The Functional Organization of Nuclear Envelope Proteins

In eukaryotic cells, the nucleus is enclosed by a double lipid membrane, termed the nuclear envelope (NE). The NE consists of the outer nuclear membrane (ONM), the inner nuclear membrane (INM), the nuclear pore complexes (NPCs) and the nuclear lamina. Recently it has been realized that the NE proteins not only serve structural functions but are also involved in a diverse group of genetic diseases collectively termed laminopathies or envelopathies. So far, only a few NE proteins have been characterized in detail. Here, we have identified and investigated a novel transmembrane protein from the NE, which is highly conserved in evolution. We termed the protein, Spindle associated membrane protein 1 (Samp1). During mitosis, a subpopulation of Samp1 is concentrated in the mitotic spindle. Samp1 has four transmembrane domains and is specifically localized to the INM. The N-terminal half of Samp1 contains a Zinc finger domain and is exposed in the nucleoplasm. Over expression of Zinc finger mutants of Samp1 gave an abnormal phenotype characterized by disruption of the localization of endogenous Samp1 and a specific set of NE proteins, suggesting that Samp1 is functionally associated with LINC complex and A-type lamina network proteins. After posttranscriptional silencing of Samp1 expression we showed that Samp1 is required for correct localization of Emerin to the NE. We also showed that Samp1 interacts with Emerin in live cells and that this interaction can occur by direct binding. The fact that the interaction between Emerin and Samp1 depended on Zinc, supports the idea that Samp1 has functional Zinc finger(s). Posttranscriptional silencing of Samp1 gave rise to an increase in the distance between the centrosome and the NE, suggesting that Samp1 is functionally associated with the microtubule cytoskeleton, most likely mediated via the LINC complexes. Using high-resolution fluorescence microscopy we showed that Samp1 is distributed in a distinct pattern in the NE and partially colocalized with the LINC complex protein, Sun1. We also showed that the Samp1 can interact with Sun1 in live cells. We developed a novel method, Membrane protein Cross-Link ImmunoPrecipitation (MCLIP) that enables detection of specific interactions of NE proteins in live cells. Using MCLIP we identified specific interaction partners of Samp1 in U2OS cells. Human induced pluripotent stem cells (hiPSCs) displayed increased expression of Samp1 during differentiation. Over expression of YFP-Samp1 induced a rapid differentiation of hiPSCs into neurons. The medium fro

Swarm Intelligence for Digital Circuits Implementation on Field Programmable Gate Arrays Platforms

Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA\u27s resources is an efficient placement and routing mechanism. This paper presents an optimization technique based on swarm intelligence for FPGA placement and routing. Mentor graphics technology mapping netlist file is used to generate initial FPGA placements and routings which are then optimized by particle swarm optimization (PSO). Results for the implementation of a binary coded decimal bidirectional counter and an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem

Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm

FPGA Placement and Routing Using Particle Swarm Optimization

Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. One of the necessary requirements to effectively utilize the FPGA\u27s fixed resources is an efficient placement and routing mechanism. This paper presents particle swarm optimization (PSO) for FPGA placement and routing. Preliminary results for the implementation of an arithmetic logic unit on a Xilinx FPGA show that PSO is a potential technique for solving the placement and routing problem

Optimal PSO for Collective Robotic Search Applications

Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET

The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019

Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome

BACKGROUND: Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5' end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed. METHODOLOGY/PRINCIPAL FINDINGS: We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement. CONCLUSIONS/SIGNIFICANCE: Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome

Immunohistochemistry on a Panel of Emery-Dreifuss Muscular Dystrophy Samples Reveals Nuclear Envelope Proteins as Inconsistent Markers for Pathology

Reports of aberrant distribution for some nuclear envelope proteins in cells expressing a few Emery–Dreifuss muscular dystrophy mutations raised the possibility that such protein redistribution could underlie pathology and/or be diagnostic. However, this disorder is linked to 8 different genes encoding nuclear envelope proteins, raising the question of whether a particular protein is most relevant. Therefore, myoblast/fibroblast cultures from biopsy and tissue sections from a panel of nine Emery–Dreifuss muscular dystrophy patients (4 male, 5 female) including those carrying emerin and FHL1 (X-linked) and several lamin A (autosomal dominant) mutations were stained for the proteins linked to the disorder. As tissue-specific nuclear envelope proteins have been postulated to mediate the tissue-specific pathologies of different nuclear envelopathies, patient samples were also stained for several muscle-specific nuclear membrane proteins. Although linked proteins nesprin 1 and SUN2 and muscle-specific proteins NET5/Samp1 and Tmem214 yielded aberrant distributions in individual patient cells, none exhibited defects through the larger patient panel. Muscle-specific Tmem38A normally appeared in both the nuclear envelope and sarcoplasmic reticulum, but most patient samples exhibited a moderate redistribution favouring the sarcoplasmic reticulum. The absence of striking uniform defects in nuclear envelope protein distribution indicates that such staining will be unavailing for general diagnostics, though it remains possible that specific mutations exhibiting protein distribution defects might reflect a particular clinical variant. These findings further argue that multiple pathways can lead to the generally similar pathologies of this disorder while at the same time the different cellular phenotypes observed possibly may help explain the considerable clinical variation of EDMD

Applications of particle swarm optimization for neural network training and digital systems

Particle Swarm Optimization (PSO) is an evolutionary computation technique similar to genetic algorithm, which is a population (swarm) based optimization tool. PSO starts with a population of random solutions called particles. Each particle is given a random velocity and is flown through the problem space. The particles work together to achieve a global task. The best particle of the entire swarm is taken as the final solution to the task. In this thesis, three problems are studied using the PSO; their results are presented, compared and contrasted with results obtained using conventional techniques. --Abstract, page iii