An automated, parallel processing approach to biomolecular sample preparation

Sample preparation for DNA and RNA assays is a prime candidate for laboratory automation. A novel, parallel processing device that performs the three separate liquid-handling functions necessary for such sample preparation-dispensing, pipetting, and pressurizing-is presented. The device comprises an array of fine nozzles connected by fluidic channels to automatically and precisely distribute flow between one source and an array of points. The design principles, as well as the experimental and computational methods used to develop the device, are described. Test results, including accuracy, uniformity, volume range, and timing, are presented. The functionality of the device is demonstrated by performing a solid-phase extraction of DNA with two types of microcolumns.     

Interaction of metal ions with carboxylic and carboxamide groups in protein structures

An analysis of the geometry of metal binding by carboxylic andcarboxamide groups in proteins is presented. Most of the ligandsare from aspartic and glutamic acid side chains. Water moleculesbound to carboxylate anions are known to interact with oxygenlone-pairs. However, metal ions are also found to approach thecarboxylate group along the C - O direction. More metal ionsare found to be along the syn than the anti lone-pair direction.This seems to be the result of the stability of the five-memberedring that is formed by the carboxylate anion hydrogen bondedto a ligand water molecule and the metal ion in the syn position.Ligand residues are usually from the helix, turn or regionswith no regular secondary structure. Because of the steric interactionsassociated with bringing all the ligands around a metal center,a calcium ion can bind only near the ends of a helix; a metal,like zinc, with a low coordination number, can bind anywherein the helix. Based on the analysis of the positions of watermolecules in the metal coordination sphere, the sequence ofthe EF hand (a calcium-binding structure) is discussed.     

Prediction of properties of rubber by using artificial neural networks

Different rubber formulations were designed using nitrile rubber and a mixed crosslinking system consisting of sulfur/accelerator and electron beam radiation. Based on the experimental results, an artificial neural network (ANN) was constructed to simulate the mechanical properties and volume fraction of rubber. The ANN could predict accurately the above properties for a series of nitrile rubber compounds. However, the number of training data played a key role in the ANN predictive quality. In addition, the more complex the nonlinear relation between input and output was, the larger was the number of training dataset required. The predicted results were further validated using another mathematical model. The constructed ANN was verified with a completely different styrene butadiene rubber system. The prediction was found to be extremely good. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 2227–2237, 2006     

Studies on engineering properties of PVC–PMMA semi‐interpenetrating polymer networks

Semi1 and semi2 interpenetrating polymer networks (IPNs) of PVC and in situ formed PMMA have been synthesized using diallyl phthalate and ethylene glycol dimethacrylate as the crosslinkers of PVC and PMMA, respectively. These two types of IPNs have been compared w.r.t their physical, mechanical, and thermal properties and an endeavor has been made to find a correlation of these properties with the morphology generated in these systems. The semi1 IPNs displayed a decrease in their mechanical parameters and the physical properties as well, while in contrast, the semi2 IPNs exhibited a marginal increase in the corresponding values after an initial drop upto about 15% of crosslinked PMMA incorporation when compared to the crosslinked PVC in the case of semi1 IPN and linear PVC in the case of semi2 IPN. The various samples of semi1 and semi2 IPNs showed a two‐stage degradation typical of PVC, while confirming the increased stability with the samples having higher percentages of PMMA. The influence of crosslinking of the major matrix in semi1 IPN was almost counterbalanced by the influence of crosslinking in the dispersed PMMA phase in the case of semi2 IPN. The softening characteristics as detected by the extent of penetration of the probe, as has been detected by thermomechanical analysis, are in conformity with their mechanicals. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 1725–1735, 2005     

An Experimental Study to Evaluate the Effect of Ambient Temperature during Manual Lifting and Design of Optimal Task Parameters

In the present study, the effect of lifting task parameters on the heart rate and oxygen uptake of workers during manual lifting tasks in different ambient conditions was evaluated. The experiments conducted in two different temperature conditions showed a significantly higher oxygen uptake and heart rate in colder conditions as compared to warmer conditions. Three other factors, namely, load, lifting frequency, and vertical distance were found to significant affect the responses. Various combinations of significant factors were used to calculate oxygen uptake and heart rate. These were then compared with the safe limits as per the maximum aerobic capacity of workers. Based on these comparisons, the safe combinations were identified that can be used to design lifting tasks in varied ambient conditions. The study further concluded that lifting tasks performed in winter should have different relaxation or fatigue allowances built into the cycle time of the task to compensate for higher exertion. © 2011 Wiley Periodicals, Inc.     

Structure-property relations for a phase-pure,nanograined tetragonal zirconia ceramic stabilized with minimum CaO doping

Dense (~97%) CaO-stabilized ZrO₂ ceramic was stabilized with minimum (3 mol%) doping (reported to date) and processed via conventional sintering at a low temperature (~1200°C); compositional analysis via X-ray florescence confirmed the CaO doping accuracy. Phase-pure tetragonal structure (characterized via both X-ray diffraction and Raman spectroscopy) along with uniform nanograins (90 nm) of the ceramic ensured the evolution of no monoclinic phase even after vigorous low-temperature degradation experiments (both thermal and hydrothermal aging for 80-100 h). The sintered ceramic recorded a high hardness (~15 GPa); the indentation toughness value was also comparable to a 3 mol% yttria-stabilized zirconia system. The remarkable structure–property correlations in the 3 mol% CaO-stabilized ZrO₂ ceramic suggests that the same may be worth examining for suitable future applications (e.g., in dental ceramics).     

A Formal Methodology for Detecting Managerial Vulnerabilities and Threats in an Enterprise Information System

From information security point of view, an enterprise is considered as a collection of assets and their interrelationships. These interrelationships may be built into the enterprise information infrastructure, as in the case of connection of hardware elements in network architecture, or in the installation of software or in the information assets. As a result, access to one element may enable access to another if they are connected. An enterprise may specify conditions on the access of certain assets in certain mode (read, write etc.) as policies. The interconnection of assets, along with specified policies, may lead to managerial vulnerabilities in the enterprise information system. These vulnerabilities, if exploited by threats, may cause disruption to the normal functioning of information systems. This paper presents a formal methodology for detection of managerial vulnerabilities of, and threats to, enterprise information systems in linear time.     

Multi‐response optimization and empirical modeling of cardiopulmonary responses during manual lifting tasks

An experimental study involving five independent lifting variables (namely weight, lift frequency, horizontal distance from the object, operator, and vertical distance) and their interactions was conducted using the Taguchi L₂₇ design. Two responses—heart rate and oxygen uptake—were measured during each trial. To optimize both responses simultaneously, the signal‐to‐noise ratio was normalized to obtain a multi‐response signal‐to‐noise (MRSN) ratio for each trial. The MRSN ratio was analyzed using analysis of variance to identify the optimal condition in which both responses were optimized at the same time. Lift frequency was found to be the most significant factor, and operator and horizontal distance were found to be least significant. The empirical modeling equation was developed and can be used to predict the response values during experimental trials involving similar work conditions. © 2010 Wiley Periodicals, Inc.     

Recognition of consonant-vowel (CV) units under background noise using combined temporal and spectral preprocessing

This paper proposes hybrid classification models and preprocessing methods for enhancing the consonant-vowel (CV) recognition in the presence of background noise. Background Noise is one of the major degradation in real-time environments which strongly effects the performance of speech recognition system. In this work, combined temporal and spectral processing (TSP) methods are explored for preprocessing to improve CV recognition performance. Proposed CV recognition method is carried out in two levels to reduce the similarity among large number of CV classes. In the first level vowel category of CV unit will be recognized, and in the second level consonant category will be recognized. At each level complementary evidences from hybrid models consisting of support vector machine (SVM) and hidden Markov models (HMM) are combined for enhancing the recognition performance. Performance of the proposed CV recognition system is evaluated on Telugu broadcast database for white and vehicle noise. The proposed preprocessing methods and hybrid classification models have improved the recognition performance compared to existed methods.     

Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data

Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.