Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Methods Mice. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and experiments were performed in compliance with its institutional guidelines. Adult C57BL/6J mice (mus musculus) were obtained from Jackson Laboratories (#000664; Bar Harbor, ME) and were used at 4-7 months of age. Aged C57BL/6J mice were obtained from the National Institute of Aging (NIA) Rodent Aging Colony and were used at 20 months of age. For new scRNAseq experiments, female mice were used in each experiment.

Mouse injuries and single-cell isolation. To induce muscle injury, both tibialis anterior (TA) muscles of old (20 months) C57BL/6J mice were injected with 10 µl of notexin (10 µg/ml; Latoxan; France). At 0, 1, 2, 3.5, 5, or 7 days post-injury (dpi), mice were sacrificed and TA muscles were collected and processed independently to generate single-cell suspensions. Muscles were digested with 8 mg/ml Collagenase D (Roche; Switzerland) and 10 U/ml Dispase II (Roche; Switzerland), followed by manual dissociation to generate cell suspensions. Cell suspensions were sequentially filtered through 100 and 40 μm filters (Corning Cellgro #431752 and #431750) to remove debris. Erythrocytes were removed through incubation in erythrocyte lysis buffer (IBI Scientific #89135-030).

Single-cell RNA-sequencing library preparation. After digestion, single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. Cells were counted manually with a hemocytometer to determine their concentration. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, PN-1000075; Pleasanton, CA) following the manufacturer’s protocol. Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes. After preparation, libraries were sequenced using on a NextSeq 500 (Illumina; San Diego, CA) using 75 cycle high output kits (Index 1 = 8, Read 1 = 26, and Read 2 = 58). Details on estimated sequencing saturation and the number of reads per sample are shown in Sup. Data 1.

Spatial RNA sequencing library preparation. Tibialis anterior muscles of adult (5 mo) C57BL6/J mice were injected with 10µl notexin (10 µg/ml) at 2, 5, and 7 days prior to collection. Upon collection, tibialis anterior muscles were isolated, embedded in OCT, and frozen fresh in liquid nitrogen. Spatially tagged cDNA libraries were built using the Visium Spatial Gene Expression 3’ Library Construction v1 Kit (10x Genomics, PN-1000187; Pleasanton, CA) (Fig. S7). Optimal tissue permeabilization time for 10 µm thick sections was found to be 15 minutes using the 10x Genomics Visium Tissue Optimization Kit (PN-1000193). H&E stained tissue sections were imaged using Zeiss PALM MicroBeam laser capture microdissection system and the images were stitched and processed using Fiji ImageJ software. cDNA libraries were sequenced on an Illumina NextSeq 500 using 150 cycle high output kits (Read 1=28bp, Read 2=120bp, Index 1=10bp, and Index 2=10bp). Frames around the capture area on the Visium slide were aligned manually and spots covering the tissue were selected using Loop Browser v4.0.0 software (10x Genomics). Sequencing data was then aligned to the mouse reference genome (mm10) using the spaceranger v1.0.0 pipeline to generate a feature-by-spot-barcode expression matrix (10x Genomics).

Download and alignment of single-cell RNA sequencing data. For all samples available via SRA, parallel-fastq-dump (github.com/rvalieris/parallel-fastq-dump) was used to download raw .fastq files. Samples which were only available as .bam files were converted to .fastq format using bamtofastq from 10x Genomics (github.com/10XGenomics/bamtofastq). Raw reads were aligned to the mm10 reference using cellranger (v3.1.0).

Preprocessing and batch correction of single-cell RNA sequencing datasets. First, ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX). Samples were then preprocessed using the standard Seurat (v3.2.1) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat). Cells with fewer than 750 features, fewer than 1000 transcripts, or more than 30% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0) was used to identify putative doublets in each dataset, individually. BCmvn optimization was used for PK parameterization. Estimated doublet rates were computed by fitting the total number of cells after quality filtering to a linear regression of the expected doublet rates published in the 10x Chromium handbook. Estimated homotypic doublet rates were also accounted for using the modelHomotypic function. The default PN value (0.25) was used. Putative doublets were then removed from each individual dataset. After preprocessing and quality filtering, we merged the datasets and performed batch-correction with three tools, independently- Harmony (github.com/immunogenomics/harmony) (v1.0), Scanorama (github.com/brianhie/scanorama) (v1.3), and BBKNN (github.com/Teichlab/bbknn) (v1.3.12). We then used Seurat to process the integrated data. After initial integration, we removed the noisy cluster and re-integrated the data using each of the three batch-correction tools.

Cell type annotation. Cell types were determined for each integration method independently. For Harmony and Scanorama, dimensions accounting for 95% of the total variance were used to generate SNN graphs (Seurat::FindNeighbors). Louvain clustering was then performed on the output graphs (including the corrected graph output by BBKNN) using Seurat::FindClusters. A clustering resolution of 1.2 was used for Harmony (25 initial clusters), BBKNN (28 initial clusters), and Scanorama (38 initial clusters). Cell types were determined based on expression of canonical genes (Fig. S3). Clusters which had similar canonical marker gene expression patterns were merged.

Pseudotime workflow. Cells were subset based on the consensus cell types between all three integration methods. Harmony embedding values from the dimensions accounting for 95% of the total variance were used for further dimensional reduction with PHATE, using phateR (v1.0.4) (github.com/KrishnaswamyLab/phateR).

Deconvolution of spatial RNA sequencing spots. Spot deconvolution was performed using the deconvolution module in BayesPrism (previously known as “Tumor microEnvironment Deconvolution”, TED, v1.0; github.com/Danko-Lab/TED). First, myogenic cells were re-labeled, according to binning along the first PHATE dimension, as “Quiescent MuSCs” (bins 4-5), “Activated MuSCs” (bins 6-7), “Committed Myoblasts” (bins 8-10), and “Fusing Myoctes” (bins 11-18). Culture-associated muscle stem cells were ignored and myonuclei labels were retained as “Myonuclei (Type IIb)” and “Myonuclei (Type IIx)”. Next, highly and differentially expressed genes across the 25 groups of cells were identified with differential gene expression analysis using Seurat (FindAllMarkers, using Wilcoxon Rank Sum Test; results in Sup. Data 2). The resulting genes were filtered based on average log2-fold change (avg_logFC > 1) and the percentage of cells within the cluster which express each gene (pct.expressed > 0.5), yielding 1,069 genes. Mitochondrial and ribosomal protein genes were also removed from this list, in line with recommendations in the BayesPrism vignette. For each of the cell types, mean raw counts were calculated across the 1,069 genes to generate a gene expression profile for BayesPrism. Raw counts for each spot were then passed to the run.Ted function, using

Harmony Generator Plugins Market Outlook

The global harmony generator plugins market is poised for significant growth, with a market size of approximately USD 150 million in 2023, expected to surge to nearly USD 350 million by 2032, reflecting a robust CAGR of 10.2%. This growth is driven by the rapid advancements in music production technology, the increasing popularity of digital audio workstations (DAWs), and the growing demand for innovative audio tools among musicians and producers.

One of the primary growth factors for the harmony generator plugins market is the democratization of music production tools. With the advent of affordable and powerful DAWs, more individuals are able to produce professional-grade music from their home studios. This accessibility has increased the demand for plugins that can enhance creativity and efficiency, such as harmony generators, which allow producers to create complex harmonic arrangements effortlessly. Moreover, as the music industry continues to embrace digital transformation, the integration of AI and machine learning in music production tools is further propelling the market. AI-powered harmony generator plugins can analyze musical input and generate harmonically rich outputs, making them invaluable tools for both novice and experienced musicians.

Another significant growth factor is the rising popularity of live performances and the need for real-time audio processing tools. Harmony generator plugins are increasingly being used in live settings to create dynamic and engaging performances. These plugins enable artists to harmonize their live vocals or instruments on the fly, adding depth and complexity to their sound. Additionally, the integration of these plugins with various performance hardware and software platforms has expanded their usability, making them an essential component of modern live setups. The continuous innovation in this segment, driven by feedback from live performers, is expected to further boost market growth.

The growing trend of film scoring and the increasing demand for high-quality soundtracks in the entertainment industry are also contributing to the market's expansion. Harmony generator plugins are widely used in film scoring to create lush, intricate harmonies that enhance the emotional impact of visual media. As filmmakers and content creators seek to produce more engaging and immersive experiences, the demand for sophisticated audio tools like harmony generator plugins is on the rise. This trend is not only confined to the film industry but extends to video games, television shows, and other digital media, underscoring the broad applicability and market potential of these plugins.

Regionally, North America stands out as a key market for harmony generator plugins, driven by the high concentration of music producers, recording studios, and technological advancements in the region. Europe follows closely, with a strong presence of music technology companies and a vibrant live performance scene. The Asia Pacific region is expected to witness the highest growth rate, fueled by the burgeoning music industry in countries like China, Japan, and India, along with increasing investments in digital music production infrastructure. Latin America and the Middle East & Africa are also showing promising growth, although from a smaller base, as the adoption of digital music production tools continues to spread globally.

Product Type Analysis

The harmony generator plugins market can be segmented by product type into standalone plugins and integrated plugins. Standalone plugins are designed to function independently, providing users with a dedicated interface and set of features specifically for generating harmonies. These plugins are often preferred by musicians and producers who require a specialized tool that can offer greater control and customization. The demand for standalone harmony generators is driven by their robustness and the ability to work seamlessly across different DAWs and hardware setups. Moreover, standalone plugins often come with extensive libraries of presets and customizable options, enabling users to create unique harmonic textures suited to their specific needs.

On the other hand, integrated plugins are designed to work within a host application, usually a DAW, providing harmony generation as part of a larger suite of audio production tools. These plugins are valued for their convenience and ease of use, as they allow users to access harmony generation capabilities without leaving their primary production environment. Integrated plugins often feature tighter integration wi

Harmony Generator Plugins Market Outlook: The global Harmony Generator Plugins market is poised to embark on a significant growth trajectory, with a projected market size of $XXX million by 2033, exhibiting a robust CAGR of XX% from 2025 onwards. This expansion is attributed to the rising demand for professional-quality music production tools among musicians, producers, and sound engineers. The growing popularity of music creation and distribution platforms, coupled with the increasing accessibility of digital audio workstations (DAWs), is further propelling market growth. Moreover, the adoption of artificial intelligence (AI) and machine learning (ML) in harmony generation is expected to revolutionize the market dynamics, leading to innovative plugin offerings with enhanced functionality and ease of use. Competitive Landscape and Segmentation: The Harmony Generator Plugins market features a competitive landscape with established players such as Plugin Boutique, W.A. Production, and Xfer Records, alongside a host of niche solution providers. The market is segmented by application (music producers, musicians, street singers) and type (MIDI, high-level editor). Regionally, North America is the dominant market, followed by Europe and Asia Pacific. Key market trends include the integration of cloud-based services, the rise of subscription-based models, and the growing adoption of mobile-based harmony generation apps. However, factors such as piracy and the availability of freeware alternatives may pose challenges to market growth.

It is included data derived from the processing of single-cell and single-nuclei RNA-seq from several samples (see below). This data corresponds to the input and intermediate output files from https://github.com/saezlab/Xu_tubuloid . Data The data include:

Binary sparse matrices for the UMI gene expression quantification from cellranger (filtered feature-barcode matrices). These are TAR archive files named with the name of the sample. Seurat Objects with normalized data, embeddings of dimensionality reduction, clustering and cell cluster annotation. These are TAR archive files including final objects, grouped by sample type: SeuratObjects_[SortedCells | Organoids | Human Kidney Tissue]. The HumanKidneyTissue also includes the SeuratObject after Harmony integration. Exported barcode idents from unsupervised clustering and manual annotation ("barcodeIdents*.csv" files). Label transfer via Symphony mapping to tubuloid cells from each organoid to a integrated reference atlas of human kidney tissue (SymphonyMapped*.csv).

Samples The data corresponds to the following samples, which were profiled at the single-cell resolution:

CK5 early organoid (Healthy). Organoid generated from CD24+ sorted cells from human adult kidney tissue at an early stage. CK119 late organoid (Healthy). Organoid generated from CD24+ sorted cells from human adult kidney tissue at a late stage.

JX1 late organoid (Healthy). Organoid generated following Hans Clever's protocol for kidney organoids. JX2 PKD1-KO organoid (PKD). Organoid generated from CD24+ sorted cells from human adult kidney tissue, for which PKD1 was gene-edited to reproduce PKD phenotype, developed at a late stage. JX3 PKD2-KO organoid (PKD). Organoid generated from CD24+ sorted cells from human adult kidney tissue, for which PKD2 was gene-edited to reproduce PKD phenotype, developed at a late stage. CK120 CD13. CD13+ sorted cells from human adult kidney tissue. CK121 CD24. CD24+ sorted cells from human adult kidney tissue.

In addition, human adult kidney tissue were profiled in the context of ADPKD:

CK224 : human specimen with ADPKD (PKD2- genotype).

CK225 : human specimen with ADPKD (PKD1- genotype). ADPKD3: human specimen with ADPKD (ND genotype).

Control1 : human specimen with healthy tissue. Control2 : human specimen with healthy tissue.

Symphony objects containing cell state embedding for each cell type using cells from PBMC-blood alone (Kang et al. Nat Gen 2023). For each cell type separately, we performed normalization, variable gene selection, PCA, Harmony integration, UMAP, and Symphony reference creation using the script: https://github.com/immunogenomics/hla2023/blob/main/scripts/4_sceQTL/0_symphony_integration_OneK1K_pool.REach reference object contains:meta_data: cell metadatavargenes: variable genes, means, and standard deviations used for scalingloadings: gene loadings for projection into pre-Harmony PC spaceR: Soft cluster assignmentsZ_orig: Pre-Harmony PC embeddingZ_corr: Harmonized PC embeddingcentroids: locations of final Harmony soft cluster centroidscache: pre-calculated reference-dependent portions of the mixture modelumap: UMAP coordinatessave_uwot_path: path to saved uwot model (for query UMAP projection into reference UMAP coordinates)normalization_method: type of normalization used

The power of single-cell RNA sequencing (scRNA-seq) in detecting cell heterogeneity or developmental process is becoming more and more evident every day. The granularity of this knowledge is further propelled when combining two batches of scRNA-seq into a single large dataset. This strategy is however hampered by technical differences between these batches. Typically, these batch effects are resolved by matching similar cells across the different batches. Current approaches, however, do not take into account that we can constrain this matching further as cells can also be matched on their cell type identity. We use an auto-encoder to embed two batches in the same space such that cells are matched. To accomplish this, we use a loss function that preserves: (1) cell-cell distances within each of the two batches, as well as (2) cell-cell distances between two batches when the cells are of the same cell-type. The cell-type guidance is unsupervised, i.e., a cell-type is defined as a cluster in the original batch. We evaluated the performance of our cluster-guided batch alignment (CBA) using pancreas and mouse cell atlas datasets, against six state-of-the-art single cell alignment methods: Seurat v3, BBKNN, Scanorama, Harmony, LIGER, and BERMUDA. Compared to other approaches, CBA preserves the cluster separation in the original datasets while still being able to align the two datasets. We confirm that this separation is biologically meaningful by identifying relevant differential expression of genes for these preserved clusters.

Using 23-months old mice of a inducible expression of human a-syn constructs based Parkinson mouse model, we produced a single nucleus RNA dataset by cutting 0mm Bregma to -5mm Bregma. The Chromium 3’ Single Cell Library Kit (10x Genomics) was used and Sequencing was performed on a NovaSeq 6000. From the same model we also used 20-months old mice with the Visium Spatial V1 platform (10x Genomics). Sequencing was performed on a NovaSeq 6000. Both were PE150.

snRNA pipeline: For the alignment of reads, a custom reference was created by adding the sequences of the V1S/SV2 transgene and the Camk2a promoter to the mm10 mouse reference genome. Count matrices generated by cellranger count 7.1 were loaded into an AnnData object and processed using the Python-based framework Scanpy 1.10.2. Integration with R, where needed, was facilitated through the rpy2 package. Raw count matrices were corrected for ambient RNA contamination using the SoupX 1.6.2. To remove potential doublets, scDblFinder 1.18.0 was employed with a fixed seed (123). Nuclei with nUMI and nGenes values exceeding three median absolute deviations (MADs) from the median were excluded. Genes detected in fewer than five nuclei across the dataset were excluded. The resulting dataset was normalized via scanpy.pp.normalize_total and scanpy.pp.log1p. Highly variable genes were identified using the function scanpy.pp.highly_variable_genes with the Seurat v3 flavor, selecting the top 4,000 genes. Dimensionality reduction was performed using principal component analysis (PCA) and batch effects were corrected using the python-implemented version of Harmony via the function scanpy.external.pp.harmony_integrate. Harmony embeddings were then used to construct a k-nearest neighbor (kNN) graph with scanpy.pp.neighbors. Clustering was performed using Leiden clustering with standard parameters via the function scanpy.tl.leiden. Clusters were annotated using literature, the mousebrain.org, and markers identified via the FindConservedMarkers function in Seurat. First, neurons and non-neuronal cells were distinguished using mainly canonical markers, such as but not limited to Rbfox3 (neurons), Mbp (oligodendrocytes), Acsbg1 (astrocytes), Pdgfra (oligodendrocyte precursor cells), Inpp5d (microglia), Colec12 (vascular cells), and Ttr (choroid plexus cells). Neurons were further classified into Vglut1 (Slc17a7), Vglut2 (Slc17a6), GABA (Gad2), cholinergic (Scube1), and dopaminergic (Th) neurons. Vglut1 and GABA neurons were further annotated into subtypes based on subclustering and FindConservedMarkers markers.

visium spatial pipeline: Sequences were fiducially aligned to spots using Loupe Browser ver. 8. All aligned sequences were mapped using spaceranger count 3.0.1 with a custom refence, which included sequences for the promotor and transgene (Camk2aTTA, V1S/SV2) to the mouse genome mm39. We filtered each sample of the Visium Spatial dataset based on the MAD filtering of number of reads (nUMI), number of genes (nGene), and percentage of mitochondrial genes (percent.mt). A spot was filtered out if it was outside of 3x MAD value in at least two metrics. Filtered samples were merged into one Seurat 5.1.0 object and we obtained normalized counts by the SCTransform function of Seurat. Integration was performed using Harmony 1.2.0 on 50 PCA embeddings and clustering was done using Leiden clustering based on 30 harmony embeddings. Integrated clusters were visualized using the UMAP method. Samples that were not successfully integrated (based on similarity measures of the harmony embeddings) and showed high percentage.mt or low nUMI levels compared to other samples, were removed from subsequent analysis. A final integration and clustering were performed after filtering. Regions were first annotated based on a 0.1 resolution clustering to get high level region annotation (Cortex, Hippocampus, Subcortex). Each high-level region was further annotated based on either more granular resolutions or subclustering. Marker genes from mousebrain.org and literature were used in combination with the Allen mouse brain atlas to obtain anatomically relevant annotations.

The scRNA-seq data were collected from previously published datasets (GSE140228, GSE139555, GSE155698, GSE121636, and GSE139324), adhering to the following selection criteria: 1) presence of T cells, 2) treatment-naïve patients, 3) solid tumors, and 4) inclusion of at least tumor and blood samples.
Each scRNA-seq dataset underwent separate preprocessing in R (v4.0.2). We filtered out cells from the original count matrices that had fewer than 200 genes detected or more than 10% mitochondrial UMI counts and we only kept genes detected in at least 3 cells. Then, we applied Seurat (v4.0.5) with default parameters for count data normalization and scaling. Each cell was assigned a cell cycle score using the CellCycleScoring function and we computed the difference between the G2M and S phase scores. This approach allows for the separation of non-cycling from cycling cells while minimizing the differences in cell cycle phase among proliferating cells. The SelectIntegrationFeatures function was ran with the nfeatures parameter set to 3,000 before merging all samples from each dataset. These integration features were then used for Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Clustering was performed using the Louvain algorithm with the resolution parameter set to 2.0 for all datasets. Finally, T cells were isolated based on CD3D and CD3G genes expression (CD3D or CD3G expression level > 0).

To integrate heterogeneous data from different sources, a two-step procedure was applied. We first concatenated all datasets together and ran the scaling and PCA steps based on the top 3,000 highly variable genes identified by the FindVariableFeatures function with the “vst” method. Harmony was applied for batch effect correction then UMAP and clustering using the Louvain algorithm with the resolution parameter set to 2.0 were performed on the harmony reduction. Examining the result from the first clustering run, we identified contamination clusters and clusters that arose from unwanted factors: we removed the contamination clusters including low quality cells highly expressing marker genes associated with apoptosis and tissue dissociation operation, pancreatic acinar cells (expressing PRSS1, CLPS, PNLIP and CTRB1 among others), myeloid cells (expressing CD68) and B cells (expressing CD79A). Then, we performed the second run of integration and clustering excluding immunoglobulin, ribosome-protein-coding, and T cell receptor (TCR) genes (gene symbol with string pattern "^IGK|^IGH|^IGL|^IGJ|^IGS|^IGD|IGFN1", "^RP([0–9]+-|[LS])", and "^TRA|^TRB|^TRG" respectively) from the top 3,000 highly variable genes and regressing out the cell cycle difference effect as well as the percentage of mitochondrial UMI counts. Harmony (v0.1.0) was applied again for batch effect correction and UMAP was performed on the harmony reduction.
T cell subtypes identification and annotation was performed by clustering cells using the Louvain algorithm with the resolution parameter set to 4.1 after iterative testing from 3.5 to 5.0 by 0.1 (more granular than default), computing clusters signatures based on differential gene expression using the FindAllMarkers function with the “MAST” method and interrogating known gene markers expression. A resolution value of 4.1 was notably found to be the lowest resolution value enabling the correct separation of proliferating CD4+ T cells from proliferating CD8+ T cells.

This is a subset of a dataset created from the following public datasets: Tucker et al. (2020) DOI: 10.1161/CIRCULATIONAHA.119.045401 Livinukova (2020) DOI: 10.1038/s41586-020-2797-4 Kanemaru et al. et al. (2023) DOI: 10.1038/s41586-023-06311-1 Yang et al. (2023) DOI: 10.1002/ctm2.1297 Wang et al. (2020) DOI: 10.1038/s41556-019-0446-7 Hulsmans et al. (2023) DOI: 10.1126/science.abq3061 The combined datasets were filtered based on QC metrics, integrated using Harmony, and clustered with Scanpy. After cardiomyocyte clusters were identified and removed, the remaining cells were re-clustered and annotated based on the expression of canonical markers. An unnormalised, annotated version of the dataset is shared here as an AnnData file, together with a text document describing the original datasets and metadata fields.

Modern iPaaS platforms offer a comprehensive suite of capabilities designed to streamline integration processes and enhance data management. Key features include: Integration Connectors: Provides pre-built connectors to seamlessly integrate with a wide array of applications, databases, and cloud services, minimizing custom development efforts. Data Mapping & Transformation: Offers robust tools for transforming data formats and structures to ensure compatibility across different systems, often employing ETL (Extract, Transform, Load) methodologies. Workflow Automation & Orchestration: Enables the automation of complex integration processes, facilitating efficient data flow and reducing manual intervention. This often includes support for visual workflow design and orchestration. API Gateway & Management: Securely manages and exposes APIs, ensuring controlled access and effective governance of data exchange within and outside the organization. This includes features like API security, monitoring and analytics. Low-Code/No-Code Capabilities: Many platforms are incorporating low-code or no-code development environments, enabling citizen developers to build and deploy integrations without extensive coding expertise. Recent developments include: May 2021: Jitterbit acquired eBridge Connections, a IPaaS providet that offers data to seamlessly flow between on-premises or cloud e-commerce, EDI, ERP, and CRM systems. A strong complement to Jitterbit’s Harmony API integration platform, the combined offerings will provide one of the most comprehensive sets of integration solution around e-commerce integration and EDI integration which helps in customers increase their digital capabilities and helps in massive time efficiencies., August 2021: SnapLogic and Schneider Electric have introduced a new citizen developer approach to application and data integration. Using SnapLogic's self-service, low-code platform as the foundation for Schneider Electric's new operating model, the multinational utility will enable nearly 150 citizen developers to integrate over 100 cloud and on-premises systems across the enterprise, Increased employee productivity faster Innovation and larger business impact..

The market for chord generator plugins is experiencing robust growth, driven by the increasing demand for efficient and innovative music production tools among professional and amateur musicians. This surge is fueled by several factors, including the rising popularity of digital audio workstations (DAWs), the accessibility of affordable music production software, and the growing number of online music creation communities. While precise market sizing data wasn't provided, considering the growth of related music software markets and the expanding user base for DAWs, we can reasonably estimate the 2025 market size for chord generator plugins to be around $50 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% (based on observed growth in related music technology sectors), the market is projected to reach approximately $115 million by 2033. This growth is further supported by evolving trends such as AI-powered chord suggestion features, integration with other plugins, and a focus on user-friendly interfaces. Key restraining factors include the availability of free or low-cost alternatives, the learning curve associated with using advanced plugins, and the potential for user saturation within certain niche markets. However, the ongoing innovation in features, functionality, and user experience is likely to outweigh these restraints, particularly as these plugins become more integrated into popular DAWs and music creation workflows. Major players like Plugin Boutique, Xfer Records, and Mixed In Key are likely to capitalize on this growing demand by investing in R&D and expanding their product portfolios. The segmentation of the market is expected to be driven by pricing tiers, functionality (e.g., basic chord generation versus advanced harmony tools), and integration capabilities with various DAWs. This evolution makes this segment a dynamic and attractive space for both established and emerging companies.

This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. Please refer to the READMEs in the directories, which are summarized below.

The record contains the following files:

`clusters.tsv`: contains the cluster id, name and colour of clusters in the paper

scATAC.zip

Analysis products for the single-cell ATAC-seq data. Contains:

- `cells.tsv`: list of barcodes that pass QC. Columns include:
- `barcode`
- `sample`: (time point)
- `umap1`
- `umap2`
- `cluster`
- `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root
- `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root
- `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.
- `features.tsv`: 50 dimensional representation of each cell
- `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`

scATAC_clusters.zip

Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data.

- `clusters.tsv`: contains the cluster id, name and colour used in the paper
- `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.
- `fragments`: contains per cluster fragment files

scATAC_scRNA_integration.zip

Analysis products from the integration of scATAC with scRNA. Contains:

- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.
- `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.
- `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.

scRNA.zip

Analysis products for the single-cell RNA-seq data. Contains:

- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)
- `genes.txt`: list of all genes
- `cells.tsv`: list of barcodes that pass QC across samples. Contains:
- `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC)
- `sample`: sample name (D0, D2, .., D14, iPSC)
- `umap1`
- `umap2`
- `nCount_RNA`
- `nFeature_RNA`
- `cluster`
- `percent.mt`: percent of mitochondrial transcripts in cell
- `percent.oskm`: percent of OSKM transcripts in cell
- `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`
- `pca.tsv`: first 50 PC of each cell
- `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`

multiome.zip

multiome/snATAC:

These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2).

- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:
- `barcode`
- `umap1`: These are the coordinates used for the figures involving multiome in the paper.
- `umap2`: ^^^
- `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq
- `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels.
- `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.
- `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.
- `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.
- `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.

multiome/snRNA:

- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.
- `genes.txt`: list of all genes (this is different from the list in scRNA analysis)
- `cells.tsv`: list of barcodes that pass QC across samples. Contains:
- `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively)
- `sample`: sample name (D1M, D2M)
- `nCount_RNA`
- `nFeature_RNA`
- `percent.oskm`: percent of OSKM genes in cell
- `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`

This is the dataset supporting the EPI-Clone manuscript: scRNA-seq profiling of hematopoietic stem and progenitor cells (HSPCs) was performed with the 3' 10x Genomics profiling. Three experiments are included: Two where HSCs were clonally labeled with the LARRY system, transplanted to recipient mouse and profiled 4-5 months later (post-transplant hematopoiesis), and one where HSPCs were profiled straight from an unperturbed mouse.Dataset is a seurat (v4) object with the following assays, reductions and metadata:ASSAYS:AB: Antibody expression dataRNA: RNA expression profilesintegrated: Integration of DNA methylation data performed across experimental batches with two batch correction methods: CCA (https://satijalab.org/seurat/reference/runcca) and harmony (https://portals.broadinstitute.org/harmony/articles/quickstart.html).DIMENSIONALITY REDUCTIONpca_cca: PCA performed on the integrated data (CCA integration)umap_cca: UMAP computed on the integrated data (CCA integration)umap_harmony: UMAP computed on the integrated data (Harmony integration)METADATAExperiment: The experiment that the cell is from, values are "LARRY main experiment", "LARRY replicate" and "Native hematopoiesis"ProcessingBatch: Experiments were processed in several batches.CellType: Cell type annotationLARRY: Error corrected LARRY barcodepercent.mt: percentage of mitochondrial DNAnCount_RNA: Read count for the RNA modalitynFeature_RNA: Number of RNAs with at least one readnCount_AB: Read count for the surface protein modalitynFeature_AB: Number of ABs with at least one read

data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:

• CellRangerCounts/
  • GSE157827/
    • post_soupX/ : contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:
      • SAMN16100290_S01_AD/
        • barcodes.tsv
        • genes.tsv
        • matrix.mtx
    • pre_soupX/ : contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:
      • SAMN16100290_S01_AD/
        • filtered_feature_ bc_matrix.h5
        • Raw_feature_bc_matrix.h5
  • GSE174367/ : contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:
    • SAMN19128610_S1_CTRL/
      • barcodes.tsv
      • genes.tsv
      • Matrix.mtx
• ccc/
  • nichenet_grn/
    • gr_network_human_21122021.rds : accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet
    • ligand_tf_matrix_nsga2r_final.rds: accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet
    • signaling_network_human_21122021.rds : accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet
    • weighted_networks_nsga2r_final.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet
  • nichenet_prior/
    • ligand_target_matrix.rds : accessed in April 2023, ligand to target matrix from NicheNet
    • lr_network.rds : accessed in April 2023, ligand-receptor matrix from NicheNet
  • nichenet_v2_prior/
    • ligand_target_matrix_nsga2r_final.rds : accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.
    • lr_network_human_21122021.rds : accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.
  • geo_multinichenet_output.rds : MultiNicheNet output for Morabito et al., 2021 data
  • geo_signaling_igraph_objects.rds : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset.
  • gse_multinichenet_output.rds : MultiNicheNet output for Lau et al., 2020 data
  • gse_signaling_igraph_objects.rds : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset
• seurat_preprocessing/
  • geo_filtered_seurat.rds : merged and filtered seurat object of Morabito et al., 2021 data
  • geo_integrated_seurat.rds : seurat object integrated using harmony of Morabito et al., 2021 data
  • geo_clustered_seurat.rds : clustered seurat object of Morabito et al., 2021 data
  • geo_processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data
  • gse_filtered_seurat.rds : merged and filtered seurat object of Lau et al., 2020 data
  • gse_integrated_seurat.rds : seurat object integrated using harmony of Lau et al., 2020 data
  • gse_clustered_seurat.rds : clustered seurat object of Lau et al., 2020 data
  • gse_processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data

The distal region of the uterine (Fallopian) tube is commonly associated with high-grade serous carcinoma (HGSC), the predominant and most aggressive form of ovarian or extra-uterine cancer. Specific cell states and lineage dynamics of the adult tubal epithelium (TE) remain insufficiently understood, hindering efforts to determine the cell of origin for HGSC. Here, we report a comprehensive census of cell types and states of the mouse uterine tube. We show that distal TE cells expressing the stem/progenitor cell marker Slc1a3 can differentiate into both secretory (Ovgp1+) and ciliated (Fam183b+) cells. Inactivation of Trp53 and Rb1, whose pathways are commonly altered in HGSC, leads to elimination of targeted Slc1a3+ cells by apoptosis, thereby preventing their malignant transformation. In contrast, pre-ciliated cells (Krt5+, Prom1+, Trp73+) remain cancer-prone and give rise to serous tubal intraepithelial carcinomas and overt HGSC. These findings identify transitional pre-ciliated cells as a previously unrecognized cancer-prone cell state and point to pre-ciliation mechanisms as novel diagnostic and therapeutic targets. Methods

Single-cell RNA-sequencing library preparation For TE single cell expression and transcriptome analysis we isolated TE from C57BL6 adult estrous female mice. In 3 independent experiments a total of 62 uterine tubes were collected. Each uterine tube was placed in sterile PBS containing 100 IU ml-1 of penicillin and 100 µg ml-1 streptomycin (Corning, 30-002-Cl), and separated in distal and proximal regions. Tissues from the same region were combined in a 40 µl drop of the same PBS solution, cut open lengthwise, and minced into 1.5-2.5 mm pieces with 25G needles. Minced tissues were transferred with help of a sterile wide bore 200 µl pipette tip into a 1.8 ml cryo vial containing 1.2 ml A-mTE-D1 (300 IU ml-1 collagenase IV mixed with 100 IU ml-1 hyaluronidase; Stem Cell Technologies, 07912, in DMEM Ham’s F12, Hyclone, SH30023.FS). Tissues were incubated with loose cap for 1 h at 37°C in a 5% CO2 incubator. During the incubation tubes were taken out 4 times and tissues suspended with a wide bore 200 µl pipette tip. At the end of incubation, the tissue-cell suspension from each tube was transferred into 1 ml TrypLE (Invitrogen, 12604013) pre-warmed to 37°C, suspended 70 times with a 1000 µl pipette tip, 5 ml A-SM [DMEM Ham’s F12 containing 2% fetal bovine serum (FBS)] were added to the mix, and TE cells were pelleted by centrifugation 300x g for 10 minutes at 25°C. Pellets were then suspended with 1 ml pre-warmed to 37°C A-mTE-D2 (7 mg ml-1 Dispase II, Worthington NPRO2, and 10 µg ml-1 Deoxyribonuclease I, Stem Cell Technologies, 07900), and mixed 70 times with a 1000 µl pipette tip. 5 ml A-mTE-D2 was added and samples were passed through a 40 µm cell strainer, and pelleted by centrifugation at 300x g for 7 minutes at +4°C. Pellets were suspended in 100 µl microbeads per 107 total cells or fewer, and dead cells were removed with the Dead Cell Removal Kit (Miltenyi Biotec, 130-090-101) according to the manufacturer’s protocol. Pelleted live cell fractions were collected in 1.5 ml low binding centrifuge tubes, kept on ice, and suspended in ice cold 50 µl A-Ri-Buffer (5% FBS, 1% GlutaMAX-I, Invitrogen, 35050-079, 9 µM Y-27632, Millipore, 688000, and 100 IU ml-1 penicillin 100 μg ml-1 streptomycin in DMEM Ham’s F12). Cell aliquots were stained with trypan blue for live and dead cell calculation. Live cell preparations with a target cell recovery of 5,000-6,000 were loaded on Chromium controller (10X Genomics, Single Cell 3’ v2 chemistry) to perform single cell partitioning and barcoding using the microfluidic platform device. After preparation of barcoded, next-generation sequencing cDNA libraries samples were sequenced on Illumina NextSeq500 System.

Download and alignment of single-cell RNA sequencing data For sequence alignment, a custom reference for mm39 was built using the cellranger (v6.1.2, 10x Genomics) mkref function. The mm39.fa soft-masked assembly sequence and the mm39.ncbiRefSeq.gtf (release 109) genome annotation last updated 2020-10-27 were used to form the custom reference. The raw sequencing reads were aligned to the custom reference and quantified using the cellranger count function.

Preprocessing and batch correction All preprocessing and data analysis was conducted in R (v.4.1.1 (2021-08-10)). The cellranger count outs were first modified with the autoEstCont and adjustCounts functions from SoupX (v.1.6.1) to output a corrected matrix with the ambient RNA signal (soup) removed (https://github.com/constantAmateur/SoupX). To preprocess the corrected matrices, the Seurat (v.4.1.1) NormalizeData, FindVariableFeatures, ScaleData, RunPCA, FindNeighbors, and RunUMAP functions were used to create a Seurat object for each sample (https://github.com/satijalab/seurat). The number of principal components used to construct a shared nearest-neighbor graph were chosen to account for 95% of the total variance. To detect possible doublets, we used the package DoubletFinder (v.2.0.3) with inputs specific to each Seurat object. DoubletFinder creates artificial doublets and calculates the proportion of artificial k nearest neighbors (pANN) for each cell from a merged dataset of the artificial and actual data. To maximize DoubletFinder’s predictive power, mean-variance normalized bimodality coefficient (BCMVN) was used to determine the optimal pK value for each dataset. To establish a threshold for pANN values to distinguish between singlets and doublets, the estimated multiplet rates for each sample were calculated by interpolating between the target cell recovery values according to the 10x Chromium user manual. Homotypic doublets were identified using unannotated Seurat clusters in each dataset with the modelHomotypic function. After doublets were identified, all distal and proximal samples were merged separately. Cells with greater than 30% mitochondrial genes, cells with fewer than 750 nCount RNA, and cells with fewer than 200 nFeature RNA were removed from the merged datasets. To correct for any batch defects between sample runs, we used the harmony (v.0.1.0) integration method (github.com/immunogenomics/harmony).

Clustering parameters and annotations After merging the datasets and batch-correction, the dimensions reflecting 95% of the total variance were input into Seurat’s FindNeighbors function with a k.param of 70. Louvain clustering was then conducted using Seurat’s FindClusters with a resolution of 0.7. The resulting 19 clusters were annotated based on the expression of canonical genes and the results of differential gene expression (Wilcoxon Rank Sum test) analysis. One cluster expressing lymphatic and epithelial markers was omitted from later analysis as it only contained 2 cells suspected to be doublets. To better understand the epithelial populations, we reclustered 6 epithelial populations and reapplied harmony batch correction. The clustering parameters from FindNeighbors was a k.param of 50, and a resolution of 0.7 was used for FindClusters. The resulting 9 clusters within the epithelial subset were further annotated using differential expression analysis and canonical markers.

Pseudotime analysis Potential of heat diffusion for affinity-based transition embedding (PHATE) is dimensional reduction method to more accurately visualize continual progressions found in biological data 35. A modified version of Seurat (v4.1.1) was developed to include the ‘RunPHATE’ function for converting a Seurat Object to a PHATE embedding. This was built on the phateR package (v.1.0.7) (https://github.com/scottgigante/seurat/tree/patch/add-PHATE-again). In addition to PHATE, pseudotime values were calculated with Monocle3 (v.1.2.7), which computes trajectories with an origin set by the user 36,55–57. The origin was set to be a progenitor cell state confirmed with lineage tracing experiments. 35. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat Biotechnol 37, 1482–1492 (2019). doi:10.1038/s41587-019-0336-3 36. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). doi:10.1038/s41586-019-0969-x 55. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology 32, 381–386 (2014). doi:10.1038/nbt.2859 56. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nature Methods 14, 309–315 (2017). doi:10.1038/nmeth.4150 57. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 14, 979–982 (2017). doi:10.1038/nmeth.4402

Rachmad, Yoesoep Edhie. 2024. Harmony in Halal: Bridging Heritage and Tourism in Indonesian Cities. Chuban Guangzhou Guoji Shuji Tebie Ban 广州白云书籍出版 2024 特别版.

"Harmony in Halal: Bridging Heritage and Tourism in Indonesian Cities" by Yoesoep Edhie Rachmad, published in 2024 by Chuban Guangzhou Guoji Shuji Tebie Ban, thoughtfully examines how halal tourism can be integrated with cultural heritage preservation to boost both the economic vitality and cultural identity of Indonesian cities. This book offers an insightful look at the harmonious blend of religious compliance and cultural preservation, providing a blueprint for sustainable and culturally respectful tourism development in Indonesia. Background Set within the bustling urban centers of Indonesia, a country rich in diverse cultural heritage and the largest Muslim-majority population in the world, the book argues for the potential of halal tourism as a driver of cultural and economic development. It explores the concept of halal tourism not just as a market niche but as a comprehensive approach that includes respecting and promoting the rich tapestry of Indonesian cultural heritage. Basic Definitions and Concepts Halal tourism is defined as tourism that adheres to Islamic principles, encompassing food, accommodation, and overall travel experiences. The book expands this concept to include the integration of these services within the broader context of preserving and celebrating Indonesia's historical and cultural sites, showing how halal tourism can support the conservation of cultural heritage. Underlying Phenomenon The global increase in Muslim travelers seeking destinations that cater to their religious needs, coupled with a growing interest in authentic cultural experiences, drives the discussion. The book highlights Indonesia's unique position to capitalize on this trend by integrating halal tourism with cultural heritage to create a more compelling and competitive tourism offering. Problem Formulation The primary challenge is how to effectively balance the strict requirements of halal certification with the need to preserve and promote local cultures and traditions in urban settings, which often feature a diverse demographic and historical complexities. Research Objectives The objective is to explore practical ways in which cities in Indonesia can integrate halal tourism with cultural heritage initiatives to create unique, appealing, and respectful tourism products that contribute to economic and social sustainability. Indicators Success is measured through increased tourist numbers, especially from Muslim-majority countries, enhanced economic benefits for local communities, and improved preservation and interpretation of cultural heritage sites. Operational Variables Variables include the number of halal-certified businesses, the effectiveness of training programs in cultural heritage for tourism providers, and the impact of tourism on local economic development. Key Factors Key factors for successful integration include strong governmental support, effective public-private partnerships, and community engagement in both tourism development and cultural preservation efforts. Implementation Strategies Strategies discussed involve developing comprehensive halal tourism certification processes that incorporate cultural heritage components, using technology to enhance visitor experiences, and marketing these unique tourism products both domestically and internationally. Challenges and Supports Challenges include navigating regulatory requirements, ensuring community benefits, and marketing effectively to diverse tourist demographics. Supports are provided by increasing international collaboration in halal tourism standards and leveraging technology for innovative tourism management. Research Findings Case studies from cities like Jakarta, Yogyakarta, and Bandung show successful examples of how local wisdom and cultural heritage have been integrated into halal tourism offerings, yielding positive social and economic outcomes. Conclusions and Recommendations The book concludes that integrating halal tourism with cultural heritage can provide substantial benefits to Indonesian cities by enhancing their tourism appeal and fostering greater cultural understanding. It recommends continued investment in capacity building, policy development, and innovative marketing strategies to ensure the sustainable growth of this sector. "Harmony in Halal" serves as a vital resource for city planners, tourism developers, and policymakers, offering a strategic vision for cultivating a vibrant, culturally rich, and economically beneficial tourism sector in urban Indonesia.

Bab 1: Pariwisata Halal dan Warisan Budaya Bab ini menggali konsep pariwisata halal dalam konteks warisan budaya di Indonesia, menjelaskan bagaimana kedua unsur ini dapat saling memperkuat. Bab ini juga menetapkan landasan teoretis tentang pentingnya menjaga warisan budaya dalam pengembangan pariwisata halal. Bab 2: Kriteria dan Standar Halal dalam Pariwisata Bab ini mendetailkan kriteria dan standar yang harus dipenuhi oleh fasilitas pariwisata untuk mendapatkan sertifikasi halal, serta implikasi dari standar ini terhadap pemeliharaan dan promosi warisan budaya. Bab 3: Kasus Studi Penerapan Halal dalam Warisan Budaya Bab ini menyajikan serangkaian kasus studi dari berbagai kota di Indonesia yang telah berhasil mengintegrasikan pariwisata halal dengan pelestarian warisan budaya, menunjukkan berbagai pendekatan dan hasilnya. Bab 4: Dampak Ekonomi dan Sosial Bab ini menganalisis dampak ekonomi dan sosial dari integrasi pariwisata halal dan warisan budaya, dengan fokus pada bagaimana ini dapat membantu dalam pengembangan ekonomi lokal dan peningkatan kualitas hidup masyarakat. Bab 5: Pengembangan Kapasitas dan Pelatihan Bab ini membahas pentingnya pengembangan kapasitas dan pelatihan bagi pelaku industri pariwisata untuk memastikan bahwa mereka dapat menawarkan produk dan layanan yang memenuhi standar halal sambil menjaga keaslian warisan budaya. Bab 6: Strategi Pemasaran dan Promosi Bab ini mengeksplorasi strategi pemasaran dan promosi yang efektif untuk menarik wisatawan yang tertarik dengan pariwisata halal dan warisan budaya, termasuk penggunaan teknologi terkini dan media sosial. Bab 7: Kebijakan Pemerintah dan Dukungan Institusional Bab ini mengkaji peran kebijakan pemerintah dan dukungan institusional dalam mendukung integrasi antara pariwisata halal dan pelestarian warisan budaya, serta memberikan rekomendasi untuk perbaikan kebijakan. Bab 8: Tantangan dan Peluang Masa Depan Bab ini mengidentifikasi tantangan utama yang dihadapi dalam menjembatani pariwisata halal dan warisan budaya serta menguraikan peluang masa depan untuk pengembangan lebih lanjut dalam sektor ini. Kesimpulan Kesimpulan dari buku ini menekankan bahwa menjembatani pariwisata halal dan warisan budaya tidak hanya menguntungkan secara ekonomi tetapi juga memperkuat identitas budaya dan sosial kota-kota di Indonesia. Melalui integrasi yang cerdas dan strategis, Indonesia dapat meningkatkan posisinya sebagai destinasi pariwisata halal global yang kaya akan warisan budaya. Inisiatif ini harus didukung oleh kebijakan yang kuat, kerjasama antar sektor, dan keterlibatan aktif masyarakat lokal untuk memastikan bahwa pertumbuhan pariwisata halal berlangsung secara berkelanjutan dan inklusif.

Harmony integrated single-cell Dataset containing all cells from 4 different murine fibrotic disease models. The dataset is largely public available but also contains unpublished (cardiac) samples https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137720https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104154https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138826https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-9816https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-7895

Building on Anderson’s nationalism concept (2006), national culture is essential as it shapes social practices and institutions; however, its complexity arises from internal diversity. This research aims to clarify how Nusantara cultural values are integrated into pupils’ management and activities to revitalize national identity and promote social harmony in Indonesia’s multicultural context. Utilizing a cross-sectional design, data were collected through surveys from 265 participants, including school leaders, teachers, staff, and pupils across ten provinces. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis, revealing significant relationships among the variables. The findings indicate that the integration of Nusantara cultural values positively influences social harmony and significantly impacts the revitalization of national identity. Furthermore, pupil management activities grounded in these cultural values enhance social harmony and positively affect national identity, with the latter significantly influencing social harmony. This demonstrates a complex interplay among these elements, where both cultural value integration and pupil management activities are pivotal, with national identity acting as a mediating factor. These results underscore the critical role of cultural values in shaping educational experiences and fostering unity amid globalization. The implications of this study suggest practical strategies for educators and policymakers to enhance cultural integration within schools, preserving Indonesia’s rich heritage while promoting inclusivity. Ultimately, this research contributes to the discourse on cultural education, offering a framework for future studies to strengthen national unity and intercultural understanding in diverse societies.

Biological possesses enriched by DAVID for hub genes of Group 2.

IntroductionNatural menopause is an inevitable biological process with significant implications for women's health. However, the molecular mechanisms underlying menopause are not well understood. This study aimed to investigate the molecular and cellular changes occurring in the ovary before and after perimenopause.MethodsSingle-cell sequencing data from the GTEx V8 cohort (30-39: 14 individuals; 40-49: 37 individuals; 50-59: 61 individuals) and transcriptome sequencing data from ovarian tissue were analyzed. Seurat was used for single-cell sequencing data analysis, while harmony was employed for data integration. Cell differentiation trajectories were inferred using CytoTrace. CIBERSORTX assessed cell infiltration scores in ovarian tissue. WGCNA evaluated co-expression network characteristics in pre- and post-perimenopausal ovarian tissue. Functional enrichment analysis of co-expression modules was conducted using ClusterprofileR and Metascape. DESeq2 performed differential expression analysis. Master regulator analysis and signaling pathway activity analysis were carried out using MsViper and Progeny, respectively. Machine learning models were constructed using Orange3.ResultsWe identified the differentiation trajectory of follicular cells in the ovary as ARID5B+ Granulosa -> JUN+ Granulosa -> KRT18+ Granulosa -> MT-CO2+ Granulosa -> GSTA1+ Granulosa -> HMGB1+ Granulosa. Genes driving Granulosa differentiation, including RBP1, TMSB10, SERPINE2, and TMSB4X, were enriched in ATP-dependent activity regulation pathways. Genes involved in maintaining the Granulosa state, such as DCN, ARID5B, EIF1, and HSP90AB1, were enriched in the response to unfolded protein and chaperone-mediated protein complex assembly pathways. Increased contents of terminally differentiated HMGB1+ Granulosa and GSTA1+ Granulosa were observed in the ovaries of individuals aged 50-69. Signaling pathway activity analysis indicated a gradual decrease in TGFb and MAPK pathway activity with menopause progression, while p53 pathway activity increased. Master regulator analysis revealed significant activation of transcription factors FOXR1, OTX2, MYBL2, HNF1A, and FOXN4 in the 30-39 age group, and GLI1, SMAD1, SMAD7, APP, and EGR1 in the 40-49 age group. Additionally, a diagnostic model based on 16 transcription factors (Logistic Regression L2) achieved reliable performance in determining ovarian status before and after perimenopause.ConclusionThis study provides insights into the molecular and cellular mechanisms underlying natural menopause in the ovary. The findings contribute to our understanding of perimenopausal changes and offer a foundation for health management strategies for women during this transition.