Advances in Research of Drought Stress in Major Pinus spp.: A Bibliometric Analysis and Narrative Review

Climate change has caused fluctuations in the frequency and severity of droughts,favoring extended periods of drought associated with anthropic actions and triggering other stressful abiotic effects that threaten terrestrial ecosystems.As climate warming intensifies,drought is a major challenge for forest growth.Pine(Pinus Linn.)is an important genus of forest in the Northern Hemisphere and has a certain tolerance to drought.This article analyzes and reviews the advances in research about drought stress of major Pinus spp.plants in recent years and discusses understanding and future core problems.To adapt to water-deficient environments,pine plants adapt to drought by changing growth traits,closing some stomata on leaves,changing the growth and structure of roots,and adjusting their physiological activities.Moreover,the expression of specific genes is altered,causing changes in the expression of several signaling molecules and metabolites to counteract drought stress.

An anisotropic Chebyshev descriptor and its optimization for deformable shape correspondence

Shape descriptors have recently gained popularity in shape matching,statistical shape modeling,etc.Their discriminative ability and efficiency play a decisive role in these tasks.In this paper,we first propose a novel handcrafted anisotropic spectral descriptor using Chebyshev polynomials,called the anisotropic Chebyshev descriptor(ACD);it can effectively capture shape features in multiple directions.The ACD inherits many good characteristics of spectral descriptors,such as being intrinsic,robust to changes in surface discretization,etc.Furthermore,due to the orthogonality of Chebyshev polynomials,the ACD is compact and can disambiguate intrinsic symmetry sinces everal directions are considered.To improve the ACD’s discrimination ability,we construct a Chebyshev spectral manifold convolutional neural network(CSMCNN)that optimizes the ACD and produces a learned ACD.Our experimental results show that the ACD outperforms existing state-of-the-art handcrafted descriptors.The combination of the ACD and the CSMCNN is better than other state-of-the-art learned descriptors in terms of discrimination,efficiency,and robustness to changes in shape resolution and discretization.