dataesr/scientific-paragraphs-categorization huggingface dataset:
Of these, five SNPs were predicted in-silico to encode protein-damaging variants: rs7271712 (T397M) in SLC17A9, rs7308720 (N551K) and rs7133914 (R1398H) in LRRK2, rs61740826 (C406Y) in ZNF678 and rs2229531 (V200M) in ACP5 (S2 Table ). The twins and their father were homozygous for N551K in LRRK2; and only the early onset twins were homozygous for LRRK2 R1398H, SLC17A9 T397M, ZNF678 C406Y and ACP5 V200M. Among these variants, LRRK2 N551K and R1398H had previously been shown to impact on LRRK2 protein activity and are established protective factors for CD and PD  [7, 17]. Conversely, the remaining three genes carrying homozygous missense variants in the twins-SLC17A9, ZNF678 and ACP5 -had no known link to infectious diseases. Hence, the two LRRK2 variants were considered the top candidates contributing to the early onset leprosy phenotype.
7 macrophages (S6 Fig). To validate these results in human cells, we obtained PBMC from two family members and conducted a colocalization analysis of LRRK2 and NOD2 in monocyte-derived macrophages (MDMs) of the father and one of the twin girls as well as from a control subject (Fig 4B  and 4C ). MDMs were differentiated with GM-CSF and co-localization was analyzed after 24h of stimulation with Nglycolyl-MDP. In concordance to the results obtained with RAW cells, we observed a lower proportion of LRRK2-NOD2 co-localization in the macrophages from the father and daughter (NOD2 R702W heterozygote), compared to the control subject who was homozygous for the R702 reference allele (Fig 4B  and 4C ). Combined, our data showed impaired LRRK2-NOD2 interaction due to the NOD2 R702W variant in both murine and human macrophages.
Whole genome sequencing WGS was performed for six family members (ID2 to ID7 from Fig 1B ) on HiSeq 2500 platform (Illumina) to generate paired-end 150 bp reads at the Genome Quebec and McGill Innovation Centre. Quality assessment of the raw data was performed using FastQC v0. 11. 4 software (Babraham Bioinformatics; http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The reads were mapped to human genome reference GRCh37+decoy using the BWA-mem algorithm on BWA v0. 7. 12  [106]. Mapped reads were sorted according to their genomic coordinate position and PCR duplicates were flagged using Picard v1. 134 (Broad Institute, GitHub Repository; https://github. com/broadinstitute/picard). Next, local realignment around indels and base recalibrations were performed using GATK v3. 5  [107].
Quality assessment of the mapped reads was performed using QualiMap v2. 1. 1  [108]. GATK HaplotypeCaller was used to call SNVs and short indels for each sample, followed by GenotypeGVCFs for the six samples together. Variant Quality Score Recalibration from GATK, using default parameters was used to reduce the amount of false positive  [109]. Genotypes with genotype quality (GQ) lower than 20 were removed. Finally, the variants were annotated using wANNOVAR (2016)  [110]. Deletion structural variants (DSVs) in autosomal chromosomes were detected using Genome STRiP v2. 0 SV pipeline with default parameters  [111]. For that, a total of 25 high-coverage WGS samples from the 1000G database were included in this step to run together with the 6 samples from the studied family  [112]. Next, GATK was used to apply hard filtering to remove low quality deletions as follow: GSELENGTH < 200; GSCLUSTERSEP � 2.
0 or NA; GSM1 � 0. 5 or � 2. 0 or NA; GLINBREEDINGCOEFF < -0. 2; GSNONVARSCORE � 13. 0; GSDUPLICATESCORE � 0 or DOSAGE_CORRELATION � 0. 5; call rate � 80%. Overlap of the DSVs to protein-coding genes was annotated using GeneOverlap command on Genome STRiP. To identify the population structure of the studied family, principal component analysis (PCA) was performed using PLINK v1. 9  [113]. For that, genotypes from VCF files were converted to PLINK format using BCFtools (http://github. com/samtools/bcftools). Autosomal variants were pruned based on LD (window size of 50kb, step size of 5 and variance inflation factor of 1. 5) and MAF > 10% as implemented in PLINK. Variant pruning was done using the samples from the 1000 Genomes Consortium (1000G) representing the five super populations worldwide: African/African American, Admixed American/Latin, East Asian, South Asian and European  [15].
Candidate variant detection from WGS data Five filtering steps (A to E) were used to select variants based on A) the variant location within protein-coding genes (coding or splice-site variants), B) its type (missense, nonsense, frameshift indels or splice-site variant), C) inheritance model (dominant or recessive), D) variant frequency in population samples from the database, and E) age-at-diagnosis of the affected family member. To apply filtering step D, we searched the variant frequency in the following population samples from the 1000G and Exome Aggregation consortium (ExAC) databases: African/African American, Admixed American/Latin, East Asian, South Asian and European (Non-Finnish European from ExAC)  [15, 16]. Using different parameters/thresholds in steps C to D, we implemented seven custom filtering approaches to identify candidate variants (S1 Fig) An additional search for candidate variants segregating in the family was done employing known leprosy genes  [10].
We included in this analysis a total of 86 genes previously associated with leprosy in GWAS and/or target association studies up to January 2023 (S5 Table ). Filtering steps were applied to identify candidate variants in these genes as implemented in the WGS data, but with a less stringent threshold for variant frequency in databases in step D (S1 Fig). For known leprosy genes, thresholds of MAF < 20% and MAF < 10% in the four populations from 1000G/ExAc were used in the recessive and dominant models, respectively (S1 Fig). Genotype validation of candidate variants was done as described in the S1 Method. Once candidate variants were identified, variant-level and gene-level metrics based on computational prediction were used to prioritize the variants that are most likely to have an impact on the protein structure and function.
For that, we used PolyPhen-2 v2. 2. 2r398 and CADD v 1. 4 as variant-level metrics, and GDI (2016) as gene-level metric  [114] [115] [116]. We focused on variants that presented the three following criteria: i) it has scaled CADD score � 20 (Top 1% most deleterious), ii) it is a missense variant with PolyPhen-2 HumVar score > 0. 446 (possibly or probably damaging), a nonsense variant, frameshift indel or splicesite variant and iii) it is located in a gene with GDI score < 13. 84 (medium or low GDI). Among compound heterozygous variants (Filtering approach #4 in S1 Fig) , we prioritized genes with GDI score < 13. 84 where both variants reached criteria one and two. Linkage disequilibrium (LD) estimates between LRRK2 N551K and R1398H were performed using Haploview software v4. 2 based on genotyping data from the five populations from 1000G  [15, 117].
Estimated frequencies of LRRK2 R1398H and NOD2 R702W combined genotypes were obtained assuming Hardy-Weinberg equilibrium for both SNPs and using MAF from gno-mAD database  [118]. For detection of candidate DSVs, deletions reported as overlapping with exon, CDS or gene were selected. Then, we applied the same filtering approaches as used for SNV and short indels for the recessive and dominant model (S1 Fig). DSV with call-rate < 80% in the 31 samples were excluded. We searched whether the candidate deletions were present on Database of Genomic Variants (DGV) catalog and kept those that were found in less than 50% of the sequenced samples from 1000 Genomes database (S1 Fig)  [15, 119].
2% BSA. The cells were washed once with 1X azide-free PBS+0. 2% BSA and then once with 1X Annexin V Binding Buffer (BD Biosciences). The cells were resuspended in 1X Binding Buffer at 2x10 6 cells/ml. Next, 5μl of Annexin V-APC were added to 100μL of the cell suspension and incubated in dark for 15 minutes at room temperature. After incubation, the cells were washed twice with 2ml of 1X Binding Buffer. The supernatant was discarded. The cells were resuspended in 200μl of 1 X Binding Buffer and immediately collected by flow cytometry with BD FACSCanto II (BD Biosciences). The data was analyzed on FlowJo v10. 4. 2 (FlowJo, LLC) with viability and Annexin V single stains as FMOs.
Cytokine and chemokine measurements For cytokine and chemokine measurements, twenty-four hours after electroporation cells were incubated with or without BCG-Russia (MOI 10:1) for another 24 hours. Cell culture supernatants were collected and centrifuged to remove debris. Cytokines and Chemokines were detected using Milliplex Map (EMD Millipore, St. Charles, MO, USA) multiplex magnetic bead-based antibody detection kits according to the manufacturer's instructions. A 6-plex premixed kit was used, which included analytes IL-1β, IL-6, IL-10, IL-12 (p40), MCP-1 and TNF. Assay was read using xPONENT 3. 1 acquisition software and MAGPIX instrument (Luminex Corporation, Toronto, ON, Canada). Data was analyzed using Mulliplex Analyst software v4. 2 (EMD Millipore) and presented as picogram of cytokine per milliliter of supernatants (pg/ml).
Statistical analysis Statistical analysis was conducted using GraphPad Prism 5 (GraphPad Software, California USA, www. graphpad. com). Kinetics of ROS production were analyzed by comparing the mutant cells with the wild-type cells at each timepoint using two-way ANOVA with Bonferroni correction. In apoptosis, NF-κB activation and cytokine secretion assays, one-way ANOVA and post hoc t test with Bonferroni correction was used to compare means between two groups. Co-localization analysis was done using one-way ANOVA, comparing each condition with the control using Kruskal-Wallis test. Adjusted P < 0. 05 was used as significance threshold, which is represented by asterisks in the figures. Data visualization for bar plots and.
Filtering approaches #1 to #4 were designed to detect variants in the recessive model, while filtering approaches #5 to #7 to detect variants in the dominant model (S1 Fig). Different thresholds of variant frequencies were used in the four approaches following the recessive model, while in the dominant model, we selected variants that were not reported in the 1000G nor ExAC databases (See S1 Fig). Based on the age-at-diagnosis of the leprosy patients in the family (Fig 1A), in filtering step E we searched for variants present in i) all the affected family members, regardless of the age-at-diagnosis (Filtering approaches #1 and #5 in S1 Fig); ii) the father and the twins, which are the cases younger than 30 years (Filtering approaches #2 and #6 in S1 Fig) and iii) only in the twin girls, that are the early-onset cases in the family (Filtering approaches #3, #4 and #7 in S1 Fig).
Acknowledgments We sincerely thank the family members who agreed to be part of this study. We thank  Marcel Behr  ( McGill University ) for the gift of N-N-glycolyl MDP, and  J. L. Casanova  ( Rockefeller University ) for comments on earlier versions of the manuscript. We thank  Linda Adams  and  Ramanuj Lahiri  from the  National Hansen's Disease Program, HRSA , for the provision of M. leprae, which was supported by  National Institute of Allergy and Infectious Diseases Interagency Agreement IAA   15006-004. We thank all members of the  Schurr lab at the MUHC-RI  and the  Mira lab at PUCPR  for useful comments and suggestions to the study and writing of the manuscript. We also thank members of da  Costa lab at UFPI, Lara and Pinheiro labs at FIOCRUZ , the  Probst lab at ICC  as well as  Geison Cambri  and  Kai Sheng  for technical support.
This work was supported by grants from the  Canadian Institutes of Health Research (CIHR)  to ES [ FDN-143332 ] and from  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq)  to MTM [Universal/2011]. MTM is supported by  CNPq  grant Productivity (PQ)  304368/2018-0  and the  Leprosy Research Initiative/Turing Foundation  ( LRI -706. 18. 39 ). MD-S was supported by a fellowship [ 3407/15-2 ] from  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES) ,  Ministry of Education of Brazil. JM was and AA is supported by  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  [grant  ANR-10-LABX-62-IBEID ], the  Investments  for the  Future program  [ ANR-10-IAHU-01 ] and MYCOPARADOX  ANR  project [ ANR16-CE12-0023 ]. FAL is supported by  CNPq  grant Productivity (PQ)  303051/2021-3  and by  FAPERJ  ( CNE 283750 ).
Background and rationale The endTB-Q trial (Evaluating Newly approved Drugs in combination regimens for multidrug-resistant Tuberculosis with fluoroquinolone resistance, ClinicalTrials. Gov identifier NCT03896685) was designed in 2019 to test recently developed drugs in combination with repurposed drugs for multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). According to the World Health Organization (WHO) Global TB Report, there were an estimated 484,000 (range, 417,000-556,000) new cases of multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB) and 214,000 (range, 133,000-295,000) total deaths from this disease in 2018  [1]. Among MDR/RR-TB patients tested for resistance to fluoroquinolones, 20. 8% (95% confidence interval [CI] 16. 3-25. 8%) had resistance to any fluoroquinolone (preextensively drug-resistant TB, pre-XDR TB)  [2].
Data are managed centrally by Epicentre. Additionally, safety data are also entered in a separate PV database at the centralized MSF PV Unit. Appropriate medical and research records are maintained for the trial, in compliance with ICH GCP and regulatory and institutional requirements. All study documents are coded with a study identification number. All study records are managed in a secure and confidential fashion. All AEs that occur during study are documented and followed to resolution or stabilization; in the case of AESIs and SAEs, this follow-up may extend beyond the normal study reporting period. SAEs are notified, within 24 h of awareness, by the Site Principal Investigator (or designee) to the MSF PV Unit. All SAEs deemed related to one or more investigational product(s) and considered unexpected with the use of such products are reported to National Regulatory Authorities and national/local IRBs.
Participants with an undefined fluoroquinolone resistance test at screening/baseline will be excluded from the mITT population if a subsequent test (phenotypic or genotypic) finds fluoroquinolone susceptibility. Participants without any post-baseline data will be also excluded from the mITT population. Participants in the mITT population will be analyzed in the arm to which they were randomly allocated (as randomized). The PP population is the same as the mITT population with the exclusion of participants who, for reasons other than treatment failure or death, do not complete a protocol-adherent course of treatment. A participant will be considered to have completed a protocol-adherent course of treatment if they have taken 80% of expected doses within 120% of the regimen duration. Participants who receive more than 7 days of either a prohibited concomitant medication or an investigational product not prescribed according to protocol will also be excluded from the PP population.
Analyses stratified by country and extent-of-TBdisease phenotype will also be performed. Kaplan-Meier estimates of probability of favorable outcomes will also be generated for the mITT population. Any imbalance between the arms (possibly due to implementation of the amended protocol) will be assessed. No interim analyses nor stopping guidelines are planned. A full description of the statistical methods, including handling of missing data and planned sensitivity analyses, will be detailed in the Statistical Analysis Plan.
Ethics and dissemination of trial findings Ethics approval for the study protocol and informed consent materials was granted before the study start from the following entities: MSF Ethics Review Board, Harvard Medical School Institutional Review Board (IRB), IRD IRB, ITM IRB, University of California San Francisco IRB and IRBs/Ethics Committees in all countries in which the study is implemented. Any amendments to the protocol or consent materials are reviewed and approved by all central IRBs, local authorities, and local IRBs before implementation. The trial is registered on ClinicalTrials. gov (NCT03896685). The results of the trial will be disseminated in peer-reviewed publications and at scientific conferences under the responsibility of the Principal Investigators of the study. Investigators/study authors will have full access to the final trial dataset.
Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if the endoscopic camera is moved out of the body of patients and out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. This study developed and validated a deep learning model for the identification of out-ofbody images in endoscopic videos. The model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries and was externally validated on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC).
The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. The model identified out-of-body images with 99. 97% ROC AUC on the internal test dataset. Mean ± standard deviation ROC AUC on the multicentric gastric bypass dataset was 99. 94 ± 0. 07% and 99. 71 ± 0. 40% on the multicentric cholecystectomy dataset, respectively. The model can reliably identify out-of-body images in endoscopic videos and is publicly shared. This facilitates privacy preservation in surgical video analysis. "And whatsoever I shall see or hear in the course of my profession, […] if it be what should not be published abroad, I will never divulge, holding such things to be holy secrets.
medical data and the identity of patients from legal inquiry. A breach of medical confidentiality by medical staff is prosecutable in most countries. Endoscopic videos which are recorded while the patient is under narcosis in the operating room (OR) are particularly sensitive. They often contain scenes of the OR that could potentially reveal sensitive information such as the identity of patients or OR staff. Moreover, if clocks or calendars present in the room are captured in the video, the time or date of the respective intervention can be identified. Information about the date and time of an operation, facilitates identification of the patient undergoing surgery. These scenes recorded outside of the patient's body are referred to as out-of-body scenes. If video recording has already been started before the endoscope is introduced into the patient, has not been stopped after the surgery was terminated or every time the endoscope is cleaned during surgery, out-of-body scenes are captured.
Recent developments in computer vision and deep learning are fueled by large-scale and publicly available datasets. In contrast, medical applications of deep learning are often limited by small-size and restricted datasets. Deidentification of endoscopic video by blurring or deleting out-of-body scenes enables recording, storing, handling, and publishing of surgical videos without the risk of a medical confidentiality breach. This article reports the development and validation of a deep learning-based image classifier to identify outof-body scenes in endoscopic videos, called Out-of-Body Network (OoBNet). OoBNet enables privacy protection of patients and OR staff through automated recognition of out-of-body scenes in endoscopic videos. External validation of OoBNet is performed on two independent multicentric datasets of laparoscopic Roux-en-Y gastric bypass and laparoscopic cholecystectomy surgeries.
Methods Datasets. The dataset used for the development of OoBNet was created from surgeries recorded at the University Hospital of Strasbourg, France  18. Four video recordings for each of the following endoscopic procedures were arbitrarily selected: Laparoscopic Nissen fundoplication, Roux-en-Y gastric bypass, sleeve gastrectomy, hepatic surgery, pancreatic surgery, cholecystectomy, sigmoidectomy, eventration, adrenalectomy, hernia surgery, robotic Roux-en-Y gastric bypass, and robotic sleeve gastrectomy. The dataset containing 48 videos was split into training-, validation-, and test-set including 2, 1 and 1 videos of each procedure, respectively. External validation of the model was done on a random sample of 5 videos from 6 centers and two independent multicentric datasets. (1) A dataset of 140 laparoscopic Roux-en-Y gastric bypass videos from the University Hospital of Strasbourg, France and Inselspital, Bern University Hospital, Switzerland  19.
(2) A dataset of 174 laparoscopic cholecystectomy videos from four Italian centers: Policlinico Universitario Agostino Gemelli, Rome; Azienda Ospedaliero-Universitaria Sant' Andrea, Rome; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan; and Monaldi Hospital, Naples. This dataset was collected for the multicentric validation of EndoDigest, a computer vision platform for video documentation of the critical view of safety (CVS)  20. An illustration of the dataset split for model development, internal and multicentric external validation is displayed in Fig. 1. Each hospital complied with local institutional review board (IRB) requirements. Patients either consented to the recording of their intervention or to the use of their health record for research purposes. All videos were shared as raw video material without identifying metadata.
Therefore, the need for ethical approval was waived, except for Inselspital, Bern University Hospital, Switzerland where ethical approval was granted by the local IRB (KEK Bern 2021-01666). Annotations. Each video was split into frames at the rate of 1 frame per second. All frames were annotated in a binary fashion, being either inside the patient's abdomen or out-of-body. The valve of the trocar was the visual cue for the transition from inside to out-of-body. All frames in which the valve of the optic trocar is visible are considered out-of-body to err on the safe side of privacy preservation. All datasets were annotated by a single  annotator (A. V. ). Edge cases were reviewed by a board-certified surgeon with extensive experience in surgical video analysis (J. L. L. ).
Model architecture and training. OoBNet is a deep learning-based image classifier, using MobileNetV2  21  as backbone followed by dropout (with dropout rate 0. 5), a long short-term memory (LSTM with 640 units)  22  , linear and sigmoid layers. Layer normalization was applied before dropout and linear layers. MobileNetV2 is a model architecture designed for image recognition at low computational resources as in mobile devices and smartphones. The LSTM layer contains memory gates that bring context awareness to frame classification. As part of preprocessing, input images were resized to 64 × 64 pixels, then augmented with random rotation and contrast. Data augmentation is a common way to generate variance in the input dataset to improve the robustness of the model. The output of OoBNet is a probability-like value that is then binarized to either 0 or 1 to predict whether the image is an inside or out-of-body frame (Fig.
2 ). The network was trained on video clips of 2048 consecutive frames for 300 epochs (cycles) with early stopping applied according to the highest F1-score obtained on the validation dataset. The optimizer used was Adam  23  with a learning rate of 0. 00009 and a batch size of 2048. The trained model and an executable application of OoBNet are available at https:// github. com/ CAMMA-public/ out-of-body-detec tor.