From Natural Methylation to Versatile Alkylations Using Halide Methyltransferases

Halide methyltransferases (HMTs) enable the enzymatic synthesis of S-adenosyl-l -methionine (SAM) from S-adenosyl-l -homocysteine (SAH) and methyl iodide. Characterisation of a range of naturally occurring HMTs and subsequent protein engineering led to HMT variants capable of synthesising ethyl, propyl, and allyl analogues of SAM. Notably, HMTs do not depend on chemical synthesis of methionine analogues, as required by methionine adenosyltransferases (MATs). However, at the moment MATs have a much broader substrate scope than the HMTs. Herein we provide an overview of the discovery and engineering of promiscuous HMTs and how these strategies will pave the way towards a toolbox of HMT variants for versatile chemo- and regioselective biocatalytic alkylations.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

Antioxidant activity of red wine phenolic extracts towards oxidation of corn oil

The antioxidant activity of Xinomavro red wine phenolic extracts in corn oil stripped of tocopherols was evaluated. One wine extract, at 100 mg/L total phenolics, rich in phenolic acids and flavonols, inhibited the oxidation of corn oil stripped of tocopherols to a greater extent than butylated hydroxyanisole, at 200 mg/L. Moreover, another extract, at 100 mg/L total phenolics, rich in flavanols, flavonols and tyrosol, also exhibited high inhibitory action. The present results indicate that some red wine phenolics – such as phenolic acids, flavonols or flavanols – may be strong antioxidants in corn oil.     

Enabling customers engagement and collaboration for small and medium-sized enterprises in ubiquitous multi-channel ecosystems

Over the last few years, we have encountered an exponential growth in online communication opportunities. Organizations have more and more ways to connect and engage with their current or future customers. The existence of more opportunities in connecting to people can be both an enabler and a burden. Being present at a multitude of different channels requires the effective management of a very large number of adapted contents, formats, and interaction patterns fulfilling the communication and cooperation needs of distributed target groups. In this respect, we integrate existing fragmented communication and monitoring approaches into a full-fledged communication model as a basis for an adequate engagement approach. We describe applications of our approach in both the eTourism and manufacturing domain. In this paper, we introduce an approach that will enable communication, collaboration and value exchange of users through a multitude of online interaction possibilities based on the use of semantic technology. Finally, we also compare our approach with existing solutions with respect to the identified challenges in this subject.     

The impact of computer self-efficacy,situational interest and academic self-concept in virtual communities of inquiry during the distance learning procedures through Second Life

The current study investigates a case where the online learning procedure in three-dimensional (3D) technologically-advanced environments of the Web 2.0 is growing at an exponential rate. In this occasion it is highly imperative need to understand students’ interactions in this innovative mode of e-Education that requires from educators and scholars not only analysis conceptually, but also an empirically-driven optimization. The community of inquiry (CoI) model (or framework) consists to be as one of the most prominent multi-dimensional constructs that it is widely used to represent several distinct dimensions of social presence, teaching presence and cognitive presence, as a unique and fundamental theoretical concept to measure students’ interactions in contemporary electronic environments. Although, the effectiveness of these multi-dimensional constructs creates a dilemma to researchers who want the breadth and comprehensiveness of this model for the precision and clarity of users’ (instructors and students) dimensions with other motivational and learning variables. To address this dilemma, the current empirical study presents statistical analyses from the “trinity” constructs of the CoI model by utilizing correlation and hierarchical regression analyses with two fundamental motivational (computer self-efficacy and situational interest) and another one learning (academic self-concept) variables. This study goes one step further and introduces the conspicuously indisputable intervention of a virtual (V)CoI and its utilization in multi-user virtual worlds, like Second Life (SL). The study findings of one hundred thirty-five (135) participants who enrolled in several online sessions unveiled that the situational interest was the only significant predictor of social presence. The computer self-efficacy was not a significant predictor of the CoI model, while on the other hand academic self-concept was a significant predictor in a revamped attempt to validate the strong relationship among constructs within it. According to the aforementioned reasons, it can be surmised that the successful combination of the VCoI in Second Life, surpassing irrefutable and inherent shortcomings to a future-driven sustainable use and growth.     

Nearly-Linear Work Parallel SDD Solvers,Low-Diameter Decomposition,and Low-Stretch Subgraphs

We present the design and analysis of a nearly-linear work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input an SDD n-by-n matrix A with m nonzero entries and a vector b, our algorithm computes a vector \(\tilde{x}\) such that \(\|\tilde{x} - A^{+}b\|_{A} \leq\varepsilon\cdot\|{A^{+}b}\|_{A}\) in \(O(m\log^{O(1)}{n}\log {\frac{1}{\varepsilon}})\) work and \(O(m^{1/3+\theta}\log\frac{1}{\varepsilon})\) depth for any θ>0, where A ⁺ denotes the Moore-Penrose pseudoinverse of A. The algorithm relies on a parallel algorithm for generating low-stretch spanning trees or spanning subgraphs. To this end, we first develop a parallel decomposition algorithm that in O(mlog ^O(1) n) work and polylogarithmic depth, partitions a graph with n nodes and m edges into components with polylogarithmic diameter such that only a small fraction of the original edges are between the components. This can be used to generate low-stretch spanning trees with average stretch O(n ^α ) in O(mlog ^O(1) n) work and O(n ^α ) depth for any α>0. Alternatively, it can be used to generate spanning subgraphs with polylogarithmic average stretch in O(mlog ^O(1) n) work and polylogarithmic depth. We apply this subgraph construction to derive a parallel linear solver. By using this solver in known applications, our results imply improved parallel randomized algorithms for several problems, including single-source shortest paths, maximum flow, minimum-cost flow, and approximate maximum flow.     

Optimization of injection molding design. Part I: Gate location optimization

The placement of a gate in an injection mold is one of the most important variables of the total mold design. The quality of the molded part is greatly affected by the gate location, because it influences the manner in which the plastic flows into the mold cavity. Some defects, such as weldline and overpack, can be effectively controlled only by the gate location. Therefore, the product quality can be greatly improved by determining the optimum gate location. In this paper, we develop a general methodology for gate location optimization. We first quantify quality in terms of flow simulation outputs. We can thus assess detrimental effects such as warpage and dimensional instability as a function of the independent variable, which is in this case the gate location. Next we develop methods to search for the optimum gate location. The search method introduced in this paper is a method that combines a deterministic hill climbing search with a stochastic annealing search method. The method is appropriately called simulated annealing and hill climbing (SANHIL). The criteria used for evaluation during the search process are a function of the flow simulation outputs. We demonstrate the success of the method for a complex industrial mold. The approach is applicable to any complex mold geometry and any plastic.     

CMMN evaluation: the modelers’ perceptions of the main notation elements

Software and Systems Modeling - Case Management Model and Notation (CMMN) has been introduced as a graphical modeling language targeting the modeling of human-centric processes. Despite its growing...