Houghtonrichter2936

Device Studying Predicts Femoral and Tibial Embed Dimensions Mismatch pertaining to Complete Joint Arthroplasty.

BACKGROUND This study investigated the effect of occupational stress and circadian clock gene polymorphism on sleep disorder of oil workers in Xinjiang, China. MATERIAL AND METHODS We enrolled 2300 Xinjiang oil workers who had been working for at least 1 year. The Chinese revised version of the Occupational Stress Questionnaire (OSI-R), the Pittsburgh Sleep Quality Index (PSQI), and General Survey Questionnaire were used. A total of 308 subjects were selected for stress hormone measurements and gene polymorphism analysis of the circadian clock genes CLOCK, PER2, and PER3. RESULTS The occupational stress scores were influenced by sex, smoking, marital status, age, and work type. Different work shift groups and different professional title groups had statistically significant sleep disorder incidences (P less then 0.05). The middle and high occupational stress groups had significantly higher subjective sleep quality, total PSQI scores, daytime dysfunction factor scores, and sleep disorder than in the low occupational stress group (P less then 0.05). CLOCK gene rs1801260 locus carrying TC genotype (OR=0.412, 95% CI=0.245-0.695), and CLOCK gene rs6850524 locus carrying GC and CC genotypes decreased sleep disorder risk (OR₁=0.357, 95% CI₁=0.245-0.695; OR₂=0.317, 95% CI₂=0.128-0.785). The main factors affecting the sleep quality of oil workers were length of service, individual strain capacity, glucocorticoid levels, Per3 gene, and the rs6850524 loci of CLOCK gene. CONCLUSIONS Occupational stress has an adverse effect on the sleep quality of workers. CLOCK gene and Per3 gene may increase risk of sleep disorders.A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an ill-posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug-drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug-drug interaction score efficiently. However, these models suffer from the over-fitting issue. Therefore, these models are not so-effective for predicting the drug-drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. find more The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.This study designs a robust closed-loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back-stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman's minimal model. find more The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super-twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller's robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.Recent experiments have shown that the biological oscillation of quorum sensing (QS) system play a vital role not only in the process of bacterial synthesis but also in the treatment of cancer by releasing drugs. As known, these five substances TetR, CI, LacI, AiiA and AI are the core components of the QS system. However, the effects of AiiA and protein synthesis time delay on QS system are often ignored in the theoretical model, which is taken as a priority in the proposed research. Therefore, the authors developed a new mathematical model to explore the effects of AiiA and time delay on the dynamical behaviour of QS system theoretically and numerically. The results show that time delay can induce oscillation of QS system. Concretely, there exists a time delay threshold [inline-formula removed]. When time delay is less than [inline-formula removed], the system is stable. With the increasing of time delay and once it passes [inline-formula removed], oscillation behaviour occurs. Moreover, the length of time delay determines the amplitude and period of the QS oscillation. In addition, the value of [inline-formula removed] is sensitive to AiiA. These results may enhance the understanding of QS oscillations and provide new insights for bacterial release drugs to treat cancer.Network motifs are recurrent and over-represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)-complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations.