dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study was supported by the  European Space Agency  through Contract No. 4000112677/14/NL/MP. The authors would like to thank all the collaborators of the project, in particular  Baptiste Battelier ,  Andrea Bertoldi ,  Sven Herrmannn ,  Merle Cornelius ,  Naceur Gaaloul ,  Dennis Becker ,  Ernst Rasel  and  Norman Guerlebeck. Comments  and feedbacks from Christian Siemes were greatly appreciated. K. Douch ,  J. Müller ,  C. Schubert  and  H. Wu  also acknowledge the  Collaborative Research Center (SFB)   1128  geo-Q, funded by the  Deutsche Forschungsgemeinschaft (DFG). C. Schubert  would.
Making reproducible, auditable and scalable data-processing analysis workflows is an important challenge in the field of bioinformatics. Recently, software containers and cloud computing introduced a novel solution to address these challenges. They simplify software installation, management and reproducibility by packaging tools and their dependencies. In this work we implemented a cloud provider agnostic and scalable container orchestration setup for the popular Galaxy workflow environment. This solution enables Galaxy to run on and offload jobs to most cloud providers (e. g. Amazon Web Services, Google Cloud or OpenStack, among others) through the Kubernetes container orchestrator.
Additionally, the development of container orchestrators enables the coordinated execution of a set of containers to host long-running services (e. g. , database, web server and application server working together in separate containers) and batch jobs, acting as a clusters for containers. Kubernetes (kubernetes. io), the current de-facto container orchestrator solution, executes container-based work-loads in scalable infrastructure on many different cloud providers. The Galaxy workflow environment  (Afgan et al. , 2018)  allows researchers to create, run and distribute bioinformatics workflows in a reproducible manner. Recent advancements in Galaxy simplified tool dependency resolutions through Bioconda  (Grüning, Dale, et al. , 2018)  and Biocontainers (da Veiga  Leprevost et al. , 2017).
These have paved the way for the use of container orchestration systems in the cloud with Galaxy. Deploying software on cloud infrastructure normally requires specialised expertise that is foreign to most bioinformaticians. Moreover, each cloud provider implements different mechanisms, further complicating the matter. In the Galaxy context, CloudMan  (Afgan et al. , 2012)  was the first solution available to deploy Galaxy on Amazon Web Services (AWS), but support for other clouds requires extensive development efforts. Here we introduce Kubernetes-based container orchestration and provisioning for Galaxy. Kubernetes acts as an abstraction layer between Galaxy and the different cloud providers, allowing Galaxy to run on every cloud provider that supports Kubernetes (>10 cloud providers currently).
ACKNOWLEDGEMENTS PM, LP, PRM, SH, AL, DS, CR, DJ, PRS, RW, RMS, NK, OS, and SN were supported by the  European Commission 's  Horizon 2020 program  grant  65424  "PhenoMeNal", and acknowledge the leadership of  Christoph Steinbeck ;  CONTRIBUTIONS Design  and envisioned Galaxy-Kubernetes integration: PM; Code contributions Galaxy Kubernetes Runner: PM, LP, PR, MVB; Write Galaxy Helm Chart: PM, LP, NG, EA, SH, AL; Contribute to docker-galaxy stable: BG, PM, LP, SN; Manuscript writing: PM, LP, PRS; Galaxy Kubernetes testing: PM, LP, PR, CR, DS, DJ, PRS, RW, RM, YP, SN; Integration Kubenow: AL; Work package lead: PRS, OS, SN; Galaxy expertise guidance: BG; Project Manager Organize hackathons. : NK; Biocontainer interface: YP; Galaxy-Kubernetes Single Cell: PM, IP.
In addition, the displacement versus voltage nonlinear relationship correlation with experiment was qualitatively, but not quantitatively, good. It is worthy to mention that unrealistic assumptions were retained for the materials, like isotropic behaviour for a piezoceramic material and nil Poisson's ratio for all materials. The present author thinks that this may explain, at least partially, the bad correlations with experiments from  [41]. Late in the previous decade, a simple phenomological model of vibrating domain wall has been implemented successfully, within the commercial FE code ANSYS®, using the latter's linear constitutive equations in order to consider the contribution of domain wall motion to the electromechanical properties of functionally graded cantilever piezoceramic bimorph actuators  [42].
To run the CRISPOR/CRISPResso containers, we suggest assigning Docker 6-8 GB of RAM. For analysis involving large sequences or many sites, it may be necessary to increase the amount of allocated memory under the 'Settings…/ Advanced' panel (Supplementary Fig. 1b ). The execution may halt with an error if enough memory is not allocated to the container (e. g. , with ' /bin/bash: line 1: 14 Killed/ crisporWebsite/bin/Linux/bwa bwas w'). In this instance, the memory allocated to the Docker container is not sufficient to run CRISPOR, and the process is terminated. Therefore, increase the memory as discussed above and run the same command again.
vi. Copy and paste the gene list into the entry field. Genes should be entered as official gene symbols, with only one entry per line. Refer to the HUGO Gene Nomenclature Committee (HGNC) (https://www. genenames. org/) and Mouse Genome Informatics (MGI) (http://www. informatics. jax. org/) for official gene symbols for human and mouse, respectively. ▲ CRITICAL STEP This entry is case-insensitive because the libraries are already species-specific. If an entry is not found in the database of genome-wide sgRNAs, sgRNAs for the entered gene will display a warning and will not be included in the output. ? TROUBLESHOOTING vii. Click on 'Submit' for gene-targeted library generation and download the output library. (C) sgRNA design using command-line CRISPOR • TIMING 1-4 h i. Type the following command into a terminal to download the latest version of the Docker container for this protocol: docker pull pinellolab/crispor_crispresso_nat_prot ii.
? TROUBLESHOOTING (C) Analysis of deep-sequencing data using CRISPR esso with Docker • TIMING 10-120 min i. Gather the required information and files as in Step 83B(i). ii. Assuming that the FASTQ files reads1. fastq. gz and reads2. fastq. gz are stored in the folder /home/user/amplicons_data/, run the command After the execution of the command, a new folder with all the results will be created; in this example, the folder will be in /home/user/amplicons_data/ CRISPResso_on_BCL11A_exon2. A summary of the different events discovered in the sequencing data is presented in the Alleles_frequency_table. txt file, and in several illustrative plots (. pdf files). Refer to the online documentation for the full description of the output: https://github. com/pinellolab/CRISPResso.
CRISPR esso analysis using CRISPRessoPooled • TIMING 20 min-4 h ▲ CRITICAL CRISPRessoPooled is a utility for analyzing and quantifying targeted sequencing CRISPR experiments involving sequencing with pooled amplicons. To use CRISPRessoPooled, it is necessary to use the command-line version of CRISPResso (see Step 83B). Although CRISPRessoPooled can be run in different modes (amplicon only, genome only, and mixed), in this protocol, we use the most reliable mode for quantification, called 'mixed mode'. In this mode, the amplicon reads are aligned to both reference amplicons and the genome to discard ambiguous or spurious reads. For more details regarding the different running modes, consult the online help (help is located below the list of files at the following link): https://github. com/pinellolab/CRISPResso.
Trimming of adapters (--trim_sequences) (optional; Step 83) This parameter enables the trimming of Illumina adapters with Trimmomatic (default: False). For custom adapters, the user can customize the Trimmomatic execution using the parameter --trimmomatic_options_string (check the Trimmomatic manual for more information on the different flags and options: http://www. usadellab. org/cms/ uploads/supplementary/Trimmomatic/TrimmomaticManual_V0. 32. pdf)  90. It is important to check with your sequencing facility to determine whether the reads were trimmed for adapters or not. If this is not possible, we suggest using the software FASTQC to determine whether reads were already trimmed (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/). To perform this analysis, open a terminal and type fastqc.
This command will open the main software window. After opening the FASTQ file(s) to analyze with FASTQC, a graphical report will be automatically generated. In the graphical report, find the section 'Adapter content'. If the reads are not properly trimmed, the adapter used will be reported. If a custom adapter was used, find the section 'Overrepresented sequences' instead, where all the sequences that are present in more than 0. 1% reads are reported. A subsequence present in more than 10% of the reads is usually an indication that the reads were not properly trimmed. An example of FASTQC report is available here: http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/RNA-Seq_fastqc. html#M9.
Acknowledgments M. C. C. was supported by a  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)  Award (F30DK103359). M. H. was funded by  National Institutes of Health (NIH)/National Human Genome Research Institute (NHGRI)  grant  5U41HG002371-15  and  NIH/National Cancer Institute (NCI)  grant  5U54HG007990-02  and by a grant from the  California Institute of Regenerative Medicine ,  CIRM GC1R-06673C. D. E. B. was supported by the  National Heart, Lung, and Blood Institute (NHLBI)  ( DP2OD022716 ,  P01HL032262 ), the  Burroughs Wellcome Fund , and a  Doris Duke Charitable Foundation Innovations in Clinical Research Award. S. H. O. was supported by an award from the  NHLBI  ( P01HL032262 ) and an award from the  NIDDK  ( P30DK049216 ,  Center of Excellence in Molecular Hematology ).
1-cm gap (Bio-Rad, cat. no. 1652089) Penicillin-streptomycin (Life Technologies, cat. no. 15140122) Plasmids/plasmid preparation • lentiGuide-Puro (Addgene, plasmid ID 52963; ref. 19) • Solid glass beads (Fisher Scientific, cat. no. 11-312-10B) • CRISPResso command-line software (https://github. com/pinellolab/ can be stored at 4 °C for years. • • • • • • 20% (wt/vol) sucrose solution-Dissolve sucrose in PBS to create a 20% (wt/vol) Fast Digest Esp3I (Thermo Fisher, cat. no. FD0454) NEB stable competent E. coli (high efficiency) (New England BioLabs, cat. no. C3040H) lentiCas9-Blast (Addgene, plasmid ID 52962; ref. 19) Corning Falcon bacteriological Petri dishes with lids (Fisher Scientific, cat. no. instructions and troubleshooting advice for CRISPResso. pXPR_011 (Addgene, plasmid ID 59702; ref.
A C C E P T E D M A N U S C R I P T Highlights 1. Even though one every fourteen patients suffering from bipolar disorder reported violent criminal behavior, the association with violent criminality was not significant in comparison with the general population. 2. The chance of committing violent criminal behavior was smaller in patients with bipolar disorder than in those suffering from psychotic disorders but higher in comparison with patients with depressive disorders. 3. In meta-regression analyses, no significant moderators emerged. The identification of potential moderators of the association between bipolar disorder and violence should be the focus of further research. Methods: A systematic review of literature from January 1st, 1980 through January 16th, 2017 from 3 electronic databases (MEDLINE/PubMed, EMBASE and PsycInfo), following the PRISMA and the MOOSE statements.
Original peer-reviewed studies reporting data on VCB in BD were included. A random-effects meta-analysis was performed. Potential sources of heterogeneity were examined through subgroup and meta-regression analyses. The protocol was registered in PROSPERO, CRD42017054070. Results: Twelve studies providing data from 58,475 BD participants. The prevalence of VCB in BD was 7. 1% (95%CI=3. 0?16. 5%; k=4). The association of BD and VCB compared to general population was not significant (OR=2. 784; 95%  CI, 0. 687?11. 287, P=. 152). The association was significant only in cross-sectional studies, in studies in which VCB was assessed through self-reported measures, and in studies conducted in the USA. BD was more likely to be associated with VCB when BD patients were compared to controls with depressive disorders, whilst it was found to be less associated with VCB when BD was compared to psychotic disorders.
Methods This systematic review and meta-analysis adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)  (Moher et al. , 2009)  and the MOOSE (Meta-analyses Of Observational Studies in Epidemiology)  (Stroup et al. , 2000)  statements. The protocol was prospectively registered in PROSPERO (CRD42017054070). Two investigators (NV and IP) independently performed the literature search, title/abstract screening, full-text review, data extraction and methodological quality assessment. A third investigator was consulted whenever a consensus could not be achieved (AM).
g. minor violent acts excluded in France) ("European Sourcebook of Crime and Criminal Justice Statistics -2010," 2010), age of the offender (overall in Europe, limiting comparison even between member Countries)  (Aebi et al. , 2014). In the United States of America (USA), two main crime databases report on VCB. In the Federal Bureau of Investigation's Uniform Crime Reporting (UCR) VCB are defined as those offenses involving force or threat of force, namely murder and nonnegligent manslaughter, legacy and revised rape, robbery and aggravated assault ("Uniform Crime Reporting Statistics," 2017). The Bureau of Justice Statistics's National Crime Victimization Survey (NCVS) provide measures for non-fatal violence reporting on rape and sexual assault, robbery, and aggravated and simple assault ("Bureau of Justice Statistics," 2017).
Data extraction A standardized extraction platform was developed in Microsoft Access 2010  ("Microsoft Access 2010, Microsoft Corporation, Redmond, Washington, USA," n. d. ). For each study, we extracted the prevalence of VCB in participants with BD (see Appendix S2 for the complete list). The full contingency tables to estimate crude ORs were also extracted, considering the general population controls without psychiatric disorders and controls with any psychiatric disorder other than BD (including severe mental illness, depression, anxiety disorders, psychotic disorders, alcohol or drug abuse disorders, or personality disorders). The type of assessment (record-based vs. selfreport) was also coded.
, 2011). Inconsistency across studies was estimated with the I 2 metric  (Higgins et al. , 2003). Evidence of publication bias was assessed with funnel plot graph and the Egger's regression test  (Egger et al. , 1997). When evidence of publication bias was observed, ES estimates were adjusted with the trim-and-fill procedure  (Fazel et al. , 2007). Potential sources of heterogeneity were explored with subgroup and metaregression analyses. The specific subgroup analyses considered a priori were reported in the Appendix S3. Meta-regression analyses were conducted when data from at least five independent datasets were available.
Results The database search generated 1,031 hits, 46 articles were identified after searching the references of included articles. After duplicates removal, the title/abstracts of 773 references were screened for eligibility; of those, 558 were excluded. Full-texts of 192 references were scrutinized in detail for eligibility, with 180 excluded (see Table  S1  in the supplementary material for reasons), and 12 references included for systematic review and meta-analysis (see flowchart in Figure  1 , characteristics in Table  S2 ). Overall, data from 9,020,778 participants (58,475 patients with BD, 8,962,303 general population controls and 231,587 patients with any psychiatric disorder) were included. Participants presenting any VCB were 91,387. Studies have followed cross-sectional (k=4), case-control (k=3), or prospective (k = 5) designs.
Acknowledgements/Funding/Support: The authors thank the support of the  Spanish Ministry of Economy and Competitiveness  integrated into the  Plan Nacional de I+D+I  and co-financed by the  ISCIII-Subdirección General de Evaluación  and the  Fondo Europeo de Desarrollo Regional (FEDER) ; the  Instituto de Salud Carlos III  that supported this work through several grants (" Miguel Servet II " post-doctoral contract and a FIS ( PI15/00588 ) to FC and a " Río Hortega " contract ( CM17/00258 ) to NV); the  CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental) ; the  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement  ( 2017 SGR 1365 ) and the  CERCA Programme / Generalitat de Catalunya. We thank all the authors of the included papers and study participants.
Declaration of interest form Dr. Norma Verdolini  is funded by the  Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III  through a " Río Hortega " contract ( CM17/00258 ) and reports no competing interests. Dr. Isabella Pacchiarotti  has received CME-related honoraria, or consulting fees from ADAMED,  Janssen-Cilag  and  Lundbeck. Dr. Cristiano A. Köhler  is the recipient of a postdoctoral fellowship award from the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Brazil). received grants, honoraria, or consulting fees from  AstraZeneca ,  Bristol-Myers Squibb, Janssen-Cilag, Lundbeck ,  Otsuka ,  Sanofi-Aventis , and  Takeda. Dr. Francesc Colom  is funded by the  Spanish Ministry of Economy and Competitiveness ,  Instituto Carlos III , through a " Miguel Servet II " post-doctoral contract and a FIS ( PI15/00588 ).
Extraction of study characteristics and description of PROM characteristics PH and KH independently extracted data on the characteristics of the studies (number, age and gender of participants, study setting and language). Characteristics of the PROMs (e. g. , items, scoring, feasibility and availability) were obtained by PH from the questionnaires, background literature, user manuals or European League Against Rheumatism (EULAR) Outcome Measures Library  [16]  or by contacting authors/copyright holders.
Reporting the results of the evidence synthesis As described by OMERACT  [9] , the COSMIN & COMET collaboration[17] and the Food And Drug Administration (FDA)  [20]  guidelines, evidence on validity (especially content validity) and reliability should be prerequisites for an instrument to be considered for further evaluation/application. If an instrument does not measure what it intends to or produces unreliable estimates, it is irrelevant to test for e. g. , responsiveness. Thus, in the result section of this systematic review, we have chosen to highlight the 'candidate' instruments per COS domain that have at least limited evidence on reliability and validity and no evidence for any poor measurement properties. The main evidence synthesis includes all studies of a PROM but conflicting evidence on measurement properties across language versions is described for 'candidate' PROMs.
[14] Literature search A research librarian (EMB) and the first-author (PH) performed a systematic search in MEDLINE via PubMed from 1966, EMBASE via OVID from 1974, and PsycINFO via OVID from 1806, all to 1 January 2017. The search was designed to identify all types of outcome measurement instruments in PsA. The search was limited to humans and consisted of two overall terms: (1) Target population: MeSH subheadings and free text words in title/abstract (ti/ab) were combined by the Boolean operator 'OR' to search for the target population (PsA) in the databases; (2) Measurement properties: Search filters have been developed to.
ACKNOWLEDGEMENT:The study was financially supported by the Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, DK, Danish Rheumatism Association (R124-A3278-B984), and Department of Rheumatology, Rigshospitalet, Gentofte Hospital, DK. The Parker Institute, Bispebjerg and Frederiksberg Hospital is supported by a core grant from the Oak Foundation (OCAY-13-309). None of the funding sources had any influence on the study design, on data collection, data synthesis, data interpretation, writing the report, or the decision to submit the manuscript for publication.
5 ). Available SMB observations  (Machguth et al. , 2016; Bales et al. , 2009)  are sparse, especially in the centre of the ice sheet, and have heterogeneous temporal coverage. However, comparison against those observations allows for a first-order evaluation of the SMB inputs chosen or produced by the modellers. Overall, positive SMB is better represented in the chosen SMB data sets than negative SMB. The order-of-magnitude difference in  root mean square error (RMSE) between the two measures is partly explained by the relatively low accumulation over a large area in the centre of the ice sheet, compared to relatively high ablation over a narrow marginal zone, which is easily misrepresented in models with too large an ice extent. While the best match with observations in both regions is produced by models using SMB derived from RCMs, good agreement with the observed SMB can also be found for some models using PDD.
The scaling of the data sets is determined based on the Greenland temperature index in the SeaRISE Greenland data set (based on ice core data; source SeaRISE reference data set: Green-land_SeaRISE_dev1. 2. nc). Temporal evolution of the sea level is also taken from the same SeaRISE Greenland data set. The ensemble of runs (PISM1, PISM2, PISM3, PISM4, PISM5) differs in the forcing applied to the GrIS. In all cases the forcing source is based on the ERA-Interim reanalysis covering the period 1979-2012. The only differences are the applied PDD factors for the determination of the SMB via PDDs. The following enumeration lists the applied different PDD factors: PISM0: PDD_snow = 0. 012 m C -1 day -2 , PDD_ice = 0. 018 m C -1 day -2 ; PISM1: PDD_snow = 0. 010 m C -1 day -2 , PDD_ice = 0. 016 m C -1 day -2 ; PISM2: PDD_snow = 0.
B14 MPIM-PISM A spin-up over one full glacial cycle (135 kyr BP to present) is performed, where model parameters are changed at 20 kyr BP. It is faster to start from a pre-spun up state at 20 kyr BP than to re-run the full glacial cycle for each parameter change. The spin-up first goes through one complete glacial cycle using a linear combination of MPI-ESM output. The scaling of the two data sets is determined based on the Greenland temperature index in the SeaRISE Greenland data set (based on GRIP data). Sea-level changes are also taken from the SeaRISE Greenland data set.
g. Snyder, 1987). Some of the variables may not be applicable for each model, in which case they were omitted. Please note that, in order to facilitate comparison with observational data sets and for consistency with future ISMIP6 activities, all model output data from initMIP-Greenland published to public archives have been conservatively interpolated to a new diagnostic grid. The grid uses EPSG:3413, a polar stereographic projection with standard parallel at 70 • N and a central meridian of 45.
Heiko Goelzer  has received funding from the programme of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Dutch Ministry of Education, Culture and Science (OCW)  under grant no. 024. 002. 001. Nicholas R. Golledge  is supported by  Royal Society of New   Zealand Rutherford Discovery Fellowship   15-VUW-004. Philippe Huybrechts acknowledges support from the  iceMOD  project funded by the  Research Foundation -Flanders (FWO-Vlaanderen). William H. Lipscomb  and  Joseph H. Kennedy  were supported by the  Regional and Global Climate Modeling and Earth System Modeling programmes of the U. S. Department of Energy's Office of Science. The  National Center for Atmospheric Research  is sponsored by the  National Science Foundation. Victoria Lee ,  Stephen L. Cornford , and  Antony J.