New Insights into the Role of F-actin in Regulation of Mitochondrial Fission

Mitochondrial dynamics, including fusion and fission, are vital for supplying cellular energy as well as controlling other tasks including apoptosis, aging and cellular differentiation. Defects of mitochondrial fission pathway have been implicated in a wide spectrum of human diseases such as Parkinson’s disease and Alzheimer’s disease. Although recent findings point to a role of the actin cytoskeleton in regulating mitochondrial division, little is known about the mechanism. Here, I report that transient de novo polymerization of F-actin on the outer mitochondrial membrane contributes to Drp1-dependent mitochondrial division in mammalian cells. Transient de novo F-actin assembly on the mitochondria occurs upon induction of mitochondrial fission and F-actin accumulates on the mitochondria without forming detectable submitochondrial foci. Impairing mitochondrial division through Drp1 knockout or inhibition prolonged the time of mitochondrial accumulation of F-actin and also led to abnormal mitochondrial accumulation of the actin regulatory factors cortactin, cofilin, and Arp2/3 complex, suggesting that disassembly of mitochondrial F-actin depends on Drp1 activity. Furthermore, downregulation of actin regulatory proteins Arp2/3 complex, cortactin and cofilin led to abnormal elongation of mitochondria, associated with mitochondrial accumulation of Drp1. In addition, depletion of cortactin inhibited Mfn2 downregulation- or FCCP- induced mitochondrial fragmentation. These data indicate that the dynamic assembly and disassembly of F-actin on the mitochondria participates in Drp1-mediated mitochondrial fission. Moreover, I also discovered a novel F-actin involved mechanism of mitochondrial fission regulated by deubiquitinase Usp30. Overexpression of Usp30CS predicted to lack deubiquitinase activity induced abnormal elongation and thinning of mitochondrial tubules. Furthermore, expression of Usp30CS preferably binds to Drp1, inducing a dramatic redistribution of Drp1 from the cytosol to the mitochondria, and accumulation of high molecular weight Drp1 species. Importantly, FCCP induced a gradual tubulation of Drp1-containing structures, accompanied with mitochondrial associated F-actin in a similar timeframe in Usp30CS-expressing cells, suggesting that inhibition of Usp30 deubiquitnase activity stalls progression of Drp1-dependent mitochondrial division. In sum, here I report that mitochondrial F-actin polymerization is a required step of mitochondrial fission, regulated by actin-modifying proteins and deubiquitinase Usp30, providing in-depth vision and a novel mechanism of actin cytoskeleton participated mitochondrial fission

A Multilayer Feed Forward Small-World Neural Network Controller and Its Application on Electrohydraulic Actuation System

Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance

DAMM: Directionality-Aware Mixture Model Parallel Sampling for Efficient Dynamical System Learning

The Linear Parameter Varying Dynamical System (LPV-DS) is a promising framework for learning stable time-invariant motion policies in robot control. By employing statistical modeling and semi-definite optimization, LPV-DS encodes complex motions via non-linear DS, ensuring the robustness and stability of the system. However, the current LPV-DS scheme faces challenges in accurately interpreting trajectory data while maintaining model efficiency and computational efficiency. To address these limitations, we propose the Directionality-aware Mixture Model (DAMM), a new statistical model that leverages Riemannian metric on $d$-dimensional sphere $\mathbb{S}^d$, and efficiently incorporates non-Euclidean directional information with position. Additionally, we introduce a hybrid Markov chain Monte Carlo method that combines the Gibbs Sampling and the Split/Merge Proposal, facilitating parallel computation and enabling faster inference for near real-time learning performance. Through extensive empirical validation, we demonstrate that the improved LPV-DS framework with DAMM is capable of producing physically-meaningful representations of the trajectory data and improved performance of the generated DS while showcasing significantly enhanced learning speed compared to its previous iterations

Learning Local to Global Feature Aggregation for Speech Emotion Recognition

Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion correlations across frames, which are key cues to represent emotion. To handle the issue, we propose a Local to Global Feature Aggregation learning (LGFA) for SER, which can aggregate longterm emotion correlations at different scales both inside frames and segments with entire frequency information to enhance the emotion discrimination of utterance-level speech features. For this purpose, we nest a Frame Transformer inside a Segment Transformer. Firstly, Frame Transformer is designed to excavate local emotion correlations between frames for frame embeddings. Then, the frame embeddings and their corresponding segment features are aggregated as different-level complements to be fed into Segment Transformer for learning utterance-level global emotion features. Experimental results show that the performance of LGFA is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202

Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach

This article presents our unimodal privacy-safe and non-individual proposal for the audio-video group emotion recognition subtask at the Emotion Recognition in the Wild (EmotiW) Challenge 2020 1. This sub challenge aims to classify in the wild videos into three categories: Positive, Neutral and Negative. Recent deep learning models have shown tremendous advances in analyzing interactions between people, predicting human behavior and affective evaluation. Nonetheless, their performance comes from individual-based analysis, which means summing up and averaging scores from individual detections, which inevitably leads to some privacy issues. In this research, we investigated a frugal approach towards a model able to capture the global moods from the whole image without using face or pose detection, or any individual-based feature as input. The proposed methodology mixes state-of-the-art and dedicated synthetic corpora as training sources. With an in-depth exploration of neural network architectures for group-level emotion recognition, we built a VGG-based model achieving 59.13% accuracy on the VGAF test set (eleventh place of the challenge). Given that the analysis is unimodal based only on global features and that the performance is evaluated on a real-world dataset, these results are promising and let us envision extending this model to multimodality for classroom ambiance evaluation, our final target application

D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation

Temporal sentence grounding (TSG) aims to locate a specific moment from an untrimmed video with a given natural language query. Recently, weakly supervised methods still have a large performance gap compared to fully supervised ones, while the latter requires laborious timestamp annotations. In this study, we aim to reduce the annotation cost yet keep competitive performance for TSG task compared to fully supervised ones. To achieve this goal, we investigate a recently proposed glance-supervised temporal sentence grounding task, which requires only single frame annotation (referred to as glance annotation) for each query. Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA). Specifically, SA-GCL samples reliable positive moments from a 2D temporal map via jointly leveraging Gaussian prior and semantic consistency, which contributes to aligning the positive sentence-moment pairs in the joint embedding space. Moreover, to alleviate the annotation bias resulting from glance annotation and model complex queries consisting of multiple events, we propose the DGA module, which adjusts the distribution dynamically to approximate the ground truth of target moments. Extensive experiments on three challenging benchmarks verify the effectiveness of the proposed D3G. It outperforms the state-of-the-art weakly supervised methods by a large margin and narrows the performance gap compared to fully supervised methods. Code is available at https://github.com/solicucu/D3G.Comment: ICCV202

Carbon Nanotube Array Based Binary Gabor Zone Plate Lenses

Diffractive zone plates have a wide range of applications from focusing x-ray to extreme UV radiation. The Gabor zone plate, which suppresses the higher-order foci to a pair of conjugate foci, is an attractive alternative to the conventional Fresnel zone plate. In this work, we developed a novel type of Beynon Gabor zone plate based on perfectly absorbing carbon nanotube forest. Lensing performances of 0, 8 and 20 sector Gabor zone plates were experimentally analyzed. Numerical investigations of Beynon Gabor zone plate configurations were in agreement with the experimental results. A high-contrast focal spot having 487 times higher intensity than the average background was obtained

Identification of a novel seed size associated locus SW9-1 in soybean

Published versionSeed size is one of the vital traits determining seed appearance, quality, and yield. Untangling the genetic mechanisms regulating soybean 100-seed weight (100-SW), seed length and seed width across environments may provide a theoretical basis for improving seed yield. However, there are few reports related to QTL mapping of 100-SW across multiple ecological regions. In this study, 21 loci associated with seed size traits were identified using a genome-wide association of 5361 single nucleotide polymorphisms (SNPs) across three ecoregions in China, which could explain 8.12%–14.25% of the phenotypic variance respectively. A new locus, named as SW9-1 on chromosome 9 that explained 10.05%–10.93% of the seed weight variance was found significantly related to seed size traits, and was not previously reported. The selection effect analysis showed that SW9-1 locus has a relatively high phenotypic effect (13.67) on 100-SW, with a greater contribution by the accessions with bigger seeds (3.69) than the accessions with small seeds (1.66). Increases in seed weight were accompanied by increases in the frequency of SW9-1T allele, with >90% of the bred varieties with a 100-SW >30 g carrying SW9-1T. Analysis of SW9-1 allelic variation in additional soybean accessions showed that SW9-1T allele accounting for 13.83% of the wild accessions, while in 46.55% and 51.57% of the landraces and bred accessions, respectively, this results indicating that the SW9-1 locus has been subjected to artificial selection during the early stages of soybean breeding, especially the utilization of SW9-1T in edamame for big seed. These results suggest that SW9-1 is a novel and reliable locus associated with seed size traits, and might have an important implication for increasing soybean seed weight in molecular design breeding. Cloning this locus in future may provide new insights into the genetic mechanisms underlying soybean seed size traits

EMG Signals based Human Action Recognition via Deep Belief Networks

Electromyography (EMG) signals can be used for action classification. Nonetheless, due to their nonlinear and time-varying properties, it is difficult to classify the EMG signals and it is critical to use appropriate algorithms for EMG feature extraction and classification. In previous studies various ML methods have been applied. In this paper, we extract four time-domain features of the EMG signals and use a generative graphical model, Deep Belief Network (DBN), to classify the EMG signals. A DBN is a fast, greedy deep learning algorithm that can find a set of optimal weights rapidly, even in deep networks with many hidden layers and a large number of parameters. To evaluate this model, we acquired EMG signals, extracted their features, and then utilized the DBN model as human action classifiers. The real data analysis results are presented to show the effectiveness of the proposed deep learning technique for 4-class recognition of human actions based on the measured EMG signals. The proposed DBN model has potential to be applied in design of EMG-based user interfaces