Mitophagy in neurodegenerative disease pathogenesis

Mitochondria are critical cellular energy resources and are central to the life of the neuron.Mitophagy selectively clears damaged or dysfunctional mitochondria through autophagic machinery to maintain mitochondrial quality control and homeostasis.Mature neurons are postmitotic and consume substantial energy,thus require highly efficient mitophagy pathways to turn over damaged or dysfunctional mitochondria.Recent evidence indicates that mitophagy is pivotal to the pathogenesis of neurological diseases.However,more work is needed to study mitophagy pathway components as potential therapeutic targets.In this review,we briefly discuss the characteristics of nonselective autophagy and selective autophagy,including ERphagy,aggrephagy,and mitophagy.We then introduce the mechanisms of Parkin-dependent and Parkin-independent mitophagy pathways under physiological conditions.Next,we summarize the diverse repertoire of mitochondrial membrane receptors and phospholipids that mediate mitophagy.Importantly,we review the critical role of mitophagy in the pathogenesis of neurodegenerative diseases including Alzheimer’s disease,Parkinson’s disease,and amyotrophic lateral sclerosis.Last,we discuss recent studies considering mitophagy as a potential therapeutic target for treating neurodegenerative diseases.Together,our review may provide novel views to better understand the roles of mitophagy in neurodegenerative disease pathogenesis.

Cerebrovascular segmentation from mesoscopic optical images using Swin Transformer

Vascular segmentation is a crucial task in biomedical image processing,which is significant for analyzing and modeling vascular networks under physiological and pathological states.With advances in fluorescent labeling and mesoscopic optical techniques,it has become possible to map the whole-mouse-brain vascular networks at capillary resolution.However,segmenting vessels from mesoscopic optical images is a challenging task.The problems,such as vascular signal discontinuities,vessel lumens,and background fluorescence signals in mesoscopic optical images,belong to global semantic information during vascular segmentation.Traditional vascular segmentation methods based on convolutional neural networks(CNNs)have been limited by their insufficient receptive fields,making it challenging to capture global semantic information of vessels and resulting in inaccurate segmentation results.Here,we propose SegVesseler,a vascular segmentation method based on Swin Transformer.SegVesseler adopts 3D Swin Transformer blocks to extract global contextual information in 3D images.This approach is able to maintain the connectivity and topology of blood vessels during segmentation.We evaluated the performance of our method on mouse cerebrovascular datasets generated from three different labeling and imaging modalities.The experimental results demonstrate that the segmentation effect of our method is significantly better than traditional CNNs and achieves state-of-the-art performance.

Targeted energy transfer of a parallel nonlinear energy sink

A parallel nonlinear energy sink(NES) is proposed and analyzed. The parallel NES is composed of a vibro-impact(VI) NES and a cubic NES. The dynamical equation is given, and the essential analytical investigation is carried out to deal with the cubic nonlinearity and impact nonlinearity. Multiple time-scale expansion is introduced, and the zeroth order is derived to give a rough outline of the system. The underlying Hamilton dynamic equation is given, and then the optimal stiffness is expressed. The clearance is regarded as a critical factor for the VI. Based on the periodical impact treatment by analytical investigation, the relationships of the cubic stiffness, the clearance, and the zeroth-order attenuation amplitude of the linear primary oscillator(LPO) are obtained.A cubic NES under the optimal condition is compared with the parallel NES. Harmonic signals, harmonic signals with noises, and the excitation generated by a second-order?lter are considered as the potential excitation forces on the system. The targeted energy transfer(TET) in the designed parallel NES is shown to be more e?cient.

Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases

A novel method based on time-dependent stochastic orthogonal bases for stochastic response surface approximation is proposed to overcome the problem of significant errors in the utilization of the generalized polynomial chaos(GPC) method that approximates the stochastic response by orthogonal polynomials. The accuracy and effectiveness of the method are illustrated by different numerical examples including both linear and nonlinear problems. The results indicate that the proposed method modifies the stochastic bases adaptively, and has a better approximation for the probability density function in contrast to the GPC method.