Evaluation on the Effect of EEG Pre-processing and Hyper parameters Tuning to the Performance of Convolutional Neural Network Motor Execution Classification

Electroencephalogram (EEG) based classification has achieved a promising performance using deep learning models like Convolutional Neural Network. Various pre-processing strategies such as smoothing the EEG data or filtering are commonly used to pre-process the captured EEG signal before the subsequent feature extraction and classification while hyperparameters tuning might help to improve the classification performance. As well, the number of layers used in the CNN can affect the performance of the classification. In this paper, the number of layers needed for the CNN to classify the EEG data correctly, the effect of apply smoothing to pre-process the EEG signal for modern end-to-end CNN and the effect of enabling hyperparameters tuning during the training phase of CNN is investigated and analyzed. Two CNN models, namely Deep CNN with 5 layers and Shallow CNN with 1 layer, with convincing classification accuracy on motor execution classification as reported in the literature were chosen for this study. Both the CNN models are trained on EEG motor execution dataset with different training strategies and dataset pre-processing. Based on the obtained training and test classification accuracy, Shallow CNN trained with enabling hyper parameters tuning and without smoothing the EEG data achieved the best classification accuracy with average training accuracy of 99.9% and test accuracy of 96.87%. This indicates that CNN does not need to have many layers to correctly classify the motor execution data and the EEG data does not require smoothing

Lipid metabolic perturbation is an early-onset phenotype in adult spinster mutants: a Drosophila model for lysosomal storage disorders

Intracellular accumulation of lipids and swollen dysfunctional lysosomes are linked to several neurodegenerative diseases, including lysosomal storage disorders (LSD). Detailed characterization of lipid metabolic changes in relation to the onset and progression of neurodegeneration is currently missing. We systematically analyzed lipid perturbations in spinster (spin) mutants, a Drosophila model of LSD-like neurodegeneration. Our results highlight an imbalance in brain ceramide and sphingosine in the early stages of neurodegeneration, preceding the accumulation of endomembranous structures, manifestation of altered behavior, and buildup of lipofuscin. Manipulating levels of ceramidase and altering these lipids in spin mutants allowed us to conclude that ceramide homeostasis is the driving force in disease progression and is integral to spin function in the adult nervous system. We identified 29 novel physical interaction partners of Spin and focused on the lipid carrier protein, Lipophorin (Lpp). A subset of Lpp and Spin colocalize in the brain and within organs specialized for lipid metabolism (fat bodies and oenocytes). Reduced Lpp protein was observed in spin mutant tissues. Finally, increased levels of lipid metabolites produced by oenocytes in spin mutants allude to a functional interaction between Spin and Lpp, underscoring the systemic nature of lipid perturbation in LSD

An extended adaptive mechanism of evolutionary based channel assignment via reinforcement

Current development in the field of wireless mobile communication is extremely limited by the capacity constraints of the available frequency spectrum. Hence proper utilisation of channel allocation techniques which are capable of ensuring efficient channel assignment is essential in order to solve the non-deterministic polynomial-time hard (NP-hard) channel assignment problem. The process of channel assignment must satisfy hard-constraints such as electromagnetic compatibility (EMC) and the demand of channels in a cell. Initial channel assignment parameters are obtained using self-learning scheme and evolutionary algorithms is used to fine-tune the estimated parameters from reinforcement learning algorithm to optimise the channel assignment problem in wireless mobile networks. Particle reselection and dynamic inertia approach in particle-swarm-optimisation (PSO) is shown to have 8 % improvement over the standard PSO algorithm. Subsequently, the introduction of PSO showed 70 -75 % power saving advantage over suboptimal resource allocation techniques

Maritime Interdiction Operations in Logistically Barren Environments

Includes supplementary materialThis report contains analysis that shows that existing technology exists to improve Maritime Interdiction Operations (MIO) by approximately 30%. Furthermore, analysis contained herein will aid MIO planning for future operations. Since MIOs are an inherently dangerous, but necessary activity with far reaching implications to theater political and economic dynamics, this improvement is of great interest. MIO is a Naval solution to the problems of smuggling weapons, explosives, people and narcotics. MIO, when employed correctly has the potential to save lives and limit economic/political damage.N

Tissue Microbiome Profiling Identifies an Enrichment of Specific Enteric Bacteria in Opisthorchis viverrini Associated Cholangiocarcinoma

Cholangiocarcinoma (CCA) is the primary cancer of the bile duct system. The role of bile duct tissue microbiomes in CCA tumorigenesis is unestablished. To address this, sixty primary CCA tumors and matched normals, from both liver fluke (Opisthorchis viverrini) associated (OVa, n = 28) and non-O. viverrini associated (non-OVa, n = 32) cancers, were profiled using high-throughput 16S rRNA sequencing. A distinct, tissue-specific microbiome dominated by the bacterial families Dietziaceae, Pseudomonadaceae and Oxalobacteraceae was observed in bile duct tissues. Systemic perturbation of the microbiome was noted in tumor and paired normal samples (vs non-cancer normals) for several bacterial families with a significant increase in Stenotrophomonas species distinguishing tumors vs paired normals. Comparison of parasite associated (OVa) vs non-associated (non-OVa) groups identified enrichment for specific enteric bacteria (Bifidobacteriaceae, Enterobacteriaceae and Enterococcaceae). One of the enriched families, Bifidobacteriaceae, was found to be dominant in the O. viverrini microbiome, providing a mechanistic link to the parasite. Functional analysis and comparison of CCA microbiomes revealed higher potential for producing bile acids and ammonia in OVa tissues, linking the altered microbiota to carcinogenesis. These results define how the unique microbial communities resident in the bile duct, parasitic infections and the tissue microenvironment can influence each other, and contribute to cancer