dataesr/scientific-paragraphs-categorization huggingface dataset:
AC K N OW L E D G E M E N T S RE acknowledges support provided by a CITA National Fellowship and by funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement no. 834148). AF is supported by a UKRI Future Leaders Fellowship (grant no. MR/T042362/1). This work used the DiRAC@Durham facility managed by the Institute for Computational Cosmology on behalf of the STFC DiRAC HPC Facility ( www. dirac. ac. uk). The equipment was funded by BEIS capital funding via STFC capital grants ST/K00042X/1, ST/P002293/1, ST/R002371/1, and ST/S002502/1, Durham University and STFC operations grant ST/R000832/1. DiRAC is part of the National e-Infrastructure. 4  , but for stellar tracers which, at t = 0, have Einasto α = 0. 5 density profiles.
RNA extraction and sequencing Total RNAs were extracted from heart tissue samples as previously described  (9). Ribosomal RNAs were depleted, and samples were prepared for sequencing according to the Illumina TruSeq RNA Preparation Kit and subjected to pairwise sequencing (2x150bp) with an Illumina HiSeq sequencer. This strategy allows to have information on only the most abundant non-coding RNAs. The RNAseq data are available under the reference : (GEO accession: GSE191081).
Quality control and alignment Raw data quality was verified with FastQC (v0. 11. 5) and reads were filtered removing the adaptors and lowquality based using Trimmomatic (v0. 39). Reads were aligned in paired-end mode on GRCh37 (hg19) human reference genome using STAR (v2. 5. 4b), and gene quantification was done with featureCounts (v2. 0. 0). All bioinformatic analyses are available at https://github. com/ pbrochet/epiChagas.
Differential expression analysis Statistical analyses were performed using R (3. 6. 2). The DESeq2 package (v1. 26. 0) was used for data normalization and differential gene expression analyses  (13) , using shrinkage function to correct log2 fold change (log2(FC)). Benjamini-Hochberg method was applied to obtain False Discovery Rate (FDR) for each analysis. Genes with an FDR ≤ 0. 05 and an absolute log2(FC) greater than 1. 5 were considered as differentially expressed (DEG).
Tissue DNA methylation analysis DNAs, extracted from whole tissue, were bisulfite converted and amplified with elongation of primers. Amplified DNAs were fragmented and hybridized in EPIC beads according to the protocol described by the manufacturer (Illumina, San Diego, California). Analysis of DNA methylation data was performed with the ChAMP package, using BMIQ for normalization and ComBat for batch-effect correction. Only genomic positions with an FDR<0. 05 and a |Db|>0. 2 were selected. A DMP (Differentially Methylated Position) is associated with a gene when the DMP is inside the gene body or in its promoter region (from gene TSS to 1. 5kb upstream). The methylation data are available under the reference : (GEO accession: GSE191082).
Transcription factor binding site characterization An analysis of differentially methylated regions (DMR) was done with the ChAMP package, using the DMRCate method, with parameters lambda=400 and C=2. A DMR of interest was defined as a region containing at least 1 DMP located in TSS ([TSS -1500bp; TSS]), 1st Exon or 5'UTR region of DEGs; and having an FDR ≤ 0. 05. In order to identify transcription factor binding sites (TFBS) affected by a difference in methylation, the ReMap database was used  (15). A total of 84 cell lines were selected (Supplementary Table  2 ), containing immune and heart-related cells, and including 151 transcription factors (TF). First, the transcription factors specifically associated with DMRs were identified with the OLOGRAM tool  (16, 17)  in a pairwise analysis, meaning we identify the individual TFs enriched with the DMRs.
Only those with an FDR ≤ 0. 05 were retained. In a second step, we studied the combinations of those selected TFs that were observed in the DMR by using the n-wise overlap option of OLOGRAM (option -more-bed-multipleoverlap, n ≤ 4). Finally, the known TFBS profiles of identified transcription factor were retrieved from JASPAR database. The location of each TFBS in DMR sequence was identified using FIMO tool.
Results Headings disregulated genes associated to severe CCC Gene expression analysis was conducted on left ventricular free wall myocardial tissue from 8 severe CCC patients and 6 healthy organ donors (see workflow: Figure  1 ). For each sample, we got between 40 and 75 million sequencing reads. Sequences were aligned to the human reference genome GRCh37/hg19. The average mappable rate of the raw reads reached 90% (+/-2%). No parasite RNA was detected. This is not surprising, since in chronic and end stage patients, the parasite ARN is no longer detected. Gene expression data were obtained from 43533 transcripts. A small fraction of these transcripts (1407/43533 (3. 23%)) were considered as differentially expressed between control and CCC (Supplementary Table  3 ), most part being upregulated (Supplementary Figure  1A ).
Moreover, the log2(FC)s obtained in analysis B are significantly higher than the ones obtained in analysis A (Wilcoxon p-value=4. 23E-07) (Figure  4C ). These two analyses clearly show that there is a stronger enrichment of TFs in DMRs than in the promoter set. To confirm these results, a third analysis (C) was performed with the DMRs compared to the promoters of the DEGs containing these DMRs. The obtained distribution of the log2(FC) with this analysis showed two distinct peaks, one around 0 and another around 1. 7 (Figure  4C ), confirming that some TFs are specifically enriched in DMRs. A total of 30 TFs were found significantly associated to the DMRs in the analyses B and C. We also considered as a Cis-Regulatory Module (CRMs) the regions where at least 2 TFs bind to the genome according to ReMap.
TFBS identified in RUNX3 regulatory region Of the 30 TFs of interest, 20 have at least one known motif in the Jaspar database, providing a total of 45 distinct motifs. After filtering predicted TFBS in our DMR overlapping at least one DMP, 423 TFBS have been identified, for all the 20 TFs (Supplementary Table  12 ). Interestingly, TBX21, RUNX3 and EBF1 are the TFs whose binding motif appears to be affected by DNA methylation in the largest number of genes. Because these TFs are involved in many complexes, regulation of their binding may affect the binding of all TFs in the complexes. Among the 25 genes with a low number of TFs binding in their promoter region (n ≤ 3), and thus being affected by specific TFs, 7 are targeted by TBX21 and 4 by RUNX3, showing again the importance of these TFs in CCC.
DEGs with DMPs were predominantly associated with the immune response, but also with several biological processes such as arrhythmia, muscle contraction, fibrosis and mitochondrial function. Here, we have set up a more advanced analysis, B A FIGURE 8 Comparison of differentially expressed genes, genes affected by methylation in tissue dataset and genes affected by methylation in blood dataset. (A) Venn diagram of top 1000 genes differentially expressed or methylated in previous tissue RNA-seq, tissue DNA methylation and blood DNA methylation analysis between control/asymptomatic and severe CCC samples. (B) Graph of the Gene ontology Biological Processes analysis of dysregulated element between control/asymptomatic and severe CCC. Nodes represents biological processes terms and are divided in 3 colors, according to the proportion of genes from RNA-seq (red), tissue methylation (blue) or blood methylation (green) analysis.
Moreover, we found a DMR in IFN-g promoter region, targeted TBX21. According to Ologram, RUNX3 could also fix this DMR, but its TFBS wasn't found in this DMR, as described in JASPAR database. Those two transcriptions factors are involved in Th1 differentiation, as well as GATA3  (55, 56). According to our analysis, the micro-RNA miR-142, associated to Th1 differentiation  (57)  in neuronal autoimmune disease, potentially targets RUNX3 and TBX21. In Chagas disease, TBX21 and IFN-g expression are correlated with the left ventricular dilation, and the ratio between TBX21 and GATA3 expression is significantly higher in CCC than in noninflammatory cardiomyopathy  (58) , which were also confirmed in our data in both control to CCC or DCM comparisons. Moreover, RUNX3 overexpression has been associated to the methylation of its promoter in CCC  (9) , as well as in our current analysis.
In total, RUNX3 and TBX21 targets 29 genes, including 14 in common (AIM2, ARHGAP30, BATF, C16orf54, CCR5, CYTIP, DENND2D, DOK2, FGR, MIR142, PTPN22, TRAF1, TRAF3IP3 and WDFY4). Moreover, RUNX3 is targeted by 7 transcription factors: EBF1, IRF4, MAX, PAX5, RUNX3, SP1 and YY1. We also found that XIST is one of the tops up-regulated lncRNA in Chagas disease. It is associated with the WNT1 gene, belonging to the Wnt family, but because of the wide sex dispersion in our dataset, we cannot conclude the direct involvement of this lncRNA in CCC. In our data, WNT1, as well as WNT10A and WNT10B are up-regulated in severe CCC. The Wnt pathway is involved in the differentiation of Th2 cells  (59)  and its inhibition in acute Chagas disease stage decrease the Th2 response (60) and increase inflammatory response, controlling the parasite  (61).
Funding This work was supported by the  Institut National de la Santéet de la Recherche Medicale (INSERM) ; the  Aix-Marseille University  (grant number: AMIDEX "International_2018" MITOMUTCHAGAS); the  French Agency for Research (Agence Nationale de la Recherche-ANR  (grant numbers: " Br-Fr-Chagas ", "landscardio"); the  CNPq (Brazilian Council for Scientific and Technological Development) ; and the  FAPESP (São Paulo State Research Funding Agency Brazil  (grant numbers:  2013/50302-3 ,  2014/ 50890-5 ); the  National Institutes of Health/USA  (grant numbers:  2 P50 AI098461-02  and  2U19AI098461-06 ). This work was founded by the  Inserm Cross-Cutting  Project  GOLD. This project has received funding from the  Excellence Initiative o f Aix -M a r s e i l l e U ni v e r s i ty -A * M i d e x a F r e n ch  " Investissements d'Avenir programme"-Institute MarMaRa   AMX-19-IET-007.
We used a questionnaire to obtain data from our study participants. The questionnaire was constructed in English and consisted of questions on socio-demographic characteristics and views on vaccines in health facilities in the sub-region (Additional le 1). The study instrument (questionnaire) was developed and grounded on literature reviews and discussions with the research team  [19, 20]. Participants were selected randomly and recruited consecutively by our research team. The questionnaire was pretested among outpatients in Gulu Regional Referral Hospital. The questionnaire had an internal validity of Cronbach's α = 0. 772. Participants were assured of con dentiality and privacy of their responses to reduce the potential bias introduced by self-reported data. In addition, the questionnaire was designed to minimize lethargy in the questionnaire and participants' responses.
Data management: Data obtained from participants were de-identi ed. Only the principal investigator and supervisors had access to and were stored in a database with restricted access. It was later archived at the Gulu University, Faculty of Medicine, Department of Surgery. Data analysis: Data analysis was performed using SPSS statistical software version 20. 0. Continuous variables are presented in means, histograms, standard deviations, modes, medians, and interquartile ranges depending on the distribution of the data. Categorical data are presented as frequencies and percentages. The Chi-square and crosstabs tests were performed on categorical data when comparing two or more groups. Multivariable Logistic regression analyses were conducted to identify independent factors associated with the COVID-19 vaccine acceptance and the relationships between independent and dependent variables.
A p-value less than 0. 05 was considered statistically signi cant. Ethical Approval: This study was approved by the St. Mary's Hospital, Lacor Institutional, Ethics, and Review Committee (Lacor IREC). Administrative clearance was obtained from the twenty-four health facilities in the Acholi sub-region. In addition, informed consent was obtained from each participant before recruitment into the study. The research team ensured that con dentiality of personal information was maintained during the investigation, and unique identi ers of participants were retained on the public records. The Principal Investigator had access to the database during the study period. At the end of the project, the database was archived at the Gulu University, Faculty of Medicine, Department of Surgery.
Strengths and limitations of this study The study has limitations in the study design, cross-sectional study where one-time information from participants is gathered and analyzed. These have shortcomings in that the views and opinions of participants are not static; they vary according to the prevailing environmental situations. In this, we suggest a need for a prospective or a longitudinal assessment of the COVID-19 vaccine acceptance in the future, ensuring that all data are measured and recorded accordingly. This data is vital as it is one of the well-documented and completed data for over 723 participants from the Acholi subregion regarding COVID-19 vaccine acceptance in the recent period. Findings from this study show a high acceptance rate despite results from other parts of Uganda.
Drought monitoring systems are real-time information systems focused on drought severity data. They are useful for determining the drought onset and development and defining the spatial extent of drought at any time. Effective drought monitoring requires databases with high spatial and temporal resolution and large spatial and temporal coverage. Recent reanalysis datasets meet these requirements and offer an excellent alternative to observational data. In addition, reanalysis data allow better quantification of some variables that affect drought severity and are more seldom observed. This study presents a global drought dataset and a monitoring system based on the Standardized Precipitation Evapotranspiration Index (SPEI) and ERA5 reanalysis data. Computation of the atmospheric evaporative demand for the SPEI follows the FAO-56 Penman-Monteith equation.
The system is updated weekly, providing near real-time information at a 0. 5° spatial resolution and global coverage. It also contains a historical dataset with the values of the SPEI at different time scales since January 1979. The drought monitoring system includes the assessment of drought severity for dominant crop-growing areas. A comparison between SPEI computed from the ERA5 and CRU datasets shows generally good spatial and temporal agreement, albeit with some important differences originating mainly from the different spatial patterns of SPEI anomalies, as well as from employing long-term climate trends for different regions worldwide. The results show that the ERA5 dataset offers robust results and supports its use for drought monitoring. The new system and dataset are publicly available at the link https://globa l-droug ht-crops.
, 2014; Dracup, 1991). The recent development of climate reanalysis datasets, updated with high frequency and a suitable spatial resolution for regional studies, allows implementing climate monitoring systems. Reanalysis datasets have several advantages over observational ones. They provide climate variables that are not commonly available through observations or are difficult to spatialize (e. g. wind speed). Moreover, they typically provide updated and detailed spatial information at high frequency, which is especially relevant during drought development and intensification. During these critical periods, determining the precise spatial extent and severity of drought conditions has the highest applied interest. Moreover, as reanalysis datasets show physical consistency between the different variables, they provide spatial and temporal coherence for many climatic variables.
Some of these variables are highly relevant for drought monitoring and are seldom registered in observational networks. In particular, global warming has increased the role of atmospheric evaporative demand (AED) in the onset and evolution of droughts and their impact on ecological and agricultural systems  (Allen et al. , 2010 (Allen et al. , , 2015;; Asseng et al. , 2015; Lobell et al. , 2011). Thus, some studies suggest that increased AED plays a pivotal role in intensifying droughts worldwide  (Dai et al. , 2018; Dai & Zhao, 2017). With the available real-time observational meteorological datasets, it is impossible to accurately quantify the AED because temperature is the only variable available in most observatories. Temperature data alone allow only rough and uncertain estimates of the AED  (Vicente-Serrano et al.
, 2020). Physically based methods that allow for an accurate estimation of the AED, such as the FAO-56 Penman-Monteith equation, consider both radiative and aerodynamic components  (Pereira et al. , 2015). In addition to temperature, these methods require information about air humidity, solar radiation and wind speed. Unlike observational datasets, reanalysis products provide these variables routinely. Although drought management is usually performed on a local to national scale, there are different drought monitoring systems available on a global scale, allowing to assess general dry or humid conditions over large areas  (Beguería et al. , 2014; Hao et al. , 2014; Turco et al. , 2020; Wood et al. , 2015). These systems can also determine drought severity in regions where operative drought monitoring is non-existing, which is the most common situation.
Even with their inherent uncertainties, mainly for precipitation  (Alexander et al. , 2020) , reanalysis products could be a landmark in developing global drought monitoring systems based on meteorological drought indices. This is mainly because they allow better characterization of the AED while offering near real-time updates. This study describes a global drought monitoring system based on the SPEI calculated from the ERA5 dataset. The information is provided at a 0. 5° longitude and latitude spatial resolution and is updated weekly. In addition, this work assesses drought severity for specific cropgrowing regions, illustrating the potential applicability of the system. In order to accomplish this task, crop yield masks for the world's most common crops have been added to the system. Importantly, this system provides information in near real time while also providing a weekly dataset dating back to 1979.
2 | DATA INPUTS 2. 1 | ERA5 data ERA5 is a new reanalysis product developed by the European Centre for Medium-range Weather Forecasts  (Hersbach et al. , 2020). It provides hourly precipitation information from 1950 and is updated regularly, providing preliminary daily precipitation data with a four-day delay. ERA5 also generates a potential evaporation (Epot) product based on a surface energy balance that could be assimilated to the atmospheric evaporative demand since it uses a constant vegetation parameter corresponding to crops/mixed farming and assumes non-limited water availability  (ECMWF, 2019). Nevertheless, the ERA5 Epot data has shown problems, as it generates a general underestimation and regional artefacts  (ECMWF, 2020). For this reason, we decided to calculate the AED using the FAO-56 Penman-Monteith reference evapotranspiration equation based on daily inputs from different ERA5 variables.
For this purpose, we used daily data of 2-m maximum and minimum air temperature, downward surface solar radiation, 10-m wind speed and 2-m dewpoint temperature. Using the ERA5 dataset with a spatial resolution of 0. 5°, all variables required to calculate the AED and precipitation were extracted. This resolution provides sufficient spatial detail for a global drought monitoring system, as it permits the identification of regional patterns of drought severity at continental and national spatial scales. Although the ERA5 dataset contains information since 1950, some problems have been identified before 1970  (Simmons et al. , 2021). For this reason, we decided to focus exclusively on the period from 1979 to the present, which corresponds to the ERA5 first release. This, of course, does not impede updating the dataset to include earlier dates once less uncertain records are released.
| DATA CALCULATION AND UPDATING Using the weekly precipitation and AED, we calculated the SPEI at different time scales  (0. 5, 1, 3, 6, 9, 12, 24, 36 and 48 months)  according to the methodology detailed in Vicente-Serrano,  Beguería, and López-Moreno (2010). As the primary purpose is to use this information for real-time monitoring, it is necessary to generate an operative update of the new SPEI data as soon as new data are available. Recalculating the entire SPEI dataset every week is not feasible due to the necessary computing time that would delay the information update. Moreover, if the whole dataset is recalculated every time new data arrives, the previous data would also change, limiting the comparability of the new data with the earlier records. For this purpose, we initially calculated the necessary parameters of the log-logistic distribution used to obtain the SPEI for 1979-2020.
| COMPARISON WITH THE SPEIBASE The SPEIbase was generated in 2010  (Beguería et al. , 2010; Vicente-Serrano, Beguería, López-Moreno, Angulo, et al. , 2010)  and updated with different versions of the Climatic Research Unit (CRU) global gridded dataset  (Harris et al. , 2020). The SPEIbase uses precipitation and potential evapotranspiration data as a surrogate of the AED to calculate the SPEI at different time scales. This dataset is available at https://spei. csic. es/spei_database, and it has been widely used in drought studies considering other points of view, from the analysis of physical processes (e. g. Das et al. , 2016; Yao et al. , 2018; Zhao et al. , 2017)  to the study of drought impacts (e. g. Bachmair et al. , 2018; Feldpausch et al. , 2016; Wang et al. , 2014). The SPEIbase is not used for real-time monitoring in any system because it is updated annually and lacks the data updates required for real-time monitoring.
Nevertheless, it provides useful long-term information for comparison with the long-term ERA5 dataset. Here, we compare the spatial and temporal agreement between the new ERA5 SPEI dataset and the SPEIbase. As the CRU data have a monthly temporal resolution, we also calculated the SPEI from monthly ERA5 data. To make both datasets comparable, we calculated the SPEI using the same reference period of 1979-2020. The SPEI sensitivity to AED is strongly dependent on the average precipitation and AED magnitude  (Tomas-Burguera et al. , 2020). Nevertheless, we do not expect a bias related to this issue in the comparability between the SPEI from CRU and ERA5 data as both datasets are similar in terms of spatial patterns and magnitude of both variables (Figures  1  and 2 ). In general, there is a high correlation (Pearson's r > 0.
9) between the 3-month and 12-month SPEI time series calculated from the CRU and ERA5 datasets in vast regions of the world (Figure  3 ). There are, however, some differences. The temporal correlation between the datasets is higher in Europe, most of Asia, Australia, North America (except for high latitudes), East and Southern South America and Southern Africa. This means that in these regions, the identification of drought conditions is independent of the dataset used. In some other areas, however, the correlation is much lower. This is particularly the case in the Amazon basin and western North America, most central Africa and hyper-arid areas of the Sahara and the Arabian Peninsula. The factors that explain these differences can be diverse. On the one hand, the spatial distribution of meteorological stations may play a role.