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

PDM: Privacy-Aware Deployment of Machine-Learning Applications for Industrial Cyber–Physical Cloud Systems

The cyber-physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning (ML) in attack detection, a wide range of ML applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, in this article a privacy-aware deployment method (PDM), named PDM, is devised for hosting the ML applications in the industrial CPCSs. In PDM, the ML applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multiobjective problem for improving the implementation performance and resource utility. Then, the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated

Effects of the probiotic Bacillus amyloliquefaciens on the growth, immunity, and disease resistance of Haliotis discus hannai

Abstract(#br)The effects of a diet containing the probiotic Bacillus amyloliquefaciens on the survival and growth of Haliotis discus hannai were evaluated by measuring growth and hematological parameters and the expression levels of nonspecific immune genes. In addition, the abalone’s response to Vibrio parahaemolyticus infection was assessed. H. discus hannai (shell length: 29.35 ± 1.81 mm, body weight: 4.28 ± 0.23 g) were exposed to an 8-week culture experiment in indoor aquariums and a 2-week V. parahaemolyticus artificial infection experiment. In each experiment, the control group (C) was fed daily with the basal feed; the experimental groups were fed daily with the experimental feed, prepared by spraying B. amyloliquefaciens onto the basal feed at final concentrations of 10 3 (group A1), 10 5 (A2), and 10 7 (A3) cfu/g. The survival rate, body weight specific growth rate, and food conversion efficiency in A2 and A3 were significantly higher than those in A1 and C ( P < 0.05). The total number of blood lymphocytes, the O 2 − and NO levels produced from respiratory burst, the activities of acid phosphatase, superoxide dismutase, and catalase, and the expression levels of catalase and thiol peroxidase in A2 were not significantly different from those in A3, but these factors were significantly higher in A2 compared to A1 and C ( P < 0.05). The total antioxidant capacity and expression levels of glutathione S-transferase in A1, A2 and A3 were significantly higher than those in C ( P < 0.05). At day 9 after infection with V. parahaemolyticus , all abalone in C were dead; at the end of the experiment, the cumulative mortality of abalone in A2 was significantly lower than that in any other group ( P < 0.05). Thus, the experimental feed containing 10 5 cfu/g B. amyloliquefaciens not only facilitated the food intake and growth of abalone, but also effectively enhanced their non-specific immunity and resistance to V. parahaemolyticus infection. In this regard, B. amyloliquefaciens may be a useful probiotic strain for abalone aquaculture

Towards Visual Foundational Models of Physical Scenes

We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "physical scene" and show that, even though different agents may maintain different representations of the same scene, the underlying physical scene that can be inferred is unique. Then, we show that NeRFs cannot represent the physical scene, as they lack extrapolation mechanisms. Those, however, could be provided by Diffusion Models, at least in theory. To test this hypothesis empirically, NeRFs can be combined with Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised representations of the physical scene. Our analysis is limited to visual data, without external grounding mechanisms that can be provided by independent sensory modalities.Comment: TLDR: Physical scenes are equivalence classes of sufficient statistics, and can be inferred uniquely by any agent measuring the same finite data; We formalize and implement an approach to representation learning that overturns "naive realism" in favor of an analytical approach of Russell and Koenderink. NeRFs cannot capture the physical scenes, but combined with Diffusion Models they ca

Facile control of nanoporosity in Cellulose Acetate using Nickel(II) nitrate additive and water pressure treatment for highly efficient battery gel separators

We succeed in fabricating nearly straight nanopores in cellulose acetate (CA) polymers for use as battery gel separators by utilizing an inorganic hexahydrate (Ni(NO3)2??6H2O) complex and isostatic water pressure treatment. The continuous nanopores are generated when the polymer film is exposed to isostatic water pressure after complexing the nickel(II) nitrate hexahydrate (Ni(NO3)2??6H2O) with the CA. These results can be attributed to the manner in which the polymer chains are weakened because of the plasticization effect of the Ni(NO3)2??6H2O that is incorporated into the CA. Furthermore, we performed extensive molecular dynamics simulation for confirming the interaction between electrolyte and CA separator. The well controlled CA membrane after water pressure treatment enables fabrication of highly reliable cell by utilizing 2032-type coin cell structure. The resulting cell performance exhibits not only the effect of the physical morphology of CA separator, but also the chemical interaction of electrolyte with CA polymer which facilitates the Li-ion in the cell.ope

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Recent development in wireless sensor and ad-hoc networks

Wireless Sensor Network (WSN) consists of numerous physically distributed autonomous devices used for sensing and monitoring the physical and/or environmental conditions. A WSN uses a gateway that provides wireless connectivity to the wired world as well as distributed networks. There are many open problems related to Ad-Hoc networks and its applications. Looking at the expansion of the cellular infrastructure, Ad-Hoc network may be acting as the basis of the 4th generation wireless technology with the new paradigm of ‘anytime, anywhere communications’. To realize this, the real challenge would be the security, authorization and management issues of the large scale WSNs. This book is an edited volume in the broad area of WSNs. The book covers various chapters like Multi-Channel Wireless Sensor Networks, its Coverage, Connectivity as well as Deployment. It covers comparison of various communication protocols and algorithms such as MANNET, ODMRP and ADMR Protocols for Ad hoc Multicasting, Location Based Coordinated Routing Protocol and other Token based group local mutual exclusion Algorithms. The book also covers a chapter on Extended Ad hoc On-Demand Distance Vector (EAODV) routing protocol based on Distributed Minimum Transmission Multicast Routing (DMTMR). One chapter is dedicated to OCDMA and its future application and another chapter covers development of Home Automation System using SWN