dataesr/scientific-paragraphs-categorization huggingface dataset:
• threadpools and processpools implementations • DISET, the heart of the DIRAC communication protocol dip, provides implementation for: -Securing sockets with SSL -marshalling and un-marshalling, done through a tiny DIRAC specific library called DEncode DIRAC implements within DISET its own RPC protocol, which can be summarized very briefly as Python sockets, secured with SSL, and using DEncode for the marshalling. It is an extremely light protocol that maximizes speed and was preferred, at the time, to the XMLRPC implementations that were native of Python. Most of the DISET functionalities have been developed long time ago, but nowadays all the aforementioned libraries are available and maintained elsewhere. Currently the adaptation of DIRAC code to fully use the standard Python libraries is ongoing.
This under-the-hood work takes long time and will significantly reduce DIRAC's codebase. DIRAC also started exploratory studies towards using HTTPS (instead of dips), to allow usage of standard libraries. HTTPS is widely used, it has a big community with a low risk of security breach, and for which it is easy to get help. Many conventions already exists to send data (like JSON or multipart/form-data), and frameworks already exist in python 2 and 3 for server-side (e. g. Tornado) and client side (e. g. requests  [10] ). For what regards the aspects of authentication (and partially of authorization), DIRAC currently relies on openSSL  [11]  and x509 certificates. DIRAC makes use of a quite thin layer on top of openSSL, named pyGSI 2  , to create proxy certificates and secure the RPC calls. This package has been created by DIRAC developers long time ago, and it is part of DIRAC's external packages bundle.
DIRAC and orchestrators Up to now we talked about scalability from the pure DIRAC software point of view, while we did not yet discuss how orchestrator software could be used for administering DIRAC's components. We made an extensive trial to use them, and achieved partially satisfactory results: on one side, high availability, scalability and ease of administration are achieved. However, a lot of training to maintain the underlying infrastructure is needed, some methodologies might need to be adapted, and client traceability is partially lost at the level of the application  [12]. We will go back to this topic once some of the developments discussed above will be completed.
Dataset growth The vast majority of DIRAC relational databases have been implemented using MySQL as a backend. At the same time DIRAC can also interact with Oracle, and within LHCbDIRAC we have implemented the Bookkeeping database within Oracle. We will keep using these RDBMS solutions, and we do not see any need to integrate more; recently, new players came on the market (e. g. cockroachDB 5  , crate. io 6  , or clustrix 7  are just some of them) but in our humble opinion it is too early to start thinking about investing time in exploring these alternatives. We have at the same time introduced ElasticSearch 8  as a NoSQL Database and search engine for some specialized purposes e. g. for monitoring.
Maintainability We believe that the scalability issue cannot be properly addressed if the system, and the software, are not easily maintainable. Throughout these years we invested into applying industrystandard software engineering practices that help developing and maintaining DIRAC. Recognizing the importance of the fact that DIRAC is an open source software used by several communities with different goals and computing models, we invested into having code that is easily extendable, both horizontally and vertically  [13].
Testing, DevOps and Continuous Delivery Writing a good test suite and a testing process is as much work as the development of the system itself, and we believe that an extensive testing phase, for a project like DIRAC, is an absolute must. For this reason, in the past few years we invested in the developement of what we called the "certification process": the certification is a lengthy, slowly automatized process of checking the quality of DIRAC software. It consists of static code analyses, unit tests, integration tests, regressions tests, system tests, and performance tests. The certification process is applied to each DIRAC minor and major releases, and it is paying off in terms of software quality and correctness. While we use and apply continuous integration techniques, we are still far from adapting the Continuous Delivery approach.
LHCb User analysis for the LHCb upgrade Everything that is done for LHCbDIRAC will also benefit LHCb user analysis. The Ganga tool  [14]  is the software of choice for LHCb users-driven analysis jobs. Ganga is a frontend to users' jobs submission, and relies on LHCbDIRAC for every interaction with LHCb distributed computing. Software maintenance, and continuous users feedback is needed for adapting to LHCb users needs in the upgrade era, and possible changes in DIRAC APIs and in the LHCb core software. Working Group productions  [15]  are encouraged as a good approach for doing analysis on the Grid where requests, coming from the working groups, are managed by the central production team. Working Group productions do not eliminate fully the need for private analysis jobs on the Grid, but are thought as a better organized way of running large sets of analysis jobs.
Individual users will still need to submit jobs to test their code before going for centralized productions, or even to launch their private production (even though the latter will be unpractical, due to the expected size of the datasets). The Ganga tool was used in Run 2 for that, and will be used in Run 3 as well. The possibility of slimming down Ganga by keeping only the features relevant to LHCb should be explored, in order to overcome possible scalability bottlenecks that might appear in case of large user productions.
Conclusions LHCb uses DIRAC for all its distributed computing activities, and will keep using it for Run3 and beyond. Many other communities use DIRAC for managing their own distributed computing processes, and some of them contribute to DIRAC's codebase as active software developers. Still, LHCb maintains a large fraction of the DIRAC code, and it is the community that stresses its capabilities the most. The LHCb upgrade should be seen not only as a challenge, but also as an opportunity to furher develop and improve DIRAC. Our focus is on usability, flexibility and scalability. We believe that DIRAC has already proven, within the past years, to be a flexible tool, capable of satisfying the needs of a growing number of users. This contribution focuses on scalability: we address it considering traffic and dataset growth, and maintainability.
Such a methodological context may limit the clinical impact of the report data. The recent recommendations from the PAMM group of the EORTC point on the necessity to apply strict rules as concerns clinical pharmacogenetics  (Robert et al. 2014 ). This includes the studied population with the number of studied cases, the assessment of diagnosis and treatments received. The recommendation also included the analyzed polymorphisms with mentioned to be made to an easy identification in the main databases. The authors also pointed to the applied statistical methods with a clear references to the Bonferroni correction, for instance. Autoimmune diseases are characterized by inflammation and tissue damage largely attributable to general deregulation of immunity cells  (Chen et al. 2018). In this respect, similar mechanisms of cell immunity deregulation can be the origin of autoimmune diseases and of excessive reactivity conferred by immunotherapy by CPI.
HLA genotype was also recently investigated regarding a possible link with response to CPI-based treatment  (Chowell et al. 2018). In this study, the authors effectively characterized the sets of patients according to the type of CPI and tumor location. In brief, the study covered a group of 1535 advanced patients on whom HAL-I genotyping was performed. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival. In contrast, the HLA-B62 genotype was associated with poor outcome. While these reported data may have potential implications for predicting response to CPI, the genetic complexity of the HLA system is such that an easy and generalizable germinal genetic-based tool is difficult to design on the currently available data.
Personal implication We recently applied a global germinal immunogenetic approach in an attempt to predict treatment outcome (toxicity and response) in patients under CPI  (Refae et al. 2018; Refae et al. 2019). The setting of potentially relevant SNPs was based on an extensive literature search for genes implicated in immune reaction, immunotherapy response and autoimmune diseases (Figure  1 ). Candidate SNPs with minor allele frequency of ≥ 5% in Caucasians according to SNPpedia (http://www. snppedia. com) and Ensemble databases (http://Ensemble. org) were selected. This led to the constitution of a custom panel of 86 genes and 166 associated SNPs. High-throughput genotyping of germinal DNA was performed by MassArray (Agena Bioscience ® ). In a group of 48 patients with NSCLC  (Refae et al.
Samples were collected from 53 non-diseased tissue sites across nearly 1000 individuals, primarily for molecular assays including WGS, WES, and RNA-Seq. The GTEx Portal provides open access to data including gene expression, QTLs, and histology images. REGULOMEDB http://www. regulomedb. org/index RegulomeDB is a database that annotates SNPs with known and predicted regulatory elements in the intergenic regions of the H. Sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of transcription factors, and promoter regions that have been biochemically characterized to regulation transcription. Sources of these data include public datasets from GEO, the ENCODE project, and published literature. SNIPMIR http://www. genomique. info:8080/merge/index?action=MISNP Tests the gain/loss of microRNA binding induced by a SNP.
Samples were collected from 53 non-diseased tissue sites across nearly 1000 individuals, primarily for molecular assays including WGS, WES, and RNA-Seq. The GTEx Portal provides open access to data including gene expression, QTLs, and histology images. REGULOMEDB http://www. regulomedb. org/index RegulomeDB is a database that annotates SNPs with known and predicted regulatory elements in the intergenic regions of the H. Sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of transcription factors, and promoter regions that have been biochemically characterized to regulation transcription. Sources of these data include public datasets from GEO, the ENCODE project, and published literature. SNIPMIR http://www. genomique. info:8080/merge/index?action=MISNP Tests the gain/loss of microRNA binding induced by a SNP Graphical abstract.
We have developed a new statistical approach (M 3 Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of 5 the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts.
Therefore we expect our work to significantly improve forthcoming GBD estimates, yielding a more accurate understanding of ozone exposure and its global health effects. Section 2 provides details of the data sources and fusion process, including the techniques to register all data sources onto a common grid, and a statistical model to minimize the difference between interpolated observations and the multi-model combination. In Section 3 the results of employing these techniques are presented, including the mapping accuracy, evaluation of regional model performance and the final multi-model bias correction. The paper concludes with a summary and discussion in Section 4. 2 Data and Method 2. 1 Observations and model output 1. Tropospheric Ozone Assessment Report (TOAR) database: In this analysis, surface ozone observations are used to evaluate the performance of 6 global atmospheric chemistry models and to also bias-correct the multi-model surface ozone product.
TOAR has produced the world's largest database of surface ozone metrics based on hourly observations at over 9000 sites around the globe  (Schultz et al. , 2017 , available for download at: https://doi. org/10. 1594/PANGAEA. 876108). Spatial coverage is high in North America, Europe, South Korea and Japan, but much lower across the rest of the world with very low data availability across Africa, the Middle East, Russia and India. In addition to data sparseness, other challenges, such as data inhomogeneity in time and the irregular spatial distribution of stations  (Chang et al. , 2017) , make the comparison between model output and observations difficult without serious statistical modeling. While satellite retrievals have been utilized by previous works for quantifying the health impacts of PM 2.
5  (Brauer et al. , 2012 (Brauer et al. , , 2015)) , satellite retrievals of tropospheric ozone have limited sensitivity near the surface and are inadequate for this analysis  (Gaudel et al. , 2018). TOAR has gathered ozone observations through 2014 at most sites, and has chosen 2008-2014 as a "present-day" window for more rigorous analysis. The purposes of the multi-year average are to reduce the effects of ozone interannual variability, which is largely driven by changes in meteorological conditions  (Strode et al. , 2015) , and to increase the number of available sites than if we used a single year. In this analysis we focus on the annual maximum of the 6-month running mean of the maximum daily 8-hour average (DMA8) at every site in the TOAR database. This metric was selected because it aligns with the ozone metric used by  Turner et al.
(2016)  to quantify the impact of long-term ozone exposure on human mortality. Hereinafter this quantity is simply referred to as "the ozone metric". 2. Atmospheric chemistry model simulations: We use output from models from phase 1 of the Chemistry-Climate Model Initiative (CCMI), downloaded from the Centre for Environmental Data Analysis (CEDA) database (http://archive. ceda. ac. uk). We choose four models (CHASER, GEOSCCM, MOCAGE and MRI-ESM1r1) that report hourly ozone output (Table  1 ). These particular simulations were part of CCMI's REF-C2 experiment  (Morgenstern et al. , 2017)  which follows the WMO (2011) A1 scenario for ozone depleting substances, and RCP 6. 0 for tropospheric ozone precursors, and aerosol and aerosol precursor emissions  (Morgenstern et al. , 2010)  for the period 1960-2100.
We propose the following procedure to combine model output and observations for data integration: 1. Interpolating irregularly located monitoring observations to the model output grid: We use a statistical kriging technique to interpolate ozone observations from irregularly located monitoring sites onto a 0. 125 • × 0. 125 • grid, using the SPDE (stochastic partial differential equation) approach  (Bolin and Lindgren, 2011; Lindgren et al. , 2011). To differentiate this result from the actual observations in the TOAR database, we refer to this interpolated surface as the "spatially interpolated ozone". We use a bilinear interpolation to smooth model output from coarser resolution to a 0. 125 • × 0. 125 • grid  (Jun et al. , 2008). The ozone metric for each model was calculated for each single grid in each year, then averaged over 2008-2014 (except for NASA G5NR-Chem, which was already in fine resolution, but only available for 1 year).
Mapping and uncertainty Ground based measurements were available from 4766 stations reported in the TOAR database  (Schultz et al. , 2017)   posterior credible interval in each cell  (Shaddick et al. , 2018). From the spatial pattern of uncertainty, we can see that relatively higher uncertainties are expected in Africa, the Middle East, South Asia and Russia, regions with very limited observations; lower uncertainty is associated with regions with a dense station network, such as North America and Europe. Due to the limitations of spatial kriging in a sparsely monitored region, the observations are often interpolated across very great distances, such as in South America, Africa and Central Asia. This method is not ideal, and instead, information from models can be used to fill in the blanks.
, 2016; Schultz et al. , 2017; Weatherhead et al. , 2017). Finally, human health studies typically adopt a fine grid resolution, such as a 0. 1 • × 0. 1 • grid product, for matching to the gridded world population database. Even though the spatial kriging surrogate can produce the predicted value at any resolution, the accuracy of the fused surface ozone product is still limited by the density of observations around that point, and by the resolution of the global model output. Let Y denote an n-vector of ozone observations measured at monitoring sites s, then a statistical model for the spatial field can be expressed as: Y = f (s) + ε, i. e. the model comprises a smooth GP spatial process f (s), capturing spatial association, and an independent normal error ε, which follows a normal error N (0, σ 2 ).
To illustrate the spatial coverage of the database, Fig 2 shows the ozone metric discretized to a 2 • × 2 • grid (a finer resolution will be too obscure for illustrative purposes), averaged over the period 2008-2014. This figure also shows our regionalized classification, including Africa, North America, South America, East Asia, Southeast and central Asia, Europe, Oceania, andRussia. Note that dense station networks are found in North America, Europe and East Asia (mostly in Japan and South Korea), while monitoring sites are more widely scattered across the remaining regions. The highest average ozone levels (> 70 ppb) are found at sites in China, South Korea, Japan, Taiwan, India, Greece, California and Mexico City.
Background: Sarcoptic mange is a contagious skin disease of wild and domestic mammals caused by the mite Sarcoptes scabiei. Reports of sarcoptic mange in wildlife increased worldwide in the second half of the 20th century, especially since the 1990s. The aim of this study was to provide new insights into the epidemiology of mange by (i) documenting the emergence of sarcoptic mange in the red fox (Vulpes vulpes) in the last decades in Switzerland; and (ii) describing its spatiotemporal spread combining data obtained through different surveillance methods. Methods: Retrospective analysis of archived material together with prospective data collection delivered a large dataset from the 19th century to 2018. Methods included: (i) a review of historical literature; (ii) screening of necropsy reports from general health surveillance ; (iii) screening of data on mange  collected during the sylvatic rabies eradication campaign; (iv) a questionnaire survey (<1980-2017) and (v) evaluation of camera-trap bycatch data (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018).
Review of the historical literature The aim of the literature review was to discover the oldest mention of MLL in foxes in Switzerland and in neighbouring countries. The following online databases were consulted: PubMed (http://www. ncbi. nlm. nih. gov/pubme d/), Google Books (http://www. books. googl e. com) Google Scholar (http://www. schol ar. googl e. com), Zobodat (http://www. zobod at. at), Gallica (http://www. galli ca. bnf. fr), E-periodica (http://www. e-perio dica. ch), E-rara (http://www. e-rara. ch), E-manuscripta (http:// www. e-manus cript a. ch), the Swiss National Library (http://www. nb. admin. ch) and the Library of the Universities of Bern and Basel (http://www. basel bern. swiss bib. ch). The following keywords were used in English, German, French and Italian: (i) sarcoptic mange (Sarcoptes scabiei, scabies, mange, itch, mite); (ii) red fox (Vulpes vulpes, fox); and (iii) Switzerland, Austria, Italy, France, Germany and Principality of Liechtenstein.
If isolation failed, the presence of intradermal mites was investigated through histopathology  [46]. Carcass collection intensity and the institutions involved varied throughout the study period: during the rabies epidemic peak  (1970-1980s [47] ), foxes were sent to the Swiss Rabies Centre (SRC) rather than to the FIWI. From 2002 to 2013, questionnaire participants (see below) were reminded each year to submit foxes with MLL to the FIWI for post-mortem examination to validate the data obtained through the questionnaire survey. In 2004-2005, foxes with MLL were actively collected for a study on sarcoptic mange  [4]. Since 2009, an increased number of foxes have been submitted due to epidemic waves of canine distemper  [48, 49].
Data on mange collected during the sylvatic rabies eradication campaign Archival data on sarcoptic mange (MLL with confirmed S. scabiei infestation) collected on foxes tested for rabies at the SRC from 1968 to 1992 (except for 1974 and 1977: missing reports on mange) were screened (n = 29,241; database of the SRC). Coordinates of mangy foxes were only available for foxes submitted from 1968 to 1990. Mite isolation was attempted through deep skin scraping and microscopic characterization. The number and geographical origin of foxes tested positive for rabies from 1967 to 1996 (last case of rabies in a fox  [50] ) were illustrated to help the interpretation of the data on sarcoptic mange (Figs. 2b, 3 ; database of the SRC  [47, 51] ).
Camera-trapping Pictures of foxes collected as bycatch data in the framework of a long-term camera-trap monitoring programme for the Eurasian lynx conducted by KORA (Carnivore Ecology and Wildlife Management) were assessed for the presence of MLL in foxes. The monitoring was standardized and performed during 23 sessions. Each session generated data recorded during subsequent 60 days between 2005 and 2018 in seven sectors belonging to six subregions (Additional file 1: Table  S1 ) using Xenon white flash camera-traps with a fast trigger speed to produce high quality pictures  [52, 53]. Because each session was performed in winter, either from December to February (phase 1) or from February to April (phase 2), the year used to designate a session corresponded to the year when the session ended (Additional file 1: Table  S1 ).
In total, 69,116 pictures were obtained and evaluated. In a first step, all pictures of foxes (n = 30,168, 44%) were screened for the presence of MLL by KORA collaborators. In a second step, 7421 of these pictures were randomly selected and re-assessed for MLL by a wildlife veterinarian (first author); the original assessment was masked to ensure an objective re-evaluation. Cohen's kappa was computed to assess inter-observer agreement between the two evaluations  [54]. According to the Cohen's kappa test value (Cohen's kappa: k = 0. 8; Z = 68. 6; P < 0. 0001; see Data analyses section below), the KORA evaluation was considered sufficient for the calculation of the MLL prevalence using all fox pictures of the database. For each session, MLL prevalence was estimated at picture level: number of pictures of foxes with MLL/total number of fox pictures.