α-Synuclein conformational plasticity: Physiologic states, pathologic strains, and biotechnological applications

α-Synuclein (αS) is remarkable for both its extensive conformational plasticity and pathologic prion-like properties. Physiologically, αS may populate disordered monomeric, helically folded tetrameric, or membrane-bound oligomeric states. Pathologically, αS may assemble into toxic oligomers and subsequently fibrils, the prion-like transmission of which is implicated in a class of neurodegenerative disorders collectively termed α-synucleinopathies. Notably, αS does not adopt a single amyloid fold , but rather exists as structurally distinct amyloid-like conformations referred to as strains . The inoculation of animal models with different strains induces distinct pathologies, and emerging evidence suggests that the propagation of disease-specific strains underlies the differential pathologies observed in patients with different α-synucleinopathies. The characterization of αS strains has provided insight into the structural basis for the overlapping, yet distinct, symptoms of Parkinson\u27s disease, multiple system atrophy, and dementia with Lewy bodies. In this review, we first explore the physiological and pathological differences between conformational states of αS. We then discuss recent studies on the influence of micro-environmental factors on αS species formation, propagation, and the resultant pathological characteristics. Lastly, we review how an understanding of αS conformational properties has been translated to emerging strain amplification technologies, which have provided further insight into the role of specific strains in distinct α-synucleinopathies, and show promise for the early diagnosis of disease

Noise reduction optimization of sound sensor based on a Conditional Generation Adversarial Network

To address the problems in the traditional speech signal noise elimination methods, such as the residual noise, poor real-time performance and narrow applications a new method is proposed to eliminate network voice noise based on deep learning of conditional generation adversarial network. In terms of the perceptual evaluation of speech quality (PESQ) and shorttime objective intelligibility measure (STOI) functions used as the loss function in the neural network, which were used as the loss function in the neural network, the flexibility of the whole network was optimized, and the training process of the model simplified. The experimental results indicate that, under the noisy environment, especially in a restaurant, the proposed noise reduction scheme improves the STOI score by 26.23% and PESQ score by 17.18%, respectively, compared with the traditional Wiener noise reduction algorithm. Therefore, the sound sensor\u27s noise reduction scheme through our approach has achieved a remarkable noise reduction effect, more useful information transmission, and stronger practicability

Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy

Emerging nanotechnology for treatment of Alzheimer\u27s and Parkinson\u27s disease

The prevalence of the two most common neurodegenerative diseases, Parkinson\u27s disease (PD) and Alzheimer\u27s Disease (AD), are expected to rise alongside the progressive aging of society. Both PD and AD are classified as proteinopathies with misfolded proteins α-synuclein, amyloid-β, and tau. Emerging evidence suggests that these misfolded aggregates are prion-like proteins that induce pathological cell-to-cell spreading, which is a major driver in pathogenesis. Additional factors that can further affect pathology spreading include oxidative stress, mitochondrial damage, inflammation, and cell death. Nanomaterials present advantages over traditional chemical or biological therapeutic approaches at targeting these specific mechanisms. They can have intrinsic properties that lead to a decrease in oxidative stress or an ability to bind and disaggregate fibrils. Additionally, nanomaterials enhance transportation across the blood-brain barrier, are easily functionalized, increase drug half-lives, protect cargo from immune detection, and provide a physical structure that can support cell growth. This review highlights emergent nanomaterials with these advantages that target oxidative stress, the fibrillization process, inflammation, and aid in regenerative medicine for both PD and AD

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%

Process Monitoring and Fault Diagnosis for Piercing Production of Seamless Tube

With the development of modernization, the application of seamless tube becomes widespread. As the first process of seamless tube, piercing is vital for the quality of the tube. The solid round billet will be transformed into a hollow shell after the piercing process. The defects of hollow shell cannot be cleared in the following process, so a monitoring model for the quality of the hollow shell is important. But the piercing process is very complicated, and a mechanism model is difficult to build between the qualities of the hollow shell and measurement variables. Furthermore, an intelligent model is needed. We established two piercing process monitoring and fault diagnosis models based on the multiway principal component analysis (MPCA) model and the multistage MPCA model, respectively, and furthermore we made a comparison between these two concepts. We took three ways to divide the period based on process, K-means, and GA, respectively. Simulation experiments have shown that the multistate MPCA method has advantage over the MPCA method and the model based on the genetic algorithm (GA) can monitor the process effectively and detect the faults

Solar light-driven photocatalytic hydrogen evolution over ZnIn2S4 loaded with transition-metal sulfides

A series of Pt-loaded MS/ZnIn2S4 (MS = transition-metal sulfide: Ag2S, SnS, CoS, CuS, NiS, and MnS) photocatalysts was investigated to show various photocatalytic activities depending on different transition-metal sulfides. Thereinto, CoS, NiS, or MnS-loading lowered down the photocatalytic activity of ZnIn2S4, while Ag2S, SnS, or CuS loading enhanced the photocatalytic activity. After loading 1.0 wt.% CuS together with 1.0 wt.% Pt on ZnIn2S4, the activity for H2 evolution was increased by up to 1.6 times, compared to the ZnIn2S4 only loaded with 1.0 wt.% Pt. Here, transition-metal sulfides such as CuS, together with Pt, acted as the dual co-catalysts for the improved photocatalytic performance. This study indicated that the application of transition-metal sulfides as effective co-catalysts opened up a new way to design and prepare high-efficiency and low-cost photocatalysts for solar-hydrogen conversion