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In the theoretical calculation of atmospheric wind fields using the cross-correlation analysis method of Medium-Frequency radar, it is necessary to compute a series of correlation parameters from the received echo signals, such as autocorrelation and cross-correlation functions, within the main lobe range of the antenna array to retrieve atmospheric parameters. However, both theoretical analysis and practical applications have shown that the shape of correlation functions can be affected by atmospheric conditions and receiver noise, leading to significant biases in the estimated correlation parameters within the main lobe range. In this study, we theoretically analyze the influence of noise on the amplitude of autocorrelation and cross-correlation functions. We propose a noise reduction method based on the characteristics of correlation functions at the zero-delay point to calculate the noise factor and process the correlation functions within the main lobe range. Furthermore, we conduct simulation analysis to evaluate the performance of this noise reduction method and summarize the effects of the number of fitting points and fitting methods on the noise reduction performance. [ABSTRACT FROM AUTHOR]

The highly glacierized eastern part of the Tibetan Plateau is the key source region for seven major rivers: Yangtze, Yellow, Lancang-Mekong, Nu-Salween, Irrawaddy, Ganges, and Brahmaputra rivers. These rivers are vital freshwater resources for more than one billion people downstream for their daily life, irrigation, industrial use, and hydropower. However, the glaciers have been receding during the last decades and are projected to further decline which will profoundly impact the water availability of these larger river systems. Although few studies have investigated glacier mass changes in these river basins since the 1970s, they are site and temporal specific and limited by data availability. Hence, knowledge of glacier mass changes is especially lacking for years prior to 2000. We therefore applied digital elevation models (DEMs) derived from large scale topographic maps based on aerial photogrammetry from the 1970s and 1980s and compared them to the Shuttle Radar Topography Mission (SRTM) DEM to provide a complete picture of mass change of glaciers in the region. The mass changes are presented on individual glacier bases with a resolution of 30 m and are also aggregated into gridded formats at resolutions of 0.1° and 0.5°. Our database consists of 13117 glaciers with a total area of ~21695 km2. The annual mean mass loss of glaciers is -0.30 ± 0.12 m w.e. in the whole region. This is larger than the previous site-specific findings, the surface thinning increases on average from west to east along the Himalayas-Hengduan mountains with the largest thinning in the Irrawaddy basin. Comparisons between the topographic map-based DEMs and DEMs generated based on Hexagon KH-9 metric camera data for parts in the Himalayas demonstrate that our dataset provides a robust estimation of glacier mass changes. However, the uncertainty is high in high altitudes due to the saturation of aerial photos over low contrast areas like snow surface on a steep terrain. The dataset is well suited for supporting more detailed climatical and hydrological analyses and is available at https://doi.org/10.11888/Cryos.tpdc.301236 (Liu et al., 2024). [ABSTRACT FROM AUTHOR]

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field. [ABSTRACT FROM AUTHOR]