dataesr/scientific-paragraphs-categorization huggingface dataset:
154)  11, 30  and derived from the current data (0. 163); and (v) CKD273 continuous, but not UAE continuous, increased IDI, while both markers continuous and CKD273 also per threshold improved NRI. The IDI and NRI indices provide complementary information. Indeed, if addition of a biomarker to a model increases the predicted probability in cases, this is reflected by a significant increase in IDI, as was the case for CKD273 continuous (Table  4 ). NRI indicates the extent to which a biomarker improves diagnostic accuracy, which in the current analyses amounted to 20. 1% and 8. 22% for UAE continuous and per threshold and to 25. 8%, 23. 3%, and 17. 9% for CKD273 continuous and per 0. 154 and 0. 343 threshold. Expert statisticians have suggested that IDI and NRI have limitations. 34 They recommend retaining existing descriptive terms, such as the true-positive and false-positive classification rates (Table  5 ), and testing the null hypothesis of no prediction increment from modeled regression coefficients (Table  2  and Figure  2 ).
Third, the participants available for analysis from the Human Urine Proteome Database  17  were enrolled in studies with different designs, including 2 randomized clinical trials,  19, 20  1 population study,  23  and 3 smaller subgroups of patients with diabetes recruited in Western Europe  16, 21  and Australia. 22 Although heterogeneity might be considered a limitation, it might also facilitate generalizability. Fourth, for 564 participants, only a single follow-up measurement of eGFR was available. However, results involving first and sustained renal endpoints were consistent (Tables  2  and 3 ). Finally, to compute albuminuria, we had to assume a urinary volume of 1500 ml/day in 165 participants (7. 9%). However, excluding these participants did not materially alter our results. In conclusion, CKD273 adds to the prediction of stage 3 CKD, at which intervention remains an achievable therapeutic target.
Data collection Data were collected retrospectively from patients' computerized medical files using the Dxcare software. Demographic data, performance status, cancer data (primary tumor, presence of metastases, metastatic sites, number of previous anticancer treatments, type of treatment and neutropenia status), reasons for ICU admission, status at admission assessed using the SAPS 2 score  [10] , life-support therapies used and the duration of use (mechanical ventilation, inotropes), and biological data at admission were collected. Reasons for admission were classified in two ways: one based on relationship with cancer (related to cancer progression or antineoplastic treatment toxicity, or no link with cancer) and the second based on medical diagnosis at admission (sepsis, acute heart failure, hemorrhagic shock, hypovolemia, acute respiratory failure, chronic bronchitis exacerbation, neurological reasons (i.
Moreover, only patients with solid tumors were included, eliminating high-risk populations (e. g. , patients with hematological malignancies. Another strength was the collection of data about tumor and metastatic sites, as well as the number and type of treatments received prior to ICU admission. Furthermore, we include data on resumption of anticancer therapy after ICU discharge, something that has only been reported in a few previous studies and is clinically relevant because it could play a part in assessing whether or not to admit a patient with cancer to the ICU. Our study also has some limitations that need to be taken into account when interpreting the data. The study was conducted in a single center and had a retrospective design. Also, there were some database limitations, including missing data on biological and clinical variables (e.
ACKNOWLEDGMENTS We gratefully acknowledge valuable contributions by the nanoSQUID team at  Tübingen ,  M. Kemmler ,  J. Nagel ,  R. Wölbing ,  T. Schwarz ,  B. Müller ,  S. Hess  and  R. Kleiner , and by our collaborators  O. Kieler  and  A. Zorin  et al. at  PTB Braunschweig ,  T. Schurig  et al. at PTB Berlin,  B. Büchner  et al. at IFW Dresden,  M. Poggio  et al. at  Univ. Basel ,  D. Grundler  et al. at  TU Munich and EPFL Lausanne ,  A. Fontcuberta i Morral  at  EPFL Lausanne  and  J. Sesé  at  INA Zaragoza. This work was funded by the  Alexander von Humboldt Foundation , the  Nachwuchswissenschaftlerprogramm of the Univ. Tübingen , the  Deutsche Forschungsgemeinschaft , via projects  KO 1303/13-1  and  SFB/TRR21 , and by the  EU-FP6-COST Action MP1201.
The Canadian Sea Ice and Snow Evolution Network (CanSISE) is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends.
CC BY 4. 0 License. Canadian cryosphere (snow, sea ice, freshwater ice, land ice, frozen ground) was compiled as part of the International Polar Year (IPY) in 2007-2008, and is described in  Derksen et al. (2012). The current study updates the IPY analysis pertaining to terrestrial snow and sea ice by adding nearly a decade of data and including climate model projections of changes over the next 30 to 40 years. This work is a core contribution of the Canadian Sea Ice and Snow Evolution Network (CanSISE), a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice. Recognizing the critical importance of the snow/sea ice component of the cryosphere to Canada's natural environment, ecosystems, and economy, this CanSISE study focused on Canadian territory, which is nearly completely covered by snow and ice for parts of each year and which features near-continuous coverage over high latitude and high elevation regions.
2007 , Smith et al. 2010 , Scaife et al. 2014) , although there is no clear consensus on how changes in Arctic sea ice and snow cover influence midlatitude climate  (Francis and Vavrus, 2012; 2015; Francis et al. , 2017; Barnes, 2013; Screen et al. , 2015). The first objective of this paper is to provide an overview of observed changes to seasonal terrestrial snow and sea ice across Canadian territory using the longest available time series of validated gridded datasets. We use a multi-data set approach, averaging multiple estimates of terrestrial snow together for a more robust trend and using an integrated, multi-source dataset for analysis of sea ice change. The second objective is to compare these recent historical changes to projected changes of snow and sea ice over a similar length of time from the near future out to the middle of the 21 st century (2020-2050).
We use simulations from state of the art climate models with confidence levels that account for uncertainty in the regional temperature response (note that the focus of this study is not model evaluationthat analysis is described in  Kushner et al. , 2017). Results are presented in two sub-sections, separated by the observational analysis period (Section 3. 1) and the future projections (Section 3. 2). Details on the datasets and methodology are provided in Section 2. We summarize our key findings in Section 4 and present remaining points of discussion in Section 5.
Terrestrial Snow Data Following  Mudryk et al. (2015) , we took a multi-dataset approach to analyze observed snow cover change in order to account for observational uncertainty. We calculated monthly snow cover fraction (SCF) and seasonal maximum snow water equivalent (SWE) using daily SWE data taken from the following four sources over the 35 year period from 1981-2015. 1) The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2),  (Reichle et al. , 2017 , updated from Rienecker et al. 2011) , is a National Aeronautics and Space Administration (NASA) atmospheric reanalysis product generated with the Goddard Earth Observing System Model, version 5. 2. 0 (GEOS-5) atmospheric general circulation model and atmospheric data assimilation system (ADAS). 2) The temperature index model described by  Brown et al.
globsnow. info) is a gridded product derived through a combination of satellite passive microwave data, forward snow emission model simulations, and climate station observations for non-alpine regions of the northern hemisphere  (Takala et al. 2011). Because the GlobSnow product is masked over complex topography, we replaced such cells with a blend of the GlobSnow data and the mean value from the other three data sources. The weighting for the blend was determined by the fraction of the grid cell area containing complex topography. For grid cells with no complex topography, unaltered GlobSnow data are used. As the fraction of complex topography increases, the weight applied to the GlobSnow data is linearly reduced, reaching zero for grid cells only containing complex topography. For a given dataset of daily SWE, we interpolated the data to a regular 0.
25 degree grid over Canada and applied a 4 mm threshold to produce a daily binary snow cover extent. We averaged this daily field over each month to produce a monthly snow cover fraction. Seasonal maximum SWE fields were calculated as the maximum value of daily SWE attained at each grid location over a given snow season. For both SCF and maximum SWE, we computed trends separately for each of the four datasets and then averaged these together into a final trend representing the mean across the observational ensemble.
, 2011a) , almost 10 years earlier than coverage by satellite passive microwave observations. The CISDA data was analyzed over two historical periods, the 1981-2015 period consistent with available snow data and a longer 1968-2016 period. We also used the Canadian Ice Service record of in situ landfast ice thickness measurements, made regularly at coastal Arctic stations since the early1950's  (Howell et al. 2016b). In general, thickness measurements are available at weekly frequency, starting after freeze-up when the ice is safe to walk on and continuing until breakup or when the ice becomes unsafe. Details of this dataset are provided by  Brown and Cote (1992)  and are available on the Canadian Ice Service Web site at http://iceglaces. ec. gc. ca.
Surface Temperature In light of documented differences in gridded temperature datasets over Canada  (Rapaic et al. , 2015) , surface temperature trends were derived from a blend of six reanalysis products: the European Centre for Mid-Range Weather Forecasting The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-198 Manuscript under review for journal The Cryosphere Discussion started: 9 October 2017 c Author(s) 2017. CC BY 4. 0 License. (ECMWF) Reanalysis (ERA-Interim,  Dee et al. , 2011) , the Japanese 55-year and 25-year Reanalyses (JRA-55,  Kobayashi et al. , 2015; JRA-25, Onogi et al, 2007) , the Modern-Era Retrospective analysis for Research and Applications version 1 and 2  (MERRA-1, Reinecker et al. , 2011; MERRA-2, Reinecker et al. , 2017) , and the Climate Forecast System Reanalysis (CFSR,  Saha et al.
, 2010). The average trend was determined by first computing the average of JRA-55 and JRA-25 (resulting in a single JRA trend), and the average of MERRA-1 and MERRA-2 (resulting in a single MERRA trend). The multi-reanalysis mean trend was computed by averaging (ERA-Interim, CFSR, JRA, and MERRA). Reanalysis was used instead of point station data in order to produce spatially continuous trends over both terrestrial and marine areas.
The SCF and SIC trends can be viewed collectively as changes in the timing and extent to which highly reflective snow and ice cover the Earth's surface, with important implications for the surface energy budget. There are no sharp boundaries in trends across adjacent land and ocean regions. This provides confidence in the consistency of the snow and ice datasets, as well as evidence of a coherent response of snow and ice to temperature forcing across terrestrial and marine regions. The reanalysis TAS trends are also seasonally and spatially consistent with the analysis of  Rapaic et al. (2015, Fig. 13 ) based on blended data from both homogenized station observations and multiple reanalyses. In both reanalysis and in situ data, Arctic trends are strongest in the fall and winter, with an increase in the magnitude of warming from the southwest to northeast regions of the country apparent in all seasons.
, 2014; Parkinson, 2014). Arctic sea ice thickness has declined in recent years (e. g. Kwok and Rothrock, 2009; Haas et al. , 2010; Laxon et al. , 2013; Richter-Menge and Farrell, 2013; Kwok and Cunningham, 2015; Tilling et al. , 2015). These studies indicate thickness declines are greater in the Beaufort Sea compared to the north facing coast of the CAA, which still contains some of the thickest sea ice in the world. Unfortunately, the spaceborne sensors used to obtain sea ice thickness information are not of sufficient spatial resolution to provide reasonable thickness estimates within the CAA. However, the Canadian Ice Service record of in situ landfast ice thickness measurements represents one of the longest datasets in the Arctic, and spans over 5 decades  (Howell et al. , 2016b). However, the seasonal behaviour of landfast ice thickness can provide useful information for understanding the interannual variability because ice growth is almost entirely due to thermodynamic forcing.
Historical datasets show the fraction of Canadian land and marine areas covered by snow and ice is decreasing. Observations show decreased SIC in all seasons and decreased terrestrial SCF in fall (delayed snow cover onset) and spring (earlier snow melt). There is regional and seasonal variability in the direction and strength of the trends (for example, some increases in spring snow cover across boreal western Canada) due to seasonal and spatial variability in surface temperature trends resulting from natural climate variability. There is evidence of decreasing pre-melt SWE (reflective of shallower snow depth) consistent with the study of  Mudryk et al. (2015)  and trends of annual maximum snow depth reported in  Vincent et al. (2015). There is only regional evidence (western CAA) of increasing winter season snow accumulation and hence higher SWE across Arctic Canada.
The majority of previous assessments of snow cover trends (i. e. Derksen and Brown, 2012; Hernandez-Henriquez et al, 2015; Derksen et al. , 2016; Kunkel et al. , 2016)  were based on the NOAA snow chart climate data record (NOAA-CDR;  Estilow et al. , 2015)  selected on the basis of the longest available record. The reliance on individual datasets such as the NOAA-CDR, however, makes trends prone to uncertainty due to the inherent uncertainties in an individual dataset (for instance, see  Brown and Derksen, 2013 and Mudryk et al. , 2017  for issues related to the NOAA-CDR). It is clear that consideration of multiple datasets either as a means of showing the range of trends from individual datasets  (Mudryk et al. , 2017) , for calculating confidence intervals around an individual dataset  (Brown and Robinson, 2011) , or to benchmark other datasets  (Brown et al.
, 2010; Hori et al. , 2017 ) is a more robust approach. However, in this case, we acknowledge that the availability and use of multiple datasets came at the sacrifice of time series length (the NOAA record extends back to 1967, whereas passive microwave satellite data and reanalyses such as ERA-interim only begin in 1979). 2. Alpine snow poses a unique challenge to trend analysis because the coarse spatial resolution of gridded products used for climate analysis cannot resolve the high degree of spatial variability (driven by land cover variability and steep topographic gradients) in alpine regions. It is imperative that we address and improve our ability to characterize variability and change in alpine snow because it is these regions that are both extremely sensitive to climate induced changes in snow accumulation (i.
, 2016a). The CAA was also a region with historically heavy MYI conditions present throughout the melt season but ice conditions have become lighter in recent years (see Figure  6b ). The replenishment of CAA MYI via FYI aging and MYI inflow from the Arctic Ocean has decreased in recent years because of increased temperature and changes in atmospheric circulation  (Howell et al. , 2015). 4. There is a strong association between the magnitude of warming and snow and ice loss both in observational datasets and climate model simulations, with projected declines in snow and sea ice cover proportional to the amount of future warming  (Thackeray et al. , 2016; Mudryk et al. , 2017; Notz and Stroeve, 2016). The multi-model mean projections indicate decreasing snow and ice cover because the multi-model mean projects a warmer climate by mid-century.
have identified spring snow cover losses that are stronger than those in Figure 1. This difference may stem from stronger spring trends in the NOAA snow chart data record (the NOAA dataset was not used in this study due to known deficiencies in the The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-198 Manuscript under review for journal The Cryosphere Discussion started: 9 October 2017 c Author(s) 2017. CC BY 4. 0 License.
Figure2shows seasonal surface air temperature (TAS) trends over the 1981-2015 period computed from a blend of six atmospheric reanalysis datasets (see Section 2. 1. 3). TAS trends are generally positive, although no trends are seen throughout the northwestern portion of the country during winter and spring, and there is significant cooling over the Canadian prairies in spring. Land areas with cooling trends are co-located with positive SCF trends in Figure1, although the region with positive SCF trends is slightly more extensive. The reduced warming and cooling over northwestern Canada is consistent with the influence of North Pacific oceanic variability over the last 35 years as analyzed in Mudryk et al. , 2014.
The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-198 Manuscript under review for journal The Cryosphere Discussion started: 9 October 2017 c Author(s) 2017. CC BY 4. 0 License. additional influence of dynamical effects (such as wind-driven redistribution of sea ice) weakens the thermodynamically driven spatial association between SIC and TAS trends. Trends in pre-melt seasonal maximum snow water equivalent (SWEmax), which indicates water resource and streamflow potential just before spring melt, are shown in Figure 4. The figure represents averages from the same datasets used to generate the SCF trends in Figure 1. Trends are negative over much of Canada, indicating a reduction over time in SWEmax.
Here we used the available high-quality genome sequence  (Dohm et al. , 2014)  and RNA-seq datasets  (Minoche et al. , 2015)  of sugar beet to identify and characterize the expression of BvAQPs. We report the distribution of BvAQPs on chromosomes, phylogeny analysis, gene structure, subcellular location, conserved resides and conserved elements. We propose putative functions of sugar beet AQPs based on their detailed genes expression patterns analysis by means of RNA-seq.
Identification and distribution of AQP genes in sugar beet BvAQPs were identified by HMM (Hidden Markov Model) and BLAST homology search. The sugar beet predicted proteome was collected using the sugar beet genome RefBeet-1. 2 (http://bvseq. molgen. mpg. de/Genome/Download/index. shtml). The Hidden Markov model (HMM) of the MIP domain (PF00230) was downloaded from the Sanger database (http://pfam. xfam. org/family/PF00230). PF00230 was then used to query the predicted Sugar beet proteome using HMMER 3. 0 software (http://hmmer. org/). 35 Arabidopsis AtAQPs were download from TAIR Database (https://www. arabidopsis. org/browse/ genefamily/Aquaporins. jsp) and then used to search BvAQPs with BLASTp tool using NCBI sugar beet genome (https://www. ncbi. nlm. nih. gov/genome/?term=Beta+vulgaris) and sugar beet genome (http://www.
genomforschung. uni-bielefeld. de/en/projects/annobeet) (Baranwal, Negi & Khurana, 2016)  with cut-off E-value of e -5. All no-redundant gene sequences were analyzed by SMART (http://smart. embl-heidelberg. de/) and Pfam (http://pfam. xfam. org/search/sequence). Sequences encoding complete MIP domain and two NPA motifs were considered as putative AQP genes. Additionally, the molecular weight (MW) and isoelectric point (PI) of BvAQPs were calculated by ExPASy (http://www. expasy. org/); transmembrane helical domains (TMHs) were assessed by TMHMM Sever v. 2. 0 (http://www. cbs. dtu. dk/services/ TMHMM/); the subcellular localization of BvAQPs were predicted using Plant-mPLoc (http://www. csbio. sjtu. edu. cn/bioinf/plant-multi/) and WolF PSORT (http: //www. genscript. com/wolf-psort. html). The position of the AQP genes on the sugar beet chromosomes were identified based on position information from the sugar beet genome database and the distribution graph of AQP genes was drawn by MapInspect software (http://mapinspect.
Phylogenetic analyses and sequence alignments BvAQPs were aligned with AtAQPs from Arabidopsis (https://www. arabidopsis. org/) DcAQPs from Dianthus caryophyllus  (Morita et al. , 2017;  http://carnation. kazusa. or. jp/blast. html), by using Clustal W. Phylogenetic tree was built by MEGA6. 0 (http: //www. megasoftware. net/history. php) using the neighbor-joining (NJ) method, with 1,000 times bootstrap replicates. BvAQPs were named based on their sequence homology and phylogenetic analyses. Greek letters (α, β) were used to denote the transcripts derived from the same gene. BvAQPs and two S. tuberosum AQPs  (Venkatesh, Yu & Park, 2013)  were aligned by DNAMAN (http://dnaman. software. informer. com/) with default parameters. Two NPA motifs, ar/R selectivity filter and Froger's position were inferred from the multiple sequence alignment result from DNAMAN.
Expression analysis of sugar beet AQP genes RNA-seq data (SRX287608-SRX287615) were collected from NCBI and used to analyse the expression profiles of AQP genes in different organs and tissues (seedling, root, leaf, inflorescence and seed). Salt and heat tolerance is largely dependent on the plant ability to maintain optimal water status in leaves. The adjustment of water relation under salinity involves changes in the transcriptional activity of genes encoding AQPs. Expression variations of BvAQPs in young leaf under salt or heat stress, were analyzed using RNA-seq data (SRX647324; SRX647712; SRX647714)  (Minoche et al. , 2015). HISAT2 was used to align raw reads to the reference genome, and StringTie was used to calculate gene expression  (Stracke et al. , 2014). The heat map for tissue-specific expression profile was generated based on the Log RPKM 2 values for each gene in all the tissue samples using R package  (Gentleman et al.