Sidan Du

FUS Interacts with HSP60 to Promote Mitochondrial Damage.

FUS-proteinopathies, a group of heterogeneous disorders including ALS-FUS and FTLD-FUS, are characterized by the formation of inclusion bodies containing the nuclear protein FUS in the affected patients. However, the underlying molecular and cellular defects remain unclear. Here we provide evidence for mitochondrial localization of FUS and its induction of mitochondrial damage. Remarkably, FTLD-FUS brain samples show increased FUS expression and mitochondrial defects. Biochemical and genetic data demonstrate that FUS interacts with a mitochondrial chaperonin, HSP60, and that FUS translocation to mitochondria is, at least in part, mediated by HSP60. Down-regulating HSP60 reduces mitochondrially localized FUS and partially rescues mitochondrial defects and neurodegenerative phenotypes caused by FUS expression in transgenic flies. This is the first report of direct mitochondrial targeting by a nuclear protein associated with neurodegeneration, suggesting that mitochondrial impairment may represent a critical event in different forms of FUS-proteinopathies and a common pathological feature for both ALS-FUS and FTLD-FUS. Our study offers a potential explanation for the highly heterogeneous nature and complex genetic presentation of different forms of FUS-proteinopathies. Our data also suggest that mitochondrial damage may be a target in future development of diagnostic and therapeutic tools for FUS-proteinopathies, a group of devastating neurodegenerative diseases.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging. Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems. The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimers disease, Parkinsons diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines.

An ALS-mutant TDP-43 neurotoxic peptide adopts an anti-parallel β-structure and induces TDP-43 redistribution

TDP-43 proteinopathies are clinically and genetically heterogeneous diseases that had been considered distinct from classical amyloid diseases. Here, we provide evidence for the structural similarity between TDP-43 peptides and other amyloid proteins. Atomic force microscopy and electron microscopy examination of peptides spanning a previously defined amyloidogenic fragment revealed a minimal core region that forms amyloid fibrils similar to the TDP-43 fibrils detected in FTLD-TDP brain tissues. An ALS-mutant A315E amyloidogenic TDP-43 peptide is capable of cross-seeding other TDP-43 peptides and an amyloid-β peptide. Sequential Nuclear Overhauser Effects and double-quantum-filtered correlation spectroscopy in nuclear magnetic resonance (NMR) analyses of the A315E-mutant TDP-43 peptide indicate that it adopts an anti-parallel β conformation. When added to cell cultures, the amyloidogenic TDP-43 peptides induce TDP-43 redistribution from the nucleus to the cytoplasm. Neuronal cultures in compartmentalized microfluidic-chambers demonstrate that the TDP-43 peptides can be taken up by axons and induce axonotoxicity and neuronal death, thus recapitulating key neuropathological features of TDP-43 proteinopathies. Importantly, a single amino acid change in the amyloidogenic TDP-43 peptide that disrupts fibril formation also eliminates neurotoxicity, supporting that amyloidogenesis is critical for TDP-43 neurotoxicity.

Application of stationary wavelet entropy in pathological brain detection

Labeling brain images as healthy or pathological cases is an important procedure for medical diagnosis. Therefore, we proposed a novel image feature, stationary wavelet entropy (SWE), to extract brain image features. Meanwhile, we replaced the feature extraction procedure in state-of-the-art approaches with the proposed SWE. We found the classification performance improved after replacing wavelet entropy (WE), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE. This proposed SWE is superior to WE, WN, and DWT.

Multi-objective path finding in stochastic networks using a biogeography-based optimization method

Multi-objective path finding (MOPF) problems are widely applied in both academic and industrial areas. In order to deal with the MOPF problem more effectively, we propose a novel model that can cope with both deterministic and random variables. For the experiment, we compared five intelligence-optimization algorithms: the genetic algorithm, artificial bee colony (ABC), ant colony optimization (ACO), biogeography-based optimization (BBO), and particle swarm optimization (PSO). After a 100-run comparison, we found the BBO is superior to the other four algorithms with regard to success rate. Therefore, the BBO is effective in MOPF problems.

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine

Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm–RTSVM— which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation

Fruit category identification is important in factories, supermarkets, and other fields. Current computer vision systems used handcrafted features, and did not get good results. In this study, our team designed a 13-layer convolutional neural network (CNN). Three types of data augmentation method was used: image rotation, Gamma correction, and noise injection. We also compared max pooling with average pooling. The stochastic gradient descent with momentum was used to train the CNN with minibatch size of 128. The overall accuracy of our method is 94.94%, at least 5 percentage points higher than state-of-the-art approaches. We validated this 13-layer is the optimal structure. The GPU can achieve a 177× acceleration on training data, and a 175× acceleration on test data. We observed using data augmentation can increase the overall accuracy. Our method is effective in image-based fruit classification.

Tea Category Identification using Computer Vision and Generalized Eigenvalue Proximal SVM

(Objective) In order to increase classification accuracy of tea-category identification (TCI) system, this paper proposed a novel approach. (Method) The proposed methods first extracted 64 color histogram to obtain color information, and 16 wavelet packet entropy to obtain the texture information. With the aim of reducing the 80 features, principal component analysis was harnessed. The reduced features were used as input to generalized eigenvalue proximal support vector machine (GEPSVM). Winner-takes-all (WTA) was used to handle the multiclass problem. Two kernels were tested, linear kernel and Radial basis function (RBF) kernel. Ten repetitions of 10-fold stratified cross validation technique were used to estimate the out-of-sample errors. We named our method as GEPSVM + RBF + WTA and GEPSVM + WTA. (Result) The results showed that PCA reduced the 80 features to merely five with explaining 99.90% of total variance. The recall rate of GEPSVM + RBF + WTA achieved the highest overall recall rate of 97.9%. (Conclusion) This was higher than the result of GEPSVM + WTA and other five state-of-the-art algorithms: back propagation neural network, RBF support vector machine, genetic neural-network, linear discriminant analysis, and fitness-scaling chaotic artificial bee colony artificial neural network. ∗Address for correspondence: School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China 326 S. Wang et al. / Tea Category Identification using CV and GEPSVM

A new method for quantifying mitochondrial axonal transport

ABSTRACTAxonal transport of mitochondria is critical for neuronal survival and function. Automatically quantifying and analyzing mitochondrial movement in a large quantity remain challenging. Here, we report an efficient method for imaging and quantifying axonal mitochondrial transport using microfluidic-chamber-cultured neurons together with a newly developed analysis package named “MitoQuant”. This tool-kit consists of an automated program for tracking mitochondrial movement inside live neuronal axons and a transient-velocity analysis program for analyzing dynamic movement patterns of mitochondria. Using this method, we examined axonal mitochondrial movement both in cultured mammalian neurons and in motor neuron axons of Drosophila in vivo. In 3 different paradigms (temperature changes, drug treatment and genetic manipulation) that affect mitochondria, we have shown that this new method is highly efficient and sensitive for detecting changes in mitochondrial movement. The method significantly enhanced our ability to quantitatively analyze axonal mitochondrial movement and allowed us to detect dynamic changes in axonal mitochondrial transport that were not detected by traditional kymographic analyses.