dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection at enrollment Participants completed the AUDIT questionnaire and donated a 25-mL blood sample at enrollment. The blood sample was used to measure Nordic biomarkers and to generate host genotyping information using the Affymetrix UK Biobank Array. Routine clinical information was extracted through medical chart review. This captured information on (i) detailed liver disease outcomes (i. e. , instances of hepatic decompensation, hepatocellular carcinoma [HCC], and liver transplantations); (ii) achievement of sustained viral response (SVR) through antiviral therapy; (iii) routine liver blood tests; (iv) screening interventions including recent ultrasound and endoscopy examinations; (v) comorbid health conditions including heart failure, angina; diabetes, kidney disease, and also history of heavy alcohol use; and (vi) medications participants were taking on the day of enrollment.
Linkage to NHS digital data For study participants in England and Wales, we linked individuallevel information acquired from the STOP-HCV cirrhosis study to individual-level information held on national registries in England and Wales. Of note, this included the admitted care hospital admission database, cancer registrations, and the mortality register held by National Health Service (NHS) Digital. Approval for this linkage was given by NHS Digital's Data Access Request Service. All participants consented to, and were successfully traced, for this linkage. At the time of analysis, cancer registrations, in-patient hospital admission, and mortality records were complete through April 1, 2017, April 1, 2018, and April 1 2019, respectively. All linked data were analyzed within the University of Glasgow's Safe Haven facility, using Stata version 12.
Study population and primary outcome events The present analysis was confined specifically to STOP-HCV cirrhosis participants from England and Wales, where record linkage to national data registries held by NHS Digital was performed. Participants were bifurcated into 2 groups, according to whether they had or had not experienced an LRO before enrollment. In other words, those patients without a previous LRO were assigned to the compensated cirrhosis group, whereas patients with a previous LRO were assigned to the decompensated cirrhosis group (Figure  1 ). An LRO was defined as decompensation (i. e. , ascites, bleeding varices, and hepatic encephalopathy), HCC, or a liver-related death. Information from patient medical records and national registries was used to ascertain whether each patient had presented with any of these conditions.
We used Rubin's rules to combine C-index and D-statistic estimates across imputation data sets. Similarly, Kaplan-Meier curves are based on the average estimate across the 20 imputation data sets created. Improving performance of validated biomarkers. We assessed the degree to which validated biomarkers are improved by adding information on enhanced biomarkers. Thus, we fitted 1 model for each validated/enhanced biomarker combination. The amount of prognostic information provided by each combination model was quantified using the likelihood ratio statistic and compared with the likelihood ratio statistic for the validated biomarker only model. We also calculated Harrell's Adequacy Index, defined as: 1-(LR SB / LR SB1EB ), where LR SB1EB is the likelihood ratio statistic for the validated biomarker 1 enhanced biomarker model, and LR SB is the likelihood ratio statistic for the validated biomarker model only  (17).
Derivation of final sample size The final sample comprised 1,196 patients with liver cirrhosis living in England or Wales. Of these, 75. 8% (n 5 907) had compensated cirrhosis at enrollment, and the remainder (24. 2%, n 5 289) had decompensated cirrhosis. Three hundred sixty-one (30. 2%) individuals were missing data for $1 biomarkers; thus, our complete case analysis, used in the biomarker improvement analysis, was based on data for 835 participants (Figure  1 ).
Finally, there were no appreciable differences between the individual biomarker performance observed in our base-case analysis (using multiple imputation), compared with the complete-case analysis restricting to participants with complete data for each biomarker (see Supplemental Figures  S6-S9 , Supplementary Digital Content 2, http://links. lww. com/CTG/A766). Improving performance of validated biomarkers. The fraction of new prognostic information provided by adding enhanced biomarkers to validated biomarkers was greatest in relation to adding CirCom, Audit, and Nordic biomarkers (generally. 30% of new information added by these biomarkers). Conversely, GRSs added relatively little additional prognostic information (,10% in general; Figure  5  and Supplemental Figure  S10 , Supplementary Digital Content 2, http://links.
A fourth limitation is that we did not consider whether change/trend in biomarker values before baseline can provide prognostic information over and above its absolute value at baseline; this would be worthy of further research. Finally, the GRSs examined in this study did not perform very well, either individually or when added to a validated biomarker. However, it is important to point out that these scores were developed to predict a different outcome from those considered in this study. Finally, we did not take account of competing risk events such as nonliver mortality on liver transplantation in this analysis. This may have affected our results. Our study has 3 important strengths. First, we recruited participants prospectively from a representative set of UK clinics. Second, we leveraged outcome data held in robust national health registries and information from medical records.
Research Foundation (award no:  C0365 ). The  STOP-HCV study  was funded by a grant from the  Medical Research Council, United Kingdom  (grant  MR/K01532X/1 ). EB is an  NIHR Senior Investigator. Hamish Innes  is supported by a viral hepatitis fellowship from the  Medical Research Foundation. Jane I Grove is supported by the  National Institute of Health Research Nottingham Biomedical Research Centre  [ BRC-1215-20003 ] The views expressed in this article are those of the author and not necessarily those of the  NHS , the  NIHR , or the  Department of Health.
Research strategy The exhaustive litterature search was conducted from July 4, 2019, to July 4, 2019 and identified eligible references published from 1914 to 2019. The search used references indexed in MedLine and the Horizon database of the French Institut de Recherche pour le Developpement (IRD) (https://horizon. documentation. ird. fr/exl-php/cadcgp. php?CMD= CHERCHE&query=1&MODELE=vues/horizon/accueil. html&AUTH=1). We also searched relevant references listed in the articles identified in the MedLine and Horizon databases, and control program and unpublished scientific reports.
Selection procedure Searches were done separately for each of the five PC-NTDs as listed by the WHO  [16]  and included articles and reports written in French or English. The general search query was "Name of the disease sought AND Congo. " We chose as inclusion criteria, community-based epidemiological studies conducted in the RoC reporting prevalence values for at least one NTD. For schistosomiasis, studies presenting both malacological and epidemiological data were retained, but only epidemiological data were extracted. We also included data presented in unpublished reports of the national control/elimination programs or doctoral theses. We excluded duplicates published in different journals or presented both in reports and in publications. In addition, we excluded non PC-NTDs-related studies, clinical case reports, animal studies, and epidemiological studies reporting data other than prevalence data (diagnostic performances, associated risk factors, etc.
Use of data Once the screening was finalized, we extracted information on the year of the study, study site location (including the village or district and the geographic coordinates), number of individuals surveyed, prevalence values, and the epidemiological index used (e. g. , the prevalence of nodules (PNod) or skin microfilariae (PMF) for onchocerciasis). When the geographic coordinates were missing, village positions were determined using maps available at the "Centre de Recherche Geographique et de Production Cartographique" (CERGEC) in Brazzaville. The maps presented in this review were produced using MapInfo v8. 5 software. For each PC-NTD, we present (i) a summary of the clinical presentation and diagnostic methods, (ii) a history of epidemiological surveys conducted, and (iii) data on the epidemiological surveys and MDA organized by the Programme National de Lutte contre l'Onchocercose (PNLO, National Onchocerciasis Control Program) which coordinates the activities for the five PC-NTDs.
Source data selection We identified 933 documents (articles and unpublished reports) in our initial search, including one duplicate. We discarded an additional 876 documents that did not meet the inclusion criteria based on title and abstract (e. g. , reports from the Democratic Republic of Congo or clinical case reports). Our systematic review is of the remaining 56 documents, including 38 articles and 15 reports, which met the inclusion criteria (Fig 1 ) and for which all references could be identified. The year of publication ranged from 1920 to 2019.
lumbricoides was 44. 4% (45. 6% in children and 42. 9% in adults) and 61. 8% with hookworm was (56. 4% in children and 69. 1% in adults). Much lower prevalence values were recorded for infections with T. trichiura (7. 8% overall, 8. 9% in children, and 6. 2% in adults) and S. stercoralis (5. 1% overall, 3. 6% in children, and 7. 2% in adults). In 1984, another Brazzaville study of 230 children aged 2-14 years reported a hookworm   (Bouenza department). The prevalence of ascariasis in these localities was 66. 8 and 58. 7%, respectively, that of trichuriasis was 86. 1 and 94. 2%, that of hookworm infection, 51. 1 and 15. 4%, and that of strongyloidiasis, 8. 9 and 1. 4%  [90]. The only other data retrieved are those collected in 1988 from laboratories in 7 localities: Ouesso and Souankein the Sangha department, Impfondo, Epena and Dongou in the Likouala department, and Owando and Mossaka in the Cuvette department.
Schistosomiasis was diagnosed only in the small village of Irebou, with a population of about 50 people, located near the border separating the Likouala and the Cuvette departments. "S. haematobium" eggs were observed in the feces of five people from the village, but no egg in the urine. It was hypothesized that these infections may have originated on the other side of the Congo River, in the Democratic Republic of Congo (formerly Zaire), where the people regularly traveled  [98]. The absence of eggs in the urine and information from later sampling in the same area suggest that the parasite present in this focus (around Impfondo, Irebou, and Loukolela) was S. intercalatum, not S. haematobium  [99, 100]. In 1964, McCullough reviewed data collected between 1956 and 1962 on schistosomiasis in the RoC.
14 he ICV data obtained from CT scanning were informative as an indirect measure of brain size, but this imaging modality does not allow for precise assessment of global or regional brain volumes. In contrast, MRI provides clearer delineation between grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). ] [26] [27]  This study aimed to use structural MRI to track both global and regional brain volume changes over the course of ovine CLN5 and CLN6 diseases. Affected CLN5 and CLN6 sheep were scanned along with age-matched healthy controls at 5, 7, 10, 14 and 18 months of age. Volumes of GM, WM and CSF were extracted from T1-weighted scans, as well as volumes of 12 cortical and subcortical structures. These specific regions were selected a priori to represent the differential patterns of degeneration seen in previous neuropathological studies.
MRI processing All sheep were given unique blinding codes prior to data processing and statistical analysis. Statistical parametric mapping (SPM8; http://www. fil. ion. ucl. ac. uk/spm) in Matlab R2015a (v 8. 5. 0. 197613, Mathworks, Massachusetts, USA) was used to perform unified segmentation and normalization of each T1-weighted image. 28 T1 images for each sheep at each time point were roughly reoriented (manually) to a standardized ovine template, provided by Ella et al. 24  T1 images were then segmented, modulated and normalized using the sheep-specific priors,  24  with affine regularization set to an average sized template. Breed-specific tissue probability maps (TPMs) were subsequently created as follows: All modulated, normalized GM, WM and CSF images for all genotypes and ages were averaged by breed, resulting in Borderdale-and South Hampshire-specific TPMs.
Discussion This study utilized MRI to assess global and regional neurodegeneration in naturally occurring sheep models of CLN5 and CLN6 NCL. The detailed dataset created using this clinically relevant and non-invasive imaging technique has established a benchmark for assessment of therapeutic efficacy in trials of gene therapy in both NCL sheep models that is translatable to human medicine. Baseline scans were performed at 5 months of age, a clinically pre-symptomatic disease stage. MRI revealed already significantly lower average ICVs in CLN5 affected sheep at this age compared to healthy controls. However, there was considerable variation between affected individuals (range 107-121 cm 3 ) at this early age, indicating that a single pre-symptomatic MRI scan could not be reliably used as an early indicator of disease status.
Identifying new DIR proteins from bacteria means identifying new substrate or product molecules from these organisms. It is the promise of going further in understanding the mechanism of action of these proteins and this will most likely have a strong impact in the fields of agricultural, pharmaceutical and/or food chemistry. Results: Our goal is to obtain as much information as possible about these proteins to unlock the secrets of their exceptional functioning. Analyzes of structural and functional genomic data led to the identification of the Pfam PF03018 domain as characteristic of DIR proteins. This domain has been further identified in the sequence of bacterial proteins therefore named as DIR-like (DIRL). We have chosen a multidisciplinary bioinformatic approach centered on bacterial genome identification, gene expression and regulation signals, protein structures, and their molecular information content.
The massive sequencing of genomes in recent years provides an immense amount of data. Databases allow classification and automatic processing of all these information. In 2020, Dabravolski highlighted 42 bacterial proteins possessing a Dirigent Protein Like (DIRL) domain in Uniprot and Interpro databases  [16]. Pursuing the study initiated on bacterial DIRL domains is important to gather more information in order to identify the most promising candidates for functional studies. Here, we performed a thorough bioinformatics analysis of DIRLs. Primary structures, predictions on secondary and tertiary structures, characterization of signals sequences, gene expression organization and regulation were compared to select bacteria candidates for further functional studies.
Gene identification, characterization, alignment of the DIRL with atDIR6 and phylogeny Interpro  [17]  was used for functional analysis of the updated list of DIRL proteins, for classifying them into families and predict domains and important sites. Like Pfam  [18] , Interpro uses the Hide Markov Model (HMM) to represent amino acids (AA) conserved in a profile, the one witch are bigger than the other (Fig. 2 ). One usually trains an HMM using an E-M algorithm. This consists of several iterations. Each iteration has one "estimate" and one "maximize" step. In the "maximize" step, each observation vector V is aligned with a state S in the model so that some likelihood measure is maximized. Uniprot  [19] , was used to retrieve links to other databases (notably Ensembl, Interpro and the NCBI) and therefore information relating DIRLs and bacteria (Additional file 3: Table  S1 ).
Protein sequences in FASTA format were used to align sequences of DIRLs initially using Clustal Omega (ClustalO). Homology and identity (%) between bacterial DIRLs and plant DIRs were retrieved from global alignments with ClustalW (Table  1 , column 6 and 7). AtDIR6 was chosen as reference in the comparison to the DIRLs of bacteria. Phylogeny was analyzed using the Seaview software version 5. 0. 5  [20]. From protein sequences in FASTA format aligned via ClustalO, the neighbor-joining agglomerative method was used and corrected by ML method (Maximum Likehood method) to create the phylogenetic tree of Fig. 3  and the sequence alignment of Additional file 1: Fig. S1. F. intermedia FiDIR1, P. sativum DRR206, G. echinata PTS1, A. Thaliana atDIR5 and A. Thaliana atDIR6 were selected among plant sequences (the 3D structures of AtDIR6, DRR206 and PTS1 are resolved and FiDIR1 and AtDIR5 are also well characterized) (Additional file 1: Fig.
Primary, second and tertiary structural analysis The SignalP 5. 0 tool (https:// servi ces. healt htech. dtu. dk/ servi ce. php? Signa lP-5. 0) was used for the prediction of signal peptides in bacterial DIRLs (Table  1 , column 9 and confirmed by Alphafold). The sequences were entered in fasta format for Glycopp server, PredictProtein and Alphafold2. Glycopp server  [21]  was used for the prediction of N-and O Glycosites in prokaryotic protein sequences (Table  1 , column 11). PredictProtein  [22]  was used to predict the secondary structure of DIRLs. β strands, interloops and signal peptides identified via PredictProtein are consistent with results from other prediction softwares as well as with the structure of AtDIR6 (data not shown). Alphafold2  [23]  (https:// colab. resea rch. google.
com/ github/ sokry pton/ Colab Fold/ blob/ main/ beta/ Alpha Fold2_ advan ced. ipynb) was used to produce 3D models of bacterial DIRLs (Fig. 5  for DIRL representing DIRL famillies). TM-Align  [24]  is an algorithm for protein structure alignment and comparison based on statistics. It allows to process a 3D visualization of the structural alignment. The RMSD (Root-Mean-Square Deviation) is the measure of the average distance between the atoms (usually the backbone atoms) of superimposed proteins. The pdb prediction structures from DIRs and DIRL protein is compare with the one from atDIR6 (LAL5) (Additional file 3: Table  S1 , column 5). This software calculs also the alignment length (Additional file 3: Table  S1 , column 3) and the Seq_ID (number of residues witch are identical / the number of the residues aligned) (Additional file 3: Table  S1 , column 5).
The second method rely on the identification of genes in the target area that may have a role in the structure of an operon. Here, these genes are often transcriptional regulators, transmembrane transporters, phosphatases or even kinases. The third method is based on the identification of promoters. An operon requires few promoters if not a single promoter. Each genomic region containing a DIRL locus was therefore analyzed with a magnifying glass. The SoftBerry software was used to identify forward sequences binding the sigma 70 promoter, characteristic of bacteria. If regions where the DIRL loci are located contain a low number of sigma 70 promoters and if such a promoter localizes upstream of an "operonic structure", then this is in favor of an operon.
Dirigent domain and data bases analyses The Pfam database contains an HMM profile specific to the Dirigent Domain: Pfam PF03018 (Fig. 2 ). This profile highlights very conserved positions within the family of DIRs. The Interpro database reference two families comprising genes encoding proteins exhibiting either a "Dirigent protein" domain (IPR004265), or an "Allene oxide cyclase/ Dirigent protein" domain (AOC/DIR IPR044859). It should be noted that in plants, the dirigent domain of DIRs is structurally close to the domain of allene oxide cyclases  [26]. These informations have been previously used by Dabravolski in 2020 to highlight the existence of genes with a putative dirigent domain in bacterial genomes  [16]. At the start of our study, we updated Dabravolski's list of bacterial candidates considering data newly deposited in Interpro (between April 2020 and October 2021).
From an analysis with the Predict-Protein software, DIRLs would have a number of strands comprised between 4 and 8. Plant DIRs genes generally encode a signal peptide that allows either the anchoring of the DIR in the plasma membrane or their secretion into the apoplasm. SignalP server was used to check for the presence or not of a signal peptide in DIRLs from bacteria. Three types of signal peptides are found in Archaea and bacteria: 1-Sec/SPI: standard secretion signal peptide transposed by the Sec translocon and cut by signal peptidase I; 2-Sec/SPII: transposed signal peptide also by the Sec transposon but cut by signal peptidase II and 3-Tat/SPI: signal peptide Tat transposed by the Tat translocon and cut by signal peptidase I). From this search, only 12 DIRLs on the 49 sequences considered would not possess an identifiable signal peptide, 9 of which being among the shortest sequences of the panel (i.
g. , end of the contig or the genome is not complete yet). To go further, the location of DIRL genes in genomes was also used to identify potential operonic structures. There are several cumulative methods of identifying operonic structure in bacteria (see Material and Method). Each genomic region containing a DIRL locus was therefore analyzed with a magnifying glass. The SoftBerry software was used to identify forward sequences binding the sigma 70 promoter, characteristic of bacteria. If regions where the DIRL loci are located contain a low number of sigma 70 promoters and if such a promoter localizes upstream of an "operonic structure", then this is in favor of an operon. This survey revealed DIRL genes probably embedded in an operonic structure in 10 bacterial genomes (species underlined in Table  1 , Additional file 2: Fig.
Discussion In this study we focalized on the identification of DIRLs candidates in bacteria from the many sequenced bacterial genomes available. The structural and functional genomic analyzes centered on the DIR domain Pfam PF03018 presented in this work allowed us to confirm the work of Dabravolski  [16]  and to identify 7 new bacteria containing a gene encoding a DIRL. Bacteria possessing a gene encoding a DIRL are mainly saprophyte and mesophilic, rarely pathogen. Most of them are soil bacteria. Few are extremophile as Sphaerobacter thermophilus strain DSM 20745, Streptomyces alboflavus str. MDJK44 or Thiogranum longum str. DSM 19610 which was isolated from a deep-water hydrothermal spring with high salt concentration  [33]. Some were isolated from lake or sea waters as C. bacterium which is a bacterium from a Siberian soda lake described to be anaerobic and photosynthetic  [34].
Concerning the secondary structures, a β sheet organization similar to the one observed for plant DIRs is predicted for all DIRLs (Plant DIRs are mainly organized in a β -barrel made of up to 8 β strands  [13, 25]. A similar structural organization is proposed for the DIRLs. From an analysis with the PredictProtein software, DIRLs would have a number of strands comprised between 4 and 8. A ninth strand, corresponding to the β1-bis strand proposed by Dabravolski  [16]  as well as by Meng et al. in 2020  [14]  could be present in some DIRLs (Additional file 1: Fig. S1 ). The 3D structure of few plant DIRs has been solved. The AtDIR6 and DRR206 β strands form anti-sense sheets that arrange in a barrel shape  [13, 32]. PsPTS1 or GePTS1 have a similar barrel structure with anti-sense leaflets but the N-terminal side leaves β strands on the outside of the barrel  [13, 32, 35].