dataesr/scientific-paragraphs-categorization huggingface dataset:
Often, validations ignore the quality flags altogether-both from the satellite as well as in situ data, leading to incorrect assessment or attribution for error calculations. In the case of ECOSTRESS, the L3_ET_PT-JPL product is the result of a combination of multiple sources of data, each with their own quality flags and sources of error. These span from the ECOSTRESS L2 LSTE and cloud mask products derived from the calibrated L1 products and ancillary numerical weather prediction data, to the Landsat surface and MODIS atmospheric data, which include varying degrees of product levels. The quality flags of the Landsat and MODIS data are worth noting here. Given the coarse temporal resolution of Landsat, especially when cloudy, ECOSTRESS shifts reliance to MODIS for surface properties (NDVI, albedo) when the latest Landsat clear overpass becomes out of date (>16 days).
This is now being improved with incorporation of Sentinel 2AB data. For the MODIS atmospheric properties, which are retrieved generally with some diurnal time separation to ECOSTRESS, if there happens to be a cloud beneath MODIS but not ECOSTRESS that day, then MODIS is in filled with numerical weather prediction data (e. g. , backup algorithm). As such, ECOSTRESS is sharper when tied more closely to Landsat than to MODIS for the surface properties and more closely to MODIS than to the numerical weather prediction for the atmospheric properties. This may or may not have bearing on the comparison to the eddy flux site, depending on landscape homogeneity/heterogeneity surrounding the site. It may be possible to incorporate higher temporal resolution visible and near-infrared (VNIR) data from commercial cubesats (e.
, 2017)  (supporting information Figure  S2 ). Because of this sensitivity to LST by PT-JPL, the accuracy and precision of the ECOSTRESS L2(LSTE) is critical to fine-scale ET estimates, such as in comparisons to eddy flux footprints  (Fisher et al. , 2013; García et al. , 2013). In turn, the accuracy and precision of the L2(LSTE) product is a result of the ECOSTRESS instrument and L1 products themselves. ECOSTRESS was built with a state-of-the-art combination of thermal bands, blackbody calibrations, spatial resolution, temporal resolution, measurement accuracy, and precision, which, when used in conjunction with the established Temperature and Emissivity Separation and atmospheric correction retrieval algorithms, allows for a high-quality measurement that propagates upward through the higher-level ET and other data products.
Conclusion ECOSTRESS provides new thermal infrared temperature measurements from the vantage point of the ISS at 70-m spatial resolution, every 1-5 days, and sampling the diurnal cycle. These measurements are used to generate a suite of data products, with the primary science focus on ET from the Level-3 latent heat flux (LE) product (L3_ET_PT-JPL). We produced a relatively rapid and robust preliminary first Stage 1 validation of ECOSTRESS clear-sky LE against 82 eddy covariance sites from around the world. ECOSTRESS LE matched well with site measurements (instantaneous: r 2 = 0. 88; overall bias = 8%; normalized RMSE = 6%), showing good correlations and bias across a range of vegetation classes, climate zones, and times of day. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and preliminary Stage 1 validation status for subsequent science that follows using these data.
Site-Level Acknowledgments • Funding for AmeriFlux Management Project was provided by the  U. S. Department of Energy's Office of Science  under Contract  N. DE-AC02-05CH11231. • The funding by  EU projects EUROFLUX, CARBOEUROFLUE , and  CARBOEUROPE-IP , by  German BMBF  project  ICOS-D and by the state of Saxony (TU Dresdon, LfULG ) is greatly appreciated. •  USDA  is an equal opportunity provider and employer. Funding for  USDA  authors comes from the  ARS Office of National Programs  (Projects  2036-61000-018-00-D, 8042-13610-029-00-D, 5012-21000-027-00D , and  2020-13660-008-00-D ). • Y. T. was supported in part by  NASA   NEWS program  Grant  NNX15AT41G. The  CCZO  project was sponsored by  NSF   CZO program  Grant  EAR-1331846. • Research at the Yatir site is supported by the  Israel Science Foundation , the  Forestry Department , KKL, and the  Wills and Lewis Center of the Weizmann Institute of Science , Israel.
•  Texas Water Observatory  is supported by  Research Development Fund of Texas A&M University. • C. E. M. and C. J. B. were partially funded by the  DOE Center for Advanced Bioenergy and Bioproducts Innovation (U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research  under Award  DE-SC0018420 ). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the  U. S. Department of Energy.
A numerical criterion can allow to STOP the algorithm, for example if |F(λ k )| < , where is a prescribed value. In this paper, a standard interior point method, which is a convex optimization algorithm, was used and implemented in Matlab. To sum up, the complete flowchart is detailed in Fig. 4. The OLTC initial reference voltage is kept at the industrial choice V re f = 1. 03 pu. The goal of the study is to optimize the DGs controllers settings in order to comply with regulatory standards without adding extra devices to the grid or reinforcement. Grid Initialization A, B, V 0 α, β, Q 0 Stochastic Power flow (I -Bα) V = A P + B(β P g + Q 0 + Q l ) + 1 V 0 Lemma 1 V = A P + B Q l + 1 V 0 + BαA P + Bβ P g + Bα1 V 0 + BαB Q l + BQ 0 Objective function max x α ,x β ,x Q min i=1•••n {p i | P(V min ≤ V i ≤ V max )) ≥ p i } Proposition 2-3 max x α ,x β ,x Q min i=1•••n µ V i -Vmin σ V i Dinkelbach's procedure α opt , β opt , Q 0 opt The means and standard deviations of parameters uncertainties are given in Table  1 Q g = α V + β P g + Q 0 (23) One must find the control parameters x α , x β , x Q of the 7 generators which optimize the criterion (24): max x α ,x β ,x Q ω 1 min i=0•••n η i + ω 2 min i=0•••n κ i - ω 3 n n i=0 Var( V i ) - ω 4 G n i=0 E[ Q p i ] P N i 2 (24) where • η i are the confidence level for which every voltage V i , i = 1 • • • 168 lies in the contractual domain, here [0.
Results It is impossible to prove formally the convexity of a problem using numerical simulations, but, similar to the Monte Carlo method, running a high number of simulations with random starting points makes it possible to infer, in practice, whether the problem is convex. This subsection displays the results of a numerical study of the convexity and the convergence speed of the proposed optimization problem using this industrial example. The optimization problem (  24 ) is more complex than the theoretical max-p programming problem, but can nevertheless be solved by the same approach as in section 3. 2, and is therefore convex. Using a standard PC equipped with Intel Core processor i7-4810MQ and 16 Go RAM, and Matlab2016b interior point routines, procedure 2 solves the optimization problem applied on a feeder within 30 seconds.
We integrated original DNA methylation data from our laboratory (the Gene SMART cohort) with available open-access data from multiple repositories and published studies. Firstly, we aimed to identify robust age-related CpGs in skeletal muscle in an EWAS meta-analysis of age, combining n = 908 samples from 10 datasets. Second, we performed enrichment analyses to unravel the potential functional consequences of these robust age-related DNA methylation changes. Thirdly, we integrated age-related methylome changes with transcriptome and proteome changes in skeletal muscle using two external, large-scale studies. Finally, we updated our skeletal muscle epigenetic clock  9  with an additional 371 samples, reaching a total of 1,053 human skeletal muscle methylomes from 16 datasets. Importantly, we have made the results of our analysis available as an open-access, user-friendly, interactive web-based tool, MetaMeth (https://sarah-voisin.
Widespread age-related DNA methylation changes at genes involved in skeletal muscle structure, development and function We first conducted a powerful EWAS meta-analysis of age in skeletal muscle using 10 datasets (total n = 908 samples), and uncovered a small, widespread effect of aging on the skeletal muscle epigenome. Eight percent of all tested CpGs were associated with age in skeletal muscle (61,006 differentially methylated positions (DMPs) corresponding to 9,986 differentially methylated regions (DMRs), both at FDR < 0. 005, Fig  1A  and Supplementary Tables  2  & 3 ). We found even numbers of hypomethylated and hypermethylated DMPs (50% hypo-and 50% hyper-DMPs, Supplementary Table  2 ) and the magnitude of age-related DNA methylation changes was small and similar for both hypo-and hyper-DMPs: hypo-DMPs lost an average of ~7% in methylation per decade of life, and hyper-DMPs gained an average of ~6% in methylation per decade of life (Fig Each dataset had a unique study design that required adjustment for factors that are known to affect DNA methylation levels, such as sex  13  , body mass index (BMI)  14  , and type 2 diabetes (T2D)  15.
We adjusted each dataset for these factors, but we noted that age was associated with BMI or T2D in some datasets (Supplementary Table  1 ). For example, older individuals from the GSE50498 dataset had a BMI than younger individuals (4. 1 kg/m 2 heavier, p = 0. 0011) so it is possible that the age-related signal captured in this dataset was partially confounded by BMI. We repeated the meta-analysis without GSE50498, but results were largely unchanged (Supplementary   Fig 3A). We also repeated the meta-analysis excluding T2D patients from the FUSION, ABOS and GSE38291 datasets, but results remained unchanged (Supplementary Fig  3B ). Finally, we repeated the meta-analysis without the ABOS dataset whose muscle of origin differed from that of the other datasets (rectus abdominis vs vastus lateralis muscle).
Supplementary Figure 1. Comparison of results from the full meta-analysis and from a meta-analysis excluding GSE50498 (A), type 2 diabetes (T2D) patients (B) or the ABOS cohort (C). Each point is one of the 61,006 differentially methylated positions (DMPs) discovered in the full meta-analysis. To compare results from the full and partial meta-analyses, we plotted the effect size (change in M-value per year of age) in the full meta-analysis (x-axis), against the effect size in the partial meta-analysis (y-axis). To show whether DMPs remained significant in the partial meta-analysis, we colored points according to the false discovery rate (FDR) in the partial meta-analysis. We then focused on the DMRs for all downstream analyses, as DMRs remove spatial redundancy (CpG sites within ~500 bp are typically highly correlated  16  ), and they may provide more robust and functionally important information than DMPs  17, 18.
g. genomic region, annotated gene, position with respect to CpG islands, chromatin states in male and female skeletal muscle, and TF binding). To download summary statistics for DMPs or DMRs in a table format, users can go to the "Summary Tables" tab, and download the data as an excel or csv file, after optionally filtering data based on genomic location and statistics. Finally, we have also displayed the scatterplot of genes showing methylation, mRNA and protein changes with age as an interactive graph: users simply need to hover their mouse on one point of the graph to be shown the name of the gene and the number of DMRs annotated to it. The code used to produce the website is available in open-access on Sarah Voisin's Github account (https://github. com/sarah-voisin/MetaMeth).
More samples in the muscle epigenetic clock does not change age prediction accuracy The present EWAS meta-analysis of age utilised all of the datasets included in the original muscle epigenetic clock (MEAT) that we recently published, with the exception of datasets that were invariant in age and the datasets that were too small (n < 20) (see Material & Methods)  9. The present study included an additional 371 samples from five datasets. Using the same algorithm and methodology, we updated the MEAT clock with these new samples, reaching a total of n = 1,053 human skeletal muscle samples from 16 datasets. The new version of the MEAT clock (MEAT 2. 0) uses DNA methylation at 205 CpGs to predict age, 98 of which were in common with the first version of MEAT. We found that MEAT 2. 0 only slightly outperforms the original version of MEAT, with an average Pearson correlation coefficient of 0.
Discussion To paint a comprehensive picture of age-related DNA methylation changes in human skeletal muscle, we conducted a powerful EWAS meta-analysis of age in human muscle across the lifespan, combining 908 samples from 10 independent datasets. In this study, we were able to demonstrate a profound effect of age on the muscle methylome. Additionally, we have provided a detailed account of the genomic context of age-affected regions, reported putatively affected pathways, and integrated methylome changes with known transcriptome and proteome changes in muscle. To maximise the usefulness of this large-scale EWAS meta-analysis to the scientific community, we created a website named MetaMeth (https://sarah-voisin. shinyapps. io/MetaMeth/), which allows researchers to visualise results in an interactive and user-friendly manner.
Finally, we updated our muscle clock  9  with 371 newly acquired DNA methylation samples and found that the first version of the MEAT clock was already at optimal prediction accuracy. Previous studies on the overall pattern of age-related DNA methylation changes in muscle showed mixed results, three reporting more hypermethylation with age  7, 8, 11  and one finding slightly more hypomethylation with age  10. We included three of these studies (GSE49908, GSE50498 and EPIK) in our meta-analysis, and found balanced amounts of hypo-and hyper-methylation. Differences in coverage between studies are unlikely to explain the discrepancy in results, since the three HumanMethylation arrays were represented in these studies (27k, 450k and 850k). It is more likely that the overall direction of age-related DNA methylation change became more nuanced once these small-scale studies were combined with the other nine datasets.
Recently, we established an epigenetic clock for human skeletal muscle, using 682 samples from 12 datasets 9. Here we updated this clock (MEAT 2. 0) by using 1,053 samples from 16 datasets, particularly adding more female and middle-aged individuals that were underrepresented in MEAT. MEAT 2. 0 automatically selected 205 CpGs for age prediction, only 98 of which were in common with the CpGs selected by MEAT. While such a small overlap may seem surprising, it likely stems from the machine learning algorithm underlying the clocks: tens of thousands of CpGs change with age, but only a handful of CpGs are selected by the elastic net model, so this group of CpGs is only one of the many possible combinations of CpGs that can predict age with high accuracy  6. We tested whether the accuracy of the muscle clock is improved by feeding more samples to the machine learning algorithm.
Surprisingly, the accuracy of the new version of the clock barely improved, from 0. 62 to 0. 66 in average correlation between predicted and actual age, and from 4. 6 to 4. 5 years in median error in age prediction. This suggests that the original muscle clock was already sufficiently accurate for age prediction in human skeletal muscle using the Illumina HumanMethylation array technology. We have however updated the R package MEAT on Bioconductor with this new clock, providing users the possibility to choose between the old version (MEAT) and new version (MEAT 2. 0) of the clock for their analyses. The age-related changes in the muscle methylome uncovered herein, and the epigenetic age calculated from the MEAT clock reflect both intracellular changes in methylation levels, and age-related changes in muscle cell type composition.
EWAS meta-analysis of age in skeletal muscle We combined four datasets of genome-wide DNA methylation in skeletal muscle (the Gene Skeletal Muscle Adaptive Response to Training (SMART)  37  , the Limb Immobilisation and Transcriptional/Epigenetic Responses (LITER) study  9  , the Biological Atlas of Severe Obesity (ABOS) study  38  , and the Epigenetica & Kracht (EPIK) study  10  ), with 5 datasets from the open-access Gene Expression Omnibus (GEO) platform (GSE49908 8 , GSE50498  7  , GSE114763  39  , GSE38291  40  , and GSE135063  41  ), and the Finland-United States Investigation of NIDDM Genetics (FUSION) Study  42 (phs000867. v1. p1 ). These summed up to a total of n = 908 skeletal muscle samples collected from men and women across the lifespan (age range 18-89 years old, Supplementary Fig  1,  Supplementary Table  1 ).
Samples were 98% Caucasian and 71% male (Supplementary Table  1 ). We excluded cohorts from our recently published paper  9  with a narrow age range (age standard deviation < 5 years) as agerelated differences in DNA methylation cannot be detected if age is invariant; we also excluded datasets with a limited number of samples (n < 20) for robustness. Samples from the Gene SMART cohort (n = 234) include two batches, and our recently published paper  9  only includes the first batch of 75 samples available on the Gene Expression Omnibus platform (GSE151407). The additional 159 samples from the second batch include both men and women, before and after exercise intervention.
Supplementary Figure 2. Age distribution in each of the 10 datasets included in the EWAS meta-analysis, and database of origin. dbGAP = database of Genotypes And Phenotypes; GEO = Gene Expression Omnibus Details on the preprocessing of each DNA methylation dataset can be found elsewhere  9. We conducted independent EWAS of age in skeletal muscle in each dataset, using robust linear models and moderated Bayesian statistics as implemented in limma  43  ; to isolate the contribution of age to DNA methylation variability, we regressed DNA methylation level against age and adjusted, when the dataset included these covariates, for sex, BMI, diabetes status, batch, and timepoint (baseline/post intervention or training); we also added, when the dataset included repeated measures on the same individuals or related individuals, a random intercept using the duplicateCorrelation function to account for repeated measures from the same individuals or to account for twinship.
We adjusted each EWAS for bias and inflation using the empirical null distribution as implemented in bacon (Supplementary Figure  2 )  34. Inflation and bias in EWAS are caused by unmeasured technical and biological confounding, such as population substructure, batch effects, and cellular heterogeneity  35. The inflation factor is higher when the expected number of true associations is high (as it is for age); it is also greater for studies with higher statistical power  34. The figures we found (Supplementary Fig 2 ) were consistent with the inflation factors and biases reported in an EWAS of age in blood  34. Supplementary Figure  3. Quantile-quantile plot of -log10 transformed P-values for each of the 10 datasets included in the EWAS meta-analysis. Right panel using uncorrected P-values and left panel using bacon bias-and inflation-corrected P-values.
Results from the independent EWAS were combined using an inverse variance weighted meta-analysis with METAL  12. We used METAL since it does not require all DNA methylation datasets to include every CpG site on the HumanMethylation arrays. Different sets of CpGs may be filtered out during preprocessing of each individual dataset, which means the overlap between the datasets is imperfect and a given CpG may only be present in 5 out of 10 datasets, or 8 out of 10 datasets. For robustness, we only included CpGs present in at least 6 of the 10 cohorts (723,655 CpGs). We used a fixed effects (as opposed to random effects) meta-analysis, assuming one true effect size of age on DNA methylation, which is shared by all the included studies. Nevertheless, Cochran's Q-test for heterogeneity was performed to test whether effect sizes were homogeneous between studies (a heterogeneity index (I 2 ) > 50% reflects heterogeneity between studies).
Enrichment of DMRs in functional regions of the genome We used a χ 2 -test to compare the distribution of hyper-and hypo-methylated DMRs with that of non-DMRs 1) at different positions with respect to CpG islands, 2) in different skeletal muscle chromatin states from the Roadmap Epigenomics Project 19 , and 3) in CTCF and EZH2 transcription factors binding sites in HSMMtube from the ENCODE project. CTCF is a multifunctional protein involved in gene regulation and chromatin organisation  45  , while EZH2 is the functional enzymatic component of the Polycomb Repressive Complex 2 (PRC2)  46. A p-value < 0. 005 was deemed significant. We performed GO and KEGG enrichment on the age-related DMRs using all tested CpGs as the background (i. e. the 723,655 CpGs included in the meta-analysis), thanks to the goregion function from the missMethyl package  47.
Update of the muscle epigenetic clock (MEAT 2. 0) Since the development of the original muscle clock that used 682 samples from 12 datasets to predict age from DNA methylation data  9  , we gathered an additional 371 samples from 5 datasets (+159 from Gene SMART, +65 from ABOS, +42 from LITER, +57 from GSE135063 and +48 from EPIK). We therefore updated the clock with these new samples, using the same algorithm and methodology  9. Briefly, we first preprocessed each dataset separately (i. e. probe/sample filtering, adjustment of type I and type II probes and correction for batch effects); then, we reduced each dataset to all the CpGs that were common between them (18,666 CpGs). To obtain DNA methylation profiles that were comparable between datasets, we calibrated each dataset to GSE50498 using an adapted version of the BMIQ algorithm  9.
Acknowledgements We are grateful for the support of the  Australian National Health & Medical Research Council (NHMRC)  via  Sarah Voisin 's  Early Career Research Fellowship  ( APP11577321 ), and  Nir Eynon's Career Development Fellowship  ( APP1140644 ). We also thank the  Australian Research Council (ARC)  for supporting this study ( DP190103081  and  DP200101830 ), The Gene SMART and LITER studies were both supported by the  Collaborative Research Network for Advancing Exercise and Sports Science (201202)  from the  Department of Education and Training, Australia. Mr Nicholas Harvey  and  Ms Jamie-Lee Thompson  was supported by a PhD stipend also provided by  Bond University CRN-AESS. This research was also supported by infrastructure.
Climate data Climate has a profound effect on the decay rate of wood. As seen in the decay model parameters, two climatic factors are key to determining a time-based decay model, average temperature, and annual rainfall. The climate model data used here comes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble  (Maurer et al. , 2007). CMIP5 provides precipitation and temperature data statistically downscaled to finer resolutions using the monthly biascorrection and spatial disaggregation (BCSD) method. Two Representative Concentration Pathways (RCP) are chosen to study the potential impact of climate change on decay and hurricane hazardRCP4. 5RCP8. 5. RCP4. 5 is chosen to represent a medium emission scenario, while RCP8. 5 represents a high emission scenario.
This is most likely due to the projections that precipitation is not expected to drastically increase in the RCP4. 5 and RCP8. 5 conditions in Miami with the RCP8. 5 scenario actually projecting that precipitation will decrease (Fig. A1 ). In all cases, the service life could not be accurately estimated past the 90-year mark as the CMIP5 data only extends through the end of the 21st century. Hence, in cases where the service life extends beyond the end of the 21st century after FRP repair, the service life is not estimated. Consequently, only two points of the New York City results are plotted. The service life of the poles for repair at 10 and 20 years extends beyond the 90-year mark.