Expanding the Scope of Sortase‐Mediated Ligations by Using Sortase Homologues

Sortase‐catalyzed transacylation reactions are widely used for the construction of non‐natural protein derivatives. However, the most commonly used enzyme for these strategies (sortase A from Staphylococcus aureus) is limited by its narrow substrate scope. To expand the range of substrates compatible with sortase‐mediated reactions, we characterized the in vitro substrate preferences of eight sortase A homologues. From these studies, we identified sortase A enzymes that recognize multiple substrates that are unreactive toward sortase A from S. aureus. We further exploited the ability of sortase A from Streptococcus pneumoniae to recognize an LPATS substrate to perform a site‐specific modification of the N‐terminal serine residue in the naturally occurring antimicrobial peptide DCD‐1L. Finally, we unexpectedly observed that certain substrates (LPATXG, X=Nle, Leu, Phe, Tyr) were susceptible to transacylation at alternative sites within the substrate motif, and sortase A from S. pneumoniae was capable of forming oligomers. Overall, this work provides a foundation for the further development of sortase enzymes for use in protein modification.     

Performance evaluation of Web server workloads in Xen‐based virtualized computer system: analytical modeling and experimental validation

Recent developments in the field of virtualization technologies have led to renewed interest in performance evaluation of these systems. Nowadays, maturity of virtualization technology has made a fuss of provisioning IT services to maximize profits, scalability and QoS. This pioneer solution facilitates deployment of datacenter applications and grid and Cloud computing services; however, there are challenges. It is necessary to investigate a trade‐off among overall system performance and revenue and to ensure service‐level agreement of submitted workloads. Although a growing body of literature has investigated virtualization overhead and virtual machines interference, there is still lack of accurate performance evaluation of virtualized systems. In this paper, we present in‐depth performance measurements to evaluate a Xen‐based virtualized Web server. Regarding this experimental study; we support our approach by queuing network modeling. Based on these quantitative and qualitative analyses, we present the results that are important for performance evaluation of consolidated workloads on Xen hypervisor. First, demands of both CPU intensive and disk intensive workloads on CPU and disk are independent from the submitted rate to unprivileged domain when dedicated core(s) are pinned to virtual machines. Second, request response time not only depends on processing time at unprivileged domain but also pertains to amount of flipped pages at Domain 0. Finally, results show that the proposed modeling methodology performs well to predict the QoS parameters in both para‐virtualized and hardware virtual machine modes by knowing the request content size. Copyright © 2015 John Wiley & Sons, Ltd.     

Correction to: Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems

Electronic Commerce Research -     

A Model to Create Organizational Value with Big Data Analytics

Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of data and create business value? The present study seeks to provide a model for creating organizational value using Big Data Analytics (BDA). To this end, after reviewing the related literature and interviewing experts, the BDA-based organizational value creation model is developed. Accordingly, five hypotheses are formulated, and a questionnaire is prepared. Then, the respective questionnaire is given to the research statistical population (i.e., IT managers and experts, particularly those specializing in data analysis) to test the research hypotheses. In next phase, connections between model variables are scrutinized using the structural equation modeling (measurement and structural models). The results of the study indicate that investigating the infrastructures of the Big Data Analytics, as well as the capabilities of the organization and those of Big Data Analytics is the initial requirement to create organizational value using BDA. Thereby, the Big Data Analytics strategy is formulated, and ultimately, the organizational value is created as well.     

A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.     

Applications of genetic algorithms in process planning: tool sequence selection for 2.5-axis pocket machining

Tool sequence selection is an important activity in process-planning for milling and has great bearing on the cost of machining. Currently, it is accomplished manually without consideration of cost factors a priori. Typically, a large tool is selected to quickly generate the rough shape and a smaller clearing tool is used to generate the net-shape. In this paper, we present a new systematic method to select the optimal sequence of tool(s), to machine a 2.5-axis pocket given pocket geometry, a database of cutting tools, cutting parameters, and tool holder geometry. Algorithms have been developed to calculate the geometric constructs such as accessible areas, and pocket decomposition, while considering tool holders. A Genetic Algorithm (GA) formulation is used to find the optimal tool sequence. Two types of selection mechanisms namely “Elitist selection” and “Roulette method” are tested. It is found that the Elitist method converges much faster than the Roulette method. The proposed method is compared to a shortest-path graph formulation that was developed previously by the authors. It is found that the GA formulation generates near optimal solutions while reducing computation by up to 30% as compared to the graph formulation.     

A study on the plastic properties of unidirectional nanocomposites with interface energy effects

In this paper, for obtaining an overall size-dependent yield function for nanocomposites containing aligned cylindrical nanofibers, the effects of interface residual stress and interface elasticity are taken into account within a micromechanical framework. Toward this goal, the modified Hill’s condition is used, and then, in order to consider effects of the interface residual stress, strains are decomposed into two parts, a part due to the external loadings and the other due to the interface residual stress. Next, utilizing the field fluctuation method, an overall yield function containing effective elastic constants of the material is derived and then simplified for practical loading conditions. Moreover, a secant modulus scheme is adopted to examine the overall nonlinear behavior of the material in plastic deformation. Finally, by some numerical examples, it is shown that the interface stress, including the interface residual stress, makes the yield strength and plastic deformation of the metal matrix nanocomposites dependent on the nanofiber size, in contrast to the classical results.     

The biological seal of the implant–soft tissue interface evaluated in a tissue-engineered oral mucosal model

For dental implants, it is vital that an initial soft tissue seal is achieved as this helps to stabilize and preserve the peri-implant tissues during the restorative stages following placement. The study of the implant–soft tissue interface is usually undertaken in animal models. We have developed an in vitro three-dimensional tissue-engineered oral mucosal model (3D OMM), which lends itself to the study of the implant–soft tissue interface as it has been shown that cells from the three-dimensional OMM attach onto titanium (Ti) surfaces forming a biological seal (BS). This study compares the quality of the BS achieved using the three-dimensional OMM for four types of Ti surfaces: polished, machined, sandblasted and anodized (TiUnite). The BS was evaluated quantitatively by permeability and cell attachment tests. Tritiated water (HTO) was used as the tracing agent for the permeability test. At the end of the permeability test, the Ti discs were removed from the three-dimensional OMM and an Alamar Blue assay was used for the measurement of residual cells attached to the Ti discs. The penetration of the HTO through the BS for the four types of Ti surfaces was not significantly different, and there was no significant difference in the viability of residual cells that attached to the Ti surfaces. The BS of the tissue-engineered oral mucosa around the four types of Ti surface topographies was not significantly different.     

Evaluation of the Effects of Cucumis sativus Seed Extract on Serum Lipids in Adult Hyperlipidemic Patients: A Randomized Double‐Blind Placebo‐Controlled Clinical Trial

Hyperlipidemia is associated with increased risk of atherosclerosis; therefore, control of this risk factor is very important in preventing atherosclerosis. Cucumber (Cucumis sativus) seed is used traditionally as a lipid‐lowering nutritional supplement. The aim of this study was to evaluate the effect of cucumber seed extract on serum lipid profile in adult patients with mild hyperlipidemia. In a randomized double‐blind placebo‐controlled clinical trial, hyperlipidemic patients with inclusion criteria were randomly and equally assigned to either Cucumis or placebo groups and used one medicinal or placebo capsule, respectively, once daily with food for 6 wk. Body mass index (BMI) as well as fasting serum levels of total cholesterol, triglycerides (TG), low‐density lipoprotein (LDL‐C), and high‐density lipoprotein (HDL‐C) were measured for all patients pre‐ and post‐intervention and finally the changes were compared between the groups. Twenty‐four patients in Cucumis group and 23 patients in placebo group completed the study. Cucumis seed extract resulted in significant reduction of total cholesterol (P = 0.016), LDL‐C (P < 0.001), TG (P < 0.001), and BMI (P < 0.001) as well as significant increase of HDL‐C (P = 0.012) compared to placebo. In conclusion, the consumption of C. sativus seed extract with daily dose of 500 mg results in desirable effects on serum lipid profile in adult hyperlipidemic patients. Therefore, cucumber seed could be considered as a food supplement for treatment of dyslipidemia.