dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Seventy-four patients (73% females) with EIA and 20 healthy controls were enrolled to the study. Mean patients' age was 48. 5 years (range: 18-85 years). Sixty-nine percent (51) of the patients were diagnosed with RA and 31%  (23)  with UA. Mean duration of joint swelling before inclusion to the study was 5. 5 months (range 1-12 months). No difference between analyzed subgroups in arthritis duration and disease activity was found. Epidemiological and clinical data of EIA patients are shown in Table  1. In RA group, RF was detected in 35 (68. 6%) patients and a-CCP in 34 (66. 7%) patients. Among RF (+) patients, high level of RF was revealed in 68% of them. On the other hand, high a-CCP level was detected in 85% of a-CCP (+) patients. Seventy-five percent of patients with RA RF(-) were also negative for a-CCP antibodies.
We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github. com/WGS-TB/MentaLiST.
Other approaches have been developed recently that bypass the need to have assembled genomes or contigs and rely on directly mapping short reads onto the allele database for a given MLST scheme  [28, 29]. However, the initial mapping phase is costly, especially for large schemes that can contain tens of thousands of alleles. Lastly, a few recent approaches have tried to avoid costly preprocessing of short read datasets by working on the principle of k-mer indexing, which has been shown to be helpful in handling large short-read datasets in other bioinformatics contexts such as metagenomics  [30]. Two tools that follow this approach currently exist: stringMLST  [31]  and StrainSeeker  [32] , although the latter assigns isolates to the nodes of a guide-tree which is required prior to the typing phase.
In this paper we introduce MentaLiST, a k-mer based MLST caller designed specifically for handling large MLST schemes. We test MentaLiST on several datasets, including a new cgMLST scheme for Mycobacterium tuberculosis composed of 553 essential genes, and compare its performance with that of two recent MLST callers, stringMLST, another k-mer based tool and ARIBA, a recent assemblybased tool. Our tests show that MentaLiST achieves comparable or better accuracy levels than both stringMLST and ARIBA while consistently using a low amount of memory and requiring much less computation time.
METHODS We propose a method for MLST calling that does not require pre-assembled genomes, working directly with the raw WGS data, and also avoids costly preprocessing steps, such as contig assembly or read mapping onto a reference. MentaLiST uses an algorithm that follows the general principle of k-mer counting, introduced in stringMLST  [31] , with some data compression improvements that lead to much smaller database sizes and a faster running time. The general principle is to find all k-mers present on the MLST scheme alleles, for each locus, and store this information as a k-mer hash map in an index file. Then, for each k-mer in the reads of a given sample, all alleles that contain this k-mer will receive one vote. The allele called for each locus is then the one with the most votes. The novelty on MentaLiST is in the construction of a coloured de Bruijn graph  [33]  for each locus of a given MLST scheme, which allows the selection of a small subset of the MLST scheme k-mers, drastically reducing the size of the index file created by MentaLiST and improving the running time, at no precision cost.
Coloured de Bruijn graph and k-mer hash map Before calling the alleles for a given sample, a preprocessing step is required, that needs to be executed only once for a given MLST scheme. The objective of the preprocessing phase is to build a k-mer database for a given MLST scheme and a given value of k. In the uncompressed version of the algorithm, for each locus in the scheme, the k-mers of all alleles of the locus are computed, and a hash table linking each k-mer to the alleles where it is present is created. In this hash table, each k-mer points to a list of all the alleles containing this k-mer. The same k-mer can be found in different loci, although the larger the value of k, the less likely this is to happen. It is possible to compress this hash map by storing a subset of the k-mers, that still represent all sequence variation seen in the alleles.
k-mer counting and voting For a given sample, MentaLiST iterates through each sequenced read, k-merizing the read and checking each k-mer hit in the k-mer hash map. Each of its k-mer hits gives a weighted vote for all the alleles in which this k-mer is present, as found through the hash map. After all the reads have been processed, the allele with the largest vote count is selected for each locus. In the case of a tie, a random allele is selected among those with the most votes. MentaLiST outputs a log file with the number of votes for each allele in each locus and a list of tied alleles. In the current version, MentaLiST expects that all genes in the MLST scheme are present in the given sample, since it focuses on core genome MLST schemes. A pseudocode for the hash map and calling functions is shown in Fig.
Experiment design We evaluated the performance of MentaLiST on several real and simulated datasets, measuring both the calling accuracy, the running time and the computational resources required by MentaLiST. Following a recent review of MLST tools  [34] , we compared MentaLiST with top-performing MLST callers, ARIBA  [22] , chosen for the accuracy of its calls, SRST2  (28) , and stringMLST  [31] , which has been shown to be the fastest. Two main datasets were used. The first dataset consists of 41 Enterococcus faecium samples, genotyped on a traditional MLST scheme based on seven housekeeping genes, taken from the ARIBA publication  [22]. The second dataset is based on Mycobacterium tuberculosis samples. We created an essential core genome MLST scheme (ecgMLST) based on selecting a subset of 553 essential genes from the full 2891 genes in the cgMLST scheme from cgMLST.
org Nomenclature Server  [35] , by intersecting the full cgMLST scheme gene set with the 615 essential M. tuberculosis genes described in Dejesus et al. [36]. Since the loci in this  ecgMLST scheme have gene annotations that match the M. tuberculosis H37Rv reference genome (NCBI accession number NC_000962. 3), we constructed simulated genomes by substituting the reference sequence with a randomly chosen allele in each locus position. We also considered two large Salmonella MLST schemes, the SISTR scheme with 330 genes  [27]  and the Enterobase cgMLST scheme with 3002 genes  [37]. In this case, since the annotations on both schema were not compatible with any Salmonella reference genome to the best of our knowledge, the simulated genomes were built by selecting a random allele for each locus and inserting random noncoding DNA from Salmonella enterica subsp.
enterica serovar Typhi strain CT18 (NCBI accession number NC_003198. 1) between each pair of consecutive loci. For all simulated genomes, WGS samples were created using the ART read simulator  [38] , using the default parameters for an Illumina HiSeq 2500 machine. The samples were generated with read length and mean fragment size of 125/ 400 and 76/200 bp, respectively, with a 100Â coverage in both cases. For the M. tuberculosis dataset, we also created a mixed strain dataset, with samples containing reads from two strains with varying proportions, and a coverage test dataset, where samples have coverages from 10Â to 100Â, in increments of 10. The tests were run on British Columbia Genome Sciences Centre's computer cluster using a single thread for each application. We used the default value of the parameters for all programs, except for the k-mer length for stringMLST where it was fixed to k=31 (the default is k=35), the same k value as MentaLiST.
Enterococcus faecium samples with a traditional MLST scheme The first dataset we considered consists of 41 real Enterococcus faecium samples which we used to evaluate ARIBA in its original publication  [22]. We used the E. faecium MLST scheme downloaded from PubMLST  [3]  containing seven housekeeping genes. This experiment provides a comparison point for the use of MentaLiST on a classical, smallscale, MLST scheme. As expected for a traditional MLST scheme, all tested methods made identical calls on all 41 samples, except for SRST2, where on two samples the call for gene ddl was different from the other callers, 11 versus 5 on both cases, and had the flags '*?' indicating mismatches and uncertainty due to a low depth of coverage in certain parts of the gene, according to SRST2 documentation.
This is intriguing since these two alleles differ by seven base pairs at the end of the sequence, and the input samples have a very large depth of coverage. The main difference between the callers was the runtime, as shown in Fig. 3  (PubMLST dataset). ARIBA, taking around 100 s per sample on average, was the fastest. This is the only dataset where MentaLiST was slower on average than ARIBA, due to the very high depth of coverage of the samples, most between 800Â and 1200Â. This indicates that ARIBA scales better than MentaLiST with the depth of coverage, but not with the number of loci in the MLST scheme, as will be shown in the results on the larger schemes.
Essential cgMLST scheme for Mycobacterium tuberculosis Our second experiment used the ecgMLST scheme for Mycobacterium tuberculosis, and focused on the impact of depth of coverage on the accuracy of type calls. We constructed three simulated M. tuberculosis datasets, and for each one we randomly selected reads to simulate a depth of coverage ranging from 10Â to 100Â in increments of 10Â. MentaLiST was at least two orders of magnitude faster than the other callers, as shown in Fig. 3  (MTB dataset). We see in Fig. 4  that MentaLiST and ARIBA were the only callers that accurately predicted all 553 genes at a 20Â coverage, with MentaLiST also making fewer errors at a 10Â coverage. With coverage larger than 60Â, all callers except SRST2 are error-free.
Mixed M. tuberculosis samples Isolates from patients that suffer from an infection with more than one strain are not uncommon, with important pathological implications  [39] , and result in challenging WGS datasets, especially in terms of identifying the types present as well as their relative abundance  [40, 41]. To assess the performance of MentaLiST in that context, we generated simulated datasets composed of two strains at various levels of relative abundance. We generated three additional random strains, as described above, and mixed them with the previously described ones to generate three samples with two strains each, and relative abundance of the major strain, denoted by p major , ranging from 50 to 95 %. The accuracy of all callers is similar, and all but SRST2 return only correct calls when p major was greater than or equal to 0.
Simulated Salmonella datasets In the last test, we used simulated genomes of the Gramnegative bacterium Salmonella using two MLST schemes, the SISTR cgMLST scheme with 330 genes  [27]  and the Enterobase cgMLST scheme with 3002 genes  [37]. We generated 10 genomes per scheme, and created two next-generation sequencing (NGS) datasets per genome with ART, as described above. Since the Enterobase scheme is a very large scheme, containing 3002 genes with up to 2000 alleles each, only Men-taLiST was able to run on this scheme, with the other callers either failing to run or taking more than 24 h per sample to run, at which point we cancelled the run. Using the SISTR scheme, stringMLST and ARIBA took an average of 10 and 50 minutes per sample to complete, respectively. This is around two orders of magnitude slower than MentaLiST, which took 15 s per sample on average (see Fig.
3 , SISTR dataset). In terms of precision, ARIBA had the best performance, especially on the 125-bp datasets, as shown in Fig. 6. For all samples but one, MentaLiST and ARIBA had either 0 or 1 error, with no errors on 80 and 25 % of the samples, respectively. For the Enterobase scheme, on average around 28 and 29 of MentaLiST's calls per sample were wrong with read lengths of 75 and 125 base pairs, respectively, for an error rate slightly below 1 %.
Comparison with other MLST software Since the majority of the existing publicly available software require either a mapping of the short reads onto a reference genome or their assembly into contigs as a preprocessing step, we did not compare our performance to theirs, as the amount of time and memory they would consume would likely make their practical application nearly prohibitive on the kind of large-scale MLST schemes that we considered here. This left us with two tools in a comparable category -StrainSeeker  [32]  and stringMLST  [31]. The former requires a guiding tree to be constructed on the alleles, and was thus excluded from the comparison. Instead, we added ARIBA  [22]  and SRST2  [28] , which, although not necessarily designed to work on large MLST schemes, are commonly used for MLST genotyping.
Our results show that MentaLiST runs faster than stringMLST and uses less memory (Fig. 7 ), while consistently providing the same or better call accuracy. Furthermore, this accuracy is always the same or better than that of ARIBA except for a slight difference on the reads generated from the SISTR database. Therefore, our results enable us to confidently state that MentaLiST is at least comparable to the best-performing MLST callers in the class of the tools able to handle large MLST schemes with a reasonable amount of computational resources.
Methods Tissue donors. Plasma and sputum samples were obtained from patients with silicosis and healthy controls, after written informed consent and with the approval of the local ethics committee (registration number 2018/41; ethics committee, Atatürk University, Faculty of Medicine, Erzurum, Turkey). The relevant patient data are displayed in Table  1. Lung tissue explants were obtained from patients with proven diagnosis of fibrotic ILD undergoing lung transplantation. All tissue samples were obtained after written informed consent of each patient and with the approval of the local ethical committee (EC-N°1147/2015; ethics committee, Medical University of Vienna, Austria). The relevant patient data are displayed in Table  2. Mice. Wild-type (WT) C57BL/6 mice and mice deficient for STING (STING -/-)  58  , cGAS (cGAS -/-)  63  , IFNAR (IFNAR -/-)  64  , TLR9 (TLR9 -/-)  65  , IL-1R1 (IL-1R1 -/-) 66 , TLR2/4 (TLR2/4 -/-) 67,68 , NLRP3 (NLRP3 -/-)  69  , and gasdermin D (GSDMD -/-)  70  were bred in our specific pathogen-free animal facility at CNRS (TAAM UPS44, Orleans, France).
Quantitative RT-qPCR analysis. Total RNA was collected and extracted in TRI-Reagent (Sigma). RNA integrity and quality was controlled using Agilent RNA 6000 Nanopuces kit ®. Reverse transcription was performed with SuperScript ® III Kit (Invitrogen), and cDNA was subjected to quantitative real-time PCR using primers for Ifnα4 (#QT01774353), Ifnβ1 (#QT00249662), Irf3 (#QT00108759), Irf7 (#QT00245266), Nlrp3 (#QT00122458), Tmem173 (#QT00261590), and Mb21d1 (#QT00131929; all from Qiagen) and GoTaq ® qPCR-Master Mix (Promega). RNA expression was normalized to Gapdh expression (Qiagen). Data were analyzed using the comparative analysis of relative expression by ΔΔ Ct methods  73. Quantification of mitochondrial versus nuclear DNA. Total DNA released in the BALF or from cell culture was purified using DNeasy Blood & Tissue kit (Qiagen) and quantified by real-time PCR (2.
Lung DNA extraction, purification, and in vitro stimulation. Total DNA was purified from lungs of silica-exposed and saline vehicle-treated mice with DNeasy Blood & Tissue kit (Qiagen)  76. DNA quantity and purity were controlled using Nanodrop 1000 (Thermo Scientific). Purified DNA was transfected into BMDMs and BMDCs (6 µg/mL) with lipofectamine 2000 according to manufacturer instructions (Thermofisher Scientific). Statistical analysis. Coefficients of correlation (r) were presented as measures of linear association for regression relationships. All data are shown as mean ± SEM. Statistical analysis was performed using Student t test, Mann-Whitney U analysis, nonparametric Kruskal-Wallis test followed by the Dunn post test or using a oneway analysis of variance (ANOVA) with Tukey post hoc test to determine significant differences between groups, as indicated.
Acknowledgements The authors thank  Glen N. Barber  for helpful discussions and sharing the STINGdeficient mice, to  Zhijian J. Chen  for providing cGAS-deficient mice, to  Peter Broz  for sharing GSDMD-deficient mice, and to  Yavuz Alper  for his contribution in collecting and handling human samples. This work was supported by  CNRS, University of Orleans, and European funding in Region Centre-Val de Loire  ( FEDER N°2016-00110366 ).
data collection Baseline surveys were administered to 869 girls and 764 caregivers from May to July 2015, and endline data were collected after intervention from August to October 2016. Survey data collectors were trained by Columbia University and IRC. Both the girls' and caregivers' surveys were translated into French, Swahili and Mashi, and interviewers and questionnaires were appropriately matched to respondents by language. All data were collected in empty, private rooms in local community-based organisations. Two methods of data collection were employed for the girls' survey. Less sensitive questions from the questionnaire were administered through face-to-face interviews using Computer Assisted Personal Interviewing (CAPI). More sensitive questions, such as those on violence exposure and sexual behaviours, were self-administered by girls using Audio Computer Assisted Self-Interviewing (ACASI).
Analysis Frequencies, ranges, means and SDs were examined for all outcomes and demographic characteristics (see online supplementary file 2-COMPASS DRC: statistical analysis plan). Pearson Χ 2 tests and t-tests were used to examine the independence of dichotomous and continuous baseline characteristics, respectively, between treatment arms, protocol adherence groups and those who did and did not complete the endline survey. To assess the effects of the intervention, both intent-to-treat (ITT) and per-protocol (PP) analyses were implemented. PP analysis was restricted to girls who attended at least 75% of sessions and examined the effect of an adolescent girl's caregiver attending at least 75% of programme sessions. Additionally, as caregivers may have had more than one girl participate in the programme, we also restricted assessment of attendance protocol data to the oldest daughter in each family.
resulTs Of the 446 and 423 girls assigned to the intervention and wait-list control arms at baseline, respectively, 408 (91. 5%) and 377 (89. 1%) participated in endline data collection (see figure  1 : Attitudes toward gender). Relocation was the most common reason for non-completion across both intervention and wait-list control arms (n=28; n=23). Among the 389 and 375 caregivers assigned to the treatment and wait-list control arms at baseline, 369 (94. 9%) and 341 (90. 9%) participated in the endline survey, respectively. Relocation was also the most frequently cited reason for non-completion among caregivers. There was no statistically significant difference *For cases where a caregiver had more than one girl participating in COMPASS, they were asked to consider their oldest daughter or girl participant.
Energetic Analysis The energetic analysis describing the energy utilisation of the materials during the compression cycle was evaluated with Origin 7. 5 software (OriginLab Corporation, Northampton, MA, USA), and aimed to determine the energies displayed in Figure  3. The total mechanical energy (MCW) involved in the compression process (ABC triangle) is composed of four partial energy segments: packaging energy (PW), friction energy (FW), net energy (NCW) and elastic energy (EW), which are used in the initial packaging of the particles, particle-particle and particle-die frictions during densification, plastic and elastic deformations respectively. With the subtraction of PW (ABA' triangle, where A' is the smallest displacement of punch that generates measurable force) from the MCW, the theoretical energy (ThCW) (A'BC triangle) may be calculated, which is related to the energy utilised for powder compaction.
Material and Methods A cohort of 69 non-small cell lung (NSCLC) cancer patients[NB1] were selected to assess the performance of existing radiobiological models for predicting RP. Patients were treated with a 6 MV photon beam using a Varian linear accelerator (Varian Medical Systems, Inc. Palo Alto, CA, USA). All 3-field treatment plans were produced using the Eclipse treatment planning system v10 using a pencil beam convolution (PCB v10. 0. 28) dose calculation algorithm and dose volume histograms were extracted. Bespoke software implementation of the Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) models were written in Matlab to calculate the NTCP for each patient. Univariate analysis was used to test for independent associations between RP and NTCP, dose per fraction, maximum dose, V20, V10, V5, and mean lung dose (MLD).
2. 6. Micro-computed tomography (μCT) Scanning and analyses followed published guidelines  [24]. Threedimensional μCT imaging was performed at various times after surgery. In brief, samples were fixed in 4% PFA at 4 °C overnight and washed in PBS. During scanning, the samples were kept in 70% ethanol solution. μCT scanning was performed on Viva CT data-acquisition system (Scanco, Brüttisellen, Switzerland) at 10. 5 μm voxel size (70 kV, 115 μA and 300 ms integration time). Bone morphometry was performed using the acquisition system's analysis software (Scanco). Multiplanar reconstruction and volume rendering were carried out using Avizo (FEI, Hillsboro, OR) and ImageJ (NIH, Bethesda, MD) software. Images were organized using Adobe Photoshop and Illustrator (Adobe, San Jose, CA). Results were presented in the form of mean ± standard deviation, with N equal to the number of samples analyzed.
Histomorphometric analyses Histomorphometric measurements were performed using ImageJ software (NIH, Bethesda, MD). To quantify the amount of new bone formation in tooth extraction sockets, Aniline blue-stained histologic sections that spanned the distance from the furcation to the apex were used. Each section was photographed using a Leica digital image system at 20× magnification. To find the percentage of new bone, the number of Aniline Blue +ve pixels within an osteotomy were calculated and divided by the number of the total pixels in the same osteotomy area.
Acknowledgements This work is supported by a grant from the  NIH  ( R01 DE024000-12  to J. A. H. ) and funds from  Nobel Biocare  (grant # 2015-1400 ). J. B. B. and J. A. H. are paid consultants for Nobel. We thank  Maziar Aghvami  for his helpful discussions and  Pedro Cuevas  for his assistance in histological experiments. All other authors declare they have no conflicts of interest.