dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples are aliquoted in FluidX tubes which were selected taking into account several criteria: • Suitable to cryostorage (liquid nitrogen vapor condition), • External thread, • High-quality virgin polypropylene, • Biocompatible (free from RNAse/DNAse, endotoxin, phthalate, biocides, no heavy metals,. ), • Tri-coded tubes (2D barcode on bottom, 1D barcode and human readable on side), • Storage in cryoboxes allowing storage optimization (low profile, 10 × 10 and 14 × 14 formats). For each serum and plasma sample Haemolysis, Lipaemia and Icterus index (HLI) measurement is performed by COBAS Integra 400 Plus (Roche Diagnostics SA CH 6343 Rotkreuz). Data related to biosampling processing, annotations and results is recorded in the LIMS.
Sample Distribution Request for distribution of biological samples for a research program is submitted to the Principal Investigator (PI) of the CONSTANCES Cohort and evaluated by the international scientific council, according to pre-established criteria  (12). If necessary, adjustments to the project, concerning either the methodology (feasibility) or the scientific objectives, might be required. If the project is accepted, the CONSTANCES team provides IBBL with a list of CONSTANCES ID numbers corresponding to the applicant's selection criteria (on the basis of clinical data, para-clinical data and questionnaires). Upon receipt, IBBL generates a working list, called picklist, containing the ID numbers of the aliquots and their storage location. The aliquot picking is performed in a CryoCart, in order to maintain a working environment below -150 • C.
IT Infrastructure The infrastructure of the Business Information System (BIS) consists of three main applications: All the data relating to the CONSTANCES Biobank, collected at different levels and by different tools, are consolidated in a single database. These data can be accessed through a data warehouse allowing the grouping and structuring of data from different sources of information; this data warehouse makes it possible, in particular, to base all management, statistical and quality control reports. All of the "technical" data relating to the biobank management process, updated daily, are transferred regularly to CONSTANCES Information Treatment team to be consolidated with the clinical and paraclinical data collected elsewhere. Thus, the CONSTANCES staff has an overview of all the information sets collected.
Non Conformity Control Communication between IBBL and the Health Prevention Centers is ensured via the CONSTANCES Service Desk, CSBC. The CSBC is used to communicate and document some nonconformities and/or deviations detected from both sides, such as: • Transport delay • Missing data entry Periodic monthly reports of non-conformities overall and for each inclusion site are published. They are reviewed at each Steering Committee (COPIL) and, if necessary, corrective measures are implemented. Table  5  lists the main nonconformities observed and their frequency.
Monitoring and Communication With Inclusion Sites Two members of the CONSTANCES team are dedicated to training, information and communication with the medical and para-medical staff of each Health Prevention Center. They intervene during the implementation of the cohort and answer daily practical questions from each Health Prevention Center in close collaboration with the IBBL staff. A CONSTANCES Service Desk (CSBC) has been specifically developed by IBBL in the form of a web portal. The CSBC consists of one interface accessible to the service desk customers and IBBL to enter requests and get information on the progress of requests. It is used to track requests from the Health Prevention Center, IBBL and the CONSTANCES team (e. g. , transport, collection, data processing and other problems) and solve support requests (also called tickets).
Management of Informed Consents Informed consent specific to the CONSTANCES biobank is collected upon arrival at the Health Prevention Center. These consents are then validated by the CONSTANCES staff, which ensures that all required information is available and recorded. Until validation of the consents, related biological samples are quarantined and cannot be distributed to researchers. Weekly, the CONSTANCES staff sends IBBL a file of CONSTANCES ID numbers for which consents have been validated to ensure the volunteers have given or withdrawn their consent. This information is recorded in IBBL's Reception database. Then, concerned samples can be used for research purposes or may be destroyed (in case of a consent withdrawal).
Only about a quarter of the volunteers will be able to match the biobank samples with the clinical, para-clinical and environmental data from the first wave of inclusion. This may be a limitation for longitudinal studies. It will also be necessary to check attrition in the second wave, a well-known phenomenon in longitudinal studies over a very long period, and to assess its impact on the representativeness of the biobank. The relatively small number of biobanked participants compared to other population-based cohorts (UK Biobank, German national cohort. ) will limit longitudinal studies based on the occurrence of outcomes to the most frequent diseases, including chronic diseases. Other diseases with low prevalence, such as certain cancers, will not be eligible for research projects because of their small number.
Quality Assurance A very careful recording of non-conformities is carried out by IBBL at each stage of the process from blood (and urine) sampling to storage. Table  5  shows that the frequency of nonconformities is low compared to the number of tubes collected and aliquots generated. The most frequent non-conformity is due to the transport time not respected by the carrier (2. 1%), leading to a delay in storage. This malfunction common in the first few months has been corrected. Logistics is a critical point for all large-scale epidemiological studies, special attention must be paid to it. Non-conformities concerning centrifugation are due to the operating constraints of the HPCs: recommendations for compliance with good practices have been made. Abnormalities in HLI (Hemolysis, Lipemia, Icterus) indices, often mentioned in the literature  (23) (24) (25) , are very limited (data not shown).
Data Collection and Information Processing System The information collection system made available to the inclusion sites is a derivative of those used in medical laboratories. Its original specifications were not really adapted to the needs of a biobank, so we had to redesign it to make it truly operational. On the other hand, the communication application with the inclusion centers (CSBC) developed specifically for the needs of the CONSTANCES biobank proved to be useful and efficient. Biobanks are very special tools that cannot be satisfied with equipment or applications designed for other purposes.
Future Research Opportunities By its thorough system for the follow-up and collection of very diverse information through a variety of methods and data sources on a large representative sample of the adult population, CONSTANCES aims to contribute to a better knowledge of the health of the French population  (8). By linking the extensive data on personal, lifestyle, environmental, occupational and social factors with the biobank data, the CONSTANCES cohort offers the opportunity to study the interplays between these factors. This extensive characterization of participants along with the biobank will also open countess opportunities of collaborations in France and abroad. The advantages of collaboration between large cohorts are unquestionable in genetics regarding e. g. , the study of rare diseases or rare alleles, of causality using Mendelian randomization, or of the population selection pressures.
As an interesting example, FinnGen is a research project that brings together almost all Finnish biobanks  (35). FinnGen plans to utilize 500,000 unique samples collected from a nationwide network of Finnish biobanks more efficiently for research purposes, aiding in the development of better treatments and medications. Among the perspectives opened by "multi-biobank" studies, it should be noted that CONSTANCES participates in several international consortia of population-based cohorts, which will facilitate research on large groups of diverse populations. Genetics data are only one of the several pieces of a bigger "biological" puzzle which includes transcriptomics, epigenetics, proteomics, and metabolomics. The proteome, transcriptome and metabolome analyses open up opportunities for the identification of new biomarkers for the diagnosis and prediction of clinical events that could be better predictors than traditional ones.
Other key strengths of CONSTANCES are the availability of urine samples and the follow-ups every 4 years with a complete examination. Urine samples will allow performing GWAS of the urinary concentrations of metabolites as done recently in the paper by Schlosser et al. among 1,627 patients with reduced kidney function  (44). In order to answer new research questions, CONSTANCES is an adapted framework to collect other fluids such as saliva or feces which will enrich the biobank, or to obtain new data though devices that can be send to the participants' home and send back by them such as electrostatic dust collectors to assess the fungal contamination in dwellings, a preventable risk factor for several respiratory diseases. It should be also noted that among the 200,000 participants 70,000 of them respond to the annual questionnaire online, allowing new questions to be rapidly implemented in response to emergency scientific programs such as those related to the new SARS-COV2  (45).
ACKNOWLEDGMENTS The authors thank the  Caisse nationale d'assurance maladie (CNAM)  and the  Centres d'examens de santé of the French Social Security  which are collecting a large part of the data, as well as the Caisse nationale d'assurance vieillesse, ClinSearch, Asqualab and Eurocell in charge of the data quality control. The authors thank all those who participated to the setting of the study and on the various aspects of the examinations involved: interviewers, technicians for lung function testing and neuropsychologists, coders, those involved in quality control, data and sample management and all the staffs from the inclusion centers (HPCs). They are indebted to all the participating individuals without whom the study would not have been possible.
FUNDING The Constances Cohort Study was supported and funded by the  Caisse nationale d'assurance maladie (CNAM). The  Constances Cohort Study is an Infrastructure nationale en Biologie et Santé and benefits from a grant from ANR  ( ANR-11-INBS-0002 ) and from the  Ministry of Research. Constances is also partly funded by  MSD ,  AstraZeneca  and  Lundbeck.
Numerical illustration: application to a remanufacturing industrial case The model (  1 )-(  6 ) as well as the nonlinear optimizations and numerical integrations are implemented in Linux using C++ on a PC with 8×CPU 2. 80 GHz and 32 Go RAM. The model (1)-(  6 ) is solved using ILOG CPLEX 12. 6. It is applied to the ball point pen instance illustrated in Figure  1  and to a remanufacturing industrial case product: a Knorr-Bremse EBS 1 Channel Module, see Figure  10. This latter represents a real case study in the automotive part remanufacturing sector. Such a product is composed of at least 45 components; the corresponding and/or graph is given in Figure  11. The input data describing each instance is given in Table  2. Recall that (see subsection 4. 1) N represents the number of all disassembly tasks, K the number of all subassemblies of a product and L the number of all parts of a product (components and subassemblies).
Study selection Search results of all databases were merged and deduplicated using Endnote. Titles were screened by one reviewer (YV); a second reviewer independently screened 10% (MHZ). Abstract and full text screening was conducted by two independent reviewers (JL and YV), following pre-established inclusion and exclusion criteria. Any discrepancies were resolved by discussion between the two reviewers. Title and abstract screening phases were conducted on Rayyan QCRI (https://rayyan. qcri. org).
Inclusion and exclusion criteria We searched for studies including adults (�18 years) with clinical suspicion or biopsy proven NAFLD, with paired data on liver histology and CK-18 (M30 or M65). Diagnostic accuracy studies reported in full articles in peer-reviewed journals, or as conference abstracts, in any language were eligible. Studies with insufficient information for making decisions on inclusion, for evaluating methodological quality, or for calculating diagnostic accuracy were excluded. Study groups with a mix of conditions (e. g. viral hepatitis) were only included if outcomes were separately reported for NAFLD patients. The target conditions for this systematic review were NASH, fibrotic NASH, and liver fibrosis. The NAFLD Activity Score (NAS)  [16]  is the most commonly used pathologic criterion for evaluating NASH.
Data extraction and quality assessment The following information was extracted: study characteristics, clinical characteristics, index test features, liver biopsy features, and data that allowed construction of a 2x2 contingency table (true positives, true negatives, false positive and false negatives) to assess the performance of the index test. For studies that reported accuracy data for multiple thresholds, all data were extracted. When pertinent data were not reported, the corresponding study author was contacted. Data were extracted independently and cross-checked by two reviewers (JL and YV). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool  [18]  was used to assess methodological quality of all available full text studies. Two reviewers (JL and YV) independently evaluated the risk of bias and concerns about applicability of the included primary studies using the four domains of QUADAS-2, assigning each study with a judgement of 'low', 'high', or 'unclear' risk.
Statistical analysis Included studies were classified into five groups for meta-analysis, based on the availability of data on the CK-18 antigens and target conditions: (1) CK-18 M30 for detecting NASH, (2) CK-18 M65 for detecting NASH, (3) CK-18 M30 for detecting fibrotic NASH, (4) CK-18 M30 for detecting significant fibrosis, and (5) CK-18 M30 for detecting advanced fibrosis. Sensitivity and specificity estimates from each study, with respective 95% confidence intervals (95% CI), were graphically illustrated as forest plots, for each reported threshold, using RevMan. Two different meta-analytical methods were applied for the combinations of CK-18 antigens and target conditions based on the number of reported threshold values. For groups 1-3, we applied a linear mixed effects multiple thresholds model (diagmeta package in R) as a majority of the primary studies reported multiple threshold values.
Quality assessment The methodological quality of the 41 studies, assessed with QUADAS-2, is summarized in S1 and S2 Figs in S1 File. Ten studies were scored as high risk of bias in the patient selection domain  [9, 10, 29, 31, 36, 37, 40, 47, 50, 58]. No study had low risk of bias in the index test domain, with 22 judged as high risk, due to the lack of a pre-established threshold value for CK-18. Seven studies were scored as unclear risk of bias in the reference standard domain, for failing to report whether biopsy reviewers were blinded to clinical data  [8, 9, 29, 32, 40, 49, 57]. Only three studies were classified as at high risk of bias for flow and timing  [10, 25, 40]. We further graded four studies with high concern regarding applicability in the patient selection domain  [30, 46, 54, 57].
This systematic review has been conducted as part of the evidence synthesis efforts in the  LITMUS (Liver Investigation: Testing Marker Utility in Steatohepatitis) study. The  LITMUS  project has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 777377  to QMA. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. The funder provided support in the form of salaries for authors JL, YV and MHZ, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the 'author contributions' section. the  IMI2 LITMUS consortium. He reports research grant funding from  Abbvie, Allergan/ Tobira ,  AstraZeneca ,  GlaxoSmithKline ,.
Spectra were normalized with a silver mirror (Thorlabs, PF10-03-P01). Scales were detached from the elytra and placed on glass slide. Spectra of different domains on each scale were measured. Domain color distribution was quantitatively analyzed by image-based RGB analysis on many single scales in each weevil with ImageJ Software (http://rsb. info. nih. gov/ij/). [39]  SEM Sample Preparation and Image Analysis: For the top view SEM images, scales were detached from the elytra and placed on a glass slide and plasma etched for 13 min to remove the outer shell of scales. The etched scales were transferred onto a SEM Stub and coated with 10 nm Pt for imaging. The SEM (Tescan MIRA3 FEG-SEM) used to observe the top view was operated at 3 kV with working distance of 5. 2 mm and using an in-beam secondary electron detector.
FDTD Simulation: Three dimensional diamond models were constructed in MATLAB and 3D images were obtained with Blender. Finite-difference time-domain (FDTD) calculations were performed with the commercial software Lumerical. Different crystal orientations, characterized through cross-sectional SEM images of scales, were oriented in the simulation box perpendicular to the direction of light incidence. Reflection properties of the chitin-air diamond structures were modelled with experimentally determined refractive index n = 1. 53 for chitin (measured via index matching oils Cargille series A), and n = 1 for the air spaces. Diamond minimum surface equation f(X, Y, Z) = cos (Z) sin (X + Y) + sin (Z)cos (X -Y)  [9]  was used for model construction, where X = 2πx/a, Y = 2πy/a, Z = 2πz/a with a the lattice constant, and x, y, and z the coordinates.
Acknowledgements The authors thank  Hsin-Chieh Tang  and  Lung-Chun Huang  ( Taipei Zoo , Taiwan) and  Wen-San Huang  ( National Museum of Natural Science, Taiwan ) for the captive-breeding technique support of weevils. The authors also appreciate the endangered species research numbers (Nos. 1061701832  and  1080231159 ) issued by the  Council of Agriculture, Taiwan. Y. O. thanks  NanoBio-ICMG platform  ( FR 2607 ) for granting access to the electron microscopy facility. This work was supported by the  Cambridge Trust , the  BBSRC   David Phillips fellowship  [ BB/K014617/1 ] and the  ERC SeSaME   ERC-2014-STG H2020 639088. Photography credits go to  Lung-Chun Huang  ( Taipei Zoo , Taiwan).
Materials and Methods The data were downloaded from the Human Protein Atlas available at (https://www. proteinatlas. org/humanproteome/sars-cov-2) and the tissue specific expressions (both mRNA and protein) of ACE2 and TMPRSS2 as yielded from the studies with RNA sequencing and immunohistochemistry (IHC) were analyzed as a function of the various components of the digestive tract. A digestive system specific functional enrichment map of ACE2 gene was created using g:profiler (https://biit. cs. ut. ee/gprofiler/gost) utility and the data were visualized using Cytoscape software, version 3. 7. 2 (https://cytoscape. org/).
Materials and Methods We analyzed the tissue specific distribution of ACE2 and TMPRSS2 (mRNA and protein) in digestive system components (GIT, liver & GB, and pancreas) using RNA sequencing and immunohistochemistry (IHC) data available in Human Protein Atlas (https://www. proteinatlas. org/humanproteome/sars-cov-2). A digestive system specific functional enrichment map of ACE2 gene was constructed using g:profiler (https://biit. cs. ut. ee/gprofiler/gost) utility and viewed with Cytoscape software, version 3. 7. 2 (https://cytoscape. org/). Since no direct subject or patient data were used in this study, clearance from the Institutional Ethics Committee was precluded.
IHC As described by the source labs, specimens containing normal tissue were collected and sampled from anonymized paraffin embedded material of surgical specimens, in accordance with approval from the local ethics committee. The specimens were derived from surgical material, normal was defined by morphological parameters and absence of neoplasia. IHC staining was performed using a standard protocol on normal tissue microarray (https://www. proteinatlas. org/download/IHC_protocol. pdf). Antibodies against human ACE2 (HPA000288, CAB026174) and TMPRSS2 (HPA035787) were labeled with DAB (3, 3'diaminobenzidine) stain. Protein expression score was done based on the staining intensity (negative, weak, moderate or strong) and fraction of stained cells (<25%, 25-75% or >75%). For each protein, the IHC staining profile was matched with mRNA expression data and gene/protein characterization data to yield an 'annotated protein expression' profile.
Transcriptomics The Human Protein Atlas collects transcriptomic data from the three databases (HPA, GTEx and FANTOM5). HPA RNAseq was performed on human tissue samples from healthy individuals (Accession no: PRJEB4337, Ensembl: ENSG00000130234 (version 92. 38). Total RNA was extracted from the tissue samples using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The extracted RNA samples were analyzed using either an Experion automated electrophoresis system (Bio-Rad Laboratories, Hercules, CA, USA) with the standard-sensitivity RNA chip or an Agilent 2100 Bioanalyzer system (Agilent Biotechnologies, Palo Alto, USA) with the RNA 6000 Nano Labchip Kit. Only samples of high-quality RNA (RNA Integrity Number 7. 5) were used for the mRNA sample preparation for sequencing.
mRNA sequencing was performed on Illumina HiSeq2000 and 2500 machines (Illumina, San Diego, CA, USA) using the standard Illumina RNA-seq protocol with a read length of 2x100 bases. Transcript abundance estimation was performed using Kallisto v0. 43. 1 (https://pachterlab. github. io/kallisto/about). The normalized Tags Per Million (TPM) for each gene from the three databases were calculated and included in the Human Protein Atlas. Each tissue was categorized for the intensity of gene expression using a cutoff value of 1 NX as a limit for detection across all tissues. A tissue was categorized (i) enriched if it had NX level at least four times higher than other tissues, (ii) low specificity if NX ≥ 1 in at least one tissue, (iii) Not detected if NX < 1 in all tissues. Further details of the assays and annotation used by the Human Protein Atlas can be accessed at: https://www.
Gene enrichment analysis and visualization Functional enrichment analysis of the ACE2 gene was performed with g: profiler web server (https://biit. cs. ut. ee/gprofiler/gost) and p-value computed using a Fisher's exact test with multiple-test correction. Enrichment map visualization was done with the help of Cytoscape software, version 3. 7. 2 (https://cytoscape. org/). Results (Fig. 1-3, S1-3, Table 1, S1-2) The transcriptomic and proteomic expression of ACE2 displayed high enrichment in the lower GIT (small intestine, colon, and rectum) (Fig. 1 , 2e-h, Table  1 ). It was highest in the parts of small intestine followed by the colon and the rectum, and nearly absent (negligible/low mRNA expression and undetectable protein expression) in the upper GIT components: mouth cavity (including tongue, oral mucosa, and salivary glands), esophagus, and stomach (Fig.