dataesr/scientific-paragraphs-categorization huggingface dataset:
Adapter sequences and low-quality bases were removed from sequencing reads using trim_galore (github. com/FelixKrueger/TrimGalore). Read pairs with one read shorter than 75 base pairs were removed. Trimmed reads were decontaminated by aligning to a custom file of bacteria (Pantoea sp. , Asaia bogorensis, Enterobacter asburiae, Klebsiella oxytoca, K. variicola, and Pseudomonas aeruginosa) and PhiX genomes using BWA v. 0. 7. 15  [48] , with only unmapped read pairs retained. Processed reads (trimmed and decontaminated) were then aligned to the An. funestus AfunF3 reference assembly  [43]  using BWA. Variants were called separately for each individual mosquito using GATK v. 3. 5  [49]  and HaplotypeCaller with options: -emit-ref-confidence GVCF -heterozygosity 0. 01 -indel-heterozygosity 0. 001 -min-base-quality-score 17.
Variant filtering was done in two steps. First, the resulting GVCFs produced by HaplotypeCaller were genotyped using GenotypeGVCFs. Variants were filtered based on the following metrics: quality by depth (QD) < 5, quality (QUAL) < 30, depth (DP) < 14, mapping quality (MQ) < 30, MQRankSum < -12. 5, ReadPosRankSum < -8. 0, strand bias (FS) > 60. 0. Filtered GVCFs were then merged into a single species GVCF using CombineGVCFs followed by GenotypeGVCFs. Second, genotypes with a GQ < 30 and DP < 20 were marked as missing. Variant quality was evaluated using scikit-allel v1. 1. 0 (doi:10. 5281/zenodo. 2652508) following Reference  [50]. Sites were masked as repetitive using RepeatMasker  [51]  with a custom repeat file  [52]. Sites were also masked if they had read coverage outside of the bounds defined by +/-3*sqrt(avgCov per chromosome) or were identified as paralogs using the methods outlined in SNPable (http://lh3lh3.
TaqMan OpenArray Assay Design and Workflow for Genotyping of Inversions 3Ra and 3Rb TaqMan OpenArray (OA) SNP genotyping assays were designed at an early stage in the assembly of the chromosome-based AfunF3 reference genome  [43] , thus for logistical reasons, we focused exclusively on inversions 3Ra and 3Rb. Beginning with the list of candidate tag SNPs ascertained as described in Section 2. 1, we filtered out any candidate tag whose flanking sequence was unsuitable for the design of forward and reverse PCR primers (e. g. , rich in low complexity or repetitive DNA), as well as any candidate containing other polymorphic sites within 30 bases of the tag SNP, as judged from our whole genome variation data (Section 2. 1). Designs for the remaining 28 tag SNPs (16 in 3Ra and 12 in 3Rb) were produced by the Dana-Farber/Harvard Cancer Center (DF/HCC) Genotyping and Genetics for Population Sciences Core, a unit of the Partners HealthCare Center for Personalized Genetic Medicine.
Amplicon Sequencing Assay Design and Workflow for Genotyping of Inversions 2Ra, 3Ra, and 3Rb We used the multiplexed amplicon sequencing (AS) approach called GT-seq (Genotyping-in-Thousands by sequencing;  [55] ). As a first step, candidate tag SNPs were evaluated with respect to primer design and primer pooling using custom perl scripts  [55] , resulting in 29, 33, and 28 assays designed for tag SNPs in 2Ra, 3Ra, and 3Rb, respectively. Following Reference  [55] , Illumina sequencing primer sites were added to locus-specific forward and reverse primer sequences to create PCR1 primers, which were ordered along with PCR2 primers (a set of 96 i5 and i7 indexes) from Integrated DNA technologies in 96-well plate format at a 25 nmole synthesis scale and a concentration of 200 µM in Tris-EDTA pH 8.
The final pooled library containing 235 An. funestus individuals (as well as another pooled library containing 957 An. gambiae and An. coluzzii individuals from a separate study  [42] ) was sequenced on a single lane of Illumina NextSeq 500 v2. 5 (75 cycle) High Output flowcell using a dual indexed 75 bp single-end read. Base calling was done by Illumina Real Time Analysis (RTA) v2 software. Using scripts described in the bioinformatics pipeline of Reference  [55]  and available on Github (https://github. com/GTseq), sequencing data were demultiplexed into single fastq files for each individual sample. Individuals were genotyped at each locus with a perl script (GTseq_Genotyper_v3. pl) that counts the occurrence of each allele at a locus within individual fastq files. The ratio of allele 1 to allele 2 counts was used to generate a genotype for each locus with total read counts > 10, following the methods and cut-offs of Reference  [55].
Converting Genotypes at Individual Tags to Multilocus Inversion Genotypes For both OA and AS approaches, data quality was checked using two measures. First, we assessed the tag SNP call rate, a tag-specific value representing the percentage of mosquito specimens in the sample with a genotype call at the focal tag. Any tag with a call rate < 80% was eliminated from the genotyping panel. Second, we calculated the specimen call rate, a specimen-specific value representing the percentage of tag SNPs that could be confidently genotyped in a focal mosquito. If the specimen call rate was <80%, that mosquito specimen was excluded from further analysis. Following data quality filtering, a multi-locus inversion genotype was calculated for each mosquito based on a custom python script. Genotypes at individual tag SNPs represent the count of alternate alleles at that tag.
Code and Data Availability Data can be found at https://figshare. com/projects/Anopheles_gambiae_An_coluzzii_and_ An_funestus_molecular_inversion_karyotyping_raw_amplicon_sequencing_data/81128 and https: //figshare. com/projects/Anopheles_funestus_SNPs_within_inverted_regions/89024, and in the Sequence Read Archive under BioProject ID PRJNA660016, accession numbers SAMN15932485 to SAMN15932719. Code used to generate the data can be found on Github (https://github. com/GTseq and https: //github. com/rrlove/molec_karyo_notebooks).
Furthermore, as detailed by Campbell et al. [55] , OA may be disadvantaged by higher genotyping costs relative to the AS approach (exemplified by GT-seq). Nevertheless, if the number of tag SNPs to be genotyped is low (50-100) and the number of samples is high (10 2 -10 3 ), OA remains a cost effective option that is still widely used  [55]. In contrast to the ascertainment of tag SNPs for inversion genotyping in An. gambiae  [40] , for An. funestus, we were constrained both by small sample size overall and limited numbers of specimens from geographic regions other than Burkina Faso. This may be one factor responsible for the apparently lower performance of molecular genotyping in An. funestus observed in this study relative to An. gambiae  [42]. A public database of natural variation in An.
funestus analogous to Ag1000G  [41] , broadly representative of the tropical African range of this species, is under development (M. Lawniczak, personal communication). Such a database will be necessary to verify or remedy the broad geographic applicability of the present tag SNP panels. Nevertheless, our focus on Burkina Faso was not accidental. The Folonzo and Kiribina chromosomal forms were discovered and have been most thoroughly studied in Burkina Faso  [35] [36] [37] [38] 44, 58] , but the burden of cytogenetic karyotyping and absence of any other molecular taxonomic tool allowing their identification represented a severe impediment to further study of these epidemiologically relevant ecotypes. By analogy to the chromosomal forms MOPTI and SAVANNA in the An. gambiae complex, we anticipate that chromosomal inversions are instruments of ecotypic differentiation rather than taxonomic boundaries  [59, 60] , but the cost-effective and high-throughput means of inversion genotyping developed here is a first step in improving our understanding of ongoing diversification within this major malaria vector.
Acknowledgments: We thank the entomology team of  CNRFP/Burkina Faso  for their fundamental contribution to the field work, as well as the inhabitants of the sampled villages for their kind collaboration. We thank  M. Kern  ( University of Notre Dame ) for assistance with DNA extraction. The  Notre Dame Center for Research Computing  provided technical support. We thank  Dana-Farber / Harvard Cancer Center in Boston, MA , for the use of the  Genotyping and Genetics for Population Sciences Core , which provided SNP analysis using the Taqman OpenArray Genotyping System. We acknowledge the  Notre Dame Genomics and Bioinformatics Core Facility  for assistance with GT-seq project development, library preparation, and sequencing.
As indicated in Table  1 , the calibrated model forced by the observed meteorological dataset of  Livneh et al. (2015)  underestimated the probability of exceedance of the minor flood level (16. 7%) compared to that observed (25. 8%), but was in good agreement with the observations for the other flood levels. In this paper, the multi-year impacts of climate change are assessed on: (1) the sum of the inflows from the 18 tributaries (sum18) of the Lake Champlain, (2) the net basin supply (NBS) of the Lake Champlain (that includes the lake inflows, direct precipitation on the lake and the evaporation of the lake), and (3) the Richelieu River discharge at Fryers station. For extreme floods, the impacts of climate change are only analyzed for the discharge at the Fryers station where discharge floods thresholds were avalable.
CONCLUSIONS The LCRR basin is a complex basin owing to the large Lake Champlain, located in a narrow valley bounded by two 1,000-m and above mountain ranges, that dampen short-term upstream river flow fluctuations. An unprecedented flood of the Richelieu River in 2011 motivated a Canadian-American study investigating possible mitigation measures to reduce flood risks and damages  (IJC 2013). In order to recommend flood mitigation measures that are resilient to climate change, this paper investigated the impacts of climate change on the recurrence of floods of the Richelieu River. To address this question, a state-of-the-art distributed hydrological model was applied to the LCRR basin  (Lucas-Picher et al. 2020). This model stands out from previous studies by the high-resolution river network, an original calibration technique considering the NBS of the Lake Champlain, a global calibration of the Lake tributaries, and the use of a new high-resolution observed gridded meteorological dataset.
DATA AVAILABILITY The observed discharge of the Richelieu River at the Fryers hydrometric station is available from the Water Survey of Canada of ECCC at https://www. ca nada. ca/en/environment-climate-change/services/wate r-overview/quantity/monitoring/survey. html. The observed discharge of the Lake Champlain tributaries is available from the U. S. Geological Survey at https://waterdata. usgs. gov/nwis/sw. The North American hydrometeorology daily dataset of  Livneh et al. (2015)  is available at http://ciresgroups. colorado. edu/ livneh/data/daily-observational-hydrometeorology-da ta-set-north-american-extent. The SWE from the Snow Data Assimilation System (SNODAS) of the National Weather Service of the National Oceanic and Atmospheric Administration is available at https://nsidc. org/data/g02158.
ACKNOWLEDGMENTS The authors are grateful to the anonymous reviewers for their valuable comments that helped in improving the manuscript. The authors thank  Dr. Jean Morin  from  ECCC  and scientists of the IJC for their important scientific advice about the paper. The authors also acknowledge the access to the NBS of Lake Champlain developed by  ECCC. This work was supported by the  International Joint Commission Lake Champlain and Richelieu River Project  and by the  D epartement de l'Expertise Hydrique du Minist ere de l'Environnement et de la Lutte contre les Changements Climatiques. The production of ClimEx was funded within the  ClimEx  project by the  Bavarian State Ministry for the Environment and Consumer Protection. The CRCM5 was developed by the  ESCER center of Universit e du Qu  ebec a Montr eal (UQAM; www.
Statistical Analysis Statistical analysis was performed with R software for Linux (version 3. 2. 3, R Core Team, Vienna, Austria, available at https://www. R-project. org/) and Rstudio for Linux (Version 1. 0. 143, RStudio Team. RStudio: Integrated Development for R. RStudio, Boston, MA). Descriptive statistics such as mean and standard deviation for continuous variables are provided. The primary analyses focused on the relationship between histologic grade and the expression of PD-L1 and CD8. Theses analyses were based on every tissue sample and realized with the Exact Test of Fisher for categorical variable. Pearson's test was used for quantitative variables. Results are reported as two-sided Ps and/or 95% confidence intervals. Failed or missing data are not included in statistical analysis.
P D-L1 and CD8 Expression and Medical History of Squamous Cell Carcinoma The main data related to PD-L1 and CD8 expression and the medical history of squamous cell carcinoma are summarized in Table  2. Twelve patients had a previous or a following history of bronchial invasive squamous cell carcinoma. CD8 expression and its localization were related to the medical history of squamous cell carcinoma (p = 0. 044). When a medical history of squamous cell carcinoma was present, CD8 positive cells were present in the epithelium and the sub-mucosa in 33% of patients whereas it was present in only 8% of patients without medical history of squamous cell carcinoma. Using image analysis with Qupath software, the mean density of lymphocytes was at 1503/mm 2 +/-231 in the submucosa. Mean density of CD8 positive cells in the submucosa was measured at 1693/mm 2 +/-334 and 1384/mm 2 +/-322 lymphocytes in patients with and without a medical history of squamous cell carcinoma, respectively (p = 0.
Context. One of the major science cases of the MUSE (Multi Unit Spectroscopic Explorer) integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Aims. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. The aim of this work is to design and validate an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity.
2015)  and more recently in the Hubble Ultra Deep Field (HUDF,  Bacon et al. (2017) ) and the CANDELS -GOOD South area  (Urrutia et al. 2018). Thanks to its unrivalled capabilities, MUSE has been able to increase the number of spectroscopic redshifts in these fields by an order of magnitude (see for example  Inami et al. 2017 ). The ORIGIN: https://github. com/musevlt/origin most spectacular increase is at high redshift (z>3), where MUSE was able to detect a large number of Lyα emitters. In the deepest exposures (10+ hours), MUSE is even able to go beyond the limiting magnitude of the deepest HST exposures. For example in the HUDF, which achieves a 5σ depth of 29. 5 in the F775W filter, MUSE was able to detect Lyα emitters without an HST counterpart  (Bacon et al. 2017; Maseda et al. 2018 ).
Objectives The detection algorithm ORIGIN is aimed at detecting point-like or weakly resolved emission line sources with emission lines at unknown redshifts. The algorithm was designed with a threefold objective : 1) Be as powerful as possible for detecting faint emission lines; 2) Be robust to local variations of the data statistics, caused for instance by bright, extended sources, residual instrumental artifacts or different number of co-added exposures ; 3) Provide a list of detected sources that achieves a target purity, this target purity being specified by the user. These objectives led us to a detection approach in five main stages outlined below and described step by step in Sec. 3.
Nuisance removal and segmentation Spatial and spectral components that are not emission lines in the datacube S (e. g. , bright sources, diffuse sources, instrumental artifacts) can drastically increase the false detection rate. The first step is thus to detect and to remove such sources, called 'nuisances' below. Regions of the field that are free from such sources are called 'background'. Nuisance features are removed by combining a continuum removal (using a Discrete Cosine Transform, DCT) and an iterative Principal Component Analysis (PCA). The estimated continuum (C) and the residual data cubes (R = S -C) from the DCT are used to perform a spatial segmentation. This provides for each spectrum a label (nuisance or background. These labels are stored in a matrix (L) and are used by the PCA, which acts differently on background and nuisance spectra.
Test statistics In the faint residual datacube (F) the point is now to capture the signatures of line emissions. These signatures essentially take the form of '3D blobs' corresponding to the convolution of a spectral line profile by the spatial PSF of the instrument. Several matched filtering  (Herenz et al. 2017)  or Generalized Likelihood Ratio (GLR) approaches  (Paris et al. 2013; Suleiman et al. 2013 Suleiman et al. , 2014) )  have been devised for that purpose. Such approaches lead to filter the data cube with a library of possible signatures, built as spectral line profiles spread spatially by the PSF, and to normalize the result. The filtering and normalization process considered here is also derived from a GLR approach but it is robust to an unknown residual continuum (see Sec. 3. 2).
Nuisance removal with DCT Each spectrum s is modeled as s = Dα + , with ∼ N(0, Σ), (1) where D is a partial DCT matrix of size N λ × N DCT , α is a N DCT × 1 vector of decomposition coefficients, Dα is the un-known continuum and is an additive Gaussian noise with covariance matrix Σ. Maximum Likelihood estimation of the continuum of spectrum s leads to a weighted least squares problem, for which the estimated coefficients α are obtained by (cf sec. 2. 1 for the notation): α := arg min α s -Dα 2 = ( D D) -1 D s. (2) The estimated continuum ĉ and residual r are obtained by ĉ = D α, r = s -ĉ. ( 3 ) These spectra are collected in continuum and residual data cubes named C and R respectively. The parameter N DCT controls the number of DCT basis vectors used in the continuum estimation. A value that is too small lets large scale oscillations in the residual spectrum, while a value that is too large tends to capture spectral features with small extension like emission lines, which become then more difficult to detect in the residual.
t(x) nuisance ≷ background γ, (5) In data like udf-10, both distributions appear to be rightskewed, with a roughly Gaussian left tail and a much heavier, possibly irregular right tail caused by nuisance sources. Hence, the distribution of the test statistics that would be obtained with pure noise (without nuisances) is estimated by means of the left part of the empirical distribution, for which a Gaussian approximation provides a reasonable fit (see Fig. 3 ). This leads to an estimated distribution of the test statistics under the noise only hypothesis which is Gaussian with estimated mean µ(t) and estimated standard deviation σ(t). For the purpose of segmentation (or classification), the user chooses a level of error in the form of an error probability, called P seg FA below. In order to tune the tests (5) at this target error rate, one needs to find the threshold value γ such that Pr (t > γ | noise only) = P seg FA , The higher the value of P seg FA , the lower the value of γ and the larger the number of spectra wrongly classified as containing nuisances.
If we denote by Φ the Cumulative Distribution Function (CDF) of a standard normal variable, the threshold γ for t 1 γ(t) = µ(t) + σ(t) • Φ -1 (1 -P seg FA ), ( 6 ) where again t is either t 1 or t 2. Two segmented maps are obtained from thresholding the maps of the N x × N y test statistics at the values γ(t 1 ) and γ(t 2 ) defined above. The nuisances regions of each map are merged into a single merged segmentation map. Because MUSE data cubes are large, a PCA working on the full data cube would be sub-optimal. Indeed, the repeated computation of the eigenvectors of a matrix composed by the whole cube would be computationally prohibitive. Moreover, the aim of the PCA is to capture spectral features corresponding to nuisances. Such nuisances have features that are locally similar, so a PCA working on patches smaller than the whole cube is also more efficient to remove the nuisances.
Application to MUSE HUDF datacube ORIGIN was initially developed for the blind search of Lyα emitters in the MUSE deep exposure of the Hubble Ultra Deep Field (hereafter HUDF). A preliminary version of the code was successfully used for the blind search exploration of the entire HUDF field of view  (Bacon et al. 2017). It resulted in the detection of 692 Lyα emitters  (Inami et al. 2017) , including 72 not detected in the HST deep broad band images  (Bacon et al. 2017; Maseda et al. 2018 ). Here we use the latest version of the udf-10 MUSE datacube (see Fig. 1  of  Bacon et al. (2017) ), the single 1 arcmin 2 datacube that achieves a depth of 30 hours. In this field  Inami et al. (2017)  report the detection of 158 Lyα emitters, including 30 not detected in the HST deep broad band images. Compared to the previous version datacube of the same field used in  Bacon et al.
Summary and conclusion The algorithm ORIGIN is designed to be as powerful as possible for detecting faint spatial-spectral emission signatures, to allow for a stable false detection rate over the data cube and to provide an automated and reliable estimation of the resulting purity. We have shown on realistic simulated MUSE data that the algorithm achieves these goals and ORIGIN was applied to the deepest MUSE observations of the Hubble Ultra Deep Field (udf-10). In this tiny 1 × 1 field, ORIGIN revealed a large population of Lyα emitters (255), including 86 sources without HST counterpart. These sources could not have been found without such a powerful blind detection method as ORIGIN. While the algorithm is mostly automated, we have presented a list of the main parameters with guidelines allowing to tune their default values.
The algorithm is freely available to the community as a Python package on GitHub. We have already identified points for improvements to the current version of ORIGIN and we are currently working on them. The first point is the greedy PCA (Sec. 3. 1. 3). Although this step works quite well and is fast, it may still leave residual nuisances that increase the false detection rate in the region of bright extended sources. Besides, we have not analyzed the behavior of this step in different acquisition settings, for instance crowded fields or fields with much lower signal-to-noise ratios than udf-10. Finally, we are developing a method for automatically tuning of the P PCA FA parameter involved in this stage. We also know that there is room for improvement in the detection power (and completeness) of the method, by accounting for a more complex model than model (7) (leading then to GLR test statistics different from (  8 )).
Acknowledgements. DM made extensive use of the free software GNU Octave (Eaton et al. 2018) for developping ORIGIN and is in particular thankful to the developers of the Octave packages statistics, signal and image. DM also acknowledges support from the  GDR ISIS  through the  Projets exploratoires program  (project  TASTY ). Part of this work was granted access to the  HPC and visualization resources of the Centre de Calcul Interactif hosted by University of Nice Côte d'Azur. RB acknowledges support from the  ERC  advanced grant 339659-MUSICOS. This research made use of the following open-source packages for Python and we are thankful to the developers of these: Astropy (Astropy Collaboration et al. 2013, 2018), Matplotlib (Hunter 2007), MPDAF (Piqueras et al. 2017),  Numpy  (van der Walt et al.