dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011). Therefore, in the future, the relationship between P-band microwave retrievals and biomass should be addressed, in addition to the calibration of historical AGB datasets (e. g. the long-term AGB dataset in this study) against the P-band SAR-based AGB benchmark map to extend the time series. In addition, an intercalibration between the AMSR-E-based VOD and the AMSR2-based VOD will further reduce the potential bias within the long-term integrated VOD datasets  (Wang et al. , 2021a, b). On the other hand, more in situ AGB and BGB measurements in larger plots are needed to further improve the estimation of belowground biomass allocation. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-897-2023-supplement. Author contributions.
Acknowledgements. This work has been supported by the  National Science Foundation of China  (grant no. 41991233 ) and the  CAS Project for Young Scientists in Basic Research  (grant no. YSBR-037 ). We are grateful to all the data contributors, especially  Xuli Tang , for sharing in situ measurements of forest aboveground biomass carbon stock across China, and  Huabing Huang , for sharing his forest aboveground biomass map. Financial support. This work has been supported by the  National Science Foundation of China  (grant no. 41991233 ) and the  CAS Project for Young Scientists in Basic Research  (grant no. YSBR-037 ). Review statement. This paper was edited by  Jia Yang  and reviewed by  Jean-Pierre Wigneron  and three anonymous referees.
and described below. 2. 1 A benchmark map of forest aboveground biomass carbon (AGBC) in China By combining multiple satellite observations of SAR backscatter, including the L-band ALOS PALSAR (Ad- vanced Land Observing Satellite Phased Array L-band Syn- thetic Aperture Radar) and C-band Envisat ASAR (Ad- vanced Synthetic Aperture Radar) around the year 2010, the first global high-resolution (100 m) forest AGB dataset, GlobBiomass 2010, was published through the European Space Agency (ESA)'s Data User Element (DUE) project (Santoro et al. , 2021), whose relative root mean square error (RMSE) was below 30 % (Mialon et al. , 2020). Apart from GlobBiomass 2010, another high-resolution (30 m) forest AGB for China was produced by relating the (lidar)-derived ICESat/GLAS (Ice, Cloud and land Elevation Satellite/Geo- science Laser Altimeter System) footprint AGB to various.
3e  and Supplementary Fig. 4c ). Leakage of myofibers in mdx mice causes a strong increase of serum creatine kinase (CK) levels, which is proportional to skeletal muscle damage. As expected, we observed a strong decrease of serum CK levels in Sirt6 mKO /mdx compared to mdx mice, further proving that suppression of SIRT6 activity in skeletal muscles increases the stability of the sarcolemma in mdx mutants (Fig. 3f ). Finally, we monitored the motility of control, mdx and Sirt6 mKO /mdx mice in Phenomaster metabolic cages. Lack of Sirt6 increased the physical activity of mdx mice, while no changes in food /liquid intake and respiratory exchange ratio (RER) were observed (Fig. 3g  and Supplementary Fig. 4d-f ). Taken together, the data clearly indicate that suppression of SIRT6 attenuates several pathological hallmarks characteristic for mdx mice.
Notably, around 46% of newly emerging H3K56ac peaks in Sirt6 mKO MuSCs localize within distal intergenic regions, which corresponds to 10-100 kb up-or downstream of TSS, regions that often contain enhancers (Fig. 5c, d ). Only 13% of upregulated H3K56ac peaks were found within promoters (Fig. 5c ). Furthermore, we compared H3K56ac peaks within promoters and enhancers of freshly isolated MuSCs to published H3K4me3 and H3K27ac ChIP-seq datasets, assuming that overlapping peaks of H3K4me3 and H3K27ac (H3K4me3+/H3K27ac+) represent promoters. Since no ChIP-seq data for enhancer markers such H3K4me1 or p300 are available for freshly isolated MuSCs, we defined H3K4me3 negative but H3K27ac positive regions (H3K4me3-/H3K27ac+) as enhancers. In line with the results shown above, upregulated H3K56ac peaks in Sirt6 mKO were mostly located at enhancers (H3K4me3-/H3K27ac+) but not at promoters (H3K4me3+/H3K27ac+) (Fig.
In total, 266 overlapping genes were categorized by Wikipathways analysis. Fisher's exact test, two-sided. e Heat map of expression of genes involved in cellular stress responses in control, mdx and Sirt6 mKO /mdx muscle (n = 3 for each group). f RT-qPCR analysis of expression of indicated genes involved in cellular stress responses in control, mdx and Sirt6 mKO /mdx muscles. m36b4 was used as reference gene (one-way ANOVA with Bonferroni multiple comparisons test: Dab2: *p = 0. 0235, **p = 0. 0017; ****p < 0. 0001; Hspa2: ns not significant, *p = 0. 0164, **p = 0. 0012; Ptpn22: **p = 0. 009, *p = 0. 0316, *p = 0. 0430; n = 4 for each group). Twelve to 16 weeks old male mice were used. Data are presented as mean ± SEM. Source data are provided in the Source Data file. experiments for SIRT6 when primary myotubes expressing lentivirally-transduced, HA-tagged SIRT6 were employed (Fig.
Visualization of binding profiles was done by using the R/BioConductor package Gviz or the Integrative Genome Viewer  64. Peak calling was done using MACS2  65. The resulting set was filtered against blacklisted chromatin regions, as detected by ENCODE. Read counts across peaks were determined using the featureCounts function of the Subread package. Differential binding analysis was performed by using DESeq2 after merging overlapping peaks into reference peak sets, using the reduce function of the BioConductor GenomicRanges package  66. H3K56ac data were compared to available data for histone modifications and other chromatin modifiers in mES cells from ENCODE  67  by downloading the raw sequencing reads of the first replicate for H3K27ac, H3k4me1, H3K4me3, H3K9ac, H3K36me3 and p300 through the UCSC browser data download portal (https://hgdownload-test.
gi. ucsc. edu/goldenPath/mm9/encodeDCC/wgEncodeLicrTfbs). Reads were processed as described above. Read counts across H3K56ac peaks and H3K56ac peaks significantly increased in Sirt6 mKO were determined using featureCounts and normalized by scale normalization within R. Wilcoxon rank sum tests were performed within R using the wilcox. test function with the alternatives "greater" or "less". ChIP-seq and data analysis in MuSCs. FACS-sorted MuSCs were cross-linked with 1% formaldehyde/PBS for 10 min, before quenching with 0. 125 M glycine. Cross-linked cells were washed with ice-cold PBS for three times and spun down. Chromatin preparation and protein-DNA complex immunoprecipitation was carried out according to established protocols  68. ChIPed DNA was purified using mini-elute PCR kits (Qiagen).
DNA concentration was measured using Qubit® Fluorometric Quantitation (Life Science). ChIP-seq libraries were generated using the Ion ChIP-Seq Library Preparation Kit (Thermo Fisher) following standard protocols. Libraries were analyzed with the Bioanalyzer 2100 and sequenced using the IonTorrent Proton platform with Ion PI Sequencing 200 Kit v2 (Thermo Fisher). Resulting raw reads were assessed for quality, adapter content and duplication rates with FastQC. Data processing of sequencing reads in FASTQ format were performed as described above for mES cells. All downstream analyses were carried out with R/BioConductor (http://www. bioconductor. org) using the packages GenomicRanges and GenomicFeatures. In order to compare H3K56ac to other histone modifications public data sets for H3K4me3 and H3K27ac in MuSCs  69  were downloaded from GEO (time-point T3 from GSE103163: GSM2756408, GSM2756400 and GSM2756402).
5% Sodium dodecyl sulfate, and freshly added 0. 1 mg/ml proteinase K). After tissue digestion, phenol/chloroform/isoamyl extraction of DNA was performed. In total, 1 µg purified DNA was subjected to enzymatic digestion with methylation-sensitive enzymes HpaII and MspI (NEB) followed by qPCR with primers flanking the restriction site (Supplementary Data 1). DNA input was measured by amplification of an unrelated fragment that contains no restriction sites. RNA-seq and data analysis in MuSCs and muscle tissue. Total RNA was isolated from muscle tissue or FACS-sorted MuSCs by the miRNeasy micro kit (Qiagen) together with on-column DNase digestion (DNase-Free DNase Set, Qiagen) and followed by QC on Bioanalyzer 2100 (Agilent). For MuSCs, Total RNA libraries were obtained by rRNA depletion with RiboMinus Eukaryote System v2 (Thermo Fisher) followed by Ion Total RNA-Seq Kit v2 (Thermo Fisher) according to the manufacturer's protocol.
4. 5-p1 tool from the Subread package  73. Only reads mapping at least partially inside exons were admitted and aggregated per gene. Reads overlapping multiple genes or aligning to multiple regions were excluded. DEGs were identified using DESeq2 version 1. 62 l 74. For MuSCs, only genes with a minimum fold change of ±2, a maximum Benjamini-Hochberg corrected p value of 0. 05, and a minimum combined mean of 5 reads were assumed to be significantly differentially expressed. For muscle tissues, only genes with a maximum uncorrected p value of 0. 05, and a minimum combined mean of 5 reads were assumed to be significantly differentially expressed. The Ensemble annotation was enriched with UniProt data (release 06. 06. 2014) based on Ensembl gene identifiers (Activities at the Universal Protein Resource (UniProt)).
Correlations of replicate gene counts were assessed with the Spearman ranked correlation algorithm included in R 3. 11 (R: A language and environment for statistical computing). Volcano plots were computed using the script run_DE_analysis. pl included in Trinity version 20140717, which employs R functions for plotting  75. Further global clustering of samples was performed using the regularized-logarithm transformation method of DESeq2, based on complete euclidean distances and hierarchical clustering. Gene ontology analysis was performed using DAVID Bioinformatics Resources (http://david. abcc. ncifcrf. gov) and Enrichr (https://maayanlab. cloud/Enrichr/)  76, 77. Heat maps were generated using function "heatmap. 2" in R package "gplots", using the version R-3. 6. 1. Statistical analysis.
For all quantitative analyses, a minimum of three biological replicates were analyzed with the exception of only two biological replicates for ChIP-seq, RNA-seq and ATAC-seq data. Statistical tests were used based on the assumption that sample data are derived from a population following a probability distribution based on a fixed set of parameters. T-tests were used to determine statistical significance of differences between two groups. One-way ANOVA were used to perform multiple comparisons. The following values were considered to be statistically significant: *p < 0. 05, **p < 0. 01, ***p < 0. 001, ****p < 0. 0001. Calculations were done using the GraphPad Prism 8 software and R. Error bars always represent standard error of the mean (mean ± SEM). No statistical method was used to predetermine sample size.
Acknowledgements We thank  Sonja Krüger ,  Kerstin Richter  and  Barbara Zimmermann  for technical help as wells as  Astrid Wietelmann  and  Ursula Hofmann  for MRI analysis. We are grateful to  Shuichi Watanabe  and  Krishna-Moorthy Sreenivasan  for the help with immunofluorescence staining and inspiring discussions, respectively. We are grateful to  Vincent Mouly ,  Anne Bigot , and the  MyoLine platform  for immortalization of human cells of the  Institut de Myologie (Paris, France)  for supplying the human cell lines used in this study. This work was supported by the  German Research Foundation (DFG) Transregional Collaborative Research Centre 81  ( TP A02 ) (T. B. and X. Y. ), the collaborative research center SFB 1213 ( TP B02 ) (T. B. and X. Y. ), the  Transregional Collaborative Research Centre 267  ( TP A05 ) (T.
Data availability Raw and processed ChIP-seq data in mESCs and MuSCs are available in the NCBI Gene Expression Omnibus (GEO), under accession number GSE168331 and GSE168330, respectively. ATAC-seq data were deposited under accession number GSE168329. RNAseq data of MuSCs and muscle tissue were deposited under accession number GSE199487, GSE168983 and GSE168984, respectively. Published data were downloaded from GEO time-point T3 from GSE103163: GSM2756408, GSM2756400 and GSM2756402. All other data generated during the current study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Objectives: Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers. Methods: Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (6 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). The model's predictions were explained using Shapley values to extract a biomarker of inadequate response. Results: We included 493 therapeutic sequences from ESPOIR, 239 from EAC and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count and corticosteroid co-treatment as predictors.
This study builds a machine learning model to predict the therapeutic response to MTX after 9 months (6 6 months) in RA patients included in two independent cohorts -respectively, the ESPOIR and Leiden EAC -based on routinely available clinical and biological data. The objectives are 3-fold. First, we aimed to assess capabilities of machine learning to predict response to treatment from routine clinical data. Second, we evaluated such models' usefulness and potential impact in clinical practice. Third, we extracted a simple biomarker-based rule from the algorithm to easily identify inadequate responders. The performances of both the model and the simple rule were assessed for validation on data from a third external and independent clinical trial, namely the tREACH.
Patients Developing prognostic criteria and tools requires developing the model on a training dataset and validating the results on a validation set independent from the training base. The more different the validation set is, the better we can assess the model's generalization. For the training dataset, we included RA patients from two longitudinal and prospective early arthritis cohorts: ESPOIR, a French multicentric cohort  [15] , and the Leiden Early Arthritis Clinic cohort (Leiden EAC)  [16]. ESPOIR and Leiden EAC are observational studies where RA patients are treated according to each centre's clinical practice. As external validation cohort, we used the tREACH trial  [17] , a randomized clinical trial comparing a triple DMARD therapy with MTX monotherapy in combination with low-dose glucocorticoid in newly diagnosed, DMARD naive, RA patients.
Only RA patients with MTX monotherapy plus potential glucocorticoid bridging therapy were selected. The different nature of training and validation cohorts helped assess the models' ability to generalize on unseen and heterogeneous data. tREACH being a clinical trial, therapeutic response is assessed more precisely at constant time points; this helped assess algorithm performances more accurately, despite tREACH being the smallest data set. Moreover, as described below, we used cross-validation on the training set to prevent overfitting, which limits the need for a large validation set. Patients were included if they fulfilled the 2010 ACR/EULAR criteria  [18, 19]  and received at least one dose of MTX (oral or SC in ESPOIR and LEAC, oral only in tREACH) and MTX monotherapy at any time of their disease; thus, combination therapies with other csDMARDs were excluded.
We included a binary variable as models' input to account for corticosteroid treatment along with MTX. Patients stopping MTX within 6 months after initiation due to pregnancy, surgery, poor compliance to the protocol, or unknown reasons were excluded from the study population. In observational cohorts, if a patient received several distinct MTX therapeutic sequences, all of them were included in the dataset, provided they fulfilled the criteria listed above. On the other hand, in the validation dataset, each patient had a unique MTX therapeutic sequence.
Without supervision, machine learning algorithms can identify known response factors and potentially unveil new biological and clinical markers. To perform this process, we used the recursive feature elimination  [23] , described in Supplementary Data S1, available at Rheumatology online. Four machine learning models were assessed: a logistic regression model, a random forest model  [24] , and two gradient boosted trees models. The Python library Scikit-Learn  [25]  was used to implement the regression and random forest models and the KNN and MICE imputation methods. LightGBM  [26]  and CatBoost  [27]  libraries were compared for the gradient boosted trees. Each model uses the variables available at the last check-up before the beginning of the MTX therapeutic sequence to predict the outcome.
Evaluation The process (recursive feature elimination, data augmentation and imputation, and model training) was evaluated using a 5fold cross-validation on the training dataset. The crossvalidation process is detailed in Supplementary Data S1, available at Rheumatology online. Cross-validation is the first way to ensure our model doesn't overfit and allows for unbiased model and feature selection. Only the best trained model was evaluated on the external validation dataset to ensure the replication of the results. We computed formal external validation sample size using methods detailed in  [28]  based on cross-validated results. Calculations are detailed in Supplementary Data S1, available at Rheumatology online. We use the AUROC to compare the models and statistical comparisons are described in Supplementary Data S1, available at Rheumatology online.
Ethics approval The protocol of the ESPOIR Cohort study was approved in July 2002 by the ethical committee of Montpellier. The data is registered as ClinicalTrials. gov NCT03666091. For Leiden EAC, ethical approval was obtained from the 'Commissie Medische Ethiek' (medical ethics committee) of the Leiden University Medical Centre (B19. 008). All participants provided written informed consent. The tREACH trial was approved by the medical ethics committees of the eight participating centres. It was registered as study ISRCTN26791028 by ISRCTN Registry. The respective scientific committees of the three cohorts approved the use of the data for this study.
Screening process In the training dataset, 674 patients with 732 therapeutic sequences were included; 493 therapeutic sequences came from the ESPOIR cohort and 239 from the Leiden EAC. A total of 674 sequences out of 732 (92%) were therefore MTX-naive. In the external validation dataset (tREACH), 138 patients were included. Conversely to other classical methodological approaches where 50/50 size is recommended between discovery and validation sets, machine learning recommends having a validation set size of around 20% of the total dataset when using cross-validation  [31] , which is what is achieved in this study. Total number of sequences above 3, 6 and 9 months is detailed in Supplementary Table  S1 , available at Rheumatology online, for each cohort. The baseline characteristics of RA patients at the beginning of the treatment sequences are displayed in Table  1.
The study's first objective was to assess the performances of a machine learning model based exclusively on clinical and biological data available in routine clinical practice. It is worth noting that the results replicate properly between the training set (ESPOIR þ Leiden EAC) and the external validation set (tREACH), with an AUC of 0. 72 compared with 0. 73 in the training dataset. This is remarkable because ESPOIR and Leiden EAC are observational studies while tREACH is a clinical trial. This is to be compared with AUROC in the range of 0. 6-0. 7 using genomic data in the DREAM RA challenge  [32, 33] , or studies using only biological data to predict response to TNF inhibitors  [34]. Performances obtained by machine learning models were slightly better than conventional methods such as logistic regression, which is consistent with previous findings  [35].
It is a first step towards precision medicine, and this 62% NPV can be improved by future studies. Using our model in clinical practice would allow physicians to prescribe MTX with a confidence of 77% (PPV) compared with the 70% of responders in the tREACH trial. It is a significant improvement compared with current clinical practice. Those results need to be validated in a broader study. The validation set size is indeed sufficient regarding the observed/ expected ratio computed in Supplementary Data S1, available at Rheumatology online (135 patients minimum) but insufficient to estimate accurate calibration slope (477 patients minimum), very important to estimate usefulness of a predicting algorithm in clinical practice. The third objective of the study was to unveil inadequateresponding patients' subgroups by exploring the algorithm's inner decision mechanisms.
Another limitation is the variability in clinical practice and measurement time points of the ESPOIR and Leiden EAC data inherent to observational studies. This variability results in a large window of evaluation (9 months 6 6 months after MTX initiation) to include more patients in the training dataset, as the timespan between treatment initiation and the next visit can be long in those observational cohorts. However tREACH being a clinical trial, performances were assessed much more accurately on the external validation set where the response was systematically evaluated at 3 and 6 months. Moreover, the training dataset and external validation are of different natures as ESPOIR and LEAC are observational cohorts while tREACH is a clinical trial, with a predefined therapeutic strategy including a higher use of glucocorticoids.
, available at Rheumatology online. LightGBM with the MICE missing value imputer performed the best on the training dataset with an AUROC of 0. 73 [95% CI (0. 62, 0. 74)]. The results replicated well on the external validation set with an AUROC of 0. 72 [95% CI (0. 63, 0. 80)]. ROC curves are presented in Fig. 1, and calibration curves are presented in Supplementary Fig. S3, available at Rheumatology online.
Acknowledgements For the first 5 years of the ESPOIR cohort, an unrestricted grant from  Merck Sharp and Dohme (MSD)  was allocated. Two additional grants from  INSERM  were obtained to support part of the biological database. The  French Society of Rheumatology ,  Pfizer ,  AbbVie ,  Lilly , and more recently Fresenius and  Biogen  supported the ESPOIR cohort study. We also thank  Nathalie Rincheval  (Montpellier), who did expert monitoring and data management, and all the investigators who recruited and followed the patients. We also thank investigators from  Leiden EAC  and tREACH studies for collecting and providing the data for this study.
This also reduces the eventual radiation damage of the sample, which might be important in the case of scanning imaging of larger samples. However, the reduced number of projections should be chosen without significantly compromising the quality of the obtained 3D mesoscale tomograms. The reconstructed sparse 3D tomograms of Mn and Fe can be seen in Fig. 2A, B. It comprises 223 virtual slices Figure  1. Semi-automatic multi-scale XRF and multimodal scanning imaging and tomography workflow implemented at Nanoscopium. After sample mounting and alignment, sparse scanning tomography is performed on the whole sample. Some minutes after the experiment, medium resolution volume rendering is available for data interpretation by the proposed semi-automatic on-site data reconstruction. This permits optimizing the strategy of successive high-resolution (HR) measurements.