Phosphate binding protein as the biorecognition element in a biosensor for phosphate

This work explores the potential use of a member of the periplasmic family of binding proteins, the phosphate binding protein (PBP), as the biorecognition element in a sensing scheme for the detection of inorganic phosphate (Pi). The selectivity of this protein originates from its natural role which, in Escherichia coli, is to serve as the initial receptor for the highly specific translocation of Pi to the cytoplasm. The single polypeptide chain of PBP is folded into two similar domains connected by three short peptide linkages that serve as a hinge. The Pi binding site is located deep within the cleft between the two domains. In the presence of the ligand, the two globular domains engulf the former in a hinge-like manner. The resultant conformational change constitutes the basis of the sensor development. A mutant of PBP (MPBP), where an alanine was replaced by a cysteine residue, was prepared by site-directed mutagenesis using the polymerase chain reaction (PCR). The mutant was expressed, from plasmid pSD501, in the periplasmic space of E. coli and purified in a single chromatographic step on a perfusion anion-exchange column. Site-specific labeling was achieved by attaching the fluorophore, N-[2-(1-maleimidyl)ethyl]-7-(diethylamino)coumarin-3-carboxamide (MDCC), to the protein through the sulfhydryl group of the cysteine moiety. Steady-state fluorescence studies of the MPBP-MDCC conjugate showed a change in the intensity of the signal upon addition of Pi. Calibration curves for Pi were constructed by relating the intensity of the fluorescence signal with the amount of analyte present in the sample. The sensing system was first developed and optimized on a spectrofluorometer using ml volumes of sample. It was then adapted to be used on a microtiter plate arrangement with microliter sample volumes. The system's versatility was finally proven by developing a fiber optic fluorescence-based sensor for monitoring Pi. In all three cases the detection limits for the analyte were in the sub-microMolar range. It was also demonstrated that the sensing system was selective for phosphate over other structurally-similar anions, paving the way for the design and development of a new family of biosensors utilizing the specific binding properties of periplasmic proteins.     

Small torsional vibration superposed on finitely deformed state of a circular cylindrical rod of transversely isotropic elastic material

Starting with a class of small deformations superposed on a finitely deformed state of a transversely isotropic elastic solid, we study a problem of small torsional vibration superposed on homogeneous finitely deformed state of a circular cylindrical rod made of transversely isotropic elastic material. It has been found that free vibration is possible and, due to anisotropy, the speed of propagation of waves of torsion along the cylinder is increased or decreased according as the initial stressed state is under tension or compression.     

On the thermodynamics of oxygen in molten copper,Cu-Sn,and Cu-Ag alloys

Measurements of oxygen activity in molten copper, Cu-Sn, and Cu-Ag alloys at 1135°C have been made utilizing the solid-state electrolyte technique. The activity coefficient of oxygen in molten copper at low oxygen levels (<0.1 pet) was found to be 0.12. The interaction parameters of silver and tin on oxygen were found to be ε _O ^Ag = 4.52 and ε _O ^Sn =-10.5. Comparison of experimental data with solution models revealed that the behavior of oxygen in Cu-O-Ag alloys is in accordance with predictions of Alcock and Richardson’s quasichemical model when a coordination number between 1 and 4 is assumed, depending on the silver and oxygen content. Furthermore, agreement with Belton’s quasichemical model based on the assumption of Ag-O dipoles serves to strengthen the evidence for the existence of Ag-O species in solution. The behavior of oxygen in the Cu-O-Sn system also shows better agreement with the quasichemical model than with the random model, with low coordination numbers favored in dilute tin solutions, increasing to larger values in Cu-60 Sn solutions.     

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.     

An immunoassay for Leu-enkephalin based on a C-terminal aequorin-peptide fusion

Recently we demonstrated that the fusion of an octapeptide to the C-terminus of a cysteine-free mutant of aequorin showed no inhibitory effect on the luminescence activity of the photoprotein. This observation is of particular importance when the use of aequorin as a label in the development of immunoassays for peptides whose activity lies in their C-terminal region or the epitope for antibody recognition is at their C-terminus is desired. In the case of opioid peptides, antibodies are directed toward their C-terminus as they differ from each other at this terminus. The goal of this study was to develop an immunoassay for Leu-enkephalin, a mammalian opioid peptide, using a C-terminal aequorin-peptide fusion protein. For that, the N-terminus of Leu-enkephalin was genetically fused to the C-terminus of a cysteine-free mutant of aequorin. It was observed that the C-terminal conjugated aequorin maintained its luminescence activity. An immunoassay for Leu-enkephalin was then developed using the aequorin-Leu-enkephalin fusion protein as a labeled analyte in a competitive as well as in a sequential binding mode. It was demonstrated that aequorin can be used as a label in peptide assays in which it is critical that the peptide's C-terminus be free for activity and/or for antibody recognition.     

Asymptotic Stability of a Rate Control System With Communication Delays

We study the issue of asymptotic stability of a family of rate control algorithms with communication delays between network elements and extend our earlier results: First, we derive delay-dependent stability conditions with a family of well-known utility and resource price functions when a finite upper bound is known on the feedback delay. These conditions are shown to be consistent with known stability conditions in two extreme cases-no delay or an arbitrarily large delay. Secondly, we provide a lower bound on the convergence rate with the same utility and resource price functions when delay-independent stability conditions hold.     

GIS Approach of Delineation and Risk Assessment of Areas Affected by Arsenic Pollution in Drinking Water

The objective of this study is to analyze health effects of arsenic pollution of drinking water using a geographical information system (GIS). The paper reports the regional impact of arsenic contamination in six administrative blocks of the central part of the Murshidabad district, West Bengal, India. In this area about 1,248,580 people are exposed to arsenic pollution out of whom 388,316 people are exposed to arsenic concentrations above 0.05?mg/L, the WHO maximum permissible level of arsenic in drinking water. The study estimates that 65% of the total area of the six blocks has arsenic concentrations below 0.05?mg/L, 26.12% of the area has arsenic concentrations above 0.05?mg/L, and for the rest of the area no arsenic distribution data available. The total number of expected death cases has been estimated considering the percent of risk involved in a concentration range and corresponding total population using such water for drinking purpose. The analysis forecasts that 11,890 people may risk death due to arsenic pollution in the whole life span. The maximum number of death cases is expected in Domkal and Beldanga 1 blocks and the minimum number of death cases is expected in Block Bhagabangola 2. This study also reports a comparison between the theoretical expectation of death cases and actual reported arsenicosis cases for the Domkal block. The areas of theoretical expectation and the areas of actually reported cases match fairly well except in a few cases. The present study helps planning and implementing of priority-based arsenic mitigation options.     

Effect of Disorder on Electronic,Magnetic, and Optical Properties of Co2CrZ Heusler Alloys (Z = Al,Ga, Si,Ge)

This paper presents the effect of disorder on electronic, magnetic, and optical properties of Co₂CrZ (Z = Al, Si, Ga, Ge) Heusler alloy using density functional theory. Binary mixing is the most common form of atomic disorder in these compounds. We have considered three types of disorders: DO ₃, A2, and B2 disorder which corresponds to X-Y, X-Z, and Y-Z mixing, respectively. After structural optimization, we found that A2 disorder has high formation energy and is most unlikely to occur. The half-metallic nature of the alloy is destroyed in the presence of DO ₃ and A2 disorder. The destruction of half-metallicity is due to reconstruction of energy states. B2 disorder retains the half-metallic nature of the alloy but spin-polarization value is reduced slightly as compared to the ordered alloy. In addition, the optical properties such as dielectric function, refractive index, absorption spectra, optical conductivity, reflectivity, and energy loss function of these alloys have also been investigated.     

An integrated framework for managing sensor data uncertainty using cloud computing

In recent years, an increasing number of data-intensive applications deal with continuously changing data objects (CCDOs), such as data streams from sensors and tracking devices. In these applications, the underlying data management system must support new types of spatiotemporal queries that refer to the spatiotemporal trajectories of the CCDOs. In contrast to traditional data objects, CCDOs have continuously changing attributes. Therefore, the spatiotemporal relation between any two CCDOs can change over time. This problem can be more complicated, since the CCDO trajectories are associated with a degree of uncertainty at every point in time. This is due to the fact that databases can only be discretely updated. The paper formally presents a comprehensive framework for managing CCDOs with insights into the spatiotemporal uncertainty problem and presents an original parallel-processing solution for efficiently managing the uncertainty using the map-reduce platform of cloud computing.