dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, we investigated whether blood DNA methylation at the DUSP22 promoters in is associated with BMI, LDL, HDL, and U N C O R R E C T E D M A N U S C Triglyceride in an EPIC methylation array dataset of 568 healthy controls. Here, we observed that several CPG sites in the DUSP22 promoter methylation displayed significant association with HDL levels, however this association was not significant following multiple testing correction (Supplementary Table  9 , Supplementary Figure  7 ). U N C O R R E C T E D M A N U S C U N C O R R E C T E D M A N U S C R I P T Down syndrome, which is proposed by some researchers also to be a segmental progeroid syndrome  (30, 31, 39). We observed no overlap when comparing CGL2 differentially methylated CpG sites to those identified in progeroid laminopathy patients, and only one common CpG site when compared to typical Werner syndrome.
Genome-wide DNA methylation measurement using EPIC arrays The EZ DNA Methylation Kit (Zymo Research, Irvine, CA, USA) was used for bisulfite conversion of ~ 500 ng of genomic DNA. Converted DNA was whole-genome amplified, enzymatically fragmented, and hybridized to Infinium Methylation EPIC Bead Chips following the manufacturer's protocol. To reduce positional effects, cases and controls were randomly hybridized on the Infinium BeadChip arrays. Arrays were scanned via the Illumina iScan, and raw intensity data (IDAT) files were exported for analysis in R using the RnBeads package  (63). Quality control and preprocessing steps included removing probes overlapping SNPs, filtering out probes with the highest fraction of cross-hybridization, and removing probes with the highest fraction of unreliable measurements via greedycut.
Following preprocessing and quality control, 340113 probes were removed and all samples were kept for further analysis. Furthermore, an additional stringent filtering step was performed via the "filtering. blacklist" option to exclude Infinium MethylationEPIC probes with potentially polymorphic targets affected by SNPs, indels, or structural variation in the following populations: European, African, admixed American, East Asian, and South Asian. Next, Dasen was used for data normalization and this step was followed by the removal of 17633 probes on sex chromosomes. In total, 518474 probes were kept for the differential DNA methylation analysis. White blood cell composition was estimated via the U N C O R R E C T E D M A N U S C R I P T 18 Houseman et al. method  (64). The comparison of deconvoluted cell proportions between cases and controls is presented in Supplementary Figure  8.
Differential DNA methylation and expression analysis A limma-based analysis was performed to adjust for cell type heterogeneity as well as other covariates, including age, gender, and surrogate variables obtained by SVA. Differential methylation was performed at the following genomic regions: promoters, CpG islands, 5 kb tiling windows, and genes. For each region, the mean difference in means for all CpG sites, the mean of quotients in mean methylation, and the combined p-value for all CpG site p-values in the region were calculated. The false discovery rate (FDR) method was used to correct p-values for multiple testing. DUSP22 gene expression analysis was performed in two publicly available GEO Datasets: 1) GSE159337, where RNA-seq was performed in 7 CGL2 patients compared to gender/agedmatched controls, and 2) GSE145070, where RNA-seq transcriptome profiling was performed in brown adipose tissue from 10-week old Bscl2 knockout (BKO) mice with mature brown adipose tissue-specific deletion of BSCL2 and 10-week old control mice  (65, 66).
R I P T 10 upregulation particularly during early differentiation (p = 0. 08). Furthermore, we measured Dusp22 mRNA expression after Bscl2 knockdown in the murine 3T3-L1 cell line, which is a commonly used in vitro model for white adipocytes, A significant downregulation of Dusp22 expression was observed in Bscl2 knockdown cells compared to control cells in both uninduced (p = 0. 013) and mature adipocytes (p = 0. 008) (Figure3 b and c). Thus, these data suggest that the two genes are linked and that Bscl2 affects the expression of Dusp22. Effect of DNA methylation changes at DUSP22 promoterHere, we checked the association of DNA methylation levels at CpG sites located in the DUSP22 promoters in adipose tissue for several metabolic phenotypes (BMI, visceral fat, insulin resistance, fasting blood glucose, incident T2D) in a 450k methylation dataset from 538 female twins (43).
Acknowledgments This work was supported with funding by  Qatar Foundation  to the  College of Health and Life Sciences, Hamad Bin Khalifa University  and in part by the  NIH  grant,  R01CA210916  ( GMM/JO ). The Qatar BioBank data analysis was made possible by a PPM2 grant # PPM2-0226-170020  from the  Qatar National Research Fund and Qatar Genome Program (QGP) (members of Qatar Foundation ). The findings herein reflect the work and are solely the responsibility of the authors. The authors would also like to acknowledge the  Qatar BioBank , the  Qatar Genome Project , and the  Genomics Core at Weill Cornell Medicine-Qatar. We would like to thank the  Caenorhabditis Genetics Center (CGC) , funded by the  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ), for providing the worm strains.
Sources of such an approach include the Climate Change Initiative (CCI) Biomass AGB maps for the epochs 2010, 2017 and 2018 with a 100 m pixel size produced using radar remote sensing  (Santoro and Cartus, 2021). Another example is the recent L-band Vegetation Optical Depth (LVOD) global AGB dataset with a pixel size of 25 km  (Wigneron et al. , 2021; Yang et al. , 2022). In contrast, with the ''gain-loss'' approach, land use-specific carbon emission and removal factors are used to derive an estimate of ΔAGB starting from an initial estimate of AGB for a given epoch  (McRoberts et al. , 2020). One example is  Harris et al. (2021) , wherein a baseline AGB map, annual forest loss and gain maps, and activity data specific to deforestation, fire, agriculture and forestry were used to assess net carbon fluxes in the past two decades at 30 m spatial resolution.
Xu et al. (2021)  used a similar approach that paired annual AGB maps with activity data for carbon fluxes analysis from 2000 to 2019 at the spatial scale of 10 km. Independent map assessments should be a standard operating procedure for EO-based products  (Nightingale et al. , 2010; Duncanson et al. , 2021). Map assessment in this context involves the comparison of ΔAGB from maps and a reference dataset, i. e. , using in-situ data from repeated AGB measurements. A common source of reference data used is represented by measurements collected at sample plots by a National Forest Inventory (NFI) data. Data from NFIs in extratropical countries are commonly being used for ΔAGB assessments and subsequent analysis mainly because these NFIs are well-established. Most NFIs, however, are not open-access data, which constrains their use for assessments beyond country scales.
Yet, countries have used NFI data to report AGB statistics (means and totals) every five years as part of United Nation's Forest Resource Assessment (FRA). The FRA data was used in global-scale studies related to ΔAGB analysis, e. g. , as a basis for comparison with map-based estimates aggregated to countries in multiple periods  (Xu et al. , 2021; Araza et al. , 2022b). At local scales, ΔAGB from airborne LiDAR-based maps provide precise estimates. While forest height derived from LiDAR is highly correlated with AGB, the local estimates they provide are considered more reliable than the ΔAGB from plot data as -unlike plots -LiDAR covers whole landscapes  (Naesset et al. , 2015). These features have allowed LiDAR as a reliable AGB reference data  (Duncanson et al. , 2021). Given that most LiDAR campaigns are being repeated for monitoring forest ecosystems, a long list of local LiDAR-based ΔAGB assessments is available.
, 2022) , and ecosystem accounting  (Hein et al. , 2020). Because of this, it makes sense to iterate map assessments over a range of aggregation levels to determine at what resolution the plot and pixel averages best match  (Moreno et al. , 2016; Santoro et al. , 2022a). Such an exercise would also inform the map users how the maps compare with certain reference data. Global ΔAGB assessment is challenged by several factors. First, there is a lack of readily available and globally suitable reference data because global mapping of multi-temporal AGB has only recently started. Second, the uncertainty about ΔAGB assessed from a global product is large. For instance, changes driven by slow regrowth and degradation are challenging to be detected from satellites  (Ryan et al. , 2011; Santoro and Cartus, 2021).
(2021)  method; the  Xu et al. (2021)  maps and the LVOD maps herein called as CCI, Flux, JPL, LVOD, respectively. Given the lack of a consistent global ΔAGB reference dataset, map-map comparisons are used to assess consistency among the maps in reporting AGB gains and losses. We (1) compile several ΔAGB reference data sources and assess their suitability for map assessment; (2) compare the ΔAGB between reference data and maps at different spatial aggregation levels; and (3) spatially assess the mutual consistency of ΔAGB maps.
Overview of the methods The assessment of four global ΔAGB Mg ha -1 is outlined in Fig. 1. Preliminary steps that include forest masking, modification of the original carbon flux model and other pre-processing steps all made sure that the maps have comparable ΔAGB. To assess the ΔAGB maps, we used three types of AGB dataset with repeated measurements as reference data: airborne LiDAR-based AGB maps (LiDAR), NFI plots and FRA. To make the map-reference data comparisons meaningful, we further selected subsets of the reference data based on data quality criteria and we applied the same forest mask (used for the maps) based on a forest definition set as >30% tree cover at 30 m pixel size  (Hansen et al. , 2013). Then, the ΔAGB of the reference data and maps were aggregated and compared at five aggregation levels between 100 m and 25 km.
ΔAGB maps CCI Biomass maps The European Space Agency Climate Change Initiative (CCI) Biomass dataset consist of annual global AGB maps with a pixel size of 100 m. The most recent version includes maps for the epochs 2010, 2017 and 2018, and we selected the 2010 and 2018 epochs for our analysis. Each map was derived from synthetic aperture radar (SAR) data where the signal is in the form of backscatter intensity. The SAR input data are then integrated into a physically-based model, accounting for the individual contributions to the backscattered signal from the forest canopy and the ground below the canopy  (Santoro and Cartus, 2021; Santoro et al. , 2021). The CCI modelling approach is reinforced by allometric equations that relate the canopy density to forest height and then height to AGB  (Santoro et al.
JPL time series AGB The JPL product  (Xu et al. , 2021)  retrieves AGB from remote sensing data following a two-step approach. First, in every 10 km grid cell covering field plots, AGB mapping is performed based on the relationship between field plot AGB and vegetation height derived from spaceborne data and airborne LiDAR, and backscatter intensity from spaceborne radar data. The resulting grid cells are then used to train and test machine learning models using covariates from MODIS optical data, topographic and climatic variables. At the pixel level,  Xu et al. (2021)  estimated and summed the uncertainties related to model residual, model parameter estimation error and plot measurement error. The km time series of above-ground carbon maps were first divided by 0. 49 to obtain AGB. We used the average of 2009-2011 and 2017-2019 to derive ΔAGB.
SMOS-LVOD time series AGB Lastly, we used the 25 km LVOD time series AGB products  (Wigneron et al. , 2021; Yang et al. , 2022)  derived from observations by the Soil Moisture and Ocean Salinity (SMOS) satellite. Spatially explicit estimates of LVOD were derived every year between 2010 and 2019. Filtering individual images was done to mitigate the signal noises but resulted in partial coverage of east Asia. Temporal aggregates of the LVOD product are results of a temporal decorrelation method to reduce the seasonal effects related to water content. The uncertainty of the LVOD dataset primarily comes from the AGB reference biomass map used. The LVOD dataset was originally projected to an Equal-Area Scalable Earth (EASE) system. We re-projected the map to WGS using bilinear interpolation. Note that owing to data availability, was used as the starting point for ΔAGB assessment by LVOD.
NFIs The first set of reference data consists of re-measured National Forest Inventories (NFI) plot data from Belgium, the Netherlands, Philippines and Sweden where the first and second measurements were surveyed at least five years apart. The re-measurements allowed estimation of ΔAGB. The NFIs have plot-level AGB estimated by the data sources, but without uncertainty estimates. We then estimated plot uncertainty from measurement and allometric model error as a function of AGB, eco-zone and plot size. We used the prediction model described in  Araza et al. (2022b)  developed from an extensive plot database spread over all major eco-zones. The model followed an error propagation method of parameters from tree measurements such as diameter, height and wood density and the parameters of the AGB allometric model such as the model coefficients and residual standard errors.
The AGB of the reference data was also adjusted to reduce the effects of temporal mismatches between the reference and maps. The AGB of datasets surveyed ± >2 years apart from the map epoch were subjected to this step using growth data from the 2019 Intergovernmental Panel on Climate Change (IPCC)  (Buendia et al. 2019). Biomass is either added or reduced depending on the number of years between the inventory date and map epoch. The associated annual uncertainty owing to these adjustments was also estimated based on the reported IPCC growth data uncertainties.
LiDAR The second reference data comprises local AGB maps derived in forests with re-measured plot inventories and two airborne LiDAR campaigns between 2010 and 2019. Maps of ΔAGB were derived by differencing AGB maps from the two survey periods. Data sources include maps from Brazil  (Longo et al. , 2016)  and the USA  (Johnson et al. , 2010)  where AGB mapping involved calibration of LiDAR height and plot AGB using power-law models. We also used LiDAR-based maps from research projects in Bulgaria, Czech Republic, Costa Rica, Poland and Spain derived using regression models that relate height and AGB. The LiDAR maps resampled to 100 m were used. Some of these maps have associated SD layers all estimated using Monte Carlo error propagation involving errors from the calibration dataset and the associated height-AGB model parameters.
FRA The third set of reference data are country-level estimates of ΔAGB from FRA reports, derived by differencing the reported AGB 2018 and 2010, where 2018 was computed as the average of 2015 and 2020 AGB. We selected the FRA data based on the capability of countries to conduct NFIs and derive FRA variables using remote sensing. We followed the FRA capacity categories based on a scale of 1 to 5 (1=very poor; 5=very good). Selected countries belonging to east Asia were excluded because of incomplete map coverage as explained in Section 2. 2. The AGB from the FRA data does not have associated uncertainty estimates.
Reference data quality assessment We further selected subsets of the reference data: 1. NFI plots were filtered using tree cover loss datasets  (Hansen et al. , 2013)   We reported the number of reference data retained after quality filtering compared to the original data, and mapped the coverage of the selected reference data over eco-zones based on Whittaker's biome  (Whittaker, 1975). The coverage per eco-zone and country determined the suitability of reference data for global map assessments. For each reference dataset, histograms of the AGB distribution in two epochs are shown in Figure  S1. We also derived the ΔAGB density for NFI and LiDAR to assess the ΔAGB distribution at every aggregation level (Section 2. 4). We aim to assess how the ΔAGB (losses, gains and no changes) are depicted from fine to coarse levels depending on the reference data.
Map-reference data ΔAGB comparisons The past decade net ΔAGB from the map products and the reference data were compared at different spatial resolutions i. e. , grid cells. The details of the comparisons are shown in Table  1  showing the grid cell selection. Grid cells were used if they met the minimum number of reference data inside grid cells  (Xu et al. , 2021; Araza et al. , 2022b) , see 2nd column of Table  1. This way, grid cells > 1 km with very few reference data were excluded from the analysis. The AGB averages per epoch from NFI plots and LiDAR pixels at grid cells were estimated as weighted means where reference data with high uncertainty received smaller weights in the averaging. The weights  () were inversely proportional to the variance  p 2 of an NFI plot or a LIDAR pixel  as shown in Eq.
(  1 )  (Araza et al. , 2022b). 𝑊 (𝑥) = 1∕𝑆𝐷 p 2 (𝑥) (1) The AGB averages of both reference data and maps were assured to correspond to a forest mask defined as 30 m pixels > 30% tree cover  (Hansen et al. , 2013). This step was separately done per epoch and for each aggregation level. Subjected to this harmonization are the ΔAGB maps from CCI and LVOD (see Table  S2 ) and the LiDAR reference data. Particularly, the maps were resampled to 30 m to match the forest mask pixel size. All non-forest pixels were masked out then the remaining pixels were averaged for each aggregation level. The assessment at 25 km level included FRA as reference data. We simply used the FRA country average AGB for the needed epochs, which are subject to the country forest definition. The ΔAGB comparisons of grid cell averages between the reference data and maps were assessed using statistical measures including mean difference (MD), Root Mean Square Difference (RMSD), coefficient of determination (R 2 ) and Nash-Sutcliffe Efficiency (NSE).
Reference data for ΔAGB global comparisons The spatial distribution of the reference datasets are shown in Fig. 2. The number of discarded data was largest for FRA (90%) since most countries do not have repeated NFIs (see Table  S3 ). More than half of the NFI plots (56%) were excluded either because they were outdated (see Section 2. 3) or the sites were deforested after the 2nd measurement and before 2018. Almost no LiDAR pixels (<1%) were filtered out as reference since the repeated LiDAR surveys all took place in the past decade and almost all pixels had valid data in both epochs. The reference data are mostly found in the temperate and tropics but underrepresents them as well as the other eco-zones. The selected FRA data, though small in size, come from all eco-zone. Despite its smaller size, the NFI dataset has broader eco-zone coverage than the LiDAR dataset.
That is because NFIs are surveyed over entire countries while LiDAR campaigns are typically confined to forests. We had no access to NFIs and LiDAR data from Africa and Australia. The ΔAGB distributions from LiDAR and NFI data at different aggregation levels are shown in Fig. 3. The highest density of data is observed for small ΔAGB but there are also several reference data implying large AGB gains and losses. Owing to spatial averaging, the density of data increases towards small ΔAGB from fine to coarse aggregation levels, especially for NFI data. The NFI captured larger AGB gains (until 1 km), while LiDAR captured more AGB losses throughout all aggregation levels. These results suggest LIDAR data showed consistent ΔAGB distributions across the aggregation levels. These observations are influenced by the forest types where the reference data are located, i.
The forest type where  the reference data are situated also drives the distribution of ΔAGB observed in Fig. 3. Future reference data should also represent different forest land uses. As the availability of reference datasets increases, the criteria for selecting them for global map assessments can become stricter. While the size of plots may need to be large enough (i. e. , > 1 ha) to fully cover the pixels of map products  (Réjou-Méchain et al. , 2019) , this will limit the reference data to permanent research plots which are always preferentially sampled. Here the effects of plot size are considered when we weighted our reference data according to plot size-driven uncertainty, and when we excluded grid cells with plots fewer than the minimum number of plots criterion. Stricter grid cell selection can be implied at coarse aggregation levels.
Also using all European NFIs,  Moreno et al. (2016)  reported that increasing the aggregation level from 1 to 5-25 km is optimal because of increased plot representation over grid cells. The country-level ΔAGB comparisons using FRA as reference depicted varying results among maps (Fig. 6 ). This variation can be attributed to the different forest definitions and AGB estimation methods of countries. Metadata of the FRA dataset is needed as basis to harmonize the map-based estimates and make them more comparable with the FRA. Nevertheless, country-level variations also reflect that there is disagreement of ΔAGB among maps.
Spatial analysis and map-map differences The purpose of inter-comparing the ΔAGB maps was to assess where maps indicate the same changes. Agreement among maps was gauged by the sign and magnitude of reported ΔAGB, and the ΔAGB crosscorrelation results ranged between 0. 11 and 0. 29 r (Figure  S5 ). The map products were produced by different remote sensing data types and AGB retrieval methodologies (Section 2. 2). The CCI ΔAGB is obtained by subtracting the 2018 and 2010 maps i. e. , stock change approach. AGB maps are indirect estimates from remote sensing signals and changes in signals are not always equivalent to ΔAGB. Past and current spaceborne radar datasets used for CCI lack sufficient sensitivity to measure gradual changes associated with regrowth and degradation especially in dense forests  (Santoro and Cartus, 2021).
Application of the work and outlook This work provides a confluence of evidence from ΔAGB mapreference data and map-map comparisons, which is a source of information for both map producers and users. Map producers can revisit their AGB retrieval process in areas where most maps disagree. We anticipate improvements of the current ΔAGB maps. Future releases of the CCI maps will make use of repeated observations instead of a single observation in the form of an image mosaic. In addition, the AGB retrieval will be supported by a much denser dataset of spaceborne LiDAR observations. The Flux model will implement improved activity data and removal factors. In terms of map users, global carbon and climate modellers mainly use global ΔAGB maps  (Herold et al. , 2019; Quegan et al. , 2019). A survey among these users revealed that most modellers prefer > 1 km maps from multiple periods  (Peylin et al.
One approach to deal with map disagreements is to produce a new map from an ensemble of existing global AGB maps using model-based approaches  (Zhang and Liang, 2020). However, doing so requires a suitable global reference dataset and does not guarantee producing accurate ΔAGB estimates. Worth exploring though is the direct mapping of ΔAGB using the global ΔAGB products and other remote sensing auxiliary data in countries with reference data. The resulting country ΔAGB map would benefit national applications that require ΔAGB map inputs e. g. , for carbon MRV  (Csillik et al. , 2022) , and ecosystem accounting  (Hein et al. , 2020). Such mapping exercise should account for the uncertainty of sampling variability. This caution also applies for sub-national to national carbon accounting using rigorous estimation methods such as an NFI-based model-assisted ΔAGB estimation.