Abnormal functional activity induces long-lasting physiological alterations in neural pathways that may play a role in the development of epilepsy. The cellular mechanisms of these alterations are not well understood. One hypothesis is that abnormal activity causes structural reorganization of neural pathways and promotes epileptogenesis. This report provides morphological evidence that synchronous perforant path activation and kindling of limbic pathways induce axonal growth and synaptic reorganization in the hippocampus, in the absence of overt morphological damage. The results show a previously unrecognized anatomic plasticity associated with synchronous activity and development of epileptic seizures in neural pathways.

Model-based analysis tools, built on assumptions and simplifications, are difficult to handle smart grids with data characterized by volume, velocity, variety, and veracity (i.e., 4Vs data). This paper, using random matrix theory (RMT), motivates data-driven tools to perceive the complex grids in high-dimension; meanwhile, an architecture with detailed procedures is proposed. In algorithm perspective, the architecture performs a high-dimensional analysis and compares the findings with RMT predictions to conduct anomaly detections. Mean spectral radius (MSR), as a statistical indicator, is defined to reflect the correlations of system data in different dimensions. In management mode perspective, a group-work mode is discussed for smart grids operation. This mode breaks through regional limitations for energy flows and data flows, and makes advanced big data analyses possible. For a specific large-scale zone-dividing system with multiple connected utilities, each site, operating under the group-work mode, is able to work out the regional MSR only with its own measured/simulated data. The large-scale interconnected system, in this way, is naturally decoupled from statistical parameters perspective, rather than from engineering models perspective. Furthermore, a comparative analysis of these distributed MSRs, even with imperceptible different raw data, will produce a contour line to detect the event and locate the source. It demonstrates that the architecture is compatible with the block calculation only using the regional small database; beyond that, this architecture, as a data-driven solution, is sensitive to system situation awareness, and practical for real large-scale interconnected systems. Five case studies and their visualizations validate the designed architecture in various fields of power systems. To our best knowledge, this paper is the first attempt to apply big data technology into smart grids.Index Terms-Architecture, big data, group-work mode, high-dimension, large-scale distributed system, mean spectral radius (MSR), random matrix, smart grid.

ObjectiveOur primary objective is to phylogenetically characterize the supragingival plaque bacterial microbiome of children prior to eruption of second primary molars by pyrosequencing method for studying etiology of early childhood caries.MethodsSupragingival plaque samples were collected from 10 caries children and 9 caries-free children. Plaque DNA was extracted, used to generate DNA amplicons of the V1–V3 hypervariable region of the bacterial 16S rRNA gene, and subjected to 454-pyrosequencing.ResultsOn average, over 22,000 sequences per sample were generated. High bacterial diversity was noted in the plaque of children with caries [170 operational taxonomical units (OTU) at 3% divergence] and caries-free children (201 OTU at 3% divergence) with no significant difference. A total of 8 phyla, 15 classes, 21 orders, 30 families, 41 genera and 99 species were represented. In addition, five predominant phyla (Firmicute, Fusobacteria, Proteobacteria, Bacteroidetes and Actinobacteria) and seven genera (Leptotrichia, Streptococcus, Actinomyces, Prevotella, Porphyromonas, Neisseria, and Veillonella) constituted a majority of contents of the total microbiota, independent of the presence or absence of caries. Principal Component Analysis (PCA) presented that caries-related genera included Streptococcus and Veillonella; while Leptotrichia, Selenomonas, Fusobacterium, Capnocytophaga and Porphyromonas were more related to the caries-free samples. Neisseria and Prevotella presented approximately in between. In both groups, the degree of shared organism lineages (as defined by species-level OTUs) among individual supragingival plaque microbiomes was minimal.ConclusionOur study represented for the first time using pyrosequencing to elucidate and monitor supragingival plaque bacterial diversity at such young age with second primary molar unerrupted. Distinctions were revealed between caries and caries-free microbiomes in terms of microbial community structure. We observed differences in abundance for several microbial groups between the caries and caries-free host populations, which were consistent with the ecological plaque hypothesis. Our approach and findings could be extended to correlating microbiomic changes after occlusion establishment and caries treatment.

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The operating status of power systems is influenced by growing varieties of factors, resulting from the developing sizes and complexity of power systems; in this situation, the modelbased methods need be revisited. A data-driven method, as the novel alternative, on the other hand, is proposed in this paper: it reveals the correlations between the factors and the system status through statistical properties of data. An augmented matrix, as the data source, is the key trick for this method; it is formulated by two parts: 1) status data as the basic part, and 2) factor data as the augmented part. The random matrix theory (RMT) is applied as the mathematical framework. The linear eigenvalue statistics (LESs), such as the mean spectral radius (MSR), are defined to study data correlations through large random matrices. Compared with model-based methods, the proposed method is inspired by a pure statistical approach, without a prior knowledge of operation and interaction mechanism models for power systems and factors. In general, this method is direct in analysis, robust against bad data, universal to various factors, and applicable for real-time analysis. A case study, based on the standard IEEE 118-bus system, validates the proposed method.