GRASP-1 A Neuronal RasGEF Associated with the AMPA Receptor/GRIP Complex

AbstractThe PDZ domain–containing proteins, such as PSD-95 and GRIP, have been suggested to be involved in the targeting of glutamate receptors, a process that plays a critical role in the efficiency of synaptic transmission and plasticity. To address the molecular mechanisms underlying AMPA receptor synaptic localization, we have identified several GRIP-associated proteins (GRASPs) that bind to distinct PDZ domains within GRIP. GRASP-1 is a neuronal rasGEF associated with GRIP and AMPA receptors in vivo. Overexpression of GRASP-1 in cultured neurons specifically reduced the synaptic targeting of AMPA receptors. In addition, the subcellular distribution of both AMPA receptors and GRASP-1 was rapidly regulated by the activation of NMDA receptors. These results suggest that GRASP-1 may regulate neuronal ras signaling and contribute to the regulation of AMPA receptor distribution by NMDA receptor activity

Synergistic Association of PTGS2 and CYP2E1 Genetic Polymorphisms with Lung Cancer Risk in Northeastern Chinese

BACKGROUND: Lung cancer is the most common cause of cancer-related deaths worldwide. The aim of this study was to investigate the association of five extensively-studied polymorphisms in PTGS2 (rs689466, rs5275, rs20417) and CYP2E1 (rs2031920, rs6413432) genes with lung cancer risk in a large northeastern Chinese population. METHODOLOGY/PRINCIPAL FINDINGS: This is a hospital-based case-control study involving 684 patients with lung cancer and 604 cancer-free controls. Genotyping was performed using the PCR-LDR method. Data were analyzed using Haplo.stats and MDR programs. There were significant differences between patients and controls in allele/genotype distributions of rs5275 (P = 0.002/0.003) and rs6413432 (P = 0.037/0.044), as well as in genotype distributions of rs689466 (P = 0.02). The risk for lung cancer associated with the rs5275-C mutant allele was decreased by 60% (95% CI [confidence interval]: 0.21-0.74; P = 0.004) under the recessive model. Carriers of rs689466-G mutant allele had a 28% (95% CI: 0.57-0.92; P = 0.008) reduced risk of developing lung cancer relative to the AA genotype carriers. In haplotype analysis, haplotype G-C-C-T (in order of rs689466, rs5275, rs2031920 and rs6413432) decreased the odds of lung cancer by 28% (95% CI: 0.51-0.93; P = 0.019) after adjusting for confounding factors, whereas haplotype A-T-T-T had 1.49-fold (95% CI: 1.21-1.79; P = 0.012) increased risk for lung cancer. Using MDR method, the overall best model including rs5275, rs689466 and rs6413432 polymorphisms was identified with a maximal testing accuracy of 66.1% and a maximal cross-validation consistency of 10 out of 10 (P = 0.003). CONCLUSIONS/SIGNIFICANCE: Our findings demonstrated a potentially synergistic association of PTGS2 and CYP2E1 polymorphisms with the underlying cause of lung cancer in northeastern Chinese

Evaluation and Quality Assurance of Fog Computing-Based IoT for Health Monitoring System

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare

A Model for the Natural Language Perception-based Creative Control of Unmanned Ground Vehicles

Mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. That is why mobile robotics problems are complex with many unanswered questions. To reach a high degree of autonomous operation, a new level of learning is required. On the one hand, promising learning theories such as the adaptive critic and creative control have been proposed, while on other hand the human brain’s processing ability has amazed and inspired researchers in the area of Unmanned Ground Vehicles but has been difficult to emulate in practice. A new direction in the fuzzy theory tries to develop a theory to deal with the perceptions conveyed by the natural language. This paper tries to combine these two fields and present a framework for autonomous robot navigation. The proposed creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The TCC module utilizes computational theory of perceptions to deal with the high levels of task planning. The authors are currently trying to implement the model on a real mobile robot and the preliminary results have been described in this paper

Evaluating the Role of Big Data in IIOT-Industrial Internet of Things for Executing Ranks Using the Analytic Network Process Approach

Due to the enhancements of Internet of Things (IoT) and sensors deployments, the production of big data in Industrial Internet of Things (IIoT) is increased. The accessing and processing of big data become a challenging issue due to the limited storage space, computational time, networking, and IoT devices end. IoT and big data are well thought-out to be the key concepts when describing new information architecture projects. The techniques, tools, and methods that help to provide better solutions for IoT and big data can have an important role to play in the architecture of business. Different approaches are being practiced in the literature for evaluating the role of big data in IIoT. These techniques are not handling the situations when complexity of dependency arises among parameters of the alternatives. The proposed research uses the approach of Analytic Network Process (ANP) for evaluating the role of big data in IIoT. The results show that the proposed research works well for evaluating the role of big data in IIoT

Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem

User Knowledge, Data Modelling, and Visualization: Handling through the Fuzzy Logic-Based Approach

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise