dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of Similar Bundles between Two Different Groups of Subjects The proposed method was applied to two independent groups of N subjects from the same database to create a robust atlas. We implemented a comparison algorithm between bundles from different groups to identify the similar bundles across both groups of subjects. First, the intersection between similar bundles is computed. For each pair of bundles, composed by bundles from different groups, the distance between all the fibers from each bundle was calculated using distance dME. For each bundle, we compute the number of fibers presenting at least one close fiber from the other bundle, with a distance smaller than 5 mm. The number of fibers of a bundle that match this criterion is used as a measure of the intersection between bundle sets.
automatic segmentation of sWM Bundles The created atlas contains the most stable bundles, common to both groups of subjects. To evaluate the reproducibility of the bundles composing the atlas, these were used for the automatic segmentation of 78 new subjects from the segmentation database. The segmentation method calculates the distance dME (equation (1)) between each atlas bundle centroid and each fiber in the subject from the segmentation data set, normalized to the difference between the atlas centroid and the subject fiber lengths  (Labra et al. , 2017) : d d A B dnf dnf d dnf abs l me me me A n = , + , > ,      = ( ) ( if otherwise with 0 2 -- , +       -, l max l l b A B ) ( ) 1 1 (2) where dnf is a normalization factor that penalizes the length difference between the atlas centroids and the subject fibers.
robust atlas creation results To analyze the registration method, the algorithm was applied to two independent groups of 37 subjects from the development HARDI database, using both types of registration. Each hemisphere was analyzed separately. Each subject hemisphere in our data set contains on average 2,345 centroids, from which 1,131 centroids on average correspond to short fibers (35-85 mm). After the comparison between the selected DWM atlas bundles, around a 17% of the centroids were discarded, leading to a mean of 939 centroids per hemisphere, per subject. This results in around 35,000 centroids per group in each hemisphere.
Clustering-Based SWM Fiber Bundle Identification We applied intersubject clustering using different values for the maximum distance dclmax, between 15 and 45 mm. The total number, shape, and size of the clusters were analyzed, as well as the reproducibility of the bundles. We looked for bundles that were large enough to ensure good reproducibility, but also preserve good shape similarity within the fibers of the bundle. The best results were obtained with a value of dclmax = 30 mm to compute the clusters. After the computation of the dendrogram, the partition stage of the algorithm selects reproducible clusters with centroids that belong to at least 75% (28 of the 37 subjects in the particular case of the atlas used for the normalization analysis). While most of the stages of the algorithm are largely implemented as Python scripts, we wrote an optimized C++ version of the dendrogram construction algorithm depicted in Algorithm 1, which accounts for most of the execution time.
While Python offers enhanced flexibility and productivity, our C++ version of the algorithm allows us to improve performance using advanced code optimizations and more efficient memory management. In fact, on our test machine, the Python code supports a maximum of 15,000 centroids, while the C++ implementation was successfully executed with up to 150,000 centroids. Table  2  lists the number of centroids and the execution times for each step of the intersubject clustering process, for each group and each hemisphere, using non-linear registration. The process is divided into the following steps: distance matrix calculation, affinity graph construction, dendrogram calculation, and dendrogram partition. The entire process takes between 2. 6 and 3. 4 h on our test machine. shows the distribution of the clustering execution time for each hemisphere in Group 1.
Validation Method and Final atlas construction To perform a better validation of the results and obtain a measure of bundle reproducibility, we embedded our method inside  a bagging strategy (bootstrap aggregating) performed over the ARCHI database (74 subjects). For the atlas creation, the robust atlas creation method uses two independent groups of subjects. Hence, for the validation, this approach was repeated 10 times, employing sets of 54 subjects sampled randomly from the ARCHI database, split into two groups of 27 subjects. This processing led to 10 SWM bundle atlases for each hemisphere. To build the final atlas, first a bundle centroid was calculated for each bundle. Then, for each hemisphere, our hierarchical clustering was performed over the bundle centroids from the 10 atlases. For cluster selection, a maximum distance between centroids equal to 30 mm was used (this distance value is the same used for the robust atlas creation method).
automatic segmentation of sWM Bundles To test the reproducibility of the SWM bundles of the final atlas, we performed the automatic segmentation of 78 subjects of the segmentation data sets. We calculated the number of subjects where each bundle is present and the variability in the number of fibers. Small bundles with less than 10 fibers were not considered in the analysis. present in at least 8 of 10 tests were considered in the final atlas. The obtained bundles were then named using the automatic labeling strategy, based on the pair of ROIs connected by each bundle. Only bundles with a strong connection, present in at least 50% of the fibers, were selected. Finally, interhemispheric correspondence was automatically calculated. To identify these common bundles between the two hemispheres, the similarity between the left atlas bundles and the reflected right atlas bundles was calculated by measuring the intersection between bundles.
With non-linear registration, we identified a greater number of similar bundles. In addition, the clusters are more dense and homogeneous, in comparison with those found through linear registration. Therefore, non-linearregistration was selected for the method validation and creation of the final atlas. A bagging strategy (bootstrap aggregating) was performed over 74 subjects. The robust atlas creation method was repeated 10 times, employing sets of 54 subjects sampled randomly from the database, split into two groups of 27 subjects. A final atlas was constructed by fusing the 10 SWM bundle atlases obtained for each hemisphere. The final atlas is composed of 44 bundles in the left hemisphere and 49 in the right hemisphere, with a reproducibility ranging from 8/10 to 10/10. These values guarantee that the obtained bundles are representative of the population of subjects and also give a measure of reproducibility of the bundles.
author): algorithm development, results evaluation, and article writing. MG: algorithm development, results evaluation, and article writing. RV: optimization of algorithms and data testing. MF: optimization of algorithms and review of the entire manuscript. JH: database generation, database preprocessing, and results evaluation. DD: development of tools for tractography data processing and database generation. CP: development of tools for tractography data processing, database generation, and results evaluation. J-FM: development of tools for tractography data processing, database generation, results evaluation, and review of the entire manuscript. PG: algorithm development, results evaluation, article writing, and review of the entire manuscript. acKnOWleDgMenTs This work was supported by CONICYT Chile grant, FONDECYT #1161427.
It was also partially funded by CONICYT Chile grants CONICYT-PIA-Anillo ACT1416 and FONDECYT #1151278, and by European Union's Horizon 2020 Framework Programmed for Research and Innovation under grant agreements No. 720270 (Human Brain Project SGA1) and No. 604102 (HBPs ramp-up phase). The "CONNECT/Archi Database" is the property of the CEA I2BM NeuroSpin centre and was designed under the supervision of Dr Cyril Poupon and Dr Jean-François Mangin and was funded by the Federative Research Institute 49, the HIPPIP European grant, and the European CONNECT project (http://www. brain-connect. eu). Acquisitions were performed by the scientists involved in the Multi-scale Brain Architecture research program of NeuroSpin and by the staff of the UNIACT Laboratory of NeuroSpin (headed by Dr Lucie Hertz-Pannier), under the ethical approval CPP100002/CPP100022 (principal investigator Dr Denis Le Bihan).
The interventions were implemented during 1 academic year, and follow-up consisted of 3 visits over 2 academic years (Fig. 1 ). The PRALIMAP-INÈS trial has been approved by the French consultative committee for treatment of information in health research (no. 12. 299), the French National Commission for Data Protection and Liberties (no. 912372) and the French Persons Protection Committee (no. 2012/15). This trial was registered at ClinicalTrials. gov (NCT01688453).
[23] ). Sociodemographic data (social and professional class of family head, residence code and family composition) were compiled from the Board of Education database but were not available at inclusion for social status appreciation but only by midyear. Lifestyle and nutritional (diet and physical activity) attitudes and behaviors: Diet was measured by a food frequency questionnaire  [24]. Physical activity and sedentary behavior were measured by the International Physical Activity Questionnaire (IPAQ)  [25]. Alcohol and tobacco consumption were investigated. An additional question explored the adolescent's needs for excess weight management. Health: The EAT-26  [26, 27] , a self-administered questionnaire with responses on a Likert scale ranging from 0 (never) to 6 (always), screened for anorexic and bulimia symptoms.
• Multidisciplinary team (MDT) meetings The MDT meetings were elaborated on the model developed in oncology care management  [32]. The MDT meetings aimed at changing professionals' perceptions and practice regarding less-advantaged adolescents. Each meeting gathered together PRALIMAP-INÈS trial professionals, school medical doctors and nurses; AVRS dieticians, psychologists and physical activity practitioners; specialized obesity professionals; and if available, the adolescent's GP. Three meetings were held to propose and follow up activities adapted to the adolescent's needs. At the first meeting, the weight history of the adolescent and sociodemographic and school characteristics were presented and shared, relying on the data collected during the adolescent's inclusion and the first collective session in addition to the school data.
Follow-up visits At the end of the intervention (end of grade 9 or 10), the adolescent was invited for a follow-up visit (T1). During the check-up, anthropometric measurements (weight, height, waist circumference) and selfadministered questionnaires (the same as in the inclusion session plus transition questions completed for each of the outcome categories and a program participation and satisfaction questionnaire) were collected. The data collection was organized in each school on the same principle as for the inclusion session one. A post-intervention follow-up visit (T2), similar to T1, was executed (whatever school the student was in) 1 year after the end of the intervention (Fig. 1 ).
Thus 2 domainsimplementation and participationwere explored according to quality and quantity and from 4 points of view: adolescents, mobile team of healthcare network specialized in nutrition, school professionals and research team. The process data were collected by observation, interviews, and self-administered questionnaires. Observation: Members of the research team observed the key processes in the intervention implementation in every school and documented the processes in activity reports. The observation included meetings with school professionals, sign-off sheets from group educational sessions and sheets reporting adverse events. Meetings were organized each year, were conducted by the PRALIMAP-INÈS team and aimed to ensure and follow up the performance of activities and uphold the dynamics of the school's investment in the process.
Data management quality control A Microsoft Access-based information system was developed to warehouse data (Microsoft Access ® , 2007). It allowed adolescents, nurses and physicians to directly complete data on a computer; data were then stored on a secured server. To ensure quality data collection, adolescents were assisted by a technician when completing questionnaires and a quality data control was computationally planned. The Board of Education and the adolescent's identification and socio-demographic data were crosschecked.
The first comparison of the primary objective analysis (A. S vs LA. S. S) will consist of an equivalence test. For the second and third comparisons (A. S vs LA. S and LA. S vs LA. S. S), superiority analyses will involve mixed models accounting for the potential confounding factors identified in the previous steps and the hierarchical (possible school and wave random effects) and longitudinal nature of the data. An unstructured correlation matrix will be initially specified and the existence of a more appropriate specific correlation structure based on the data at hand will be. Additional analyses concerning changes in secondary outcomes (anthropometric, nutritional, attitudes and behaviors, health, transition questions) will involve models similar to those specified for the primary outcomes.
From the second year, the declaration was eliminated and all adolescents were invited to be measured. Additionally, middle schools were committed. The modification of the strategy of inclusion after the first year did not change the implementation of activities but allowed for achieving the sample size. A good quality of the inclusion and follow-up data was warranted because of the unified procedure for collecting anthropometric, selfadministered questionnaire and medical visit data. The computer- assisted questionnaire completion was easier than the paper version and also, the adolescents are assisted by a trained technician. BMI and WC alone were insufficient to accurately diagnose overweight, especially among athletic adolescents, generally grouped in sport-curriculum classes, as shown by the 138 adolescents (29% of those not proposed for inclusion by the physician) of whom none were clinically diagnosed as overweight although fulfilling the BMI or WC criteria.
Acknowledgments Many people worked together selflessly and enthusiastically to make the PRALIMAP-INÈS trial a success. The PRALIMAP-INÈS trial group warmly acknowledges the students and their parents who participated in the measurements and interventions and the school professionals (nurses, teachers, administrative staff, and headmasters' staff) who contributed to the recruitment of students and delivery of the interventions. Participating schools: Le Chesnois high school,  Bains Les Bains ;  Jean Lurcat  high school, Bruyeres;  Marcel Goulette  high school, Charmes; Pierre Mendès France high school, Contrexeville; Pierre Mendès France high school, Epinal;  Isabelle Viviani  high school, Epinal; Claude Gellée high school, Epinal; Louis Lapique high school, Epinal; EREA middle and high school, Epinal;  Pierre Gilles de Gennes high school, Gerardmer; Jean-Baptiste Siméon Chardin high school, Gerardmer; La Haie Griselle high school, Gerardmer ;  Jean-Baptiste Vuillaume  high school, Mirecourt;  Pierre et Marie Curie  middle and high school, Neufchâteau; Louis Geisler high school,  Raon L'Etape ;  Camille Claudel  high school, Remiremont; André Malraux high school, Remiremont; Jules Ferry middle and high school, Saint-Dié-des-Vosges;  Georges Baumont high school, Saint-Dié-des-Vosges; Jacques Augustin high school, Saint-Dié-des-Vosges; La Haute Mosellotte high school, Saulxure-sur-Mosellotte ;  Emile Gallé  high school, Thaon Les Vosges; Fleurot D'herival middle school, Le Val-D'ajol; Fleurot D'herival middle school, Plombières; Charlet middle school, Remiremont; Le Tertre middle school, Remiremont;  René Cassin  middle school, Eloyes;  Hubert Curien  middle school, Cornimont; Les Boudieres middle school,  La Bresse ;  Maurice Barres  middle school; Charmes; Jean Montemont middle school,  Rupt-Sur-Moselle.
We warmly thank  Mrs Henry-Wittmann  and her statistical team of  Nancy-Metz Academy Board of Education  for making the required student database available. We thank all administrative and technical staff of the Nancy National conservatory of arts and crafts (CNAM), the  Nancy School of Public Health , the Lorraine University EA 4360 APEAMAC team and the epidemiology and clinical evaluation department of  Nancy University Hospital  for their contribution to data collection, data entry and management, activity reporting, and logistic and.
Database Of the 819 transplantations performed at the University Hospital of Limoges (France) between december 1984 and december 2011, 664 were included in the primary cohort (development database). A flow-chart showing patient selection is shown in Fig 1. All 664 transplants studied came from heart-beating deceased donors and had a follow-up of at least one year after transplantation. The maintenance immunosuppressive regimen consisted mainly of one calcineurin inhibitor (cyclosporine or, since 2001, tacrolimus) associated with azathioprine (until 1996) or mycophenolate mofetil (after 1996) and corticosteroids (generally stopped between 3 and 6 months post-transplantation). All patients received induction therapy. Patient outcome was known for each patient at the date of the last follow-up.
Patient ethnicity was not recorded since it is not authorized by French law. RSF analysis was performed to select and rank the most predictive covariates of graft failure using the date of transplantation as time origin  [11]. RSF was implemented in the 'randomFor-estSRC' R-package (version 2. 0. 0). Briefly, a RSF was generated by creating 1000 trees, each tree built on a randomly selected bootstrap sample (using 63% of the original data) using a randomly selected subset of covariates. Each bootstrap sample excluded, on average, 37% of the data, which were reserved for a test set called "out-of-bag" data (OOB). RSF evaluated the change in prediction error attributable to each covariate. The prediction error (i. e. the percentage of patients misclassified) was assessed with the Harrell's concordance index (Harrell's cindex) using OOB data  [19].
The c-index was computed using an OOB set constructed with the 1000 OOB datasets provided by the 1000 bootstrap samples used in growing the forest. The OOB prediction error is defined as 1 minus Harrell's c-index  [11]. The prediction error ranges between 0 and 1, where a value of 0. 5 corresponds to a prediction no better than random guessing and a value of 0 reflects perfect accuracy. The parameter "nsplit" used to specify random splitting was fixed at 3. The predictive performance of the studied variables was evaluated by their "variable importance" (VIMP), calculated by RSF. VIMP measures the change in prediction error for a forest grown with or without this variable. Variables selection was successively done by (1) fitting data by RSF and ranking all available variables and (2) iteratively fitting RSF by removing at each iteration a variable from the bottom of the positive variable importance ranking list.
Prediction of graft failure Score calculations were derived from both the VIMP sourced from the final RSF model and the conditional survival tree. The weight of each variable (i. e. each risk factor) was based on the ratio between its VIMP and the VIMP of the last predictive variable retained. A same value of weight was allocated for variables split at the same tree-depth in the conditional survival tree. The weighted risk score was calculated by adding the weights of the different risk factors within each branch of the conditional survival tree. This strategy led to a score for each patient subgroup identified at each terminal node of the conditional survival tree. Time-dependent receiver operating characteristic (ROC) curves with area under the curve (AUC) for censored survival data were used to evaluate the discrimination of the developed score.
External validation External validation of the developed score was performed in patients transplanted between 2002 and 2010 in two independent French transplantation centers (CHU Tours n = 706; CHU Poitiers n = 190). As in the development cohort, patients with pre-transplant DSA were excluded. All anti-HLA antibodies screenings were performed using Luminex1. The validation database (Astre database) was approved by the CNIL (Authorization number DR-2012-518). Validation procedure included: recalculation of the Scr clusters considering the external database only, calculation of the individual scores using the developed scoring system, determination of the time-dependent ROC AUC at ten years post-transplantation and calibration based on Hosmer-Lemeshow goodness-of-fit test adapted for survival data  [21].
Kaplan-Meier analysis was used to assess graft survival (graft loss, i. e. return to dialysis). Graft survival in different patient subgroups was compared using the log rank test. Statistical analyses were performed with MedCalc for Windows, version 14. 10. 2. (MedCalc Software, Ostend, Belgium) and R version 2. 15. 1 (www. R-project. org). The R packages are freely available through the Comprehensive R Archive Network distribution system (http:// cran. r-project. org).
Development database The characteristics of the studied kidney transplants are listed in Table  1. During the whole study period, 137 patients have been treated for a first acute rejection among them 122 (89%) were biopsy proven. One hundred nine first rejections occurred during the first year post-transplantation. Borderline rejection was evidenced in 36 patients and T-Cell mediated rejection (TCMR) in 105 patients, Antibody-mediated rejection (ABMR) in 14 patients and mixed (ABMR + TCMR) in three patients. Only two patients displayed ABMR criteria on a biopsy done before the definition of ABMR in the Banff classification. During follow-up, dnDSA were present in 62 patients. The median time to dnDSA diagnosis was significantly lower in patients who exhibited pretransplant NDSA than in patients who did not (1.
External validation of AdGFS Table  1  reports the characteristics of the patients. Graft survival within each risk group was similar in the development and external validation datasets (Fig  7 ). The accuracy of the score at predicting graft failure remained high in the validation dataset, with a time-dependent ROC AUC of 0. 79 (CI 95% 0. 74-0. 84) at ten years after transplantation. Results of calibration evaluation of AdGFS in the external dataset were good: observed numbers of patients with graft failure were close to the expected numbers using the AdGFS risk groups ((χ 2 = 2. 39, p = 0. 30) (Table  3 ).
The database is one of the largest in which the impact of dnDSA was analyzed. Our study has some limitations. It included fewer patients than some previously published cohorts and was validated (i) beyond one year of transplantation, (ii) in deceased donors' grafts, and (iii) in patients without DSA at the time of transplantation. Thereby, AdGFS can be cannot be calculated before one year post-transplantation neither in case of living donor nor in patients with DSA before transplantation. In conclusion, we propose an adjustable score for risk stratification of graft failure at different post-transplantation times. AdGFS showed good discrimination and could be more useful than scores ignoring onset of dnDSA, for decisions regarding more or less intensive surveillance and treatment of the patients.