dataesr/scientific-paragraphs-categorization huggingface dataset:
-Using pressed dried leaf material, we prepared a slide-mount of each leaf's venation network following standard chemical clearing and staining protocol, with leaf epidermal layers removed using a small brush taBle 1. Characteristics of communities sampled along the Kosñipata tropical forest elevation gradient. Trait columns indicate abundance-weighted means and standard deviations. Columns "species/branches measured" refer to the number of units for which angiosperm trait measurements were available. Plot code   (Pérez-Harguindeguy et al. 2013). We then photographed each slide-mounted leaf using a Olympus SZX-12 microscope with transillumination and coupled to a Canon T1i digital camera. Images were obtained with a final resolution of 179 pixels mm -1 , with a full extent of 12. 8 mm × 19.
3 mm. Images were enhanced using in MATLAB by applying contrast limited adaptive histogram equalization to the green channel of each image, using a sliding window of 200 pixels and a gain of 0. 01. We traced all veins within a well-cleared polygonal region of interest of each image (mean area 36 ± 23 SD mm 2 ). We calculated VD raw in MATLAB by dividing the total length of the skeletonized traced veins by the total area of the region of interest. We calculated IVD raw by performing a Euclidean distance transformation on the skeletonized image. Within each areole, the maximum of this distance transformation is equivalent to the maximum distance from a vein. We then calculated IVD raw as the mean of this distribution. We then corrected raw measurements for shrinkage using a factor using the relationship between areal and linear scales as Latitude (°) Longitude (°) Elevation (m) MAT (°C) MAP (mm yr -1 ) αPET (mmol m -2 s -1 ) VD (mm -1 ) IVD (mm) r v (mm) d y (mm) E max (mmol m -2 s - Vein radius (r v ; mm).
It therefore provides a first approximation for how venation networks constrain water flow within a leaf. Gap-filling. -A small number (<10%) of trait measurements for leaf mass per area, fresh lamina area, and d y were missing at random. We filled missing values using multiple imputation via chained equations, with predictive mean matching. We imputed 10 datasets for a matrix including these variables, plot code and sun/shade status. We then used mean values across these replicates to gapfill missing observations in the original data matrix.
Climate data We obtained climate data from a set of weather stations located adjacent to each one of the study sites (Table  1 ). The most complete annual time series for most weather stations were for year 2013 and incoming radiation, temperature, precipitation, and relative humidity were recorded at 30-min intervals. We used these time series to estimate the average daily climate. For days that the diurnal profile was not available, the average daily values were obtained by interpolating the daily parameters of the previous and following 3 d. Because of poor humidity sensor performance at TRU-04, we estimated relative humidity there following a calibration based on dewpoint temperature at WAY-01 using the methods of  Ephrath et al. (1996). Daily total αPET was estimated within the climate submodel of the Trait-based Forest Simulator model  (Fyllas et al.
Phylogenetic analysis We constructed a phylogenetic tree for all species for which trait measurements were obtained using standardized names. Voucher specimens can be viewed at https://cao. carnegiescience. edu/spectranomics using the branch codes in Data S2. We built the phyogenetic tree using the phylomatic function in Phylocom 4. 2  (Webb et al. 2008)  using the "R20120829" megatree. We then calculated approximate crown ages for each clade using Phylocom's bladj function, with constraints for internal nodes originally provided by  (Wikström et al. 2001 ) and corrected for file transcription errors by  Gastauer and Meira-Neto (2013). We then assigned trait values to the tips of this tree by calculating mean trait values across all branches and sites for which a measurement was available.
Range of trait variation Our final dataset included trait measurements for 811 leaves from 130 taxa. This reflected collections at each site of 17 ± 6 SD species and 81 ± 18 SD branches (Table  1 ). All venation network traits showed extensive variation. Vein density (VD) varied from 4. 8 mm -1 for Clusia alata (Clusiaceae) to 21. 7 mm -1 for Pourouma bicolor (Urticaceae) (Fig. 2 ), while IVD showed inverse variation, from 0. 043 mm for Pourouma bicolor to 0. 277 mm for Clusia alata. Median minor vein radius (r v ) varied from 0. 018 mm in Rauvolfia leptophylla (Apocynaceae) to 0. 07 mm in Clusia alata (Clusiaceae). Leaf thickness (d y ) varied from 0. 128 mm in Rinorea viridiflora (Violaceae) to 0. 805 mm in Clusia flaviflora (Clusiaceae). Boxplots of trait distributions across individuals within each species and plot are available for VD (Appendix S1: Fig.
We also repeated this analysis for only sun leaves and for only shade leaves. Results were broadly consistent with the complete dataset (sun leaves; Appendix S1: Fig. S6 ; shade leaves, Appendix S1: Fig. S7 ). Shade leaves showed stronger relationships than seen in the complete dataset. For example, the R 2 value for the mean annual temperature -VD relationship increased from 0. 50 to 0. 80, and the R 2 value for the E max and α PET max relationship increased from 0. 41 to 0. 59. However, this stratified analysis is potentially biased because some common species had canopies with no sun leaves or no shade leaves and as a result only contributed to the community-weighted trait in some cases. Nevertheless,.
In this dataset, we found sun-shade differences in some venation network traits, and evidence for stronger TERs within shade and sun leaves compared to within the entire dataset. However, we did not find consistent evidence intraspecific venation network TERs, despite evidence for these in other systems  (Blonder et al. 2013 (Blonder et al. , 2015)). However, the, high Andean beta diversity and the taxonomic breadth of our study necessarily led to limited within-species replication. Thus it seems possible that measuring microclimate variation may help to improve the strength abundance-weighted TERs. Fourth, the model's assumption about leaf-scale transpiration matching (Eq. 10) may not hold for species that have conservative water-use strategies. However, the high precipitation (>1,500 mm yr -1 ) at all communities suggests that these strategies are unlikely here.
acknowledGmentS This work is a product of the  Global Ecosystems Monitoring (GEM)  network (gem. tropicalforests. ox. ac. uk) the  Andes Biodiversity and Ecosystems Research Group ABERG  (andesresearch. org),the  Amazon Forest Inventory Network RAINFOR  (www. rainfor. org),and the  Carnegie Spectranomics Project  (spectranomics. carnegiescience. edu)research consortia. The field campaign was funded by grants to  Yadvinder Malhi  from the  UK Natural Environment Research Council  (Grant  NE/J023418/1 ), with additional support from  European Research Council  advanced investigator grants  GEM-TRAITS (321131 ),  T-FORCES  ( 291585 ), and a  John D. and Catherine T. MacArthur Foundation  grant to  Gregory Asner. We thank the  Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP)  and personnel of Manu and  Tambopata National Parks  for logistical assistance and permission to work in the protected areas.
Brian J. Enquist was supported by the  National Science Foundation  ( Macrosystems-1065861  and  DEB-1457812 ). Yadvinder Malhi  was supported by the  Jackson Foundation. Sandra Díaz  was supported by the  Leverhulme Trust (UK) , the  Inter-American Institute for Global Change Research, and FONCyT and CONICET (Argentina). Gregory Goldsmith  was supported by funding from the  European Community 's  Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  290605  (COFUND:  PSI-FELLOW ). Cyrille Violle  was supported by a  Marie Curie International Outgoing Fellowship  within the  7th European Community Framework Program (DiversiTraits project , no. 221060 ) and by the  European Research Council (ERC)  Starting Grant Project StG-2014-639706-CONSTRAINTS. We thank several anonymous reviewers for comments that greatly improved the manuscript.
The risk of being killed by a hunter has been shown to be related to relative brain size  [6]. The probability of being killed in traffic might not be independent of the probability of being killed by a hunter. Therefore, we excluded all specimens killed by a hunter. The conclusions remained the same in this restricted dataset (electronic supplementary material, table  S1 ). The effects of sex and age did not reach statistical significance (electronic supplementary material, table  S1 ). In contrast to the effect of brain mass, there was no similar effect for liver mass (F = 0. 47, d. f. = 1, 2339, p = 0. 49), heart mass (F = 0. 88, d. f. = 1, 1563, p = 0. 35) or lung mass (F = 0. 53, d. f. = 1, 140. 6, p = 0. 47).
SIRT1 represses MKK6 in an epigenetic manner. To determine whether SIRT1 directly regulates the MAP2K6 gene, we performed chromatin immunoprecipitation (ChIP) assays. We selected 12 specific primer pairs encompassing the 5′ untranslated region (5′UTR) of the MAP2K6 gene, including the transcription start site (TSS), the enhancer sites within the first intron and the peaks enriched in histones H3K27ac and H3K9ac in HUVEC, described in the UCSC database  34  (Fig. 7A ). Within this 16,547-bp region, we found significant  The SIRT1 enrichment at the distal 5′ regulatory region (-437 to -329 bp) and in the first strong enhancer region of intron 1 (+3,005 to +3,196 bp) in CT-ECFC (Fig. 7B ). Prematurity led to significantly decreased SIRT1 binding at these regions (Fig. 7C ), suggesting that SIRT1 directly binds in proximity to the MAP2K6 TSS to regulate its transcription.
Some reports indicate that EV released upon inflammatory stimulation can contain cytokines such as members of IL-1 family or IL-6  [49] [50] [51]. In our study, analysis of the proadhesive phenotype of HUVEC co-cultured with PT-EMP demonstrated that PT-EMP have not a prominent role in the pro-inflammatory response orchestrated by the SASP. This precipitated genomic DNA was analyzed via RT-qPCR using primers for the specific promoter (Prom_2 and 8) and enhancer (Enh_1 and 2) regions. The data for all of the bar graphs are presented as the means ± SEM. (Statistical analysis: Two-way ANOVA; *p < 0. 05, **p < 0. 01, ***p < 0. 001). (I) Proposed signaling mechanism controlling the release of EMP and their functional impact in the SIPS context of prematurity. data may indicate that despite the relationship between EMP and IL-6 levels released by PT-ECFC, pro-senescent EMP do not carry biologically relevant amounts of IL-6.
Methods Detailed methods are provided in the online data supplement. Patients. Eighteen term (control, gestational age (GA) >37 weeks, appropriate weight) and twenty-nine preterm neonates (GA 24 to 35 weeks with appropriate or small weight for GA), were included. Exclusion criteria were congenital viral infections, major congenital heart or structural brain malformations, genetic abnormalities and metabolic diseases. This research was approved by a local ethic committee Assistance Publique Hôpitaux de Marseille and the study was performed conform the declaration of Helsinki. All the parents have provided written informed consent for the use of cord blood. The patient characteristics are shown in Table  S1. Isolation of endothelial colony-forming cells. ECFC were isolated and expanded from mononuclear cell fraction (MNC) obtained from the cord blood of term (CT) and preterm (PT) neonates, cultured and characterized as previously described  12.
Antibody arrays. Conditioned media for antibody array analysis were prepared by washing cells with PBS and incubating them in basal medium for 48 h. The conditioned media were collected in a centrifuge tube, and the cells remaining on the dish were counted to normalize conditioned media volumes by cell number. The conditioned media were clarified by brief centrifugation, 0. 2 µm filtered, diluted with a serum-free medium to a concentration equivalent to 1. 35 × 10 5 cells per 1. 3 ml, and applied to the antibody arrays (Raybiotech; AAH-CYT-1) as described previously by Freund et al. 19  and as recommended by the supplier. The signals were detected using a G-BOX Imaging System (GeneSys) and were analyzed using specific software (GeneTools, Syngene). Signals were averaged and displayed as described in the figure legend.
ELISA. Conditioned media were prepared by incubating cells for 48 h as described above. The CM were analyzed using the Human IL-6 ELISA Kit II and reagents, following the procedures described by the manufacturer (BD OptEIATM; 550799). Counting and measuring particles. Tunable resistive pulse sensing. The concentration and size distribution of particles was analyzed with TRPS (qNano, Izon Science Ltd, Christchurch, New Zealand), a relatively new technology that allows the detection of particles passing through a nanopore by way of single-electrophoresis  66. Calibration was performed using CPC200 OR SKP400 calibration particles (Izon) as a standard according to the manufacturer's instructions. Data were recorded and analyzed using the Izon Control Suite software version 3. 2. 2. 234. Flow cytometric analysis of EMP.
EMP were analyzed using a Gallios flow cytometer, as previously described by Robert et al. 67. Briefly, conditioned medium was collected after 48 hours of ECFC culture (passage 3 to 4) in 0. 22 µm filtered complete medium. After two initial centrifugation to discard debris (300 × g, 5 minutes) and apoptotic bodies (2000 × g, 15 minutes), EMP in resulting supernatant were quantified withth Annexin A5-FITC (AnnV-FITC, Tau Technology BV, Netherlands). CytoCount beads (Cyto-Count, Dako, Copenhagen, Denmarrk) were added as internal standard to samples before FC analysis to determine the concentration of EMP. The samples analyses were performed with the Kaluza ® Analysis software (Beckman Coulter), as already described 68, 69. Functional Analysis. Preparation of Conditioned Medium. Conditioned medium (CM) was collected after 48 hours of incubation with PT or CT-ECFC in completed medium and then clarified by two serial centrifugation steps (300 g for 5 min and 2000 g for 15 min, at 4 °C) to remove cells debris and apoptotic bodies.
HUVEC were incubated for 24 hours with CM or EMP from each condition, harvested, fixed with 70% cold ethanol, and stained with propidium iodide. At least 10,000 events were acquired per sample with a Gallios flow cytometer system and analyzed using Kaluza ® Analysis software (Beckman Coulter). THP-1 adhesion to HUVEC. THP-1 monocytic cells were cultured in RPMI 1640 with 10% heat-inactivated FCS. Adhesion of the monocytic cell line THP-1 to HUVEC was performed as described by Akeson et al. 71  using calcein-labeled cells. HUVEC were stimulated with CM or EMP from each condition, for 48 h. At the end of the stimulation, calcein-labeled THP-1 were incubated with HUVECs for 30 min in RPMI medium. Adhesion to HUVEC was measured according to the method published by Vaporcyan et al. 72. Experiments were performed in triplicates.
cRNA labeling and hybridization performance were performed and all parameters checked were found within the manufacturers specifications. Signal intensities on 20 bit tiff images were calculated by Feature Extraction software (FE, Version 8. 5; Agilent Technologies). Data analyses were conducted with GeneSpring GX software (Vers. 13. 1. 1; Agilent Technologies). We considered genes to be differentially expressed when the adjusted p value was below 0. 05, as determined by a moderated T-test supplemented with Benjamini-Hochberg multiple testing corrections, and the absolute fold-change (FC) was higher than 1. 5. Unsupervised analyses were performed using principal component analysis (PCA) and hierarchical clustering. The average linkage was based on the Pearson correlation distance. Microarray data are available in the ArrayExpress database (www.
ebi. ac. uk/arrayexpress) under accession number E-MTAB-4860. Chromatin Immunoprecipitation (ChIP) and real-time PCR. Chromatin immunoprecipitation was performed as described previously  74. Precleared chromatin was incubated overnight at 4°C with 5 μl of primary antibodies specific for SIRT1 (#07-131) acH3K9 (#07-352) and H3 (#04-928) purchased from Millipore. After collection of immune complexes, DNA was recovered and quantified. Analysis of ChIP DNA samples was performed by quantitative PCR with the specific promoter regions described in the Supplemental Table  VII. Real-time PCR experiments were performed using the SYBR ® Premix Ex Taq TM (Takara Bio Inc, Japan) and analyzed using the stepOnePlus ™ Real-Time PCR System (Applied Biosystems). qPCR values were normal- ized to the values obtained with the positive control (Chromosome 5) and to input DNA.
Statistical Analysis. All the statistical analyses were performed using the software GraphPad Prism software and significance was calculated with 95% confidence interval (alpha < 0. 05) Data are expressed as means ± SEM. Demographic data of the preterm and term populations were analyzed qualitatively using the chi2 test of Pearson and quantitatively using the 2-tailed unpaired t test. Normality was confirmed with a D' Agostino and Pearson omnibus test. For data normally distibuted, statistical significance was assessed by an unpaired or pair Student t-test, as appropriate. For data not normally distributed, we used a Mann-Whitney or Wilcoxon test, as appropriate. When ANOVA was utilized, post-hoc intergroup comparison were analyzed for statistical significant differences using either Tukey's (all group compared to each other) or Dunnet's (groups compared to control group) methodology, as appropriate.
Acknowledgements The authors thank  Vallat Christelle  for assistance with qNano system and the staff of the obstetric departments (Conception and  Saint Joseph Hospitals ,  Marseille ). This work has been carried out thanks to the support of the A* MIDEX project  (no  ANR-11-IDEX-0001-02 ) funded by the  «Investissements d' Avenir» French Government program , managed by the  French National Research Agency (ANR) ", the  Fondation pour la Recherche Médicale  (grant  R08046AA ), and  Inserm.
As the second in a series  7, 8, 10, 11  of now annual updates, the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) is the most comprehensive and current source of summary health metrics. The Global Burden of Disease (GBD) is based on development of the largest available database of health outcomes, risk factor exposure, intervention coverage, and sociodemographic factors related to health. We applied analytical techniques to reduce data biases and support comparability, propa gated the uncertainty in these estimates, and provided insights at the highest temporal and spatial resolution afforded by the data. The purpose of this study is to present the results of GBD 2016 for DALYs and HALE, building on updated estimates of mortality, causes of death, and nonfatal health loss  7, 10  to identify nations with notable variation in health performance from that expected on the basis of SDI.
Overview We used the results of GBD 2016 to evaluate trends in epidemiological patterns and health performance on a global, regional, national, and subnational scale using DALYs and HALE as summary measures of changes in health states. Greater detail than presented in this section for methods used to estimate DALYs and HALE, including analytic approaches for assessment of relative morbidity and mortality from individual diseases and injuries, is provided in related publications in this series 8,10 and the appendix. This analysis follows the Guidelines for Accurate and Transparent Health Estimates Reporting,  13, 14  which include recommendations on documentation of data sources, estimation methods, and statistical analysis. We did analyses using Python version 2. 7. 12 and 2. 7. 3, Stata version 13.
1, and R version 3. 2. 2. For more information on Guidelines for Accurate and Transparent Health Estimates Reporting compliance, please refer to the appendix (pp 13-15). Additionally, interactive online tools are available to explore GBD 2016 data sources in detail. Causespecific estimation for GBD 2016 covers the years 1990-2016. For a subset of analyses, we focus on the last decade, from 2006 to 2016, to address current policy priorities. The GBD 2016 results for all years and by location can be explored further with dynamic data visualisations.
The full GBD cause hierarchy, including corresponding International Classification of Diseases (ICD)9 and ICD10 codes, is detailed in GBD 2016 publications on causespecific mortality  10  and nonfatal health outcomes, 8 with causespecific methods detailed in the corresponding appendices. The GBD study is organised by a geographical hierarchy of seven superregions containing 21 regions, with 195 countries and territories nested within those regions. 12  GBD 2016 included new subnational assessments for Indonesia by province and for England by local govern ment area. In this study, we present subnational data for the five countries with a population greater than 200 million people in 2016: Brazil, China, India, Indonesia, and the USA.
10  The database for GBD 2016 now includes data for the 333 causes estimated for DALYs and new subnational units for Indonesia (n=34) and England (n=150). For GBD 2016, we included substantial amounts of additional data sources from new studies and our network of collaborators; details of the types of data added can be found in the GBD 2016 cause of death  10  and non fatal 8 publications. Additionally, research teams did systematic reviews to incorporate literature data into fatal and nonfatal models. Further details on search strings are available in the GBD 2016 nonfatal 8 and cause of death  10  publication appendices. The Registrar General of India provided improved verbal autopsy data collected through their Sample Registration System, enabling a more detailed and thorough analysis of subnational data for India than in GBD 2015.
Tuberculosis We made important changes to the modelling strategy for tuberculosis in GBD 2016. For fatal tuberculosis, we first modelled the prevalence of active disease and latent infection, which we then used as covariates for the Cause of Death Ensemble model. For nonfatal tuberculosis, we strengthened our statistical triangulation approach, which enforces consistency between data for different parameters by modelling tuberculosis incidence, prevalence, and mortality among those with latent infection. Application of MIRs estimated on the basis of SDI to better reflect incidence in lowincome and middleincome countries has also enhanced consistency between fatal and nonfatal estimates of tuberculosis. All of these changes have resulted in global tuberculosis DALYs that are 15% higher than in GBD 2015 for the year 2010, with prominent increases occurring in several African countries, including Uganda, the Central African Republic, and Zambia.
Inclusion of diarrhoeal diseases as a fatal discontinuity in YLL estimation also affected the overall estimation for GBD 2016. Overall, the YLLs due to lower respiratory infections decreased slightly (by 0•626%) for the year 2010 from GBD 2015 to GBD 2016 and those due to diarrhoeal diseases increased (by 14•3%). We believe that these modelling changes improved under5 mortality estimates for lower respiratory infections and diarrhoea. Although responsible for a smaller contribution to DALYs than cause of death models, the nonfatal models of lower respiratory infections and diarrhoea included new data sources and processing. An example is that we now account for seasonality from the population representative surveys that provide most prevalence data from subSaharan Africa and south Asia in the lower respiratory infection and diarrhoeal disease models.