dataesr/scientific-paragraphs-categorization huggingface dataset:
Results The PRO-MSACTIVE study enrolled 422 patients in 46 participating French centers between July 2018 and August 2019. Of these 422 patients, 376 (89. 1%) suffered from RRMS and 46 (10. 9%) from active SPMS. The six PRO questionnaires were fulfilled and then analyzed in most patients, whatever the self-reported questionnaire was: 417 patients with at least one baseline score for the SymptoMScreen, 416 for the MFIS, 414 for the EQ-5D-5L, 410 for the WPAI:SHP, 416 for the MusiQoL, and 392 for the TSQM-14. Overall, 384 patients (91. 0%) of patients completed the study. SM data review meetings (Fig. 3 ) were conducted at the following trigger points: 60% (April 2019), 90% of patient recruitment (July During the first data review meeting, 48 outputs using 14 planned tests were run on baseline characteristics and follow-up data, with a total of 9 findings on 7/41 (17.
6%) sites identified as needing review or investigation. 4 days were necessary for this analysis. 4A  displays the difference between relative study day and theoretical date for each visit. For each site, the distribution of the difference was compared to the mean distribution of all other sites combined using Cochran-Mantel-Haenszel test for ordinal data and represented by a volcano plot  [4] : x-axis represents the difference in differences for study day (standardized in base 100), y-axis represents the p-value (using minus log10 scale). Any site above the red line needed investigation. Fig. 4B  displays the test based on available data to identify subject inliers and outliers in multivariate space from the multivariate mean. Patients with highest Malahanobis distance  [5]  have 'outlying' values for a number of variables, given their values for other variables in the database, which may indicate the possibility of an incorrect value entered or some other issue underlying this erroneous value.
Discussion SM is complementary to traditional monitoring of data and improves data integrity of a clinical trial by identifying clinical data discrepancies that may occur during study conduct and before any database is locked so that issues may be detected early and before any major problems occur, which allows to implement necessary actions to resolve these issues. Anticipation is key for the success of SM, as it takes time to prepare the SM plan and associated programs necessary for the SM data review meetings, especially if recruitment of patients is planned to be fast and active. The development of an interactive SM tool using R Shiny allows drill down into individual patient data to display identified issues. The first SM review meeting is important to adjust, if necessary, the planned tests and statistical monitoring outputs in the dedicated SM plan.
Pharmacokinetic analysis PK and statistical evaluations were performed using the standard software Phoenix 64 (WinNonlin®, version 8. 1; Pharsight Corporation (a Certara Company), Mountain View, California, USA). Separate evaluations were performed for the different AIs and study groups. Calculations comprised descriptive statistics on individual concentration data, PK analysis, and descriptive statistics on derived PK parameters (e. g. geometric mean (GM) and geometric standard deviation, minimum and maximum, 95% confidence interval). Plasma concentration-timeprofiles were plotted individually and as geometric mean (AE geometric standard deviation) curves by AI or study group. Graphic presentation was done using Prism version 8 (GraphPad Software, San Diego, CA, USA). PK evaluation of the derived plasma concentrations were performed on the observed concentrations and planned sampling times (if actual times did not deviate outside the permitted time window) using noncompartmental methods.
Each AI and treatment were evaluated separately. The software calculated a full set of PK parameters automatically. The software selected the time points used for the terminal phase elimination rate constant calculation (λ z -calculation) automatically using the "best fit" approach calculated by means of log-linear regression. All parameters were derived from individual animal data sets. Plasma drug concentrations analysed as being below the quantification limit (< LLoQ) were always entered into the system as "missing". Relative data set weight was always set to 1.
Only 4 of 260 (1. 5%) concentration-time data points represented values below the limit of quantitation (BLOQ); therefore, a separate handling of BLOQ data was not included in the model. Model quality was assessed using a set of accepted graphic and numerical tools  (Pelligand et al. , 2016; Nguyen et al. , 2017). Convergence of the SAEM algorithm was checked by inspection of the stability of parameter search and by the precision of parameter estimates. This was measured by the relative standard error (RSE) of the estimate as obtained by the Fisher Information Matrix. The condition number of the eigenvalues was assessed to check for over-parameterization. Standard goodness-of-fit (GOF) plots were used to assess the performances of the different models: individual fits, individual predictions vs observations, normalized prediction distribution errors (NPDE), and visual predictive check.
Correlation of random effects was applied when correlation coefficients were estimated to be high, met the threshold for inclusion (P < 0. 05) and improved model performance. As recommended by earlier studies  (Lavielle & Ribba, 2016; Pelligand et al. , 2016) , multiple samples from the posterior distribution obtained at the last SAEM iteration were preferred over the empirical Bayes estimates (EBEs) during the evaluation of parameter correlations. 2. 3. 5. 2. Simulation of a multiple-dose administration. After model selection and fit, the R 3. 4. 4 package Simulx 3. 3. 0 (Monolix 2021R2) was used to simulate tigolaner plasma disposition kinetic profiles from final Monolix run files. First, Monolix file was exported to Simulx and used to visualize the entire distribution of predicted tigolaner concentration time courses in cats, following a single administration of 14.
Analysis of clinical data The results of the pre-and post-treatment physical examinations and assessments were evaluated but not statistically analysed. Body weights were summarized as arithmetic mean and standard deviation per measurement and fluctuations in BW during the in-life phase were calculated. Any adverse event observed was described and assessed for a relation to treatment. Haematology and clinical chemistry data were analysed by means of Advia® 120 Haematology System (Bayer Diagnostics, Tarrytown, USA). Clinical chemistry was determined using a reflection photometer (VetTest 8008, IDEXX GmbH, 55286 W€ orrstadt, Germany).
10 -5. mm 2 s -1 , a power of 80% and a first-species risk of 5%, the sample size for each group was set at or above 16 women/group. In order to compensate for possible problems (trial dropout, missing data…), we have recruited 26 subjects per group but several women were removed from the study for various reasons (i. e. , patients taking continuous contraception, fortuitous discovery of a drop in ovarian reserve, or dropout from the clinical investigation). At the end of the study, we could analyse the data from 17 healthy volunteers and 23 women with PCOS. Taking into account the stringent screening of the population of subjects to restrict as much as possible inter-individual variability, the final sample size of the two groups ensured adequate power to detect significant differences while also allowing for natural variation in responses.
The procedure for quantifying metabolite concentrations consists in fitting the experimental data to a parametric model describing the resonances of each metabolite. Relative Cramér-Rao lower bound (rCRB) is used as a benchmark of precision for the concentration measurements. We set a rejection threshold for CRB at 30%, above which the noise-to-background signal is too high to allow precise measurements of metabolites. Automatic segmentation and fiber tractography Segmentation of regions of interest (ROI) Hypothalamic segmentations were obtained using a convolutional neural network (CNN) to form five subunits comprised of multiple hypothalamic nuclei trained on manually parcellated T1-weigthed images and which are defined as follows: Anterior-inferior (suprachiasmatic nucleus; supraoptic nucleus); Anterior-superior (preoptic area; paraventricular nucleus); Posterior (mamillary body, lateral hypothalamus; tuberomamillary nucleus, TMN); Inferior tuberal (infundibular or arcuate nucleus, ARC; ventromedial nucleus; supraoptic nucleus, SON; lateral tubular nucleus); Superior tuberal (dorsomedial nucleus; paraventricular nucleus, PVN; lateral hypothalamus).
Fiber tractography Diffusion-weighted imaging, DWI, were corrected for Eddy currents, geometrical and signal distortions and rigidly co-registered with T1 images using HCP toolbox as described in Ref. 38. The obtained transformation matrix was then applied to seeds defined in native T1 space. DWI data were processed using the MRtrix software (v3. 0. 1). For each subject, an FA map was calculated and threshold (FA > 0. 7) to determine the response function of highly anisotropic voxels, further used for constrained spherical deconvolution to provide sharp fiber orientation distribution (FOD) estimates. Fiber tracts were generated using a probabilistic tracking algorithm. Tracking parameters were left to defaults (step size = 0. 1 mm, minimum radius curvature = 1 mm, FOD cutoff = 0. 2, minimum length = 10 mm), the maximum harmonic order set to 10 and the number of generated tracts set to 2,000,000.
Quantitative RT-PCR analyses For gene expression analyses, mRNAs obtained from FACS-sorted tanycytes were reverse transcribed using SuperScript® IV Reverse Transcriptase (Life Technologies, USA) and a linear preamplification step was performed using the TaqMan PreAmp Master Mix Kit protocol (P/N 4366128, Applied Biosystems, USA) as previously described. 4 Real-time PCR was carried out on an Applied Biosystems 7900HT Fast Real-Time PCR System, using exon-boundary-specific TaqMan Gene Expression Assays (Applied Biosystems, USA) as follows: Amh (Amh-Mm00431795_g1), Amhr2 (Amhr2-Mm00513847_m1). Control housekeeping genes were r18S (18S: Hs99999901_s1) and Actb (Actb: Mm00607939_s1). Gene expression data were analysed using SDS 2. 4. 1 and Data Assist 3. 0. 1 software (Applied Biosystems, USA).
The goat anti-AMH (AF2748; 1:100) and the goat anti-AMHR2 (AF1618; 1:400) antibodies were purchased from R&D Systems, USA. Immunoreactions were detected with horseradish peroxidase-conjugated secondary antibodies (Sigma; USA 1:10,000). Protein expression was densitometrically analysed using Scion Image software (Scion Corporation, MA, USA).
Wound-healing assay Primary tanycytes were seeded at a high density (1 × 10 5 cells/cm 2 ) into cell culture insert (Ibidi) to generate a wound with a gap of 500 μm. Cells were cultures for 48 h in DMEM F-12 high-glucose medium supplemented with 2% calf serum and 2 mM L-glutamine and treated with PBS (control) or recombinant hAMH (100 ng/ml; rhMIS 1737-MS-10, R&D Systems, USA). Then, the cells were fixed in 4% PFA in 0. 1 M phosphate buffer (PB) (4% PFA/0. 1 M PB; pH 7. 6) and permeabilized with 0. 3% Triton X-100 (TBS-T; Sigma-Aldrich, USA, T8787) for 1 h before staining of the actin cytoskeleton using Alexa Fluor 568 Phalloidin (1:100; ThermoFisher Scientific, USA, A12380). The area occupied by tanycytic processes was measured using ImageJ software (Bethesda, USA).
Ethics statement Human studies The human study was performed in accordance with the Declaration of Helsinki. All participants gave written informed consent prior to inclusion in the study. The study was carried out with the approval of regional ethics committee (Comité de Protection des Personnes Nord Ouest IV; No: 2016-A01391-50). The clinical protocols have been declared at ClinicalTrials. gov ll efforts were made to minimize animal suffering and animal care was supervised by veterinarians and animal technicians skilled in rodent healthcare and housing.
Acknowledgments This work was supported by  INSERM  ( U1172  to P. G. and V. P. ), by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement n • 725149 to P. G. ), and by the  CHU de Lille, France (Bonus H , to P. G. ). We thank the  BICeL core facility of Lille ,  Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille ,  US 41-UMS 2014-PLBS ,  F-59000 Lille ,  France  for the access to the animal facility and to the preclinical imaging core facility. We thank the volunteers who participated to the MRI study.
Study design and patient population This retrospective study used medical data from patients with a UCD due to a defect in NAGS, CPS1, OTC, or ORNT1, who had been exposed to citrulline and/or arginine since 1990 at the Reference Center for Inherited Metabolic Disorders, Necker University Hospital -Enfants Malades, in Paris, France. Standard treatment practices for UCDs have not undergone major changes since 1990, even after new guidelines  [1, 2]  were implemented in 2012. Amino acids (citrulline and arginine) and ammonium scavengers have commonly been prescribed, with doses dependent on the severity of the disease, together with a protein restriction diet. Data from newborn patients (up to 1 month of age) who did not survive the initial episode of hyperammonemia were excluded. This research was conducted according to the principles of the Declaration of Helsinki and local and international laws.
Statistical analyses The exposed population included patients who were receiving or had received oral treatment with citrulline and/or arginine for their UCD and had at least one assessment after initiation of this treatment. All analyses were performed on the exposed population, except the analysis of decompensation, which was performed on the evaluable population. The evaluable population included patients from the exposed population who, based on the investigator's judgement through analysis of the patient files, had good treatment compliance and did not present with several episodes of decompensation despite appropriate medical care, as these factors could have interfered with the study evaluation. This population was validated during a data review meeting before database lock. For some relevant data, results were displayed to show distribution between neonatal (revealed before 1 month old) and late onset (revealed after 1 month old) form of the disease.
The statistical analyses were performed by eXYSTAT (Malakoff, France) using SAS software version 9. 4 (SAS Institute, North Carolina, USA). Descriptive statistics were used with continuous variables presented as number (n), number of missing data, mean ± standard deviation (SD), and median with minimum and maximum, and discrete variables presented as n, missing n, frequency, and percentages. Comparisons of the quantitative data were made using analysis of variance (ANOVA) or the non-parametric Kruskal Wallis test. Qualitative data comparisons were made using the chi-squared test or Fisher's exact test based on population.
Discussion This retrospective study analyzed data from 79 patients with UCDs who were treated with arginine and/or citrulline associated with nitrogen scavengers and followed up for between 0. 2 and 36 years (median of 9. 5 years; mean 11. 2 years). To our knowledge, this is the first study to analyze longitudinal clinical data from one of the largest French reference centers managing patients with UCDs with a focus on the use of citrulline and/or arginine. The most common initial supplementation after diagnosis was arginine monotherapy, followed by arginine + citrulline, and then citrulline monotherapy. The duration between diagnosis and initial treatment with arginine monotherapy was shorter than for citrulline monotherapy (mean 1. 5 vs. 2. 9 years). There are at least two possible reasons for this difference.
Materials/subjects and method Subjects. Subjects (n = 310) were participants from a large prospective study conducted at the University Hospital of Grenoble Alps (HYPINT: NCT02642705); we measured and analyzed body composition using MRI in 70 women and 240 men aged 50. 8 ± 10. 6 years (range: 20-66 years) with a BMI of 31. 3 ± 5. 6 kg. m -2 (range 17. 9-51. 0) (Table  1 ). Thirty-eight participants had a BMI lower than 25 kg. m  -2  , 93 between 25 and 30 kg. m -2 , and 179 greater than 30 kg. m -2. Experimental design. We built equations in the total population (n = 310) and tested their validity in this population and in different BMI subgroup split into 2 parts according to sex (Table  1 ). Then we implemented a longitudinal phase consisting in a 2-month exercise training program during which we tested the sensitivity of these equations in a subgroup of participants with overweight/obesity (n = 79, 20 women, age: 52.
7 ± 9. 1 years; BMI: 32. 1 ± 4. 6 kg. m -2 ) by comparing the exercise-induced variations between predicted and measured AT and ATFM. We used data from a completed clinical trial (NCT02642705) testing the cardiometabolic effects of different exercise training modalities (i. e. three 45-min sessions of ergocycle training per week for 2-months): (1) moderate intensity continuous training (MICT; n = 32) and (2) high intensity intermittent training (HIIT; n = 47). Body Table  1. Demographics, total adipose tissue (AT), total adipose tissue free mass (ATFM) of total population, women and men with overweight/obesity, and trained subgroup with overweight/obesity. 1. The study was approved by the local ethics committee (Comité de Protection des Personnes Sud-Est V) and performed according to the Declaration of Helsinki.
Tissue measurement by magnetic resonance imaging (MRI). MRI is a reference method for assessing body composition including AT, ATFM and its regional distribution, and particularly visceral adipose tissue  16, 36. In order to determine full body composition (called measured body composition), the reference method includes 41 slices with a thickness of 10 mm spaced by 40 mm  16. MRI data were acquired in 30 min for each participant on a General Electric Signa Advantage 1. 5-T scanner (General Electric Medical Systems, Milwaukee, WI, USA). For the specific purpose of the present study (prediction of body composition), three single slices (thickness of 10 mm) were acquired in 10 min at 3 different localizations: T6-T7, L4-L5 and mid-thigh. Mid-thigh level for the single slice acquisition was determined by computing half of the overall femoral length.
Calculation of adipose tissue and adipose tissue free mass volumes. Each slice was analyzed using Matlab-based software (Matlab ® , Mathworks, Inc. ) developed by the radiology department of University Hospital of Grenoble Alps. The brightness level of tissue distinguished AT and ATFM, using the graphical interface of Matlab ®. More specifically, we measured adipose tissue and calculated adipose tissue free mass by subtracting adipose tissue area to slice area. Moreover, determination of tissue area on a given MR image is performed by subjecting the data to various segmentation techniques. In our laboratory we have developed a computer software specifically designed for MR image analysis similar to that developed by Ross et al. 20. The program features an interactive slice editor routine that allows for the verification of the segmentation result.
Acknowledgements We thank the funders of this study that was supported by the  French National Research Agency  within the framework of the  Investissements d'avenir program  ( ANR-10-AIRT-05 ), by the  "Thematic Doctoral Contract" of Grenoble Alpes University and the Projets Exploratoires Premier Soutien (PEPS) Interdisciplinaires of National Center for Scientific Research and Grenoble Alpes University , the " Fond de Dotation Agir pour les maladies chroniques " and the  Lebanese University  for a PhD grant. Our warmest thanks to  Bernard Wuyam  and  Pauline Maffre  for their invaluable contribution.
Plasma cytokine analysis Baseline cytokine levels were evaluated using the Luminex Flexmap3D platform with analysis performed on thawed plasma samples using the Thermofisher 9-Plex Human ProcartaPlex Panel as per manufacturer's instructions. Briefly, plasma samples were incubated with magnetic capture beads with specific spectral properties for 18 hours, prior to streptavidin-RPE being added followed by a biotinylated detection antibody. Data was acquired on the Luminex Flexmap3D.
Their results indicate that generalization of urban vitality is possible, but generalizing their model to Rome resulted in a notable decrease in accuracy, as it is historically, culturally, and naturally distinct from the other cities within their dataset. Huang and Liu (2022)  use a deterministic approach to model liveability of 101,630 communities in China in 42 major cities, guided by expert decisions. A total of 27 liveability factors are extracted using highresolution satellite imagery and subsequently weighted according to expert opinions. Their work presents the first large-scale assessment of the liveability of urban communities in China. Previous work has attempted to study remotely-sensed liveability by observing a limited number of components relating to liveability at a time, and without taking into account surveyed resident opinions.
Lastly, we evaluate how well liveability domains can adapt to unseen geographical contexts, as well as building typologies. We formulate and address two research questions for our research: 1. How well can we predict different domains of liveability? 2. How well does a bottleneck model predict compared to an unconstrained model? The remainder of the paper is as follows. In Section 2 we present the dataset used in our study and our model architecture. In Section 3 we present the metrics and maps for our experiments. Lastly, in Section 4 we reflect on our results and their relevance for liveability monitoring.
Methods We are interested in training a deep learning model to predict liveability on a neighbourhood scale by first predicting domain-specific liveability contribution scores as a set of interpretable semantic intermediate concepts. For this purpose we use a semantic bottleneck model  (Marcos et al. , 2021; Koh et al. , 2020) , which uses an intermediate linear layer with semantic concepts which are then used to predict a final objective. For this purpose we need a dataset of overhead aerial images, neighbourhood-scale labels of liveability, and a deep learning model architecture which can first predict individual domain scores, and then regress the overall liveability score through the domain specific scores. We discuss these requirements in order.
Dataset design To train our model we require a labelled dataset of liveability scores and overhead aerial imagery (Fig. 1 ). Additionally, we make use a series of domain scores, which decompose the liveability score into a series of explainable aspects. To build this dataset we use nationally available data sources in the Netherlands. Specifically, we consider 13 built-up areas of varying sizes, ranging from village (Beesel) to metropolis (Amsterdam). Selected built-up areas are listed in Table  1.
Liveability reference data The reference data for liveability used in our research is made available by the Leefbaarometer (LBM ) project  (Leidelmeijer et al. , 2014) , an ongoing liveability monitoring project initiated by the Dutch government. For this purpose, the authors collected a dataset with over 100 variables for use in regression models to predict liveability. These variables are available for all neighbourhoods in the Netherlands at the scale of an individual street. Where applicable, variables are summed over a radius of 200 m around each neigbhourhood to reduce the occurrence of outlying neighbourhood with few respondents in the dataset. The input variables can be designated to five domains. The following broad groups of variables are considered for each domain: • Population: Welfare factors, age groups, residuals for family composition and ethnic composition after controlling for income • Physical Environment: Green/gray area descriptors, proximity to water/green areas, proximity to nuisances (e.