dataesr/scientific-paragraphs-categorization huggingface dataset:
The average cost for care to PLCs (medical and general costs) can therefore be determined from the financial statements of each case in the study based on public reports. Physician fees, which include all medical procedures invoiced by physicians to the RAMQ that are not included in these reports, will be captured from the RAMQ along with the relative intensity levels of resources used. These data will provide an individual cost estimate for hospitalizations. Unit costs of medical services will be calculated for the most recent full fiscal year using a methodology published by two of the co-investigators  [110]. Costs for outpatient oncology clinic visits and emergency room visits will be assessed on a cost-per-visit basis. Costs for hospitalizations include costs for hospital stay (cost per day) and day surgeries (cost per surgery).
Quantitative analyses The validity of quantitative results is based on the use of questionnaires with demonstrated psychometric qualities and implementation of the analysis plan under the guidance of co-investigators with solid expertise. Statistical analyses will be performed using IBM SPSS Statistics version 25  [111]  and SAS 9. 4  [112]. Values of p < 0. 05 will be considered significant. Missing data for the variables will be imputed using the multiple imputation technique. Descriptive statistics will be used to summarize sociodemographic and clinical variables for PLCs and professional experience variables for care providers at each site. Student tests or non-parametric tests such as the Mann-Whitney test and ANOVA tests will be used for continuous variables. For categorical variables, square Chi-square tests will be used to identify differences between groups.
Therefore, more attention should be given to human factors to ensure productivity, health and safety requirements. As a matter of fact, several approaches and tools were proposed to consider human factors during manufacturing system design process. Tools such as Human Digital Models, Predetermined Motion Time Systems  [6]  and Discreet Event Simulation software  [7]  can be used to predict respectively the biomechanical hazards, standard times and workload and how human factors (such as learning and fatigue) impact the task processing time and error probabilities  [8]. Regarding the whole manufacturing design process, there is three main approaches for achieving human factors integration using these tools  [9]  : • Classical approaches which use the available knowledge in shape of models that can be incorporated in form of criteria to predict working conditions and validate the tasks requirement regarding ergonomics, health and safety of human operators; • Socio-technical approach which considers that a manufacturing system is composed of two sub-systems that must be jointly optimized: the sub-technical system, referring to the manufacturing equipment (machines, workstations…) and the social sub-system (workers within a specific organization); • Human centred-approach which aims at establishing an anthropocentric manufacturing system by driving the design objectives toward reinforcing the human position in the system (enhancing workers collaboration, knowledge…).
Design assessment The simulation conducted under simulation software SIMIO gives an estimation of the system throughputs including rejected parts due to human errors: 601 assembled blinders, including 41 rejected. A further examination of fatigue distribution shows that worker 2 and 3 have a fatigue index relatively high in comparison with the rest of workers, leading to a rise in human error probability and altering working conditions. The case study introduced in the previous section showed that balancing the system based on capacities is inefficient. Focus should rather be given to flow with consideration of variations by including behaviours in the analysis. To reduce workstation 2 and 3 workload, reallocation of operations between stations 2, 3, 5 and 7 is made. W7 performs operation 14 and 13, W3 get operations 1, 12 and 10, W5 performs 7 and 11.
Acknowledgement The authors gratefully acknowledge the support of the common laboratory LC2S between LCFC and INRS; and the  Industrial Research chair "SYSTEMES DE PRODUCTION RECONFIGURABLES, SURS ET PERFORMANTS" of Arts et Métiers , co-founded by  Thyssenkrupp Presta France, UIMM Lorraine  (fond  F2i ) and the  European Regional Development Fund  (" Programme opérationnel FEDER-FSE Lorraine et Massif de Vosges 2014-2020 ").
(2013) , when they record the SNR data. Contrary to classical tide gauges that are located in protected areas to dampen the effect of the waves on the signal, the GNSS-R based time-series of Sea Surface Height (SSH) contains both of the signatures of the tide and other information, like the waves  [26] , as well as the surges signatures. In this study, the SNR data records from the Socoa geodetic station (south west of France) were used to determine the SSH variations from January to March 2010 in the Saint Jean de Luz Bay. Two methods (Singular Spectrum Analysis-SSA and Continuous Wavelet Transform-CWT) were applied to the SSH derived from SNR records to separate the tide signature from other geophysical signals. Comparisons were performed against the tide gauge records from the Socoa station.
GNSS Data GNSS data, including SNR, have been continuously acquired since the 1st of April 2006 at a frequency of 1 Hz while using a Trimble NETR5 receiver (from 2009 until 2010) and a Trimble Zephyr model 2 (TRM55971. 00) geodetic antenna in Socoa (43°23'42. 83"N, 01°40'54. 05"W) at 10. 66 m above the mean sea surface (Figure  1b ). This station (SCOA) is part of RGP (Réseau GNSS Permanent-http://rgp. ign. fr/) from IGN (Institut National de l'Information Géographique et Forestière). In this study, the data used were acquired at L1 frequency from GPS and GLONASS constellations from the 1st of January to 31 March 2010.
Tide Gauge Data SSH records from the Socoa gauge station that was operated by the French REFMAR network (http://data. shom. fr/donnes/refmar/) were used for comparison with SSH estimates, derived from the SNR technique, from January to March 2010. SSH measurements are available with a temporal resolution of 10 minutes. The instrumental zero (of this tide gauge) is set to the hydrographic zero. The Socoa tide gauge is located (43°23'42. 86"N; 01°40'53. 83"W) at the end of Socoa's breakwaters, protecting it against wave effects, 5 m away from the GNSS station. The Socoa station is equipped with a digital coastal tide gauge that is composed of an Optiwave 7300C sensor and a MARELTA acquisition unit.
Meteorological Data The Socoa meteorological station provides hourly estimates of rainfall, wind speed, and atmospheric pressure. Records from the period January-March 2010 were used in this study. Infoclimat (https://www. infoclimat. fr) made them available. The atmospheric pressure was converted into inverted barometer effect (ℎ ) using the following equation: ℎ = - - ρg (1) where is the time varying mean of the global surface atmospheric pressure over the oceans; ρ = 1,020 gcm -3 is density of seawater; and, g = 9,81cms -2 is the mean acceleration of gravity  [32].
This means that: -many other GNSS geodetic stations from permanent networks around the world can also be used as accidental tide gauges to complement or improve the existing tide gauge networks, -GNSS geodetic stations offer the opportunity to record other geophysical phenomena such SWH (eg. Roussel et al. , (2015)  [22] ) or surge or inverted barometer (this study), and -if located on a better environment as the top of a hill or a mast, better accuracy can be reached (e. g. , 0. 12 m see  [47] ). The choice of the location and the altitude of deployment can be facilitated by the use of dedicated softwares, such as GNSS Reflected Signals Simulations (GRESS)  [48]  or the GPS tool box  [49] , which provide a simulation of the position of the reflection points depending on the location of the GNSS geodetic station.
Acknowledgments: This work was supported by  Centre National de la Recherche Scientifique (CNRS)  through the  MISTRAL H2020  project and in the framework and by the  Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/pnts),grant n°  PNTS-2017-05. Phuong-Lan Vu gratefully acknowledge the Vietnamese Government 's 911 project for funding during my thesis, without which the present study could not have been completed. We thank the  French REFMAR network  for providing us with the tide gauge records from Socoa station and the  RGP (Réseau GNSS Permanent  -http://rgp. ign. fr/)from  IGN (Institut National de l'Information Géographique et Forestière)  for providing the GNSS data at the SOCOA GNSS tide gauge station. We thank three anonymous Reviewers for their very helpful comments that helped us in improving the quality of the manuscript.
Study population, blood collection, and follow-up EPIC is an ongoing multi-center cohort study including approximately 520,000 participants recruited between 1992 and 2000 from ten European countries  [19]. Female participants (n = 367,903) were aged 35-75 years old at inclusion. At recruitment, detailed information was collected on dietary, lifestyle, reproductive, medical, and anthropometric data  [19]. Around 246,000 women from all countries provided a baseline blood sample. Blood was collected according to a standardized protocol in France, Germany, Greece, Italy, the Netherlands, Norway, Spain, and the UK  [19]. Serum (except in Norway), plasma, erythrocytes, and buffy coat aliquots were stored in liquid nitrogen  (-196 °C)  in a centralized biobank at IARC. In Denmark, blood fractions were stored locally in the vapor phase of liquid nitrogen containers (-150 °C), and in Sweden, they were stored locally at -80 °C in standard freezers.
6 years/more than 8. 6 years), and at recruitment waist circumference (WC) (< 80 cm/≥80 cm), BMI (< 25 kg/m 2 /≥25 kg/m 2 ), and country, by introducing interaction terms in the models. Subgroup analyses were conducted on the raw models. For WC, unconditional logistic regression adjusted for each matching factor was used. P values were not corrected for multiple tests since heterogeneity was investigated only for metabolites showing statistically significant associations with risk overall, after correction for multiple testing. A sensitivity analysis of all 127 metabolites was performed on hormone non-users (1124 cases and 1124 controls) and by cancer subtype. Analyses were conducted using SAS software for Windows (version 9. 4, Copyright© 2017, SAS Institute Inc. ) and R software (packages Epi and NPC)  [25, 26].
Funding This work was funded by the  French National Cancer Institute  (grant number  2015-166 ). Mathilde His' work reported here was undertaken during the tenure of a postdoctoral fellowship awarded by the  International Agency for Research on Cancer , financed by the  Fondation ARC. The coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Ministry of Education and Research (BMBF) ,  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany) ; the  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  European Research Council  ( ERC-2009-AdG 232997 ), and  Nordforsk, Nordic Centre of Excellence Programme on Food, Nutrition and Health (Norway) ;  Regional Governments of Andalucía, Asturias ,  Basque Country ,  Murcia  (no.
Primary hyperoxaluria type 2 is a rare inherited disorder of glyoxylate metabolism causing nephrocalcinosis, renal stone formation and ultimately kidney failure. Previously, primary hyperoxaluria type 2 was considered to have a more favorable prognosis than primary hyperoxaluria type 1, but earlier reports are limited by low patient numbers and short follow up periods. Here we report on the clinical, genetic, and biochemical findings from the largest cohort of patients with primary hyperoxaluria type 2, obtained by a retrospective record review of genetically confirmed cases in the OxalEurope registry, a dataset containing 101 patients from eleven countries. Median follow up was 12. 4 years. Median ages at first symptom and diagnosis for index cases were 3. 2 years and 8. 0 years, respectively.
10 Mapping of the genomic location for the GRHPR gene has allowed molecular diagnostics, and thus far 39 disease-causing mutations have been identified (Human Gene Mutation Database [assessed February 2018] and https://www. ncbi. nlm. nih. gov/clinvar/ [accessed April 5,  2018]). Despite advances in our understanding of molecular etiology, the limited number of patients makes it difficult to relate genotype to clinical phenotype at this time. In early reports, PH2 had been considered to have a milder phenotype than PH1 despite urine oxalate excretion of similar magnitude  [10] [11] [12]  and a similar age at onset of disease. 0] [21] [22] [23]  Conservative treatment consisting of high fluid intake (>3 l/m 2 body surface area) and calcium oxalate crystallization inhibitors (e. g. , potassium citrate) is similar to that prescribed for PH1, with the exception that pyridoxine (vitamin B6) is ineffective in treating PH2.
RESULTS We obtained data for 101 patients with PH2 (46 females and 55 males) from 75 families. The overall frequency of PH2 in the OxalEurope registry was 8. 7% compared with 84% for PH1 and 6. 3% for PH3. Eleven European countries contributed, but more than half of all patients lived in the United Kingdom. Cohort characteristics are shown in Table  1.
The exception, c. 740A>G (p. Asp247Gly), was flagged as a likely benign variant but was not reported in any of the single nucleotide polymorphism databases (ClinVar, ExAC, and gnomAD) and thus, by definition, it is not a common polymorphism. This change occurred in cis with another variant, c. 743T>A (p. Val248Asp), which was predicted to be pathogenic. One mutation, c. 352_365dup (p. Val116fs*) was a complex change involving insertion of a portion of intron 2 into the exon, along with duplication of nucleotides 352_365. Three major genotype groups occurred: c. 103delG, c. 404þ3_404þ6del, and c. 494G>A, accounting for 56 (27. 7%), 40 (19. 8%), and 36 (17. 8%) pathological alleles, respectively. The first of these was only found in White individuals, whereas the other 2 mutations were solely in persons of Asian origin.
METHODS A retrospective review was performed of all patients with PH2 who were registered in the OxalEurope database and in whom data of sufficient detail had been recorded. OxalEurope is an international European network of specialists involved in the treatment and research of PH. At this moment, the OxalEurope registry includes more than 1100 patients with PH from more than 21 countries. Previously published PH1 data  33  were used to compare cumulative renal survival. All patients were identified after presentation to one of the participating OxalEurope centers. Diagnosis was confirmed by either liver GR/HPR enzymatic analysis activity in liver biopsy and/or genetic testing. In addition, age and symptoms at time of diagnosis (e. g. , nephrocalcinosis, urolithiasis, and renal function) were required for inclusion.
Four patients with PH2 were excluded based on insufficient data, because we had no other clinical information available besides their genotype at the time this article was prepared. To improve the quality of data entry, 1 investigator (SFG) performed checks on data entry for inconsistencies by local review of medical charts. Informed consent was obtained for all patients. Follow-up data that were not primarily entered into the database could not be included without patient consent because of European data privacy legislation. For genetic testing, the numbering was based on reference sequence NM_012203, nomenclature follows the Human Genome Variation Society guidelines (http://www. hgvs. org/), and sequence variants were classified according to the American College of Medical Genetics and Genomics guidelines.
ACKNOWLEDGMENTS This project received funding in part from the  European Union 's  Horizon 2020 research and innovation programme  under the  ERA-NET  Cofund action number  643578 DFG grant BE 60721/11  (BBB) and  Spanish ministry of science  grant  SAF2015-69796  (ES). The authors thank all the patients and their clinicians and the scientists in the  OxalEurope Consortium  who assisted with the data collection.
Pole Vault Biomechanics and Injuries Conclusion: Biomechanical pole vault patterns during the competition day were associated with a higher proportion of history of all injuries. Although the injury data collection was retrospective leading to recall bias risk, and do not allow determining cause-consequence relationships regarding biomechanical patterns and injury occurrence, this present study is the first to analyze potential association between the biomechanical pole vault patterns and injury occurrence, which is of great help to provide hypotheses/ideas to design future studies and to move forward into prevention measures.
Study Design and Procedure We conducted a study over national-level pole vaulters including the prospective collection of pole vault biomechanical data during a competition in the context of the national Elite indoor championship and youth national indoor championship (U17 and U20) and the retrospective collection of injuries during the 12 preceding months through an online questionnaire. Those data were collected as part of national Elite follow-up programme from French Athletics Federation (https://www. athle. fr). The study protocol was reviewed and approved by the Saint-Etienne University Hospital Ethics Committee (Institutional Review Board: IORG0007394; IRBN322016/CHUSTE).
Population We proposed to all pole vaulters participating at national Elite indoor championship and youth national indoor championship (U17 and U20) to be volunteer for this study. Pole vaulters were included if they were registered with the French Athletics Federation, had no contra-indication for athletics participation, were able to participate at the pole vault competition, were able to read and reply to survey in French sent by internet, and accepted to participate at the study. The day of the competition, athletes (and their parents when minors) were informed about the study aim and procedure, and gave their consent to participate and their data being used for research.
Injury Data Collection At the time of the competition the included pole vaulters were asked to complete an online survey about number of year of pole vault practice, mean number of hours of athletics training per week, if they had an injury history during the last 12 months, and if they have currently a pain or discomfort during pole vault. For the purpose of the study injury was defined as: "Any pain, discomfort, or lesion of the musculoskeletal system (e. g. , bones, muscles, tendons, ligaments. ), which occurred during sports practice (i. e. , training or competition), regardless of the consequences on sport and medical attention, occurring in the last 12 months. " If athletes replied yes, they were asked to detail for each injury the injury location (e. g. , hamstring, ankle. ).
Pole Vault Biomechanical Data Acquisition Pole vault biomechanical data were collected, in the context of a national level competition, during run-up until take-off with the similar set-up than during previous studies  (Cassirame et al. , 2018). Twenty meters of optoelectronic system (Optojump Next Microgate, Bolzano, Italy) was installed on the official lane to measure run-up kinematics. Due to the landing mat, optoelectronic system could not be installed until the planting box and was installed up until 2. 00 or 2. 20 m before the box (Figure  1 ). This material permits measurement of contact time on the floor (t c ), aerial time (t a ), stride rate (SR), and stride length (SL). SL asymmetry (SL asy ) was calculated as the absolute difference of distance covered on three left-foot strides minus the distance covered on three right-foot strides.
SL variability (SL var ) was calculated as the mean of the differences between stride length over successive steps. SR, SL, SL asy , SL var , t a, and t c were measured and averaged from the 3rd up to 8th last stride of the approach. Last two strides of the run-up were not take into account because they are commonly used to adjust takeoff distance and are not representative of the running kinematic  (Makaruk et al. , 2016). Finally, last stride adjustment (SL adj ) was calculated as the final SL minus the penultimate SL. Negative SL adj indicated a reduction in the last SL, and a positive value indicated a longer final stride  (Cassirame et al. , 2018). Position of the foot at take-off (PoTk) was calculated using position data output from the optoelectronic system and the distance from the planting box (Figure  1 ).
Horizontal running velocity was measured using Radar gun (Stalker Pro II, Stalker ltd, Plano, TX) positioned behind the landing mat at 1. 4 meter height offering no angle deviation with athletes trajectory (Figure  1 ). Data output from radar were collected at 46. 9 Hz by MookyStalker software (Matsport, Saint-Ismier, France) and synchronized with the Optojump Next system to calculate approach speed between 10 and 5 meters to the box (Spd) and speed increase in last 5 m of the runup ( Spd). During the take-off phase, a video analysis was performed with a Gopro Hero 5 camera (San Mateo, California, United-States) using a sampling rate of 240 frames per seconds and a resolution of 1280 × 720 pixels. The camera was positioned at a distance of 4 m perpendicular to the lane at a 3. 5 m distance from the box to avoid parallax error.
Before each competition, calibration videos were collected using a calibration stick of known length (2. 40 m) in the plane of measurement. Video analyzes were manually performed with Kinovea software 08. 15 (Joan Charmant & Contributors, Bordeaux, France) to output several length measurements in two different positions. The Position 1 occurred when the athlete was in contact with the ground at the instant of pole plant in the box, and the Position 2 occurred when the athlete subsequently took off from the ground (Figure  2 ). At both positions, the height of the grip (superior) hand from the ground was measured and noted as H1 and H2 for Positions 1 and 2, respectively. In addition, the anteroposterior distance between the grip hand and the take-off foot's toes was calculated at the two positions and noted as U1 and U2 (Figure  2 ).
If the grip hand was posterior to the toes, this value was negative. From these four measurements, H and U were calculated in order to obtain vertical and horizontal displacements of the grip hand between the two positions. H and U were calculated as follows: H = H2-H1 and U = U2-U1. To complete this analysis, the distance between hands (HD) and the distance between the grip hand and pole extremity was also measured. This last measurement permits to calculate the grip height (Grip) used by athlete during trials. Finally, data related to the poles used during the competition were collected from coach and/or athlete interview. For each trial, pole length and stiffness index (P Stiff ) were collected. Pole length information was use to deduct grip (Grip) used by athlete use measurement processed by video analysis (Distance upper hand to extremity of the pole in the box).