dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors gratefully acknowledge  Carolina Morell-Perez  of the  Health and Environmental Sciences Institute  for her enduring support of the committee work and resulting publications. We would like to thank  Claire Mariet ,  Lydie Pilatre  et  Martine Courcol  from  Sanofi  for technical We also would like to thank  Deidre Dalmas ,  Gregory Travlos , and  Julie Foley  for critical reading of the article. In addition, we appreciate support from  B. Alex Merrick  and  Julie Foley  of  NIEHS  and  Dr Matthew Toma  of  Covance Genomics Laboratory  for technical support of sRNA-seq. Members of the  Health and Environmental Sciences Institute (HESI) Biomarkers of Nephrotoxicity Technical Committee. HESI is a nonprofit organization whose mission is to collaboratively create science-based solutions to various challenges affecting human health and the environment.
In contrast, no effect on ATPase activity was observed for the one mutation that lies within the RNA-binding motif Ia, and, interestingly, its phenotype was slightly distinct. To analyze the impact on RNA-binding, the authors took advantage of the publicly available CLIP dataset (ENCODE). RT-qPCR analysis of immunoprecipitated GFP-mutant DHX30 showed a reduced interaction with selected target mRNAs compared with wild-type DHX30. While DHX30 has a diffuse distribution in the cytosol, it accumulates in SGs upon heat stress. Moreover, overexpression of mutant but not wild-type DHX30 enhances SG formation, in the absence of stress, accompanied by strongly reduced protein synthesis  [91]. Clearly, follow-up studies are needed to further dissect and delineate the role of DHX30 in translation.
Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between the intestine and hepatopancreatic caecum.
This methodology is based on protein shotgun analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) and interpretation of the MS/MS spectra with available protein sequence databases. The peptide and protein data obtained by metaproteomics can be used for proteotyping the microbial communities present in the sample  5  , estimating biomass contributions  6  , and obtaining a direct understanding of the biochemical functions and interactions of the community  7. Due to the paucity of molecular information for numerous animal species such as arthropods, microbial communities from these animals remain poorly characterized. Animal metaproteomics have focused on exploring the microbiomes from large animals such as mammals or birds  8, 9  and studies on small invertebrates are scarce  10, 11.
Ideally, metaproteomic data should be interpreted with metagenomics data acquired on the same sample. Similarly, RNA sequencing (RNA-seq) can be used to sequence extracted mRNAs and translate them into putative protein sequences  12. However, this implies higher costs, time and efforts, and may be challenging when dealing with limited animal resources. The 16S (and/or 18S) rRNA genes of the taxa present can be sequenced and, based on these results, a customized protein database specific to the taxa identified can be proposed. Alternatively, metaproteomic data may be assigned using generalist databases encompassing all known genomes  13. When the microbial communities of a given host are unknown, assignation of peptide/protein from metaproteomics studies is done only at the highest taxonomical ranks.
The absence of a reference genome for the animal host can also hinder the reliable interpretation of the proteomic data, as host peptides that are shared with other taxa can be attributed to non-host proteins present in the database. In any case, the use of metagenomicsderived or multi-organism genome-derived databases enlarges significantly the search space, challenging the search algorithms and compromising the sensitivity of peptide identification  14. In this context, the use of multi-step database searches helps improve metaproteomics outputs  15, 16. Here we propose an iterative generalist database search strategy combining high-resolution MS of proteins and RNA-seq of the host to characterize the microbiome of an organism not yet genome-sequenced. In the absence of genomic information on Gammarus fossarum from generalist databases, host MS/MS spectra which represent most experimental spectra in the sample could be wrongly attributed.
Therefore, we propose to complement generalist databases with an RNA-seq-based protein database specific for the host. Here, we used a previously RNAseq data set acquired on G. fossarum. This data set comprises paired-end Illumina sequences of total mRNAs of specific organs from a large number of male and female gammarids. Reads with at least 90% overlap were assembled into contigs. Contigs were then translated into six reading frames, resulting in a customized protein sequence database (GFOSS) for interpreting MS/MS data (details can be found in ref. 17  ). The workflow was applied for the study of the microbiome of two tissues from the digestive tract of G. fossarum-a key species for aquatic systems (body length 2-15 mm) and an emergent model in environmental toxicology  18. Characterization of the microbiomes associated with the intestine (INT) and hepatopancreatic caeca (HC) is of considerable interest, to better understand their role and possible involvement in mediating the impact of toxicants for the host.
Taxon-discriminant peptide sequences highlighted the potential presence of 749 taxa (Supplementary Data 1) whose annotated proteomes were included in three dedicated sub-databases for step 2. In step 2, we performed sequential follow-up searches for each sample to identify the following: (i) host-associated peptides, using DB-GFOSS-M, a database comprising the protein sequences specific to the host organism obtained by RNA-seq  16  , and additional metazoan sequences identified in step1; (ii) food-associated peptides, using DB-streptophyta, which comprises the remaining multicellular eukaryotic proteomes; and (iii) the unicellular microorganism-associated peptides using all eukaryotic and prokaryotic unicellular organisms (DB-unicellular). Most MS/MS signals from the tissues were assigned to the host (38%), the microbiome (3%), and the residual food (<3%; Supplementary Data 2).
Approximately 2000 taxon-to-spectrum matches (TSMs) were used for the confident taxonomic identification of the microbiota of the digestive tract (false discovery rate (FDR) < 1%), and quantification of their relative biomasses (Supplementary Data 3). The final database in step 3, DB-metaP, contains all of the protein sequences identified in step 2 for assessing the metabolic activities of both the microbiome and the host. A total of 9532 polypeptides were identified with an FDR of 1% and were grouped into 6466 protein groups (Supplementary Data 4), from which 3717 were attributed to the host organism, 422 to the food, and 1422 to the microbiota. The microbiome of G. fossarum comprises 52 genera: 1 archaeon, 40 bacteria, and 11 eukaryota. Remarkably, we observed that protein biomass contributions were almost equal between bacteria (48%) and unicellular eukaryotes (52%).
The pipeline proposed in this study allows discriminating host, residual food, and microbiome proteins, and identifies the main taxa and their functions at an unprecedented range, including archaea, bacteria, fungi, and parasites. This approach gives more flexibility in database construction, cascade layout, and parameter optimization than currently available tools such as Unipept 5 , MetaLab  15  , MPA  30  , or ProteoClade 31 , which have not been specifically conceived for the analysis of microbiomes from nonsequenced hosts. We believe this strategy can be of great value for future microbiome studies of numerous non-model animals, providing a valid alternative for studies in diverse areas such as environmental biomonitoring, aquaculture, parasitology, or biocontrol. This is especially true for small organisms, for which metagenomics is limited by the small amount of initial sample, or for large cohorts of animals for which metagenomics will be too costly.
We capitalized on the rapid advances of protein and RNAseq technologies to propose an innovative solution in database creation and application for metaproteomics studies of nonsequenced hosts. This workflow can be adapted to a broad range of species and one single sample can provide information on the taxonomic and functional composition of a microbiome, plus host proteome characterization. These approaches pave the way for performing deep mechanistic studies in both host and hostassociated microorganisms, and for fuller understanding of microbiome-host relationships.
Proteome extraction and digestion Tissues were homogenized in 75 µl of LDS buffer (Invitrogen) containing β-mercaptoethanol. Samples were vortexed, heated for 5 min at 99 °C, incubated for 5 min in an ultrasound bath, and then mechanically homogenized through bead beating in a Precellys® tissue homogenizer (Bertin Technologies). A volume of 30 μL of supernatant was transferred into a new tube and heated for 5 min at 99 °C. Each sample was then subjected to a short SDS-polyacrylamide gel electrophoresis migration at 200 V. The whole protein content from each well was fractionated in three bands per sample, processed, and digested with Trypsin Gold (Promega) using 0. 011% ProteaseMAX surfactant (Promega), as described  33. Peptide separation and MS data acquisition LC-MS/MS analysis was carried out using a data-dependent approach with a Q-Exactive HF mass spectrometer (Thermo Fisher) coupled to an UltiMate 3000 LC system (Dionex-LC Packings) operated as described  34.
txt" and "assembly_summary_genbank. txt", downloaded from "ftp://ftp. ncbi. nlm. nih. gov/genomes/ASSEMBLY_REPORTS," were used to map taxids to RefSeq assemblies (GCF) and GenBank assemblies (GCA). Then, the "GCF/ GCA *_assembly_report. txt" files were used to map GCF/GCA to nucleotides and the "*_genomic. gff. gz" files were used to map GCF/GCA to protein accessions. A collation of information was performed following the NCBI taxonomical tree from direct assignation to "canonical" taxonomical levels-species, genus, family, order, class, phylum, and superkingdom. For each taxon at each level, an identification of matching peptide sequences and TSMs was performed, as well as a count of specific or unique peptide sequences, and corresponding specific TSMs. The DB-GFOSS-M, DB-streptophyta, and DB-unicellular databases were built using the proteomes from species identified with (i) more than 9, 7, and 1 specific peptides or (ii) more than 109, 74, and 6 added TSMs for eukaryota, bacteria, and archaea species, respectively.
For each species retained, a maximum of ten representative taxa were selected, ordered by the number of TSMs, and with a minimum of two added TSMs. The FDR for genus identification was calculated after a search against the target database DB-unicellular (#hits_target) and its reversed decoy version (#hits_decoy) and taxonomical assignation of peptide sequence hits. Peptides were assigned to the genus taxonomical rank using the Unipept 4. 0 web interface  5  with default parameters (equate I/L, filter duplicate peptides). The FDR was estimated through comparing the number of taxonomic hits from forward and decoy peptides identified from both databases assuming a threshold of at least three taxon-specific peptides. Protein identification and quantification for functional characterization For step 3, spectra were searched against DB_metaP to identify and quantify all peptides present in the sample.
PSMs with an FDR < 1% were filtered and selected for peptide and protein inference. Proteins were grouped if they shared at least one peptide and label-free quantification was based on PSM counts for each protein following the principle of parsimony. Spectral counts from all the proteins in a group were summed for assigning each protein group with an abundance value. Host proteins were analyzed individually, while microbial proteins were grouped in GOslims. Briefly, GO annotations of proteins were performed using a DIAMOND search against the UniRef50 database and the homologs found in this search were mapped to GO terms through the GO annotation database. GOslim terms were obtained by using the Map2Slim option in OWLTools and the generic GOslim list of GO entries. The spectrum count values of all proteins associated with a GOslim term were summed, granting each GOslim term with an abundance value.
Statistical analyses Quantitative count values from both taxonomic (number of PSMs) and functional data (spectrum counts) were scaled to their total sum in the sample. The R package metacoder  35  was used for representing taxonomic abundance as a differential heat tree. Univariate differential analysis of taxon and GOslim abundances between conditions was performed through non-parametric Wilcoxon tests corrected for multiple comparisons (Benjamini-Hochberg). The pairwise Bray-Curtis β-diversity indices were calculated using the function vegdist from the vegan package in R. NMDS ordination of Bray-Curtis distances among samples was performed with the function metaMDS. PLS-DA, sPLS-DA, and heatmap clustering were performed with the package mixOmics  36. The PLS-DA and NMDS plots were drawn with GraphPad Prism v8.
Methods: A search of Medline and Embase databases was undertaken to identify relevant publications published in the last 25 years. Results: Results from 32 original publications, 11 of which were case reports, suggest a significant burden of meningococcal disease and related complications. Meningococcal disease is increasingly reported among adolescents and adults, and large outbreaks have been reported in this population. Meningococcal disease in India is caused almost exclusively by serogroup A; serogroups B, C, W and Y have also been documented. Meningococcal disease burden data remain unreliable because of limited disease surveillance, insufficient laboratory capacity, misdiagnosis and prevalence of extensive antibiotic use in India. Lack of access to healthcare also increases under-reporting, thus bringing the reliability of the data into question.
Conjugate meningococcal vaccines are being used for disease prevention by national governments and immunization programs globally. In India, meningococcal vaccination is recommended only for certain high-risk groups, during outbreaks and for international travelers such as Hajj pilgrims and students pursuing studies abroad. Conclusion: Meningococcal disease is prevalent in India but remains grossly underestimated and under-reported. Available literature largely presents outbreak data related to serogroup A disease; however, non-A serogroup disease cases have been reported. Reliable epidemiologic data Digital Features To view digital features for this article go to https://doi. org/10. 6084/m9. figshare. 12613760.
METHODS The literature search for this narrative review was conducted according to the Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses (PRISMA) guidelines  [19]  to obtain relevant information using a reproducible, robust and transparent methodology. In line with these guidelines, we developed a search strategy and defined eligibility criteria prior to conducting the review. Searches were performed and retrieved publications were assessed for eligibility by two independent reviewers in a two-phase screening process based on the pre-defined eligibility criteria. Data were extracted from the final list of publications that were considered relevant for this review, the scope of data extraction was established a priori.
Search Sources and Strategy We searched the Medline (via PubMed) and Embase databases to identify peer-reviewed publications on meningococcal disease in India. The search strategy included both free-text and Emtree/MeSH terms such as ''meningococcal infections,'' ''meningococcus,'' ''N. meningitidis'' and ''India'' combined with Boolean operators (Table  S1 ). National and regional World Health Organization (WHO) websites were also searched for information on vaccination recommendations; these searches were not systematically conducted.
Article Eligibility and Screening Publications on meningococcal disease in India were considered eligible for inclusion based on the criteria provided in Table  1. The screening process was limited to articles reported during the last  25 years (1994-2019). Eligible publications were based on: studies on meningococcal disease that focused on the disease burden and epidemiologic outcomes from observational studies, surveillance studies and case reports in India. Reference lists of reviews were consulted to identify additional original studies that may not have been captured by the search in Medline and Embase. Letters to the Editor were included if they contained original data on the disease burden and epidemiology of meningococcal disease in India. The publications retrieved from databases were screened by two independent researchers based on the eligibility criteria in two phases.
Data Collection and Reporting Data extracted from the eligible publications included contextual details (year, study design, geographic region etc. ), information on the epidemiology of meningitis (incidence [no. of cases, age-specific estimates, serogroups]), morbidity (carriage, clinical presentation and sequelae) and mortality. Incidence rates are based on suspected cases as defined in the individual studies. Age groups such as neonate (0-30 days of age), pediatric (1 month-12 years of age), adolescent (12-18 years of age) and adult ([ 18 years of age) were defined according to the WHO pediatric age categories  [20]. We defined meningococcal meningitis according to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM): a fulminant infection of the meninges and subarachnoid fluid by the bacterium Neisseria meningitidis, producing diffuse inflammation and peri-meningeal venous thromboses  [21].
Overview of Included Studies A total of 262 publications were identified in the Medline and Embase databases (search cutoff date: August-September 2019). Excluding duplicates, 210 publications were screened based on their titles and abstracts; after excluding ineligible articles, 56 publications were further screened for eligibility based on full-text contents. Finally, 32 publications reporting data from original studies were included in this review  [16,  (Fig. 2 ). These 32 publications reported data on meningococcal disease from different regions of India: Delhi (n = 11)  [16, 24-26, 34, 40, 45-47, 52, 53] , Karnataka (n = 5)  [36, 43, 44, 49, 50] , Assam (n = 3)  [28, 32, 33] , Kashmir (n = 2)  [27, 41] , Chandigarh (n = 2)  [37, 51] , Meghalaya (n = 2)  [30, 31] , multiple states (n = 2)  [38, 39] , Maharashtra (n = 1)  [29] , Uttar Pradesh (n = 1)  [23] , Odisha (n = 1)  [48] , Tripura (n = 1)  [42]  and Tamil Nadu (n = 1)  [35]  (Table  2 ).
LIMITATIONS AND FUTURE RESEARCH This narrative review has several limitations related to the methodology such as the exclusion of gray literature sources (i. e. , literature not peer-reviewed prior to publication such as government databases and reports) in the systematic search, conducted in Medline and Embase. As we aimed to obtain a better understanding of published epidemiology and disease burden data, broadening our search to other sources and having a risk-of-bias analysis was not deemed necessary. Generalizability of the results from this review should be done cautiously as incidence rates were presented based on suspected cases as defined by the individual studies and case reports. Furthermore, not all articles provide adequate data regarding methods used for microbiologic diagnosis and serogroup analysis.
In addition, the routine immunization of high-risk individuals as well as adolescents and adults involved in carriage and transmission of the disease could be beneficial to prevent the occurrence of outbreaks. GSK group of companies, holds shares as part of his employee remuneration and declares no non-financial conflicts of interest. Sripriya Sathyanarayanan is an employee of the GSK group of companies and declares no non-financial conflicts of interest. Compliance with Ethics Guidelines. This article is based on previously conducted studies and does not contain any studies performed by any of the authors with human participants or animals. Data Availability. Data sharing is not applicable to this article as no datasets were generated or analyzed in the current study. Open Access. This article is licensed under a Creative Commons Attribution-Non-Commercial 4.
In a sufficiently long simulation (coupled atmosphere-ocean models take millennia), the climate system reaches a new equilibrium when R = 0. As G can be approximated via backward regression towards T as = 0, ECS can be estimated as ECS = -G/λ. As the ECS is the theoretical equilibrium temperature for doubling CO 2 , in a quadrupling of CO 2 it is common to divide the result derived from 4xCO 2 simulations by 2  (Andrews et al. , 2012). By using this linear forcing response framework, we can estimate climate sensitivity, radiative forcing, and feedback parameters following the method proposed by  Gregory et al. (2004). The values of λ (slope) and G (y intercept) are estimated via the ordinary least-squares regression of the global annual mean of R against T as under all-sky conditions. Using the same linear technique, we decompose the feedback parameter into shortwave (SW) and longwave (LW) radiation components, and we extract the clear-sky radiative flux components from the BESM and CMIP databases to estimate the cloud radiative forcing or cloud radiative effect ( CRE) defined as the difference between the all-sky and clear-sky feedback parameters  (Andrews et al.
Furthermore, it is important to mention that the radiative energy imbalance of -4 W m -2 at the TOA, arising from our ocean-atmosphere coupling, is seen as an issue that is to be tackled in ongoing model development work. We hope that the next version will include improved energy flow diagnostics and that it will include physical parameterizations of atmosphere-ocean interactions that lead to better agreement with other models and with observations. Code and data availability. The BESM-OA2. 5 source code is freely available subject to a license agreement. Please contact Paulo Nobre to obtain the BESM-OA2. 5 source code and data. Author contributions. VBC conducted the analysis and wrote the paper. VBC, PN, MB, SNF, PYK, EG, and JPRF were responsible for the development of the new version of the model.
Acknowledgements. The authors are grateful to the two anonymous reviewers for their useful comments and suggestions. This work was supported by the  São Paulo Research Foundation (FAPESP) , the  National Coordination for High Level Education and Training (CAPES) , the  CAPES/National Water Agency (CAPES/ANA) , the  Brazilian National Council for Scientific and Technological Development (CNPq) , the  Brazilian Research Network on Global Climate Change (FINEP/Rede Clima) , the  National Institute for Science and Technology on Climate Change (INCT-MC) , and INCT-MC Phase 2 funded by  CNPq. Financial support. This work was supported by the  São Paulo Research Foundation (FAPESP)  (grant nos. 2009/50528-6 ,  2014/50848-9 , and  2018/06204-0 ), the  National Coordination for High Level Education and Training (CAPES)  (grant no.