dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEGMENTS We would like to thank Dr Christine Jaxel for all the support and discussions during this project, and to Dr. Thibaud Dieudonné, Dr Guillaume Lenoir, Dr Cédric Montigny, Dr Eugene Diatloff and Dr Jing Yuan for crytically reading the manuscript. We also thank Quentin Rochette for his early work on this project. This work was supported by the ANR grants ANR-14-CE09-0022 to Dr Guillaume Lenoir, and ANR-18-C811-0009-02 to JLVI, the French Infrastructure for Integrated Structural Biology (FRISBI; ANR-10-INSB-05), and the Centre National de la Recherche Scientifique (CNRS). In memory of Prof Ronald H. Kaback.
Molecular Identification of Re-crustin The nucleotide sequence of the Re-crustin was obtained by RT-PCR using 2 µg of template cDNA, with the forward primer 5 ′ -GACAAACACCTCCTCCTCCTCCA-3 ′ designed from the incomplete 5 ′ coding sequence of a crustin sequence available in GenBank (accessing number FJ573157) and the oligo (dT)18 primer.
Molecular Cloning and Sequence Analysis PCR products from each replicate were pooled and then purified with the NucleoSpin R Gel and PCR Clean-up kit (Macherey-Nagel). Purified PCR products were cloned using the TOPO-TA kit (Invitrogen, Carlsbad, CA, USA). Clones were sequenced according to the Sanger method (50) on a 310 ABI prism (Applied Biosystems). Sequences were imported into Geneious © version 8. 1 software (Biomatters, available from http://www. geneious. com/). Prediction of signal peptide was performed with the SignalP 4. 1 program (51) and the presence of conserved domains was tested using the SMART 7. 0 protein analysis tool  (52). Homology searches were performed using BLAST from NCBI. Multiple alignments of the deduced amino acid sequences (Type I, Type IIa, and Type IIb crustins) were generated using the MAFFT software (scoring matrix BLOSUM62)  (53).
Determination of the Level and Site of Re-crustin Gene Expression by RT-qPCR Tissues from branchiostegite, scaphognathite, gills, abdomen, stomach, hepatopancreas and eggs dissected aboard as wells as whole adults and juveniles were ground in Trizol ReagentTM using FastPrep-24 R 5G (MP Biomedicals). Total RNA extraction and RT were performed as described in the previous section. The primers used for the quantitative PCR were designed with the Primer3 Input software (http://bioinfo. ut. ee/primer3-0. 4. 0/ primer3/primer3/; www. cgi). -crustin primers: forward: 5 ′ -ACTGCTGTGAGAACGGGA AC-3 ′ ; reverse: 5 ′ -AACATGTTTGAGGGGGTCCT-3 ′ -Rpl8 primers: forward: 5 ′ -GAAGCTCCCATCAGGTGC CAAGAA-3 ′ ; reverse 5 ′ -TTGTTACCACCACCGTGAG GATGC-3 ′. The Rpl8 gene was used as the reference gene  (55). Real-time quantitative PCR reactions (RT-qPCR) were conducted on a LightCycler R 480 system (Roche) using a hot start enzyme.
RT-qPCR assays were submitted to an initial denaturation step of 10 min at 94 • C followed by 40 cycles of denaturation at 94 • C for 15 s, annealing at 59 • C for 1 min and extension at 72 • C for 30 s. Reference and target were amplified in separated wells. After amplifications, a melting curve analysis was performed in order to confirm the specificity of the PCR products. Re-crustin primers generated a single and discrete peak in the dissociation curve (data not shown). A negative control and a 5-fold dilution series protocol of pooled cDNAs were included in each run. The 5-fold dilution series were used to construct a relative standard curve to determine the PCR efficiencies and for further quantification analysis. In all experiments, all primer pairs gave amplification efficiencies of 90-100%.
Re-crustin, a Novel Member of Type IIa Crustins and a Novel AMP From an Extremophile Organism The complete nucleotide sequence of Re-crustin was obtained by 5 ′ -RACE RT-PCR from total RNA extracted from the entire shrimp R. exoculata (GenBank accession number: MT102281). Only one sequence of crustin was identified from our molecular subcloning and sequencing. The complete cDNA sequence encodes a precursor of 190 amino acid residues, which includes a 15-residue signal peptide (Figure  2 ). The mature polypeptide is predicted to consist of 175 residues with a calculated molecular weight of 17. 82 kDa and a theoretical isoelectric point (pI) of about 8. 5. The mature Re-crustin is composed by a hydrophobic glycine-rich region followed by a C-terminus containing a cysteine-rich region (with 4 conserved cysteine residues) and a single WAP domain (Figure  2B ).
FUNDING This work was supported by  Ifremer ,  LabexMer , and  ANR Carnot EDROME Institute. The pressure equipment was funded by a  European Community program EXOCET/D  ( FP6-GOCE-CT-2003-505342 ) and by the  University Pierre et Marie Curie , under the  BQR UPMC 2008. RR was funded by the  Brazilian funding agencies CNPq  ( MEC/MCTI/CAPES/CNPq/FAPs PVE 401191/2014-1  and  MCTI/CNPq Universal 406530/2016-5 ) and  CAPES  ( CIMAR 1974/2014 ).
Electronic databases We searched the six following electronic databases: The Ovid MEDLINE search strategy was presented in the protocol  (Li et al. , 2018). The full search strategies for all databases were revised by an information scientist and are presented in Appendix 3 in the Supplementary data. Searches were performed in electronic databases operated in the English language using a search strategy in the English language. When we neared completion of the review, we conducted a top-up search of the MEDLINE database on 27th August 2019 to capture the most recent publications (e. g. , publications ahead of print). Deviations from the proposed search strategy and the actual search strategy are documented in Section 8.
Grey literature databases We also searched the two following two grey literature databases in July 2018; OpenGrey (http://www. opengrey. eu/) Grey Literature Report (http://greylit. org/) We used the following search strategy: (("work hours" OR "working hours" OR "long work" OR "long working" OR "long hours" OR overtime OR overwork OR workload OR employee*) AND (myocardial OR heart OR coronary OR cardiovascular OR angina)).
Hand-searching and expert consultation We hand-searched for potentially eligible studies in: • Reference lists of previous systematic reviews. • Reference lists of all included trials register records. • Study protocols published over the past 24 months in the three peer- reviewed academic journals with the largest number of included studies. • Published study protocols that have cited the included studies (identified in Web of Science citation database). • Collections of the review authors. Additional experts were contacted with a list of included studies, with the request to identify potentially eligible additional studies.
Selected studies Study selection was carried out with the Covidence software (Veritas Health Innovation). All records identified in the search were downloaded and duplicates were identified and deleted. Afterwards, two review authors independently and in duplicate screened titles and abstracts (step 1) and then full texts (step 2) of potentially relevant records. A third review author resolved any disagreements between the two review authors. 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, the record was reassigned to another review author for study selection. The study selection is presented in a flow chart, as per PRISMA guidelines  (Liberati et al. , 2009).
Types of exposures We included studies that defined long working hours in accordance with our standard definition (Table  1 ). We again prioritized measures of the total number of hours worked, including in both of: main and secondary jobs, self-employment and salaried employment, whether in the informal or the formal economy. We included studies with objective (e. g. , by means of time recording technology) or subjective measurements of long working hours, whether, including studies that used measurements by experts (e. g. , scientists with subject matter expertise) and self-reports by the worker, workplace administrator or manager. If a study presented both objective and subjective measurements, then we prioritized objective ones. Studies with measures from any data source, including registry data, were included.
, 2011)  and the US Agency for Healthcare Research and Quality  (Viswanathan et al. , 2008). Some further refinements of the Navigation Guide method may be warranted  (Goodman et al. , 2017) , but it has been successfully applied in several completed and ongoing systematic reviews  (Johnson et al. , 2016 (Johnson et al. , , 2014;; Koustas et al. , 2014; Lam et al. , 2016a Lam et al. , , 2014 Lam et al. , , 2017 Lam et al. , , 2016b;; Vesterinen et al. , 2014). In our application of the Navigation Guide method, we draw heavily on one of its latest versions, as presented in the protocol for an ongoing systematic review  (Lam et al. , 2016c). Risk of bias was assessed on the individual study level and across the body of evidence for each outcome. The nine risk of bias domains included in the Navigation Guide method for human data were: (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.
Assessed strength of evidence Our systematic review included observational epidemiologic studies of human data only, and no other streams of evidence (e. g. , no studies of nonhuman data). The standard Navigation Guide methodology  (Lam et al. , 2016c)  allows for rating human and non-human animal studies separately, and then combining the strength of evidence for each stream for an overall strength of evidence rating. However, the Navigation Guide also allows for rating one stream of evidence based on the factors described above (i. e. , risk of bias, indirectness, inconsistency, imprecisions, publication bias, large magnitude of effect, dose-response and residual confounding) to arrive at an overall rating of the quality of evidence as 'high', 'moderate' or 'low' (see above and the protocol).
Limitations Our systematic review has several limitations. First, while we conducted a broad and sensitive search, we may have missed eligible studies, for example, due to them being published in languages other than English. However, we searched many academic and grey literature databases using a comprehensive search strategy and consulted additional experts who also did not identify any additional eligible studies. Considering the large number of included studies, the size of participants and the number of disease events, it seems unlikely that the overall results would have been affected by this fact. Second, we did not receive a substantial amount of the missing data we requested for the studies included in this systematic review. We requested missing data from principal study authors at least three times, but the principal study authors generally did not share these requested missing data with us or only shared selected data.
As a result, we can only present limited evidence in this systematic review on: • The subgroup analyses by age, sex, occupation and industrial sector. • Dose-response associations between the different exposure cate- gories and the outcomes (statistical testing was not possible). In some cases, we know that the missing data requested were readily available to the principal study authors, but these requested missing data were nevertheless not shared with us. This has introduced some uncertainties in the evidence that could have been resolved had the principal study authors shared the requested missing data. Third, the validity of exposure assessment was somehow restricted, not only due to lack of objective measurements, but also since exposure to long working hours was assessed at baseline only, thus preventing the analysis of potential changes of exposure over time.
med. yale. edu). We then observed a substantially higher ability of identifying relevant studies by the latter strategy, based on 34 included studies of two previously published systematic reviews  (Kivimaki et al. , 2015; Virtanen et al. , 2012). • Our protocol said that we would search the Embase database, but since we did not have access to this database, the Scopus database was searched instead, which includes all records from Embase. • In the protocol, we planned to convert OR into RR, if possible. To conduct conversion, information on "prevalence of outcome in reference group or baseline risk" is required. However, such information was not available from any included studies. For casecontrol studies, ORs were reported and were synthesized directly. For cohort studies, ORs, HRs, and RRs were reported and were used for meta-analyses without any conversion, in line with an earlier systematic review and meta-analysis on this topic [  (Kivimaki et al.
Acknowledgments We thank  Paul Whaley  ( Associate Editor for Systematic Reviews, Environment International ;  Lancaster Environment Centre, Lancaster University, United Kingdom ) and  Tim Driscoll  ( University of Sydney, Australia ) for the editorial guidance and support. The following individuals shared data from primary included studies with us:  Yoshimi Fukuoka ,  Harald Hannerz ,  Inchul Jeong , and  Mika Kivimaki. 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.
In recent years, there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their applications. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their locations, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.
COMPARISON OF METHODS We now present a comparison of the performance of these different methods across a range of scenarios. Presenting these comparisons is challenging due to wishing to consider a number of distinct scenarios for the mean (differing numbers of changes, sizes of change, lengths of segment, etc. ); the noise (IID Gaussian, heavy-tailed and auto-correlated); different measures of accuracy (error of estimating the segmentation of the data or the mean function); and the large number of methods that have been proposed for this problem, each of which can have a different tuning parameter that impacts on the number of changepoints detected. As a result, we present indicative results in this section, with the complete results available in Supporting Information. Moreover, the Supporting Information is available as a markdown document with associated code available.
Epidemiological trend and description of cases A total of 93 EV-D68 infections were reported between 1 January 2019 and 15 January 2020 (Denmark n = 21, France n = 6, Germany n = 36, the Netherlands n = 19, Sweden n = 11). Epidemiological and clinical information was collected for cases where possible (Table  1 ). As surveillance samples and data are collected retrospectively, data for December 2019 and January 2020 are not complete. Cases were identified through enterovirus surveillance (n = 31), rhinovirus surveillance (n = 3), influenza and respiratory infection community surveillance (n = 12) or hospital-based diagnostics of respiratory infections (n = 47). Following diagnostic testing of respiratory samples for enterovirus and/or rhinovirus using commercial or in-house assays, EV-D68 cases were identified by either EV-D68-specific real-time PCR or partial sequencing of the VP1 and/or VP4/VP2 region of the genome  [9] [10] [11].
Phylogenetic analysis of enterovirus D68 strains Full-or nearly full-length genome sequencing was successfully carried out for seven strains (Denmark and the Netherlands: in-house protocols, Sweden  [12] ). Sequences which were available up to and including 6 December 2019 are available on GenBank (Table  2 ). Phylogenetic analysis of VP1 sequence data was carried out using the Nextstrain augur pipeline  [13]. We included samples from this study with ≥ 300 bp in VP1 (Table  2 ), alongside all available VP1 sequences in GenBank of ≥ 700 bp, randomly down-sampled to 20 samples per country per month to avoid sampling bias and overrepresentation of some countries (particularly during the 2014 and 2016 epidemics; no 2019 samples were down-sampled). The code is available at https://github. com/enterovirus-phylo/evd68-2019; the analysis can be viewed at https://nextstrain.
This would suggest either a very low circulation of strains of this cluster or a lack of sustained global surveillance for EV-D68 and/or submission of sequences in GenBank, or both. The estimated MRCA in mid-2018 suggests that these viruses were already circulating and diversifying during and after the 2018 EV-D68 epidemic, but were not sampled until August 2019. In previous European seasons of EV-D68, Denmark has only reported respiratory infections caused by EV-D68, whereas Germany, France, the Netherlands and Sweden have experienced paralytic cases  [11, 18, 19]. The EV-D68 cases were detected through routine surveillance networks in each country, with alerts to physicians posted in Denmark and the Netherlands. The number of samples processed in each country was the expected range for the enterovirus season, but the detection rate of EV-D68 was higher than expected in Denmark, Germany and Sweden, where the number of detections for the 2018 season had been unexpectedly low.
Acknowledgements The authors wish to thank the  European non-polio Enterovirus Network (ENPEN)  for facilitating contact between the countries. The authors are grateful to all the scientists and researchers who have made their EV-D68 samples and metadata available publicly, often in a very timely manner, which enables the analysis of recent sequences in a global context. The Danish authors wish to thank the clinical microbiology departments for sending entero-and rhinovirus-positive samples to SSI for further characterisation. We also wish to thank the clinicians in the hospitals for ensuring appropriate sampling and diagnostic testing and providing us with clinical information about enterovirus-positive cases. Finally, we wish to thank  Shukriya Barzinci  and  Mille Weismann Poulsen  for carrying out the laboratory analyses at  SSI.
The Dutch authors wish to thank the  Dutch medical microbiology laboratories  for their contribution on the EV surveillance programme and VIRO-TypeNed, in particular thanks to the clinicians, molecular medical microbiologist and technicians in the Dutch hospitals and laboratories for providing us with sequence and clinical information about the EV-D68 cases ( Richard Molenkamp  ( Erasmus Medical Center, Rotterdam ),  Judith Hoogenboom-Beuving  ( Reinier Haga ,  Medical Diagnostic Center, Delft) ,  Hubert GM Niesters ,  Coretta Van Leer-Buter ,  Marjolein Knoester  ( University Medical Center Groningen, Groningen ), and  Lieuwe Roorda  ( Maasstad Hospital, Rotterdam )). We would also like to thank  Gé A. Donker  coordinator of the Nivel Primary Care database -sentinel Practices, Utrecht, The Netherlands and technicians (Bas van der Veer,  Anne-Marie van den Brandt ,  Sharon van den Brink ,  Lisa Wijsman ,  Elsa Poorter ,  Barbara Favier ,  Jeroen Cremer  and  Pieter Overduin ) running molecular diagnostics and sequencing at RIVM.
Biological sources of the data Data from different studies that incubated eggs at constant temperatures were extracted from publications (available in the ''databaseTSD'' file, as part of the R package embryogrowth  (Girondot, 2019a). New data from our own unpublished studies  (Navarro Sánchez, 2015)  were also included (Table  1 ). The version of the database available in the package can be obtained using the R command DatabaseTSD$Version  [1]. The version used here (from embryogrowth ver. 7. 6. 7) is the 2019-11-19 version with 670 records for 25 species or subspecies. Lepidochelys olivacea data including geographic origin, incubation temperatures and their amplitude were retrieved from this file. The Regional Management Units (RMUs) of olive ridley marine turtles as defined in  Wallace et al.
(2010)  were inferred from the geographic origins. Whenever available, a temperature correction factor (difference between the measured incubator temperature and true internal egg temperature) was also obtained from the database. This correction factor has been reported in some publications (e. g. ,  Godfrey & Mrosovsky, 2006)  and has been shown to be relevant particularly when the substrate contains humidity  (Tezak, Sifuentes-Romero & Wyneken, 2018). However, as few papers measure or report this parameter, it can be ignored when comparing studies where some, or all, do not report it. An alternative is to use an average correction factor based on all studies, but doing so will not contribute statistical information and will only shift fitted P values proportional to the correction factor.
In the current analysis, the correction factor was not used and we used only sex ratio data from eggs incubated in temperature-regulated chambers. Incubation. temperature. Amplitude and 2ndThird. Incubation. temperature. Amplitude columns from databaseTSD refer to a measure of the variability of temperatures during the whole experimental incubation and the middle-third of the incubation, respectively. Mrosovsky & Pieau (1991)  define the thermosensitive period (TSP) for sex determination as the interval of time when a change of incubation temperature results in a change in resultant sex ratio. The TSP begins with the formation of the genital ridge at stage 21 (sensu  Miller, 1985) , which corresponds to   Behra & Dimond (1985 ), 4-McCoy, Vogt & Censky (1983 ), 5-Wibbels, Rostal & Byles (1998 ), 6-Merchant-Larios et al.
1 ), belonging to 3 RMUs (West Atlantic, Northeast q q q q q q q q q q q q 1 2 3 Indian, East Pacific). Though olive ridleys nesting on beaches in Pacific Mexico and Pacific Costa Rica belong to the same East Pacific RMU  (Wallace et al. , 2010) , we analyzed these data separately because embryo growth dynamics from the two areas respond differently under a range of incubation temperatures  (Morales Mérida et al. , 2015). Data included 40 incubations at various constant temperatures with a total of 464 sexed embryos (277 males, 168 females, and 19 intersexes). The 19 intersexes were reported from only 2 studies out of 8, from incubation temperatures ranging from 27. 6 to 32. 3 • C. An intersex is a transient state during development when the gonads are ovotestes that exhibit characteristics of both testes and ovaries  (Pieau & Dorizzi, 2004).
After hatching, ovotestes generally evolve as normal testes  (Pieau et al. , 1998). Some adult gonads retain traces of intersexual characters when some oocytes may persist at the surface of testis in some species, whereas for others, no signs of intersex at the adult stage are observable. Because the criteria to define a gonad as an ovotestis are not entirely objective, we excluded data from hatchlings that were classified as intersex (n = 19 values, or 3. 9% of all sexed turtles, Table  1 ). The East Pacific RMU was overrepresented in the database (384 embryos, 190 from Mexico and 197 from Costa Rica) as compared to Northeast India (31 embryos) and West Atlantic (50 embryos). The number of incubation temperatures that produced mixed sex ratios was 2 for West Atlantic, 1 for Northeast Indian, 4 for East Pacific (Costa Rica), and 1 for East Pacific (Mexico).