dataesr/scientific-paragraphs-categorization huggingface dataset:
Empirical application The above DNN prediction techniques are applied to a toy example with real data on house prices. For comparability purposes, we choose a popular dataset widely used in the literature on linear and nonlinear prediction, see for example, Al  Bataineh and Kaur (2018). The Boston Housing dataset initially studied in  Harrison and Rubinfeld (1978)  consists of 506 datapoints split between 404 training and 102 test observations. This dataset is concerned with housing values in the suburbs of Boston. The dependent variable is the median value of the Boston house prices in thousands of dollars, that is explained by thirteen independent variables, which for sake of space are not reported, and described in details by  Harrison and Rubinfeld (1978). In order to guarantee a proper training of the ReLu DNN, a featurewise normalization consisting on transforming the observations into zero-mean and unit standard deviation random variables is performed.
The mean and the standard deviation used for the feature-wise normalization are computed considering only the train dataset. The first step of the neural network prediction exercise is to set the optimal neural network architecture. Different optimization algorithms, weights initializers, learning rates, number of epochs, drop-out rates, and total number of hidden nodes are considered. In particular, the learning rates 0. 0001, 0. 001, 0. 01, and 0. 1 for the Adam optimizer (𝛽 1 = 0. 9, 𝛽 2 = 0. 999), for the Stochastic Gradient descent (SGD) with Nesterov momentum of 0. 9, and the RMSProp optimizer with 𝜌 = 0. 9 are tuned. When the Adam optimizer is considered, we use the He normal initializer that draws samples from a truncated normal distribution with 𝜇 = 0 and 𝜎 = √ 2∕Indim, where ''Indim'' is the number of input units in the weight tensor; conversely, when the SGD is tuned, a truncated normal distribution with 𝜇 = [0.
This novel procedure is shown to outperform k-fold cross-validation procedures for prediction in nonlinear models. In linear settings, in which standard OLS methods are optimal, our approach is competitive and provides comparable mean square error values. We illustrate the ability of our optimal deep neural network architecture to predict median house prices from the Boston Housing dataset. This dataset is extensively used by the machine learning literature to validate new learning techniques. Our neural network architecture reduces the out-of-sample mean square prediction error between 28. 55% and 47. 32% compared to recent studies fitting neural networks to this dataset. By optimizing width and depth prior to training for a given choice of nodes, our proposed method substantially saves upon the necessary time and computing power involved in fine tuning while training.
Study design and population This retrospective observational cohort included children < 18 years of age in 2018 and followed up for one year between 2018 and 2019 according to their birth or birthday. In mainland France (population of 64. 9 million), there were 13. 99 million children < 18 years of age on 1 January 2019, according to the National Institute for Statistics and Economic Studies (Institut national de la statistique et des études économiques)  [20]. Those excluded from the analysis included children who did not receive any refund during the one-year follow-up period and those of the same sex resulting from a multiple birth (twins, triplets, etc. ) who could not be unambiguously identified in the database, as well as those with a false number of linkages or those lacking critical information, such as the month of birth (N = 13.
Data source The SNDS collects comprehensive anonymous data concerning all reimbursed prescriptions, examinations, and procedures performed in the outpatient setting and hospitalisations. Information about the beneficiaries themselves (date of birth, sex, place of residence, deprivation index, etc. ) are also available  [19]. This database does not record primary medical consultation diagnoses or the results of clinical examinations and investigations. Nevertheless, information is available concerning chronic longterm disease status (LTD). The LTD status is requested or renewed by the patient's general practitioner (GP) and guarantees 100% reimbursement for all healthcare expenditures related to the LTD for at least five years. Diagnoses are made and validated after medical examination and, in certain cases, hospitalisation or chronicization during follow-up, by the patient's GP using national recommendations for diagnosis, treatment, and follow-up.
The list of LTDs is published and updated by decree after expertise from the Haute Autorité de Santé (French National Authority for Health)  [21, 22]. A guide is also sent to the patient and information is also available on the French National Authority for Health website  [22]. Through a pseudonymised identification number, this information is all linked via the national hospital discharge database to data concerning public and private hospital stays: emergency department (ED), SSH, psychiatric hospitals or rehabilitation facilities, home hospitalisation. Hospital and LTD diagnoses are coded according to the International Classification of Diseases 10th revision (ICD-10). Normal stays in hospitals/maternity units for childbirth without the need for neonatal care were excluded. The one-day services provided are equivalent in nature, complexity, and the medical supervision they require to services generally provided in the context of full-time hospitalisation.
Statistical analysis We conducted a descriptive analysis for each outcome group using information detailed above. Having an LTD status was considered for children with at least one of the most frequent LTDs for each outcome, detailed by diagnosis, or those with one or more of the less frequent LTDs, pooled as other LTD. The results for the use of primary or other healthcare services during the year are expressed by group as the rate of individuals who used such services at least once during the year of follow-up. The median number of instances of healthcare use is reported, together with the interquartile range (IQR), to assess the intensity of healthcare use during the year of follow-up. Data on the SSH principal diagnosis are expressed as the proportion of hospitalisations for the corresponding ICD-10 chapter among all hospitalisations and the most frequent specific conditions for certain chapters by group.
Children with a SSH stay < 1 night and of another type were counted in each group. Data on potentially preventable admissions are expressed as the proportion of SSHs of the corresponding diagnosis among children between the age of 0 and 10 years based on a tool developed in New Zealand  [25, 26]. Quasi-Poisson regression models were used to identify factors associated with the various outcomes of stay or readmission. Social deprivation was not considered in the full analysis due to collinearity with the variable CMUC and was only adjusted for age and sex. This was also true for the class "at least one LTD", because the 10 most frequent LTD diagnoses were detailed in the full model analysis with the "other LTD diagnoses" class. The crude relative risk (RR) and 95% confidence interval (95%CI) are presented for each outcome in the supplementary material and then adjusted in the full models in the text.
In Italy, 10. 5% of hospitalisations were judged to be preventable in 2016. Dehydration (30%), pneumonia (18%), seizures (16%), and bronchitis (3%) accounted for 90% of preventable paediatric hospitalisations. They are mostly related to acute illness, highlighting difficulties in managing acute diseases of the youngest in primary care  [31]. Nevertheless, the criteria for preventable admissions and their numbers or coding (ICD9 vs. ICD10) varied between studies, as did the age range: 50% of potentially preventable hospital charges were associated with asthma and bacterial infection in the 2006 kid's inpatient database  [32]. Furthermore 15% of hospital discharges followed preventable hospitalisation for children < 21 years of age with a non-complex chronic disease and 5% for children with a medically complex condition  [33].
Strengths and limitations The main strength of this study was the use of the SNDS, which allowed us to include more than 13 million children, representing 94. 4% of the metropolitan French population in this age group listed by the National Institute for Statistics and Economic Studies, and which exhaustively collects hospital discharge database information. Nevertheless, we observed a difference with the National Institute for Statistics and Economic Studies population, which increased with age, possibly due to the non-inclusion of children who had no reimbursements during the year in our study, leading to a slight overestimation of healthcare consumption. Most individuals without reimbursements during the year appeared to be adolescents. In addition, younger children born alive but not discharged from the hospital may have not been included in the study because they did not have an outpatient refund.
INCA and CIQUAL databases Dietary data on breakfast consumption in France came from the INCA3 study on dietary intakes of the French population  (24). INCA3 is based on three non-consecutive 24 h dietary recalls for 1,907 men and 2,207 women (2 weekdays and 1 weekend day). Only data for adults aged ≥18 years (n = 2,121) were analyzed. As part of the dietary intake assessment, participants needed to name the eating occasion. There were 10 possibilities (including "breakfast"). Only foods consumed during the declared breakfast meal were analyzed. Breakfasts consisting of coffee or tea only (sweetened or not) were excluded. A total of 4,478 breakfasts were analyzed. The associated ANSES CIQUAL 2016 nutrient composition database (25) provides energy and nutrient values per 100 g edible portion for all the foods consumed by INCA3 participants.
The model that correlated best with NRF9. 3 was selected and called "BQS" in the rest of the article. BQS values for 4,478 breakfasts in the INCA3 database were split into tertiles. The general linear model then compared values of energy and nutrients (for which IBRI recommendations were available), sub-scores eLIMf, PF, and VM, and grams of eight dietary components across the BQS tertiles. The eight dietary components of interest were fruits and vegetables, whole-grain, refined-grain, milk and dairy, plant fats, animal fats, sugary foods, and sweet-tasting beverages (e. g. , soda and fruit juices). A post-hoc comparison (Tukey's HSD test) was performed when the difference was significant. Statistical analyses used the R software version 4. 1. The level of significance was set to 5% for all tests.
Alternative BQS models with complete VMn sub-scores Breakfast meals in the INCA 3 database were evaluated using the four alternative BQS models. All four BQS models were highly correlated with each other (range r = 0. 76 to r = 0. 99; results not shown). With all the alternative BQS models, none of the 4,478 breakfasts got a 100% adequacy score. Mean BQS values ranged from 51. 6% (balanced model) to 58. 5% (unweighted model) and were comparable for the four models. The balanced model showed the highest correlations with NRF9. 3 nutrient density scores (r = 0. 55) and the lowest correlations with energy density (r = -0. 15) of breakfasts in the INCA database (Figure  2 ). The balanced model had a moderate correlation with LIM.
Sensitivity through three examples of breakfasts The sensitivity of the BQS was tested using three alternative breakfasts. Breakfast 1 ("tartine") was a baguette with jam; Breakfast 2 ("cereal") was ready-to-eat (RTE) cereal and milk; and Breakfast 3 ("sandwich") was a savory sandwich. Four to five versions of each breakfast (v0, v1, v2, v3, v4, and v5) were constructed (Table  4 ). Breakfast nutrient content was calculated using the "Breakfast Calculator" online tool, developed by our group and available at https://ms-nutrition. com/web-app/ breakfast-calculator/. The version used for the article contained the nutritional composition of foods that corresponded to the dietary intake data (INCA3) and has since been updated to reflect the most up-to-date food composition database (i. e. , CIQUAL 2020).
The present approach differs from that of nutriscore, which does not include vitamins or minerals but awards points for the content of fruits, vegetables, legumes, nuts, and seeds. The number of vitamins and minerals was based on the IBRI recommendations. The performance of VMn scores was tested when the number of vitamins and minerals was allowed to vary from 0 to 14. This was done to determine the minimum number of vitamins and minerals that are necessary to assess breakfast quality. It was found that less than three vitamins and minerals should be critical to derive a robust BQS. The best choice is to conserve the 14 micronutrients. However, not all food databases, especially those currently available for low-and middle-income countries, have a full set of nutrients, so our results demonstrated that using <14 micronutrients could be a viable alternative.
We discuss their role as a tracers of the physical conditions of gas and provide constraints for parameters commonly used in PDR models. The observations and data reduction are described in Sect. 2, whereas the observational results are presented in Sect. 3. In Sect. 4 we describe the radiative transfer modeling of the molecular emission and derive constraints on the physical conditions of the gas. In Sect. 5 we describe the PDR models that best reproduce the observations. We discuss the evidence of a steep density gradient and dynamical effects in the Horsehead in Sect. 6, and we summarize the results and conclusions in Sect. 7.
g. , SH + , SO + , and HOC + ), but their analysis will be discussed in a future work. The data reduction and imaging were performed using the Common Astronomy Software Application (CASA 1  ) version 5. 6. 1  (McMullin et al. 2007 ). The different execution blocks were calibrated following the standard pipeline of The Joint ALMA Observatory (JAO). The calibrated visibilities of the 12 m array and ACA were merged into a single Measurement Set with the concat task, and then the data was imaged with the tclean function. The spectral cubes were cleaned using a Briggs weighting with a robust parameter of 0. 5. After trying different cleaning masks, we cleaned the images without any mask in order to include all the line emission from the molecular cloud and the H II region, avoiding any possible biases 2.
Acknowledgements. The authors thank the referee for the constructive comments that improved the content of this work. We also thank the  ALMA Data Analysts  and Support Scientists from the  North American ALMA Science Center (NAASC)  for their support in data reduction. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2019. 1. 00558. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The  Joint ALMA Observatory  is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the  National Science Foundation  operated under cooperative agreement by  Associated Universities , Inc.
C. H. -V. acknowledges support from the  National Agency for Research and Development (ANID) -Scholarship Program  through the  Doctorado Nacional  grant no. 2021-21212409. V. V. G. gratefully acknowledges support from  FONDECYT Regular   1221352 ,  ANID BASAL  projects  ACE210002  and  FB210003 , and  ANID ,  -Millennium Science Initiative Program   -NCN19_171. J. R. G. thanks the  Spanish MICINN  for funding support under grant  PID2019-106110GB-I00. V. M. , J. P. , F. L. P. , M. G. , E. B. , E. R. , P. G. acknowledge support by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP , co-funded by  CEA  and  CNES.
Setting and data sources This is a single center, retrospective, cohort study using institutional electronic medical records and a national health administrative database in Strasbourg University Hospital France. The study protocol was approved by the French Society of Anesthesiology Institutional Review Board on July 6, 2020 (IRB 00010254-2020-150). As per French law  [21] , individual patient informed consent was not required for this retrospective cohort research. The study was registered to the institutional Data Protection Officer registry (#20-042). The French National Uniform Hospital Discharge Database (Programme de Médicalisation des Systèmes d'Information, PMSI), which includes medical and administrative data for every hospitalization, was screened to select eligible patients (see below).
Covariates The following patient characteristics previously associated with mortality after hip fracture were recorded  [8] : age, sex and medical comorbidities including chronic obstructive pulmonary disease, heart failure (HF), and coronary artery disease. These comorbidities were extracted from the PMSI database using the ICD-10 codes(supplemental Table  1 ). Surgery duration and preoperative DOA dosing were extracted from the EMR. Medications taken at home at admission were extracted from the EMR and the use of DOA was recorded (apixaban, rivaroxaban or dabigatran). The administration of Fresh Frozen Plasma (FFP) or Prothrombin Complex Concentrate (PCC) during hospital stay was extracted from the EMR. The dispensation of idarucizumab during the study period was extracted from the hospital pharmacy charts and the medical indications in the corresponding EMR were analyzed.
Outcomes The primary outcome was all-cause mortality within 30 days after hip fracture surgery. This outcome was extracted from the open database of deceased people in France, published by the French National Institute of Statistics and Economical Studies (INSEE). This 30-day follow-up period is among the most common outcome criteria to describe the association between wait-time and postoperative mortality after hip fracture surgery  [8]. Secondary outcomes were 90-day mortality, medical complications during initial hospital length of stay including deep venous thrombosis, pneumonia, myocardial infarction, and stroke. Medical complications during initial hospital length of stay were extracted from the PMSI database using ICD-10 codes (supplemental Table  1 ). Red blood cell transfusions during the hospitalization were extracted from the EMR.
We considered SMD of more than 0. 1 or less than -0. 1 as significant. The study size was established on a pragmatic basis considering the exposure time period to maximize sample size while minimizing chronological bias. For each analysis, patients with relevant missing data were excluded, so no imputation was performed. The analyses were carried out using the R software (with ad hoc packages, e. g. , R2jags). The study was carried out with institutional funding only.
Second, the primary exposure, time from hospital admission to surgery was based on the EMR, which allowed for a precision at the level of hours and minutes. In contrast, in many administrative datasets (PMSI for example), time from hospital admission to surgery is only available with a precision at the level of days, not hours as in our study. Third, our study offers time specific epidemiologic description of the increased prevalence of DOA in patients admitted for hip fracture surgery. We observed a gradual increase in DOA prevalence during the years 2015 to 2019 from 2 to 12%. These epidemiological data emphasize the need to improve our knowledge on the best therapeutical options required for patients receiving DOA who are admitted for hip fracture surgery.
The mileage is computed from the acquired runner servomotor position, the contact pressure comes from FEMs, and the wear coefficient is chosen according to the literature. Wear in [mm] is converted into a non-dimensional KDI by defining a critical limit value for wear. The status of fatigue of the runner regulating mechanism: From the recorded servomotor's opening and closing pressures, a servomotor force is computed that is then converted into stress by applying FEM results. The number of load cycles is determined using rain flow counting. Using Miner's rule and an appropriate S-N curve yields cumulated damage for the considered period. The same method can be applied to different parts of the runner regulating mechanism. In order to compute these indicators based on real operation, operation data are acquired from the MVX monitoring system for unit 1 and unit 3.
e. , it is a digital twin  [28] [29] [30]. This system allows continuous diagnosis of the health of a hydropower plant with digital cloning of the main hydraulic and electrical components of the plant. The Hydro-Clone's general concept is illustrated in Figure  14. The system manages the tasks of real-time acquisition and transfer of boundary conditions and measured quantities to the SIMSEN model, as well as data processing and diagnosis of the HPP health status. A custom-built archival storage system and an associated database allow for the display and analysis of previous results. The analysis and comparison of simulated and measured quantities enable us to understand, at any time, the fitness and behavior of all essential components of the system and to estimate non-measured/non-measurable quantities throughout the entire system.
Biolayer interferometry Biolayer interferometry (BLI) performed on an Octet RED384 instrument (FortéBio) was used to determine the binding affinity between different BTB-Kelch and CUL3 protein constructs as indicated. Biotinylated protein ligand buffered in 50 mM HEPES, 300 mM NaCl, 0. 5 mM TCEP, and 10 mM DTT was used at 0. 16 mg/mL to immobilise ligand onto streptavidin-coated fiber optic tips (FortéBio) to yield a binding response of 7-8 nm. Serial dilutions of the test analyte protein in the same buffer supplemented with 0. 01% TWEEN-20 were placed in the relevant wells, with matching buffer in the reference wells. Association and dissociation phases were recorded as indicated. Steady-state equilibrium and kinetic fits were performed by global data analyses in the ForteBio Data Analysis 9.
TR-FRET measurements Unless otherwise noted, experiments were performed in white, 384well microtiter plates (Corning 3572) in 30 μL assay volume. TR-FRET measurements were acquired on a Tecan SPARK plate reader with SPARKCONTROL software version V2. 1 (Tecan Group Ltd. ), with the following settings: 340/50 nm excitation, 490/10 nm (Tb), and 520/10 nm (AF488) emission, 100 μs delay, 400 μs integration. The 490/10 nm and 520/10 nm emission channels were acquired with a 50% mirror and a dichroic 510 mirror, respectively, using independently optimized detector gain settings unless specified otherwise. The TR-FRET ratio was taken as the 520/490 nm intensity ratio on a per-well basis.
Determination of equilibrium dissociation constant (K D ) of AF488labeled CUL3 toward CoraFluor-1-labeled KEAP1 and KLHL11 CoraFluor-1-labeled KEAP1 and KLHL11 were diluted to 1. 5x (30 nM and 3 nM, respectively) in assay buffer (25 mM HEPES, 150 mM NaCl, 1 mM DTT, 0. 5 mg/mL BSA, 0. 005% TWEEN-20, pH 7. 5). AF488-labeled CUL3 NTD was added in serial dilution from 3x stock solutions in assay buffer (1:2 titration, 12-point, c max = 1000 nM) and allowed to equilibrate for 2 h at room temperature before TR-FRET measurements were taken. Data were fitted to a One Site -Total Binding model in Prism 9.
Measurement of dissociation rate constants (k off ) by TR-FRET Solutions of: (i) 20 nM CoraFluor-1-labeled KEAP1, 300 nM AF488labeled CUL3 NTD , and (ii) 2 nM CoraFluor-1-labeled KLHL11, 45 nM AF488-labeled CUL3 NTD were prepared in assay buffer and allowed to equilibrate for 2 h at room temperature before initial (t = 0) TR-FRET measurements were taken. Following addition of 25 μM unlabeled KLHL11, the time-dependent change of TR-FRET intensity was recorded (in 30 s intervals) over the course of 30 min. Data were normalized and fitted to a one-phase decay model in Prism 9.