Seurat object stored in RDS file format. Three fresh tumor samples (respectively from INI254, INI255 and INI267) were prepared and loaded in 10x Chromium instrument (10x Genomics). Libraries were prepared using a Single Cell 3’ Reagent Kit (V2 chemistry, 10X Genomics) and sequenced on an Illumina HiSeq2500 using paired-end 26x98 bp as sequencing mode, targeting at least 50 000 reads par cell. Mapping and UMI counting per gene were performed using cellranger tool (version 3.1.0) and the hg19 reference genome version. Cells with both a low number of genes and a high proportion of mitochondrial RNA were discarded. The threshold of the minimum number of detected genes was set as the 5th percentile of the distribution of the number of detected genes in all cells while the maximum proportion of mitochondrial genes were set by visual inspection of the plot of the number of detected genes versus the percentage of mitochondrial gene of each sample. scRNA-seq data integration was performed using the CCA-based implemented in Seurat version 3. The clustering was conducted using the graph-based modularity optimization Louvain algorithm implemented in Seurat v3. The resolution 0.2 (integrated_snn_res.0.2) was choosen for the final result.

List of genes and fatty acids after functional grouping with REACTOME and STRING using.

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

Arsenic exposure via drinking water is a serious environmental health concern. Epidemiological studies suggest a strong association between prenatal arsenic exposure and subsequent childhood respiratory infections, as well as morbidity from respiratory diseases in adulthood, long after systemic clearance of arsenic. We investigated the impact of exclusive prenatal arsenic exposure on the inflammatory immune response and respiratory health after an adult influenza A (IAV) lung infection. C57BL/6J mice were exposed to 100 ppb sodium arsenite in utero, and subsequently infected with IAV (H1N1) after maturation to adulthood. Assessment of lung tissue and bronchoalveolar lavage fluid (BALF) at various time points post IAV infection reveals greater lung damage and inflammation in arsenic exposed mice versus control mice. Single-cell RNA sequencing analysis of immune cells harvested from IAV infected lungs suggests that the enhanced inflammatory response is mediated by dysregulation of innate immune function of monocyte derived macrophages, neutrophils, NK cells, and alveolar macrophages. Our results suggest that prenatal arsenic exposure results in lasting effects on the adult host innate immune response to IAV infection, long after exposure to arsenic, leading to greater immunopathology. This study provides the first direct evidence that exclusive prenatal exposure to arsenic in drinking water causes predisposition to a hyperinflammatory response to IAV infection in adult mice, which is associated with significant lung damage.

Methods Whole lung homogenate preparation for single cell RNA sequencing (scRNA-seq).

Lungs were perfused with PBS via the right ventricle, harvested, and mechanically disassociated prior to straining through 70- and 30-µm filters to obtain a single-cell suspension. Dead cells were removed (annexin V EasySep kit, StemCell Technologies, Vancouver, Canada), and samples were enriched for cells of hematopoetic origin by magnetic separation using anti-CD45-conjugated microbeads (Miltenyi, Auburn, CA). Single-cell suspensions of 6 samples were loaded on a Chromium Single Cell system (10X Genomics) to generate barcoded single-cell gel beads in emulsion, and scRNA-seq libraries were prepared using Single Cell 3’ Version 2 chemistry. Libraries were multiplexed and sequenced on 4 lanes of a Nextseq 500 sequencer (Illumina) with 3 sequencing runs. Demultiplexing and barcode processing of raw sequencing data was conducted using Cell Ranger v. 3.0.1 (10X Genomics; Dartmouth Genomics Shared Resource Core). Reads were aligned to mouse (GRCm38) and influenza A virus (A/PR8/34, genome build GCF_000865725.1) genomes to generate unique molecular index (UMI) count matrices. Gene expression data have been deposited in the NCBI GEO database and are available at accession # GSE142047.

Preprocessing of single cell RNA sequencing (scRNA-seq) data

Count matrices produced using Cell Ranger were analyzed in the R statistical working environment (version 3.6.1). Preliminary visualization and quality analysis were conducted using scran (v 1.14.3, Lun et al., 2016) and Scater (v. 1.14.1, McCarthy et al., 2017) to identify thresholds for cell quality and feature filtering. Sample matrices were imported into Seurat (v. 3.1.1, Stuart., et al., 2019) and the percentage of mitochondrial, hemoglobin, and influenza A viral transcripts calculated per cell. Cells with < 1000 or > 20,000 unique molecular identifiers (UMIs: low quality and doublets), fewer than 300 features (low quality), greater than 10% of reads mapped to mitochondrial genes (dying) or greater than 1% of reads mapped to hemoglobin genes (red blood cells) were filtered from further analysis. Total cells per sample after filtering ranged from 1895-2482, no significant difference in the number of cells was observed in arsenic vs. control. Data were then normalized using SCTransform (Hafemeister et al., 2019) and variable features identified for each sample. Integration anchors between samples were identified using canonical correlation analysis (CCA) and mutual nearest neighbors (MNNs), as implemented in Seurat V3 (Stuart., et al., 2019) and used to integrate samples into a shared space for further comparison. This process enables identification of shared populations of cells between samples, even in the presence of technical or biological differences, while also allowing for non-overlapping populations that are unique to individual samples.

Clustering and reference-based cell identity labeling of single immune cells from IAV-infected lung with scRNA-seq

Principal components were identified from the integrated dataset and were used for Uniform Manifold Approximation and Projection (UMAP) visualization of the data in two-dimensional space. A shared-nearest-neighbor (SNN) graph was constructed using default parameters, and clusters identified using the SLM algorithm in Seurat at a range of resolutions (0.2-2). The first 30 principal components were used to identify 22 cell clusters ranging in size from 25 to 2310 cells. Gene markers for clusters were identified with the findMarkers function in scran. To label individual cells with cell type identities, we used the singleR package (v. 3.1.1) to compare gene expression profiles of individual cells with expression data from curated, FACS-sorted leukocyte samples in the Immgen compendium (Aran D. et al., 2019; Heng et al., 2008). We manually updated the Immgen reference annotation with 263 sample group labels for fine-grain analysis and 25 CD45+ cell type identities based on markers used to sort Immgen samples (Guilliams et al., 2014). The reference annotation is provided in Table S2, cells that were not labeled confidently after label pruning were assigned “Unknown”.

Differential gene expression by immune cells

Differential gene expression within individual cell types was performed by pooling raw count data from cells of each cell type on a per-sample basis to create a pseudo-bulk count table for each cell type. Differential expression analysis was only performed on cell types that were sufficiently represented (>10 cells) in each sample. In droplet-based scRNA-seq, ambient RNA from lysed cells is incorporated into droplets, and can result in spurious identification of these genes in cell types where they aren’t actually expressed. We therefore used a method developed by Young and Behjati (Young et al., 2018) to estimate the contribution of ambient RNA for each gene, and identified genes in each cell type that were estimated to be > 25% ambient-derived. These genes were excluded from analysis in a cell-type specific manner. Genes expressed in less than 5 percent of cells were also excluded from analysis. Differential expression analysis was then performed in Limma (limma-voom with quality weights) following a standard protocol for bulk RNA-seq (Law et al., 2014). Significant genes were identified using MA/QC criteria of P < .05, log2FC >1.

Analysis of arsenic effect on immune cell gene expression by scRNA-seq.

Sample-wide effects of arsenic on gene expression were identified by pooling raw count data from all cells per sample to create a count table for pseudo-bulk gene expression analysis. Genes with less than 20 counts in any sample, or less than 60 total counts were excluded from analysis. Differential expression analysis was performed using limma-voom as described above.

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`

The Continuous Configuration Automation (CCA) tool market is experiencing robust growth, driven by the increasing demand for streamlined software deployment and infrastructure management. The market's expansion is fueled by several key factors, including the rising adoption of DevOps methodologies, the need for enhanced security and compliance, and the growing complexity of modern IT environments. Businesses are increasingly seeking solutions that automate the configuration and deployment of applications and infrastructure, minimizing human error and accelerating release cycles. This trend is evident across various industries, including financial services, healthcare, and technology, where rapid innovation and agility are critical for competitive advantage. We project a Compound Annual Growth Rate (CAGR) of 15% for the CCA tool market between 2025 and 2033, based on observed market trends and expert analysis. This growth is expected to be particularly strong in regions like North America and Europe, driven by early adoption of DevOps practices and a mature technological landscape. However, challenges remain, including the need for skilled professionals to implement and manage these tools, and the complexities involved in integrating CCA solutions with existing IT infrastructure. Nevertheless, the long-term outlook for the CCA tool market remains positive, with significant opportunities for vendors offering innovative and user-friendly solutions. The segmentation within the market reflects varying application needs and technological approaches, impacting the adoption rate across different sectors. The competitive landscape is characterized by a mix of established players and emerging startups, each offering unique features and functionalities. The market is expected to witness increased consolidation in the coming years, as vendors strive to expand their market share and offer comprehensive solutions. Key strategies employed by companies in this space include partnerships, acquisitions, and the development of advanced capabilities such as AI-powered automation and integrated security features. Regional variations in market growth are anticipated, with regions such as Asia-Pacific showing potentially higher growth rates compared to more mature markets in North America and Europe. This is attributed to the rapid digital transformation currently underway in the Asia-Pacific region and increasing investments in IT infrastructure. Continued focus on improving user experience and addressing integration challenges will be crucial for sustained market growth in the coming years.

Recent research in C. elegans and the rodent has identified correlations between gene expression and connectivity. Here we extend this type of approach to examine complex patterns of gene expression in the rodent brain in the context of regional brain connectivity and differences in cellular populations. Using multiple large-scale data sets obtained from public sources, we identified two novel patterns of mouse brain gene expression showing a strong degree of anti-correlation, and relate this to multiple data modalities including macroscale connectivity. We found that these signatures are associated with differences in expression of neuronal and oligodendrocyte markers, suggesting they reflect regional differences in cellular populations. We also find that the expression level of these genes is correlated with connectivity degree, with regions expressing the neuron-enriched pattern having more incoming and outgoing connections with other regions. Our results exemplify what is possible when increasingly detailed large-scale cell- and gene-level data sets are integrated with connectivity data.

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure.

Integrating community based disaster risk reduction (DRR) and climate change adaptation (CCA) is identified at the policy and practical level as crucial to aid effectiveness. Successful integration reduces both duplication of efforts and confusion at the community level. This research focuses on Pacific community based DRR and CCA initiatives, and draws upon the knowledge and insight of key stakeholders from multiple backgrounds to develop an understanding of the current status of DRR and CCA in the region. Additional understanding is gained through detailed case studies of current projects in Fiji and Samoa which highlight the challenges and best practice methods used to integrate DRR and CCA in current community based projects.Available online|Also available on CD: Contents: Full report. 2. Appendicies A-F. 3. Pacific Insight Newsletters. 4. Conference Paper: Gero et al., 2009Call Number: 554.12 GER,[EL],CD206Physical Description: various pagings ; 29 cm

The Collaborative Combat Aircraft (CCA) market is poised for substantial growth, projected to reach $736 million in 2025. While the precise Compound Annual Growth Rate (CAGR) isn't provided, considering the advancements in autonomous systems, artificial intelligence, and the increasing demand for improved interoperability among military platforms, a conservative estimate of a 10% CAGR over the forecast period (2025-2033) is plausible. This translates to significant market expansion, driven by factors such as the need for enhanced situational awareness, reduced pilot workload, and improved collaborative capabilities in modern warfare. Key market drivers include the rising geopolitical tensions and the subsequent increase in defense budgets globally, along with technological advancements in areas such as sensor fusion, data-linking, and unmanned aerial vehicle (UAV) integration within CCA systems. The market is segmented by type (stealth and non-stealth), application (sea, land, and airspace), and production (including World Collaborative Combat Aircraft). The leading players, including Lockheed Martin, Boeing, Northrop Grumman, and General Atomics, are actively investing in research and development to maintain their competitive edge. The market's growth, however, is subject to restraints such as high development and operational costs, and the complexity of integrating diverse systems for seamless collaborative operation. The regional breakdown reveals a strong presence of North America, driven by the significant investments in defense technology and the considerable market share held by US-based companies. However, the Asia-Pacific region is expected to witness rapid growth due to increasing defense spending and modernization initiatives by several countries in the region. Europe, while a key player, is anticipated to demonstrate moderate growth, primarily influenced by the existing defense collaborations and technological advancements within the region. The market is likely to be shaped by the adoption of advanced technologies, evolving defense strategies, and changing geopolitical landscapes over the next decade. The increasing focus on network-centric warfare will further fuel the demand for sophisticated CCA systems, thereby contributing to the market's projected growth.

Normative models of neural computation offer simplified yet lucid mathematical descriptions of murky biological phenomena. Previously, online Principal Component Analysis (PCA) was used to model a network of single-compartment neurons accounting for weighted summation of upstream neural activity in the soma and Hebbian/anti-Hebbian synaptic learning rules. However, synaptic plasticity in biological neurons often depends on the integration of synaptic currents over a dendritic compartment rather than total current in the soma. Motivated by this observation, we model a pyramidal neuronal network using online Canonical Correlation Analysis (CCA). Given two related datasets represented by distal and proximal dendritic inputs, CCA projects them onto the subspace which maximizes the correlation between their projections. First, adopting a normative approach and starting from a single-channel CCA objective function, we derive an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron. To model networks of pyramidal neurons, we introduce a novel multi-channel CCA objective function, and derive from it an online gradient-based optimization algorithm whose steps can be interpreted as the operation of a pyramidal neuron network including its architecture, dynamics, and synaptic learning rules. Next, we model a neuron with more than two dendritic compartments by deriving its operation from a known objective function for multi-view CCA. Finally, we confirm the functionality of our networks via numerical simulations. Overall, our work presents a simplified but informative abstraction of learning in a pyramidal neuron network, and demonstrates how such networks can integrate multiple sources of inputs.

The mature HIV-1 capsid is stabilized by host and viral determinants. The capsid protein CA binds to the cellular metabolites inositol hexakisphosphate (IP6) and its precursor inositol (1, 3, 4, 5, 6) pentakisphosphate (IP5) to stabilize the mature capsid. In target cells, capsid destabilization by the antiviral compounds lenacapavir and PF74 reveals an HIV-1 infectivity defect due to IP5/IP6 (IP5/6) depletion. To test whether intrinsic HIV-1 capsid stability and/ or host factor binding determines HIV-1 insensitivity to IP5/6 depletion, a panel of CA mutants was assayed for infection of IP5/6-depleted T cells and wildtype cells. Four CA mutants with unstable capsids exhibited dependence on host IP5/6 for infection and reverse transcription (RTN). Adaptation of one such mutant, Q219A, by spread in culture resulted in Vpu truncation and a capsid three-fold interface mutation, T200I. T200I increased intrinsic capsid stability as determined by in vitro uncoating of purified cores and partially reversed the IP5/6-dependence in target cells for each of the four CA mutants. T200I further rescued the changes to lenacapavir sensitivity associated with the parental mutation. The premature dissolution of the capsid caused by the IP5/6-dependent mutations imparted a unique defect in integration targeting that was rescued by T200I. Collectively, these results demonstrate that T200I restored other capsid functions after RTN for the panel of mutants. Thus, the hyperstable T200I mutation stabilized the instability defects imparted by the parental IP5/6-dependent CA mutation. The contribution of Vpu truncation to mutant adaptation was linked to BST-2 antagonization, suggesting that cell-to-cell transfer promoted replication of the mutants. We conclude that interactions at the three-fold interface are adaptable, key mediators of capsid stability in target cells and are able to antagonize even severe capsid instability to promote infection.

The National Marine Sanctuary Program (NMSP) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, NMSP and NOAA?s National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).

Survey of advanced technology, acquisition or integration of advanced technologies, by methods of acquiring or integrating advanced technologies, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2014.

MOTIVATION:

Many microarray datasets are available online with formalized standards describing the probe sequences and expression values. Unfortunately, the description, conditions and parameters of the experiments are less commonly formalized and often occur as natural language text. This hinders searching, high-throughput analysis, organization and integration of the datasets.

RESULTS:

We use the lexical resources and software tools from the Unified Medical Language System (UMLS) to extract concepts from text. We then link the UMLS concepts to classes in open biomedical ontologies. The result is accessible and clear semantic annotations of gene expression experiments. We applied the method to 595 expression experiments from Gemma, a resource for re-use and meta-analysis of gene expression profiling data. We evaluated and corrected all stages of the annotation process. The majority of missed annotations were due to a lack of cross-references. The most error-prone stage was the extraction of concepts from phrases. Final review of the annotations in context of the experiments revealed 89% precision. A naive system, lacking the phrase to concept corrections is 68% precise. We have integrated this annotation pipeline into Gemma.

AVAILABILITY:

The source code, documentation and Supplementary Materials are available at http://www.chibi.ubc.ca/GEOMMTX. The results of the manual evaluations are provided as Supplementary Material. Both manual and predicted annotations can be viewed and searched via the Gemma website at http://www.chibi.ubc.ca/Gemma. The complete set of predicted annotations is available as a machine readable resource description framework graph.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Community Choice Aggregation Billing Platform Market Outlook

According to our latest research, the global Community Choice Aggregation (CCA) Billing Platform market size reached USD 1.41 billion in 2024, demonstrating robust expansion driven by the increasing adoption of renewable energy initiatives and the growing demand for transparent, flexible energy billing solutions. The market is projected to grow at a CAGR of 13.8% from 2025 to 2033, with the total market value forecasted to reach USD 4.16 billion by 2033. This dynamic growth is propelled by the acceleration of community-based energy programs, regulatory support for energy choice, and the digital transformation of utility billing systems worldwide.

The primary growth driver for the Community Choice Aggregation Billing Platform market is the surge in community-driven energy procurement programs, particularly in North America and Europe. As municipalities and local governments strive to provide residents and businesses with access to renewable energy options, the complexity of billing, customer management, and compliance increases. This has led to a significant demand for advanced billing platforms that can handle multifaceted energy transactions, dynamic pricing models, and seamless integration with distributed energy resources. Furthermore, the shift toward decarbonization and sustainability goals is encouraging more communities to establish CCAs, thereby expanding the addressable market for sophisticated billing solutions that can support these evolving requirements.

Another critical factor fueling market growth is the rapid digitization of the utility sector. The integration of smart meters, IoT devices, and cloud-based platforms is enhancing the efficiency and accuracy of billing operations for CCAs. These technological advancements facilitate real-time data collection, advanced analytics, and automated billing processes, reducing manual errors and operational costs. The adoption of artificial intelligence and machine learning within billing platforms is also improving customer engagement, enabling predictive analytics, and supporting personalized energy management services. As utilities and CCAs continue to modernize their infrastructure, the demand for scalable, secure, and interoperable billing platforms is expected to rise substantially.

Regulatory frameworks and policy incentives play a pivotal role in shaping the Community Choice Aggregation Billing Platform market landscape. Governments worldwide are enacting policies that promote energy choice, grid modernization, and renewable integration. These regulations often mandate transparent billing, data privacy, and compliance reporting, creating a fertile environment for the deployment of specialized billing platforms. In addition, the proliferation of distributed energy resources such as rooftop solar, battery storage, and electric vehicles is adding further complexity to billing processes, necessitating advanced solutions capable of managing diverse energy flows and tariff structures. The interplay between regulatory support and market innovation is anticipated to sustain high growth rates for the foreseeable future.

From a regional perspective, North America continues to dominate the CCA Billing Platform market, accounting for over 45% of the global market share in 2024. This leadership is attributed to the extensive rollout of CCAs in states like California, Illinois, and New York, coupled with progressive regulatory environments and high levels of digital adoption among utilities. Europe follows closely, benefiting from ambitious decarbonization targets and a strong focus on energy democratization. Meanwhile, emerging markets in Asia Pacific and Latin America are witnessing increased interest in community energy programs, although regulatory and infrastructural challenges persist. As these regions invest in smart grid technologies and renewable integration, they are expected to contribute significantly to market growth over the next decade.

Component Analysis

The Pan-Canadian Wind Integration Study (PCWIS), completed in 2016, assessed the operational and economic implications of integrating large amounts of wind energy into the Canadian electricity system. The PCWIS study generated a significant amount of high-resolution modelled wind data at many locations across Canada. This dataset contains over 54,000 “cells”, with each cell representing one node on a 2×2 km grid. Each cell has an associated time history of three years of modelled wind data, from 2008 to 2010, at 10-minute intervals. The interactive map allows a user to readily visualize the geographic distribution of Canada’s wind resources, as well as to quickly estimate the strength of the wind resource at a particular location.

The Office of National Marine Sanctuary Program (ONMS) updates and revises the management plans for each of its 13 sanctuaries. This process, which is open to the public, enables each site to revisit the reasons for sanctuary designation and assess whether they are meeting their goals, as well as to set new goals consistent with the mandates of the National Marine Sanctuaries Act. Issues raised by the public during this process are evaluated and a determination is made as to whether they will be incorporated into the updated plan. Many of these issues focus on topics such as the implementation of marine zoning or sanctuary boundary adjustments, both of which require information on the distribution of resources within and around the sanctuary. Recognizing this, ONMS and NOAA's National Centers for Coastal Ocean Science (NCCOS) formalized an agreement to collaborate in the revision process by developing such information through a series of biogeographic assessments conducted in selected sanctuaries. The resulting products are then supplied to sanctuary managers and staff for use in the policy and decision making process. This collaborative effort began along the west coast of the U.S. with the Cordell Bank, Gulf of Farallones, and Monterey Bay national marine sanctuaries, and is herein centered on the Channel Islands National Marine Sanctuary (CINMS).

Average percentage of integrations (with standard deviation) into the indicated genomic annotations. (XLSX)