive dataset of all publications citing DSI but is limited to the open-access publications available for text mining in ePMC, as well as other dataset limitations explained in  [11]. Furthermore, we note that the dataset would be further improved if the country of origin information (14.
6% of sequences in this dataset had such information) provided by scientists submitting sequence data to INSDC were more consistent. Encouragingly, INSDC recently announced a new requirement for provenance information  [14, 16]. With improved geographical and temporal information, clearer references to regional conditions would be possible and thus more valid scientific statements and analyses will be possible. For example, gene-function relationships could be mapped more precisely with climatic, geological, or atmospheric features. However, acknowledging the aforementioned limitations, this analysis is the largest and only comparison of this size and perspective to date and represents a novel attempt to bring data and a new perspective on DSI to the policymaking process. We encourage other groups to expand and build upon this dataset for other policy environments and to reuse and complement these data with additional perspectives.
Potential Implications Future political decisions around how to handle DSI should account for the complexity of geographical provision and use trends presented here. A DSI framework that requires benefit sharing from individual sequences should anticipate the high level of LMIC-LMIC DSI provision and use and high level of HIC-HIC DSI provision and use. Benefit-sharing expectations should be adjusted accordingly. If policymakers do not want to require LMICs to provide benefits to other LMICs, then a simple, decoupled multilateral mechanism should be considered that decouples access and use of individual sequences from benefit-sharing requirements and instead requires benefits further downstream in the value chain. Policymakers also need to appreciate the tremendous contribution towards non-monetary benefit sharing that these global biological and publication databases make towards broader CBD goals and towards the UN Sustainable Development Goals.
They should build in incentives in any DSI beneft-sharing framework to support these contributions. When policymakers meet in June 2022 to make a decision on DSI and the Global Biodiversity Framework, we hope these data will make a constructive contribution to evidence-based DSI policymaking. Finally, these data also raise a fundamental question about the current ABS frameworks already in place for GR, especially the Nagoya Protocol. For GR there is no central repository for movement across national borders as there is for DSI, but these data suggest that the provider-user relationship, or the lack thereof, for GR could follow similar patterns to those observed for DSI. If so, this could suggest that the existing bilateral system and the predominance of user checks in HICs (rather than globally) is perhaps not the most appropriate way to ensure benefit sharing.
We therefore measured the length of bacteria (Fig 7B ) and we found that the distribution of bacterial length was shifted toward the smaller size for the Δrv0180c::km mutant in comparison to H37Rv and the complemented strains (Fig  7B ). In contrast, electronic microscopy experiments indicated that the diameter was larger for the Δrv0180c::km mutant than for the WT strain or complemented strain (Fig 7C ). Taken together, these data indicated that the deletion of rv0180c affects the shape of M. tuberculosis. As the bacterial shape is determined by the cell envelope ultrastructure, we explored the functionality of this barrier by testing the sensitivity of the WT or mutant strains to stresses such as antibiotics or a detergent. First, we examined the minimum inhibitory concentrations (MIC) for rifampicin, a large polyketide that inhibits RNA polymerase, isoniazid and ethambutol, which block the biosynthesis of mycolic acids, and vancomycin that targets peptidoglycan synthesis.
For each DNA sample, five independent PCR amplification were performed and sequenced (technical replicates). Size selection was performed on the PCR product. Library quality was assessed using an Advanced Analytical Fragment Analyzer and libraries were quantified by qPCR using the Kapa Library Quantification Kit (Roche). DNA-sequencing was performed on an Illumina HiSeq3000 using a paired-end read length of 2x150 pb with the Illumina HiSeq3000 Reagent Kits. More than 5 millions reads were generated for each sample. The sequencing reads were aligned to the H37Rv reference genome  [61] , and unmapped, unpaired reads, and PCR duplicates were removed using BWA and Samtools softwares. The sequencing reads were analyzed to identify the Himar inverted terminal repeat and the adjacent TA using a custom script (available upon request) allowing one mismatch.
The sequence following the TA insertion site was aligned to the H37Rv genome sequence to identify the TA dinucleotide insertion site coordinates. For each sequencing data set, a table was generated containing the number of read for each TA insertion site. For TnSeq analysis, each insertion count from mouse samples was compared to the insertion count obtained with the inoculum sample using both the ESSENTIAL  [18]  and TRANSIT  [19]  methods. For each comparison and each method, a list of genes with significant different insertion count (adjusted p-values <0. 05 and Log2 Fold change <-2 or >2) was generated. Genes identified in three out of four comparisons using the same statistical analysis method were retained. We then selected only the genes retained using the two methods to produce two short lists of genes with differential insertion count in comparison to the inoculum after 1 day or 8 days in mice (S1 Fig).
At the end of infection, extracellular bacteria were removed by three successive washes with fresh medium. At the indicated time points, cells were fixed with 3. 7% PFA. Coverslips were then quenched with 50mM NH 4 Cl in PBS for 2 min and permeabilized with 0. 03% triton X100 for 10 min before washing 3 times with PBS. Nuclei and F-actin were labelled with DAPI (1/1000) and with rhodamine-phalloidin antibodies (1/2000) (Life Technologies), respectively. Coverslips were mounted on glass slides using mounting medium and samples were imaged using a confocal Spinning Disk microscope (Olympus, IX83) equipped with a Yokogawa X1 head and an EMCCD camera (iXon Ultra, Andor). The system was driven by Andor IQ 3 software. Images were taken using a 60X oil immersion objective (1. 35 NA, UPlanSAPO) and acquisition was performed using the following settings: scanning mode xyz, sequential color acquisition and pixel resolution 1,024 x 1,024.
The pellet was then discarded and bacteria resuspended with PBS. For freezing, 3 μL of sample were deposited onto glow-discharged lacey carbon grids and placed in the thermostatic chamber of a Leica EM-GP automatic plunge freezer, set at 20˚C and 95% humidity. Excess solution was removed by blotting with Whatman n˚1 filter paper for 0. 5 seconds, and the grids were immediately flash frozen in liquid ethane at -185˚C. The frozen specimens were placed in a Gatan 626 cryo-holder, and Cryo-EM was carried out on a Jeol 2100 microscope, equipped with a LaB 6 cathode and operating at 200 kV, under low dose conditions. Images were acquired with SerialEM software, with defocus of 1-2 μm, on a Gatan US4000 CCD camera. This device was placed at the end of a GIF Quantum energy filter (Gatan, Inc. ), operated in zero-energy-loss mode, with a slit width of 25 eV.
2% formic acid) and 5% solvent B (80% acetonitrile and 0. 2% formic acid). Peptides were eluted using a 5-50% gradient of solvent B over 105 min and at a flow rate of 300 nL/min. The OrbiTrap Fusion was operated in Data Dependent Acquisition mode to automatically switch between full scan MS and MS/MS acquisition using Xcalibur software (Thermo Fisher Scientific). Survey scan MS was acquired in the Orbitrap over the m/z 400-1500 range, with the resolution set to a value of 120,000 (m/z 400). The Top Speed mode (3s) was used to perform the DDA Aquisition, the most intense ions per survey scan were selected for higherenergy collisional dissociation, and the resulting fragments were analysed in the linear ion trap. Dynamic exclusion was employed within 60s to prevent repetitive selection of the same peptide.
Mass spectrometry raw files were analyzed using the Proline software version 1. 6  [69]. MS/ MS spectra were searched in the Mascot search engine (Matrix Science Inc. , Boston, MA, USA) against the forward and reverse MTB H37Rv native 20201001 database. The digestion enzyme was set to trypsin/P with up to two missed cleavages. Methionine were searched as variable modifications and carbamidomethyl of cysteine as fixed modification. Parent peptide masses and fragment masses were searched with maximal mass deviation of 10 ppm and 20 ppm respectively. A first level of false discovery rate (FDR) filtration was done on the peptide-spectrum match level, and this was followed by a second level of FDR control on the protein level. Both filtrations were performed at a 1% FDR. These filtrations were done using a standard target-decoy database approach.
Acknowledgments The authors are grateful to  Dr Rainer Kalscheuer  for sharing with us the M. tuberculosis H37Rv ΔtreSΔglgC double mutant deficient in capsular glucan synthesis and to  Dr B. J. Appelmelk  and  J. J. Maaskant  for the generous gift of antibodies raised against various mycobacterial glycoconjugates. The authors thank  Dr A. Arbues  for technical assistance with siRNA transfection. The authors acknowledge support from the  GenoToul (Toulouse, France) Bioinformatics platform and GeT-PlaGe core facility  for help with the TnSeq analysis, the Anexplo platform for animal experimentation and the  TRI  platform for imaging. This work also benefited from the assistance of  Vanessa Soldan  from the  Multiscale Electron Imaging platform (METi) of the Centre de Biologie Integrative (CBI).
This project has received funding from the  Fondation pour la Recherche Medicale (Equipe FRM   2016 DEQ20160334879 ), the  Fondation Bettencourt Schueller  (www. fondationbs. org)and the  Fondation MSD Avenir  (evenement. msdavenir. fr) to C. G. This work was supported by grants from the  French Ministry of Research  ( Programme Investissement d'Avenir, PIA ,  Equipex  ANINIMIP  ANR-10-INBS-08  to O. B. -S. ). This work was also supported by  France Genomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contrat  ANR-10-INBS-09  to O. B. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These are both regulators of the complement cascade and provide the impetus for this analysis of complement genetics in LOAD. To test whether complement genes beyond CLU and CR1 (both genome-wide significant (GWS) in the International Genomics of Alzheimer's Project (IGAP) dataset) influence the risk of LOAD, we compiled a comprehensive complement gene-set containing only those genes that encoded proteins directly involved in complement activation, regulation, or recognition. Then, we undertook several methods of pathway analysis to test whether additional genes within the complement gene-set were associated with LOAD risk.
Complement Genes and Gene Exclusion Analyses in LOAD In order to understand the genetics of the complement pathway in AD, we compiled a comprehensive gene-set comprising all complement genes and associated regulators and receptors. Genes were selected for inclusion based upon known biological relevance to the complement system rather than by using often inaccurate annotations in public databases. The resultant complement gene-set contained 56 genes, subdivided into their relevant functional groups (Table  1 ).
AD Summary Statistics This study utilised summary statistics from the International Genomics of Alzheimer's Project (IGAP). IGAP is a large three-stage study based upon GWAS on individuals of European ancestry. In stage 1, IGAP used genotyped and imputed data on 11,480,632 single nucleotide polymorphisms (SNPs) to meta-analyse GWAS datasets consisting of 21,982 Alzheimer's disease cases and 41,944 cognitively normal controls from four consortia: the Alzheimer Disease Genetics Consortium (ADGC); the European Alzheimer's disease Initiative (EADI); the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE); and the Genetic and Environmental Risk in AD Consortium Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer's Disease Consortium (GERAD/PERADES).
In stage 2, 11,632 SNPs were genotyped and tested for association in an independent set of 8362 Alzheimer's disease cases and 10,483 controls. Meta-analyses of variants selected for analysis in stage 3A (n = 11,666) or stage 3B (n = 30,511) samples brought the final sample to 35,274 clinical and autopsy-documented Alzheimer's disease cases and 59,163 controls. Gene-set analysis was performed using the complement gene-set and stage 1 summary statistics from the International Genomics of Alzheimer's Project  [14]. The individual and combined effects of the genome-wide significant (GWS) genes CLU and CR1 within the complement gene-set were investigated by removing these genes individually and together. We utilised the most up-to-date publicly available GWAS dataset at the time of writing  [14] , and calculated the complement gene-set p-values when including and excluding those loci that reached genome-wide significance in the IGAP dataset.
The recently identified LOAD-associated C1S variant  [13]  does not show genome-wide statistical significance in the IGAP dataset; and therefore was not removed in the gene-set analysis. Complement gene-sets were tested for enrichment using the IGAP stage 1 summary statistics  [14]  in MAGMA version 1. 06  [21]. Summary statistics were filtered for common variants (MAF ≥ 0. 01) and all indels and merged deletions were removed; 8,608,484 SNPs were analysed. Genes were annotated using reference data files from the European population of Phase 3 of 1000 Genomes, human genome Build 37 using a window of 35 kb upstream and 10 kb downstream of each gene  [22]. Ten thousand permutations were used to estimate p-values, corrected for multiple testing using the family-wise error rate (FWER). Gene-sets with a FWER-corrected p-value < 0.
Complement Risk Score Analysis A complement risk score combining the effects of all SNPs in the complement gene-set was produced. POLARIS  [23]  was used to compute risk scores in GERAD-genotyped data (3332 cases, 9832 controls) using SNP effect sizes from IGAP stage 1 summary statistics  [14, 16, 20]  (excluding GERAD subjects). Linkage disequilibrium (LD) was estimated from the GERAD data, and POLARIS was used to adjust the scores for LD between SNPs. The overall association of the complement gene-set with LOAD was determined using a logistic regression model, adjusting for population covariates, age, and sex. The logistic regression model included the baseline polygenic risk scores for all SNPs in the model, thereby testing for any association beyond the baseline polygenic effect. Data used in the preparation of this article were obtained from the Genetic and Environmental Risk for Alzheimer's disease (GERAD) Consortium.
The imputed GERAD sample comprised 3177 AD cases and 7277 controls with available age and gender data. All AD cases met criteria for either probable (NINCDS-ADRDA, DSM-IV) or definite (CERAD) AD. All elderly controls were screened for dementia using the MMSE or ADAS-cog and were determined to be free from dementia at neuropathological examination or had a Braak score of 2. 5 or lower. Genotypes from all cases and 4617 controls were previously included in the AD GWAS by  Harold and colleagues (2009)   [20]. Genotypes for the remaining population controls were obtained from WTCCC2. Imputation of the dataset was performed using IMPUTE2 and the 1000 genomes (http://www. 1000genomes. org/, accessed on 15 March 2021) Dec2010 reference panel (NCBI build 37. 1).
Risk Score Analysis Supports the Impact of Complement Genes To further explore the impact of complement genes on LOAD risk, we adopted a polygenic approach. We first applied risk score analysis to the dataset, then used logistic regression to explore the association between LOAD and complement gene-set risk scores in GERAD individuals (Table  3 ). The complement gene-set as a whole was strongly associated with AD in this analysis (p = 0. 003). Removal of CLU from the gene-set caused the largest reduction in significance (p = 0. 003 vs. p = 0. 053). Removal of CR1, or the CR1/CR1L locus had minimal impact on the significance of association in the gene-set, although when CLU, CR1 and CR1L were eliminated, the significance was further reduced compared to the elimination of CLU alone (p = 0. 148 vs.
Likelihood Ratio Analysis Confirms No Significant Impact of Other Complement Genes We next tested complement gene-set effects using likelihood ratio analyses. Models in which CLU, CR1 and CR1/CR1L were removed individually, showed significant residual impact in the gene-set (p = 0. 0136; p = 0. 0091; p = 0. 0063 respectively); after removal of CR1 and CLU or CR1, CLU and CR1L, there was no significant residual impact in the gene-set, demonstrating that there was no significant polygenic effect of the remaining complement genes in the datasets used (Table  4 ). These results further support the conclusion that the complement gene-set association with LOAD is driven predominantly by CLU and CR1, but with no significant contribution from other complement gene-set members (p = 0. 1457; Table  4 ).
Indeed, the demonstration that multiple complement genes can collaborate to cause dysregulation and disease informed the concept of the "complotype", the set of complement gene variants inherited by an individual that dictates complement activity and disease risk  [35]. The genetic associations in these other chronic inflammatory diseases influence systemic or local complement regulation and/or amplification of activation; these in turn cause complement dysregulation that drives inflammation. Our demonstration that the complement genetic signature in LOAD is restricted to the genes encoding clusterin and CR1 suggests that complement dysregulation is not critical in the disease process. However, it should be noted that this finding is dependent on the dataset being investigated. At the time of writing, we utilised the largest publicly available AD GWAS dataset  [14].
A recent study by the European AD Biobank, currently a preprint, reported an LOAD GWAS-significant association with the complement gene C1S  [13] ; this suggests that larger datasets and different analytical methods may implicate other complement genes and further elucidate roles of the complement system in LOAD. Additionally, because of the highly repetitive nature of a number of the complement loci, for example, the regulators of complement activation (RCA) clusters on chromosome 1  [36] , many complement genes may be hidden from standard sequencing technologies; the application of emerging long-range sequencing methods may reveal additional genetic variation in complement genes linked to LOAD missed in current GWAS and whole exome/genome sequencing studies using short read sequencing technologies  [37].