dataesr/scientific-paragraphs-categorization huggingface dataset:
Current climate change scenarios predict the evolution of the components of the European cryosphere relatively reliably until 2050. In the second half of the century, however, the development is much more uncertain. This is linked to, inter alia, the uncertainties affecting future precipitation evolution. Shifts in air temperature and precipitation will clearly be significant drivers for future changes in the mountain cryosphere, but so will changes in shortwave and longwave radiation processes (related to greenhouse gas forcing, changes in moisture and clouds, etc. ) that are also key drivers for glacier melting and permafrost thawing. Besides the emphasis on challenges in the modeling domain and cascading mass movement processes and process chains, this paper has highlighted data issues.
Indeed, there are numerous limits to data availability, related to spatial and temporal sparseness and restricted access. Financial and institutional barriers, as well as non-harmonized data policies, add to the problem. Mountain cryosphere research urgently needs open-access data of high quality for both understanding the functioning of the various elements and for building reliable models capable of projecting their evolution. Continuous improvement of physical models and free access to high spatial and temporal resolution simulations are essential to furthering our understanding of feedbacks between the atmosphere, the hydrosphere, and the cryosphere and to translate greenhouse gas emissions scenarios into cryospheric impacts. The spatial resolution of GCMs is rapidly increasing, but much of the information still remains too coarse for mountain cryosphere research.
Physically based, nested global-to-regional modeling techniques are expected to provide adequate data in the future but simulation results will remain highly dependent on the models' initial conditions. Providing an adequate observational data basis for this purpose is therefore of paramount importance. Finally, communicating research results on climate and cryospheric science is a challenge in itself. In general, the imminence of climatic change is more convincing to a lay audience when changes become visible. A prominent example is the retreat of mountain glaciers, which can convincingly be brought to the public through repeated photography. In this sense, climate-induced changes in the cryosphere enable a unique and effective form of communication. More effort should be dedicated to illustrate how these changes can affect water resources, mountain ecosystems, natural hazards, and a wide range of related economic activities.
By highlighting the impacts of a changing cryosphere as climate evolves, this review has attempted to emphasize the central role of the cryosphere as a key element of environmental change in high mountains. To develop appropriate adaptation strategies to respond to the changes in climate, we anticipate an increasing need for knowledge on the cryosphere. Data availability. Due to the broad range of topics -and the large amount of data that have been compiled -anyone interested in accessing the information used in the paper should contact the following lead authors: for snow, Ulrich Strasser, Austria, at ulrich. strasser@uibk. ac. at; for glaciers, Liss M. Andreassen, Norway, at lma@nve. no; for permafrost, Christian Hauck, Switzerland, at Christian. Hauck@unige. ch; for observational data, Hendrik Huwald, Switzerland, at Hendrik.
Data and Methods The effect of agricultural subsidies, off-farm income and farm characteristics on farm income risk in Slovenian agriculture is analysed using farm-level data from the Slovenian farm accountancy data network (FADN) for the period 2004-2013. The FADN is an unbalanced panel dataset. For the period 2004-2013, the Slovenian FADN contains a total of 8272 observations. The analysis focuses on gross farm income. Farmers' income risk is measured by the coefficient of variation (i. e. CV is the ratio of standard deviation and mean) at the farm level in order to facilitate comparison of income risks across farms and over time. Formally, , where σ is the standard deviation of farm income (FI), and μ is the mean of farm income over five years for each farm. Based on earlier research (see the surveys of el  Benni et al.
Combination treatment of FK866 with JPH203 was performed and 48 h after combination treatment, Cell viability was determined using XTT assay (Invitrogen) and DAPI staining. Percentage of cell death was subjected for drug combination analysis as described by combination index (CI). CI was analyzed using CompuSyn software V1. 0 by the method of Chou and Talalay  [31]. CI < 1 indicates drug synergistic effect, CI > 1 indicates drug antagonistic effect, and CI = 1 indicates drug additive effect. For the HPLC analysis of NAD and NMN (nicotinamide mononucleotide), nucleotides were extracted by resuspending cell pellets in 0. 4 M HClO 4. After 5 min on ice, samples were centrifuged at 16,000×g for 5 min. Pellets were resuspended in 100 μl formic acid, incubated 5 min at 37 °C, and used for the determination of the protein concentration (Bradford reagent).
ACTIN and GAPDH were used as protein loading control. Densitometric analysis and normalization relative to control was conducted using ImageJ software. A representative western blot of three-independent biological experiments is shown. shRNA and siRNA transfection Lentiviral vector particles (LVPs) were generated in Lenti-HEK293 cells. Briefly, cells were co-transfected with a Δ891 and a VSV-G encoding vector along with a shRNA transfer lentiviral plasmid PLKO vector and a shNAPRT plasmid (a gift of Dr. Alessio Nencioni). Seventy-two hours post-transfection, viral supernatants were collected, filtrated, and performed quantification. MDA cells were stably transduced with shNAPRT, and selection of puromycin (2. 5 μg/ml) was performed. Downregulation of LDHA in MDA cells was performed by transient transfection of siRNA of LDHA using INTERFERin in vitro siRNA/miRNA transfection reagent (Polyplus) for 48 h.
Data were expressed as nmol ATP produced/ min/10 6 cells  [35]. The oxidative phosphorylation efficiency (P/O ratio) was calculated as the ratio between the concentration of the produced ATP and the amount of consumed oxygen in the presence of respiring substrate and ADP. When the oxygen consumption is completed devoted to the energy production, the P/O ratio should be around 2. 5 and 1. 5 after pyruvate + malate or succinate addition, respectively  [36].
Glucose consumption Glucose consumption was evaluated by the hexokinase (HK) and glucose-6-phosphate dehydrogenase (G6PD) coupling system, following the reduction of NADP at 340 nm. The assay medium contained 100 mM Tris-HCl, pH 7. 4, 2 mM ATP, 10 mM NADP, 2 mM MgCl2, 2 IU of hexokinase, and 2 IU of glucose-6-phosphate dehydrogenase. Data was normalized to the cell number and expressed as mM glucose consumed/10 6 cells  [37].
Lactate release assay Lactate concentration was assayed spectrophotometrically in the growth medium, following the reduction of NAD + at 340 nm. The assay medium contained 100 mM Tris-HCl (pH 8), 5 mM NAD + , and 1 IU/ml of lactate dehydrogenase. Samples were analyzed before and after the addition of 4 μg of purified lactate dehydrogenase. Data was normalized to the cell number and expressed as mM lactate released/10 6 cells  [37].
Microarrays Total RNA extracted from CEM RES cells was hybridized in quadruplicate on the Agilent Human GE 4x44K v2 Microarray (G2519F-026652) and compared with total RNA from parental cells. After quantile normalization and removal of low signals, differentially expressed genes were selected according to a double threshold on the log2 fold change (absolute value > 0. 75) and moderated t test p value (< 0. 05) calculated with the Limma package. Functional annotation enrichment analysis of the resulting lists of DEGs was performed with the clusterProfiler Bioconductor package. The significance of over-representation was determined using a p value threshold of 0. 05. Gene ontology terms with less than 500 associated genes were used for the analysis. Microarray raw data are available on Gene Expression Omnibus under the following accession number: GSE96636.
Acknowledgements We thank  Johanna Chiche  ( C3M-U1065 ,  Nice, France ) for setting up the OXPhos/glycolysis ATP production assay,  Mickaël Ohana  for the helpful discussion about the NAD + metabolism, and  Hitoshi Endou  ( JPharma, Yokohama, Japan ) and  Michael F Wempe  ( University of Colorado, Aurora, CO, USA ) for the JPH203.
2-5 Previous GBD papers have reported that increases in HALE have been slower than increases in life expectancy, resulting in more years of poor health, and suggesting an absolute expansion of morbidity. 6-9 However, details of how many of the additional years of life gained are spent in good health versus poor health across countries and sociodemographic groups have not been well characterised. As people live longer, such information becomes increasingly relevant for policy development, health systems planning, and resource allocation, the effects of which cannot be understated for population health. The estimates herein provide insight into the importance of access to services and comparability of the cause of death data were evaluated and improved through several steps, including adjustment of data from vital registration systems for incompleteness, conversion of causes found in the original data to the GBD 2017 cause list, and redistribution of deaths assigned to ICD codes that cannot be underlying causes of death.
16  The programme then selects an ensemble of models that performs best on out-of-sample predictive validity tests for each cause of death. Ensemble models have been shown to produce smaller errors in estimated cause-specific mortality and more accurate trends than single-component models. 16  Additional detail, including model specifications and data availability for each causespecific model, can be found in the appendices of the GBD 2017 causes of death11 and mortality13 publications. We calculated YLLs from the sum of each death multiplied by the standard life expectancy at each age. The standard life expectancy was taken from the lowest observed risk of death for each 5-year age group in all populations greater than 5 million people. For consistency across all fatal and non-fatal estimates in GBD 2017, we calculated our own population and fertility estimates.
17  We then used the GBD world population age standard to calculate age-standardised rates for cause-specific deaths and YLLs. The GBD world population age standard and the standard life expectancies are available in the appendix of the GBD 2017 mortality publication. 13  Changes we have implemented since GBD 2016 for cause-specific mortality include the addition of important sources of new mortality data (detailed at the beginning of this section) and the expansion of the GBD location hierarchy, refinements in the calculation of Sociodemographic Index (SDI), and disaggregation of specific causes into subgroupings to provide additional detail. We estimated the following specific causes separately for the first time: invasive non-typhoidal salmonella disease; liver cancer due to non-alcoholic steatohepatitis (NASH); cirrhosis due to non-alcoholic steatohepatitis; myelodysplastic, myeloproliferative, and other haemopoietic neoplasms; benign and in-situ intestinal neoplasms; benign and in-situ cervical and uterine neoplasms; other benign and in-situ neoplasms; subarachnoid haemorrhage; non-rheumatic valvular heart disease; non-rheumatic calcific aortic valve disease; non-rheumatic degenerative mitral valve disease; other nonrheumatic valve diseases; gastro-oesophageal reflux disease; type 1 diabetes; type 2 diabetes; chronic kidney disease due to type 1 diabetes; chronic kidney disease due to type 2 diabetes; poisoning by carbon monoxide; and poisoning by other means.
Specific data sources are available in the appendices of the GBD 2017 non-fatal diseases and injuries12 and causes of death11 publications. Additional information on data sources used can be found in our online source tool. For estimation of non-fatal health loss, we most commonly used the Bayesian metaregression tool DisMod-MR 2. 1, which synthesises variable data sources to produce internally consistent estimates of incidence, prevalence, remission, and excess mortality. 18 If DisMod-MR 2. 1 did not capture the complexity of the disease, or if incidence and prevalence needed to be calculated from other data, we used custom models; detailed methods for each cause are in the appendices of the GBD 2017 non-fatal diseases and injuries publication. 12  We estimated each non-fatal sequela separately and assessed the occurrence of comorbidity for each age group, sex, location, and year separately using a micro-simulation framework.
12 Disability estimated for co-morbid conditions was distributed to each contributing cause during the comorbidity estimation process. Although the distribution of sequelae and the severity and cumulative disability per case of a condition might be different by age, sex, location, and year, previous studies have found that disability weights do not substantially vary between locations, income per capita, or levels of educational attainment. 19, 20  Additional details, including model specifications, data availability, data adjustments to enhance comparability for each cause-specific model, and the development of disability weights by cause and their use in the estimation of non-fatal health loss, are available in the appendices of the GBD 2017 non-fatal diseases and injuries publication. 12.
Estimation of DALYs, HALE, and corresponding uncertainty To calculate HALE, we used the following inputs from GBD 2017: age-specific mortality rates; estimates of the prevalence of sequelae by age, sex, location, and year; and disability weights for all unique health states. We used the method originally developed by Sullivan21 to estimate HALE (appendix 1). We calculated DALYs as the sum of YLLs11 and YLDs12 for each location, year, age group, and cause, by sex. We calculated 95% uncertainty intervals (UIs) on the basis of 1000 draws from the posterior distribution of each step in the estimation process using the 2•5th and 97•5th percentiles of the ordered 1000 values. We attributed the uncertainty associated with estimation of mortality and YLLs to multiple sources, including sample size variability in data sources, adjustment and standardisation methods applied to data, and model specifications.
Estimates of DALYs due to congenital birth defects were higher in GBD 2017 than in GBD 2016 for two reasons. 9 First, all data sources that captured only a small subset of congenital causes (eg, only congenital heart disease and neural tube defects) were excluded for GBD 2017, leading to increased mortality estimates. Second, for GBD 2017 we implemented an algorithm to empirically identify data sources for registration of birth defects and for administrations (ie, hospital and claims) that had systematic under-reporting by age, sex, and defect. This change led to exclusion of several sources and, in many cases, higher prevalence estimates. The estimated DALYs due to malaria were lower in GBD 2017 than in GBD 2016 because of newly included Plasmodium parasite rate survey data and updates to insecticidetreated bednet coverage, which decreased incidence estimates, particularly in Nigeria.
The decrease in incidence was in agreement with additional verbal autopsy data for Nigeria, which resulted in a decrease in estimated YLLs and DALYs. The global age-standardised DALY rate for drug use disorders in GBD 2017 was higher for the most recent decade than in GBD 2016, because of a better fit to the most recent years of cause of death data in the USA and the use of more appropriate covariates, including the prevalence of injecting drug use and an estimate of consumption (measured by import and domestic manufacture of scheduled substances, including opioids, expressed as daily doses per 1000 people per day). Estimates of DALYs due to acute hepatitis overall are very similar to GBD 2016, but our assessments of the relative contribution of different viruses changed. We used clinical administrative data sources to calculate location-specific and age-specific case-fatality ratios for acute hepatitis infection, pairing these ratios with incidence estimates of each virus.
Limitations This study has several limitations. First, the GBD 2017 YLL and YLD analyses had limitations as described in the GBD 2017 cause of death,11 disease and injury,12 and mortality papers. 13 Second, we assumed that uncertainty is independent between YLLs and YLDs because little empirical evidence exists to establish this correlation; however, this assumption could result in an under-estimation of the total uncertainty for DALYs. Third, HALE and DALY estimates are influenced by the availability of data for YLL and YLD estimations. Because of time lags in the reporting of health data by countries and their subsequent incorporation into the GBD estimation, recent changes in health states might not have been captured in our estimates. The scarcity of data for a particular location is reflected by wider uncertainty intervals.
In addition to broadening our estimation by cause, location, and time, a substantial amount of new data were added for GBD 2017. For cause-specific non-fatal estimations, we added new data from epidemiological surveillance, disease registries, scientific literature sources, and survey sources. Similarly, for cause-specific fatal estimation, we added new data from verbal autopsy studies, vital registration, and cancer registries. For age-specific all-cause mortality estimations, we added vital registration data, complete birth history sources, summary birth history sources, and sibling history surveys. These improvements are reflected in the summary measures of population health, DALYs and HALE, reported in this paper. We also provided a more detailed assessment for HALE than in previous GBD papers by examining the following: distinguishing the years of life gained over the past 28 years into years spent in good health and in poor health, by sex, for each location; determining which extra years lived were spent in good health and in poor health for females compared with males for each location; and assessing the male-female difference in HALE and years lived in poor health for the period 1990-2017 across Socio-demographic Index (SDI) quintiles.
Louisa Degenhardt reports grants from  Indivior ,  Mundipharma , and  Seqirus. Seana Gall  reports grants from the  National Health and Medical Research Council  and the  National Heart Foundation of Australia. Panniyammakal Jeemon reports a Clinical and Public Health Intermediate Fellowship from the  Wellcome Trust-DBT India Alliance  ( 2015-20 ). Jacek Jóźwiak  reports a grant from Valeant; personal fees from Valeant,  ALAB Laboratoria , and Amgen; and non-financial support from  Microlife and Servier. Nicholas Kassebaum  reports personal fees and other support from  Vifor Pharmaceuticals. Srinivasa Vittal Katikireddi  reports grants from  UK NHS Research Scotland  ( SCAF/15/02 ),  UK Medical Research Council  ( MC_UU_12017/13  and  MC_UU_12017/15 ), and the  Scottish Government Chief Scientist Office  ( SPHSU13  and  SPHSU15 ).
Therefore the determination of the spatial and kinematical properties of OCs and a better knowledge of how they evolve with time provide strong constraints for testing the dynamical processes that occur at local and Galactic scales. The information about OCs is compiled in large catalogues and databases, such as WEBDA  (Mermilliod & Paunzen 2003) , and the regularly updated catalogues of  Dias et al. (2002) , hereafter DAML, and of  Kharchenko et al. (2013) , hereafter MWSC. With this observational material available before the Gaia era, several studies have drawn a picture of the kinematical behaviour of the OC system using several hundred objects. Dias & Lépine later became DAML. The authors used the sample to study the pattern speed of the nearby spiral structure. Wu et al. (2009)  analysed the kinematics and orbits for a sample of 488 OCs extracted from DAML.
The dramatic improvement of the 3D OC velocities with Gaia DR2 allows us to revisit the global properties of the OC system. In this paper we compute mean RVs of 861 OCs from Paper I using only Gaia DR2 data. Section 2 describes the procedure we used, the assessment of the precision and accuracy of the catalogue by comparison to ground-based datasets, and the definition of a high-quality sample. We combine the mean RVs with the astrometric parameters derived in Paper I to compute their 3D Galactic velocities (Sect. 3). We investigate the kinematics versus age of that sample and try to identify pairs and groups.
Comparison to other datasets We compared our mean RV with that of different catalogues. The largest RV source for OC candidates is the MWSC, which provides basic parameters for 3006 entries, including RVs for 953 of them. With this catalogue, we obtain a median difference of 1 km s -1 with a MAD of 5. 5 km s -1 for 374 OCs in common. Several outliers with an RV difference larger than 20 km s -1 mostly correspond to OCs in which the RV relies on one or two members, either in the MWSC or in our sample. When restricted to the 140 common OCs with at least three RV members in each catalogue, the median difference remains the same, but the MAD decreases to 2. 5 km s -1. There are still outliers, such as FSR 0866, for which the Gaia RV is 65. 5 ± 0. 5 km s -1 based on three members, while the MWSC RV is 0.
The MWSC individual RVs seem of good quality, while the 12 Gaia RVs are more dispersed, as shown in Fig. 4. The 3 stars on which the mean Gaia RV is based have never been studied before, so that their measurement cannot be assessed against an external source. For this object the MWSC mean RV looks more reliable, although there is no clear explanation why the Gaia RV would be in error. According to the Simbad database, NGC 2244 is an ionising cluster of the Rosette Nebula with a nearby associated stellar cluster, NGC 2237. It is therefore possible that two clusters with different RV overlap in that area. We compared our catalogue to that of  Conrad et al. (2014) , who provide the mean RV of 110 OCs based on RAVE DR4  (Kordopatis et al. 2013) , 62 of which are common with our sample. The median difference for these common OCs is 0.
Acknowledgements. This work has made use of results from the  European Space Agency (ESA) space mission Gaia , the data from which were processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. C. S. and L. C. acknowledge support from the  "programme national cosmologie et galaxies" (PNCG)  of  CNRS/INSU. This work was supported by the  MINECO (Spanish Ministry of Economy ) through grant  ESP2016-80079-C2-1-R  ( MINECO/FEDER, UE)  and  ESP2014-55996-C2-1-R (MINECO/FEDER , UE) and MDM-2014-0369 of  ICCUB (Unidad de Excelencia 'María de Maeztu '). U. H. acknowledges support from the  Swedish National Space Agency (SNSA/Rymdstyrelsen ).
aureus cells were enriched with StrataClean resin beads according to the method of Bonn et al. (80)  with minor modifications, namely, using 50 mM Tris buffer (pH 7) instead of TE buffer (50 mM Tris, 10 mM EDTA, pH 7) and determination of peptide concentrations by the Pierce quantitative colorimetric peptide assay (Thermo Fisher Scientific). Tryptic digest and peptide purification were performed as previously described (81) using a C 18 ZipTip column (Millipore, Billerica, MA, USA) with a loading capacity of 5 g. Before subjecting the samples to mass spectrometry, HRM spike-in mix (Biognosys AG, Schlieren, Switzerland) was added. Mass spectrometric measurements and data analysis. Tryptic peptides were separated by reversed-phase liquid chromatography (Dionex Ultimate 3000 system; Thermo Fisher Scientific) coupled online to electrospray ionization mass spectrometry using a Q Exactive mass spectrometer (Thermo Fisher Scientific).