dataesr/scientific-paragraphs-categorization huggingface dataset:
Prediction of TF complex Considering the fact that transcription factors often act together, forming complexes, an analysis of interaction between those transcription factors was performed using the OLOGRAM tool  (44). Here, we considered as a Cis-Regulatory Module (CRMs) the regions where at least 2 TFs bind to the genome according to ReMap. After data filtering and considering combinations of up to 4 TFs, we have identified 16 regions significantly associated with our DMRs, involving a total of 12 transcription factors (Supplementary table  10 ). Among the top-regulators, BRD4 which appear in 10 (83. 33%) of the identified complex and overlap a total of 3090bp (number S). Comes after EED (S = 2777), EBF1 (S = 2389), BCLAF1 (S = 2312), TBX21 (S = 2271), RUNX3 (S = 2092) and RUNX1 (S = 2052).
TFBS pattern affected by DNA methylation To confirm that DNA methylation is indeed what affects the binding of the identified TFs, an analysis was conducted on their binding motif. Of the 30 TFs of interest, 20 have at least one known motif in the Jaspar database, providing a total of 45 distinct motif. 127,158 genomic sequences included in 92 of our DMR were significantly associated with at least one motif,  35 ,091 are present in the DMRs predicted with OLOGRAM, 22,325 have at least one CG and 939 have at least one DMP. After removing redundancy in TFBS, 423 TFBS-DMR specific sequence pairs have been identified. None of the TFs of interest were discarded by the set of filters used, all 20 having TFBSs in the DMRs. These sequences are located in the promoter regions of 48 DEGs. Among the TFs mainly involved in complexes (BRD4, EED, EBF1, BCLAF1, TBX21, RUNX3, RUNX1), TFBS pattern is known for EBF1, TBX21, RUNX3 and RUNX1.
Evaluation of cell types proportions in RNA-seq Given the infiltration of immune cells in CCC myocardium, we looked for characterize and quantify the proportions of these cell types in our samples. To do so, we applied to our data, ADAPTS, a R tool to deconvolute the cell type proportions in RNA-seq by comparison to cell type signatures extracted from single-cell RNA-seq datasets (see Methods: Evaluation of cell types in heart tissue). In our study, we used two different single-cell datasets. First of all, immune cell signatures (36) (22 cell types) were used and showed in general a higher All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted December 21, 2021.
In addition, several lncRNAs were also differentially expressed in CCC patient tissues. Among them, MIAT has been associated to multiple diseases leading to various phenotypes  (88). In breast cancer, it targets the microRNA-133, dysregulated in CCC  (18) , and down-regulated in patients with heart failure (89). This lncRNA is always acting as a sponge for specific miRNAs. Moreover, XIST is one of the top up-regulated lncRNA in our disease. It is associated with the WNT1 gene, belonging to the Wnt family. Because of the sex dispersion in our dataset, it is All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted December 21, 2021.
TGF-β receptor overexpression has already been observed in acute phase of CCC  (97) , and his role in CCC development has been confirmed, including parasitic invasion, inflammation, immune response, heart fibrosis and heart conduction  (98). A therapeutic strategy, based on TGF-β inhibition, has already been proposed and try on mouse  (80). Moreover, some study suggest an interaction between the Wnt pathway, the TGF-β signalling and the NOTCH signaling  (99, 100). Our result showing the involvement of the Wnt pathway in the evolution of the disease and the potential implication of NOTCH1 in the genetic deregulation in severe CCC reinforce the hypothesis of the Wnt-NOTCH-TGF-β in CCC evolution. For the others, their modes of action are not obvious at all. Several approaches are ongoing such as Loss-of-Function Strategies for lncRNAs, lncRNA-Interactome Analyses, Identification of lncRNAs Associated with Translation Machinery (review in (  101 )) but a database, namely a well annotated one, is slow to come.
In tissue samples, differentially methylated regions located in differentially expressed genes regulatory region (from TSS-1500 to first exon) were searched with ChAMP. Transcription factor binding site (TFBS) were predicted with OLOGRAM using ReMap database, and TFBS profiles were identified with FIMO, using JASPAR database. In blood samples, two analyses were performed: DMP between asymptomatic and severe CCC, and DMP between moderate and severe CCC. In both cases, gene enrichment was performed in the same way than tissue methylation data. Finally, in blood samples, blood biomarkers were identified, for disease diagnosis and disease stage diagnosis.
Fundings This work was supported by the  Institut National de la Santé et de la Recherche Médicale (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 of Aix-Marseille University  -A* Midex a French "Investissements d'Avenir programme"-Institute MarMaRa   AMX-19-IET-007.
PCA) were computed with factoextra (v1. 0. 7) and FactoMineR (v2. 3) packages (http://lib. stat. cmu. edu/R/CRAN/). medRxiv preprint doi: ; https://doi. org/10. 1101/2021. 12. 20. 21267972 this version posted December 21, 2021. The copyright holder for this analysis ( ). Subsequent diagrams were produced with Enhanced Volcano (v1. 4. 0) (https://github. com/kevinblighe/EnhancedVolcano) or ggplot2 packages (28). Moreover, hierarchical clustering (HCA), using Spearman correlation distance as distance metric, was computed using gplots (v3. 0. 4) package (https://rdrr. io/cran/gplots/). Principal component All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in.
Cloud masks EUREC 4 A's HALO was designed to observe different properties of clouds using the richness of their interaction with electromagnetic radiation. Different instruments (Table  4 ), by virtue of their differing measurement principles and footprints, see clouds in different ways. To provide an overview of the cloud fields sampled by HALO, a trinary cloud mask is created for each cloudsensitive instrument, as described in Appendix B. Access to the cloud mask data is outlined in Table  6. The value of the cloud mask denotes measurements that each instrument identifies as cloud-free (0), probably cloudy (1), or most likely cloudy (2). The introduction of the probably cloudy flag reflects the ambiguity in cloud detection faced by many instruments. Especially for the passive instruments (HAMP radiometer, specMACS, KT19, VELOX), a range of thresholds were applied to separate cloudy and cloud-free observations.
Accessing EUREC A's HALO data EUREC 4 A was a large field campaign, which involved hundreds of people from dozens of institutions spread over a score of countries across three continents. Measurements were collected from more sensors than there were people involved in the campaign. The task of quality controlling and curating the resultant data is immense and time-consuming. Making the data visible and usable by a broader community is even more daunting, all the more so for those same qualities that made EUREC 4 A's execution so successfulnamely the multiplicity of people, institutions, and countries involved. EUREC 4 A's HALO is a microcosm encapsulating many of the challenges faced by EUREC 4 A as a whole. HALO deployed instruments developed and operated by different groups, funded by different agencies, and designed to collect very different types of data.
The chapters of How to EUREC4A are built from a combination of code and explanatory markdown files. The use cases range from simple examples that show how to work with HALO flight segments to simple quick looks at data from an individual instrument and to more elaborate analyses that combine measurements from different instruments. 1 Jupyter is an interactive development environment supporting several programming languages (https://jupyter. org, last access: 29 September 2021). 2 https://howto. eurec4a. eu (last access: 29 September 2021). For example, the comparison of cloud cover shown in Fig. 6  is one of the use cases documented in the book. Code examples are written in Python, but the methods employed are readily transferred to other languages -even by those unfamiliar with Python. All example scripts can be run interactively in the browser via a binder integration such that no local setup and memory resources are necessary.
The code examples can also be downloaded and run locally with the respective requirements for the Python environment installed. How to EUREC4A thus provides a common starting point for those interested in working with EUREC 4 A's HALO data and at the same time serves as a tutorial to help inexperienced users begin using the data. How to EUREC4A is a living document. It continues to mature through the addition of chapters on new instrument platforms, through the addition of new or corrections of old analyses, and through the ingestion of new data or data releases and their provenance. For this latter purpose and to help disassociate the indexing of data from their archiving, How to EUREC4A accesses EUREC 4 A data through an intake catalog 3  , which is continuously updated to contain links to the most recent versions of the publicly available EUREC 4 A data.
To provide users with a more narrative description of the available HALO data, How to EUREC4A also contains a section that links the HALO scientific payload with its institutional owners, contact information to its data providers, citations to reference material, and links to data. For users concerned about the volatility of an online book, a snapshot of this information, valid at the time of submission, has been compiled into a machine-readable YAML (YAML Ain't Markup Language) file  (Kölling et al. , 2021) ; it includes the information shown graphically in Fig. 9  and is included as an immutable asset with this paper.
Summary We describe the operation of the German research aircraft HALO during the EUREC 4 A experiment and its associated data. HALO flew 15 scientific missions during EUREC 4 A. These are described not only by the scientific measurements made from instruments aboard the aircraft but also through the provision of auxiliary data: timestamp intervals that segment the flight paths, selected and curated photographs from each flight, movies showing the evolving satellite presentation and flight tracks for each flight, subset aircraft state data, and cloud masks from instruments sensitive to the presence of clouds. In addition, metadata are provided describing deployed instruments, their particular configuration, the contact information of those responsible for each instrument, and the instruments' data, and (when available) a URL to the data themselves is included, along with the aforementioned auxiliary data, through a machine-readable text (YAML) file.
Literature search and collection This paper uses the scoping review method for the literature search and collection regarding EV smart energy solutions and business models in the energy system. A scoping review aims to map rapidly the key concepts underpinning a research area  (Ma et al. 2019; Arksey and O'Malley 2005). A scoping review can be used to: examine the extent, range, and nature of research activity; determining the value of undertaking a full systematic review; summarize and disseminate research findings; identify research gaps in the existing literature  (Arksey and O'Malley 2005). This study uses the scoping review to summarize and disseminate the research findings. The used approach starts by designing a search string for relevant databases. The overall purpose is to find EVrelated smart energy solutions and business models.
Hence, five databases are chosen based on the relevance of the domain: Academic Search Premier EBSCO ACM Digital Library IEEE Xplore Web of Science Science Direct Academic Search Premier EBSCO, Web of Science, and Science Direct include a wide range of research fields including engineering and social science. They are included in the paper to make sure that business-related articles are covered. Many smart energy solutions are related to information technology and computing, in particular, EVs' charging solutions. Therefore, the ACM Digital Library database is included. IEEE Xplore is included due to its focus on electrical engineering, computer science, and electronics. The following search string is an example of a string used for the database of Web of Science. (TI=((smart OR intelligent) AND (energ* OR electric* OR heat*) AND (solution* OR technolog* OR product* OR service*))) The search string is modified for the other databases following their searchingsystem syntax.
Literature search results This section presents the scoping review search results. Table  2  shows the used search strings for the different databases including the literature results after removing all duplicates. The removal of all duplicates is done after all searches have been performed and citations are downloaded to the reference management software tool, which in this case is EndNote X9  (Zhu et al. 2016). A literature search is done before the one seen in Table  2  for practicing search strings in different databases. The search criteria are overall based on smart energy solutions and business models within that field. The search is based only on title results to reduce the number of results. Synonyms and notations for the different searching keywords are used to make sure relevant literature is not excluded.
For example, the keyword 'energy' is also representing electricity and heating. From the searching results shown in Table  2  and the initial practicing search, a total of 558 results are found. The found results' citations are downloaded to EndNote. In EndNote a keyword search on the keywords of 'solution', 'business model', and 'solution AND (business model)' is used to remove non-relevant literature. Table  3  shows that a total number of 240 references are related to the scope. The last step of the quantitative scoping review search process is to identify literature for the qualitative scoping review analysis. To do so, the 240 references' titles and abstracts are evaluated and assessed into different categories. The categories are physical components/technologies, software, service, smart house/building, and EV oriented.
Limitation and future works The paper conducts the literature search in scientific databases to investigate the technical details, and other references, e. g. , patents or technical reports are not included due to the main focus of the paper is to introduce the proposed methodology. Therefore, a more comprehensive and broad search is recommended especially for the technical details that have not been covered by the scientific literature. Meanwhile, to prove the proposed methodology can contribute to the mentioned simulations, agent-based simulation for EV solutions and business models are recommended with various scenarios, e. g. , EV smart charging and participation in various electricity markets. Furthermore, although business and market are included in the SGAM model, the aspects of policies, regulations, social and financial aspects are missing in the literature due to its technical focus.
Distribution and transmission: Data collector or databases such as data  (Nicanfar et al. 2013 )  (Santos et al. 2015)  Station: The data collector or database is a data concentration  (Nicanfar et al. 2013 )  (Santos et al. 2015)  Component: Data collector or database e. g. data center  (Nicanfar et al. 2013 )  (Santos et al. 2015)  Function: A cloud solution, smart grid server  (Nicanfar et al. 2013)  13 Grid operator or DSO control operations.
2013) using CANbus (McGee et al. demand side (McGee et al. n. d. ) n. d. ) (Nicanfar et al. 2013) Identity-based cryptography (Nicanfar et al. 2013) Information: Voltage level as control signal (McGee et al. n. d. ) EV identity (Nicanfar et al. 2013) 11 Distribution: Process: Component: EV charging at Public charging points in EV Charging (Nicanfar et al. EVs at public charging public charging distrbuition grid (Nicanfar 2013) points and fast-charging sta- points/ fast- et al. 2013) tions (Nicanfar et al. 2013) charging stations Transmission: Fast charging stations in transmission grid (Nicanfar et al. 2013) 12 Data collector or database (data center).
Identification of the Fungal Strain Although ITS is presently the best barcode sequence for fungi  (Schoch et al. , 2012) , it did not allow the assignment of the selected fungal strain, FEP 16, at the species rank. The BLAST top score results in GenBank (GB) indicated that the FEP 16 ITS sequence was 100% similar with a 100% coverage to the sequences of several species: F. petroliphilum (Q. T. Chen and X. H. Fu) (D. Geiser, O'Donnell, Short and Zhang) (2013) (GB accession number: LC512834), F. macroceras (Wollenweber and Reinking 1925) (MH854821), and F. solani (Mart. 1842) (MH855493). These species, based on ITS BLAST results, belong to the F. solani species complex in which many species are still not formally described  (Short et al. , 2013; Chehri et al. , 2015; O'Donnell, 2019). Trying to further determine to which species FEP 16 belongs, we sequenced four more loci for that strain (see the experimental section) and combined these data with data sampled in the study by  Bohni et al.
aeruginosa (Table  1 ). The pqsA gene encodes an enzyme involved in the synthesis of PQS, which is a signaling molecule of QS, while the lasB gene encodes an important elastase enzyme  (Jimenez et al. , 2012). These primary bioassay results encouraged us to go for further investigations. Natural Products Social Molecular Networking (GNPS)  (Wang et al. , 2016)  and predicted spectra obtained using the in silico MS/ MS fragmentation database (ISDB-DNP)  (Allard et al. , 2016). For each node of the MN, possible structure candidates were listed according to MS/MS similarity (initial rank) up to a maximum of the top 50 compounds reported to occur in fungi. To increase the level of confidence in annotation, a reweighting step based on taxonomy was performed. This step takes into consideration the matching between the biological source reported in the DNP at the level of species > genus > family, resulting in a maximum of the top 5 candidates (final rank) for each annotated node  (Rutz et al.
The full MNs with the complete annotation are deposited in Figshare (https://doi. org/10. 6084/m9. figshare. 14706198. v1). The same MN will be used later on in this study to apply annotation propagation through the location of isolated molecules in the MN. Most of the annotated metabolites belong to pyran and pyrone derivatives, furan lactones, naphthoquinones, isocoumarins, terpenes, and sterols. Such data are consistent with previously reported studies on the chemical content of Fusarium species  (Wei and Wu, 2020) , in particular, the presence of some frequently reported compounds in Fusarium, such as gibepyrone D  (Wang et al. , 2011) , aloesol  (Kashiwada et al. , 1984) , fusarubin  (Tatum and Baker, 1983) , anhydrofusarubin  (Shao et al. , 2010) , and bostrycoidin  (Arsenault, 1965) , which are highlighted in Table  2.
R t or fraction N o ). This plot simulates the actual output of a classic on-flow LC-NMR analysis  (Queiroz et al. , 2002). The chromatographic dimension of the plot was expressed either as fraction number or retention time since all spectra were stacked according to their elution order, which allows a straightforward correlation with the corresponding analytical HPLC-DAD trace (Figure  1F ). An interactive version of the plot can be explored here: (https:// oolonek. github. io/pseudo_lcnmr_plotter/2dNMR. html). The generated 2D plot from collected and dried micro-fractions has several advantages over classical LC-NMR. In this case, all 1 H-NMR signals are perfectly aligned, since all spectra are recorded with the same solvent across the whole separation (DMSO-d 6 ). By comparing our approach to on-flow LC-NMR, no solvent suppression is necessary.
Most of these fatty acids were found to be unsaturated fatty acids and exhibited a characteristic signal of their ethylenic protons around δ H 5. 3, whereas fractions F117, 122, and 123 contained saturated fatty acids. In addition to fatty acids, steroids and triterpenes were also identified in this chromatographic region. For example, ergosterol (  22 ), a main constituent of the fungal membrane, could be identified in F132, yielding an intense signal in the histogram (Supplementary Figure  S3A ). Its 1 H-NMR spectrum was in good agreement with reported data  (Kwon et al. , 2002; Yang et al. , 2003)  and confirmed the dereplication results (Table  2 ). Moreover, cerevisterol (20) could be confirmed in F116 by comparing its 1 H-NMR spectrum with those in the literature  (Kawagishi et al. , 1988).
Since all NMR spectra were recorded in DMSO-d 6 , the low field region (δ H 11. 0-14. 0) of the pseudo-LC-NMR 2D plot highlighted characteristic deshielded mobile protons. Among them, the acidic protons of fatty acids were clearly seen (δ H 12. 0) in F113-F119, and quinonic-phenolic groups of fusarubin (7) and 3-O-methylfusarubin (8) were observed (Figure  1F ). Several similar types of signals were linked to the unknown metabolites in F35 and F69. These selected examples show a good complementarity of NMR and HRMS/MS data for the dereplication of main constituents and help highlight new Fusarium metabolites. For full de novo structure identification, 2D-NMR spectra were recorded for the compounds of interest and those exhibiting a sufficient S/N ratio in the 1 H-NMR spectra.
This yielded a good baseline separation in most compounds that ease the peak targeted collection. Purity of collected fractions was checked by 1 H-NMR and LC-ELSD-MS (data not shown) and enabled the bioactivity assessment of compounds 1-14. Since Z9 was also a bioactive target, the apolar hexane fraction was purified similarly. Interestingly, by comparing the dry weights of collected fractions from enriched extract with the equivalent ones from crude extract, all compounds were collected in amounts higher than 500 µg, which was the threshold for the bioassay we selected. This also granted supplementary amounts to perform further bioassay experiments such as quantitative PCR. As shown, in our strategy, the precise assignment of the bioactive LC peaks was dependent on the amounts of extract injected.