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Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets.

The respiratory system undergoes a diversity of structural, biochemical, and functional changes necessary for adaptation to air breathing at birth. To identify the heterogeneity of pulmonary cell types and dynamic changes in gene expression mediating adaptation to respiration, here we perform single cell RNA analyses of mouse lung on postnatal day 1. Using an iterative cell type identification strategy we unbiasedly identify the heterogeneity of murine pulmonary cell types. We identify distinct populations of epithelial, endothelial, mesenchymal, and immune cells, each containing distinct subpopulations. Furthermore we compare temporal changes in RNA expression patterns before and after birth to identify signaling pathways selectively activated in specific pulmonary cell types, including activation of cell stress and the unfolded protein response during perinatal adaptation of the lung. The present data provide a single cell view of the adaptation to air breathing after birth. The respiratory system is transformed in terms of functional change at birth to adapt to breathing air. Here, the authors examine the molecular changes behind the first breath in the mouse by Drop-seq based RNA sequencing, identifying activation of the unfolded protein response as a perinatal adaptation of the lung.

‘LungGENS’, our previously developed web tool for mapping single-cell gene expression in the developing lung, has been well received by the pulmonary research community. With continued support from the ‘LungMAP’ consortium, we extended the scope of the LungGENS database to accommodate transcriptomics data from pulmonary tissues and cells from human and mouse at different stages of lung development. Lung Gene Expression Analysis (LGEA) web portal is an extended version of LungGENS useful for the analysis, display and interpretation of gene expression patterns obtained from single cells, sorted cell populations and whole lung tissues. The LGEA web portal is freely available at http://research.cchmc.org/pbge/lunggens/mainportal.html.

A tungsten (W) material is a candidate for the first wall and silicon carbide (SiC) composites are candidates for the structural materials applied in nuclear fusion. SiC fiber-reinforced W composites are also developed for nuclear fusion applications. An effective diffusion barrier is required to prevent reaction between W and SiC. Therefore, in this work, advanced ceramics coatings, such as oxides (ZrO2, TiO2 and Er2O3), nitrides (ZrN and TiN), carbides (TiC and ZrC) were chosen to assess abilities to suppress the reactions. Various films were coated on a CVD (chemical vapor deposition)-SiC plate using the dipping method. Additionally, nitrides coatings prepared by the sputtering method were also investigated in this work. Then evaluations were carried out by joining the coated CVD-SiC plates with W foils. Only the multi-dipped Er2O3 coating and the sputtered nitrides worked well compared with the other coatings. For the other oxide coatings, reactions were identified between oxides and SiC, and for the dipped nitrides and carbides films, cracks were observed on the coating, generated from the coefficient of thermal expansion (CTE) mismatch with the SiC substrate and volume change for the oxides changing to nitrides and carbides. This work provides suggestions about choosing an appropriate interface material between SiC and W.

We developed LungGENS (Lung Gene Expression iN Single-cell), a web-based bioinformatics resource for querying single-cell gene expression databases by entering a gene symbol or a list of genes or selecting a cell type of their interest. Gene query provides quantitative RNA expression of the gene of interest in each lung cell type. Cell type query returns associated selective gene signatures and genes encoding cell surface markers and transcription factors in interactive heatmap and tables. LungGENS will be broadly applicable in respiratory research, providing a cell-specific RNA expression resource at single-cell resolution. LungGENS is freely available for non-commercial use at https://research.cchmc.org/pbge/lunggens/default.html.

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs. Accurate cell-type identification is vital for single-cell analysis. Here, the authors develop a computational pipeline called “LungMAP CellRef” for efficient, automated cell-type annotation of normal and disease human and mouse lung single-cell datasets.

Abstract An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts, we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multiomics data, and a diverse collection of image data that span mammalian lung development and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances, including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, and mouse) to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, reanalysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP, and external research efforts.

The communication unit adaptation specification protocol of the customer-side energy measuring equipment and its conformance test is important for realizing massive equipment access to the energy internet flexibly and efficiently. The conformance test of communication protocol is the key to generating complete and efficient test sequences. In allusion to the problem of conformance test sequence generation of communication unit adaptation specification protocol, the paper first formally describes the protocol based on a finite state machine, and then further carries out the research on conformance test sequence generation based on T method, U method and U+ method. The results show that the test sequence generated by U+ method has the ability of state verification and the length is 22.1% shorter than that of U method. Its redundancy is slightly less than that of T method and the error detection ability is much better. Therefore, U+ method is more suitable for the conformance test sequence generation of communication unit adaptation specification protocol.