dataesr/scientific-paragraphs-categorization huggingface dataset:
Payne  carried out work as part of the UKESM contribution for CMIP6. Mathieu Morlighem was supported by  NASA 's  Cryospheric Sciences Program  (no. NNX15AD55G ) and the  National Science Foundation's Arctic System Science Program (ARCSS)  (no. 1504230 ). Christian Rodehacke (DMI)  has received funding from the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  610055  as part of the  Ice2Ice  project  as well as the Nordic Center of Excellence eSTICC (eScience Tool for In-vestigating Climate Change in northern high latitudes ) funded by  Nordforsk  (grant 57001 ). Florian A. Ziemen was supported by the  BMBF  project  PALMOD. Computational resources for MPI-PISM were made available by DKRZ through support from  BMBF.
IGE-ELMER simulations were performed using the Froggy platform of the CIMENT infrastructure -which is supported by the  Rhône-Alpes region  (grant  CPER07_13   CIRA ), the OSUG@2020 laBex (reference  ANR10 LABX56 ), and the  Equip@Meso project  (reference  ANR-10-EQPX-29-01 ) -and using HPC resources from  GENCI-CINES  (grant  2016-016066 ). Ralf Greve ,  Ayako Abe-Ouchi , and  Fuyuki Saito  were supported by the  Arctic Challenge for Sustainability (ArCS)  project of the  Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT) , and by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  under grant no. 17H06104 ,  Ralf Greve  and  Ayako Abe-Ouchi  were additionally supported under  JSPS  grant no. 16H02224 , and  Fuyuki Saito  under  JSPS  grant no. 17K05664.
Ayako Abe-Ouchi  and  Fuyuki Saito  were supported by the  Integrated Research Program for Advancing Climate Models (TOUGOU program ) from  MEXT. Reinhard Calov  was funded by the  Leibniz Association  grant  SAW-2014-PIK-1  and is now funded by the  Bundesministerium für Bildung und Forschung (BMBF)  grants  PalMod-1. 1-TP5and PalMod-1. 3-TP4. Edited by:  G. Hilmar Gudmundsson  Reviewed by: two anonymous referees.
Data availability. The model output from the simulations described in this paper will be made publicly available with a digital object identifier (DOI) https://doi. org/10. 5281/zenodo. 1173088. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6, ISMIP6 and the participating modelling groups. The forcing data sets are equally made publicly available via https://doi. org/10. 5281/zenodo. 1173088.
, 2014)  or an alternate desired state  (Acreman et al. , 2014; Dallas, 2013) , but scarcity of hydrological data hampers such differentiation. In response, and building on  Richter et al. 's (1996)  perennial-focussed 'indicators of hydrologic alteration', the 'MIRAGE toolbox'  (Prat et al. , 2014)  and 'Temporary Rivers Ecological and Hydrological Status' (TREHS) software  (Gallart et al. , 2017)  outline a means of distinguishing natural from artificial IRES (see Section 5. 1). Guidance developed for perennial systems (EC, 2003b) may also inform identification of reference sites and subsequent definition of reference conditions in and beyond Mediterranean IRES. Identifying undisturbed reference sites may be unfeasible across much of Europe, due to widespread, long-term human influences encompassing land use change, river regulation, and physical habitat modification  (EC, 2003b) , and in IRES, the problem of reference site identification may be compounded by difficulties in characterizing the natural intermittence regime.
, 2016c). Such variability as well as low taxa richness compared to perennial systems may mean that the aquatic groups defined as WFD BQEs (Van de Bund, 2009) are inappropriate, in particular to represent systems with ephemeral or episodic flow (e. g. Cazemier et al. , 2011). Debate is therefore needed to either: (a) select a single state in which status can be adequately represented using an established bioindicator (e. g. the benthic invertebrate BQE sampled late in a flowing phase in a predictable, near-perennial river;  Sánchez-Montoya et al. , 2012) ; (b) select a single state in which status may be effectively characterized by a novel bioindicator (e. g. terrestrial biota in an IRES with long, uninterrupted dry phases; see Section 4. 4. 2); or (c) integrate aquatic and terrestrial assemblage data collected across flowing, pool, and dry phases to provide a holistic picture of ecosystem health.
1 Symbol size is proportional to richness; differences between weak and strong dispersers will decrease as flowing phase duration increases (temporal changes not shown). variable, which can impede accurate status assessment  (Bady et al. , 2005). Functional approaches can complement taxonomic community characterization by exploring the biological and ecological traits possessed by a sampled assemblage. Despite limitations of biological trait databases  (Wilkes et al. , 2017) , measures of functional diversity can be reliable indicators of human impacts in perennial rivers  (Charvet et al. , 2000; Dolédec et al. , 1999), and Bruno et al. (2016)  found functional redundancy (i. e. the number of taxa performing a comparable ecosystem function) of woody riparian vegetation lining seasonal Mediterranean intermittent rivers to be a sensitive indicator of anthropogenic alteration that remained stable among sites of equivalent status.
Secondly, the 'PTH fam ' index (informed by Plecoptera,  Trichoptera, and Hydrachnidia [Acari]  families;  Miccoli et al. , 2013 ) may be a sensitive status indicator in IRES compared to other taxa combinations  (Pozojević and Pešić, unpublished). Research is ongoing to evaluate and test the PTH fam index, prior to potential recommendation for wider use. Community-level and taxon-specific intermittence sensitivity scoring can be informed by research initiatives compiling IRES biodiversity information. Notably, the Intermittent River Biodiversity Synthesis (IRBAS) project (www. irbas. cesab. org/irbas) has created an international, open-access database of hydrological and biological data, primarily reporting aquatic invertebrate assemblages sampled from IRES during flowing phases  (Leigh et al.
Acknowledgements This article is based upon work from  COST Action CA15113 (SMIRES, Science and Management of Intermittent Rivers and Ephemeral Streams , www. smires. eu),supported by  COST (European Cooperation in Science and Technology). We gratefully acknowledge the input of SMIRES Working Group 4 members who, in addition to the authors, liaised with national colleagues to contribute information about national biomonitoring activity:  Maria Helena Alves  (Portugal),  Amélie Barthès (France) ,  Silviu Bercea  (Romania),  Núria Bonada (Spain) ,  Eman Calleja  (Malta),  Gerald Dörflinger  (Cyprus),  Judy England (UK) ,  Tadeusz Fleituch  (Poland),  Jani Heino (Finland) ,  Ifigenia Kagalou  (Greece),  Maria Lazaridou  (Greece),  Zlatko Levkov  ( Republic of Macedonia ),  Djuradj Milošević  ( Republic of Serbia ),  Jón S.
Ólafsson  (Iceland),  Amael Paillex  (Switzerland),  Marek Polášek  ( Czech Republic ),  Ana Previšić  (Croatia),  Maria del Mar Sánchez-Montoya  (Spain),  Christopher Robinson  (Switzerland),  Janne Soininen  (Finland),  Maria Soria  (Spain),  Michal Straka  ( Czech Republic ),  Leonidas Vardakas (Greece) ,  Gábor Várbiró (Hungary) ,  Christian G. Westwood (UK) ,  Paul J. Wood (UK) , and  Annamaria Zoppini (Italy). Núria Cid  was supported by the  EU Project LIFE+ TRivers  ( LIFE13 ENV/ES/000341 );  Zoltán Csabai  by the  EU -funded project  EFOP-3. 6. 1. -16-2016-00004 ;and  Petr Pařil  by the  INTER-COST  project  INTER-EXCELENCE  ( MSMT LTC17017 ). We thank  Judy England  and  Clara Mendoza Lera  and four anonymous reviewers, whose constructive comments greatly improved earlier drafts of the manuscript.
For each of the obtained read 1 contigs (i. e. , RAD loci), we further assembled the associated reads 2 using CAP3  (Hang & Madan, 1999)   and percentage of identity above 95), both contigs were concatenated. Otherwise, fifteen "Ns" were inserted between both contigs. Sequence reads were aligned to this assembly using the programs aln and sampe implemented in bwa 0. 5. 9 and ran with default options. The resulting bam files were then jointly analyzed with the mpileup program from the Samtools v0. 1. 19 suite. We used default options except for the minimum mapping quality for alignment (-q option) that was set to 20. The mpileup file was further processed using a custom awk script to perform SNP calling and derive read counts for each alternative base (after discarding bases with a Base Alignment Quality score <25) as previously described  (Gautier, 2015; Gautier et al.
| Characterization of genetic diversity and population structure We performed genetic diversity analyses on the genotypes inferred at candidate gene SNPs. Gene diversities within (H o ) and among (H e ) individuals, as well as the inbreeding coefficient F IS , were estimated using Genepop v4. 2  (Rousset, 2008). Deviation from Hardy-Weinberg equilibrium was assessed using exact tests implemented in Genepop v4. 2. Linkage disequilibrium (LD) was estimated using the program Linkdos implemented in Genetix v4. 05  (Belkhir, Borsa, Chikhi, Raufaste, & Bonhomme, 1996 -2004). Significance was assessed using permutation tests. For RAD-seq markers, we characterized the extent of genetic differentiation between populations using F ST. Yet, because computing F ST from read counts as if they were allele counts ignores the extra sampling error brought by the random sampling of reads from the gene pool during sequencing, we used the method-of-moments estimator developed by  Hivert, Leblois, Petit, Gautier, and Vitalis (2018)  for Pool-seq data, based on an analysis-of-variance framework.
SelEstim uses a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the model parameters, in a hierarchical Bayesian framework. To prevent any convergence issue with SelEstim, the final dataset was transformed by randomizing the reference allele for each and every locus, using the randomize. reference. allele() R function provided in the package. For each analysis, twenty-five short pilot runs (1,000 iterations each) were set to adjust the proposal distributions for each model parameter and, after a 100,000 burn-in period, 100,000 updating steps were performed. Samples were collected for all the model parameters every 40 steps (thinning interval), yielding 2,500 observations. Three different SelEstim analyses were run on the same data, to assess convergence using the Gelman-Rubin diagnostic implemented in the CODA package for R  (Plummer, Best, Cowles, & Vines, 2006).
Provided convergence was assessed, one replicate analysis was picked at random for the rest of the study. Candidate markers under selection were selected on the basis of the distance between the posterior distribution of the locus-specific coefficients of selection and a "centering distribution" derived from the distribution of the genome-wide parameter that accounts for the variation of selection strength across loci. SelEstim uses the Kullback-Leibler divergence (KLD) as a distance between these two distributions, which is calibrated using simulations from a posterior predictive distribution based on the observed data  (Vitalis et al. , 2014). Here, we generated a pseudo-observed dataset (POD) from the inference model, with hyperparameters set to their respective posterior means, using a rejection-sampling algorithm.
, 2018). In order to appreciate the differential strength of selection at any locus across populations, we therefore used the composite parameter ζ ij ≡ 2 σ ij (κ ij -0. 5), computed from the posterior means of σ ij and κ ij. This composite parameter tends to 0 when κ ij tends to 0. 5 (which is expected for a neutral marker); it tends to -σ ij when κ ij tends to 0 (which is expected when the reference allele is selected for); it tends to σ ij when κ ij tends to 1 (which is expected when the alternative allele is selected). Broadly positive or negative values of ζ ij are therefore expected when selection is strong at one locus in one population. Then, we examined the correlation of ζ ij with some environmental variables. Next, we used the core model implemented in the software package BayPass  (Gautier, 2015)  to characterize the genetics structure of populations and identify outlier loci.
| Enrichment analysis of outlier candidates For enrichment analyses, the Blast2GO program v5. 0. 13  (Conesa et al. , 2005)   ENSEMBL gene id for all R AD outliers that were aligned on a mouse gene was extracted along with the gene id of the candidate Tlr7 (ENSMUSG00000044583). As background set the BioMart annotations for 53,946 mouse genes were obtained online via the Blast2GO interface. The overrepresentation of outlier GO terms was tested using Fisher's exact test and a p-value threshold of 0. 05 corrected using FDR  (Benjamini & Hochberg, 1995). Furthermore, we ran the web pro- The above analyses were also conducted using genome assemblies of two alternative species: the prairie vole, Microtus ochrogaster (ENSEMBL assembly MicOch1. 0), and the Chinese hamster, Cricetulus griseus (ENSEMBL assembly CriGri_1.
| Genomic differentiation and limits of the sampling design The phylogeography of bank voles is characterized by a bidirectional colonization of Sweden, resulting in a contact zone between two genetically differentiated mitochondrial lineages from southern and F I G U R E 5 Outlier detection based on 95,988 SNPs in the six bank vole populations using BayPass. (a) Correlation between the Kullback-Leibler divergence (KLD) for all SNPs and the X T X statistics estimated using BayPass. Horizontal lines correspond to the 99. 90% (solid), 99. 95% (dashed), and 99. 99% (dotted) quantiles calculated from the KLD calibration procedure. Vertical lines correspond to the 99. 90% (solid), 99. 95% (dashed), and 99. 99% (dotted) quantiles of the X T X distribution calculated using simulations from a predictive distribution based on the observed dataset.
gram REVIGO (web version of 02/14/2018, Supek, Bošnjak, Škunca, & Šmuc, 2011)  to group significantly enriched Gene Ontology (GO) terms into GO categories and to summarize the GO terms to common terms. We applied the SimRel semantic similarity measure with the default GO term similarity of 0. 7, associated corrected p-values to the enriched GO terms, and the M. musculus database (Supek et al. , 2011). In addition, the Reactome, KEGG, and INOH databases were searched using InnateDB analysis webtool (Breuer et al. , 2013)  for overrepresented metabolic pathways which were associated to the mouse gene ids of the aligned outliers. A metabolic pathway was considered as significantly overrepresented if the associated p-value was <0. 05 after FDR correction.
A minimal QA program comprises of quality control (QC), external quality assessment (EQA), standard operating procedures (SOP), and competency assessment (CA) of coworkers. To achieve certification by the International Organization for Standardization (ISO), the ISO 15189:2012 regulation specifies requirements for quality and competence in medical laboratories  [3]. QC is an internal system focusing on intrinsic performance of tests used, quality of reagents and materials used, and inclusion of positive and negative controls. CA probes the ability of laboratory staff to properly execute their tasks and can be evaluated through internal quality assurance (IQA) systems, such as reprocessing a number of samples and comparing results  [4]. The central concept of EQA is to monitor the level of performance of clinical laboratories by comparing them to a national or supranational standard.
The EQA systems in use in disease control programs are often-but not always-based on the dispatching of a "proficiency panel," i. e. , a set of well-characterized samples with a known result prepared by a reference laboratory and sent for reanalysis and rereading to the clinical laboratories. One possible downside of EQA based on testing of proficiency panels is that it monitors the optimal performance of a laboratory but not necessarily its routine performance level  [5]. TB control programs relying on sputum smear microscopy for diagnosis have for many years understood the need for permanent QA systems. Both IQA and EQA systems are in use. The recommended system of EQA for Ziehl-Neelsen staining relies on a re-examination of slides from each laboratory involved in the program  [6]. All slides have to be stored, and periodically, a laboratory supervisor will take a random sample of slides and dispatch it to the reference lab for rechecking.
A recent pilot study conducted by the national HAT control program of the DRC (PNLTHA) and the Institute of Tropical Medicine (ITM) showed that it was technically possible to record a short video of the moving trypanosomes in the mAECT collector tube. Using a smartphone application developed by our team, the microscopy technician was able to record an image of any positive confirmation test. We fit the smartphone on the trinocular microscope with a custom-designed adapter. This allows recording a video through the lens of the microscope, making use of the camera of the smartphone. In a preliminary evaluation of 114 CATT and/or RDT positive HAT suspects in Bandundu province examined in their villages by a lab technician, two were confirmed as HAT cases. For both patients, good quality videos of the parasite were recorded, which could be rechecked at a distance by a senior reader, leaving no doubt about the diagnosis.
The app ensures that the recorded videos are automatically saved with the registration number of the patient and with a timestamp for easy and foolproof linkage to the corresponding patient record. This innovative digital imaging system allowed us to overcome the issue of not being able to store positive T. b. gambiense slides. The same app also addresses EQA of the screening tests (CATT or RDT), as a picture of the screening test can be taken and stored for later rechecking by the supervisor. Synchronization of the data in the tablet or smartphone with a central database on a network associated server (NAS) occurs whenever a WiFi connection is made. At least equally important in EQA is a system to ensure that samples read as negative are truly negative. Here, TB control programs recognize that errors may occur because a technician will never be able to examine each and every field in a slide.
As in the TB EQA system, finding more than the expected number of positives among supposedly negative samples does not necessarily equate poor performance but should trigger further investigation, e. g. , a visit to the lab concerned by a laboratory supervisor. Whereas in TB control, microscopy is usually performed in fixed health facilities that are part of the general healthcare system, as explained above; microscopy for HAT is also performed by specialized mobile teams that are part of a vertical program. In the DRC, there are currently 35 such teams. The EQA system described above can easily be applied to such mobile teams as well. It will enable the HAT control program management to focus its limited supervisory and training capacity on those teams that need it most. As HAT is increasingly becoming a rare disease, it is also important to bear in mind the lessons learned in the early days of TB control programs.
We gratefully acknowledge financial support of the  Belgian Development cooperation , the  Bill and Melinda Gates Foundation , and the development of the Android 5 compatible application (DRIA-2014-306-DiTECT-HAT) is funded by the EDCTP2 programme, which is supported under  Horizon 2020 , the  European Union 's  Framework Programme for Research and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Detection of composite genes and ChiC genes in Haloarchaea We clustered 1,816,486 archaeal proteins from 802 genomes into 49,269 families. In total, 6417 families (including 132,458 proteins) were found in at least three different haloarchaeal genomes and were exclusive to Haloarchaea. These 132,458 proteins were further aligned over an extended bacterial database of 7,239,663 sequences from 2078 bacterial genomes in order to remove families with full-length similarities to bacterial proteins. A total of 5558 families were retained from this additional screen and are therefore good candidates for novel, clade-specific genes because they likely originated during or after the emergence of Haloarchaea, since homologs of these Haloarchaeal genes cannot be found in any other taxa. We tested whether these exclusive haloarchaeal genes were composite, i.
A reanalysis of this dataset has argued that the acquisition of genes by LGT may have been a more piecemeal process  [7]. However, the methodological basis of this reanalysis has very recently been challenged as artificially inflating the number of more recent events  [28]. We tested whether the composite genes reported in our study could provide additional (although clearly distinct) elements to the debate regarding the tempo of acquisition of bacterial gene fragments into Haloarchaea. Our dataset of composite gene families has no overlap with the LGTs identified in the study focusing on full-sized genes if bacterial origins  [6, 7]  and has very limited overlap with LGTs identified in other recent studies  [5, 9]  (Additional file 1). Instead, our dataset represents a novel contribution of gene families to Haloarchaea created by gene rearrangement, some of which include fragments of bacterial origin.
Domain and functional annotations Domains were predicted using the conserved domain database (CDD) (version 3. 13)  [40]  (default parameters). Sequences were functionally annotated with the halobacteria profiles dataset from the EggNog database (version 4. 5)  [41]  (default parameters). For each family, if > 60% of gene members share the same EggNog annotation, this EggNog annotation has been assigned to the family, if not, the family function was considered as unknown. Cellular localization was investigated using the PSORTdb (version 3. 0)  [42]  (default parameters for archaea). For each family, the more abundant localization annotation has been used as family localization.
If the component family contained < 5 sequences, or if archaeal and bacterial sequences were both present among the five best sequences, we considered the component to originate from prokaryotes. To explore whether use of the top five BLAST hits was a good proxy for assessing component origin, phylogenies were generated for all composite gene domains and their corresponding component families. Sequences were aligned using MAFFT  [43]. A HMM profile was constructed for each alignment and used as a query to search the gene family including directly annotated composite genes  [44]. This search was used to identify components within the composite gene family that were not directly detected using BLAST and add their sequence to the dataset. The final dataset was aligned using MAFFT, regions of uncertain alignment were trimmed using trimAL in automated 1 mode  [45] , and phylogenies were inferred using the LG + G model implemented in IQ-tree  [46, 47].
Availability of data and materials All the genomes we used to generate the results are publicly available in the NCBI genome database. The accessions of the genome assemblies are listed in Additional file 4. All sequences used in the analysis are publicly available from NCBI genome database. Sequences for all composite gene families are available at https://figshare. com/s/778c566b568c24d9ec83  [50]. All sequences and alignments and phylogenies used to assign component origins are available at https://figshare. com/s/906f41485528e4a99173  [51].