Investigation of human papilloma viruses infections in prostate cancer

Human papillomavirus (HPV) infections are associated with benign and malignant lesions of the female and male anogenital tract. In the current study, we aimed to investigate the role of high-risk HPVs infection in the pathogenesis of prostate cancer among nations or ethnic groups, in addition to testing the role of homozygosity of arginine form at codon 72 of the p53 gene among prostate cancer patients whose prostate tissues were infected with high-risk HPVs. Formalin-fixed paraffin-embedded tissue samples of 123 primary prostate adenocarcinoma cases and 267 control tissues of benign prostatic hyperplasia were used in the study. Genomic DNA was purified and amplified through MY09/MY11 degenerate primers, GP5+/GP6+ consensus primers, SPF1/2 cocktail of six primers using conventional, multiplex and nested PCR techniques, and subsequently subjected to viral load quantification, genotyping, testing of polymorphism of codon 72 of the p53 gene and apoptosis index assessment by in situ assay. Also, the status of the p53 tumour suppressor gene, p16INK4a transcription factor as well as the E6 protein of the high risk HPVs have been tested by immunohistochemistry in both the study and control groups. High-risk HPVs were detected in 30 of 123 (24.3%) PCa and 16 of 267 (5.9%) BPH samples with positive HPV-DNA. The detection rate of the high-risk HPV infections was 4%, 44% and 29% among the ethnic subgroups from the Middle Eastern, Caucasian, and Afro-Caribbean of the PCa patients. There was no association between the existence of high-risk HPV infections and their viral load in PCa patients and the tumour staging, grading, PSA level and patient survival rate in those patients. Likewise, there was no significant difference in the frequency of p53 Arg 16 homozygosity between the high-risk HPV-positive and the HPV-negative PCa samples. Moreover, it has been found that the existence of the highrisk HPV E6 protein within the PCa samples was independent of the status of the p53 gene, p16INK4a transcription factor, and the apoptosis index in these samples. Our data showed that HPV infections do exist in PCa and BPH samples with different prevalence within ethnic groups with the least occurrence in the Middle Eastern patients. However, the infections with high-risk HPVs are not associated with the prostate cancer grade, stage, patient’s PSA level, and survival rate. Therefore, our data do not support the role of HPV infection in the pathogenesis of prostate carcinoma

Computational models and approaches for lung cancer diagnosis

The success of treatment of patients with cancer depends on establishing an accurate diagnosis. To this end, the aim of this study is to developed novel lung cancer diagnostic models. New algorithms are proposed to analyse the biological data and extract knowledge that assists in achieving accurate diagnosis results

Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Aircraft Abnormal Conditions Detection, Identification, and Evaluation Using Innate and Adaptive Immune Systems Interaction

Abnormal flight conditions play a major role in aircraft accidents frequently causing loss of control. To ensure aircraft operation safety in all situations, intelligent system monitoring and adaptation must rely on accurately detecting the presence of abnormal conditions as soon as they take place, identifying their root cause(s), estimating their nature and severity, and predicting their impact on the flight envelope.;Due to the complexity and multidimensionality of the aircraft system under abnormal conditions, these requirements are extremely difficult to satisfy using existing analytical and/or statistical approaches. Moreover, current methodologies have addressed only isolated classes of abnormal conditions and a reduced number of aircraft dynamic parameters within a limited region of the flight envelope.;This research effort aims at developing an integrated and comprehensive framework for the aircraft abnormal conditions detection, identification, and evaluation based on the artificial immune systems paradigm, which has the capability to address the complexity and multidimensionality issues related to aircraft systems.;Within the proposed framework, a novel algorithm was developed for the abnormal conditions detection problem and extended to the abnormal conditions identification and evaluation. The algorithm and its extensions were inspired from the functionality of the biological dendritic cells (an important part of the innate immune system) and their interaction with the different components of the adaptive immune system. Immunity-based methodologies for re-assessing the flight envelope at post-failure and predicting the impact of the abnormal conditions on the performance and handling qualities are also proposed and investigated in this study.;The generality of the approach makes it applicable to any system. Data for artificial immune system development were collected from flight tests of a supersonic research aircraft within a motion-based flight simulator. The abnormal conditions considered in this work include locked actuators (stabilator, aileron, rudder, and throttle), structural damage of the wing, horizontal tail, and vertical tail, malfunctioning sensors, and reduced engine effectiveness. The results of applying the proposed approach to this wide range of abnormal conditions show its high capability in detecting the abnormal conditions with zero false alarms and very high detection rates, correctly identifying the failed subsystem and evaluating the type and severity of the failure. The results also reveal that the post-failure flight envelope can be reasonably predicted within this framework

Application and evaluation of the neural network in gearbox

We developed old designed of a Back-Propagation neural network (BPNN), which it was designed by other researchers, and we made modification in their structure. The 1st velocity ratio was discriminated by lowest speed, and highest twist. The 6th velocity ratio was discriminated by highest speed, and lowest twist. The aim of this paper is to design neural structure get best performance to control an electrical automotive transportation six-speed gearbox of the vehicle. We focus on the evaluation of the BPNN to select the suitable number of layers and neurons. Experimentally, the structure of the proposed BPNN are constructed from four layers: eight input nodes in the first layer that received data in binary number, 45 neurons in 1st hidden-layer, 25 neurons in 2nd hidden-layer, and 6 neurons in the fourth layer. The MSE and number of Epochs are the main factors used for the evaluation of the proposed structure, and compared with the other structures which was designed by other researchers. Experimentally, we discovered that the best value of Epoch and MSE was chosen when the BPNN consisted of two hidden-layers, 45, and 25 neurons in the 1st and 2nd hidden-layer respectively. The implementation was applied using MATLAB software