Forest aboveground biomass estimation based on spaceborne LiDAR combining machine learning model and geostatistical method.

Estimation of forest biomass at regional scale based on GEDI spaceborne LiDAR data is of great significance for forest quality assessment and carbon cycle. To solve the problem of discontinuous data of GEDI footprints, this study mapped different echo indexes in the footprints to the surface by inverse distance weighted interpolation method, and verified the influence of different number of footprints on the interpolation results. Random forest algorithm was chosen to estimate the spruce-fir biomass combined with the parameters provided by GEDI and 138 spruce-fir sample plots in Shangri-La.

Unlocking vegetation health: optimizing GEDI data for accurate chlorophyll content estimation.

Chlorophyll content is a vital indicator for evaluating vegetation health and estimating productivity. This study addresses the issue of Global Ecosystem Dynamics Investigation (GEDI) data discreteness and explores its potential in estimating chlorophyll content. This study used the empirical Bayesian Kriging regression prediction (EBKRP) method to obtain the continuous distribution of GEDI spot parameters in an unknown space.

Spatial effects analysis of natural forest canopy cover based on spaceborne LiDAR and geostatistics.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC.

Identification of cell-type-specific spatially variable genes accounting for excess zeros.

Motivation: Spatial transcriptomic techniques can profile gene expressions while retaining the spatial information, thus offering unprecedented opportunities to explore the relationship between gene expression and spatial locations. The spatial relationship may vary across cell types, but there is a lack of statistical methods to identify cell-type-specific spatially variable (SV) genes by simultaneously modeling excess zeros and cell-type proportions.

Results: We develop a statistical approach CTSV to detect cell-type-specific SV genes.

Single-Cell RNA Sequencing Revealed CD14 Monocytes Increased in Patients With Takayasu's Arteritis Requiring Surgical Management.

Takayasu Arteritis (TA) is a highly specific vascular inflammation and poses threat to patients' health. Although some patients have accepted medical treatment, their culprit lesions require surgical management (TARSM). This study aimed at dissecting the transcriptomes of peripheral blood mononuclear cells (PBMCs) in these patients and to explore potential clinical markers for TA development and progression.

Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data.

Recent advances in single-cell technologies enable spatial expression profiling at the cell level, making it possible to elucidate spatial changes of cell-specific genomic features. The gene co-expression network is an important feature that encodes the gene-gene marginal dependence structure and allows for the functional annotation of highly connected genes. In this paper, we design a simple and computationally efficient two-step algorithm to recover spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data.