MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals

Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning methods, which, however, require high-cost clinical labels. In addition, the huge difference in the clinical patterns of brain signals measured by invasive (e.g., SEEG) and non-invasive (e.g., EEG) methods leads to the lack of a unified method. To handle the above issues, we propose to study the self-supervised learning (SSL) framework for brain signals that can be applied to pre-train either SEEG or EEG data. Intuitively, brain signals, generated by the firing of neurons, are transmitted among different connecting structures in human brain. Inspired by this, we propose MBrain to learn implicit spatial and temporal correlations between different channels (i.e., contacts of the electrode, corresponding to different brain areas) as the cornerstone for uniformly modeling different types of brain signals. Specifically, we represent the spatial correlation by a graph structure, which is built with proposed multi-channel CPC. We theoretically prove that optimizing the goal of multi-channel CPC can lead to a better predictive representation and apply the instantaneou-time-shift prediction task based on it. Then we capture the temporal correlation by designing the delayed-time-shift prediction task. Finally, replace-discriminative-learning task is proposed to preserve the characteristics of each channel. Extensive experiments of seizure detection on both EEG and SEEG large-scale real-world datasets demonstrate that our model outperforms several state-of-the-art time series SSL and unsupervised models, and has the ability to be deployed to clinical practice

BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the patient's brain. Existing works have begun to employ machine learning models to detect epileptic waves via cortical electroencephalogram (EEG). However, the recently developed stereoelectrocorticography (SEEG) method provides information in stereo that is more precise than conventional EEG, and has been broadly applied in clinical practice. Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. While offering new opportunities, SEEG also poses several challenges. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. These propagation paths, also known as the epileptogenic network, are deemed to be a key factor in the context of epilepsy surgery. However, the question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience. To address these challenges, we propose a novel model (BrainNet) that jointly learns the dynamic diffusion graphs and models the brain wave diffusion patterns. In addition, our model effectively aids in resisting label imbalance and severe noise by employing several self-supervised learning tasks and a hierarchical framework. By experimenting with the extensive real SEEG dataset obtained from multiple patients, we find that BrainNet outperforms several latest state-of-the-art baselines derived from time-series analysis

The following performance between particle and fluid medium inside hydrocyclone with double vortex finders

In this paper, we used numerical simulations to study the effect of size, density and concentration of particles on the relative motion between two phases in a cyclone separator with double vortex finders, which is different than a traditional separator that has only one overflow pipe

Study on effect of the inner vortex finder length on the flow properties of the hydrocyclone with double vortex finders

The traditional hydrocyclone can only obtain two products: overflow and underflow. In the paper, we propose three-products hydrocyclone with double vortex finders. The hydrocyclone is designed with two coaxial overflow tubes with different diameters. During overflow, light and fine particles exit from the inner overflow tube. The mid-size particles overflow from the outer overflow tube, and the coarse particles through the underflow pipe. Therefore, one classification can obtain three different narrow-grade-classification products. The inner vortex finder length is the important influent factor on the flow performance of the hydrocyclone. This paper is mainly focused on the study of the flow field of both the air and the liquid phase, and of the effects of the inner vortex finder length on the velocity field, pressure field and the air column of the hydrocyclone with double vortex finders

Engineered Reproductively Isolated Species Drive Reversible Population Replacement

Engineered reproductive species barriers are useful for impeding gene flow and driving desirable genes into wild populations in a reversible threshold-dependent manner. However, methods to generate synthetic barriers have not been developed in advanced eukaryotes. To overcome this challenge, we engineered SPECIES (Synthetic Postzygotic barriers Exploiting CRISPR-based Incompatibilities for Engineering Species) to generate postzygotic reproductive barriers. Using this approach, we engineer multiple reproductively isolated SPECIES and demonstrate their threshold-dependent gene drive capabilities in D. melanogaster. Given the near-universal functionality of CRISPR tools, this approach should be portable to many species, including insect disease vectors in which confinable gene drives could be of great practical utility

Data Transmission and Access Protection of Community Medical Internet of Things

On the basis of Internet of Things (IoT) technologies, Community Medical Internet of Things (CMIoT) is a new medical information system and generates massive multiple types of medical data which contain all kinds of user identity data, various types of medical data, and other sensitive information. To effectively protect users’ privacy, we propose a secure privacy data protection scheme including transmission protection and access control. For the uplink transmission data protection, bidirectional identity authentication and fragmented multipath data transmission are used, and for the downlink data protection, fine grained access control and dynamic authorization are used. Through theoretical analysis and experiment evaluation, it is proved that the community medical data can be effectively protected in the transmission and access process without high performance loss

Deficient O-GlcNAc Glycosylation Impairs Regulatory T Cell Differentiation and Notch Signaling in Autoimmune Hepatitis

Post-translational modifications such as glycosylation play an important role in the functions of homeostatic proteins, and are critical driving factors of several diseases; however, the role of glycosylation in autoimmune hepatitis is poorly understood. Here, we established an O-GlcNAc glycosylation-deficient rat model by knocking out the Eogt gene by TALEN-mediated gene targeting. O-GlcNAc glycosylation deficiency overtly aggravated liver injury in concanavalin-A induced autoimmune hepatitis, and delayed self-recovery of the liver. Furthermore, flow cytometry analysis revealed increased CD4+ T cell infiltration in the liver of rats with O-GlcNAc glycosylation deficiency, and normal differentiation of regulatory T cells (Tregs) in the liver to inhibit T cell infiltration could not be activated. Moreover, in vitro experiments showed that O-GlcNAc glycosylation deficiency impaired Treg differentiation to inhibit the Notch signaling pathway in CD4+ T cells. These finding indicate that O-GlcNAc glycosylation plays a critical role in the activation of Notch signaling, which could promote Treg differentiation in the liver to inhibit T cell infiltration and control disease development in autoimmune hepatitis. Therefore, this study reveals a regulatory role for glycosylation in the pathogenesis of autoimmune hepatitis, and highlights glycosylation as a potential treatment target