dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonlinear response In the absence of out-of-time pileup, the PCC luminosity is expected to be linear, according to simulations, so no corrections are applied. Moreover, the ratios of PCC to both DT and RAMSES luminosity measurements are highly compatible as a function of the instantaneous luminosity without any corrections. The HFOC response in 2015 and 2016, on the other hand, exhibits significant nonlinearity compared to the other luminometers. The main source of nonlinearity is the uncalibrated ADC-to-charge conversion applied at the time of data taking. Data from fill 5416, which exhibit a wide range of instantaneous luminosity, are used to model the correction for HFOC with a fourth-order polynomial. This smooth function extrapolates to the σ vis calibration at low pileup within uncertainty.
PCC measurements are valid for 98. 3 (94. 3)% of the data set in 2015 (2016). The rest of the luminosity is provided by the next most stable luminometer, which is RAMSES (HFOC) for 2015 (2016). The primary luminosity, which is PCC or luminosity from the next most stable detector when PCC is unavailable, is compared with the next-best available luminometer (secondary). In Fig. 18 , the latter is selected using the lowest standard deviation in the ratio relative to PCC over fixed time intervals of approximately 20 min each. The position of the mean shows the agreement between the luminometers on the integrated luminosity. The width reflects stability effects, as well as residual statistical uncertainty in the luminosity measurement in each interval. From the distribution over the course of each year, the width is an upper limit on the uncertainty due to time dependencies in the luminometers.
Time dependence of linearity We make use of two methods for assessing the detector linearity. The primary method compares the ratio of the instantaneous luminosity from two luminometers per fill as a function of  the instantaneous luminosity, which is estimated from the numerator. A first-order polynomial fit is performed and the slope is extracted. The slopes per fill are then studied as a function of time. No significant deviation over time is observed between DT/PCC or RAMSES/PCC and HFOC/PCC, DT/PCC, or RAMSES/PCC in 2015 and 2016, respectively. To estimate the uncertainty, the fitted slopes are weighted according to the per-fill integrated luminosity. The mean values deviate slightly from 0, and the largest deviation is the systematic uncertainty in the linearity of PCC luminosity. Figure  19  shows the summary of these slopes for 2015 and 2016 at √ s = 13 TeV both for the whole year, and for subsets of each data set with equal luminosity.
Since the source of instability cannot be assessed and contains time-dependent features, the uncertainty is not correlated. The tool used for providing luminosity values to physics analyses applies the corrections to the raw luminosity values using the average per-bunch luminosity, rather than the individual bunch-by-bunch values. This potentially introduces an error in the case where these corrections include a nonlinear term and the bunch-by-bunch luminosity varies significantly among bunches. We evaluated the effect of this approximation on 2016 data, and found that the overall impact on the integrated luminosity was <0. 1%. Finally, the quantity measured by the luminometers is the luminosity delivered to CMS; however, the quantity of interest to most physics analyses is the luminosity corresponding to the data actually recorded by the CMS DAQ system.
Summary The luminosity calibration using beam-separation (van der Meer, vdM) scans has been presented for data from proton-proton collisions recorded by the CMS experiment in 2015 and 2016 when all subdetectors were fully operational. The main sources of systematic uncertainty are related to residual differences between the measured beam positions and the ones provided by the operational settings of the LHC magnets, the factorizability of the transverse spatial distributions of proton bunches, and the modeling of effects on the proton distributions due to electromagnetic interactions among protons in the colliding bunches. When applying the vdM calibration to the entire data-taking period, the relative stability and linearity of luminosity subdetectors (luminometers) are included in the uncertainty in the integrated luminosity measurement as well.
Regardless, the pandemic has clearly changed the treatment of patients with acute appendicitis as most surgeons understand it. This should be a wake-up call to everyone, as we are at a time when different parts of our country are experiencing their own realities regarding the pandemic. It is worth noting the limitations of this analysis. The sample of 382 surgeons may not be adequate, as there was no sample calculation. Due to problems related to databases, we were unable to cross-reference more data for statistical analysis. Perhaps the approaches would be different had we compared more seasoned surgeons with less experienced ones, for example. There is a lot of variability in the availability of materials for laparoscopy in a continental country like Brazil. Therefore, the option for the open route, more than a technical option, may reflect the lack of specific material in public services.
Data Collection The data collection was performed through semi-structured interviews, following a guide with a set of open-ended questions to facilitate the in-depth discussion of the subjects of interest. The interviews were conducted during the month of October 2021, at the homes of the informants in order to establish an adequate environment to facilitate expressions of feelings and emotions in an atmosphere of sincerity. The mean duration of the interviews was forty minutes. The main researcher recruited the participants through non-governmental organizations working in the different neighborhoods, which provided the contact information of Romani women (RW) who met the inclusion criteria. The interviews were recorded with the participant's permission. When 16 individuals were interviewed, the topics became repetitive.
Data Management and Analysis The recorded interviews were transcribed and edited by the interviewer to guarantee the exactness of the transcription. The data were stored, managed, classified, and organized with the help of the analysis software ATLAS-ti 8. 0. Firstly, an iterative reading of the transcripts was performed. The themes initially identified were aligned to the 4 open-ended questions formulated in each semi-structured interview. These questions were related to the participants' perceptions about their Romany identity, social and economic conditions, health and sickness concepts, and experiences with health services. It should be noted that the theoretical framework to build these questions was based on the previous scientific literature and the objectives established in our study.
Geometry and Material Properties An aTAA patient-specific morphology was reconstructed based on CT data. The data were acquired in vivo on a patient harboring an aTAA, just before surgical intervention at the Heart Hospital of Massa. The CT images were obtained with a 320-detector scanner (Toshiba Aquilon One, Toshiba, Japan) by adopting a iodinated contrast medium. The clinical data in the diastolic phase were analyzed through a threshold segmentation algorithm to obtain the aortic morphology including the aTAA section, the supra-aortic branches and the descending aorta. After assessing the morphology, material properties were defined on the basis of ex vivo tissue characterization. In particular, a tissue sample was collected from the aTAA after the surgical intervention. A square specimen of about 30 × 30 mm was cut in the outer curvature region of the harvested aTAA tissue.
Reverse Displacement Algorithm Before performing FSI simulations, the unloaded ZP configuration was firstly reconstructed. To obtain the ZP configuration from the in vivo diastolic configuration, a reverse-displacement method was implemented, as introduced by  Raghavan et al. (2006). The material properties were the ones fitted against the biaxial test data. The software ANSYS mechanical was used for all the computations. Fiber-based constitutive relationships according to Equation (15) were implemented in the ANSYS environment with a custom made FORTRAN script. The reverse displacement method workflow is represented schematically in Figure  2. The main principle is to find a scalar quantity to describe the size change of the aTAA structure on the basis of a distance score, evaluated with respect to the DIA configuration.
SOL-Linearized Simulation After assessing the ZP configuration, the SOL-Linearized simulation was setup. All the steps for the SOL linearization procedure are summarized in Figure  3. Software ANSYS mechanical and ANSYS Fluent were used for all the simulations. All computation were achieved with the same machine (Processor : Intel Core i9-9900, 4 cores, CPU@3. 10 GHz; RAM: 32 GB). The main steps are given by the Large Deformation and the Small Deformation steps.
A physiological velocity profile was imposed as inlet at the aortic valve level. In particular, the profile was obtained from 4D flow MRI datasets, performed by means of a 3T MR-scanner (Ingenia, Philips Medical Systems, Amsterdam, Netherlands). Given the aortic valve section, the imposed velocity resulted in a cardiac output of 4. 8 l/min with an heart rate of 77 bpm. A total of 5 cardiac cycles were simulated. The pressure conditions were maintained at the brachio-cephalic artery (BCA), the left common coronary artery (LCCA), the left subclavian artery (LSA) and descending aorta (DA) outlets according to the three-element Windkessel model. The values of the lumped parameters were calculated according to a state-of-the-art algorithm  (Boccadifuoco et al. , 2018a)  and set to maintain a pressure range of 0-40 mmHg.
The linearized stiffness tensors were evaluated with a specific Matlab script by processing the previous large deformation step results. Then, the same script generates an APDL routine to re-assign the material properties of each element. Finally, the APDL routine is loaded within the ANSYS environment. The load condition was imposed on the basis of the data exchange of the FSI. The same boundary condition constraints from the previous LD simulation were imposed for this step. The pressure and flow conditions were calculated and post-processed. The circumferential and longitudinal stress distributions were evaluated along a cardiac cycle and presented as a result. The computation times were recorded to permit a comparison with the non-linear simulation.
Study Design and Setting We conducted a historical prospective cohort study from 1 January 2009 to 31 December 2016, with prospective measurement of exposure using population-based French insurance health databases. We investigated a 1/97 random sample of all beneficiaries of the French health insurance called "échantillon généraliste de bénéficiaires" (EGB)  [11]. The EGB constitutes a representative sample of the French population in terms of age, sex and geographical location, and it includes more than 660,000 individuals, whether they receive healthcare or not  [11]. The EGB is an open cohort that is continuously updated with new beneficiaries and newborn infants and contains exhaustive information on all outpatient healthcare reimbursements. Data from individuals affiliated with the main French health insurance scheme (general scheme, covering 76% of the French population) have been stored since 2005, whereas data from individuals affiliated with other schemes have been available since 2011.
Participants All women over 50 years old who were included in the database on 1 January 2009, i. e. , who were covered by the general scheme, were eligible  [12]. Women living outside metropolitan France were excluded because the risk of osteoporotic fracture depends on ethnicity  [13]. To be included, a four-year period of historical data had to be available because the risk of recurrence of osteoporotic fracture remains significantly higher during the four years after a previous fracture  [14]. Women with a history of metastatic cancer, renal failure, treatment for osteoporosis with medications other than bisphosphonates (i. e. , teriparatide, raloxifene, denosumab or strontium ranelate) and treatment with hormone replacement therapy or antithyroid drugs were excluded  [15, 16]. Because residual bisphosphonates can remain for up to 18 months, women who received at least two bisphosphonate prescriptions in the two years before 1 January 2009 were excluded  [5, 17].
Covariates Patient comorbidities were ascertained based on ICD-10 codes extracted from the hospitalization records or from the records of patients' long-term diseases available in the database  [11]. Long-term diseases correspond to diseases in which the severity and/or the chronicity require a long-term and particularly costly treatment entirely covered by the health insurance. In addition, some illnesses have been identified by specific treatments, such as dementia, chronic pulmonary disease, human immunodeficiency virus (HIV), insulin-dependent diabetes and hyperthyroidism. The Charlson Index  [20]  was computed at the start of the follow-up. All the covariates used in the Charlson Index and their weighting are described in Supplementary Material, Table  S1. Covariates known as osteoporotic fracture risk factors could be identified by the fracture risk assessment tool (FRAX)  [21]  (Supplementary Material, Table  S2 ).
As not all covariates composing the FRAX are available in our database, we included the following available covariates: age, history of hip fracture, corticosteroid intake (more than 5 mg per day within three months), history of rheumatoid arthritis, alcohol intake identified by diseases related to alcohol (Supplementary Material, Table  S3  and history of secondary osteoporosis (Supplementary Material, Table  S4 ). Women with at least 1200 mg of calcium prescribed per day during the three years before the start of follow-up were considered treated. Similarly, women with at least 500 IU of vitamin D per day were considered treated  [22, 23].
This result was not found with the weighted models. This could be explained by an indication bias: patients in poor health with a high fracture risk are probably treated less than others because of their many other health problems. The use of a marginal structural Cox model has corrected this bias. The third strength of this study was the use of the Charlson Index, which allowed us to estimate the patients' health status. We also used FRAX variables to identify risk factors for hip fracture. This allowed us to control for confounding factors related to the risk of osteoporotic fracture. The main limitations are related to the administrative medical databases. First, the EGB contains data about drug prescriptions and not information on real use. To limit this bias, we considered women as exposed if they filled two prescriptions.
Second, we could not consider zoledronic acid (administered only intravenously) because data about this treatment were not available in the database. Third, for outpatients, diagnoses were not collected by the health insurance system. Ascertainment of chronic conditions was based on the registered patient's long-term diseases, reimbursements for disease-specific treatments and hospitalization diagnoses. Fourth, we did not study bisphosphonate side effects because they do not require systematic hospitalization or result in long-term diseases.
Histological analysis Muscle samples were mounted in tissue freezing medium (tragacanth gum) and snap frozen in nitrogen-cooled isopentane. The sections, 5 μm thick, were mounted on HistoBond adhesion microslides (Marienfeld, Lauda-Königshofen, DE). Skeletal muscle sections were stained with haematoxylin and eosin (HE) to quantify myofibre area using NIH Image Software ImageJ64 (National Institutes of Health, Bethesda, MD, USA). For immunofluorescence analysis, skeletal muscle sections were permeabilized and blocked with PBST [1% Triton X-100, 1% Tween 20, 5% heat-inactivated goat serum, 0. 2% bovine serum albumin in phosphate-buffered saline (PBS)] for 30 min and incubated with primary antibodies diluted in PBST overnight at 4°C, washed with PBS, and then incubated with a mixture of appropriate secondary antibodies for 45 min at 37°C.
Fig. 4  b) shows the area probed during the measurements. The samples were cleaved in order to expose the sidewalls of a 50μm diameter via. This large via size was chosen to minimize the possible luminescence contribution from the cleaved sidewall. The via sidewalls were probed on a centered 8*8μm raster. Furthermore, a 3 keV beam energy was chosen to limit the bulk luminescence as the beam only probed the first ~50nm. The cathodoluminescence data are analyzed with the Labspec software that enables to extract the cathodoluminescence coming from the InGaP top cell and the (In)GaAs middle cell only.
Acknowledgement LN2 is a joint  International Research Laboratory (IRL 3463 ) funded and co-operated by  Université de Sherbrooke (Canada)  and  CNRS (France)  as well as  INSA Lyon , ECL,  UCBL ,  Université Grenoble Alpes  ( UGA ) as well as the  French national nanofabrication network RENA-TECH. The  TOF-SIMS  and cathodoluminescence measurements were supported by the  French"Recherches Technologiques de Base " program and were performed on the  Nano Characterization Platform (PFNC) of the CEA Grenoble. The support from  NSERC , Prompt and STACE through the  MARS-CPV  project is acknowledged. M. de Lafontaine  acknowledges  FRQNT  and  Université Grenoble Alpes  for financial support. The authors would like to thank  T. Chevolleau ,  T. Charvolin ,  S. Litaudon  and  D. Drouin  for fruitful discussions.
Cells were scanned with an increasingly focused FOV and a greater resolution-from a 35. 5 mm pixel size, corresponding to a 70 3 70 mm FOV, to 1. 76 3 1. 76 mm pixel resolution, and 3. 6 3 3. 6 mm FOV (imaging configurations are shown in Table  1 ). The highest resolution interior volumetric data acquisitions were required to provide visualization of the electrode layers post-testing-in particular, the PCC, which surrounded the nail. 6] [47] [48]  This was required to visualize the CC adequately. An initial coarse scan was conducted to establish the orientation and axes of the sample before a more detailed interior tomography was acquired. Post-reconstruction, visualization of the reconstructed tomograms was processed using Avizo Fire 9. 5 software (ThermoFisher Visualization Sciences Group [VSG], Merignac, France).
Imaging configurations for lab-based X-ray micro-CT is a proprietary filter provided by Zeiss as part of the Xradia 520 Versa. XRM, X-ray microscopy. of the UK Department of Business, Energy, and Industrial Strategy. The authors would also like to acknowledge the Royal Academy of Engineering for funding P. R. S. through CiET1718/59 and the Faraday Institution (EP/S003053/1, FIRG001). The authors also acknowledge the STFC for supporting P. R. S. and D. J. L. B. and for an Experiment Design Award for M. T. M. P under the auspices of the STFC Batteries Network (ST/R006873/1). This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DE-AC36-08GO28308. Funding was provided by the US DOE Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office.
ACKNOWLEDGMENTS The authors would like to acknowledge the expertise provided by  Zoran Bilc  and  Erman Cihan  during the calorimeter design, assembly, and instrumentation. Special thanks to the supporting facilities and technical staff at the  NASA JSC Energy Systems and Test Area (ESTA). We are grateful for the provision of synchrotron beamtime by the  ESRF  on beamline ID19 and for allowing us to use their facilities (Experiment ME 1515). The authors would like to acknowledge the  EPSRC  for supporting the energy storage work in the  Electrochemical Innovation Lab  ( EP/R020973/1 ;  EP/R023581/1 ;  EP/N032888/1 ;  EP/N001583/1 ;  EP/P009050/1 ;  EP/M009394/1 ;  EP/M014371/1 ). This work was partly funded by the  National Measurement System.
At least 1 kg of OW, composts, or digestates was collected per treatment. Samples were coded as indicated in Le  Bars et al. (2018). For each sample, certain pollutants and physicochemical parameters were measured (Supplementary Tables  1, 2 ). These datasets have been presented in Le  Bars et al. (2018)  and  Sertillanges et al. (2020)  and further analyzed in order to verify the chemical differentiation of samples according to their origin (urban, agricultural, and central). Total nitrogen (ISO 13878) and total carbon (ISO 10694) were determined by dry combustion with an elemental NC 2100 Soil Analyzer (Thermo Electron Corp. ). Inorganic carbon (ISO 10693) was determined with a Bernard Calcimeter by adding HCl and measuring released CO2. Organic carbon (C org ) was deduced from the difference between total and inorganic carbon.
d Raw organic wastes were used for the composting at the Urban-3 and Agri-3 sites. (Supplementary Table  4 ). Standard curves were generated using 10-fold serial dilutions (ranging from 10 5 to 10 0 gene copies) of cloned targeted sequences  (Marti et al. , 2017). MST qPCR datasets were expressed per copy number of DNA targets per gram of dry sample and normalized by dividing their numbers by the 16S rRNA gene copy numbers per gram of dry sample.
The generated sequences were processed with the Mothur package  (Schloss et al. , 2009)  according to a standard operation protocol (SOP) for MiSeq-based microbial community analysis  (Schloss et al. , 2009; Kozich et al. , 2013) , so-called MiSeq SOP, and available at http://www. mothur. org/wiki/MiSeq_SOP. Briefly, primers and barcodes were trimmed according to quality and size filtration, followed by sequence alignment. Size filtration was refined, and unique sequences were extracted. The pre. cluster command was applied in order to mask the remaining sequencing errors as well as the split. abund command where the cutoff was set to 1. Chimera removal was performed using the chimera. uchime command and chimeric sequences were removed. At this stage, sequences were processed following two normalization strategies: (i) subsampling according to the sample containing the lowest number of sequences, and (ii) no subsampling (see statistical analysis for normalization strategy).
Finally, for both datasets, the SILVA 16S rRNA Bacterial reference library was used for taxonomic allocation of the OTUs (cutoff 80) 1 and OTU clustering was performed with a similarity threshold of 97% for subsampled reads (Supplementary Table  5 ) or using the phylotype command at the genus level, for non-subsampled reads (Supplementary Table  6 ). The Mothur package was also used to calculate diversity indices (Shannon, Simpson, evenness, and richness) on the 16S rRNA subsampled OTU table (Supplementary Table  7 ).
Statistical Analyses Relations between sample groupings and chemical datasets were tested by principal component analysis using the rda function and plotted using the biplot function; confidence ellipses were drawn to show the variance observed per group. Kruskal-Wallis tests were performed using the kruskal. test function on 16S rRNA diversity indices, MST markers ratio, and physical-chemical data. When significant differences were found (p < 0. 05), a Dunn test was applied using the kwAllPairsDunnTest function from the PMCMRplus package  (Pohlert, 2020)  for post hoc analysis. Hellinger transformation was applied on the 16S rRNA subsampled OTU table using the decostand function (method = "total"), and Bray-Curtis dissimilarities were computed between pairs of samples using the vegdist function ("bray").