dataesr/scientific-paragraphs-categorization huggingface dataset:
Personal support to JGG was provided by the  Fondation pour la Recherche Médicale, France  [ FRM-SPE20051105175 ],  EMBO  [ ALTF676-2005 ] and  EU Marie Curie  [ MEIF-CT-2006-039676 )]. This work was supported by the  Ludwig Institute for Cancer Research, Cancer Research UK  ( C23338/ A10200  and  C23338/A15965 ) (to BV),  EU-ERG   FP7  ( 270696-PaCa/PI3K ) (to JGG),  MRC  ( G1100354 ) (to LBS),  Agence de la Biomedecine  (to GH) and  AstraZeneca. The funders had no role in study documentation of the involvement of a signalling enzyme,  PI3K , in the regulation of AR activity during spermatogenesis. where p110β and AR have previously been reported to functionally interact.
11 More generally, changes may disproportionately affect very preterm infants who face the highest risks of mortality. Recent studies have reported mortality reductions for this population, attributed to continued medical advances and more active perinatal management. 12 13 A final question relates to how much these declines reflect changes in gestation-specific mortality or whether they could result from changes in the gestational age distribution and, in particular, reductions in the preterm birth rate. To better understand recent trends in stillbirth and neonatal mortality in Europe, we used data from the Euro-Peristat project collected in 2004 and 2010 to investigate the changes in stillbirths and neonatal mortality by gestational age and Open Access Scan to access more free content across countries according to their level of mortality in 2004.
Data sources This study was embedded within the Euro-Peristat project, which developed a list of valid and reliable indicators for monitoring and evaluating perinatal health in the EU and has produced perinatal health reports on 2004 and 2010 data. 1 4 8  Twenty-five EU member states and Norway participated in data collection in 2004 and 2010. The Euro-Peristat project collects data from routine national-level population-based sources. Data come from medical birth registers, civil registration, child health systems and routine surveys, as described previously. 1 4 7 14  In this study, we used aggregate data on the numbers of live births, stillbirths and neonatal deaths by gestational age for the years 2004 and 2010. Owing to missing data on mortality by gestational age, Cyprus, Greece and Spain were excluded for all comparisons and France, Germany, Hungary, Ireland and Italy were excluded for comparisons of neonatal mortality.
Slovakia was excluded because birth and death registration procedures changed substantially over the study period. Thus, 22 and 18 countries were included for the analyses of stillbirths and neonatal mortality, respectively. Data for Belgium came from the Flanders region only and for the UK, data were provided separately for England and Wales, Northern Ireland and Scotland. Data from France came from national representative surveys in 2003 and 2010. For the first period, data for England and Wales were for births in 2005, and for Italy for births in 2003.
Variables and definitions Our principal outcomes were stillbirth and neonatal mortality rates overall and by gestational age subgroup. We also calculated preterm birth rates in order to relate changes in the gestational age distribution to those observed for mortality. Euro-Peristat requests gestational age data using the best obstetrical estimate in completed weeks (ie, 28 weeks is equal to 28 +0 to 28 +6 weeks). The stillbirth rate was defined as the number of deaths before or during birth at or after 28 weeks of gestation in a given year per 1000 live and stillbirths in the same year. The threshold of ≥28 weeks was used because of differences in limits for registration of stillbirths and in policies towards screening for congenital anomalies and terminations of pregnancies. 7 The neonatal mortality rate was defined as the number of deaths after live birth during the neonatal period (days 0-27) at or after 24 completed weeks of gestation in a given year per 1000 live births in the same year.
In fact, absolute declines in the number of deaths were highest for term births, followed by births between 24 and 27 weeks. For stillbirths, reductions were greatest among moderately preterm and term births. These results underscore the importance of a focus on improving outcomes across the gestational age spectrum. This study has strengths and limitations. Euro-Peristat collects population-based data on births and deaths using a common protocol. Information is requested by week of gestation making it possible to compute gestation-specific mortality rates which are unavailable in other international databases. Owing to differences in recording practices for births and deaths in Europe, we excluded stillbirths at 22-27 weeks and neonatal deaths at 22-23 weeks, allowing like-with-like comparisons across countries and across time.
7 17 However, owing to these exclusions, we were unable to assess changes in stillbirth before 28 weeks. Another limitation is that we had only two time points, as Euro-Peristat collects data periodically for its reports. This increases variation due to annual fluctuations, especially in small countries, and the influence of specific situations linked to the years selected for our studies. In Italy, for instance, data covering the first period were produced after institutional changes in the processing of birth certificates; weights were used to compute total numbers, a procedure that was no longer used in 2010. 18 inally, as Euro-Peristat collects national-level aggregate data, we are limited by the lack of information on other factors to explain the observed changes in outcomes. 1] [22]  Part of these differences may be related to how stillbirth rates are measured.
Acknowledgements The authors would like to acknowledge the following contributors to the European Perinatal Health Report: Health and Care of Pregnant Women and Babies in Europe in 2010:  Austria ,  Gerald Haidinger , The  Medical University of Vienna, Department of Epidemiology, Centre of Public Health; Jeannette Klimont, Statistics Austria; Belgium ,  Sophie Alexander ,  Wei-Hong Zhang ,  Michèle Dramaix-Wilmet ,  Mélissa Van Humbeeck ,  Université Libre de Bruxelles ,.
Sequence data were compared by individual BLASTn (Basic Local Alignment Search Tool) searches using NCBI (National Center for Biotechnology Information) BLAST database. A 100% sequence similarity with Trichosporon faecale L8331-EB087B (GenBank accession number KM488281) and Trichosporon faecale L8333-EB108A (GenBank accession number KM488280) was obtained. Antifungal susceptibility was investigated using E-test s (bioMérieux). The minimal inhibitory concentrations (MICs) were 1 mg/ml for amphotericin B, 32 mg/ml for 5-fluorocytosine, 2 mg/ml for fluconazole, 0. 32 mg/ml for voriconazole and 432 mg/ml for micafungin. On day 12, the fever persisted with poor performance status (ECOG 3: capable of only limited selfcare; confined to bed or chair more than 50% of waking hours)  [11] , jaundice and inflammatory nodular skin lesions on both legs whose biopsy revealed the presence of Trichosporon faecale.
Among the 7742 remaining participants, 258 (3. 3%) were lost to follow-up, leaving a study sample of 7484 participants. Face-to-face examinations took place every two years during follow-up. Trained nurses and psychologists conducted interviews and made physical and cognitive measurements at the participant's home and at the study centre. Data collection included sociodemographic characteristics (education, occupation, and income), lifestyle (smoking, drinking, and food frequency questionnaire), assessment of disability (Instrumental Activities of Daily Living scale), global cognitive functioning (Mini Mental State examination) and depression (Centre for Epidemiologic Studies-Depression scale), and recording of height and weight. 13 14 15 ast history of cardiovascular disease included a history of coronary heart disease, stroke, arrhythmia, and peripheral artery disease.
Brain imaging data were available for more than 80% of validated stroke cases (computerised tomography 82%; magnetic resonance imaging 15%) and Doppler ultrasound for 62%. When no brain imaging was available, the diagnosis was based on signs and symptoms. Stroke was confirmed if the participant had a new focal neurological deficit of sudden onset attributable to a cerebrovascular event that persisted for more than 24 hours. The panel classified stroke as ischaemic stroke, intracerebral haemorrhage, or of unspecified type.
strengths and limitations Strengths of the study include the prospective data collection, general population setting, large number of participants and outcome events, high quality data on many factors (including medical history, vascular risk factors, lifestyle, and drug use), and validation of cardiovascular events. Validity of data about exposure to lipid lowering drugs was assessed in a previous study in the Three-City study participants. This study showed a very good agreement between self reported use of lipid lowering drugs and claims for reimbursements registered in the French national healthcare insurance database (κ coefficient 0. 85, 0. 84 to 0. 87). 37 troke events can be difficult to diagnose with certainty, especially when self reported, but detailed medical records including brain imaging were available for more than 80% of adjudicated stroke cases.
These unrepresented factors may have an impact on the reported signals for northern areas, which will suffer more from the low irradiance performance of PV modules than sites in the south of Europe where the solar In grey shadows: spatial density (in %) of the distribution of the PV powerinstalled capacity by the year 2050 as obtained with the CLIMIX model over the EURO-CORDEX spatial grid considering the regional targets proposed by the European Climate Foundation in its 80% RES pathway  7. These targets are provided as GW of installed PV power capacity per region, as listed, with each region comprising either one or several countries, namely: Denmark, Finland, Norway and Sweden compose region 1; Ireland and UK, region 2; Belgium, Germany and The Netherlands, region 3; Portugal and Spain, region 4; Estonia, Latvia, Lithuania and Poland, region 5; Austria, Czech Republic, Slovakia, Slovenia and Switzerland, region 6; Bulgaria, Cyprus, Greece, Hungary and Romania, region 7; Italy and Malta, region 8; and France, region 9.
005 °C À 1 , considering the typical response of monocrystalline silicon solar panels  40. Finally, T cell is modelled considering the effects of TAS, RSDS and VWS on it as: Hence, if ambient conditions (RSDS, TAS and VWS) correspond to the STCs, PVpot equals 1 and PV power production reaches the rated value. If they are so that T cell is higher (lower) than 25 °C and/or RSDS lower (higher) than 1,000 W m À 2 , PVpot will be lower (higher) than the unit and the PV power output will be lower (higher) than the nominal power of the module. T cell t ð Þ ¼ c 1 þ c 2 TAS t ð Þ þ c 3 RSDS t ð Þ þ c 4 VWS t ð Þ ð3Þ The spatial distribution of PV power-installed capacity was obtained with the CLIMIX model as in ref. 17. Here it is applied to the 2050 regional targets set by the European Climate Foundation Roadmap 2050 in its 80% RES pathway 18 using the 0.
Acknowledgements This study was supported by the  Spanish government  and the  Fondo Europeo de Desarrollo Regional (FEDER)  through the project  REPAIR  ( CGL2014-59677-R ), the  European Union   7th Framework Programme  ( FP7/2007-2013 ) through the project  IMPACT2C  (grant no. 282746 ) and the  CLIMIX DSM-Energy project from the Commissariat al'Energie Atomique et aux Energies Alternatives (CEA ). S. J. also thanks the  University of Murcia  for her postdoctoral grant. B. B. also thanks the financial support received from the  SCIEX Programme. Special acknowledgements to the  EURO-COR-DEX initiative  for providing the climate simulations. In this sense, the CLMCOM simulations were carried out at the  Swiss National Supercomputing Centre (CSCS)  under the project  ID s78  and the KNMI simulations were supported by the 7th Framework EU-projects IMPACT2C and ECLIPSE (grant no.
2011 and Porter et al. 2014) , thus playing a critical role for the interpretation of observational data at these scales  (Fontanot et al. 2011b). Moreover, in our models we also do not consider tidal stripping of stars in satellite galaxies. All three SAMs considered in this work implemented merger prescriptions based on a broad division between major and minor mergers: in a major merger (i. e. mass ratio >0. 3), the entire stellar and gaseous content of the two merging galaxies is given to the spheroidal remnant, which becomes an elliptical galaxy (B/T = 1). In minor mergers, the stellar mass of the satellite is given to the bulge of the central/remnant, while the gaseous component is given either to the disc (DLB07; SC-SAM) or to the bulge (MORGANA): in both cases, the merger triggers a starburst.
support from the grants PRIN MIUR 2009 'The Intergalactic Medium as a probe of the growth of cosmic structures', PRIN INAF 2010 'From the dawn of galaxy formation' and from the Klaus Tschira Foundation. MH acknowledges financial support from the European Research Council via an Advanced Grant under grant agreement no. 321323 (NEOGAL). RSS thanks the Downsbrough family for their generous support. DJW thanks the Deutsche Forschungsgemeinschaft for funding via project WI 3871/1-1 R E F E R E N C E S.
Several studies reported extensive P. vivax microsatellite diversity even in regions of moderate endemicity, and high multiplicity of infection was frequently observed. While some P. vivax populations showed pronounced structuring on small geographical scale  [10, 13] , this was not the case for other populations  [11, 23]. Differences in method of sampling with respect to geographical space as well as different panels of markers used for genotyping make direct comparison of results difficult  [24]. Lacking so far was a comprehensive comparison of P. vivax diversity based on samples collected across many different sites and typed with the same set of markers. Therefore we compiled data from published studies that included samples from Peru  [10] , Brazil  [14, 21] , Sudan  [25] , Cambodia  [26] , Vietnam  [27] , Papua New Guinea (PNG) and Solomon Islands  [11] , and complemented this dataset with previously unpublished typing results from Central America and Mexico, Madagascar and Central Asia (Armenia, Azerbaijan and Uzbekistan).
All samples were typed with 11 published microsatellite markers  [28]. This global data set included 841 isolates from regions of different levels of transmission intensity (Fig 1A  and Table 1 ) and representing 6 out of 9 malaria transmission zones recently shown to differ in relapse patterns  [29]. The highest P. vivax prevalence ever has been recorded in the lowlands of PNG (e. g. reaching >50% by PCR in children in East Sepik Province  [19] ). These South Pacific parasite populations are relatively isolated due to limited migration of human hosts. In Southeast (SE)-Asia transmission is also high, yet often focal  [30]. Migration of hosts is high SE-Asia and a major complication of eradication efforts. In Latin America transmission is lower, but increased since the 1960s when the number of P.
Genotyping and data analysis All samples were typed with the same set of 11 published microsatellite markers  [28]. Three additional markers of that panel were excluded. MS3 and MS16 had not been typed in all populations, and MS8 showed signals of positive selection and variation in allele sizing between labs (details below). Microsatellites are considered neutral markers; however, some of them might lie within coding regions or be linked to genes under selection. Selection had been tested using Lositan software  [39]. Only marker MS8 showed a weak signal for positive selection. A relationship between P. vivax microsatellite alleles and clinical disease or acquired immunity has never been reported, thus it was not to be expected that different age groups sampled across populations or different proportions of febrile and asymptomatic individuals would influence population genetic parameters.
Alleles were binned using TANDEM software  [40]  and formatted using PGDspider  [41]. Expected heterozygosity (H E ) of markers and allelic richness were calculated using FSTAT  [42]. H E is the chance that two unrelated parasites carry a different allele of a given marker, and allelic richness is a measure of alleles per each marker adjusted for the different numbers of isolates per site. When the effective population size is reduced, e. g. as consequence of intensified malaria control, rare alleles are expected to disappear first. As a result, allelic richness changes more rapidly than H E , as the influence of low-frequency alleles on H E is small. The number of unique haplotypes was calculated by Dropout  [43]  and linkage disequilibrium (LD) by LIAN 3. 5 with 100,000-fold re-sampling  [44].
LIAN compares the observed association of markers to the values expected for random association based on the population diversity. Only unique haplotypes with no missing data were included for calculating LD, resulting in 633 samples in this analysis. Three of the markers used (MS2, MS4, MS5) localize to chromosome 6 and two markers (MS12, MS15) to chromosome 5, thus these markers are physically linked. To assess linkage disequilibrium irrespective of physical linkage of markers, LD was also calculated excluding markers MS2, MS4 and MS12, i. e. with 8 markers located on 8 different chromosomes. As compared to the 11-marker panel, fewer isolates had to be excluded due to missing data, but identical 8-marker haplotypes occurred more often, resulting in 637 samples for this analysis. Relatedness between haplotypes was assessed by pairwise comparison of all samples within a population and calculating the proportion of shared alleles.
Twenty iterations for K = 1 to K = 12 (K being the number of clusters) were run, each with a burn-in period of 10'000 steps and then 100'000 MCMC iterations. A method developed through simulation studies  [48]  was applied to estimate the most likely number of clusters. In addition, the optimal number of clusters was assessed using the program STRUCTURAMA  [49]. F ST values among populations were calculated using FSTAT  [42]. To compute Principal Components Analysis (PCA) the smartPCA application of EIGENSOFT was used  [50]. In contrast to STRUCTURE analysis, PCA attempts to maximize variance between populations based on the known origin of samples. While smartPCA was designed for SNPs it can be used with microsatellites; each microsatellite allele was treated as a SNP. Preliminary analysis had shown no population substructuring between samples collected in Brazil in 2004 and 2006  [21] , in the lowlands of PNG  [11]  and in Madagascar, thus samples were combined for calculation of F ST values and PCA.
Results A total of 841 P. vivax isolates were available for this study. Fig  1A  shows the origin of isolates on a published map of P. vivax endemicity in 2010, represented as parasite rate  [1]. All isolates were typed for the same set of 11 microsatellite markers. For all 841 isolates genotyping results were obtained from !7 markers. For marker MS4, no amplification product was obtained from 70 isolates (i. e. data was obtained from 91. 7% of all isolates), for all other markers data was obtained from 823-834 (97. 9-99. 2%) out of 841 isolates.
Haplotype diversity Across all populations a total of 759 individual haplotypes were found in 818 isolates. 11-loci haplotypes were shared only within the same country. Again isolates from Uzbekistan were  To assess discrimination power of a smaller panel of markers compared to the full set of markers, microsatellites were removed successively and haplotype counts recorded from each individual population, as well as for the full dataset (Fig  3 ). MS4 was removed first, as no data for this marker could be obtained from 70 isolates. The remaining 10 markers were sequentially removed according to their diversity across all populations, starting with the least diverse one. Only taking into account haplotypes shared within populations, a set of 4 markers (MS2, MS9, MS15, MS20) identified 710/760 (93.
When the optimal number of clusters was calculated as described  [48] , high values were observed for K = 2, K = 5 and K = 7 (Fig  4 ). In addition the program STRUCTURAMA was used to assess the best number of clusters. The 99% CI for estimates of cluster number showed a wide range (79-115 clusters) and was thus not informative As the clear separation of Latin American and South Pacific isolates from those from Africa and Asia might interfere with more subtle population structure within Africa and Asia, For K = 2 African and Azerbaijani isolates clustered separately from SE-Asian ones. Indian isolates clustered with Africa. As with the full set of isolates, the clonal samples from Uzbekistan clustered with different populations in individual runs for the same number of clusters (e. g. with Africa or with SE-Asia if K = 2).
The data was searched against the Swiss-Prot database (26/01/2014 release) restricted to Mus musculus (16656 entries) using Mascot (2. 4. 1). All Mascot result files were loaded into Scaffold (4. 3. 4). Peptide and protein thresholds were set to 95. 0% with a 2 unique peptide criterion to report protein identification. For analysis of MFF sites phosphorylated in vivo, dried peptides were dissolved in 3% acetonitrile/0. 1% formic acid. RPLC-MS/MS analysis was performed on a LTQ-Orbitrap Elite equipped with an Ultimate 3000 RSLC nano system. The analytical separation was run for 70 min using an Acclaim PepMap RSLC 75 μm × 50 cm (C18, 2 μm, 100 Å) column. For survey scans, the Orbitrap resolution was 120,000 with an m/z window from 300 to 1500. The top 20 precursor ions were selected for MS/MS in the LTQ with CID (NCE 30%).
The data was searched against the Q6PCP5-4|MFF_MOUSE sequence from Swiss-Prot (07/03/ 2014 release) using Mascot (2. 4. 0). All Mascot result files were loaded into Scaffold (4. 3. 2). Peptide and protein thresholds were 95. 0% and 99. 0% respectively with a 2 unique peptide criterion.
1B ). Possible reasons for such a modest difference between the treated and the untreated group (as compared to the phospho-AMPK motif immunoblot of the total extract in Fig. 1A ) could be that AMPKactivator-regulated bands might have been masked by non-specific bands or the abundance of the regulated bands was too low to be detected by Coomassie staining. Another reason could be that as the phospho-AMPK motif antibody was raised against a short linear peptide, it will not recognise all the phospho-sites in folded native proteins. Tryptic peptides derived from the experiment shown in Fig. 1B  were analysed by LC-MS/MS and the resulting data were searched against the Swiss-Prot mouse database. To demonstrate consistency and reproducibility of the analysis a technical replicate (immunoprecipitation plus LC-MS/MS) using the same hepatocyte lysates was performed (exp1a and exp1b).
Immunoprecipitates were separated by SDS-PAGE and the gel was stained using colloidal Coomassie. Boxes on the image indicate where gel bands were excised for subsequent sample processing (trypsin digestion and MS/MS analysis). (C) Samples from panel B were analysed by LC-MS/MS, searched against the Swiss-Prot mouse database and the amount of protein detected was quantified by total spectral counting. 99 proteins were enriched in the stimulated versus basal samples in the first run (exp1a) and 59 in the technical replicate using the same starting material (exp1b) (upper diagram). In total, 101 proteins were enriched in the stimulated versus basal samples (exp1). In a biological replicate of the experiment, 157 proteins were enriched in the stimulated versus basal samples (exp2). Across both biological replicates, 64 proteins were enriched (lower diagram).