Synergistic interaction of biocatalyst with bio-anode as a function of electrode materials

Comprehensive study was performed to understand the synergistic interaction between the biocatalyst and anode in terms of electron discharge (ED) pattern and microbial growth by varying electrode (bio-anode) materials viz., graphite, aluminum, brass, copper, nickel and stainless steel. Experiments were performed in bio-electrochemical cell consisting of three electrodes (bio-anode as working electrode, carbon rod as counter electrode and Ag/AgCl(S) as reference electrode) employing anaerobic mixed culture as anodic biocatalyst. Voltammetric and chronoamperometric analysis were used to enumerate the ED and redox reactions. Presence of higher microbial population and dominance of Gram positive bacteria with higher ED supported graphite function as a good bio-anode material. Nickel and stainless steel showed higher ED after graphite associated with dominance of Gram positive bacterial population. Although higher ED was noticed with brass, metal oxidation and decrement in ED with time doesn’t support its function as bio-anode. In spite of higher ED than nickel and stainless steel, aluminum and copper showed significant metal oxidation leading to change in both physical and electrochemical properties along with dominant growth of Gram negative bacteria. This study gives a comprehensive idea on biocatalyst interaction with anode in extracellular electron transfer which is important in improving the anode performance. Juxtaposing the results, it can be deduced that the outcome of the present study can be extended to all bio-electrochemical systems including microbial fuel cell (MFC).     

Amperometric determination of tyramine in sauce and beer by epoxy resin biocomposite membrane bound tyramine oxidase

A method is described for construction of an amperometric biosensor for specific determination of tyramine, using black gram tyramine oxidase immobilized covalently on an epoxy resin membrane. The biosensor had optimum response within 10 s at pH 8.5 and 35 °C. A linear relationship was observed between tyramine concentrations and current (mA) in the range of 0.24 to 3.47 mg/dL. The biosensor was employed for determination of tyramine in beer and sauce. The detection limit of sensor was 0.24 mg/dL. The mean analytical recovery of added tyramine (0.5 and 1.0 mg/dL) was 97.3 ± 2.3 and 95.9 ± 3.4%. Within and between batch coefficient of variation were 5.1 and 5.34%, respectively. Enzyme electrode showed 35% loss in its initial activity after its regular use over a period of 2 months. The biosensor has the advantage that it does not suffer from leaching of enzyme and measures tyramine specifically.     

Systemic inflammatory load in humans is suppressed by consumption of two formulations of dried,encapsulated juice concentrate

Chronic inflammation contributes to an increased risk for developing chronic conditions such as cardiovascular disease, diabetes, and cancer. A high “inflammatory load” is defined as elevated inflammation markers in blood or other tissues. We evaluated several markers of systemic inflammation from healthy adults and tested the hypothesis that two formulations of encapsulated fruit and vegetable juice powder concentrate with added berry powders (FVB) or without (FV) could impact markers of inflammatory load. Using a double‐blind, placebo‐controlled approach, 117 subjects were randomly assigned to receive placebo, FV, or FVB capsules. Blood was drawn at baseline and after 60 d of capsule consumption. We measured inflammatory markers (high sensitivity C‐Reactive Protein, Monocyte Chemotactic Protein‐1, Macrophage Inflammatory Protein 1‐β, and Regulated upon Activation, Normal T cell Expressed and Secreted), superoxide dismutase, and micronutrients (β‐carotene, vitamin C, and vitamin E). Results showed Monocyte Chemotactic Protein‐1, Macrophage Inflammatory Protein 1‐β, and RANTES levels were significantly reduced and superoxide dismutase and micronutrient levels were significantly increased in subjects consuming both FV and FVB, relative to placebo. Data suggest a potential health benefit by consuming either formulation of the encapsulated juice concentrates through their anti‐inflammatory properties.     

Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.     

A Physics-driven Neural Networks-based Simulation System (PhyNNeSS) for multimodal interactive virtual environments involving nonlinear deformable objects

BACKGROUND: While an update rate of 30 Hz is considered adequate for real time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. METHODS: In this work we present PhyNNeSS - a Physics-driven Neural Networks-based Simulation System - to address this long-standing technical challenge. The first step is an off-line pre-computation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. RESULTS: We present realistic simulation examples from interactive surgical simulation with real time force feedback. As an example, we have developed a deformable human stomach model and a Penrose-drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. CONCLUSIONS: A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based pre-computational step allows training of neural networks which may be used in real time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.     

Microstructure and residual stress distribution of similar and dissimilar electron beam welds – Maraging steel to medium alloy medium carbon steel

The influence of parent metal heat treatment condition on the residual stress distribution in dissimilar metal welds of maraging steel to quenched and tempered medium alloy medium carbon steel has been investigated. It has been observed that the residual stress distribution would be more compressive if the maraging steel is in soft condition. This is attributed to stress absorbing nature of highly yielding soft maraging steel.     

Specifying and Monitoring Interactions and Commitments in Open Business Processes

Business processes are increasingly complex and open because they rely on services that are distributed geographically and across organizations. So, they're prone to several points of failure. Monitoring, therefore, remains an important concern. A new approach specifies and monitors interactions among heterogeneous services by tracking their commitments. This approach extends recent research that views business process design as a composition of interaction protocols. It specifies and monitors policies and commitments as a way to monitor service-level agreements and recover from process failures.     

Practical methods for constructing suffix trees

Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized. In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n²) worst-case complexity outperforms popular linear time algorithms like Ukkonen and McCreight, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we describe two approaches. First, we present a buffer management strategy for the O(n²) algorithm. The resulting new algorithm, which we call “Top Down Disk-based” (TDD), scales to sizes much larger than have been previously described in literature. This approach far outperforms the best known disk-based construction methods. Second, we present a new disk-based suffix tree construction algorithm that is based on a sort-merge paradigm, and show that for constructing very large suffix trees with very little resources, this algorithm is more efficient than TDD.     

Design patterns for open tool integration

Design tool integration is a highly relevant area of software engineering that can greatly improve the efficiency of development processes. Design patterns have been widely recognized as important contributors to the success of software systems. This paper describes and compares two large-grain, architectural design patterns that solve specific design tool integration problems. Both patterns have been implemented and used in real-life engineering processes.