dataesr/scientific-paragraphs-categorization huggingface dataset:
Database Data were selected retrospectively from the ARTIX study (Adaptive Radiotherapy to Decrease Xerostomia in Oropharynx Carcinoma)  [33]. All the patients were adults (>18 and <75 years of age) with locally advanced non-metastatic carcinoma of the oropharynx limited to T3 and T4 and N2-N3 treated with arc-IMRT (Intensity-Modulated Radiation Therapy). Data comprised HN CT scans from 48 patients treated with RT in the CLCC Eugène Marquis between 2013 and 2018. For each patient, all contours were delineated by a single expert radiation oncologist amongst a team of ten who participated in the whole study. The patients underwent adaptive RT with weekly replanning, with between two and six CT scans acquired and delineated during the treatment course. Two hundred and sixty-nine CT scans were included in our study.
The standard CT in-plane pixel spacing was 1. 131 mm × 1. 131 mm, whereas slice thickness varied between 2 mm and 3 mm. The clinical data comprised inconsistent data annotations with missing ground truth contours for some anatomical structures. In other words, a different number of OAR (between 5 and 30) were segmented in each CT image. In some cases, even for the same patient, particular OARs were delineated in some CT scans but not others. We selected the fifteen HN OARs with the highest availability throughout the cohort: brain, brainstem, right/left (R/L) inner ear, R/L parotid glands, R/L temporomandibular joint, mandible, R/L submandibular glands, lips, larynx, esophagus, and spinal canal. OAR contouring frequency is depicted in Table  1. From the 24 patients for which the described fifteen OARs were fully contoured in all CT scans, we randomly selected a subset of 8 patients.
Proposed Framework We exploited the longitudinal information in our dataset to solve the partial annotations problem with the following two-step workflow (Figure  1 ). After preprocessing, we firstly trained a single-class OAR-specific 3D U-Net-based network for each organ. These trained models were used to generate pseudo-contours for the missing OAR delineations, obtaining multi-class masks for every CT in the training set. Secondly, a nnU-Net  [34]  was trained with the constructed multi-class masks. This final model exploits the anatomical spatial interdependence among the OARs, yielding the final delineations. We evaluated the workflow in the independent test group. The experiments were performed on a CUDA-enabled Nvidia Quadro RTX 8000 GPU with 48 GB of memory. DL-based models were implemented in PyTorch 1.
11. Table  1. OAR contouring frequency in the dataset used to train the workflow. # stands for "Number of". The first column depicts the OARs segmented in the study. The second and third columns correspond to the total number of patients for which each OAR segmentation was available in at least one CT, and the number of CTs contoured for that OAR, respectively. These CTs were then divided into train/test sets for the OAR-specific models. The division is depicted in the following two columns. Then, the OAR-specific networks are trained to generate the pseudo-contours, which are ensembled together with the available ground truth segmentations into a multi-class mask. These fully segmented images are used to train nnU-Net to predict the OAR contours. During inference, it is only necessary to preprocess the CT image and predict contours with the trained nnU-Net.
Each fold was completed in 23 h and 14 min on average. The inference was implemented using an overlapping sliding window. The Dice score coefficient (DSC), surface Dice similarity coefficient (sDSC), and average surface distance (ASD) were computed to evaluate the performance of the trained model. The sDSC evaluates the overlap between two surfaces at a specified tolerance  [3] , so a perfect sDSC would imply that approximately 95% of the surface is correctly outlined whereas 5% must be refined. We implemented the sDSC using pre-computed tolerances presented in  [3]. All the experiments were evaluated before and after data curation in the test set. We conducted two experimental studies exploiting our longitudinal data. First, we evaluated the feasibility of adapting the model to new data for adaptive RT, following a similar approach to  [43].
OAR-Specific Models Table  2  depicts the evaluation of each OAR-specific model in its own test and independent test sets before and after data curation. The results were highly accurate in the OAR-specific test sets, with the DSC above 81% for all OARs except the lips and submandibular glands. On the independent test set, the DSC was still higher than 81% for the brain, mandible, spinal canal, and brainstem. On the contrary, the larynx, lips, and submandibular glands presented the most substantial deviations (66. 73 ± 9. 31%, 45. 17 ± 18. 18%, and 55. 40 ± 21. 14%, respectively). As expected, the four refined OARs underwent a substantial increase in performance after data curation, especially significant for the temporomandibular joints and inner ears (from 67. 26% to 83. 34%, and 55. 49% to 83.
Discussion This paper proposes a DL-based workflow to automatically delineate fifteen OARs for HN radiotherapy treatment planning. We exploited a longitudinal partially labeled database to (i) generate accurate OAR segmentations, and (ii) demonstrate the possibility of enriching the model robustness with a baseline image from each patient. In the pseudo-contouring step, we trained eleven OAR-specific models to generate pseudo-contours to complete the missing labels in the dataset (Table  1 ). As our data were longitudinal, including images from a patient in the training group was highly beneficial for robustly segmenting missing labels for that patient. The models showed high perfor- (e) 3D GT contours, (f) 3D predicted contours, and comparison between them in the (g) sagittal and (h) coronal planes.
Discussion This paper proposes a DL-based workflow to automatically delineate fifteen OARs for HN radiotherapy treatment planning. We exploited a longitudinal partially labeled database to (i) generate accurate OAR segmentations, and (ii) demonstrate the possibility of enriching the model robustness with a baseline image from each patient. In the pseudo-contouring step, we trained eleven OAR-specific models to generate pseudo-contours to complete the missing labels in the dataset (Table  1 ). As our data were longitudinal, including images from a patient in the training group was highly beneficial for robustly segmenting missing labels for that patient. The models showed high performance in the independent test for most OARs except the larynx, lips, and submandibular glands (DSC < 70% and ASD > 2 mm).
Four OARs (spinal canal, esophagus, temporomandibular joints, and inner ears), showed a meaningful improvement in performance after data curation (Table  2 ), as the DSC increased by an average of 14. 67% and the ASD decreased by a mean of 0. 788 mm. Even if the models' performances were not entirely satisfactory for some OARs, it must be considered that the goal of this workflow step was to complete the database by generating pseudo-contours with a simple training architecture. The reduced-data experiments demonstrated similar performance in the independent test between the original and reduced-data models (Figure  2 ). Therefore, we demonstrated that our approach for this first segmentation step was feasible in longitudinal databases with between 30 and 40 patients. The generated pseudo-contours were then ensembled together with the available ground truth delineations, building a multi-class segmentation for each CT scan to train nnU-Net.
The DSC does not assess the surface fraction that needs to be redrawn, as it does not account for whether there are numerous minor surface deviations across the volume or only a significant deviation at a single point, largely penalizing small volumes. Thus, comparing the results for large HN OARs, such as the brain of the spinal canal, with smaller OARs, such as the inner ears or the temporomandibular joints, results in unfair assessments. The sDSC was therefore of great aid to contrast the results of the DSC and get a broader evaluation. The larynx, lips, and submandibular glands obtained the worst performance overall, in agreement with other published papers (Table  4 ). These results showed that our model was comparable to previously published studies for HN OAR segmentation regarding the DSC  [19]  (Table  4 ), and also to other studies using partially labeled or unlabeled datasets and following similar approaches to ours  [7, 11, 25].
Previous works  [10, 44]  have shown how implementing DL-based OAR segmentation algorithms in new medical centers without retraining renders less agreement between predictions and ground truth segmentations. These discrepancies usually come from deviations in segmentation protocol  [14] , medical imaging machines with different technical characteristics  [44] , and overfitting to the training data. Therefore, it is evident that training DL-based models with data from the hospital where they will be implemented is very advantageous. To avoid the high computational cost of training a DL network from the beginning and the restricted sizes of homogenized labeled datasets, a more efficient approach would be performing transfer learning with pre-trained DL models. This reduces the computational requirements and includes additional data variability in the model.
A further step, particularly valuable in adaptive radiation therapy, is to retrain the last epochs of a model with fully labeled CT scans from new patients being treated to teach the model to segment future CT scans from those patients with higher accuracy. Tables  3b  and 4  illustrate the significant performance improvements with this self-supervised approach, especially for the larynx, lips, and submandibular glands, which were the most difficult OARs to segment. Figure  S2  also depicts the DSC improvement when using the self-supervised model (Figure  S1 ), demonstrating the capability of the self-supervised model to yield more robust and personalized predictions. The proposed DL-based workflow can automatically contour fifteen OARs in the clinic. It has proven to be an efficient way to exploit the available partially labeled data from a medical center and presented a more realistic solution than studies that rely on large databases segmented from the beginning.
Our model showed comparable performance to the literature, with a more straightforward and pragmatic implementation in new scenarios. Our solution was developed using CT scans derived from routine clinical practice, belonging to patients with advanced local tumors, and therefore should be applicable in a hospital setting. Moreover, we demonstrated the feasibility of integrating our workflow for adaptive RT, yielding robust patient-adapted contours requiring fewer human and computational resources. However, there were some limitations to this study. First, the cohort included only 48 unique patients. Although we applied numerous data augmentation tools to increase data variability, only 40 unique patients were used for training. Moreover, the contours of the larynx and lips were not refined to ensure homogenization in the segmentation protocol, possibly downgrading model performance.
Conclusions This paper presents a novel DL-based weakly supervised workflow for HN OAR segmentation exploiting partially labeled datasets and longitudinal data. Experimental results showed its superior performance for fast segmentation of fifteen OARs of the head and neck anatomy. The model can be easily integrated into the clinical environment as it has been trained with data derived from clinical practice. Our results also show the feasibility of implementing the workflow for adaptive radiotherapy, contributing to the availability of DL-based auto-segmentation tools for clinical users in the near future.
Data processing and bioinformatics analysis Raw sequence data were aligned to the human reference genome (Build-UCSC hg19) using BWA  (Li & Durbin 2009). Somatic substitutions and insertions/deletions were detected using CaVEMan and Pindel, respectively  (Raine et al. 2015 , Jones et al. 2016). Somatic mutations were validated by manual inspection in Integrative Genomics Viewer and the COSMIC database. Mutations were restricted to those affecting protein-coding regions. In order to provide a complete overview of the mutations in the 360 genes in GEP-NEN, the data set was not restricted to driver mutations. Allele-specific copy number analysis and estimation of purity and ploidy were performed using FACETS  (Shen & Seshan 2016). GISTIC 2. 0  (Mermel et al. 2011 ) was used to identify frequent amplifications and deletions.
Targetable molecular alterations were identified based on a predefined list and, in addition, by application of the OncoKB database (Supplementary Methods and Supplementary Table  1 ). A prediction model for the classification of tumours into the categories LC-NEC or NET G3 was built, based on mutational status of nine genes (APC, ATRX, BRAF, DAXX, KRAS, MEN1, MYO5B, SMAD2 and TP53). Classification was performed using C5. 0 decision tree algorithm implemented in R package C50 (v0. 1. 2; Supplementary files 1, 2 and 3) (https://www. rulequest. com/).
Among the 152 samples, we found 101 (66%) with one or more alterations that could be targeted by available drugs. The majority of these alterations were related to defects in DNA repair, making tumours potentially sensitive to PARP inhibition, but frequent targetable alterations were also seen in BRAF, MTOR signalling as well as several other genes (Fig. 1  and Supplementary Table  1 ). In addition to the analysis of potentially targetable mutations, we performed a highly stringent assessment of those alterations listed as established biomarkers in the OncoKB database. Even in this restricted analysis, as many as 22% of the tumours harboured one or more targetable alterations.
Prognosis Overall survival (OS) for GEP-NEC patients was significantly worse than for NET G3 patients (11 vs 18 months; P = 0. 049; Supplementary Fig. 15 ). Within the NEC group, patients with SC-NEC had a significantly worse OS than those with LC-NEC (9 vs 12 months; P = 0. 025; Supplementary Fig. 15 ). Although trends for the prognostic value of key features such as MSI and TMB were observed, these did not reach statistical significance in the Endocrine-Related Cancer present data set. Neither did mutation status for any of the investigated single genes (in univariate analyses).
2019). Separation of NET G3 from NEC based on morphology can be challenging. Some have suggested to use MEN1/ATRX/DAXX and RB1/TP53 to aid in separation of these entities  (Tang et al. 2016a). The ESMO 2020 GEP-NEN guidelines suggest using RB1 mutations or RB1 loss to discriminate between NET G3 and NEC  (Pavel et al. 2020). Our data highlight that testing for RB1 loss would perform better than RB1 mutations in such an approach. Applying molecular data, we assessed the main differences between NEC and NET G3. Given that SC tumours per definition are NEC, we considered the subgroups that are clinically important to distinguish to be LC-NEC from NET G3. Mutations in APC, TP53, KRAS and BRAF were enriched in LC-NEC as compared to NET G3, and the mutation status of these genes yielded a classifier with surprisingly high sensitivity, specificity and positive predictive value.
Statistical analysis The multifactorial analysis (PCA) which was made by the XLSTAT software, allowed classifying and processing the information related to the metallic trace elements in the sediments during the sampling campaigns during the two years 2018 and 2019 by the establishment of the correlations between all the variables of Oued Inaouene. This PCA is performed on a data matrix consisting of 10 samples (6 stations, 2 periods) during which the 13 variables (P, Zn, Cu, Sr, Ni, pH, M. o etc. ) were measured.
Relationship between metals The correlation plots above, obtained by XLSTAT software, were used to determine the relationships that may exist between the metals studied (Table  3  and 4 ). According to  Waris et al. (2010) , the correlation coefficient determines the relationship between two variables and measures the strength of that relationship. Positive correlations and trends suggest a partly common origin and/or chemical similarities. This common origin may be natural and/or related to anthropogenic activity. Negative trends may be the consequence of different chemical behavior between metals. The analysis of the correlation matrix tables revealed a good correlation between lead, copper, cobalt, zinc, and nickel; on the other hand, strontium does not present a link with any of these metals except nickel for the samples of 2019 (Table  4 ).
Results (effects/changes) Using population survey information and databases on the locations of programmes, facilities and green space, our analysis has proven what works and what doesn't in each of the cities. For example, ethnic groups with lower levels of physical activity show higher levels of motivation to be active than other groups. Suggesting that lack of interest is not the reason for lower participation. We also found clear evidence of the impact and importance of facility access. Across all cities, access to facilities is correlated with levels of activity -with more active areas on average having almost 2. 5 times more facilities than less active areas.
Methods A systematic literature search of observational and experimental studies on the association between the participation in sport and health outcomes was conducted through electronic databases: MEDLINE via PubMed, Scopus, and Web of Science. The inclusion criteria were: participants heathy adults; type of studies cross-sectional, cohort or intervention designs; study group participation in specific sport disciplines; non-participation comparison group; health-related outcome variables in terms of mortality, morbidity, disease risk factors and function. Studies on sport injuries were excluded.
Results The SCforH online learning tool includes: a participation consent form; a module for setting learning objectives, textual, pictorial, and video learning materials; interactive learning exercises; in-course quizzes and self-assessment modules; a link to the database of SCforH initiatives and other related practices in the European Union; links to other available online materials for those who want to know more; a module for the assessment of acquired knowledge; a participant feedback module; an optional survey including questions relevant for the evaluation of SCforH activities.
Methods Six databases (MEDLINE (Ebsco), SportDiscus, Cinahl, Cochrane library, Web of Science, and Scopus) were searched for key concepts of policy, transport, evaluation and PA. Methodological quality was assessed using standardised tools. The strength of the evidence of policy impact was described based on pre-determined categories of positive, negative, inconclusive or untested. Results 17 of 2,549 studies were included in the data synthesis. The authors identified three main transport policy areas with 60 individual policy actions that had a direct or indirect effect on PA. The policy areas were: convenient transport infrastructure development, active travel promotion and shift of transport mode. These areas correspond to Haddon's methodological approach of the transport system division in to three elements  (human, vehicle, road).
Background According to a socio-ecological perspective, the neighbourhood environment plays a critical role in promoting physical activity across all population groups. A comprehensive assessment of environmental characteristics is a prerequisite to the planning, implementation and evaluation of physical activity interventions. This work aims to identify existing audit tools for the assessment of physical activity related environmental features in order to evaluate a) their applicability in the European context and b) their relevance in the urban and rural context. Methods Within the research project EUBeKo[1] we searched relevant websites (e. g. Active Living Research) and scientific databases (e. g. PubMed) in order to identify existing audit instruments. We then developed a categorization system for the integration of the topics and items retrieved from the identified tools.
Methods A systematic review with narrative synthesis was conducted. The search was carried out in the databases PubMed, Scopus, CINAHL, PsycINFO/SportDiscus, and WebofScience. Searches were limited to only include studies published up until 04/ 2020, in English and German. Altogether, qualitative, quantitative, ''mixed-method'' studies, reports and reviews were considered. Articles referring to factors of sustainable implementation of community-based physical activity promotion with a whole-system approach were included. Although this was not an inclusion criterion, articles addressing accessibility for SDI were specifically taken into account. The framework by  Schell et al. (2013)  was used for data extraction and analysis, while the four main elements from  Popay et al. (2006)  were applied in the narrative synthesis process.
Results The screening resulted in a total of 49 articles included in the analysis. Preliminary results show a diverse use of methods for measuring capabilities for healthy lifestyles. Preliminary results show that three categories of instruments can be identified: (a) Five studies employed secondary data analysis of specific datasets to extrapolate capabilities for healthy living; (b) five articles dealt with measuring capabilities using qualitative approaches (interviews, video recordings); (c) 39 articles reported on a total of 10 different questionnaires to measure capabilities. We identified only one instrument (employing both a questionnaire and qualitative measures) that explicitly measured capabilities for PA, albeit only for a specific target group.
Results Between 2015 and 2019, prevalence of overweight and obesity increased from 9. 1% to 14. 1%, with this increase greater among low-SES girls. We observed an increase in both the proportion of adolescents playing 7+ hours of sport (8. 0% to 12. 3%) and those not playing sport (34. 2% to 35. 9%). These changes varied according to adolescents' SES. In 2019, low-SES adolescents were 1. 5 times less likely to play sport (95% CI:1. 17-1. 99) while no change was observed among high-SES adolescents. Finally, 7+ adolescents were no more likely to be overweight or obese in 2019 compared to 2015. Contrarily, NS and 7-adolescents were 1. 7 and 1. 8 times more likely respectively to be overweight or obese in 2019 compared to 2015. Methods A total of 4 databases were examined: PubMed-Medline, PsycINFO, ScienceDirect and Google Scholar.
Background The World Cancer Research Fund International's (WCRF International) analysis of global research shows a strong link between diet, physical activity, and risk of cancer. To address this, WCRF International has developed a package of policy resources to promote healthy diets and physical activity to support reporting, categorising and monitoring of policy actions. The MOVING policy framework was developed as a complement to the well-established NOURISHING framework of diet-related policies-outlining a comprehensive set of areas in which governments should take action to promote physical activity. The new framework forms the basis for the MOVING database of implemented policy actions. Methods For the MOVING framework, literature reviews were undertaken, and the results were distilled into policy categories on which academics and policy experts provided feedback.
The Downloaded from https://academic. oup. com/eurpub/issue/32/Supplement_2 by guest on 12 September 2022 framework is the basis for a specific inclusion criteria and search strategy to determine how policy actions will be collected, reviewed and categorised for the MOVING database. A comprehensive scanning methodology was designed to identify all relevant policies across the MOVING policy areas. Relevant policies are then verified with in-country experts and uploaded to the database. This methodology was tested, refined and will be applied to 27 European countries.