dataesr/scientific-paragraphs-categorization huggingface dataset:
This is also true for the RMSE: it is the same for a given station whatever the database, and conversely, for a given database, it varies only slightly from one station to another. The RMSE ranges between 70 and 90 Wh m -2 and is 11 to 16 % of the mean of the measurements, which is very good for hourly SSI estimated from satellite imagery. Performances are still far from WMO standards: 95 % of the deviations less than 20 % for data of moderate quality  (WMO, 2008). Despite the identified drawbacks, this validation of the three databases for this arid region which is on the edge of the Meteosat coverage reveals satisfactory results. The three databases capture accurately the temporal and spatial variability of the irradiance field. It is found that the three databases do not exhibit noticeable geographical changes in performances and are reliable sources to assess the SSI in this region.
Data availability Station data for surface solar irradiance were received from the Directorate General of Meteorology of Oman and the National Center for Meteorology and Seismology of the United Arab Emirates after writing a request. Time-series of CAMS-Rad data may be downloaded for free after registration at the Copernicus CAMS web site (https://atmosphere. copernicus. eu, CAMS, 2016) or the SoDa web site  (www. soda-pro. com, SoDa, 2016). The output comprises the irradiation at the top of atmosphere. Time-series of HelioClim-3v4 or HelioClim-3v5 data may be downloaded from the SoDa web site (www. soda-pro. com) managed by the company Transvalor. Data is available to anyone for free for years 2004-2006 as a GEOSS Data-CORE (GEOSS Data Collection of Open Resources for Everyone) and for-pay for the most recent years with charge depending on requests and requester.
Acknowledgements. The authors thank the  Directorate General of Meteorology of Oman  and the  National Center for Meteorology and Seismology of the United Arab Emirates  for providing measurements of solar radiation. The authors thank the two anonymous reviewers whose comments greatly help in improving the quality of the text. The research leading to these results has been partly undertaken within the  Copernicus Atmosphere Monitoring Service (CAMS)  of the  European Union. Edited by: S. -E. Gryning Reviewed by: two anonymous referees.
However, these studies had limitations. They mostly focused on weight variation after cancer diagnosis and thus did not collect data regarding weight before diagnosis  [2, 8, 14, 15, 25]. Few studies provided weight information before diagnosis, but all relied on prediagnosis anthropometric data collected retrospectively  [9] [10] [11] 13 ]. To our knowledge, no study investigated weight change between before and after cancer diagnosis with anthropometric data collected prospectively, which would lower memory bias and substantially increase data quality. In addition, very few studies investigated a wide range of potential predictors of weight variation in the same dataset  [10, 13, 25]. The aim of this study was to quantify weight variation (overall and by sex, cancer location and cancer prognosis) between before and after cancer diagnosis in a large cohort, relying on prospective anthropometric data.
RESULTS Characteristics of the study population are presented in Table  1  and Table 2. 68% of subjects were women. Mean age at diagnosis was 58±10. 9 years. 40. 1% had excess weight before cancer diagnosis. Mean time between inclusion in the cohort and diagnosis was 24. 6±14. 7 months. Main cancer locations were: breast (37%), prostate (15%), skin (10%) and colon-rectum (7%). Sex and age at diagnosis were similar between included and excluded cases (p=0. 6 and 0. 3 respectively) but breast and prostate cancers were more frequent in included cases (p=0. 0007). Mean weight before (p=0. 4) and respectively after (p=0. 7) cancer diagnosis were similar between included and excluded cases with available weight data (375 excluded cases had weight data before diagnosis and 20 after respectively) (Appendix 1).
DISCUSSION To our knowledge, this large cohort study was the first to investigate weight variations between before and after cancer diagnosis with anthropometric data collected prospectively, and a follow-up beginning in average 2y before diagnosis. Different weight trajectories were observed according to cancer locations. While weight loss was reported in many colorectal cancer patients, a substantial proportion of breast and skin cancer patients gained weight. Sociodemographic and economic factors appeared as important determinants of weight gain: patients with lower educational level or lower monthly income, women, and those aged below 60y were more likely to experience moderate-to-severe weight gain. Physical activity after diagnosis was associated with lower weight gain. In contrast, initial excess weight and smoking cessation after cancer diagnosis were strongly associated with weight gain.
However, most of these studies considered cancer diagnosis as baseline, while this potentially corresponds to a period of strong weight fluctuation  [11]. Indeed, it has been shown that patients who gained weight after cancer in a long-term basis were likely to first lose weight just before/around diagnosis  [11]. Thus, in the present study, we excluded anthropometric data collected 3 months before and 6 months after (12 and 24 months in sensitivity analyses) cancer diagnosis, in order to focus on stabilized periods. In our study, we did not observe any association between clinical characteristics and treatments of breast tumors and the risk of weight gain. These results should be considered with caution since statistical power was limited for several parameters. However, these results were consistent with previous studies that showed no association between tumor stage  [9, 11]  or hormone receptor status  [13]  and weight gain after breast cancer.
Moderate-to-intense physical activity after diagnosis was associated with lower weight gain in sex and age-adjusted models. This point is debated in the literature, some studies suggesting an impact of decreasing physical activity on weight gain  [25, 46] , while a recent review in prostate cancer patients concluded that exercise alone did not lead to weight loss  [47]. Strengths of this study pertained to a large population-based cohort with incident cancer cases, prospective anthropometric data collected before and after cancer diagnosis and detailed information on sociodemographic, economic, lifestyle and clinical parameters. Several limitations should be acknowledged. First, caution is needed in extrapolating our results to all French cancer cases, since the NutriNet-Santé study involved volunteers who accepted to participate in a survey on nutrition and health.
Furthermore, even if we have tested all relevant factors available in our cohort; some others (such as psychological factors) were not taken into account. Next, weight and height were self-reported, thus classification bias could not be excluded. However, in a previous validation study on 199 subjects, we showed that self-reported weight and height data from the NutriNet-Santé web-based study were valid and strongly correlated with anthropometric data measured by study staff  [50]. Finally, while medical records were available for validation of all cancer cases, exhaustive clinical data were not systematically recorded in these files. These missing values for clinical factors in some patients may have impaired our ability to detect some of the hypothesized associations. In conclusion, this large cohort of cancer patients provided original prospective results on weight variation between before and after cancer diagnosis, highlighting different weight trajectories.
ACKNOWLEDGMENTS The authors thank  Ludivine Ursule  for her assistance to the medical team. We also thank  Younes Esseddik ,  Paul Flanzy ,  Mohand Ait Oufella ,  Yasmina Chelghoum ,  Than Duong Van  and  Mac Rakotondrazafy  (computer scientists); and  Nathalie Arnault ,  Véronique Gourlet ,  Dr. Fabien Szabo ,  Charlie Ménard ,  Laurent Bourhis , and  Stephen Besseau  (data-manager/biostatisticians) for their technical contribution to the  NutriNet-Santé study. We thank all the volunteers of the NutriNet-Santé cohort. This project has been conducted in the framework of the  NACRe network (National network on Nutrition and Cancer).
FUNDING The present work was supported by the 2013  Research Prize  from the  French Nutrition Society  and the  French National Cancer Institute  (no. DEPREV14-027 ). Philippine Fassier  and  Mélanie Deschasaux  were funded by a PhD grant from the  Cancéropôle Ile-de-France  (public funding from the  Paris region). 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) ,  Fondation pour la Recherche Médicale (FRM) ,  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.
| Plant material sampling One hundred and sixty-five Vitis individuals (i. e. , physically separated above ground) were found in the study area. Fifteen individuals could not be found or were not reachable. Each sample location was recorded by GPS. For each sample, we collected the following data: geographic coordinates, morphological data (number of stems at the base, DBH, and height of the main stem), and ecological data: number of host trees identified by species used for ascending, situation related to the dykes (within or outside) and habitat (forest edge with meadow, forest edge with channel or forest interior in a gap).
25 mM dNTPs, 0. 25 μM of each primer, 0. 5 U GoTaqG2 DNA Polymerase (Promega), and 2-5 ng of template DNA. The PCR cycling conditions consisted of an initial activation step of 4 min at 94°C, followed by 30 cycles each of 60 s at 92°C, 50 s at 52-56°C (Appendix 1), and 60 s at 72°C, with a final extension step of 10 min at 72°C. Macrogen did the genotyping. Amplified fragment lengths were assigned to allele sizes with GeneMapper software v 3. 7 (Applied Biosystems). Among the 24 pairs of markers, four markers (VVMD-28, VMC-5A1, VMC-1C10, VVS2) did not amplify correctly. Five samples that did not amplify at least 15 pairs of markers were also removed from statistical analysis. All grape varieties and rootstocks amplified correctly. As a result, we retained 200 samples (i. e. , 160 grapevines found in the wild; 21 cultivars; 19 rootstocks), analyzed with 20 microsatellite (nSSR) loci and five chloroplastic (cp) DNA loci.
ACKNOWLEDGMENTS The authors would like to thank  Christian Fraissl ,  Constanze Rak Karoline Zsak  and  Teresa Knoll  for their support on the field part of the project and constructive comments on the paper. The authors thank the  Institut für Rebenzüchtung Geilweilerhof (Germany)  and the  Agroscope Viticulture Research Centre Pully (Switzerland)  for the selection of cultivars and rootstocks. The Wüthrich and  Matthey-Dupraz Funds  financed part of this research. The authors also thank the reviewers for their useful comments, and  Catherine Stott  for the correction of the manuscript.
Socio-economic position and demographic data were collected at 24 months after inclusion, which is close to the administration of the food preparation questionnaire. This study was conducted according to guidelines laid down in the Declaration of Helsinki, and all procedures were approved by the Institutional Review Board of the French Institute for Health and Medical Research (IRB Inserm n°0000388FWA00005831) and the Commission Nationale Informatique et Libertés (CNIL n°908,450 and n °909,216). This study is registered in EudraCT (n°2013-000929-31). Written electronic informed consent to participate in the study was obtained from all subjects.
Socio-economic position and demographic characteristics All data used in this study were collected at 24 months after inclusion, which is close to the administration of the cooking questionnaire in month 22. Socio-economic position of participants was assessed using three indicators: education, income and occupation, using categories consistent with the French National Institute of Statistics definitions  [41]. If subjects were unemployed or retired, we noted the occupational category of their last job. Occupation was recoded into 5 classes: manual and office worker, intermediate profession (technician, skilled employee, teacher, nurse, etc. ), and managerial staff, self-employed (craftsman, shopkeeper, company manager, farmer) and never-employed (homemaker, student, disabled). Participants were asked their monthly household income, including salary, social benefits, family allowance and rental income.
An extensive literature review on different indicators such as frequency and time spent preparing food, self-estimated cooking skills and knowledge, enjoyment of cooking, preparation from scratch or complex food preparation techniques was performed to develop the questionnaire. However, dimensions of food preparation used in our analysis were constructed using a posteriori approach (score computation) from descriptive data instead of a priori approach testing reliability of the questions and consequently the tool may be not adapted to other population. In addition, test-retest reliability was not assessed, which does not allow an estimation of the repeatability of the questionnaire. Another limitation was that the data were self-reported, which may induce misreporting. Bias associated with social desirability is lower in studies using self-reported questionnaires, rather than face to face interviews, because it introduces distance between the investigator and the subject  [59, 60].
Acknowledgments We thank the scientists, dietitians, technicians and assistants who helped carry out the  NutriNet-Santé study , and all the dedicated and conscientious volunteers. We especially thank  Younes Esseddik ,  Paul Flanzy ,  Mohand Ait Oufella ,  Yasmina Chelghoum , and  Than Duong Van  (computer scientists),  Nathalie Arnault ,  Véronique Gourlet ,  Fabien Szabo ,  Charlie Menard ,  Laurent Bourhis , and  Stephen Besseau  (data managers), and the dietitians Cédric Agaesse and  Claudia Chahine  for their technical contribution to the  NutriNet-Santé study.
In the plant, these are particularly conjugates with glucose or sulfate and in animals' glucuronic acid. Previous risk assessments from EFSA on T2/HT2 and on modified mycotoxins have been used as a starting point for the present assessment. In addition, a systematic literature search has been carried out to obtain up-to-date and comprehensive information on T2/HT2 and its modified forms. In this opinion, the general principles for risk assessment were followed. Before assessing whether modified forms of T2/HT2 could be included in a group HBGV for T2/HT2, the CONTAM Panel decided to review new relevant data on T2/HT2 published after the European Food Safety Authority (EFSA) assessment in 2011 to evaluate whether the established tolerable daily intake (TDI) for T2/HT2 needed to be revised and if in addition there was a need also to set an acute reference dose (ARfD) for T2/HT2.
Emetic events observed in mink upon single oral and intraperitoneal exposure to both T2 and HT2 were identified as critical effects for setting an acute HBGV in the present opinion. A benchmark dose (BMD) analysis was performed using a combined data set on the incidence of emetic events in mink exposed to either T2 or HT2 in two independent experiments. This approach was justified considering that the two toxins show similar acute toxicity, are equipotent with regard to emetic response and appear to work via the same mode of action. In addition, T2 is rapidly metabolised to HT2 in the organism. Using a benchmark response (BMR) of 10% resulted in a benchmark dose 10% (BMD 10 ) total confidence interval (BMDL 10 -BMDU 10 ) of 2. 97-49. 8 lg T2 or HT2/kg body weight (bw) per day across the applied models.
Also more data on the absorption and bioavailability of the major phase II metabolites present in plant products used for food and feed are needed. Further chemical characterisation of not fully characterised modified forms should be performed. Certified reference materials and standards for the modified forms of T2 and HT2 should be made available and more data on the occurrence of modified forms of T2 and HT2 in food are needed in order to characterise risks using the group ARfD and group TDI and the RPFs established in this opinion. Background and Terms of Reference as provided by the requestor Following a request from the European Commission, the risks to human and animal health related to modified forms of the Fusarium toxins zearalenone, nivalenol, T-2 and HT-2 toxins, and fumonisins were evaluated in the scientific opinion on the risks for human health related to the presence of modified forms of certain mycotoxins in food and feed, 1  adopted by the EFSA Panel on Contaminants in the Food Chain (CONTAM) on 25 November 2014.
In line with the previous EFSA opinion on modified mycotoxins (EFSA CONTAM Panel, 2014), modified T2 and HT2 occurring in plants (arising from both plant and fungal metabolism), formed as a consequence of food processing and transfer from feed to livestock were considered for possible inclusion in group HBGVs. In order to assess whether it was appropriate to include the modified forms of T2 and HT2 in group HBGVs with T2 and HT2, all data available and relevant for that task were evaluated.
Methodology for data collection and study appraisal The CONTAM Panel considered the previous assessments on T2 and HT2 (EFSA CONTAM Panel, 2011) and on modified fusarium toxins including modified T2 and HT2 (EFSA CONTAM Panel, 2014) as comprehensive, covering all relevant publications on T2 and HT2 and its modified forms, respectively, until those dates. All publications referenced therein have been considered, wherever appropriate, also for the present evaluation. In order to cover also new publications not considered in these previous assessments, a systematic and comprehensive search for literature was conducted for peer-reviewed original research pertaining to T2 and HT2 and its modified forms published after 2010 including scientific literature dealing with analytical determination, chemistry, occurrence, toxicokinetics and toxicity of T2 and HT2 and/or its modified forms.
Studies on analytical methods, chemistry and occurrence of the parent compounds T2 and HT2 only, however, were excluded since not considered of relevance for the present assessment. Date, search strings, data bases used and numbers of publications retrieved and used for assessment are presented in detail in Appendix A. In total, 1,087 citations/abstracts were obtained. Those considered relevant by expert judgement were included in the present assessment. Only papers in English language were considered for inclusion in the assessment. Since the 2011 opinion on T2 and HT2 did not include information on metabolites, in order to obtain information on the toxicity of T2 metabolites additional publications on T2-tetraol and neosolaniol were collected using the same databases as above, search terms T2-tetraol AND tox* and neosolaniol AND tox*, but applying no time limit.
9. Mode of action for toxicity of T2 and HT2 toxins and their metabolites Most of the data on the biochemical mode of action are concerning T2 with fewer studies available for HT2. The mode of action of T2 and HT2 has been reviewed review in the previous EFSA opinion (EFSA CONTAM Panel, 2011). The following chapter will briefly summarise the major finding and discuss in more detail pertinent studies since 2011.
10. Structure-activity relationships for T2 and HT2 and their phase I and phase II metabolites In contrast to the chemical structures of the numerous phase I metabolites of T2 and HT2, which have been well elucidated (see  Section 7. 3) , only little information is available on the toxicological activity of these metabolites. Most of the studies on structure-activity relationships (SARs) of T2 metabolites have utilised various cell systems in vitro, and only very few in vivo studies have been conducted. Most of these SAR studies on T2 and its metabolites together with other trichothecenes have recently been reviewed by  Wu et al. (2013b). From the toxicological data of the phase I metabolites, none of them appears to exceed the potency of the parent toxin T2, unlike in the case of zearalenone, a mycotoxin that can be biotransformed to a metabolite of much higher oestrogenic potency (zearalenol) than its parent compound (EFSA CONTAM Panel, 2016).
14. 7. Acute and chronic dose-response analysis • The CONTAM Panel selected the emetic effects of T2 and HT2 for the acute hazard characterisation. A BMD analysis was performed on a combined data set on the incidence of emetic events in mink exposed to either T2 or HT2. • The BMD modelling using a BMR of 10% resulted in a benchmark dose 95% lower and upper confidence interval (BMDL 10 -BMDU 10 ) of 2. 97-49. 8 lg T2 or HT2/kg bw across the applied BMD models. • For chronic toxicity, the CONTAM Panel selected the decrease in total leucocyte count observed in a subchronic study on T2 in male Wistar rats as the critical effect. For this BMD analysis, the CONTAM Panel selected a BMR of 10%. A 95% BMDL 10 -BMDU 10 confidence interval of 3. 33-27. 6 T2/kg bw per day across the applied models was calculated.
Evolutionary rates of olfactory genes We resequenced the genome of a single female D. orena and mapped the reads to the D. erecta reference genome to a mean per-site coverage of 26. 16X (D. erecta is D. orena's sister species; see SI for details). We computed the ratio of synonymous (K s ) to nonsynonymous (K a ) substitutions (ω) in both the D. orena and D. erecta lineages using codeml from the PAML 4. 8 package  (Yang 2007). We computed ω for 13,605 D. erecta genes using a three-species alignment of D. orena, D. erecta, and D. yakuba and polarized substitutions as being derived in either the D. orena or D. erecta lineage, whenever possible, using D. yakuba as the outgroup (D. yakuba is the sister species of D. orena and D. erecta; see SI for information regarding the D. yakuba sequence we used in our analysis).
test function in R). We also compared the distribution of ω for each of the classes of olfactory genes to that of the remaining genes in our dataset using Wilcoxon rank sum tests (WRSTs; wilcox. test function in R). Because we have estimates of ω for both D. orena and D. erecta, we tested whether any olfactory genes showed evidence of positive selection along both these lineages. For each class of olfactory gene, we determined whether the proportion of genes with ω ࣙ 1 along both the D. orena and D. erecta lineage was enriched relative to random expectations. To test for convergent positive selection, we only considered genes with ω ࣙ 1 as a result of different substitutions along the D. orena and D. erecta branches. We took this approach because we cannot rule out the possibility that a shared substitution evolved independently along both the D.
All D. orena we collected were sampled above 1200 m at cool and wet sites relative to the rest of the island (Figs. 1 and 2A; SI). No Pandanus spp. plants, the preferred host of D. erecta was found in Bioko, decreasing the possibility of our collections being D. erecta. We confirmed that the flies we sampled were not D. erecta by performing controlled crosses between the isofemale lines we established and the one D. orena line that was collected from Cameroon in 1975. All flies mated readily and produced fertile offspring (data not shown).