dataesr/scientific-paragraphs-categorization huggingface dataset:
An important consequence of our observations for the clinical management of ODCD is the need for regular screening, including neuroimaging and ophthalmoscopy, to prevent secondary complications from hydrocephaly and unrecognized renal failure. Splice mutations of TRIP11 cause ODCD. The underlying genetic defect of ACG1A has been identified as recessive loss-of-function of the TRIP11 gene (1), which encodes GMAP-210  (16, 17). The phenotypic similarities prompted us to perform direct mutation analysis of TRIP11 in ODCD. In all affected individuals, Sanger sequencing revealed biallelic changes that cosegregated with the disease phenotype (Figure  2A , Supplemental Figure  1 , and Table  2 ). Parents were heterozygous; unaffected siblings were heterozygous or WT. None of the changes were known polymorphisms or were listed in public exome databases.
The following antibodies were used: rabbit anti-TRIP11 (HPA002570, MilliporeSigma), mouse anti-TRIP11 (E-2, Santa Cruz Biotechnology Inc. ), mouse anti-GAPDH (G-9, Santa Cruz Biotechnology Inc. ), rabbit anti-GM130 (anti-N73pep) (52), mouse anti-GM130 (clone 35, BD Biosciences), rabbit anti-IFT20 (13615-1-AP, Proteintech), sheep anti-TGN46 (provided by Sreenivasan Ponnambalam, University of Leeds, Leeds, United Kingdom), sheep anti-ZFPL1 (53), mouse anti-GalNAc-T2 (provided by Henrik Clausen, University of Copenhagen, Denmark), mouse anti-myc (9B11, Cell Signaling Technology), goat anti-Myc (ab9132, Abcam), rabbit anti-GORAB (generated in house), sheep anti-golgin-84 (54), rabbit anti-LAMP1 (D2D11, Cell Signaling Technology), mouse anti-LAMP2 (H4B4, Developmental Studies Hybridoma Bank, University of Iowa, Iowa City, Iowa, USA), anti-decorin (14667, Proteintech), mouse anti-acetylated tubulin (clone 6-11B-1, MilliporeSigma), and mouse anti-α-tubulin (T9026, Milli-poreSigma).
Membranes were washed 3 times in PBST buffer for 5 minutes each at RT (subsequent steps at RT) and incubated with secondary antibody solution (5% skimmed milk in PBST) for 1 hour. Membranes were washed 3 times in PBST buffer for 5 minutes and once in PBS for 5 minutes. The immunoreaction was detected with SuperSignal West Pico ECL chemiluminescent substrate (Thermo Fisher Scientific) using Bio-Rad Chemidoc MP Imager. Data were analyzed using Image Lab software (Bio-Rad). Analysis of molecular weight of GMAP-210 variants was conducted using a dedicated molecular weight analysis tool in Image Lab software (Bio-Rad) by comparing bands with known protein standards. Immunodetection of proteins and histochemistry. Cells and supernatants were harvested 48-72 hours after transfection or at the indicated time points of the FDC protocol; prior to protein extraction, cells were washed twice with PBS.
Cells were permeabilized by 4-minute incubation in 0. 1% (wt/vol) Triton X-100 in PBS. Cells were incubated with primary antibody diluted in PBS for 1 hour at RT, washed 3 times with PBS, and then incubated for 1 hour with secondary antibody diluted in PBS, sometimes together with 100 ng/ml Hoechst 33342 dye (Thermo Fisher Scientific) to stain DNA. Following another 3 washes with PBS and 2 in ddH 2 0, coverslips were mounted in Mowiol 4-88. Images were acquired using an Olympus BX60 upright microscope equipped with a Micro-Max cooled, slow-scan CCD camera (Princeton Instruments) driven by Metaview software (University Imaging Corporation). Images were processed using ImageJ software (NIH). Electron microscopy. Cells were grown on 10-cm dishes until they reached 80% confluency. The samples were fixed with 4% formaldehyde + 2.
Signals from radiolabeled proteins were quantified using AIDA software (Elysia-raytest). Statistics. Statistical analyses were conducted with use of GraphPad Prism software (GraphPad Software). D'Agostino-Pearson and Shapiro-Wilk tests were used for comparison of the distribution of data with a Gaussian distribution. Depending on the result, a Mann-Whitney U test or 1-or 2-way ANOVA with Bonferroni's correction for multiple comparisons was performed. The number of cells with large vesicular profiles and the Golgi morphology classes in the rescue experiments of Golgi compaction/fragmentation in case A2 fibroblasts were compared using a χ 2 test. Amounts of secreted proteins were compared using 1-way ANOVA followed by Dunnett's multiple comparisons test. Statistical significance cut-offs were set as follows: *P ≤ 0.
Acknowledgments We are thankful to all families and clinicians participating in this study. This work was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) -Projektnummer 246781735 -SFB 1140. EL is supported by the  European Commission  (SYBIL consortium grant agreement no. 602300 ,  Rarenet [a project of INTERREG V], MCDS-Therapy  grant agreement no  754825 ). The authors wish to thank  Dr. Aleksandr Mironov  for the assistance in electron microscopy and the  Wellcome Trust  for equipment grant support to the  EM Facility at the University of Manchester. MS acknowledges funding from the  European Research Council  ( ERC StG TREATCilia , grant no. 716344 ). ML and TMW were supported by a research grant from the  MRC  ( MR/N000366/1 ). Address correspondence to:  Andrea Superti-Furga ,  Division of Genetic Medicine, University of Lausanne, Centre Hospitalier Universitaire Vaudois, Rue du Bugnon 21, 1011 Lausanne, Switzerland.
38 under the assumption of mechanical dissipation. The uncertainty of the experimental data was estimated at 1/4 of the marker size. We observe in the inset of Fig. A6  that both relative and absolute uncertainties decrease sharply with the age of the muscle. Consequently the data obtained for the oldest ages are dramatically more weighted in the fitting process. The shaded regions represent the best fit of the normalized and aggregated data for each muscle at plus and minus one standard deviation. We then have ζ EDL = 0. 27 ± 0. 02 and ζ SOL = 0. 23 ± 0. 02, which verifies that r SOL < r EDL. By recognizing that ζ EDL ζ SOL = 1-r SOL 1-r EDL , we obtain the condition r EDL ≥ 1 -ζ SOL ζ EDL. A strict equality in the latter expression would imply that r SOL is exactly zero, i. e. , the soleus muscle is under the Dirichlet boundary condition.
Moreover, SCN astrocytes exhibited circadian variations in cytosolic calcium concentration in antiphase with neurons and contributed to the nocturnal silencing of neuronal activity by regulating extracellular glutamate levels  [100]. This mechanism enables astrocytes to communicate their own tempo to the SCN, and to play the role of circadian pacemaker in absence of other clock cells  [101]. Altogether, these data indicate that glial transmission interacts with neuronal signaling to regulate circadian activity in the SCN. Whether several astrocyte sub-types exist and regulate different neuronal cell types to generate specific neuron-glia networks in the SCN remains unknown. Importantly, because voluntary locomotor rhythmicity is commonly the single measured circadian output, one may tend to generalize behavioral observations and assume that one specific SCN cell type must regulate all circadian rhythms.
The translation of these approaches to mouse models will reveal the circuit architecture of the mammalian SCN and its contribution to jetlag. Author Contributions: Writing-original draft preparation, X. B. ; writing-review and editing, L. E. C. H. , P. M. , and X. B. ; funding acquisition, P. M. Funding: Funded by Agence Nationale de la Recherche, grant number ANR-15-CE14-0012 and ANR-18-CE14-0017-01, to P. M.
Summary Goudsmit-Sounderson model, which is the most accurate one for e ± transport, was improved in Geant4 10. 4. The model may be recommended for a wide variety of simulation applications. Improvements in modelling of other processes, as well as in EM code infrastructure were introduced. Mott corrections were added to the WentzelVI model used for simulation of high energy scattering. In Geant4 10. 5beta the new 5D gamma conversion model was added. More effective handling of the material properties is provided, the interface to EM parameters is extended, and the set of EM physics list components is improved. We continue reviewing EM models in order to take into account various next-to-leading-order corrections to the cross sections and to the final state generation. We acknowledge the support of the French National Research Agency (ANR-13-BS05-0002); Fermi Research Alliance, LLC under Contract No.
Biofilm formation of P. aeruginosa H103. A flow-cell device with glass surface was used to investigate the biofilm formation of P. aeruginosa H103 in dynamic conditions to mimic in vivo conditions (Fig. 4 ). For both concentrations of Epi (1 and 10 µM) administrated continuously during biofilm formation, no modifications of biofilm architecture were observed compared to control (Fig. 4a ). However, images analysis using COMSTAT software showed that average and maximum thicknesses were significantly increased in presence of 10 µM Epi compared to untreated biofilm, with +29% ± 10% (p = 0. 0450) and +44% ± 22% (p = 0. 0140), respectively. No significant difference of the average and maximum thicknesses were observed with 1 µM of Epi, but the biovolume reflecting the bacterial biomass was increased with +18% ± 5.
aeruginosa H103 (10 8 CFU/mL) in molted agar (supplemented or not with 1 µM or 10 µM Epi) and then poured on the top of LB agar plate. The plates were incubated at 37 °C for 24 h. Lysis plaques were then counted, and results were expressed in PFU/mL. Data were obtained from four independent experiments. Biofilm culture. Biofilms grown under hydrodynamic conditions were performed in a three channel flow cell (1 × 40 × 44 mm; Biocentrum, DTU, Danemark)  52. The flow system was assembled, prepared and sterilized as described by Tolker-Nielsen and Sternberg  53. The substratum consisted of a microscope glass coverslip (24 × 50 st1 [Knittel Glasser, Germany]). Each channel was inoculated with 250 µl of an overnight culture of P. aeruginosa H103 diluted to an OD 580 of 0. 1 (10 8 CFU/mL) in physiological water (NaCl 0.
Confocal laser scanning microscopy (CLSM). Biofilm observations were performed with a Zeiss LSM710 (Zeiss, Germany) using a 40x oil immersion objective. Bacteria were detected by monitoring the GFP fluorescence. GFP was excited at 488 nm and fluorescence emission was detected between 500 and 550 nm. Images were taken every micrometer throughout the whole biofilm depth. For visualization and processing of three-dimensional (3D) image data, the Zen 2. 1 software (Zeiss, Germany) was used. Quantitative analyses of image stacks were performed using the COMSTAT software (http://www. imageanalysis. dk/)  54. For bacterial adhesion, the percentage of covered surface was determined using ImageJ software  55. At least three image stacks from each of three independent experiments (nine stacks in total) were used for each analysis.
Statistical analysis. Statistical analysis was performed using GraphPad Prism (GraphPad Prism 8. 1. 2; GraphPad Software, San Diego, California, USA). For the G. mellonella killing assay, larvae survival was calculated by the Kaplan-Meier method, and survival differences were tested for significance using the log rank test. In all other assays, t-test were used to compare the means within the same set of experiments. Statistical significance was determined at p < 0. 05.
Background A reimbursement claim database enables large cohorts to be set up, providing comprehensive data at a relatively low cost  [1]. Their use in pharmacoepidemiology has considerably increased in recent years  [2]. In France, numerous studies have been conducted using the French National Health database (SNDS, previously known as SNIIRAM)  [3] [4] [5] [6] [7]. The French hospital database (PMSI), part of the SNDS, provides a discharge diagnosis (ICD-10 codes) for all patients admitted to hospital in France. It is considered that hospital-based data, and discharge codes in particular, can be used as valuable sources of information to define patient populations, assess comorbidities  [8]  or the severity of disease, determine patient outcomes  [3]  and drug effectiveness  [4] , and detect adverse events, including major bleeding  [5] [6] [7].
Statistical analysis From contingency tables, indicators of diagnostic accuracy, sensitivity, specificity, negative and positive predictive values, and positive and negative likelihood ratios were calculated along with an exact 95% confidence interval, using SAS 9. 4 software (SAS Institute, Cary, NC. , USA). Briefly, sensitivity or true positive rate is the number of positive index test (true positive) out of all stays with a target condition as defined by the standard procedure (true positive plus false negative). Specificity also called the true negative rate is the number of negative index test (true negative) that are identified as negative by the standard procedure (true negative plus false positive).
Discussion First, we observed overall low sensitivity (65%); a third of the major bleeding events identified by our standard procedure were not detected by the ICD-10-based algorithm (false negatives). Second, we highlighted differential sensitivity and specificity across types of bleeding, with the highest values for intracranial hemorrhage. One hundred fifty-five cases out of 637 (24%) were false positives, with a large majority being non-serious, involving mostly "other" bleeding and GI bleeding events. Major bleeding is an adverse event common to all types of antithrombotic drugs. An automated approach to identifying major bleeding in real time would be particularly useful. Indeed it would enable continuous monitoring and early signal detection in the area of pharmacovigilance. However, accuracy in coding for major bleeding is required to yield trustworthy results on the basis of hospital databases.
reported a positive predictive value of 98 and 91% for ICH and subarachnoid hemorrhage respectively  [10]. Cunningham et al. showed The index test is an ICD-10-based algorithm (previously published  [5] ) ICH denotes intracranial hemorrhage, GI Gastrointestinal that an algorithm identifying bleeding-related hospitalizations from the primary discharge diagnosis had a positive predictive value of between 89 and 99% in distinguishing specific bleeding sites  [11]. The accuracy of upper GI bleeding codes in one Dutch administrative database using the ICD-10 coding system showed a positive predictive value of 77%  [12]. In an assessment of four different coding systems in different European countries, it was concluded that positive predictive value is associated not only with the code itself, but is also with the way the code is used  [12] ; France was not part of this study.
In France, main discharge diagnoses may not reflect the motive for referral, but rather what most impacts hospital resources; indeed the PMSI database has a primarily financial objective, not an epidemiological point of view. Lastly, Delate et al. recommended a manual chart review to validate warfarin-related bleeding events from administrative data  [13]. Ruigomez et al. also advocated additional information to prevent misclassification as regards major GI or urogenital bleeding events  [14]. Our findings are in line  Codes used by the ICD-10-based algorithm (but on discharge diagnoses) are in italics with these previous results and have highlighted differential measures of accuracy between ICH and GI. When evaluating anticoagulant safety profiles, it would be wise to perform separate analyses according to these outcomes.
The PMSI database does not include laboratory results. In addition, to take account of a drop in hemoglobin level, a reference level is required, which is not straightforward. Our definition applied only to hospital-based care. Therefore, data from individuals who do not seek medical attention or who are only seen in outpatient clinics or surgeries was not captured. For major bleeding, we thought this would not lead to a substantial bias, except for bleeding-related sudden death. Thirdly, we used a two-step approach as the standard procedure; an extensive chart review was simply unrealistic, because of the expense involved in reviewing so many charts. A previous pilot study has shown good sensitivity and specificity for the first automated step. Of course, medical review of all charts would have been the true reference standard.
Statistics Demographic data are presented as absolute number and median with first and third quartiles. Utilizations of HFNC, non-invasive ventilation (NIV) and invasive mechanical ventilation for 1000 admissions were calculated. Results concerning HFNC daily practice are presented as percentages and proportions of responses. Comparisons between the two groups ("juniors" vs "seniors") were performed using a Fischer's exact test or a χ 2 test as needed. A p value < 0. 05 was considered as statistically significant. All data were analyzed with Prism 6. 0 (GraphPad, USA).
Demographic data All 34 ICU Medical Heads (from 7 university hospitals, 26 general hospitals and 1 private hospital) answered the demographic data of the survey. HFNC was available and regularly used in all ICUs. The median number of physicians in each medical team was 5. 75  [5, 6]  ICU physicians and 4  [3] [4] [5] [6]  residents. The median number of beds was 10. 5  [8] [9] [10] [11] [12]  for ICU and 5. 5  [4-6. 5] Regarding the second part of the survey, among the 235 ICU physicians contacted, 137 responded, i. e,. a response rate of 58. 3%, with a median ICU expertise of 9  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]  years and a median HFNC use of 5  [3. 5-7]  years. All regularly used HFNC for ARF management (100%, 137/137). Of these responders, 20 did not answer to the items concerning indications or usual practice of HFNC, 6 answered less than 50% and 111 more than 50% of the items, resulting in a mean completion of the survey of 80.
Acknowledgements The authors are grateful to  Nikki Sabourin-Gibbs ,  Rouen University Hospital , for her help in editing the manuscript. The authors also thank the other participating  intensive care units of the Boreal study group  (by alphabetical order):  G Alvado  ( Bayeux hospital ),  C Andrejak  ( Amiens university hospital ),  G Barjon  ( Compiègne hospital ),  C Boulle  ( Douai hospital ),  O Delastre  (Elbeuf hospital),  P Dubosq  ( Evreux hospital ),  H Dupont  ( Amiens university hospital ),  J Elbaaj  ( Lisieux hospital), JC Guilbaud (Laon hospital ),  M Hajjar  ( Cambrai hospital ),  F Hadjslimane  (Calais hospital),  P Hazera  ( St-Lo hospital ),  P Herbecq  ( Roubaix hospital ),  O Leroy  (Tourcoing hospital),  P Jeanjean  ( Amiens university hospital ),  M Moubarak  ( Amiens university hospital ),  S Moulront  (Dunkerque hospital),  R Pordes  ( Boulogne hospital ),  E Renaud  (Creil hospital),  J Rigaud  (Dieppe hospital),  A Riviere  ( Abbeville hospital ),  D Samiec  (Montreuil hospital),  H Shetiwy  (Soissons hospital),  D Thevenin  (Lens hospital),  T Vanderlinden  ( Lomme hospital ),  C Vinsonneau  (Béthune hospital).
Questionnaire development and population A web questionnaire to explore the factors influencing the choice of ID residency was developed using Survey-Monkey® by a working group of 15 ID specialists. The working group included one professor, two hospital practitioners, six fellows and six residents. The questionnaire took into account previous questionnaires on the topic  [12, 13]  and was adapted for the French curriculum. The questionnaire was reviewed and tested by two first-year ID residents to check for simplicity and comprehensiveness and was modified according to comments. The survey was anonymous and received approval from the French data protection authority (CNIL, registration number: 2213137). The questionnaire was split into three parts (supplementary material). The first part collected information about demographics, medical school curriculum, expectations from ID residency and career plan.
Statistical analysis Data from the survey were imported into R software (version 3. 2. 4). Numerical data are presented as absolute numbers, proportion, median ± interquartile range (IQR). To represent the choice of medical and surgical specialties by the medical students, we drew a ridgeline plot which represents a superposition of density curves by choice of specialty according to the ranking of medical students in 2017 and 2018. We used the R packages "ggridges" and "ggplot2" for the ridgeline plot.
Acknowledgements We thank the RéJIF board:  Simon Bessis ,  Kévin Bouiller ,  Mathieu Cabon ,  Vincent Dubée ,  Maxime Hentzien ,  Capucine Martins ,  Marion Le Maréchal ,  Julien Gras. We thank the  French National Society for Infectious Diseases (SPILF) , the  Collège des Universitaires de Maladies Infectieuses (CMIT)  and the  Association de Chimiothérapie Anti-Infectieuse (ACAI)  for their support.
018 cm/s and 0. 027 cm/s). Thus, the implementation of this method equalled a classical filtration approach at low fabric face velocities. This literature review highlights the fact that the ISO standardized test for Type-5 protective clothing does not provide enough knowledge that could allow workers finding the right balance between comfort and the protection they need. It also shows the paucity of data in the literature regarding the performance of Type-5 protective clothing fabrics. Finally, it is worth pointing out that all the previously cited studies dealt with particles smaller than 500 nm (nanoparticles and sub-micron particles), while the Type-5 certification assesses the clothing performances using an aerosol with a broad particle size distribution, from 20 nm to 2000 nm. Therefore, this project aimed at studying and explaining the performances of a large panel (20) of protective fabrics against solid aerosols.
Meltblown Layer Thickness It was not possible to use the same standard for measuring the thicknesses of meltblown layers because layers of the SMS assemblies cannot be separated without being damaged or modified. The meltblown layer thicknesses were then measured on cross-sectional images of SMS samples. These photos were taken with a stereomicroscope (SMZ-18, Nikon Instruments Inc. , Tokyo, Japan) providing a large enough observation distance and magnification, i. e. , 135x. For these observations, the samples were inserted between microscope and cover slides to hold them without significant compression. After acquisition, the images were automatically analyzed with a MATLAB® (Mathworks, Natick, Massachusetts, USA) code detecting and measuring the M layer thickness.
Fiber Diameter Fiber diameters were measured for the SMS fabrics and spunbonded layer of the microporous materials. The measurements were performed using photographs taken by optical microscopy (Nikon Eclipse E400, Nikon Instruments Inc. , Tokyo, Japan) set at 400x magnification. Some 100 measurements per fabric were performed with the ImageJ software (National Institutes of Health, Bethesda, Maryland, USA). In the case of the SMS materials, measurements were performed on the two spunbonded layers (outer and inner layers) and on the M layer.
Stochastic mechanistic epidemiological models largely contribute to better understand pathogen emergence and spread, and assess control strategies at various scales (from withinhost to transnational scale). However, developing realistic models which involve multi-disciplinary knowledge integration faces three major challenges in predictive epidemiology: lack of readability once translated into simulation code, low reproducibility and reusability, and long development time compared to outbreak time scale. We introduce here EMULSION, an artificial intelligence-based software intended to address those issues and help modellers focus on model design rather than programming. EMULSION defines a domain-specific language to make all components of an epidemiological model (structure, processes, parameters.