Ultrastructural features and synaptic connections of hilar ectopic granule cells in the rat dentate gyrus are different from those of granule cells in the granule cell layer.

Several investigators have shown the existence of dentate granule cells in ectopic locations within the hilus and molecular layer using both Golgi and retrograde tracing studies but the ultrastructural features and synaptic connections of ectopic granule cells were not previously examined. In the present study, the biocytin retrograde tracing technique was used to label ectopic granule cells following injections into stratum lucidum of CA3b of hippocampal slices obtained from epileptic rats. Electron microscopy was used to study hilar ectopic granule cells that were located 20-40 microm from the granule cell layer (GCL). They had ultrastructural features similar to those of granule cells in the GCL but showed differences, including nuclei that often displayed infoldings and thicker apical dendrites. At their origin, these dendrites were 6 microm in diameter and they tapered down to 2 microm at the border with the GCL. Both biocytin-labeled and unlabeled axon terminals formed exclusively asymmetric synapses with the somata and proximal dendrites of hilar ectopic granule cells. The mean number of axosomatic synapses for these cells was three times that for granule cells in the GCL. Together, these data indicate that hilar ectopic granule cells are postsynaptic to mossy fibers and have less inhibitory input on their somata and proximal dendrites than granule cells in the GCL. This finding is consistent with recent physiological results showing that hilar ectopic granule cells from epileptic rats are more hyperexcitable than granule cells in the GCL

Suppression of hippocampal neurogenesis is associated with developmental stage, number of perinatal seizure episodes, and glucocorticosteroid level.

Seizures increase dentate granule cell proliferation in adult rats but decrease proliferation in young pups. The particular period and number of perinatal seizures required to cause newborn granule cell suppression in development are unknown. Therefore, we examined cell proliferation with bromodeoxyuridine (BrdU) immunohistochemistry during the peak of neurogenesis (e.g., P6 and P9) and at later postnatal ages (e.g., P13, P20, or P30) following single and multiple episodes of perinatal status epilepticus induced by kainate (KA). Because an inverse relationship exists between glucocorticosteroids (CORT) levels and granule cell proliferation, plasma CORT levels and electroencephalographic (EEG) activity were simultaneously monitored to elucidate underlying mechanisms that inhibit cell proliferation. In control animals, the number of BrdU-labeled cells increased then declined with maturation. After 1x KA or 2x KA administered on P6 and P9, the numbers of BrdU-labeled cells were not different from age-matched controls. However, rat pups with 3x KA (on P6, P9, and P13) had marked suppression of BrdU-labeled cells 48-72 h after the last seizure (43 +/- 6.5% of control). Cell proliferation was also significantly inhibited on P20 after 2x KA (to 56 +/- 6.9%) or 3x KA (to 54 +/- 7.9%) and on P30 with 3x KA (to 74.5 +/- 8.2% of age-matched controls). Cell death was not apparent as chromatin stains showed increased basophilia of only inner cells lining the granule cell layers, in the absence of eosinophilia, argyrophilia, or terminal deoxynucleotidyl dUTP nick endlabeling (TUNEL) labeling at times examined. In P13 pups with 3x KA, electron microscopy revealed an increased number of immature granule cells and putative stem cells with irregular shape, condensed cytoplasm, and electron dense nuclei, and they were also BrdU positive. The EEG showed no relationship between neurogenesis and duration of high-synchronous ictal activity. However, endocrine studies showed a correlation with BrdU number and age, sustained increases in circulating CORT levels following 1x KA on P6 (0.7 +/- 0.1 to 2.40 +/- 0.86 microg/dl), and cumulative increases that exceeded 10 microg/dl at 4-8 h after 3x KA on P13 or P20. In conclusion, a history of only one or two perinatal seizure(s) can suppress neurogenesis if a second or third seizure recurs after a critical developmental period associated with a marked surge in CORT. During the first 2 weeks of postnatal life sustained increases in postictal circulating CORT levels but not duration or intensity of ictal activity has long-term consequences on neurogenesis. The occurrence of an increased proportion of immature granule cells and putative stem cells with irregular morphology in the absence of neurodegeneration suggests that progenitors may not differentiate properly and remain in an immature state

Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

Seizure-induced basal dendrites on granule cells

Seizure-induced hilar basal dendrites on dentate granule cells are observed in several rodent models of temporal lobe epilepsy. Ultrastructural evidence showed that basal dendrites receive predominantly excitatory synapses, including many from mossy fibers. Such highly interconnected granule cells with basal dendrites are suggested to enhance hyperexcitability within the dentate network. For an expanded treatment of this topic see Jasper's Basic Mechanisms of the Epilepsies, Fourth Edition (Noebels JL, Avoli M, Rogawski MA, Olsen RW, Delgado-Escueta AV, eds) published by Oxford University Press (available on the National Library of Medicine Bookshelf [NCBI] at). © 2010 International League Against Epilepsy

A comparative study of Persian sentiment analysis based on different feature combinations

In recent years, the use of internet and correspondingly the number of online reviews, comments and opinions have increased significantly. It is indeed very difficult for humans to read these opinions and classify them accurately. Consequently, there is a need for an automated system to process this big data. In this paper, a novel sentiment analysis framework for Persian language has been proposed. The proposed framework comprises three basic steps: pre-processing, feature extraction, and support vector machine (SVM) based classification. The performance of the proposed framework has been evaluated taking into account different features combinations. The simulation results have revealed that the best performance could be achieved by integrating unigram, bigram, and trigram features

A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter

In the literature, limited work has been conducted to develop sentiment resources for Saudi dialect. The lack of resources such as dialectical lexicons and corpora are some of the major bottlenecks to the successful development of Arabic sentiment analysis models. In this paper, a semi-supervised approach is presented to construct an annotated sentiment corpus for Saudi dialect using Twitter. The presented approach is primarily based on a list of lexicons built by using word embedding techniques such as word2vec. A huge corpus extracted from twitter is annotated and manually reviewed to exclude incorrect annotated tweets which is publicly available. For corpus validation, state-of-the-art classification algorithms (such as Logistic Regression, Support Vector Machine, and Naive Bayes) are applied and evaluated. Simulation results demonstrate that the Naive Bayes algorithm outperformed all other approaches and achieved accuracy up to 91%

Commission des Communautes Europeennes: Groupe du Porte-Parole = Commission of European Communities: Spokesman Group. Spokesman Service Note to National Offices Bio No. (81) 276, 8 July 1981

This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse – but still acceptable – performance when compared to the single language model, while benefiting from better generalization properties across languages

Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning

This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning(RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with aXilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms