dataesr/scientific-paragraphs-categorization huggingface dataset:
Exposure studied All studies measured exposure to long working hours by self-report, either by pen-and-paper-survey (four studies), face-to-face-survey (three studies), telephone survey (four studies), an interview (unclear if faceto-face or telephone) (two studies) or did not specify which of these methods were used (nine studies). No included study used non-selfreported measures, such as official or company records of hours worked. Only two of the 22 included studies (NLSY OA Cohort, 2019;  Zadow et al. , 2019)  provided the standard exposure categories that we had predefined in the protocol. We therefore attempted to contact all study authors asking them to either provide us with the original data or conducting a re-analysis of the data using our standard exposure categories. The author of one study provided us with the original data  (Kim, 2013) , and the authors of three studies re-analysed the data according to our request  (Ahn, 2018; Wang et al.
Risk of bias at individual study level We assessed risk of bias based on the information we retrieved from the included study records and additional records on the included studies. For the ten previously unpublished studies presented in the  Virtanen et al. (2018)  systematic review, risk was assessed based on both information provided by  Virtanen et al. (2018)  and information from previous publications on the cohorts and datasets analysed by  Virtanen et al. (2018). This systematic review only included evidence on one eligible outcome: "Acquired depression" (depression incidence). The risk of bias rating for each domain for all 22 included studies for this outcome are presented in Fig. 3. The justification for each rating for each domain by included study is presented in risk of bias tables in Appendix 6 in the Supplementary data.
Compared to the previous systematic reviews, our systematic review and meta-analysis had some added value. First, we published a protocol detailing all methods of the systematic review and meta-analysis before commencing literature search  (Rugulies et al. , 2019). Second, by searching seven academic databases and two grey literature databases, our search was more comprehensive than the searches in the previous reviews. Third, we searched the literature up to July 2018 with a supplementary top-up search of Medline shortly before finalizing the article (November 2019) resulting in the most up-to-date review. We considered all studies included in the previous reviews and identified two additional studies  (Ahn 2018; Zadow et al. , 2019)  not included in the previous reviews. Fourth, we were able to re-analyse six studies  (Ahn 2018; Kim 2013; NLSY OA Cohort, 2019; Wang et al.
Limitations Our systematic review has several limitations. First, we may have missed eligible studies, for example due to them being published in languages other than English. However, as we searched seven academic and two grey literature databases using a comprehensive search strategy, conducted an extensive hand search and consulted additional experts who also did not identify any additional eligible studies, we believe it is unlikely that we missed any important study. 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.
It is conceivable that these variations modify association between working conditions, including exposure to long working hours, and risk of depression  (Dragano et al. , 2011; Lunau et al. , 2013) , yet our data did not allow us to examine such a possible effect modification. Sixth, the subgroup analyses by sex, age and SES could not been performed with all studies, but only with a subsample of those studies that provided estimates stratified by these variables. We cannot rule out that subgroup analyses would have provided different results, had we been able to conduct them with all included studies. Seventh, as in all systematic reviews, we included studies that were conducted in the past. Work environment conditions, including working time arrangements, from the time period of the studies that we included in the review might be different from current or future work environment conditions, and changes might sometimes occur rapidly and to a dramatic extent, for example in the global financial crisis that emerged in 2007  (Torá et al.
Conclusions We did not identify studies providing evidence on the association between long working hours and depression prevalence and depression mortality. Regarding depression incidence, we identified 22 studies. From these bodies of evidence, we are very uncertain regarding the effect of exposure to long working hours on having, acquiring and dying from depression and judged the existing bodies of evidence from human data as "inadequate evidence for harmfulness" for the exposure categories 41-48, 48-54 and ≥55 h/week, compared with 35-40 h/week.
Acknowledgments We thank  Dr Paul Whaley  ( Associate Editor for Systematic Reviews, Environment International ; and  Lancaster Environment Centre, Lancaster University ) and  Professor Tim Driscoll  ( University of Sydney ) for the editorial guidance and support. We thank  Dr Ivan Ivanov  (WHO) and  Nancy Leppink  for their coordination and other support for this systematic review; research librarian  Elizabeth Bengtsen  for her help in developing the search strategy and searching the electronic databases; and  Dr Natalie Momen  ( WHO ) for technically editing the manuscript. Professor Anne H. Garde  was a member of the Working Group for Systematic Review 13 on the effect of exposure to long working hours on depression from 17 October 2017 to 25 March 2020. WHO gratefully acknowledges her participation at the meetings and her contribution to the work of the Working Group.
Differential expression analysis of paired samples for hallmark gene sets showed a significant upregulation of IFN alpha/gamma response, oxidative phosphorylation, and DNA repair pathways after treatment (FDR < 0. 1, Figure  3c , Table  S3 ). Clustering of paired samples based on computational deconvolution of immune cell estimates from bulk expression derived using CIBERSORT  (Newman et al. , 2015)  showed a separation of pre-and post-treatment samples particularly driven by infiltration of CD8+ T cells (Figure  3d, e ). Significant increase in immune infiltration (wilcoxon signed-rank test p<0. 05; Figure  S3d ) and specifically CD8+ T cells was also observed post-therapy from differential analysis of paired pre-treatment and posttreatment samples (FDR < 0. 1, Figure  3f , Table  S3 ). Several clinical and pre-clinical studies have generated bulk or single-cell RNAseq datasets to identify gene sets associated with ICB resistance and T cell dysfunction.
Generation of the molecular dataset Tumor tissue samples from pre-and post-treatment timepoints were obtained from a subset of NSCLC patients treated with PD-1 blockade (n = 29). All samples were processed as FFPE. 16 samples were obtained prior to initiation of therapy (pre-treatment) and 37 samples were obtained at time of acquired resistance. Most patients had a best overall response of CR or PR per RECIST criteria (n = 22, 76%), with a small subgroup with stable disease (SD) less than -10%. Lesionlevel response was obtained for all samples (Figure  S2 ). Pre-treatment lesions were those that had at least a -30% reduction in size on treatment or were resected prior to initiating therapy (n = 4). In patients with resected lesion samples, lesion-level response could not be obtained so overall patient-level must have been CR or PR per RECIST.
Samples were molecularly profiled by microarray-based transcriptome sequencing and/or whole exome sequencing. Expression and exome data were available on the same sample for 28 tumor lesions. The following antibodies were used for the Immunohistochemistry of the clinical : B2M (B2M Polyclonal, DAKO, A0072; RRID: AB_812325) and HLA (HLA-1/MHC-1, Clone: A4, eBioscience, 14-9958; RRID: AB_1210772).
Processing and analysis of microarray data for the clinical cohort Global RNA expression was measured using the human Affymetrix Clariom D Pico assay. The RNA samples quantification on Affymetrix Arrays was performed in two separate batches. Samples from each batch were processed independently using Affymetrix Expression Console Software. Initially the samples were normalized using the SST-RMA algorithm and outlier samples were excluded. Samples from the two batches were then combined together into a single dataset and subject to batch normalization using ComBat. Finally all the samples were further normalized together using LOESS normalization. For genes with multiple measurements, we selected the measurement with the highest coefficient of variation. The data analysis was focussed on 14,668 annotated protein-coding genes with expression measurements in the arrays.
The expression dataset was thoroughly evaluated for technical artifacts such as batch effects (Figure  S3a ). Differential expression analysis was performed using the limma  (Ritchie et al. , 2015)  package in R. Normalized expression data of protein-coding genes were fitted to a linear model using lmFit function and subject to empirical Bayes (eBayes) moderated t-statistics test to identify differentially expressed genes in paired lesions.
Estimation of gene set enrichment scores from expression data Enrichment scores were calculated for gene sets from the normalized expression matrix of protein-coding genes using the GSVA package  (Hänzelmann et al. , 2013)  in R with default parameters except for method = 'ssgsea' and norm = 'TRUE'. This approach was used to estimate enrichment scores for the Hallmark Gene sets (msigdb v6. 1 database  (Liberzon et al. , 2015) ) and non-redundant cancer and immune-related gene sets in literature. Although clinical data for acquired resistance to immunotherapy is fairly limited, recent studies have investigated the impact of chronic ICB treatment in in vitro cell lines and in vivo settings using mouse models generating either bulk or single-cell RNAseq datasets. We manually collated gene sets reported in many of these studies to build an extensive resource of biological processes and gene sets associated with cancer and immune pathways and more specifically ICB resistance (Table  S4 ).
Detection of somatic mutations in tumor samples Mutation calling for SNPs and Indels was performed for each tumor-normal(serum) pair using GATK-Mutect2 (v. 4. 0. 2. 1) with default parameters and additional filters to remove germline mutations including SNPs detected in gnomAD (Genome Aggregation Database)  (Karczewski et al. , 2020)  and mutations in PoN (panel of normal) samples obtained from combining all normal samples in the cohort. Since exome samples were generated in multiple batches with different sequencers and using different capture kits (Illumina's Rapid Capture Exome Kit (38Mb target territory), Agilent SureSelect Human All Exon V2 (44Mb target territory), Agilent SureSelect Human All exon V4 (51MB target territory)), mutations were only called on common regions captured by the three different kits.
Estimation of somatic signatures in tumor exome data Mutational signatures were estimated using the Sigfit  (Gori and Baez-Ortega, 2018)  package in R. For each exome sample, the proportion of mutations associated with each of the 30 mutational signatures in the COSMIC  (Tate et al. , 2019)  database were estimated. Signature 4 corresponds to smoking signature while Signature 13 corresponds to APOBEC signature.
Analysis of selection pressure in mutation data Gene-level selection pressure was quantified for pre-treatment and post-treatment samples using the dNdScv  (Martincorena et al. , 2017)  package in R. The dNdScv approach quantifies dN/dS ratios based on missense, truncations (nonsense and essential splice site) and indel mutations in a group of samples and identifies genes under positive selection in cancer based on the global pvalues derived from likelihood tests. Selection pressure was calculated for each gene for pretreatment and post-treatment samples separately and the difference in selection pressure between the two groups was used to identify potential biologically important genes associated with acquired resistance to ICB treatment. Gene sets with significant change in selection pressure between pre and post-treatment samples were identified via the GSEA  (Subramanian et al.
Phylogeny Tree Reconstruction For 12 patients with both pre and post-treatment WES available, mutations were filtered based on the following criteria: 1) total coverage for tumor ≥10, 2) variant allele frequency (VAF) for tumor ≥4%, 3) number of reads with alternative allele ≥9 for tumor, 4) total coverage for normal ≥7, and 5) VAF for normal ≤1% at a given mutation. These filters applied to all mutations except for mutations in the KRAS gene. Then pre-and post-therapy mutations were aggregated per patient. PhyloWGS  (Deshwar et al. , 2015)  software package (https://github. com/morrislab/phylowgs) was used to infer the clonal structures and estimate clone sizes.
Neoantigen Prediction and Fitness Score Filtered mutations were annotated with snpEff. v4. 3t software  (Cingolani et al, 2012, PMID: 22728672)  with options set as "-noStats -strict -hgvs1LetterAa -hgvs -canon -fastaProt [fasta file name]". All wild-type (WT) and mutant genomic sequences corresponding to coding mutations were translated to an amino acid sequence consistent with the GRCh37 reference genome (GRCh37. 75). Only annotations without "WARNING" or "ERROR" were kept and the most deleterious missense mutation was prioritized in mapping a genomic mutation to a gene. The mutant amino acid from a missense mutation was centered in a 17 amino acids long peptide. Then 9-mers were extracted in a left-to-right sliding fashion. Each mutant 9-mer contained the mutant amino acid on one of the 9 positions.
In essence, one missense mutation produced up to nine 9-mer peptides. Predictions of MHC Class-I binding for both wildtype peptide (P WT ) and mutant peptide (P Neo ) were estimated using the NetMHC 3. 4  (Lundegaard et al. , 2008)  software with patient-specific HLA-I types. All P Neo s with predicted IC50 affinities below 500 nM to a patientspecific HLA-I type were defined as neoantigens. Filtered neoantigens were aligned to the known positive epitopes in the Immune Epitope Database (IEDB, http://www. iedb. org) (Vita et al. , 2019)  for all human infectious disease, class-I restricted targets with positive immune assays using blastp  (Altschul et al. , 1990)  software (https://blast. ncbi. nlm. nih. gov/Blast. cgi). We then calculate the alignment scores with the Biopython Bio. pairwise2 package (http://biopython.
Transcriptomic profiling of anti-PD1 resistant cell lines Three distinct vials of parental CT26 cells (ATCC; 'experimental replicates'), two independently isolated tumors from '2nd round' mice, and four independently isolated tumors from '4th round' mice (both 'biological replicates'), were cultured +/-20 ng/ml of mouse IFNγ (Biolegend) for 24 hours at 37 o C/5%CO2. The following day, RNA was isolated from cells using Qaigen RNAeasy reagents according to manufacturer's instructions, including QiaShredder homogenization and on-column DNaseI digestion. Isolated RNA was sent to Genewiz (www. genewiz. com) for library generation, RNA-sequencing, and data processing. Briefly, sequencing libraries were generated and sequenced on an Illumina HiSeq (2x150 paired end reads), targeting >20x10 6 reads per sample.
Sequences were trimmed using Trimmomatic v. 0. 36  (Bolger et al. , 2014)  and mapped to Mus Musculus GRCm38 reference genome using STAR aligner v. 2. 5. 2b  (Dobin et al. , 2013). Unique gene hits were calculated by using featureCounts from the Subread package v. 1. 5. 2. Only unique reads that fell in exonic regions were counted. The Transcript Per Million (TPM) values were obtained for each protein-coding gene and subsequently log-transformed (log2(TPM +1)) for downstream analysis. Mouse orthologs of genes in hallmark gene sets and Antigen Processing Machinery  (Şenbabaoğlu et al. , 2016)  and regulons of IRF1 and STAT1 were identified using Ensembl 87  (Howe et al. , 2021)  and single sample gene set enrichment analysis was performed in a similar fashion as described for the clinical cohort.
MDH. and this research are supported in part by the Damon Runyon Cancer Research Foundation (grant CI-98-18), the Memorial Sloan Kettering Cancer Center (support grant/core grant P30 CA008748), Stand Up to Cancer (SU2C)-American Cancer Society Lung Cancer Dream Team Translational research grant (SU2C-AACR-DT17-15), and by The Mark Foundation for Cancer Research (Grant # 19-029 MIA). SU2C is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. DM was a fellow of the joint EMBL-EBI & NIHR Cambridge Biomedical Research Centre Postdoctoral (EBPOD) program when part of this work was conducted. MLM was supported by a Cancer Research UK core grant (C14303/A17197). Pact Pharma; Regeneron; Shattuck Labs; Syndax; as well as equity options from Arcus, Factorial, Immunai, and Shattuck Labs.
Acknowledgements We thank  Natasha de Manincor  and the students that helped with field work and data collection. We thank  Patrick Lhomme ,  Silvio Erler  and two anonymous reviewers for providing useful comments on the manuscript. MG conceived the study; AF, GB, LB and MG collected data and determined bumblebees; AF and FM analysed data; AF and GB wrote the first draft and all authors contributed to the final version of the manuscript. Version 1. 2 of this preprint has been peer-reviewed and recommended by  Peer Community In Zoology  (https://doi. org/10. 24072/pci. zool. 100009).
I. INTRODUCTION In many countries, funded collaborative projects involving academic partners and industrial ones, either SMEs or large enterprises, are a preferential way of implementing ambitious Research and Innovation Actions (RIAs)  [1] ,  [2]. Such projects are also frequently used in order to foster international collaborations and to develop corresponding long-term partnerships between organizations from various countries. This is notably the case in Europe, where the European Commission has several active funding bodies proposing various kinds of funding programmes targeting different societal, economical and scientific grand challenges  [3]. In the context of such collaborative projects in the Software and Systems Engineering area, possibly important in terms of the number and variety of involved partners and countries, the main expected results are generally large and complex This work has received funding from the ECSEL Joint Undertaking under grant agreements No.
737494 (MegaM@Rt2 project) and No. 101007350 (AIDOaRt project), from ITEA3 (REVaMP2 project No. 15010) and from H2020 under grant agreements No. 732064 (DataBio project) and No. 957212 (VeriDevOps project). integrated frameworks or tool sets. In order to allow for their actual design, development and deployment, it is thus fundamental to be able to support and manage as efficiently as possible the corresponding Requirements Engineering (RE) processes  [4] , from the initial identification of the industrial needs to their actual realization within the final innovative solutions. As it can be observed in the literature, the state-of-art in RE is already rich in terms of approaches and corresponding technical solutions  [5]. Among other paradigms, some of these approaches rely on model-based concepts  [6].
This notably reflects the fact that model-based principles and techniques have become more popular in industry over the last two decades, as they can provide relevant abstraction, genericity or reusability capabilities (for example)  [7]. In this paper, we report on our practical experience of proposing and applying a Model-based Requirements Engineering (MBRE) approach and language during 5 years in the context of three different large European collaborative projects, each one of them providing as a result various complex software solutions (e. g. frameworks, integrated tool sets, etc. ). The approach was mainly developed by the SOFT-EAM company and applied in 3 different research projects in which SOFTEAM participated in the technical coordination activities. With this reporting and data analysis work, we notably intend to show that such a MBRE approach can bring interesting benefits in terms of scalability (e.
IV. PROPOSED APPROACH As a solution to the problem of covering complete RE processes in the context of large collaborative projects involving many different kinds of partners, we propose a MBRE approach accompanied by a dedicated modeling language and the corresponding tool support. We give an overview of this approach in Section IV-A and we describe the corresponding RE language we designed in Section IV-B. In Section IV-C, we also provide more details on how these approach and language have been concretely implemented and supported in the Modelio tool.
g. Eclipse RCP, a Java virtual machine, etc. ). A given component can be connected to different nodes, i. e. its deployment platforms. 2) Concrete Syntax: Attached to the abstract syntax of our language as introduced just before, various kinds of concrete notations could be envisioned, either graphical, textual or combining both. Based on our own experiences, and notably the industrial experience of SOFTEAM when using Modeliobased solutions for their customers' projects, we decided to consider a combination of tabular views and UML/SysMLlike diagrams as the syntax for our RE language. In terms of diagrams, a SysML Requirements diagram is used to graphically map the different tool components to their respective tool requirements/purposes and then to their corresponding case study requirements.
These tool requirements are grouped in a same requirements container named "Modelio (SOFT)" (cf. left panel). On the related diagram (cf. bottom view), we can see how a given Modelio tool requirement "MODELIO-030" is traced to some framework requirements "SYS-020201", etc. and then, by transitivity, to some case study requirements "NOK-02", etc. We can also see that a given tool component "Modelio (SOFT)" satisfies a corresponding tool requirement "MODELIO-030". 3) Semantics: As partially extending UML core concepts, the semantics of our RE language is directly connected to the semantics of UML. For instance, we consider the concepts of Package, Component, Interface, Node and various types of links as having the same meaning that prescribed by UML. However, in order to make the models universally understandable and reusable by other persons relying on our RE language, we limited their usage to the strict definitions provided in Section IV-B1.
C. A Modelio-based Implementation of the RE Approach and Language The complete MBRE approach and language presented in this paper have been implemented in the Modelio tool  [34]  developed by SOFTEAM. In the DataBio project, we first experimented with extending/refining ArchiMate as the highlevel representation for architecture. However, due to the constraints imposed by the ArchiMate metamodel, collaborative modeling was quite tedious in Modelio. For example, the locking of a single element was leading to the locking of a large portion of the model, possibly impacting other users. Moreover, ArchiMate is quite new and much less adopted in the industry. This would have resulted in a steeper learning curve, such a limitation being partially reflected in the survey data presented in Section V-B.