dataesr/scientific-paragraphs-categorization huggingface dataset:
Observations This study uses gridded MDA8 O 3 concentrations created with an objective-mapping algorithm developed by  Schnell et al. (2014). They applied a new interpolation technique over hourly observations of stations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air-quality database (AirBase) to calculate surface ozone averaged over 1 • by 1 • grid cells (see  Schnell et al. , 2014 Schnell et al. , , 2015)). Otero et al. (2016)  used this data set to examine the influence of synoptic and local meteorological conditions over Europe. This interpolated product offers a possibility to establish a direct comparison between observations and CTMs. However, it must be acknowledged that for some areas with a low number of stations (i. e. the south-eastern or north-eastern European regions) the values interpolated into the 1 • × 1 • degree grid cells may not be representative of such large scales.
Acknowledgements. This work was hosted by  IASS Potsdam , with financial support provided by the  Federal Ministry of Education and Research of Germany (BMBF)  and the  Ministry for Science, Research and Culture of the State of Brandenburg (MWFK). The authors acknowledge  Jordan L. Schnell  for providing the interpolated data set of MDA 8 O 3. Modelling data used in the present analysis were produced in the framework of the  EURODELTA-Trends  project initiated by the  Task Force on Measurement and Modelling of the Convention on Long Range Transboundary Air Pollution. EURODELTA-Trends is coordinated by  INERIS  and involves modelling teams from  BSC, CEREA, CIEMAT, ENEA, IASS, JRC, MET Norway, TNO  and  SMHI. The views expressed in this study are those of the authors and do not necessarily represent the views of EURODELTA-Trends modelling teams.
The MATCH participation was partly funded by the  Swedish Environmental Protection Agency  through the research programme  Swedish Clean Air and Climate (SCAC)  and  NordForsk  through the research programme  Nordic WelfAir  (grant no. 75007 ). Jana Sillmann is supported by the  Research Council of Norway  through the  CIXPAG  project (grant no. 244551 ). Edited by:  Jørgen Brandt Reviewed  by: four anonymous referees.
In our experiments, we rst study the optimal number of Gabor lter directions for AD detection. In addition, we compare dierent texture lers such as local variance or entropy. We also compare our new adaptive fusion method with dierent fusion schemes. Finally, to highlight the improvement of classication performances provided by our new framework, we compare our new method with the state-of-the-art approaches and demonstrate its eciency. Data used in this work were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset  1. ADNI is a North American campaign launched in 2003 with aims to provide MRI, positron emission tomography scans, clinical neurological measures and other biomarkers. The data used in this study are all the baseline T1-weighted (T1w) MRI of the ADNI1 phase.
Patch-based grading Grading framework uses patch-based techniques to capture signal modications related to anatomical degradations caused by AD  [9]. To date, patch-based grading methods demonstrate state-of-the-art performances to detect the earliest stages of AD  [20; 33]. To determine the pathological status of a subject, Figure  2 : Sample of absolute value of complex number computed by Gabor lters. The hippocampal area is ltered with gabor at dierent directions. From left: Original image, textural maps provided by our Gabor lters bank given by the parameter set {(θ = 0; φ = 0), (π/2; π/2), (0; π/2), (0; π/4), (0; -π/4), (-π/4, π/4), (π/4, π/4)}. It can be noticed that for each direction, dierent textures are extracted. grading-based methods estimate at each voxel the state of cerebral tissues using anatomical patterns extracted from a training library T composed of two datasets, one with images from CN subjects and one with AD patients.
Implementation During our experiments, texture maps were obtained using one scale and 11 dierent directions. The texture-based grading maps were estimated using patches of 5 × 5 × 5 voxels. The grading step based on an optimized Patch-Match  [17]  was performed using K = 50. The required computational time was 3s per texture maps, thus the global grading step required 15 seconds with our setup. A support vector machine (SVM) with a linear kernel was used to classify each test subject. We used the Matlab function provided by the Statistics and Machine Learning Toolbox. In our experiments, the soft margin parameter C was optimized with a Bayesian optimization method. The results of each experiment were compared in terms of accuracy (ACC) and area under the ROC curve (AUC), specicity (SPE), and sensitivity (SEN).
On the other hand, our proposed method applied into hippocampus is compared with methods based on a whole brain analysis using similar dataset (see the lower part of Table  4 ). Indeed, we compare our texture-based grading approach applied on HIPP with an ensemble grading that proposed to extend the original patch-based grading to a whole brain analysis based on grey matter maps  [20] , the patch-based grading method based on a sparse representation applied on the whole brain  [33] , a sparse ensemble grading method that analyzes the whole brain  [21] , and a Deep Learning (DL) method based a whole brain analysis  [31]. The results show that our method obtains the best accuracy for AD versus CN. This result is similar to classication results obtained with a DL and sparse ensemble grading method  [31; 21].
Acknowledgements We thank  D. Mokranjac  and  D. Pacheu-Grau  for the support, critical reading, and discussion. We thank  D. Görlich  for the His-SUMOstar construct. Supported by the  Deutsche Forschungsgemeinschaft   SFB860  project  B01  (P. R. ), the  Max Planck Society  (P. R. ), the  Copernicus Award  (P. R. and A. C. ),  Boehringer Ingelheim Fonds  (C. S. ),  Foundation for Polish Science First TEAM Program  co-financed by the  European Union  under the  European Regional Development Fund  (P. B. ), and the  PhD program Molecular Biology -International Max Planck Research School  and the  Göttingen Graduate School for Neurosciences and Molecular Biosciences (GGNB; DFG  grant  GSC 226/1 ) (A. S. , N. N. , R. G. , and C. S. ).
Data collection At inclusion, participants completed a set of five questionnaires related to sociodemographic and lifestyle characteristics (for example, date of birth, sex, occupation, educational level, smoking status, number of children),  32  anthropometry (height, weight), dietary intakes (see below),  33 34  physical activity (validated seven day International Physical Activity Questionnaire (IPAQ)),  35  and health status (personal and family history of diseases, drug use including use of hormonal treatment for menopause and oral contraceptives, and menopausal status). 7] [38]  To be included in the nutrition component of the NutriNet-Santé cohort, only two dietary records were mandatory. We did not exclude participants if they did not complete all optional questionnaires. We averaged mean dietary intakes from all the 24 hour dietary records available during the first two years of each participant's followup and considered these as baseline usual dietary intakes in this prospective analysis.
The NutriNet-Santé web based, self administered 24 hour dietary records have been tested and validated against an interview by a trained dietitian and against blood and urinary biomarkers. 36 37 Participants used the dedicated web interface to declare all food and drinks consumed during a 24 hour period for each of the three main meals (breakfast, lunch, dinner) and any other eating occasion. Portion sizes were estimated using previously validated photographs or usual containers. 39 We identified dietary under-reporting on the basis of the method proposed by Black, using the basal metabolic rate and Goldberg cutoff, and excluded under-reporters of energy intake. 40 e calculated mean daily alcohol, micronutrient and macronutrient, and energy intake by using the NutriNet-Santé food composition database, which contains more than 3300 different items.
Case ascertainment Participants self declared health events through the yearly health status questionnaire, through a specific check-up questionnaire for health events (every three months), or at any time through a specific interface on the study website. For each incident cancer declared, a physician from the study team contacted participants and asked them to provide any relevant medical records. If necessary, the study physicians contacted the patient's physician and/or hospitals to collect additional information. Afterwards, an expert committee of physicians reviewed all medical data. Our research team was the first in France to obtain the authorisation by decree in the Council of State (No 2013-175) to link data from our cohorts to medicoadministrative databases of the national health insurance system (SNIIRAM databases).
We therefore completed declared health events with the information from these databases, thereby limiting any potential bias due to participants with cancer who may not report their disease to the study investigators. Lastly, we used an additional linkage to the French national cause specific mortality registry (CépiDC) to detect deaths and potentially missed cases of cancer for deceased participants. We classified cancer cases by using the international classification of diseases, 10th revision (ICD-10). In this study, we considered all first primary cancers diagnosed between the inclusion date and 1 January 2017 to be cases, except for basal cell skin carcinoma, which we did not consider as cancer. We obtained medical records for more than 90% of cancer cases. Because of the high validity of self reports (95% of self reported cancers for which a medical record was obtained were confirmed by our physicians), we included as cases all participants who self reported incident cancers, unless they were identified as non-case participants by a pathology report, in which case we classified them as non-cases.
Similarly, we tested models including all participants with at least one 24 hour dietary record during the first two years of follow-up. We also tested associations between the quantity (g/d) of each ultra-processed food group and risk of cancer. Secondary analyses tested the associations between the proportion in the diet of each of the three other NOVA categories of food processing (continuous) and risk of cancer, using multivariate Cox models adjusted for model 1 covariates. All tests were two sided, with P<0. 05 considered to be statistically significant. We used SAS version 9. 4 for the analyses.
Among these 2228 cases, 108 (4. 8%) were identified during mortality followup with the national CépiDC database. The dropout rate in the NutriNet-Santé cohort was 6. 7%. Table  2  shows associations between the proportion of ultraprocessed foods in the diet and risks of overall, breast, prostate, and colorectal cancer. Figure  2  shows the corresponding cumulative incidence curves. In model 1, ultra-processed food intake was associated with increased risks of overall cancer (hazard ratio for a 10 point increment in the proportion of ultra-processed foods in the diet 1. 12 (95% confidence interval 1. 06 to 1. 18), P<0. 001) and breast cancer (1. 11 (1. 02 to 1. 22), P=0. 02). The latter association was more specifically observed for postmenopausal breast cancer (P=0. 04) but not for premenopausal breast cancer (P=0.
These points would tend to lead to underestimation of the strength of the associations. However, the possibility that selection bias may have led to an overestimation of some associations cannot be totally excluded. Secondly, some misclassification in the NOVA "ultra-processed food" category cannot be ruled out. Thirdly, despite a multi-source strategy for case ascertainment (combining validation of health events declared by participants, medicoadministrative databases from the health insurance, on 15 April 2022 by guest. Protected by copyright. http://www. bmj. com/ BMJ: first published as 10. 1136/bmj. k322 on 14 February 2018. Downloaded from and national death registry), exhaustive detection of cancer cases cannot be guaranteed. Furthermore, statistical power was limited for some cancer locations (such as colorectal cancer), which may have impaired our ability to detect hypothesised associations.
TF did the statistical analysis, supervised by MT and BS. TF and MT wrote the paper. BS did sensitivity analyses and was in charge of the revision of the paper. All authors contributed to the data interpretation, revised each draft for important intellectual content, and read and approved the final manuscript. MT is the guarantor. Funding: The NutriNet-Santé study was supported by the following public institutions: Ministère de la Santé, Institut de Veille Sanitaire (InVS), Institut National de la Prévention et de l'Education pour la Santé (INPES), Région Ile-de-France (CORDDIM), Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Recherche Agronomique (INRA), Conservatoire National des Arts et Métiers (CNAM), and Université Paris 13. MD and PF were funded by a PhD grant from the Cancéropôle Ile de France/Région Ile de France (public funding).
Materials and methods This study was conducted as a part of SEYLE (Saving and Empowering Young Lives in Europe), an EU-funded project designed to collect epidemiological data and to conduct a randomized trial of suicide-preventive interventions  [21, 22]. Between 2009 and 2011, 11,110 adolescents were recruited to the SEYLE trial from 168 randomly selected schools in 10 European countries: Austria, Estonia, France, Germany, Hungary, Ireland, Italy, Romania, Slovenia and Spain. The eligibility of schools and adolescents was determined by previously published criteria  [21]. The sample size was based on the expected effect of the trial's suicide-preventive interventions and calculated in excess of the requirements to detect statistically significant changes  [21]. The sample had a high external validity and was fairly representative of each national population  [23].
Further details on sample selection, size and randomization are elsewhere published  [21]. Once the consent of pupils and their caregivers were obtained locally, a baseline self-report questionnaire addressing sociodemographics, behaviours, life-events, relations, and mental health, was completed during one classroom session. A follow-up questionnaire was delivered after 12 months. The questionnaires were prepared in English and underwent coordinated translation and cultural adaptation in each country  [23]. Ninety-seven percent of SEYLE trial's pupils (N 10,757; median age 15; interquartile range [IQR] 14-15; females 59. 6%) answered a baseline question about sexual experience and were included in a cross-sectional analysis. Follow-up data of the pupils without sexual experience at baseline were analysed through a longitudinal nested case-control design (Fig  1 ).
All predictor variables showing a significative association with the outcome at unadjusted analyses were initially included, and only predictors showing significant association in at least one stratum were kept in the final multivariable analyses. Odds ratios (OR) were calculated together with 95% confidence intervals (CI). Statistical significance of differences was defined by p < 0. 05, two-tailed. The analyses were performed with IBM1 SPSS1 Statistics ver. 23 for Mac OsX.
nor by the participants, who were adolescent school pupils. All data are from the  SEYLE  study project, and are available upon request. Data requests may be sent to the SEYLE coordinating center at the  National Centre for Suicide Research and Prevention of Mental Ill-Health (NASP), Department of Learning, Informatics, Management and Ethics (LIME) ,  Karolinska Institutet , , http://ki. se/en/nasp/). The  SEYLE  project was funded by the  European Union   Seventh Framework Program  ( FP7 ;  HEALTH-F2-2009-223091 ). Judit Bala  ´zs was supported by the  Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background What are sarcomas? Sarcomas are a family of rare cancers with a combined incidence of six people per 100,000 population, with 28,000 new cases each year in Europe  [1]. They develop in the connective tissues and bones, and can occur anywhere in the body, at any age  [1]. There are more than 50 subtypes of soft tissue sarcoma (STS) alone, each with unique clinical, prognostic and treatment characteristics  [1, 2]. Because of this heterogeneity, sarcoma epitomises many of the challenges encountered with rare cancers. Patients often lack access to appropriate information about their condition, specialised care centres, appropriate courses of care, and ongoing clinical trials  [3]. The heterogeneity of sarcoma also complicates research efforts: recruiting sufficient numbers of patients to large clinical trials is often impossible, epidemiological data are scarce and the evidence base to guide clinical practice is inadequate  [1, 4, 5].
The charity has also made a strategic commitment to fund £3 million in scientific research by 2020  [25]. In Spain, research is taking place on very rare and ultra-rare sarcomas to determine their burden and improve treatment pathways. As has been mentioned previously, recruiting sufficient numbers of patients into sarcoma clinical trials remains an ongoing challenge due to the small number of patients with each specific type of sarcoma  [3] [4] [5]. Real-world data pooled from multiple centres as well as established registries at national and international level are therefore critical to gather sufficient evidence of how treatments work in practice, and guide quality improvement efforts. In Sweden, the National Sarcoma Quality Registry (INCA), which collects sarcoma patient data from all regions, provides an interesting opportunity for real-world data analyses.
[40]pean initiatives that focus on improving sarcoma care This paper outlines what patients expect sarcoma treatment to look like, how they expect services to be structured and developed to respond to patient needs, and how referral practices should evolve internationally[7]. http://www. sarcoma-patients. eu/en/dings/19-spaen-policy-paper-onquality-care-in-sarcomas-now-availableEUROEWINGconsortiuminternationalclinicaltrials to improve survival from Ewing sarcoma (EEC project)(2014)(2015)(2016)(2017)(2018). This initiative is coordinated by University College London and involves 20 European partners. It is funded by the European Union's Framework Programme 7 (FP7). It is a coalition of clinical study groups to bring greater patient access to clinical trials, promote sharing of knowledge and improve patient results[39].
http://www. euroewing. eueuroSARCproject. Funded by the European Union's Framework Programme 7 (FP7), it aims to design, structure and implement European clinical trials on rare sarcoma subtypes within an integrated translational study network[40]. approach to care should be implemented across all expert centres for sarcomaand link with community providers as well. National and European training programmes are needed to build specialised expertise in the diagnosis and care of sarcomas for all healthcare professionals involved in sarcoma care. Both European-and national-level collaborations are needed to achieve sustainable and equitable solutions in all countries. DKG: German Cancer Society; ECCO: European CanCer Organisation; EORTC: The European Organisation for Research and Treatment of Cancer; ERN: European Reference Network; ESSO: European Society of Surgical Oncology; EURACAN: European Reference Network on rare adult cancers; FP7: Framework Programme 7; GP: General practitioners; HTA: Health technology assessment; INCA: National Sarcoma Quality Registry; JARC: Joint Action for Rare Cancers; MDT: Multidisciplinary team; NEMDT: National Ewing Sarcoma Multidisciplinary Team; NetSarc: French Clinical Reference Network for soft tissue and visceral sarcomas; RCE: Rare Cancers Europe; ResOs: French Reference Network for bone sarcoma and rare bone tumours; RRePS: French Sarcoma Pathological Reference Network; RRePS: Sarcoma Pathological Reference Network; SPAEN: Sarcoma PAtients EuroNet; STBSG: Soft Tissue and Bone Sarcoma Group; STS: Soft tissue sarcoma.
http://www. euroewing. eueuroSARCproject. Funded by the European Union's Framework Programme 7 (FP7), it aims to design, structure and implement European clinical trials on rare sarcoma subtypes within an integrated translational study network[40]. approach to care should be implemented across all expert centres for sarcomaand link with community providers as well. National and European training programmes are needed to build specialised expertise in the diagnosis and care of sarcomas for all healthcare professionals involved in sarcoma care. Both European-and national-level collaborations are needed to achieve sustainable and equitable solutions in all countries. DKG: German Cancer Society; ECCO: European CanCer Organisation; EORTC: The European Organisation for Research and Treatment of Cancer; ERN: European Reference Network; ESSO: European Society of Surgical Oncology; EURACAN: European Reference Network on rare adult cancers; FP7: Framework Programme 7; GP: General practitioners; HTA: Health technology assessment; INCA: National Sarcoma Quality Registry; JARC: Joint Action for Rare Cancers; MDT: Multidisciplinary team; NEMDT: National Ewing Sarcoma Multidisciplinary Team; NetSarc: French Clinical Reference Network for soft tissue and visceral sarcomas; RCE: Rare Cancers Europe; ResOs: French Reference Network for bone sarcoma and rare bone tumours; RRePS: French Sarcoma Pathological Reference Network; RRePS: Sarcoma Pathological Reference Network; SPAEN: Sarcoma PAtients EuroNet; STBSG: Soft Tissue and Bone Sarcoma Group; STS: Soft tissue sarcoma Initiative [41]://eurosarc.
IFA IFAs of Toxoplasma-infected host cells were undertaken using standard procedures detailed in S1 Text. Images were captured on an AP DeltaVision Elite microscope (GE Healthcare) equipped with a CoolSnap2 CCD detector and captured with SoftWorx software (GE Healthcare). Images were assembled using ImageJ and Adobe Illustrator software. Ty (mAb BB2), HA antibodies (3F10) (Roche), glydeosome-associated protein 45 (GAP45)  [58] , and ISP1  [30]  were all used at 1:1,000 in 3% BSA/PBS. Secondary Alexa Fluor conjugated antibodies (Molecular Probes) were all used at 1:1,000. Images were adjusted in ImageJ and imported into Adobe Illustrator CC 2017.
Acknowledgments We are most appreciative of ongoing help from  Kelly Rogers  and  Lachlan Whitehead  ( WEHI ) for helping with image acquisition and analysis. We would like to thank  Giel van Dooren  ( Australian National University ) and  Dominique Soldati-Favre  (The  University of Geneva ) for plasmids and antibodies. We would also like to acknowledge  Vern Carruthers  ( University of Michigan ) and  David Sibley  ( Washington University ) for providing us with antibodies. We are also indebted to  Cody Allison  ( WEHI ) for providing us with immortalised tdtomato: ROSA26  MEFs.
National Health and Medical Research Council  (grant number  APP1123218 ,  APP1047806 ,  APP1025598 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Australian Research Council  (grant number  FT120100164 ,  DE160101035 ). The funder had no role in study design, data collection and analysis, decision to publish, manuscript. Agence Nationale de la Recherche  (grant number  LABEX PARAFRAP ANR-11-LABX-0024 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CNRS-INSERM-Fondation FINOVI. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Victorian State Government Operational Infrastructure Support  and the  Australian Government.