Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

Progress in the generation of spinal cord organoids over the past decade and future perspectives

Spinal cord organoids are three-dimensional tissues derived from stem cells that recapitulate the primary morphological and functional characteristics of the spinal cord in vivo.As emerging bioengineering methods have led to the optimization of cell culture protocols,spinal cord organoids technology has made remarkable advancements in the past decade.Our literature search found that current spinal cord organoids do not only dynamically simulate neural tube formation but also exhibit diverse cytoarchitecture along the dorsal-ventral and rostral-caudal axes.Moreover,fused organoids that integrate motor neurons and other regionally specific organoids exhibit intricate neural circuits that allows for functional assessment.These qualities make spinal cord organoids valuable tools for disease modeling,drug screening,and tissue regeneration.By utilizing this emergent technology,researchers have made significant progress in investigating the pathogenesis and potential therapeutic targets of spinal cord diseases.However,at present,spinal cord organoid technology remains in its infancy and has not been widely applied in translational medicine.Establishment of the next generation of spinal cord organoids will depend on good manufacturing practice standards and needs to focus on diverse cell phenotypes and electrophysiological functionality evaluation.

双圆盘上Hardy-Toeplitz算子的几个问题

本文分别提出关于双圆盘Hardy空间上的Toeplitz算子的不变子空间、特征值与特征向量以及数值域的刻画等问题,并对这些问题均给予肯定的回答.

Nyström-based spectral clustering using airborne LiDAR point cloud data for individual tree segmentation

The spectral clustering method has notable advantages in segmentation.But the high computational complexity and time consuming limit its application in large-scale and dense airborne Light Detection and Ranging(LiDAR)point cloud data.We proposed the Nyström-based spectral clustering(NSC)algorithm to decrease the computational burden.This novel NSC method showed accurate and rapid in individual tree segmentation using point cloud data.The K-nearest neighbour-based sampling(KNNS)was proposed for the Nyström approximation of voxels to improve the efficiency.The NSC algorithm showed good performance for 32 plots in China and Europe.The overall matching rate and extraction rate of proposed algorithm reached 69%and 103%.For all trees located by Global Navigation Satellite System(GNSS)calibrated tape-measures,the tree height regression of the matching results showed an value of 0.88 and a relative root mean square error(RMSE)of 5.97%.For all trees located by GNSS calibrated total-station measures,the values were 0.89 and 4.49%.The method also showed good performance in a benchmark dataset with an improvement of 7%for the average matching rate.The results demonstrate that the proposed NSC algorithm provides an accurate individual tree segmentation and parameter estimation using airborne LiDAR point cloud data.

Transplantation of bone marrow mesenchymal stem cells improves cognitive deficits and alleviates neuropathology in animal models of Alzheimer’s disease: a meta-analytic review on potential mechanisms

Background Alzheimer’s disease is a neurodegenerative disorder.Therapeutically,a transplantation of bone marrow mesenchymal stem cells(BMMSCs)can play a beneficial role in animal models of Alzheimer’s disease.However,the relevant mechanism remains to be fully elucidated.Main body Subsequent to the transplantation of BMMSCs,memory loss and cognitive impairment were significantly improved in animal models with Alzheimer’s disease(AD).Potential mechanisms involved neurogenesis,apoptosis,angiogenesis,inflammation,immunomodulation,etc.The above mechanisms might play different roles at certain stages.It was revealed that the transplantation of BMMSCs could alter some gene levels.Moreover,the differential expression of representative genes was responsible for neuropathological phenotypes in Alzheimer’s disease,which could be used to construct gene-specific patterns.Conclusions Multiple signal pathways involve therapeutic mechanisms by which the transplantation of BMMSCs improves cognitive and behavioral deficits in AD models.Gene expression profile can be utilized to establish statistical regression model for the evaluation of therapeutic effect.The transplantation of autologous BMMSCs maybe a prospective therapy for patients with Alzheimer’s disease.