dataesr/scientific-paragraphs-categorization huggingface dataset:
'SHF' total RNA-seq library preparation and data analysis Five control (Mesp1-Cre) and six cKO (Mesp1-Cre; Chd7 fl/fl ) samples were processed using the KAPA RNA HyperPrep Kit with RiboErase according to manufacturer's instructions. ERCC RNA Spike-In Mix was added to all RNA samples. Libraries were pooled in equimolar quantities, calculated from Qubit and Bioanalyser fragment analysis, and multiplexed in the same run. Samples were sequenced on the NextSeq 500 instrument (Illumina) in a 43 bp paired end run. Raw data were demultiplexed and converted to fastq files using Illumina's bcl2fastq Conversion Software (v2. 18) on BaseSpace. Reads were then aligned to the mouse reference genome (UCSC mm10) using STAR (v2. 5. 0b) on the BaseSpace RNA-seq alignment app (v1. 1. 0, Illumina). Reads per transcript were counted using HTSeq (RnaReadCounter by Illumina) and differential expression was estimated using the BioConductor package DESeq2 on BaseSpace app (v1.
Whole mount in situ HCR Embryos E9. 5 were dissected and processed for whole mount ISH. For each target mRNA a DNA probe set, DNA HCR amplifier, and hybridization, wash and amplification buffers were purchased from Molecular Instruments (https://www. molecularinstruments. com/). DNA probe sets were designed and manufactured by Molecular Instruments, using the Mus Musculus accession number for the target mRNA sequences from the NCBI database. In situ HCR was performed using the whole-mount mouse protocol. 35 Following HCR embryos were counter stained with DAPI and mounted using CitiFluor AF1 mounting media into a 'homemade chamber slide' that provided enough space for the embryos to retain their 3D structure for imaging. Each HCR was repeated on at least four stagematched embryos. Details on image acquisition and analysis are available in Supplementary material online, supplementary methods.
Genome-wide analysis of CHD7 binding sites in CP cells To identify primary and direct targets of CHD7 during the early stages of cardiac commitment and differentiation, we decided to map the genomewide binding sites of CHD7. We used an in vitro approach of directed differentiation of mouse ESCs towards CMs in order to overcome technical difficulties encountered with in vivo derived tissues. Furthermore, in vitro CM differentiation allows comparison of our data with several published datasets from equivalent in vitro systems that map the dynamic enhancer landscapes along CM differentiation. In order to identify a suitable timepoint for in vitro differentiation that best reflects the stage of 'SHF' cells in vivo we first compared our control RNA-seq data with the RNA-seq datasets published from Wamstad et al.
In summary, these data indicate that both up-and down-regulated genes may be targeted by CHD7, which acts primarily at cardiac enhancers dynamically regulated during cardiac morphogenesis. Next, we decided to mine other published datasets for additional evidence of CHD7 interacting transcriptional regulators, especially in CP cells. We took three approaches: we looked for TF motifs enriched at the CHD7 binding sites (for TFs without published ChIP-seq data in CP cells), we unbiasedly searched for TFs exhibiting binding patterns significantly overlap with CHD7 (where ChIP-seq data from CP and other cardiac related cells were available), and we investigated TFs binding at the promoters of CHD7-bound genes (to account for co-regulation and looping without direct co-binding).
Sites of CHD7 binding co-localize with a subset of those of the LIM/homeodomain TF ISL1 As an unbiased approach, we used the BARTweb 51 to search through thousands of public ChIP-seq datasets for transcription regulators with genomic binding patterns significantly similar to our CHD7 peaks. The analysis identified ISL1 (P = 2. 2e-14, Wilcoxon rank test) among the top five regulators (the other four are chromatin factors, e. g. SMARCA4). Interestingly the similarity to published CHD7 data from non-cardiac tissues was not statistically significant (P = 0. 17), indicating tissue specificity of CHD7 co-binding. The ISL1 ChIP-seq data were from CP cells (dataset GSE79701  52  ), and we found 150 common peaks between CHD7 and ISL1 datasets. GO analysis of genes associated with these co-binding peaks showed enrichment for heart development, cardiac ventricle formation and RV morphogenesis (Figure  6D ).
control (dataset GSE80383, Supplementary material online, Figure  S9B ). Similarly, there was significant overlap of Isl1 -/-CPC down DEGs and 'HEART' down DEGs (see Supplementary material online, Figure  S9B ). The second is from additional analysis of genes associated with CHD7 CUT&RUN peaks. Using The ChEA function within Enrichr, containing results of ChIP-seq studies, we searched for TFs bound to the promoter of genes associated with a CHD7 binding site. Third listed in terms of probability value was again the CP cell ISL1 ChIP-Seq (dataset GSE79701,  52  Figure  6F , enrichment P-value = 1. 394e-49). ISL1 has been shown to bind at promoters as well as enhancers/distal elements in CP cells. 52, 53 In addition, ChEA analysis uncovered possible cooperation with WT1 (expressed in the sinus horns,  54  20-25% of the embryonic CMs,  55  and important for epicardial development  56  and EMT within mesoderm  57  ).
Discussion Given the varied and severe heart malformations seen in the Mesp1-Cre; Chd7 cKO embryos we set out to identify genes whose dysregulation and H3K27ac from E10. 5 hearts (green). In (C ), CP enhancers are expanded and their activity at each stage is indicated as absent (-1), poised (0) or active (1). Details on the tracks used can be found in the methods section. CHD7 regulates a cardiogenic GRN in mesoderm are likely to contribute to these phenotypes. We paid particular attention to genes whose loci were bound by CHD7 as these would more likely be direct targets. Examination of existing datasets, TF binding motif analysis and gene ontology were employed to assess the most important TF interactions and pathways involved. Although our E9. 5 'SHF' bulk RNA-seq contained cells outside the Mesp1 lineage e.
60 Notably, the Semaphorin3 receptor PlexinA2 is a BRG1:CHD7 target in NCCs. 19 CHD7 has similar phenotypes in the NCC and mesoderm conditional mutants, in particular both have 4th PAA and OFT septation defects; mesodermal mutants have more severe AVSDs. It is known that the SHF and NCC ingressing into the OFT reciprocally interact. 61 One possible mechanism is via Semaphorin signalling. For example, NCC derived-Sema3c is required for EMT during OFT septation,  13  and the Sema3 receptor PLXNA2 is similarly required in the cardiac NCC. 62 e compared the E9. 5 data with E11. 5 and E13. 5 transcriptomic data from Mesp1 cKOs that we published previously. Of interest, the failure of CM differentiation culminating in abnormal coupling of calcium signalling to pacing  13  was foreshadowed by the decreased expression of early CM markers shown most evidently in Supplementary material online, Figure  S1C.
g. via semaphorins, FGF10). Disturbance of relative levels of expression of anterior vs. posterior SHF genes could affect both cardiac outflow and inflow patterning. The AVSDs seen in Mesp1 cKOs may be secondary to anomalous SHF or FHF contributions, or both. SHF derivatives are found in the dorsal mesenchymal projection (DMP), a structure vital for atrial and atrioventricular septation. A boundary between TBX1-positive arterial pole and TBX5-positive venous pole progenitors is vital for the DMP contribution  45  but is established normally in Chd7 cKOs. Several genes specifically involved in AV canal morphogenesis showed altered expression e. g. Wnt4 (HCR confirmed, with CHD7 binding sites). Wnt2, which also displayed increased expression in the 'SHF' sample, is expressed specifically in the developing inflow tract mesoderm and required for its remodelling, although we could not obtain good quality HCR data for this gene.
Osr1 and Foxf1 are both involved in remodelling the AV canal via a hedgehog cascade,  68  and Fendrr is itself vital for normal heart development. 69 Myosin heavy chain gene (Myh) mutations have also been described in human and murine heart defects (reviewed in  70  ). Thus, a substantial number of genes orchestrating atrioventricular septation are dysregulated in Mesp1 cKOs. As there is substantial extant data on regulatory elements modulating cardiac mesoderm differentiation, our results were compared to genome wide datasets to establish whether CHD7 mainly acted at known regulatory sites and TFs likely to be involved in co-regulation. There was significant overlap of DEGs and the presence of one or more CHD7 binding sites at that locus. The majority of binding sites were at sites where histone modifications suggested an enhancer element, either in differentiating mESCs,  37  or in murine E10.
There are indications an ISL1-CHD7 relationship exists outwith the cardiovascular system. Hurd et al. suggest CHD7 regulates Fgf10 expression in neuroblasts during inner ear neurogenesis through one or both of inhibition of Tbx1 and activation of Isl1/NeuroD/Ngn1. 73 In zebrafish, knockdown of CHD7 produces abnormal organization of cranial motor neurons tagged with GFP driven by ISL1 regulatory elements. 74 Moreover, we performed HOMER motif enrichment analysis of previously published CHD7 ChIP-seq data from cerebellar granule cells  75  and found significant enrichment for the neuronal ISL1 motif (with P-value 0. 0001). We examined CHD7 binding sites for specific DNA sequence motifs. The GATA motif was the most over-represented. Notably, it has been shown that CHD7 interacts directly with GATA4 protein in CP cells derived from human iPS cells,  76  that ISL1 and GATA4 often co-occupy sites with open chromatin,  53  and ISL1 and GATA4 co-operate in regulation of Fgf10.
Conclusion Our data reveal that CHD7 fine-tunes the expression of genes in the cardiogenic GRN during cardiac morphogenesis. This includes critical TFs such as Isl1, Hand1, Hand2, Tbx5, Foxf1, Osr1 as well as signalling proteins such as Fgf10, Wnt4, and Semaphorins. We present evidence that CHD7 acts at sites bound by the pioneer TF ISL1, and at enhancers >50 kb from the TSS, a feature also noted for ISL1. CHD7 physically interacts with ISL1 in CP cells, we therefore suggest important co-operation during mesodermal through CM differentiation. TF-CHD7 interactions may be important where dynamic changes in expression level are required during mesodermal differentiation as exemplified by Fgf10 expression within a SHF domain and its repression in FHF lineages contributing to the left ventricle.
Acknowledgements We thank  Yang Liu  for helping with the scRNA-seq analysis,  Louisa McGuinness  and  Diana Gold Diaz  for genotyping,  UCL genomics Facility  for  NGS , and  UCL Biological Services Unit  for mouse husbandry. Microscopy was performed at the  Light Microscopy Core Facility ,  UCL GOS ICH  supported by  NIHR GOSH BRC  award  17DD08.
Funding This work was supported by grants  RG151431880  ( British Heart Foundation ) (P. J. S. ),  Transatlantic Network of Excellence   15CDV01  (the Leducq Fondation) (P. J. S. , R. G. K. );  Agence National pour la Recherche  grant  ANR-18-CE13-0011  (R. G. K. ) and  Fondation pour la Recherche Medicale  (C. T. ), with additional support from the  NIHR Great Ormond Street Hospital Biomedical Research Centre.
The implementation is described in further detail in the Supplementary Information together with a template project for new users. PhysiBoSS 2. 0 code is open-source distributed under BSD 3-clause license (https://github. com/PhysiBoSS/PhysiBoSS). In addition, we also provide a nanoHUB GUI-based tool implementing an example model, to ease its use at https://nanohub. org/tools/pba4tnf/.
, 2022). The third model is a model of the gastric adenocarcinoma cell line  (Flobak et al. , 2015) , developed to predict drug synergies among seven drugtargeted genes part of the network (Fig. S11 ). The model has 90 total nodes with no input nodes and 2 output nodes (Pro-survival and Anti-survival). This model is available as a Supplementary material of the corresponding paper  (Flobak et al. , 2015)  and can be used to screen combinations of drugs (Fig. S12 ). All these models are available in the dedicated repository: https://github. com/PhysiBoSS/boolean-models.
Model exploration Extreme-scale Model Exploration With Swift (EMEWS)  (Ozik et al. , 2018)  is a framework that combines model exploration with distributed simulations using the Swift/T language. Our model exploration strategy takes advantage of the DEAP package  (De Rainville et al. , 2012; Fortin et al. , 2012) , a python package that implements different evolutionary algorithms, including classic Genetic Algorithms, that can be used to perform model calibration or explore parameters that optimise a user-defined goal (e. g. minimise cancer cells). We adapted the framework from PhysiCell-EMEWS  (Ozik et al. , 2018)  (available at https://github. com/MathCancer/PhysiCell-EMEWS) and expanded it by including a novel search strategy, Covariance Matrix Adaptation, as well as different sampling methods for performing parameter sweeps  (Akasiadis et al.
, 2022; Ponce-de-Leon et al. , 2022). Herein, we tested 3375 sets of parameter values for three kinetic constants of the TNF receptor model and selected the set that produces the results closest to those reported by  Letort et al. (2019). The source code to reproduce this model exploration, as well as other experiments, is available at: https://github. com/PhysiBoSS/spheroid-tnf-v2-emews.
Personalisation of Boolean model using PROFILE methodology The prostate Boolean model was tailored to different datasets using PROFILE methodology to obtain personalised models that capture the particularities of a set of patients  (Béal et al. , 2019)  and cell lines  (Béal et al. , 2021). Proteomics, transcriptomics, mutations and copy number alteration (CNA) data can be used to modify different variables of the MaBoSS framework, such as node activity status, transition rates and initial conditions. The resulting ensemble of models is a set of personalised variants of the original model that can show great phenotypic differences. Different recipes (use of a given data type to modify a given MaBoSS variable) can be tested to find the combination that better correlates to a given clinical or otherwise descriptive data.
A Index (CI) below 1 shows synergism while a value above 1 indicates antagonism. This methodology can be found in its own repository: https://github. com/PhysiBoSS/PROFILE_v2. PhysiBoSS 2. 0, diffusing drugs in the microenvironment target and inhibit a specific node in the Boolean model. As modellers, the goal is to identify drugs that target candidate nodes that might be relevant in producing desired therapeutic outcomes such as increased apoptosis or reduced proliferation in LNCaP. We used a list of target nodes from  Montagud et al. (2021)  that have yielded interesting results using prostate Boolean models. Suitable drugs were then identified using the DrugBank database  (Wishart et al. , 2018)  and matched with their availability on the Genomics of Drug Sensitivity in Cancer (GDSC) database  (Yang et al.
0 allows us to better model the effect that the local concentration of a drug in the nearby surroundings of a cell has on the intracellular signalling model. GDSC dose-response data was used to fit the raw cell viability data to a multi-level fixed effect model  (Vis et al. , 2016)  with which a specific sigmoidal dose-response curve was obtained for each drug and cell line pair. The code to reproduce this can be found in the gdscIC50 R package (https://github. com/CancerRxGene/gdscIC50) We integrated the specific dose-response curves for the selected drugs (Table  S1 ) on LNCaP into the drug mapping function that governs the inhibition of a Boolean target node upon drug availability in the cell's surroundings. A cell is informed of the drug concentration for each simulated substrate at its nearest voxel, then the respective target node is inhibited with a probability based on the dose-response curve.
Additionally, our design allows PhysiBoSS 2. 0 to ease the integration of new Boolean models by using custom modules that connect intracellular variables to agent-based ones and configuration XML files with custom data for different cell types and environments. Together with PhysiBoSS 2. 0, we have developed the PhysiCell ToolKit (PCTK) a python-based package that includes a library and command-line scripts to process and analyse simulation outputs. Although there are already available tools for handling PhysiCell outputs (such as https://github. com/PhysiCell-Tools/python-loader), with PCTK we aim to gather and organise different pieces of python code that have been recurrently useful in different projects involving PhysiCell and PhysiBoSS. Currently, the package implements a simple module to parse and handle the MultiCellDS standard and uses an efficient schema to process the output files containing the cells and microenvironment data.
Moreover, on the top of this module we have implemented different command-line tools for processing and creating basic plots, including time courses of the number of alive and dead cells and generating POV files used as inputs for the 3D rendering of the multicellular models using POV-Ray (Persistence of Vision Raytracer, PovRay 2004). PCTK can be used both as a callable library and as a stand-alone command-line tool and can be found at https://github. com/PhysiBoSS/pctk.
Acknowledgements We would like to thank  Åsmund Flobak  from  Norwegian University of Science and Technology (NTNU)  for facilitating the use of the AGS model. The authors acknowledge the technical expertise and assistance provided by the  Spanish Supercomputing Network (Red Española de Supercomputación) , as well as the computer resources used: the  LaPalma Supercomputer  ( RES-BCV-2019-2-0008 ), located at the  Instituto de Astrofísica de Canarias  and  MareNostrum4  ( RES-BCV-2019-2-0013 ,  RES-BCV-2020-3-0016 ,  RES-BCV-2021-2-0020 ), located at the  Barcelona Supercomputing Center.
Funding This work has been partially supported by the  European Commission  under the  INFORE  project ( H2020-ICT-825070 ) and  PerMedCoE  project ( H2020-ICT-951773 ). This work was funded in part by the  French  under the management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ).
B. SIMILAR SOFTWARE TOOLS FOR MICROGRID PLANNING In the market, there are multiple software tools for microgrid design and optimization, including HOMER, RETScreen, Hybrid2, GridLAB-D, TRNSYS, among others. Each one has its specificities, main objectives, advantages, and limitations. In this paper, HOMER  [30]  and RETScreen  [31]  were selected for comparison with the new software tool as they are considered the most well-regarded among the available software tools for microgrid design and optimization. HOMER (Hybrid Optimization of Multiple Energy Resources) is a software tool developed by the National Renewable Energy Laboratory (NREL) in the United States for microgrid design and optimization. It allows users to model and optimize hybrid energy systems that integrate various renewable and conventional energy sources, energy storage systems, and demand-side management strategies.
This requires a high level of technical expertise and knowledge of microgrid design and optimization. Another limitation of HOMER is its cost. While a free version of the software is available, it has limited features and capabilities. The full commercial version of HOMER can be expensive, making it less accessible to small businesses and organizations with limited budgets. The cost of HOMER may also be prohibitive for researchers and policymakers in developing countries, where the need for microgrid design and optimization is particularly acute. RETScreen is a software tool developed by Natural Resources Canada for renewable energy and energy efficiency analysis. It provides a wide range of modules and tools for designing, analyzing, and optimizing renewable energy systems, such as photovoltaic systems, wind turbines, and hybrid systems.
Another limitation of RETScreen is its lack of support for complex optimization algorithms. This may make it less suitable for applications where precise optimization is required, such as microgrids in remote and off-grid areas. As a consequence of the limitations of HOMER and RETScreen, the software being discussed emerges as a viable alternative. With its user-friendly interface and open-source nature, it fills the gap for users who lack the resources to invest in commercial tools. By providing a simpler and more accessible option for microgrid design and optimization, it offers a promising solution to address electricity shortages and promote the integration of renewable energy in rural areas.
B. ARCHITECTURE The web application was developed using the Model-View-Controller (MVC) software design pattern. This pattern divides the system into distinct parts, each with a specific function. The screenshots accompanying this text were taken from a model built using data from an existing real-world system in Darewadi, a village in the Nagar Taluka of Ahmednagar District in Maharashtra State, India. The first component is the view layer, which contains everything related to the user interface's structure. This is where the application's different pages are designed, enabling the user to interact with the system's functionalities such as creating new projects, entering data, and presenting dynamic graphs necessary for evaluating data results. The second component of the design pattern is the controller layer, which is the system's main layer as it acts as The last component of this design pattern is the model layer, responsible for handling everything related to reading and writing information in the database.
For the toolkit, this component helps to load information when creating a project and entering the required data. Additionally, it facilitates storing the information, making it possible to extract data for generating results. Each queried data from the database is processed through functions containing the formulas established in the pre-feasibility calculation model. The workflow of the web application is illustrated in Figure  2. It begins with user interaction through the web interface, which initiates the processing of the request. The application adds the information to the database, makes calculations as required, and generates a new interface that displays the response to the user.
A. CASE STUDY The case study used for demonstrating the application is based on an existing and operational system, located in the Indian village of Darewadi, Nagar Taluka, in the Ahmednagar District of Maharashtra State. The data collected for this case study is divided into three groups, corresponding to the data called for in the input screens as previously described. In order to validate the calculations and outputs of the Economic Toolkit, three model systems were created. These models were derived from existing systems by using published literature and making realistic assumptions to fill in gaps in information that could not be obtained from public sources. The three model systems selected were: • The village of Dharnai, Bijar province, India  [40]  [41] • The village of Myin Chi Niang, Myanmar  [42]  • A health clinic and school in the village of Sapra, Jharkand, India  [43]  Each of the three model systems was analyzed using the Economic Toolkit, as well as commonly used publicly available tools, HOMER and RETScreen.