dataesr/scientific-paragraphs-categorization huggingface dataset:
One strong advantage is that no manual annotation is required during training and no additional optimization, is performed at inference time. Furthermore, in addition to the regressed kinematics, the model directly provides a segmentation mask of the tools. Experimental evaluation was carried out using a particular robotic endoscope having two flexible robotic arms, each one characterized by three joint values, on three datasets: semisynthetic, phantom and in-vivo. Results show a consistent improvement with respect to the baselines trained directly on the raw instruments kinematics. While the results are promising, the proposed approach presents a few limitations that could be addressed. First, the kinematic model of the instruments should be improved, in order to take into account phenomena as tool-channel interactions and slackening, particularly evident in non-controlled environments, as the in-vivo dataset.
This paper was recommended for publication by Editor Eric Marchand upon evaluation of the Associate Editor and Reviewers' comments. This work was supported by the  ATLAS  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No. 813782. This work is also partially supported by  French State Funds  managed by the  Agence Nationale de la Recherche (ANR)  through the  Investissements d'Avenir Program  under Grant  ANR-11-LABX-0004  ( Labex CAMI)  and Grant  ANR-10-IAHU-02  ( IHU-Strasbourg ).
Acknowledgements This paper represents independent research part-funded (LHG) by the  National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre  at the  South London and Maudsley NHS Foundation Trust and King's College London. The views expressed in this publication are those of the author and not necessarily those of the  NHS , the  NIHR  or the  Department of Health and Social Care. LHG is a Director of the  FND Society  and a member of the  Medical Advisory Board for FND Action. She receives royalties from Wiley  and from  Taylor & Francis. (DLP) Funding: None. Disclosures: D. L. P. has received honoraria for continuing medical education lectures on functional neurological disorder and is on the editorial board of  Epilepsy & Behaviour. (BT) Funding: None. Disclosures: BT has received honoraria from  Columbia University Medical Centre , the  International League against Epilepsy , and the  American Academy of Neurology.
Data collection Clinical data were collected retrospectively from Amiens University Hospital medical records. This collection was carried out in a standardized way by a single investigator. Demographic variables such as sex, date of diagnosis and smoking status at the time of surgery were collected; clinical variables included phenotype, disease location according to the Montreal classification  [18] , a history of bowel surgery as well as treatment exposition before and at the time of surgery. Surgical variables were also collected: date of surgery, indication, degree of emergency, laparoscopic or laparotomy approach, type of anastomosis, length of resection, date of stoma closure in case of temporary ileostomy and type of anastomosis. The histologic variables were prospectively collected including histologic involvement of resection margins (ileal and colic sections) and the presence of granuloma.
Statistics The quantitative variables were calculated as median (interquartile range), and the qualitative variables were calculated as percentages. The comparison of quantitative variables with the normal distribution was performed by the Student t-test or by the Mann-Whitney test for variables with a non-normal distribution. For qualitative variables, a chi-2 test or the exact Fisher test was used. A Cox model was used to identify factors associated with time to recurrence, each variable's effect was expressed as hazard ratio (HR) (95% confidence interval (CI)). All variables identified with univariate p < 0. 20 were included in a multivariate model with backward selection method. The analysis was performed with SAS ® software version 9. 4 (SAS Institute, Cary, NC). The study was approved by the ethical review board / CNIL committee (Comite Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Sante -N • T.
56, 57 Furthermore, ORR may not reliably predict PFS and OS in this patient population. 58 To date, there are no phase III data available comparing a bevacizumab biosimilar with bevacizumab RP in mCRC, and no studies of this type are currently listed in ClinicalTrials. gov. In the comparative clinical pharmacology study of BEVZ92 and bevacizumab RP in mCRC, the preliminary efficacy and safety results showed no differences in clinical benefit, PFS, safety, or immunogenicity between the treatment arms. 31.
Acknowledgments Medical writing assistance was provided by  Dan Booth, PhD  ( Bioscript Medical Ltd, Macclesfield, UK ), and funded by  Amgen (Europe) GmbH (Rotkreuz, Switzerland). Editorial support was provided by  Carine Thual of Amgen (Europe) GmbH. This work was funded by  Amgen (Europe) GmbH (Rotkreuz, Switzerland)  and did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Fixed bacteria were then permeabilized by incubation with 0. 25% Triton X-100 in PBS for 15 min at room temperature, and a click reaction was performed as described above. Stained bacteria were washed three times in ice-cold Wash Buffer and resuspended in 7H9 plus 0. 85 g/L NaCl and 0. 05% tyloxapol. Flow cytometry data were collected on a CytoFLEX flow cytometer (Beckman Coulter) and analyzed using FlowJo software.
25 µm film thickness). Oven temperature was programmed from 110°C to 220°C at a rate of 2°C/min, and the carrier gas was hydrogen (0. 5 bar). Injector and detector temperatures were set at 225 and 245°C, respectively. Peak detection and integration analysis were done using Azur software. FAs were quantified by measuring their area under the peak and normalizing to the internal standard. These quantities were then normalized by cellular DNA content.
Data was collected by the NanoString nCounter Digital Analyzer, quality checked, and normalized to the housekeeping controls using nSolver analysis software according to NanoString analysis guidelines. For quantitative RT-PCR analysis, RNA quantity was assessed using the NanoDrop-1000 spectrophotometer and cDNA was prepared with the High-Capacity cDNA Reverse Transcription Kit. Quantitative PCR was performed using Power SYBR Green PCR Master Mix and gene-specific primers (Supplementary file 3). Amplification conditions on an Applied Biosystems StepOnePlus Real-time PCR system were 2 min at 50°C, 10 min at 95°C, followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. Variations in expression were calculated by the 2 -ΔΔCt method using Rpl19 as an endogenous control.
Acknowledgements This study was supported by the  Institut Pasteur (TL, CD)  and  INSERM  ( U1224 , CD). We thank the  Cytometry and Biomarkers (UTechS CB) Technological Platform  for help with nCounter FLEX Nano-String use and the  Image Analysis Hub of Institut Pasteur  for confocal microscopy data analysis. We gratefully acknowledge the  MetaToul (Toulouse metabolomics  and fluxomics facilities, https://www.
Video Microscopy The germination process was followed over a 24-h time period under a Nikon TI microscope equipped with a 37 • C incubator and a 40× objective, with 1 picture captured every 4 min. Data were analyzed using ICY software  [21] [22] [23]. We set up a specific program to automatically detect the conidia and measure their size over time. The size was evaluated as the area measured in pixels and was kept as such since pixels were the true measures recorded by the software. For a better understanding of the size of the fungal cell, pixels were sometimes translated into µm 2 with a value of 0. 0256 µm 2 per pixel.
Further, our sample was close to the representative pre-restriction PA levels reported: baseline compliance (81%) with WHO recommendations was only slightly higher than the pooled percentage (73%) of 358 previous surveys, including a total of 1. 9 million participants  [34]. However, it should also be noted that determining response rates is difficult with social media recruitment, and it is possible that persons with low internet affinity or limited access to technology did not participate in our investigation. Another issue relates to the mode of outcome assessment. Like most large-scale studies assessing PA, we used self-reported data instead of objective instruments such as accelerometers. Typically, subjective measures tend to overestimate the actual PA levels, and moderate activities are recalled less precisely than vigorous activities  [35].
org/, accessed on 3 May 2021). Moreover, the database LIPID MAPS (Metabolites and Pathways Strategyhttps://www. lipidsmaps. org/, accessed on 3 May 2021) constitutes an important tool for lipidomic analysis, with structures and annotations of bulk lipids  [5] [6] [7]. Importantly, most organic molecules are largely mobile depending on their physical and chemical environment (cf. Figure  2 ). The molecular diversity of phospholipids is very large and depends not only on the polar head, the carbon length (short/medium/long/very long), and the number of carbon chains (from one in lyso-PL to four in cardiolipin), but also on the degree of double bonds (responsible for molecular rigidity) and their position (cf. Figure  2 ). There are also plasmalogens, PL analogs containing an ether or vinyl ether group instead of the usual ester function  [8].
Study Design A population-based cross-sectional study was conducted using an anonymous online survey which was distributed directly by the thirty-three participating universities to students and academic staff and through respective websites. The survey was also distributed through local press, social media, professional networks, local hospitals and health centers, social institutions, and through professional groups' email lists in the participating countries. Data were collected for a period of two months-between 7 April and 7 June 2020-during which time a state of emergency for COVID-19 was declared by the World Health Organization (WHO) as well as in the majority of countries participating in the study.
Acknowledgments: We wish to thank the following people for their work in helping to implement the study:  Spyros Demosthenous ,  Christiana Karashali ,  Diamanto Rovania  ( University of Cyprus );  Maria Antoniade  ( European University of Cyprus );  Ioanna Menoikou  ( Cyprus University of Technology );  Elias Ioannou  ( University of Nicosia );  Sonja Borner ,  Victoria Firsching-Block ,  Alexander Fenn  ( University of Basel );  Cristīne Šneidere ,  Ingrīda Trups-Kalne ,  Lolita Vansovica ,  Sandra Feldmane , ( Riga Stradin  , š University);  David Nilsson  ( Lund University ); M. A. S. -V. ( Fundación Universitaria Konrad Lorenz );  Claudia Lenuţa Rus ,  Catalina Otoiu , and  Cristina Vajaean  ( Babes-Bolyai University ).
Funding Information This work was supported by the  Assistance Publique-Hôpitaux de Paris (APHP) , the  University Paris-Saclay, INSERM , the  Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT)  supported by a grant from the  French National Research Agency  ( ANR-10-LABX-33 ), and the  Commisariat a l'Energie Atomique (CEA).
Study design, setting, and population We identified consecutive adults (�18 years of age) admitted to the ICU of the Nantes University Hospital between January 1, 2017, and December 31, 2019, and registered in the electronic hospital database with any of the codes for AWS in the ICD-10 (F10. 3, F10. 30, F10. 31, F10. 4, F10. 40, F10. 41, F10. 03, F10. 05, F10. 06). For patients who had multiple admissions during the study period, only the first admission was considered. In our institution, coding is done at the time of ICU discharge by the physician in charge, using the patient's formal discharge summary. Each medical file was reviewed by AV and EC to confirm the diagnosis of AWS based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)  [26]. All four major criteria had to be present in the electronic medical record of each patient to diagnose AWS.
Data collection Data were extracted from the electronic medical records of the ICU (CERNER Millenium 1 , Nantes, France). We obtained data for baseline patient characteristics, including demographics, comorbidities, chronic medications, and habits of alcohol consumption. Habits of alcohol consumption and alcohol history are part of the standard intake procedure in our hospital. Data were obtained from patients' interview. When the patient's clinical condition made the interview impossible, data were obtained either from the next of kin or from the patient at the time of discharge. The onset of AWS was the date when AWS was first recorded in the medical file. For each patient, AWS recovery was assessed by reading the daily notes of nurses and doctors from the EMR. The date of resolution was either the date of resolution recorded in the medical file or the last date of recording of AWS with no further signs or symptoms of AWS recorded for at least 48 hours.
If neither of these two conditions was met, the episode of AWS was classified as persistent. When patients had underlying dementia or other neurocognitive disorders, a worsening of the clinical state during hospitalization had to be mentioned in the patients' EMR to classify a patient with persistent confusion. AWS severity was assessed using Cushman's score  [27]. The following complications of AWS during the ICU stay were recorded: delirium tremens, seizures, status epilepticus, rhabdomyolysis, acute kidney injury, and pneumonia. The drugs administered intravenously or orally during the AWS episode were extracted from the electronic prescription database of the hospital. The life-sustaining therapies used during the ICU stay (high-flow oxygen, non-invasive ventilation, mechanical ventilation (MV), vasopressors, and/or renal replacement therapy) were extracted from the electronic medical records.
Strengths and limitations This study has a number of strengths. First, we used both the ICD-10 coding system and a detailed review of each medical file for DSM-5 criteria to identify patients with AWS. This minimized potential bias related to the retrospective selection of the study patients. Second, we evaluated and identified risk factors for clinically relevant endpoints (ICU stay �7 days and in-hospital mortality). Thus, we provide new data for identifying patients at risk for poor outcomes. Third, we obtained detailed information on the outcome after ICU discharge, an area rarely explored in previous ICU studies. Our study also has several limitations. First, the retrospective design implies information bias with a possibility of missing data. For example, we had no information on other potential causes of health disparities, such as income, health care coverage or country of birth, which may have influenced patients' outcomes.
1% (vol/vol) formic acid in water], and mobile phase. Data were processed using Xcalibur® software (Thermo Fisher Scientific, San Jose, CA). A library of standard compounds (Mass Spectroscopy Metabolite Library of Standards MSMLS supplied by IROA TechnologiesTM) were analyzed with the same conditions and gradient of mobile phases than those used to analyze the extracted metabolites. For data processing, briefly, peaks with greater than 30% variance (CV %) in quality control samples were removed. The normalization was done to the total area of the peaks of interest. The multivariate analyses were done using Simca-P+-15 software (Umetrics, Umeå, Sweden) as previously described  25. Briefly, the data analyses were first conducted using principal component analysis (PCA) to detect outliers. Discriminant metabolites were obtained after orthogonal partial least squares discriminant analysis (OPLS-DA), after elimination of metabolites with low impact in the separation of the different groups.
Statistical analysis All values were presented in the form of mean±SEM. Statistical evaluation of differences between the experimental groups was done by Mann-Whitney non-parametric test, or Log Rank test for survival. All tests were performed with GraphPad Prism [GraphPad Software Inc. , San Diego, CA, USA; www. graphpad. com]. A P-value <0. 05 was considered significant and symbolized with *; ** for P<0. 01 and *** for P<0. 001.
Renal failure in Mocos KO mice Kidney dysfunction in Mocos deficient mice was further evaluated by monitoring biochemical markers for the diagnosis of the progression of kidney damage. Our data denote profound metabolic changes, including drastic increases of serum creatinine and urea in Mocos - /-mice when compared to controls (Figure  4A, B ). As expected from the essential role of Mocos in XDH activity, Mocos KO mice had an almost undetectable level of uric acid (Figure  4C ). In addition, the serum levels of some hepatic enzymes were altered with a reduced level of alanine aminotransferase (ALAT) and an increased level of alkaline phosphatase (ALP) in Mocos deficient mice (Figure  4D -F), which were not observed in heterozygous Mocos animals when compared to wild type mice at 4 weeks of age or later in adult mice (Supplemental Figure  4A-F ).
Data were processed using Xcalibur® software (Thermo Fisher Scientific, San Jose, CA). A library of standard compounds (Mass Spectroscopy Metabolite Library of Standards MSMLS supplied by IROA TechnologiesTM) were analyzed with the same conditions and gradient of mobile phases than those used to analyze the extracted metabolites. For data processing, briefly, peaks with greater than 30% variance (CV %) in quality control samples were removed. The normalization was done to the total area of the peaks of interest. The multivariate analyses were done using Simca-P+-15 software (Umetrics, Umeå, Sweden) as previously described  36. FIGURE LEGENDS Figure 2 Figure 3 Figure 3 Figure 4 Figure 5 Figure 5 Figure 6 Figure 6 Figure 7 Briefly, the data analyses were first conducted using principal component analysis (PCA) to detect outliers.
Acknowledgments We would like to thank  PHENOMIN  and the  Mouse Clinical Institute (ICS, Strasbourg, France)  for the generation of Mocos KO mice. Our special thanks are going to  François Erard  and to  Melody Thilloux  and  Mathilde Favrat  for technical assistance and animal handling. We are also grateful to the  Center for Typing and Archiving of Animal Models (TAAM, Orleans, France ) and more specifically to  Emilie Bouvier  and  Severine Grieszmann  for taking care of the animals. We also thank  the department "Analyse des Systemes Biologiques" (PST ASB ,  Universite de Tours, France ) for their help with sample analyses.
Study design and participants We performed a retrospective study in eight ICU within seven French hospitals (Gustave Roussy, Villejuif; Cochin Hospital, Paris; Hotel Dieu Hospital, Nantes; Jean Minjoz Hospital, Besançon; Centre Hospitalier Intercommunal, Alençon; Centre Hospitalier Mémorial, Saint-Lô and Lariboisière Hospital, Paris). The study gathered consecutive adult patients (aged ≥ 18 years old) admitted in the ICU with a PCR-con rmed SARS-CoV-2 infection and acute respiratory failure. Demographic, clinical, laboratory, treatment, and outcome data were collected from electronic medical records using a standardized data collection form. An independent physician (CdR) checked all data. The Research Ethics Commission of the Gustave Roussy Institute approved the study. The study was registered at the French National Commission on Informatics and Liberty and the French National Institute for Health Data.
One RSV-A (ON1) was detected in a pediatric patient (patient 18, Fig. 1a ). The others were RSV-B, all belonging to RSV BA genotype. Alignment of their sequences is shown on Fig. 1b. The 7 analyzed sequences of RSV detected in SPR-R and SPR-M units during this outbreak were exactly identical and exhibited 6 to 9 differences with cocirculating RSV-B isolates analyzed here. These data strongly support the circulation of a unique RSV strain in SPR-R and in SPR-M unit over a 3 weeks period. The 12 RSV cases from SPR-R and SPR-M were thus considered as part the same nosocomial outbreak and are referred as "outbreak cases". Molecular analysis has also enabled the definition of outbreak start by identifying the source patient (patient 5, Fig. 1a ). Strikingly, the 7 studied sequences resulting from person-to-person transmission over 20 days were absolutely identical.
Precautions were implemented as soon as the patients were symptomatic for all the following cases (Fig. 2 ). The fifth first cases were detected in SPR-R ward (ground floor), and then the outbreak reached the SPR-M ward (first floor) from day 14. No other patient was diagnosed after day 20, it can be assumed that the intensified infection control measures stopped further transmission. Unfortunately, staff members were not investigated. Considering only the SPR-R unit and supposing all cases were diagnosed, the attack rate of this outbreak would be 35% (10/28). The 12 patients' files were reviewed retrospectively to assess their underlying conditions, symptoms and outcomes. Socio-demographic and clinical data are indicated in the Table  1  and detailed for each case in Supplementary Figure  1.
During this outbreak, patients could not be transferred into single bed-rooms, given the lack of available rooms and the psychological impact of confinement. The other infection control measures listed in methods section, were sufficient to contain and end the outbreak, suggesting cohorting measures might be adapted to patients' specificities. This study has certain limitations. Retrospective data collection could have led to bias and some clinical or epidemiological data are missing. Furthermore, sequencing of about half the samples failed, most likely due to low viral load. Notably, the RSV case from the SPR-C ward close to SPR-R one, couldn't be linked to the outbreak. Finally, health care workers part of the transmission chain is unclear as information regarding symptoms, laboratory confirmation and adherence to control measures are lacking.
Data Source Nationally representative cross-sectional data from the Pakistan Demographic and Health Survey (PDHS) 2012-2013 were used for analysis (available at measuredhs. com (accessed on 14 August 2021)). Using a two-stage, stratified selection technique, a sample of 14,000 households was chosen. Using the probability proportional to size technique, 500 primary sampling units (252 rural and 248 urban) were selected in the first step. In the second stage, a systematic sampling process was used to choose 28 households from each primary sampling unit (a predefined number). In total, 14,569 eligible women of ages 15-49 years were selected for interview, and of these, 13,558 women were successfully interviewed. Within five years of the study, these mothers had given birth to a total of 11,763 living children.
The socioeconomic status (SES) (in the PDHS data this variable is labeled as wealth index (v190)) was computed by employing the principal component analysis (PCA) technique on a set of household durables (table, chair or bench, watch, radio, television, bicycle, telephone, etc. ), housing characteristics such as having electricity, type of source of drinking water, access to a sanitation facility, availability of cooking fuel, main roof material, main wall material, floor material items. This proximate variable was then divided into socioeconomic quintiles: SES-I (poorest), SES-II (poor), SES-III (middle), SES-IV (rich) and SES-V (richest), based on the factor scores (PDHS report, 2013). This indicator may serve as a household wealth status and is consistent with income and expenditure measures  [19].