Drivers of spatial structure in thinned forests

Background:As is widely known,an increasing number of forest areas were managed to preserve and enhance the health of forest ecosystems.However,previous research on forest management has often overlooked the importance of structure-based.Aims:Our objectives were to define the direction of structure-based forest management.Subsequently,we investigated the relationships between forest structure and the regeneration,growth,and mortality of trees under different thinning treatments.Ultimately,the drivers of forest structural change were explored.Methods:On the basis of 92 sites selected from northeastern China,with different recovery time (from 1 to 15years) and different thinning intensities (0–59.9%) since the last thinning.Principal component analysis (PCA)identified relationships among factors determining forest spatial structure.The structural equation model (SEM)was used to analyze the driving factors behind the changes in forest spatial structure after thinning.Results:Light thinning (0–20%trees removed) promoted forest regeneration,and heavy thinning (over 35% of trees removed) facilitated forest growth.However,only moderate thinning (20%–35%trees removed) created a reasonable spatial structure.While dead trees were clustered,and they were hardly affected by thinning intensity.Additionally,thinning intensity,recovery time,and altitude indirectly improve the spatial structure of the forest by influencing diameter at breast height (DBH) and canopy area.Conclusion:Creating larger DBH and canopy area through thinning will promote the formation of complex forest structures,which cultivates healthy and stable forests.

Overview of High Voltage SiC Power Semiconductor Devices: Development and Application

Research on high voltage(HV)silicon carbide(SiC)power semiconductor devices has attracted much attention in recent years.This paper overviews the development and status of HV SiC devices.Meanwhile,benefits of HV SiC devices are presented.The technologies and challenges for HV SiC device application in converter design are discussed.The state-of-the-art applications of HV SiC devices are also reviewed.

In silico design of anti-tumor mini-protein targeting MDM2

We designed a disulfide-crosslinked mini-protein with a two-helical topology consisting of L-and Damino acids,which was exceptionally stable in serum.Therefore,we further used it as a scaffold to design mini-proteins targeting p53 positive tumor cells.Based on bifunctional grafting,key residues from the transactivation domain of p53 and a designed unnatural amino acid were grafted into the helix constituted by L-amino acids to confer the mini-protein with MDM2 inhibitory activity.Meanwhile,ten Arg residues were introduced to improve its membrane penetrating capacity.Among the mini-proteins,UPROL-10e showed nano-molar binding affinity on MDM2 and cellular toxicity on p53 expressing HCT116cells.

An integrative profiling of metabolome and transcriptome in the plasma and skeletal muscle following an exercise intervention in diet-induced obese mice

Exercise intervention at the early stage of type 2 diabetes mellitus(T2DM)can aid in the maintenance of blood glucose homeostasis and prevent the development of macrovascular and microvascular complications.However,the exercise-regulated pathways that prevent the development of T2DM remain largely unclear.In this study,two forms of exercise intervention,treadmill training and voluntary wheel running,were conducted for high-fat diet(HFD)-induced obese mice.We observed that both forms of exercise intervention alleviated HFD-induced insulin resistance and glucose intolerance.Skeletal muscle is recognized as the primary site for postprandial glucose uptake and for responsive alteration beyond exercise training.Metabolomic profiling of the plasma and skeletal muscle in Chow,HFD,and HFD-exercise groups revealed robust alterations in metabolic pathways by exercise intervention in both cases.Overlapping analysis identified nine metabolites,including beta-alanine,leucine,valine,and tryptophan,which were reversed by exercise treatment in both the plasma and skeletal muscle.Transcriptomic analysis of gene expression profiles in the skeletal muscle revealed several key pathways involved in the beneficial effects of exercise on metabolic homeostasis.In addition,integrative transcriptomic and metabolomic analyses uncovered strong correlations between the concentrations of bioactive metabolites and the expression levels of genes involved in energy metabolism,insulin sensitivity,and immune response in the skeletal muscle.This work established two models of exercise intervention in obese mice and provided mechanistic insights into the beneficial effects of exercise intervention on systemic energy homeostasis.

Nanomaterials toward the treatment of Alzheimer's disease:Recent advances and future trends

Alzheimer’s disease(AD)is a progressive and fatal neurodegenerative condition and the most prevalent cause of dementia.This disease is characterized by progressive cognitive impairment.The prevalence of AD is currently affecting more than 35 million people and is rising worldwide.No efficient therapy is currently available due to low drug potency and a number of various obstacles to delivery.Recent nanotechnological advancements have the potential to offer promising therapeutic options.Progress on nanomaterials as well as their applications in biomedicine is receiving increasing attention,especially the advantages of nanomaterial-based drug delivery systems.The aim of this review is to comprehensively summarize the latest developments in nanomaterial-based strategies for AD treatment,including nanoparticles,liposomes and other options for the delivery of therapeutic compounds and scaffolds for cell delivery strategies.Future research directions are also proposed.We hope this review can provide important information to guide the future development of nanomaterials in AD treatment.

Latent Co-interests' Relationship Prediction

With the development of the social media and Internet, discovering latent information from massive information is becoming particularly relevant to improving user experience. Research efforts based on preferences and relationships between users have attracted more and more attention. Predictive problems, such as inferring friend relationship and co-author relationship between users have been explored. However, many such methods are based on analyzing either node features or the network structures separately, few have tried to tackle both of them at the same time. In this paper, in order to discover latent co-interests' relationship, we not only consider users' attributes but network information as well. In addition, we propose an Interest-based Factor Graph Model (I-FGM) to incorporate these factors. Experiments on two data sets (bookmarking and music network) demonstrate that this predictive method can achieve better results than the other three methods (ANN, NB, and SVM).