dataesr/scientific-paragraphs-categorization huggingface dataset:
Information sources and search 3. 1. 2. 1. Electronic academic databases. We (that is a research team formed from researchers across the four long working hour review groups, including JLAM, MB, MC, CDT, BMR, KS and KT from this review group) will, at a minimum, search the following seven electronic academic databases: The Ovid Medline search strategy for Systematic Review 1 is presented in Appendix B. We will perform searches in electronic databases operated in the English language using a search strategy in the English language. Consequently, study records that do not report essential information (i. e. title and abstract) in English will not be captured. We will adapt the search syntax to suit the other electronic academic and grey literature databases. When we are nearing completion of the review, we will search the PubMed database for the most recent publications (e.
g. , e-publications ahead of print) over the last six months. Any deviation from the proposed search strategy in the actual search strategy will be documented. 3. 1. 2. 2. Electronic grey literature databases. We will, at a minimum, search the two following electronic grey literature databases: 1. OpenGrey (http://www. opengrey. eu/) 2. Grey Literature Report (http://greylit. org/) 3. 1. 2. 3. Internet search engines. We will search the Google (www. google. com/) and GoogleScholar (www. google. com/scholar/) Internet search engines and screen the first 100 hits for potentially relevant records, as has been done previously in Cochrane Reviews  (Pega et al. , 2015; Pega et al. , 2017). 3. 1. 2. 4. Organizational websites. We will search, at a minimum, the websites of the following seven international organizations and national government departments: 1.
Study selection Study selection will be carried out with Covidence (Babineau, 2014; Covidence systematic review software) and/or the Rayyan Systematic Reviews Web App  (Ouzzani et al. , 2016). All study records identified in the search will be downloaded and duplicates will be identified and deleted. Afterwards, at least two review authors (from researchers across the four long working hour review groups, including JLAM, MB, MC, CDT, BMR, KS and KT from this review group), working in pairs, will independently screen against eligibility criteria titles and abstracts (step 1) and then full texts of potentially relevant records (step 2). A third review author will resolve any disagreements between the pairs of study selectors. If a study record identified in the literature search was authored by a review author assigned to study selection or if an assigned review author was involved in the study, then the record will be re-assigned to another review author for study selection.
Data extraction and data items A data extraction form will be developed and piloted until there is convergence and agreement among data extractors. At a minimum, two review authors (from researchers across the four long working hour review groups, including JLAM, MB, MC, CDT, BMR, KS and KT from this review group), will independently extract the data on exposure to long working hours, disaggregated by country, sex, age and industrial sector or occupation. A third review author will resolve conflicting extractions. At a minimum, we will extract data on study characteristics (including study authors, study year, study country, participants and exposure), study design (including study type), risk of bias (including missing data, as indicated by response rate and other measures) and study context.
The estimates of the proportion of the population exposed to the occupational risk factor from included studies will be entered into and managed with, the Review Manager, Version 5. 3 (RevMan 5. 3) (2014) or DistillerSR (EvidencePartner, 2017) softwares. We will also extract data on potential conflict of interest in included studies, including the financial disclosures and funding sources of each author and their affiliated organization. We will use a modification of a previous method to identify and assess undisclosed financial interests  (Forsyth et al. , 2014). Where no financial disclosure/conflict of interest is provided, we will search declarations of interest both in other records from this study published in the 36 months prior to the included study record and in other publicly available repositories  (Drazen et al.
Strength of evidence assessment To our knowledge, no agreed method exists for rating strength of evidence in systematic reviews of prevalence studies. We will rate the strength of the evidence for use as input data for estimating nationallevel exposure to the risk factor. Our rating will be based on a combination of the following four criteria: (i) quality of the entire body of evidence; (ii) population coverage of evidence (WHO regions and countries); (iii) confidence in the entire body of evidence; and (iv) other compelling attributes of the evidence that may influence certainty. We will rate the strength of the evidence as either "potentially sufficient" or "potentially inadequate" for use as input data (Appendix E).
Types of populations. We will include studies of working-age (≤15 years) workers in the formal and informal economy. Studies of children (aged < 15 years) and unpaid domestic workers will be excluded. Data on the formal and informal economy that the workers work in will be extracted, if feasible. Participants residing in any WHO and/or ILO Member State and any industrial setting or occupation will be included. We note that occupational exposure to long working hours may potentially have further population reach (e. g. across generations for workers of reproductive age) and acknowledge that the scope of our systematic reviews will not be able capture these populations and impacts on them. Appendix F provides a complete, but briefer overview of the PECO criteria.
Types of exposures. We will include studies that define long working hours in accordance with our standard definition (Table  1 ). We will again prioritize measures of the total number of hours worked, including in both of: main and secondary jobs, self-employment and salaried employment and informal and formal jobs. We will include all studies where long working hours were measured, whether objectively (e. g. by means of time recording technology), or subjectively, including studies that used measurements by experts (e. g. scientists with subject matter expertise) and self-reports by the worker or workplace administrator or manager. If a study presents both objective and subjective measurements, then we will prioritize objective measurements. We will include studies with measures from any data source, including registry data, in the same analyses and description.
ii. Diagnosis by a physician, psychologist or other qualified health professional. iii. Hospital admission or discharge record. iv. Administrative data (e. g. , disability pensioning with the diagnosis of depression). v. Register data of treatment for depression, with antidepressant medication, psychotherapy or both; will only be included if there is documentation that the treatment was for depression and not for other types of disorders. vi. Self-administered rating scale for depression that was previously validated against a clinical measure of depression and that dichotomized respondents into cases versus non-cases (e. g. , Center of Epidemiological Studies Depression Scale (CES-D)  (Radloff, 1977)  or Major Depression Inventory (MDI)  (Bech et al. , 2001) ) or other validated self-administered rating scales.
The Ovid Medline search strategy for Systematic Review 2 is presented in Appendix G. We will perform searchers in the electronic databases operated in the English literature using a search strategy in the English language. We will perform searches in electronic databases operated in the English language using a search strategy in the English language. We (CDT, KS and RR) will adapt the search syntax to suit the other electronic academic and grey literature databases. When we are nearing completion of the review, we will search the PubMed database for the most recent publications (e. g. , e-publications ahead of print) over the last six months. Any deviation from the proposed search strategy in the actual search strategy will be documented.
3. 2. 2. 2. Electronic grey literature databases. We (MB, CDT, BMR and KS) will, at a minimum, search the two following electronic grey literature databases: 1. OpenGrey (http://www. opengrey. eu/) 2. Grey Literature Report (http://greylit. org/) 3. 2. 2. 3. Internet search engines. We (MB, CDT, BMR and KS) will search the Google (www. google. com/) and GoogleScholar (www. google. com/ scholar/) Internet search engines and screen the first 100 hits for potentially relevant records.
Organizational websites. We (MB, CDT, BMR and KS) will search the websites of the six following international organizations and national government departments: 1. International Labour Organization (www. ilo. org/) 2. World Health Organization (www. who. int) 3. EUROSTAT (www. ec. europa  • Reference lists of previous systematic reviews. • Reference lists of all included study records. • Study records published over the past 24 months in the three peer- reviewed academic journals with the largest number of included studies. • Study records that have cited the included studies (identified in Web of Science citation database). • Collections of the review authors. Additional experts will be contacted with a list of included studies, with the request to identify potentially eligible additional studies.
Study selection Study selection will be carried out in a reference manager database, such as Covidence  (Babineau, 2014;  Covidence systematic review software) or the Rayyan Systematic Reviews Web App  (Ouzzani et al. , 2016). All study records identified in the search will be downloaded and duplicates will be identified and deleted. Afterwards, at least two review authors (out of: RR, EA, JLAM, MB, MC, CDT, ND, QDM, HE, JG, AHG, SI, IEHM, BMR, KS, KT and AZ), working in pairs, will independently screen titles and abstracts (step 1) and then full texts (step 2) of potentially relevant records. Any disagreements between the two review authors will be resolved by discussion and the involvement of a third review author (MB, CDT, BMR or KS). If a study record identified in the literature search was authored by a review author assigned to study selection or if an assigned review author was involved the study, then the record will be re-assigned to another review author for study selection.
Data extraction and data items A data extraction form will be developed and trialed until data extractors reach convergence and agreement. At a minimum, two review authors (out of: MB, CDT, BMR and KS) will extract data on study characteristics (including study authors, study year, study country, participants, exposure and outcome), study design (including summary of study design, comparator, epidemiological models used and effect estimate measure), risk of bias (including selection bias, reporting bias, confounding, and reverse causation) and study context (e. g. data on contemporaneous exposure to other occupational risk factors potentially relevant for risk of depression). A third review author (out of: MB, CDT, BMR, KS, RR) will resolve conflicts in data extraction. Data will be entered into and managed with the RevMan 5.
3 software (2014). We will also extract data on potential conflict of interest in included studies, including the financial disclosures and funding sources of each author and their affiliated organization. We will use a modification of a previous method to identify and assess undisclosed financial interests  (Forsyth et al. , 2014). Where no financial disclosure or conflict of interest statements are provided, we will search declarations of interest both in other records from this study published in the 36 months prior to the included study record and in other publicly available repositories  (Drazen et al. , 2010a; Drazen et al. , 2010b). We will request missing data from the principal study author by email or phone, using the contact details provided in the principal study record. If we do not receive a positive response from the study author, we will send follow-up emails twice, at two and four weeks.
, 2016c). Should a more suitable method become available, we may switch to it. We will assess risk of bias on the individual study level and on the body of evidence overall. The nine risk of bias domains included in the Navigation Guide method for human studies are: (i) source population representation; (ii) blinding; (iii) exposure assessment; (iv) outcome assessment; (v) confounding; (vi) incomplete outcome data; (vii) selective outcome reporting; (viii) conflict of interest; and (ix) other sources of bias. While two of the earlier case studies of the Navigation Guide did not utilize outcome assessment as a risk of bias domain for studies of human data  (Johnson et al. , 2014; Koustas et al. , 2014; Lam et al. , 2014; Vesterinen et al. , 2014)  all of the subsequent reviews have included this domain  (Johnson et al.
Acknowledgments We thank  Stavroula Leka  for her preliminary contribution towards the establishment of the systematic review before the review commenced and  Alexis Descatha ,  Diana Gagliardi ,  Jian Li ,  Grace Sembajwe ,  Johannes Siegrist  and  Mark van Ommeren  for their feedback on an earlier version of this protocol. We thank research librarian  Elizabeth Bengtsen  for her assistance with developing the search strategy. We thank  Frida Fischer ,  Anders Knutsson  and  Mikael Sallinen  for their feedback on the search strategy. We are grateful to  Lisa Bero ,  Rebecca Morgan ,  Susan Norris ,  Holger J. Schünemann ,  Patrice Sutton  and  Tracey Woodruff  for their feedback on the methods for this protocol. We thank  Paul Whaley  and  Tim Driscoll  for their editorial guidance. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.
Data The variables used in this study were the water temperature, concentrations of chlorophyll-a (Chla), phytoplankton and zooplankton, dissolved oxygen (DO), ammonium (NH 4 ), nitrate (NO 3 ), orthophosphates (PO 4 ), silicate (SiO 2 ), total phosphorus (TP), particulate organic matter (POC, PON and POP), dissolved organic matter (DOC, DON, DOP), particulate inorganic matter (PIM) and transparency (SDD, Secchi disk depth). The years, 2009 The years, , 2010 The years, , 2011 The years, and 2012  were chosen for this study because the hydrodynamics model had already been validated by  Soulignac et al. (2018). The data were available from the SOERE-OLA database and the Canton of Geneva. Sampling took place at two stations from 2009 to 2012, straight up the deepest point of the lake in the large basin (SHL2: 46.
Modeling The open source Delft3D software (version 4. 03. 00) used in this study has been applied to lakes of different sizes all around the world  (Chanudet et al. , 2012; Kacikoc and Beyhan, 2014; Razmi et al. , 2013; Soulignac et al. , 2017). Delft3D is composed of a 3D hydrodynamic and a 3D water quality modules. For this study, the hydrodynamic model has been validated, and its configuration was described in  Soulignac et al. (2018). The model was calibrated by performing a sensitivity analysis on several physical parameters (background coefficients for horizontal viscosity and diffusion of heat, coefficient for heat transfer at the atmosphere-lake interface), and it was validated based monthly temperature profiles taken at the two monitoring stations, SH2 and GE3. The model was applied on a grid representing Lake Geneva.
Lake ecological status The French Ministry of the Environment, Energy and the Sea imposes a protocol for the calculation of the ecological status of lakes. Ecological status is calculated annually using data from vertical integrated samples in the euphotic zone. The calculation is based on four samples per year. In situ measurements are performed during two years, and the obtained ecological status states for the entire six years' management plan. This protocol has been designed to allow a comparison between lakes. These samples are taken over the growing season, above the deepest point of the lake and during different temporal windows (TW) (Fig. 3 ). For Lake Geneva, the first TW corresponds to the end of the winter mixing period, typically from mid-February to end of March. The three other TWs are during the thermal stratification.
The second TW is from mid-May to end of June, the third TW extends from July to August and the fourth TW from September to mid-October. The lake ecological status varies from "very good", "good", "medium", "poor" or "bad". Five variables used in the lake ecological status evaluation were tested in this study, Chla, NH 4 , NO 3 , TP and SDD. The annual metric for Chla is calculated as the mean value of three samples from TW2-4. Annual metrics for NH 4 and NO 3 are the maximum values among the four TWs. Annual metrics for TP and SDD are the median values over the four TWs. The sensitivity of the lake ecological status to the timing of the sampling dates and the location of the sampling station were assessed by using simulations performed for the year, that presented the best fit between model and in situ data (Tab.
Sensitivity to the timing of the sampling is due to strong short-term fluctuations in phytoplankton abundance. This variability has been underline by high frequency monitoring or satellite data  (Kiefer et al. , 2015) , and for the first time we provide evidence that it can strongly impact results of the water quality assessment. Spatial heterogeneity in phytoplankton abundance are well known, and here results indicated that the location of the sampling station could bias the estimation of lake ecological status. Rimet et al. (2016)  identified two types of areas with different status in Lake Geneva. A more oligotrophic status was measured near the shoreline with low population densities and more eutrophic areas situated in more densely inhabited areas. Our results showed that the western part of Lake Geneva presented a better quality compared to the eastern part.
Acknowledgments. This work was supported the  French Agency for Biodiversity (AFB , previously the  French National Agency for Water and Aquatic Environments (ONEMA) ),  ModeL  contract  N°15000239 , and by the  European Space Agency (ESA) Scientific Exploitation of Operational Missions element (SEOM) ,  CORESIM  contract  N°A0/1-8216/15/I-SBo. We wish to thank the  French Alpine Lakes Observatory (SOERE-OLA)  and the  International Commission for the Protection of Lake Geneva  (CIPEL). Data were from © SOERE OLA-IS,  AnaEE-France ,  INRA Thonon-les-Bains , CIPEL (2016), developed by  Eco-Informatics ORE INRA Team. We also wish to thank the Department of environment, transport and agriculture (DETA) of the Geneva water ecology service for providing additional in situ data as well as  Isabel Kiefer  for satellite data.
Protein Extraction and Western Blot Analysis Proteins were extracted using a lysing solution (RIPA, anti-protease cocktail). Total protein concentration was evaluated with a Pierce TM BCA Protein Assay Kit (Thermo Fisher Scientific). Next, 20 µg of total protein extract was reduced and size-separated on 10% SDS-polyacrylamide gels, transferred to nitrocellulose or PVDF membranes (Invitrogen, Paris, France), blocked in TBS-T milk, and incubated with specific primary antibodies against IL1R1 (# HPA005823 at 1:500), RANK (# PA80145 at 1:1,000), IKKβ (# 2884 at 1:1,000), and βactin (# 3700 at 1:10,000) (Cell Signaling Technology, Danvers, MA, USA). The proteins of interest were detected, imaged, and quantified (Chemidoc; Bio-Rad, Marnes-la-Coquette, France).
RESULTS Expression of miR-636, IL1R1, RANK, and IKBKB in CF and Non-CF ALI Cell Cultures In our previous work, miR-636 expression was found to be significantly overexpressed in CF ALI cell cultures compared to non-CF cultures  (18). Small RNAseq raw data files are available in the European Nucleotide Archive (ENA) (primary accession number ERP116236; http://www. ebi. ac. uk/ena/data/ view/ERP116236). In order to validate the expression patterns obtained from small RNAseq and RNAseq, we conducted RT-qPCR on the samples to confirm expression of miR-636 and the mRNA targets (IL1R1, RANK, and IKBKB). We observed a significant increase in miR-636 expression but no significant differences in IL1R1, RANK, and IKBKB mRNA expression in the CF ALI cell cultures compared to that in the non-CF cultures (Figure  1 ).
Figures 2D,F confirmed and reinforced the data published previously  (18). miR-636 Directly Regulates IL1R1, RANK, and IKBKB Expression Levels but Not FAM13A In CF patients, we hypothesised that miR-636 is able to act on the four predicted targets, thereby influencing pulmonary inflammation. The CF cells were transfected with miR-636 at different concentrations  (10, 30, 60 , and 100 nM) or with mimic controls and cultured for 48 h (Figure  S1A ). miR-636 expression was analysed by RT-qPCR. The level of miR-636 in the presence of 30 nM of the mimic during 48 h increased by a factor of 10,000 compared to the control (Figure  S1B ). We also showed that the levels of endogenous miR-636 decreased by a factor of 40% in the presence of 30 nM of antagomiR-636 compared to that in the antagomiR control (Figure  S1C ).
Furthermore, only one study has reported a direct link between this miRNA and one of the predicted targets at the 3 ′ -UTR region of GRα  (22). Thus, miR-636 expression is associated with various cellular processes, such as the survival, proliferation, inflammation, and repair of DNA. To identify specific miR-636 targets related to inflammation, we used bioinformatics prediction in silico databases that highlighted four actors: IL1R1, RANK, IKBKB, and FAM13A. We, therefore, focused our study on these four potential targets using functional analysis. In the CF context, we showed that miR-636 specifically binds to the 3 ′ -UTR regions of IL1R1, RANK, and IKBKB, but not to FAM13A mRNA. The specificity of binding is shown using a mutated luciferase plasmid with a 3-5-nucleotide mismatch mutation into the seed region of miR-636.
We will use a database to predict the interaction between miRNA and lncRNA  (57)  to determine, in the context of CF, which lncRNA is overexpressed and be able to block the expression of miR-636. A more thorough understanding of miR-636 will allow us to consider this miRNA as a possible therapeutic target to reduce inflammation in CF patients. We also showed in our cell model, that stimulation with heatkilled P. aeruginosa, the pathogen most commonly found in the airways of CF patients, at an MOI of 0. 25 for 24 h, had no effect on the miR-636, IL1R1, RANK, and IKBKB expression levels. This experiment revealed that the virulence factors are not implicated in this activation. We selected a protracted-time interval to assess the possible impact on the expression of our miRNA, but it is possible that it was too long to observe a significant impact on the expression levels in mRNA.
FUNDING This research was funded in part by grants from  Inserm ,  Sorbonne Université, Faculté des Sciences , and the non-profit organization Vaincre la Mucoviscidose. PB received a Ph. D. grant from  Ministère de la Recherche et de l'Enseignement Supérieur, Sorbonne Université. CR was funded by an  EU   Marie Curie Fellowship  and JP by an  MRC New Investigator Research Grant.