Influence of acupuncture on cognitive function and markers of oxidative DNA damage in patients with vascular dementia

AbstractObjectiveTo test the influence of acupuncture on cognitive function and a marker of oxidative DNA damage in patients with vascular dementia (VD).MethodsSixteen VD patients were evaluated before and after acupuncture, using the Folstein Mini-Mental State Examination-Revised (MMSE-R) to assess cognitive function, and the ADL-R scale to assess independence in activities of daily living (ADL). Life quality was evaluated using the DEMQOL (Dementia quality of life questionnaire) questionnaire, and syndromes and expression of vascular dementia were evaluated with the Scale for the Differentiation of Syndromes of Vascular Dementia (SDSVD). In addition, the urine concentration of 8-hydroxy-2′-deoxyguanosine (8-OHdG) —a marker of oxidative damage—was quantified with enzyme-linked immunosorbent assay.ResultsThe MMSE-R and DEMQOL scores were higher after acupuncture than before (P<0.05), while there were no obvious differences in the ADL-R or SDSVD scores (P>0.05). The 8-OHdG content in urine significantly decreased after acupuncture (P<0.05).ConclusionAcupuncture reduces the levels of 8-OHdG and improves cognitive function and quality of life in VD patients, suggesting that acupuncture is beneficial at least in part by preventing oxidative damage

Optimal estimation and control for lossy network: stability, convergence, and performance

In this paper, we study the problems of optimal estimation and control, i.e., the linear quadratic Gaussian (LQG) control, for systems with packet losses but without acknowledgment. Such acknowledgment is a signal sent by the actuator to inform the estimator of the incidence of control packet losses. For such system, which is usually called as a user datagram protocol (UDP)-like system, the optimal estimation is nonlinear and its calculation is time-consuming, making its corresponding optimal LQG problem complicated. We first propose two conditions: 1) the sensor has some computation abilities; and 2) the control command, exerted to the plant, is known to the sensor. For a UDP-like system satisfying these two conditions, we derive the optimal estimation. By constructing the finite and infinite product probability measure spaces for the estimation error covariances (EEC), we give the stability condition for the expected EEC, and show the existence of a measurable function to which the EEC converges in distribution, and propose some practical methods to evaluate the estimation performance. Finally, the LQG controllers are derived, and the conditions for the mean square stability of the closed-loop system are established

Research on Chloride Penetration Resistance of Hybrid Fiber Reinforced Self-Compacting Concrete

The properties of chloride penetration of hybrid fiber reinforced self-compacting concrete (SCC) were investigated in this study. The results show that, the chloride penetration resistance of concrete can be improved by single incorporation either carbon or cellulose fibers. The concrete chloride diffusion coefficient DRCM of 12-cm length carbon SCC with fiber content of 1.7 kg/m3, 2.72 kg/m3, and 3.4 kg/m3 decreases by 10.3%, 25.5%, and 18.2% compared to reference concrete without any fibers, respectively. Moreover, the concrete chloride diffusion coefficient DRCM of cellulose SCC with fiber content of 1.2 kg/m3, 1.6 kg/m3, and 2.0 kg/m3 decreases by 18.8%, 22.4%, and 26.7% compared to reference concrete, respectively. Based on the results of orthogonal experimental design, the chloride diffusion coefficients DRCM of hybrid fiber reinforced SCC are listed in order of importance, as follows: length of carbon fiber \u3e content of carbon fiber \u3e content of cellulose fiber; furthermore, the hybrid of 2.72-kg/m3 carbon fiber with length of 12mm and 2.0-kg/m3 cellulose fiber exhibits the most significant effect on chloride diffusion coefficients DRCM of SCC

Global Protein Interactome Exploration Through Mining Genome-Scale Data in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e

Background Many essential cellular processes, such as cellular metabolism, transport, cellular metabolism and most regulatory mechanisms, rely on physical interactions between proteins. Genome-wide protein interactome networks of yeast, human and several other animal organisms have already been established, but this kind of network reminds to be established in the field of plant. Results We first predicted the protein protein interaction in Arabidopsis thaliana with methods, including ortholog, SSBP, gene fusion, gene neighbor, phylogenetic profile, coexpression, protein domain, and used Naïve Bayesian approach next to integrate the results of these methods and text mining data to build a genome-wide protein interactome network. Furthermore, we adopted the data of GO enrichment analysis, pathway, published literature to validate our network, the confirmation of our network shows the feasibility of using our network to predict protein function and other usage. Conclusions Our interactome is a comprehensive genome-wide network in the organism plant Arabidopsis thaliana, and provides a rich resource for researchers in related field to study the protein function, molecular interaction and potential mechanism under different conditions