dataesr/scientific-paragraphs-categorization huggingface dataset:
Study setting and timeline This study will be conducted at the Departments of Neurosurgery, Neurology and Nuclear Medicine at Aarhus University Hospital in Denmark. Recruitment of patients started in the fall of 2021 and all patients are expected to be included before fall 2022. The study duration includes a 1-year follow-up and all examinations will be completed before the end of 2023. PET imaging data analysis Quantification of  18  F-FDG PET scans will be performed using previously reported procedures. 14 15 The optimal modelling approach for  18  F-FEOBV PET is as previously referenced. 16 nalysis of PET scans will be performed using both a region of interest (ROI) approach sampling hypothesised areas and exploratory statistical parametric mapping (SPM). For each subject, ROIs will be defined on the individual CT and copied onto coregistered PET images.
ROIs will include putamen, caudate nuclei, ventral striatum, thalamus, red nucleus, amygdala, hypothalamus, locus coeruleus, median raphe and the ventral tegmental area. In addition, an anatomical probabilistic template that divides the entire brain into 62 cortical and subcortical volumes of interest, which has been defined on the Montreal Neurological Institute brain template, will be used to extract cortical data (eg, anterior cingulate, posterior cingulate). SPM will allow automated interrogation of parametric images across the whole brain volume at a voxel level to localise significant differences in tracer uptake without a priori selection of target regions. The primary end points for the imaging analyses are the between-group differences in striatal and extrastriatal tracer uptake/binding.
Statistics Between-group comparisons of clinical scores from baseline to follow-up will be analysed using difference between means. We estimated a power of 96% for the sample size of 12 participants on the primary clinical end point, the PIGD subscore. An SD of normal progression over 1 year in patients with PD with gait problems (MDS-UPDRS Motor score Part III Gait score ≥ 1) was obtained using the Parkinson's Progressive Markers Initiative database  17  and a significant difference in scores set to 3 points. Betweengroup comparisons of PET findings will be performed using analysis of variance (ANOVA). Based on the previous studies and previous experience with  18  F-FEOBV PET,  16  we estimate the mean values of uptake for the tracers within striatal and extrastriatal structures will have an SD of 20%.
ETHICS AND DISSEMINATION This study was reviewed and approved by the Committee on Health Research Ethics-Central Denmark Region and will be conducted according to the Danish Act on Data Protection (Registration number: 1-10-72-44-21). All participants give written informed consent and are free to withdraw without giving reason at any time without prejudicing further treatment. Any adverse events, related or unrelated to this trial, will be treated, and records will be kept for all participants. Adverse events will be reported to the authorities according to Danish law. We intend to disseminate and publish the results of this study whether they are positive, negative or inconclusive. The results of the study will be published in high-impact, interdisciplinary journals and presented at international and national conferences.
Acknowledgements We thank our study coordinator  Rikke Nan Valdemarsen  ( Department of Nuclear Medicine and PET Aarhus University Hospital and Aarhus University ), neurosurgical nurse  Anne Lene Knudsen  ( Department of Neurosurgery, Aarhus University Hospital ), and the  Danish Parkinson's Association  for distributing our call for participants. We used the SPIRIT checklist when writing our report (11). Contributors MHT, VSH and NP have drafted the first version of this paper. The remaining authors, who have contributed to the study design and will be involved in the conduction of the trial, have revised it and provided criticisms and suggestions.
Therefore, the tracing of individual study measures of efficacy to its cause, i. e. , exposure to germs versus strength and duration of the antimicrobial activity in real-life conditions, is needed. In vitro studies and experiments in rodents have shown TS antimicrobial activity lasting several days  [8] [9] [10] [11] [12] 16]. However, a literature review has shown that animal models of human surgery often translate poorly to clinical conditions  [17]. Moreover, published studies do not relate the antimicrobial activity to triclosan bioavailability on TS. A review of publication databases found articles on the range of triclosan load on a TS to meet antimicrobial efficacy and non-toxicity objectives, on TS surgical properties, microbiological assays and clinical trial outcomes  [8] [9] [10] [11] [12] [13] [14] 16, 18].
Data Management and Statistical Analysis The determined concentrations with sample identifiers and preparation details were recorded in a database. Each pair of copies provided a minimum, a maximum, and a mean. Groups of six copies provided 95% confidence intervals (CI). Statistical analysis computed the time plots of the marginal release rates for each type of suture and solvent. Marginal ex vivo release rates and their controls were computed with CI and tested against their respective controls with two-sided t-tests and a type-I error of 5%. Ex vivo benchmark release rates from subcutaneous and intramuscular explanted fragments were compared. A mathematical model was fitted to in vitro marginal release rates measured in accelerated conditions and the ex vivo benchmark release rates and residual triclosan over time.
Ln2 L (5) The double-decay model decomposed the single decay baseline rate and half-life into an early decay with a partial baseline rate related to a short half-life overlapping with a later decay with another partial baseline related to a long half-life (6)  [41] : R predicted (t) = R 1 •e -t. Ln2 L 1 + R 2 •e -t. Ln2 L 2 (6) The models that best predicted the animal ex vivo benchmarks were selected. The observed residual amount of triclosan on the suture at time t was the extracted initial amount less the sum of the observed released amounts at each time slot since immersion started to time (7): A observed (t) = A0 observed - t ∑ t=0 A observed (t) (7) The predicted residual amount at time t was the extracted initial amount less the integral of the fitted model from time 0 to t (8): A predicted (t) = A0 observed - t t=0 R predicted (t)dt (8) Data management and computations were performed in Stata 17, StataCorps LLC, College Station, TX, USA.
Acknowledgments: The authors thank the following individual for their guidance and support:  Nicolas Borenstein  and  Angélique Ente  at  IMMR, 75014 Paris, France , who provided ex vivo samples for benchmarking. M. Arnaud Zabala  and  Fatima M'Zali ,  Université de Bordeaux ,  Aquitaine Microbiologie , UMR 5234 CNRS, 33000  Bordeaux, France , who allowed the use of their laboratory to prepare the samples and storing the supplies. Nadège Pineau , Medextens, 75015 Paris, France, who helped prepare the samples, checked and entered the data.
Methods This prospective observational, multicenter cohort study was conducted in two French hospitals (Institut Curie, Saint-Cloud and Foch Hospital, Suresnes). The study was initiated after receiving Ethics Committee approval (East Regional Research Ethics Committee, Chairperson JP Quenot, ID RCB 2018-A00396-49 on 12 March 2018) and registration at ClinicalTrials. gov (NCT03472118 on 21 March 2018). Written informed consent was obtained from patients prior to their participation in the study. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational cohort studies. 8 t was decided that the sponsor of the study (i. e. , the data monitoring committee) would be kept informed of the occurrence of adverse events and would make the decision whether to continue the study.
Data Extracted from Medical Files Demographic characteristics, clinical data, diagnostic test results, and treatments were finally collected from the medical files. These included parameters potentially or really associated with the risk of VTE during pregnancy and puerperium. The following data were obtained when the women were seen for thromboprophylaxis prescription after their positive pregnancy test: age, type of COC-associated initial VTE event, time elapsed between VTE and pregnancy, pregnancy after assisted conception techniques, quantitative D-dimer plasma concentrations (categorized as high if >500 μg. L À1 ), HERDOO2 score categorized as high if !2, 27 BMI (BMI with obesity defined as values >30 kg. m À2 ), family history of VTE in a first degree relative, family history of atherothrombosis, active smoking, varicose veins, hypercholesterolemia (fasting cholesterol above 5.
Statistical Analysis There was no a priori study size calculation. The study was only based on our possibilities of recruitment and selection of medical files corresponding to the chosen criteria. We minimized the amount of missing data by establishing an initial consensus between the three centers, planning to carefully collect the essential information prone to describing the risk of VTE during regular pregnancy follow-up visits. Quantitative data are given as means, [standard deviation], (range), and qualitative data as numbers (%). The follow-up period was defined including pregnancy and the first 6 weeks of puerperium. For patients lost to follow-up, the last evaluation date was considered as the follow-up period. Incidence rates of thrombosis were calculated as the number of thrombotic events over the number of personyears of pregnancy and puerperium follow-up, defined as up to the end of the first 6 weeks postpartum.
Acknowledgments We wish to thank all the study participants and colleagues who supported us. We also wish to thank the following key members of the research team at the  "Direction de la Recherche Clinique et de l'Innovation," Nîmes University Hospital :  S. Clément ,  C. Meyzonnier ,  N. Best ,  A. Megzari ,  E. Dupeyron ,  S. Granier ,  B. Lafont ,  C. Masseguin ,  O. Albert ,  S. Guerrero ,  P. Rataboul , and  M. P. Francheschi. We are grateful to all the staff at the  BESPIM (Département de Biostatistique, Epidémiologie, Santé Publique et Innovation en Methodologie ) at  Nîmes University Hospital  under the direction of  P. Fabbro-Peray  and  T. Mura. We thank  Sophie de Boüard  for her administrative help and data management. Our thanks also go to  Teresa Sawyers  for her expert editorial assistance.
The questionnaire has been filled with the information gathered from patients and their family over the first weeks of treatment and was updated during follow-up if new information emerged. Follow-up assessments were conducted after 2, 6, 12, 18, 24, 30 and 36 months of treatment. They explored various aspects of treatment and co-morbidities, as well as the evolution of psychopathology and functional level. They were conducted by psychologists trained in the assessment of psychopathology and by CMs for descriptive measures. Psychopathology was assessed with The Positive and Negative Syndrome Scale (PANSS)  (Kay, Flszbein, & Opfer, 1987)  with good inter-rater reliability between psychologists. The local Research and Ethics Committee granted access to the clinical data for research purposes (CER-VD; protocol #2020-00272).
Statistical analysis Differences between groups in terms of demographic data were assessed with chi-square and Student t tests. To answer our research question we first assessed to what extent impulsivity level at the entry into the program was different between VP and NVP, whether these patients were users or not. To do this, we conducted a two-way ANCOVA with two factors, Patient Group (VP v. NVP) and use (users v. Non-users), while controlling for the level of alcohol consumption at the entry into the program. We also further examined with the same method what were the differences in the level of impulsivity between early and late consumers, in relationship to the group (VP v. NVP). To assess the evolution of impulsivity during the program and analyze the relationship between Group and CU, we compared the VP and NVP and the presence or absence of CU on impulsivity with a linear mixed-effects model, which included a random intercept for each individual.
Statistical significance was set at p ≤ 0. 05. In addition, the histograms and empirical distributions of O-dT/T ratio values were plotted and visually compared between groups. Several approaches were used to identify cut-off values of O-dT/T ratio for CYP2D6 phenotype discrimination. First, an empirical search without any model was performed by testing various cut-off value and computing sensibility and specificity for the discrimination between PM and non-PM subjects in the entire dataset. Then, more sophisticated and robust statistical approaches were used to identify variables predicting CYP2D6 phenotype. We randomly split the overall dataset into a learning (80%) and validation (20%) dataset. Random sampling was set to respect the proportions of phenotypes from the original full dataset.
The validation group of 50 patients had phenotype proportions as follows: 25 EM (51%), 20 IM (39%), 4 PM (8%) and 1 UM (1%). Then, we used univariate and multivariate classification trees (CT), which are machine learning methods, to identify cut-off values of the metabolic ratio that discriminated CYP2D6 phenotypes in the learning dataset. Univariate models only included O-dT/T ratio at 24 h or 48 h. Then, a bivariate model including the ratio at both times was assessed. Multivariate models including all available variables were also investigated (metabolic ratios at 24 h and 48 h, sex, age, weight, serum creatinine, prothrombin time). The Gini diversity index was used for defining binary splits in trees. In order to avoid overfitting, a minimal number of five observations was set in child nodes.
Predictive performance of the prediction trees was assessed by computing sensitivity (Se), specificity (Sp), positive (PPV) and negative (NPV) predictive values in the learning dataset. Finally, the robustness of the prediction trees was assessed by computing their predictive performance in the validation dataset. Statistical analysis was performed by using the Orange toolbox  [22]  and the Statistica software (version 13. 3, Tibco, Palo Alto, CA, USA). Plots were builtwith R.
Patients' Characteristics Patients were enrolled from September 2009 to June 2011. A total of 401 European patients were eligible during this time frame. The study flow-chart is summarized in Figure  1. CYP2D6 genotyping was performed in 310 patients. Nine patients were excluded from the study after genotype testing for various reasons as shown in Figure  1. Genotype data were further analyzed in 301 patients. Based on the alleles identified, the distribution of CYP2D6 phenotype was as follows: 23 PM (8%), 117 IM (39%), 154 EM (51%), and 7 UM (2%). Regarding tramadol concentration at 24 h, data from 297 out of 301 patients were available. Data from 4 patients were excluded (two EM and two PM individuals): one because of missing blood sample at 24 h, and three because of outlier results. Concentration data at 48 h were available in 227 patients.
Prediction of CYP2D6 Phenotype Based on O-dT/T Ratio In the model-free analysis of the entire dataset, a metabolic ratio cut-off ≤0. 1 was associated with the best performance in discriminating PM from non-PM, with Se = 87%, Sp = 94. 5%, PPV = 87%, and NPV = 94. 5% at 24 h. Results for the ratio calculated at 48 h were as follows: Se = 87. 5%, Sp = 83. 8%, PPV = 88%, and NVP = 83%. However, model-based analysis with external validation showed poorer results. Univariate and bivariate classification tree models-based O-dT/T ratio at 24 and 48 h showed low to moderate predictive performance in both the learning and validation datasets, as shown in Tables  3  and 4. No model achieved the predefined acceptable Se and Sp of 90% in the learning set and performance worsened in the validation group.
UM patients did not show an increased value of the metabolic ratio compared with EM, but the number of UM subjects in the study (N = 7) preclude from a definitive conclusion. Of note, the conversion of tramadol to O-dT by CYP2D6 also has implications for drug-drug interactions  [23]. When O-dT/T ratio was used to predict CYP2D6 phenotype, a cut-off value of 0. 1 appeared to separate PM and non-PM with good predictive performance in the entire dataset. Unfortunately, performance worsened when prediction was applied in a validation group after data splitting. Indeed, no single value of the metabolic ratio could discriminate the various phenotypes with acceptable performance. This is consistent with the large overlap of distributions of the metabolic ratio between phenotypes (see Figures  2  and 3 ).
Sympathetic Nerve Activity Recording Mice were anesthetized using a mixture of ketamine (75 mg/kg)/xylazine (10 mg/kg), and the carotid artery was exposed. The sympathetic nerve filament was dissected free of underlying tissues on a distance of 1 cm until the superior cervical ganglion. The nerve was covered with paraffin oil and placed on a pair of recording silver electrodes (0. 6 mm diameter) connected to a high-impedance probe, action potentials were saved after initial amplification through a low-noise amplifier (BIO amplifier, ADInstrument, Paris, France). Unipolar nerve activity was recorded continuously for 15 min. Data were digitized with PowerLab/4sp digitizer (ADInstrument, Paris, France). Signals were amplified 105×, filtered using low/highfrequency cut-offs of 100 and 1,000 Hz, and monitored using the Chart 4 computer program (ADInstrument, Paris, France).
, 2000). Given the absence of precise data in the literature concerning the bioavailability and pharmacokinetics of GW4064 in the brain, we empirically determined the active dose by performing an effect/dose experiment in metabolic cages. Chow fed lean mice in metabolic cages received increasing doses of GW4064 by ICV administration and modification of metabolic parameters as well as FXR target genes expression were monitored. A dose of 0. 9 mM; 522 ng/µl; 1 µl injected, of GW4064, was found to modify metabolic parameters and to increase the expression of established FXR target genes in the hypothalamus, indicative of a pharmacological activity and a cerebral activation of FXR. Indeed, mRNA levels of the FXR target genes Small Heterodimer Partner and Bile Salt Export Pump (Shp and Bsep) were induced in the ARH upon GW4064 treatment (Figures  3A, B ), but not in the paraventricular nucleus of the hypothalamus (PVH) (  Figures 4A, B ), indicating specific activation of FXR in the ARH.
ACKNOWLEDGMENTS We thank  M. Tardivel  and  A. Bongiovanni  of the  BioImaging Center (Lille University School of Medicine Research center ) for their expertise and training us on the used devices. We thank  C. Piveteau  for the GW4064 assay. We thank  V. Prevot  and  P. Giacobini  for brain slices of NPY-gfp mice. We also thank the animal core facility (animal facilities of  Univ. Lille-Inserm ).
FUNDING This research was performed using funding provided by  National Research Agency BABrain  ( ANR-17-CE14-0007 ),  European Research Council Immunobile  ( 694717 ) project and the  Agence Nationale de la Recherche (ANR)  grants «  European Genomic Institute for Diabetes » E. G. I. D ,  ANR-10-LABX-0046 , a  French State  fund managed by  ANR  under the  frame program Investissements d'Avenir I-SITE ULNE/ ANR-16-IDEX-0004 ULNE.
C4 haplotype diversity and its correlation with classical HLA alleles The complex genetic variation of C4A and C4B, which consists of many haplotypes with different numbers of C4A and C4B genes was recently made accessible for analysis in large cohorts  28. We imputed 29 C4 haplotypes independently in both cohorts. Haplotype frequencies were found to be similar in both cohorts and comparable to published results  28  but varied substantially across countries (Supplementary Fig. 2  and Supplementary Table  2A ). We determined nine C4 haplotypes to have a moderate to strong correlation (r 2 > 0. 4) to at least one classical HLA allele (Supplementary Dataset 1). Correlation to classical HLA alleles associated to SSc on a genome-wide level was found to be small (r 2 < 0. 3) for most haplotypes with the exceptions of HLA-B*08:01 and HLA-C*07:01 which had a strong correlation (r 2 = 0.
82 and 0. 67, respectively) with the BS haplotype and HLA-C*16:01 with AL-BS (H2111B) of 0. 63 (Supplementary Dataset 1). The association between C4 copy number variants and SSc is modified by sex and HERV-K We found a higher C4 CN to be protective for SSc (Fig. 1a  and Table  1 ). Less than four copies of C4 were found in 28. 6% of SSc patients, and 2. 8% had less than three copies. Interestingly, less than four copies of C4 were found in 28% of female and 32% of male patients. In a simple additive model C4A, C4B and HERV-K CNs exhibited 1. 8-fold variation in their relative risk of SSc (95% confidence interval (95% CI), [1. 51-2. 33]; P = 1. 04 × 10 -16 ). Logistic-regression analysis estimated a rather small difference between the protection afforded by each copy of C4A (odds ratio (OR) = 0.
C4 genetics can explain a part of the SSc association to the MHC region We performed conditional association analysis for genetic markers across the MHC genomic region. Conditioning on C4A, C4B, and HERV-K CN or on a risk score calculated using the complex C4 CN interaction model derived above, showed an impact on residual association levels limited to the vicinity of the C4 gene (Supplementary Fig. 3B, C ). In addition, we calculated a C4 risk score recently proposed for SLE and SjS based only on C4A and C4B CN (as: risk = (2. 3)C4A CN + C4B CN)  28. We applied the SLE/SjS score for conditional association analysis in the first dataset and found almost no effect (Supplementary Fig. 3A ). Given the strong association of C4 CN with SSc and its rather local impact in conditional analysis, we focused on C4 eQTLs as potential modifiers of C4 CN risk as both CN and eQTLs affect C4 expression levels.
Our analysis above suggests that the predictive power of C4 CN and C4 eQTLs on C4 expression levels is at least partly redundant. In fact, C4 eQTLs alone can explain more C4 expression variance than C4 CNs and C4 eQTLs are significant predictors for copy numbers of C4. We obtained 10,680 eQTLs of C4A and C4B from the GTEx v8 database and found that 37% of these are associated to SSc with p GWAS < 10 -5 using the first dataset (Fig. 3a ). This encouraged us to find independent C4 eQTLs using forward selection to explain their contribution to the MHC association to SSc. Conditioned association analysis for genetic markers across the MHC genomic region on ten independent C4 eQTL SNPs rendered most association to SSc nonsignificant (P > 5 × 10 -8 ), except for a peak around HLA-DPB1, the initial association of which was enhanced by conditioning (Fig.
3b ). While conditioning on 13 independent C4A exclusive eQTL SNPs had a similar effect on the residual association profile, conditioning on 12 C4B exclusive eQTL SNPs had a smaller effect with residual association (P < 5 × 10 -8 ) to SSc in three regions centered on HLA-DPB1, HLA-DRB1, and HLA-B (Supplementary Fig. 4B, C ), which suggests a stronger contribution of C4A to SSc. While the ten independent C4 eQTL SNPs absorb as much SSc association as possible by design (forward selection), they might have been selected due to the extensive genetic linkage in the MHC region without being implicated in SSc pathogenesis. We therefore asked if SSc association with the MHC could be explained by C4 eQTLs selected to explain C4A and C4B expression variance in the second dataset. Using expression-model eQTL SNPs, selected in the second cohort to explain expression variance, as co-factors in the first cohort to determine residual genetic association with SSc again rendered most MHC association with SSc nonsignificant (P > 5 × 10 -8 ), except for the peaks shown in Fig.