Studies on the mechanism of follitropin action at the cellular level

With a view toward understanding better the mechanism of action of follitropin, an attempt was made using granulosa cells obtained from intact immature estrogenized rats to study in short-term incubations the effect of highly purified ovine follitropin on the binding of the hormone to the cells and the associated aromatase response. A modified radioimmunoassay procedure has been used to monitor unlabeled physiologically fully active follitropin bound to the cell. A linear relationship between the actual amount of hormone bound to the cells and the estradiol produced in vitro has been established. The amount of ovine follitropin bound that can elicit a half-maximal response in estrogen production was calculated to be 400 pg. The number of follitropin binding sites per cell was 375 and the Kd of binding was 3.03 × 10−10 Image . By the addition of ovine follitropin antiserum at different time points of a 4-h incubation period, a continual need for follitropin support for estradiol production has been established.

Multi-state-choice automata model for expedient learning in random environment

A new automata model Mr,k, with a conceptually significant innovation in the form of multi-state alternatives at each instance, is proposed in this study. Computer simulations of the Mr,k, model in the context of feature selection in an unsupervised environment has demonstrated the superiority of the model over similar models without this multi-state-choice innovation.

Arabidopsis thaliana J-class heat shock proteins: cellular stress sensors

Plants are sessile organisms that have evolved a variety of mechanisms to maintain their cellular homeostasis under stressful environmental conditions. Survival of plants under abiotic stress conditions requires specialized group of heat shock protein machinery, belonging to Hsp70:J-protein family. These heat shock proteins are most ubiquitous types of chaperone machineries involved in diverse cellular processes including protein folding, translocation across cell membranes, and protein degradation. They play a crucial role in maintaining the protein homeostasis by reestablishing functional native conformations under environmental stress conditions, thus providing protection to the cell. J-proteins are co-chaperones of Hsp70 machine, which play a critical role by stimulating Hsp70s ATPase activity, thereby stabilizing its interaction with client proteins. Using genome-wide analysis of Arabidopsis thaliana, here we have outlined identification and systematic classification of J-protein co-chaperones which are key regulators of Hsp70s function. In comparison with Saccharomyces cerevisiae model system, a comprehensive domain structural organization, cellular localization, and functional diversity of A. thaliana J-proteins have also been summarized. Electronic supplementary material The online version of this article (doi:10.1007/s10142-009-0132-0) contains supplementary material, which is available to authorized users.

A Team of Continuous-Action Learning Automata for Noise-Tolerant Learning of Half-Spaces

Learning automata are adaptive decision making devices that are found useful in a variety of machine learning and pattern recognition applications. Although most learning automata methods deal with the case of finitely many actions for the automaton, there are also models of continuous-action-set learning automata (CALA). A team of such CALA can be useful in stochastic optimization problems where one has access only to noise-corrupted values of the objective function. In this paper, we present a novel formulation for noise-tolerant learning of linear classifiers using a CALA team. We consider the general case of nonuniform noise, where the probability that the class label of an example is wrong may be a function of the feature vector of the example. The objective is to learn the underlying separating hyperplane given only such noisy examples. We present an algorithm employing a team of CALA and prove, under some conditions on the class conditional densities, that the algorithm achieves noise-tolerant learning as long as the probability of wrong label for any example is less than 0.5. We also present some empirical results to illustrate the effectiveness of the algorithm.

Automata and logics over finitely varying functions

We extend some of the classical connections between automata and logic due to Büchi (1960) [5] and McNaughton and Papert (1971) [12] to languages of finitely varying functions or “signals”. In particular, we introduce a natural class of automata for generating finitely varying functions called View the MathML source’s, and show that it coincides in terms of language definability with a natural monadic second-order logic interpreted over finitely varying functions Rabinovich (2002) [15]. We also identify a “counter-free” subclass of View the MathML source’s which characterise the first-order definable languages of finitely varying functions. Our proofs mainly factor through the classical results for word languages. These results have applications in automata characterisations for continuously interpreted real-time logics like Metric Temporal Logic (MTL) Chevalier et al. (2006, 2007) [6] and [7].

Cellular and molecular regulation of mammalian blastocyst hatching

In mammals including humans, failure in blastocyst hatching and implantation leads to early embryonic loss and infertility. Prior to implantation, the blastocyst must hatch out of its acellular glycoprotein coat, the zona pellucida (ZP). The phenomenon of blastocyst hatching is believed to be regulated by (i) dynamic cellular components such as actin-based trophectodermal projections (TEPs), and (ii) a variety of autocrine and paracrine molecules such as growth factors, cytokines and proteases. The spatio-temporal regulation of zona lysis by blastocyst-derived cellular and molecular signaling factors is being keenly investigated. Our studies show that hamster blastocyst hatching is acelerated by growth factors such as heparin binding-epidermal growth factor and leukemia inhibitory factor and that embryo-derived, cysteine proteases including cathepsins are responsible for blastocyst hatching. Additionally, we believe that cyclooxygenase-generated prostaglandins, estradiol-17 beta mediated estrogen receptor-alpha signaling and possibly NF kappa B could be involved in peri-hatching development. Moreover, we show that TEPs are intimately involved with lysing ZP and that the TEPs potentially enrich and harbor hatching-enabling factors. These observations provide new insights into our understanding of the key cellular and molecular regulators involved in the phenomenon of mammalian blastocyst hatching, which is essential for the establishment of early pregnancy.

Uplink Power Control and Base Station Association in Multichannel Cellular Networks

A combined base station association and power control problem is studied for the uplink of multichannel multicell cellular networks, in which each channel is used by exactly one cell (i.e., base station). A distributed association and power update algorithm is proposed and shown to converge to a Nash equilibrium of a noncooperative game. We consider network models with discrete mobiles (yielding an atomic congestion game), as well as a continuum of mobiles (yielding a population game). We find that the equilibria need not be Pareto efficient, nor need they be system optimal. To address the lack of system optimality, we propose pricing mechanisms. It is shown that these mechanisms can be implemented in a distributed fashion.

Optimal non-linear reinforcement schemes for stochastic automata

Two optimal non-linear reinforcement schemes—the Reward-Inaction and the Penalty-Inaction—for the two-state automaton functioning in a stationary random environment are considered. Very simple conditions of symmetry of the non-linear function figuring in the reinforcement scheme are shown to be necessary and sufficient for optimality. General expressions for the variance and rate of learning are derived. These schemes are compared with the already existing optimal linear schemes in the light of average variance and average rate of learning.

An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

Simultaneous effect of lead and cadmium on granulosa cells: A cellular model for ovarian toxicity

Lead (Pb) and cadmium (Cd) are known reproductive toxicants, which accumulate in granulosa cells of the ovary. Female Charles foster rats were treated with sodium acetate (control), lead acetate and cadmium acetate either alone or in combination at a dose 0.05 mg/kg body weight intra-peritoneally for 15 days daily. Animals were killed at proestrous stage and granulosa cells were isolated from the ovaries. Binding of I-125-luteinizing hormone (I-125-LH), I-125-follicle stimulating hormone (I-125-FSH) and 17 beta-hydroxysteroid dehydrogenase activity were measured. As these receptors are localized on the surface of the cell membrane, we also estimated the membrane parameters of these cells. Our results demonstrated that both lead and cadmium caused a significant reduction in gonadotropin binding, which altered steroidogenic enzyme activity of granulosa cells. These changes exhibited a positive correlation with membrane changes of the granulosa cells.

Genetic Heterogeneity in Wild Isolates of Cellular Slime Mold Social Groups

This study addresses the issues of spatial distribution, dispersal, and genetic heterogeneity in social groups of the cellular slime molds (CSMs). The CSMs are soil amoebae with an unusual life cycle that consists of alternating solitary and social phases. Because the social phase involves division of labor with what appears to be an extreme form of "altruism", the CSMs raise interesting evolutionary questions regarding the origin and maintenance of sociality. Knowledge of the genetic structure of social groups in the wild is necessary for answering these questions. We confirm that CSMs are widespread in undisturbed forest soil from South India. They are dispersed over long distances via the dung of a variety of large mammals. Consistent with this mode of dispersal, most social groups in the two species examined for detailed study, Dictyostelium giganteum and Dictyostelium purpureum, are multi-clonal.

A Cellular Array for Multivalued Logic Functions

An algebraic generalization of the well-known binary q-function array to a multivalued q-function array is presented. It is possible to associate tree-structure realizations for binary q-functions and multivalued q-functions. Synthesis of multivalued functions using this array is very simple