dataesr/scientific-paragraphs-categorization huggingface dataset:
The study area covers from latitude 50°to 64°N and longitude 6°W to 22°E (see Fig. 7 ). Ocean winds may exhibit seasonal and diurnal variations. Therefore observations were collected during all months and in ascending and descending modes. The data are listed in Table  1. Since 2006 we mainly use the ESA rolling archive. CLS developed a reprocessing chain able to massively process images using the method described in  (Portabella, Stoffelen, & Johannessen, 2002). This methodology was revisited by  Kerbaol (2007)  to reduce the computational cost. CLS used the latest version of the algorithm for the reprocessing. For HH data the polarization ratio with both incidence angle and wind direction relative to the radar look angle is used  (Mouche, Hauser, Daloze, & Guerin, 2005). At CLS the European Centre for Medium-range Weather Forecasts (ECMWF) atmospheric model wind directions  (Brown et al.
, 2005; Vogelzang et al. , 2011)  were used in the backscatter-to-wind inversion scheme. Ancillary data are given by ECMWF at spatial grid of 0. 125°(or 0. 5°when 0. 125°is not available) and 3-hourly (or 6-hourly, when 3-hourly is not available). The data are interpolated in space to the SAR scenes before wind inversion. The collocation in time is so that the closest ECMWF output in time is used. To omit areas with sea ice before wind inversion the mask by OSI SAF is applied. Fig. 1  shows a wind map processed by CLS. ASCAT ocean surface vector winds were collected in gridded version by KNMI. KNMI produces since November 2009 the coastal ASCAT scatterometer swath product within the context of the Eumetsat OSI SAF. This product consists of measurements of wind speed and direction (equivalent neutral at 10 m) within a wind vector cell of size 12.
5 km (ASCAT Wind Product User Manual, 2012) oriented according to the satellite swath. In the context of (MyOcean Catalogue, 2014) a version of this product is produced on a fixed geographical grid since the beginning of 2012, the MyOcean L3 Global wind product. For the period of 1 June 2007 to 31 December 2008 the Full Resolution product of ASCAT was reprocessed into the ASCAT coastal L2 product  (Verhoef, Vogelsang, Verspeek, & Stoffelsen, 2010; Verspeek, Portabella, Stoffelen, & Verhoef, 2011)  on a grid of 12. 5 km. The input ASCAT Full Resolution data was retrieved from the NOAA CLASS archive. The reprocessing was done using the ASCAT Wind Data Processor package version 2. 1 of the Eumetsat Numerical Weather Prediction (NWP) SAF  (Verhoef et al. , 2010). This product was then gridded into the L3 ASCAT 12.
Wind resource statistics methodology Wind resource estimation is usually based on meteorological observations collected as time-series of hourly or 10-minute recorded observations (usually from 10-min averages) over a minimum of one year, but preferable from several years as this reduces the uncertainty of the estimation. The data can be treated in the Wind Atlas Analysis and Application program (WAsP)  (Mortensen et al. , 2000)  which uses the wind atlas methodology. This is the de facto standard method in wind resource assessment. The usual procedure is to divide the data into twelve 30°wind direction sectors and determine the Weibull scale and shape parameters in each sector  (Troen & Petersen, 1989). The number of satellite ocean wind observations in a single direction bin may be very small due to the observational frequency from space (e.
Acknowledgements Funding for and collaboration within the  FP7 NORSEWInD  project  TREN-FP7-219048  coordinated by  Dr. Andy Oldroyd  at  Oldbaum Services  is warmly acknowledged. Envisat data are provided by the  European Space Agency. ASCAT data are provided by the  Eumetsat OSI SAF  and the  Copernicus MyOcean services and QuikSCAT from Remote Sensing Systems. At DTU Wind Energy  we are very thankful for collaboration with Frank Monaldo on the SAR processing. Meteorological observations are kindly made available for FINO1 from  Federal Ministry for Environment (BMU) ,  Horns Rev M2  from  DONG energy and Vattenfall, Egmond aan Zee from NUON , and  Greater Gabbard  from  SSE.
MATERIAL AND METHODS Study population. The study design has been previously detailed  (Riboli et al, 2002). In brief, the EPIC cohort includes 4500 000 individuals, of which approximately 370 000 are women. The participants were mainly recruited from the general population who resided in 23 study centres in 10 European countries, Denmark, France, Spain, Germany, The Netherlands, Greece, Italy, Norway, Sweden and the United Kingdom, between 1992 and 2000. Exceptions to this included: French participants were recruited through health insurance databases; some members of the Italian and Spanish cohorts were recruited via local blood donor registries; some members of the Utrecht and Florence cohorts were recruited through local breast screening programmes; and approximately half of the Oxford cohort included individuals who did not eat meat.
Ethics approval for the study was obtained from the International Agency for Research on Cancer and the local review boards at the participating centres. Outcome assessment. According to the improved understanding of ovarian cancer pathogenesis  (Jarboe et al, 2008; Kurman and Shih, 2011) , we defined incident 'ovarian' cancer cases using the International Classification of Disease for Oncology (ICD-O-3) codes C56. 9, C57. 0 and C48, including primary peritoneal and fallopian tube cancers. Cases were identified through linkage with national cancer registries in Denmark, Italy, The Netherlands, Norway, Spain, Sweden and the UK. In France, Germany and Greece, cases were identified using insurance records, cancer registries and active follow-up of participants. Data on tumour invasiveness, histology, stage and grade were available from cancer registries and a pathology record review.
Information on vital status, the causes and dates of death were obtained using record linkages with cancer registries, boards of health and death indices in Denmark, Italy (except Naples), The Netherlands, Norway, Spain, Sweden and the UK or through active follow-up (inquiries by mail or telephone to participants, municipal registries, regional health departments, physicians and hospitals) in Germany, Greece, Naples (Italy) and France. Causes of death were reported as specified by the International Statistical Classification of Diseases, Tenth revision (ICD-10). As different procedures were used to obtain follow-up data on vital status across the study centres, this resulted in differences across the centres in the timing to report the causes of death. To account for this, the follow-up dates were truncated to when 80% of the causes of death at each centre were known (June 2005-June 2009) with the following exceptions; in Greece and Germany, the date of last known contact was the date of censoring and this extended to November 2009 for Greece and February 2010 for Germany.
Of the n ¼ 554 total deaths that were observed, an EOC-specific death was recorded for n ¼ 511 (92%) and deaths that occurred owing to other causes (n ¼ 43) were censored. Exposure assessment. At the time of study recruitment, participants completed questionnaires on reproductive history, diet and lifestyle. Data collection procedures were centralised as a single study with multiple centres. Reproductive variables that were investigated included parity (live births and still births only; number and age at first FTP), ever breastfed and duration, OC use and duration, age at menarche and menopause, hysterectomy and total ovulatory lifespan. For the MHT variables (assessed at recruitment), participants were asked if they had ever used MHT, the timing of use (whether they were current users), their age at start and total duration of use.
75 (equivalent to 9 months), and/or used OCs. Data on tubal ligation, family history of ovarian cancer and BRCA1/2 status were not available for any of the cases. Age at last FTP was not assessed because this information was unknown for 88% of the parous cases and family history of breast cancer was not examined as a confounder because this information was missing for 56% of the cases. Statistical analysis. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression models. Person-time was calculated as the number of days between EOC diagnosis and the date of death, emigration, loss to follow-up or censoring, whichever occurred first. Multivariable models were adjusted for covariates that were selected a priori because of their known influence on risk of EOC death; age at diagnosis (continuous), BMI (o23 kg m À 2 , X23-o25 (reference), X25-o30, X30), tumour stage (local (reference), regional, metastatic, unknown) and smoking status (never (reference), former, current, unknown).
These null associations were consistent with the earlier Australian study and systematic review of seven studies  (Nagle et al, 2008)  and subsequent reports  (Yang et al, 2008; Zhang and Holman, 2012; Robbins et al, 2009) , with the following exceptions: reproductive factors that were associated with a worse survival included an older age at first delivery  (Jacobsen et al, 1993) , younger age at menarche and increasing ovulatory years  (Kjaerbye-Thygesen et al, 2006; Robbins et al, 2009) , while breastfeeding was associated with a better survival  (Nagle et al, 2008). Key strengths of this study include the large number of cases and the representation of findings from 10 European countries. Potential limitations of this study were that the exposure data were collected on average 6 years prior to diagnosis; however, the impact of this was likely limited for reproductive exposures that occurred during childbearing years; the majority (63%) of the cases in the study were postmenopausal at recruitment and had completed their reproductive history.
ACKNOWLEDGEMENTS The coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale ,  Institut National de la Santeet de la Recherche Medicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Ministry of Education and Research (BMBF) ,  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany) ; the  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  ERC-2009-AdG 232997  and  Nordic Center of Excellence programme on Food , Nutrition and Health.
Materials and methods All consecutive patients admitted between June 2006 and October 2007, to nine multidisciplinary, tertiary-care PICUs of university-affiliated hospitals (eight French and oneBelgian, all member of the Groupe Francophone de Réanimation et Urgences Pédiatriques-GFRUP) were prospectively included. Exclusion criteria were: age 18 years or older; premature at entry into PICU; pregnancy; total length of stay in PICU less than 4 h; admission in a state of continuous cardiopulmonary resuscitation without achieving stable vital signs for at least 2 h; transfer from another PICU; and admissions for scheduled procedures normally performed in other hospital locations. The study and its database were declared safe and were approved by the French authorities (Commission Nationale de l'Informatique et des Libertés) on 7 February 2007.
Results The study included 3,669 patients, two patients of the original database  [14]  with incomplete data being excluded. Characteristics of the population are reported in Table  1. The mortality rate was 6. 1 % (222 deaths). Administrative median length of stay in PICU was 3 days  [2] [3] [4] [5] [6]  in survivors and 4 days  [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]  in non survivors (p = 0. 055). Daily PELOD-2 score was measured on 7,983 days (Fig. 1 ). MODS was present on day 1 in 2,024 of the patients in the whole population (55. 2 %). The dPELOD-2 score was available at least at day 2 in 2,057 patients (Fig. 1 ) allowing the identification of new and progressive MODS: among the 796 patients without MODS (one or no organ dysfunction) on day 1, 186 (23. 3 %) acquired the syndrome during their PICU stay (new MODS); the mortality rate was 4.
Discussion In this study, we report that the dPELOD-2 scores calculated on the 7 days previously identified, had good discrimination and calibration. This study confirms that the progression of the severity of organ dysfunctions can be evaluated by measuring the dPELOD-2 scores during a specified set of days in PICU (admission and days 2, 5, 8, 12, 16 and 18). Our data not only showed that the PELOD-2 score on day 1 was a significant prognostic factor, but also that mortality was significantly higher in children in whom MODS worsened after day 1 as compared with those in whom MODS remained unchanged or improved. Thus, it makes sense to collect data daily on the severity of MODS in order to take into account this time factor  [15, 16]. It has been suggested that MODS scores are effective in quantifying the severity of each organ failure during the first ICU days, in adults  [17] [18] [19]  as well as in children  [20].
Indeed, we have taken into account the dynamics of organ dysfunctions throughout the entire PICU stay. This study is not without limitations. First, the dataset was collected 8 years ago and the case mix and mortality rate may have changed over this time period. Second, it was conducted in only two European countries (France and Belgium); our population is different from the US and UK populations  [14]. Last, the number of deaths was quite low after 7 days in the PICU (there were 54 deaths in 557 patients with a length of stay in the PICU longer than 7 days), but higher than that in our previous study (33 deaths in 338 patients)  [13]. Therefore, a large prospective study that is not country-specific would be useful to evaluate external validity of the PELOD-2 score; it should include more critically ill children with a long-term PICU stay and consider two groups of patients, those with a short PICU stay (<7 days) and those with a long PICU stay (≥7 days), order to collect a sufficient number of deaths in each group  [40].
Acknowledgements This study was supported by the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique n°1 , 997/122 ). GFRUP collaborators were:  D. Biarent  ( Bruxelles, Belgium ),  R. Cremer  ( Lille, France );  S. Dauger  ( Robert Debré-Paris , France),  M. Dobrzynski  ( Brest, France ),  G. Emériaud  ( Grenoble, France ),  S. Renolleau  ( Trousseau-Paris, France ),  M. Roque-Gineste  ( Toulouse, France ),  D. Stamm ,  N. Richard  ( Lyon, France ), and  I. Wroblewski  ( Besançon, France ).
Since the 2000s, social networks have grown spectacularly until they are now regarded as indispensable and introduced as a daily practice of millions of users. Enterprises have become aware of the need and the importance of these collaborative tools, and the concept of Enterprise Social Network (ESN) has now emerged. As such, OpenPaaS is an innovative ESN that aims at facilitating inter-organizational collaborations. In this sense, this paper describes the new OpenPaaS platform to address current issues of ESNs: (i) the management and exchange of large amount of data during inter-organizational collaborations; (ii) the ability of the system to provide synchronous communications between collaborative partners; and finally, (iii) the establishment of transverse collaborations. This last point is a key feature of OpenPaaS, which goal is to automatically generate new inter-organizational coalitions by deducing collaborative processes in response to an opportunity of collaborations brought by any enterprise of the ESN.
Overall Architecture We illustrate the overall architecture of OpenPaaS in Figure  1. Our approach consists on a PaaS (Platform as a Service) technology dedicated to enterprise collaborative applications deployed on hybrid cloud. It provides an innovative Enterprise Social Networks that innovates both at collaborative level by its capacity to leverage heterogeneous cloud technologies at the IaaS (Infrastructure as a Service). Within OpenPaaS Platform, a set of collaborative services is made available in an IaaS infrastructure. The ESN is itself a service available in SaaS (Software as a Service) and serves as a point of access to applications deployed in the PaaS. Video Conferencing, messaging, calendar management and file sharing services are included in OpenPaaS platform and are presented in the next section.
Membership to a community depends on its type. For open ones, the user can freely join it. For the other types, a membership request shall be sent by the user and, then, be validated or denied by the community manager (CM), or an invitation is sent by a CM to a user to become a member who decides whether to accept or not. • Project. While communities result from an organic self-organization of the users, a project is more tied to the activity of the company. As such, a project has a start and end dates. Besides, project's members can be users or/and communities. • Conference consists on a short-lived collaboration. It includes instant messages and WebRTC (Web Real-Time Communication) videoconference. • Synchronization with external contacts lists, which allows to import contacts from an existing database in order to invite them to join.
Automated Creation of New Collaborations In addition, a collaborative module has been implemented and aims at facilitating new inter-organizational collaborations: it gathers knowledge on collaborative contexts and needs (profiles and objectives of collaboration) through two modelers and exploits it to create new collaborations: (i) the Profile Modeler lets the enterprises describe their business capabilities (e. g. produce cars) and (ii) the Objective Modeler offers a way to propose new collaborative objectives (e. g. buy cars) and answers to them by setting up the corresponding optimal (e. g. on cost, delivery time, quality criteria, etc. ) collaboration (i. e. selecting the best partners to answer the objectives, and order their corresponding capabilities into a business process). A last Collaborative Business Process Modeler allows the users to adapt the latter process and to orchestrate it on the later run-time phase.
Conclusion This paper presents an innovative Enterprise Social Network which originality relies on its ability to support and facilitate intra-and inter-organizational collaboration by many ways. First, it provides a complete package of social services that helps coordinating the daily working life of the collaborators by providing easy way to share various types of information. This contribution to the current offers of social and professional networking platforms fits a deep need of the companies today, by providing easy-to-use tools. More than facilitating coordination among collaborators, it also provides a service to create new collaborations to fit current business opportunities of the OpenPaaS' users. The optimal selection of partners (and consequently of their capabilities) can, for example, help the enterprises in their bidding process: it can indeed be performed on a very large scope of candidate partners and thus provides quickly an efficient set of partners.
Acknowledgements We thank the end-users in Kampot Province for participating in the study. We are grateful to health workers and staff of the  Ministry of Health of Cambodia in Kampot Province , the  National Centre for Parasitology, Entomology  and  Malaria Control , for their collaboration. We are thankful to  Nicolas Fortin  and  Anne Bertrand  ( IMACCESS, Lyon, France ) for their support and relevant advice. This study was supported by  IMACCESS (Lyon, France).
Disease severity was assessed for each patient according to the 1997 WHO criteria and to the 2009 WHO guidelines  [1, 31]  using clinical, biological and paraclinical examination data recorded at admission and throughout the entire hospitalization period. Patients were classified into 2 groups: non-severe dengue (dengue fever (DF) according to the 1997 guidelines and DF without warning signs according to the 2009 WHO guidelines) and severe dengue (dengue hemorrhagic fever (DHF) and DSS (dengue shock syndrome) according to the 1997 guidelines and dengue with warning signs as well as severe dengue according to the 2009 WHO recommendations). Urine and saliva were collected daily during hospitalization and then one week, two weeks, three weeks, one month and three months after the discharge from hospital.
The limit of detection was 200 copies/ml, 50 copies/ml, 200 copies/ml and 350 copies/ml for DENV-1, -2, -3 and -4, respectively, with the qRT-PCR in urine and plasma specimens spiked with quantified synthetic plasmids. As DENV viremia lasts only a few days after the onset of fever (DAOF), the samples selected for the detection of the viral genome were mainly those collected during hospitalization. Additional urine and saliva specimens collected one to three weeks after discharge from the hospital were also added as some data previously published suggested that the DENV genome could be detected in urine until day 16 after the onset of fever  [22]. Virus isolation in C6/36 cells C6/36 cells were used in attempt to isolate DENV from urine and saliva samples. Before inoculation, urine samples were dialyzed and concentrated in PBS using the 100K Microsep ultrafiltration system (Pall, USA) and then filtered through 0.
The AUC ranges between 0. 5 for random prediction to 1, the higher the AUC the better the model performance. For models with a continuous response variable, the performance of the model was estimated by a correlation coefficient between observed and predicted datasets. This correlation coefficient ranges between 0 (no correlation) and 1 (perfect correlation), with values superior to 0. 5 suggesting a high correlation. The analysis was carried out using the "dismo" package (version 1. 0-5)  [37]  and the "gbm" package (version 2. 1)  [38]  under the R statistical environment (R Foundation, Vienna, Austria). A learning rate of 0. 001, a bag fraction of 0. 5 and a tree complexity of 3 were used. The explanatory variables used are summarized in Table  1. The dengue serotype was not included in the analyses as most of the patients included in this study were infected with a DENV-1 strain during the 2013 epidemic in Cambodia.
Discussion One of the objectives of this study was to assess the possibility to use saliva or urine specimens instead of blood for the diagnosis of dengue infections in specific situations. The evaluation included the assays most routinely used for dengue diagnosis: viral genome detection by qRT-PCR, virus isolation in mosquito cell lines, NS1 antigen and anti-DENV antibody detection by ELISA methods. In order to generate the strongest data possible, a large number of samples obtained from children hospitalized in Cambodia for dengue of varying degrees of severity were tested. This is the first study in which plasma, urine and saliva obtained from the same patients, at the same time-points, were tested in parallel and the results compared to estimate the performances of the various dengue diagnostic assays.
Acknowledgments We would like to thank the patients, nurses and doctors who participated in this study at Kampong Cham reference hospital and at the 2 district hospitals of  Tbong Khmum  and  Prey Chhor. We also thank the field teams of the  Epidemiology Unit of Institut Pasteur of Cambodia  who went all over Kampong Cham province to collect the clinical specimens during the patient's follow-up after hospital discharge.