In vitro antioxidant activity and inhibitory hepatic steatosis effect on oleic acid-induced fatty liver model of consecutive extracts from Rosa davurica Pall

The nutrient and phytochemical composition of Rosa davurica Pall. fruit (RDF) from China were determined, including sugar, reducing sugar, ascorbic acid, caroteniods and phenolics. RDF was successively extracted with chloroform, ethyl acetate (EA), water-saturated n-butanol, ethanol and distilled water, respectively. Among the five fractions, EA fraction revealed the highest total phenolic content and the strongest antioxidant ability in cyclic voltammograms (CV) and oxygen radical absorbance capacity (ORAC) assays. Moreover, the strongest inhibitory activity against oleic acidinduced (OA-induced) fatty liver in vitro was also the EA fraction. Further, it was separated by a semipreparative high performance liquid chromatography (HPLC) on C18 column. The most active fraction (EA-FII) of EA in all assays above, which mainly included catechin, quercetin-sedoheptulose and quercetin-94, was identified by LC/MS, HPLC and ultraviolet (UV)/V spectrum analysis.Keywords: RDF extracts, composition properties, antioxidant ability, inhibitory activity, oleic acid-induced, HPLC, LC/MS.African Journal of Biotechnology Vol. 12(31), pp. 4944-495

Robustness of maintenance support service networks : attributes, evaluation and improvement

Maintenance support service network (MSSN) is used to provide maintenance services and maintain the operational status of equipment. However, the performance of MSSN has been significantly influenced by inevitable disturbance, which makes it vital to maintain its robustness. Existing research on robustness of MSSN mainly focuses on single-layer rather than two-layer network, which imposes constraints on the disturbances and limits its application. To solve these issues, this study develops a two-layer MSSN, consisting of a directed entity-layer and an undirected cyber-layer focusing on supporting maintenance service. A definition of robustness for two-layer MSSN is proposed, and effect propagation models are established to evaluate its robustness of MSSN, followed by its improvement strategies. In particular, two strategies applied in the single-layer MSSN are modified to adapt to the two-layer MSSN, and a novel greedy partnership building approach is proposed to find an optimal strategy under cascading failure, to maintain the robustness of MSSN from a complex network perspective. Finally, numerical examples are presented to illustrate the effectiveness of the proposed approach

Reliability analysis of multiplex control system of subsea blowout preventer based on stochastic Petri net

Višestruki (MUX − multiplex) upravljački sustav za sprečavanje podmorske erupcije bušotine (BOP − blowout preventer) ima bitnu ulogu u stvaranju sigurnih radnih uvjeta kod podmorskih aktivnosti bušenja. U skladu s radnim stanjima i kritičnim načinima kvara višestrukog upravljačkog sustava, u radu se predstavlja njegov stohastički model Petri mreža (SPN), uzimajući u obzir nesavršenu sposobnost otkrivanja greške. Predlaže se metoda numeričke analize temeljena na istolikom (izomorfnom) trajnom Markovljevom lancu modela. Istraživani su i uspoređivani pokazatelji pouzdanosti, odnosno pouzdanost, raspoloživost i MTTF višestrukog (MUX) upravljačkog sustava i probnog hidrauličkog upravljačkog sustava. Uz to, istraživani su učinci faktora prikrivenosti grešaka na vjerojatnosti stanja i dostupnost MUX upravljačkog sustava, a izvršena je i analiza nesigurnosti brzina paljenja u odnosu na MTTF.The multiplex (MUX) control system of subsea blowout preventer (BOP) plays a vital role in providing safe working conditions for the subsea drilling activities. According to the working states and critical failure modes of the MUX control system, this paper presents its stochastic Petri nets (SPN) model, taking into account the imperfect fault detection capacity. The numerical analysis method is proposed based on the isomorphic continuous-time Markov chain of the model. The reliability indexes, namely reliability, availability and MTTF of the MUX control system and pilot hydraulic control system are obtained and compared. In addition, the effects of fault coverage factor on state probabilities and availability of the MUX control system are researched and the uncertainty analysis of the firing rates related to MTTF is also performed

Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries

The proper monitoring of quality-related but hard-to-measure variables is currently one of the bottlenecks limiting the safe and efficient operations of industrial processes. This paper proposes a novel ensemble learning algorithm by coordinating global and local Gaussian process regression (GPR) models, and this algorithm is able to capture global and local process behaviours for accurate prediction and timely process monitoring. To further address the deterioration in predictions when using the off-line training and online testing strategy, this paper proposes an adaptive ranking strategy to perform ensemble learning for the sub-GPR models. In this adaptive strategy, we use the moving-window technique to rank and select several of the best sub-model predictions and then average them together to make the final predictions. Last but not least, the least absolute shrinkage and selection operator (Lasso) works together with factor analysis (FA) in a two-step variable selection method to remove under-correlated model input variables in the first stage and to compress over-correlated model input variables in the second stage. The proposed prediction model is validated in two real wastewater treatment plants (WWTPs) with stationary and nonstationary behaviours. The results show that the proposed methodology achieves better performance than other standard methods in the context of their predictions of quality-related variables

Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery

Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes

Kaposi's Sarcoma Associated Herpes Virus (KSHV) Induced COX-2: A Key Factor in Latency, Inflammation, Angiogenesis, Cell Survival and Invasion

Kaposi's sarcoma (KS), an enigmatic endothelial cell vascular neoplasm, is characterized by the proliferation of spindle shaped endothelial cells, inflammatory cytokines (ICs), growth factors (GFs) and angiogenic factors. KSHV is etiologically linked to KS and expresses its latent genes in KS lesion endothelial cells. Primary infection of human micro vascular endothelial cells (HMVEC-d) results in the establishment of latent infection and reprogramming of host genes, and cyclooxygenase-2 (COX-2) is one of the highly up-regulated genes. Our previous study suggested a role for COX-2 in the establishment and maintenance of KSHV latency. Here, we examined the role of COX-2 in the induction of ICs, GFs, angiogenesis and invasive events occurring during KSHV de novo infection of endothelial cells. A significant amount of COX-2 was detected in KS tissue sections. Telomerase-immortalized human umbilical vein endothelial cells supporting KSHV stable latency (TIVE-LTC) expressed elevated levels of functional COX-2 and microsomal PGE2 synthase (m-PGES), and secreted the predominant eicosanoid inflammatory metabolite PGE2. Infected HMVEC-d and TIVE-LTC cells secreted a variety of ICs, GFs, angiogenic factors and matrix metalloproteinases (MMPs), which were significantly abrogated by COX-2 inhibition either by chemical inhibitors or by siRNA. The ability of these factors to induce tube formation of uninfected endothelial cells was also inhibited. PGE2, secreted early during KSHV infection, profoundly increased the adhesion of uninfected endothelial cells to fibronectin by activating the small G protein Rac1. COX-2 inhibition considerably reduced KSHV latent ORF73 gene expression and survival of TIVE-LTC cells. Collectively, these studies underscore the pivotal role of KSHV induced COX-2/PGE2 in creating KS lesion like microenvironment during de novo infection. Since COX-2 plays multiple roles in KSHV latent gene expression, which themselves are powerful mediators of cytokine induction, anti-apoptosis, cell survival and viral genome maintainence, effective inhibition of COX-2 via well-characterized clinically approved COX-2 inhibitors could potentially be used in treatment to control latent KSHV infection and ameliorate KS

Preparation and Application of Electrodes in Capacitive Deionization (CDI): a State-of-Art Review

As a promising desalination technology, capacitive deionization (CDI) have shown practicality and cost-effectiveness in brackish water treatment. Developing more efficient electrode materials is the key to improving salt removal performance. This work reviewed current progress on electrode fabrication in application of CDI. Fundamental principal (e.g. EDL theory and adsorption isotherms) and process factors (e.g. pore distribution, potential, salt type and concentration) of CDI performance were presented first. It was then followed by in-depth discussion and comparison on properties and fabrication technique of different electrodes, including carbon aerogel, activated carbon, carbon nanotubes, graphene and ordered mesoporous carbon. Finally, polyaniline as conductive polymer and its potential application as CDI electrode-enhancing materials were also discussed