Gossypol-induced hypokalemia and role of exogenous potassium salt supplementation when used as an antispermatogenic agent in male langur monkey

In our earlier study, we have observed that hypokalemia in langur monkeys, following gossypol acetic acid (GAA) treatment (5 mg dose level) when used as an antispermatogenic agent, and potassium salt supplementation partially maintained body potassium level of the animals. The aims of the present investigation was to confirm further occurrence of hypokalemia in the monkey (comparatively at two higher dose levels) and the role of potassium salt in preventing occurrence of gossypol-induced hypokalemia. Highly purified gossypol acetic acid alone at two dose levels (7.5 and 10 mg/animal/day; oral) and in combination with potassium chloride (0.50 and 0.75 mg/animal/day; oral) was given for 180 days. Treatment with gossypol alone as well as with the supplementation of potassium salt resulted in severe oligospermia and azoospermia. Animals receiving gossypol alone showed significant potassium deficiency with signs of fatigue at both dose levels. Enhanced potassium loss through urine was found in potassium-deficient animals, whereas animals receiving gossypol acetic acid plus potassium salt showed normal serum potassium with a less significant increase in urine potassium level during treatment phases. Other parameters of the body remained within normal range except gradual and significant elevation in serum transaminases activity. The animals gradually returned to normalcy following 150 and 180 days of termination of the treatment.

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Structural and functional characteristics of homing endonucleases

Mobile genetic elements constitute a remarkably diverse group of nonessential selfish genes that provide no apparent function to the host. These selfish genes have been implicated in host extinction, speciation and architecture of genetic systems. Homing endonucleases, encoded by the open reading frames embedded in introns or inteins of mobile genetic elements, possess double-stranded DNA-specific endonuclease activity. They inflict sequence-specific double-strand breaks at or near the homing site in intron- or intein-less allele. Subsequently, through nonreciprocal exchange the insertion sequence (intron or intein) is transferred from an intein- or intron-containing allele to an intein- or intron-less allele. The components of host double-strand break repair pathway are thought to finish the "homing" process. Several lines of evidence suggest that homing endonucleases are capable of promoting transposition into ectopic sites within or across genomes for their survival as well as dispersal in natural populations. The occurrence of inteins at high frequencies serves as instructive models for understanding the mechanistic aspects of the process of homing and its evolution. This review focuses on genetic, biochemical, structural, and phylogenetic aspects of homing endonucleases, and their comparison with restriction endonucleases.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

On generating optimal signal probabilities for random tests: A genetic approach

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed, A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance pf our GA-based approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger. To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms.

A Change in the 3(10)- to alpha-Helical Transition Point in the Heptapeptides Containing Sulfur and Selenium

Crystal structures of three heptapeptides Boc-Ala-Leu-Aib-XXX-Ala-Leu-Aib-OMe (where XXX = methionine in peptide A, selenomethionine in peptide B, and S-benzyl cysteine in peptide C) reveal mixed 3(10)-/alpha-helical conformations with R factors of 6.94, 5.79, and 5.98, respectively. All the structures were solved in the P2(1)2(1)2(1) space group. 3(10)- to a-helical transitions are observed in all of these peptides. The helices begin as a 3(10)-helical segment at the N-terminus and then transit for peptides A and C at residue Aib(3) carbonyl (O(3)), while for peptide B the transition occurs at residue Leu(2) carbonyl oxygen (O(2)). There are water molecules associated in the crystal of each of these peptides and they form different types of hydrogen bonding patterns in each crystal. The observations suggest that 3(10)- to alpha-helical transition is sequence dependent in these short heptapeptide sequences.

Pickup and delivery problem using metaheuristics techniques

Dial-a-ride problem (DARP) is an optimization problem which deals with the minimization of the cost of the provided service where the customers are provided a door-to-door service based on their requests. This optimization model presented in earlier studies, is considered in this study. Due to the non-linear nature of the objective function the traditional optimization methods are plagued with the problem of converging to a local minima. To overcome this pitfall we use metaheuristics namely Simulated Annealing (SA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Immune System (AIS). From the results obtained, we conclude that Artificial Immune System method effectively tackles this optimization problem by providing us with optimal solutions. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

Shape optimization of RC flexural members

A natural velocity field method for shape optimization of reinforced concrete (RC) flexural members has been demonstrated. The possibility of shape optimization by modifying the shape of an initially rectangular section, in addition to variation of breadth and depth along the length, has been explored. Necessary shape changes have been computed using the sequential quadratic programming (SQP) technique. Genetic algorithm (Goldberg and Samtani 1986) has been used to optimize the diameter and number of main reinforcement bars. A limit-state design approach has been adopted for the nonprismatic RC sections. Such relevant issues as formulation of optimization problem, finite-element modeling, and solution procedure have been described. Three design examples-a simply supported beam, a cantilever beam, and a two-span continuous beam, all under uniformly distributed loads-have been optimized. The results show a significant savings (40-56%) in material and cost and also result in aesthetically pleasing structures. This procedure will lead to considerable cost saving, particularly in cases of mass-produced precast members and a heavy cast-in-place member such as a bridge girder.

Heat shock protein 70 as a biomarker of heat stress in a simulated hot cockpit

Background: Fighter pilots are frequently exposed to high temperatures during high-speed low-level flight. Heat strain can result in temporary impairment of cognitive functions and when severe, loss of consciousness and consequent loss of life and equipment. Induction of stress proteins is a highly conserved stress response mechanism from bacteria to humans. induced stress protein levels are known to be cytoprotective and have been correlated with stress tolerance. Although many studies on the heat shock response mechanisms have been performed in cell culture and animal model systems, there is very limited information on stress protein induction in human subjects. Hypothesis: Heat shock proteins (Hsp), especially Hsp70, may be induced in human subjects exposed to high temperatures in a hot cockpit designed to simulate heat stress experienced in low flying sorties. Methods: Six healthy volunteers were subjected to heat stress at 55degreesC in a high temperature cockpit simulator for a period of 1 h at 30% humidity. Physiological parameters such as oral and skin temperatures, heart rate, and sweat rate were monitored regularly during this time. The level of Hsp70 in leukocytes was examined before and after the heat exposure in each subject. Conclusions: Hsp70 was found to be significantly induced in all the six subjects exposed to heat stress. The level of induced Hsp70 appears to correlate with other strain indicators such as accumulative circulatory strain and Craig's modified index. The usefulness of Hsp70 as a molecular marker of heat stress in humans is discussed.

Impact of Mutating the Key Residues of a Bifunctional 5,10-Methylenetetrahydrofolate Dehydrogenase-Cyclohydrolase from Escherichia coli on Its Activities

Methylenetetrahydrofolate dehydrogenase-cyclohydrolase (FolD) catalyzes interconversion of 5,10-methylene-tetrahydrofolate and 10-formyl-tetrahydrofolate in the one-carbon metabolic pathway. In some organisms, the essential requirement of 10-formyl-tetrahydrofolate may also be fulfilled by formyltetrahydrofolate synthetase (Fhs). Recently, we developed an Escherichia coli strain in which the folD gene was deleted in the presence of Clostridium perfringens fhs (E. coli Delta folD/p-fhs) and used it to purify FolD mutants (free from the host-encoded FolD) and determine their biological activities. Mutations in the key residues of E. coli FolD, as identified from three-dimensional structures (D121A, Q98K, K54S, Y50S, and R191E), and a genetic screen (G122D and C58Y) were generated, and the mutant proteins were purified to determine their kinetic constants. Except for the R191E and K54S mutants, others were highly compromised in terms of both dehydrogenase and cyclohydrolase activities. While the R191E mutant showed high cyclohydrolase activity, it retained only a residual dehydrogenase activity. On the other hand, the K54S mutant lacked the cyclohydrolase activity but possessed high dehydrogenase activity. The D121A and G122D (in a loop between two helices) mutants were highly compromised in terms of both dehydrogenase and cyclohydrolase activities. In vivo and in vitro characterization of wild-type and mutant (R191E, G122D, D121A, Q98K, C58Y, K54S, and Y50S) FolD together with three-dimensional modeling has allowed us to develop a better understanding of the mechanism for substrate binding and catalysis by E. coli FolD.