N-formyl hydroxylamine containing dipeptides: generation of a new class of vasopeptidase inhibitors

Four primary zinc-binding pharmacophores (thiols, carboxylates, phosphorus acids, and hydroxamates) have been utilized in generating inhibitors of zinc metalloproteases such as ACE, NEP, the MMPs, and ECE. Although compounds which inhibit the activity of both ACE and NEP (vasopeptidase inhibitors, VPIs) have been reported which incorporate a thiol, carboxylate, or phosphorus acid pharmacophore, the generation of hydroxamate based vasopeptidase inhibitors has remained elusive. Herein we report the first potent vasopeptidase inhibitors which were generated from the incorporation of conformationally restricted dipeptide mimetics to an N-formyl hydroxylamine zinc-binding group. Compounds such as 13c and 13d are among the most potent in this series, exhibiting in vitro activity comparable to other classes of inhibitors.     

The Body as a Tool for Anger Awareness—Differential Effects of Angry Facial and Bodily Expressions on Suppression from Awareness

Emotional signals are perceived whether or not we are aware of it. The evidence so far mostly came from studies with facial expressions. Here, we investigated whether the pattern of non-conscious face expression perception is found for whole body expressions. Continuous flash suppression (CFS) was used to measure the time for neutral, fearful, and angry facial or bodily expressions to break from suppression. We observed different suppression time patterns for emotions depending on whether the stimuli were faces or bodies. The suppression time for anger was shortest for bodily expressions, but longest for the facial expressions. This pattern indicates different processing and detection mechanisms for faces and bodies outside awareness, and suggests that awareness mechanisms associated with dorsal structures might play a role in becoming conscious of angry bodily expressions.     

Time Perception and Dynamics of Facial Expressions of Emotions

Two experiments were run to examine the effects of dynamic displays of facial expressions of emotions on time judgments. The participants were given a temporal bisection task with emotional facial expressions presented in a dynamic or a static display. Two emotional facial expressions and a neutral expression were tested and compared. Each of the emotional expressions had the same affective valence (unpleasant), but one was high-arousing (expressing anger) and the other low-arousing (expressing sadness). Our results showed that time judgments are highly sensitive to movements in facial expressions and the emotions expressed. Indeed, longer perceived durations were found in response to the dynamic faces and the high-arousing emotional expressions compared to the static faces and low-arousing expressions. In addition, the facial movements amplified the effect of emotions on time perception. Dynamic facial expressions are thus interesting tools for examining variations in temporal judgments in different social contexts.     

Survival analysis of longitudinal microarrays

MOTIVATION: The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or continuous, but there is less work in relating expressions to an observed event time such as time to death, response or relapse. When gene expressions are measured over time, there are methods for differentiating temporal patterns. However, methods have not yet been proposed for the survival analysis of longitudinally collected microarrays. RESULTS: We describe an approach for the survival analysis of longitudinal gene expression data. We construct a measure of association between the time to an event and gene expressions collected over time. Statistical significance is addressed using permutations and control of the false discovery rate. Our proposed method is illustrated on a dataset from a multi-center research study of inflammation and response to injury that aims to uncover the biological reasons why patients can have dramatically different outcomes after suffering a traumatic injury (www.gluegrant.org).     

Individual Differences in the Recognition of Facial Expressions: An Event-Related Potentials Study

Previous studies have shown that early posterior components of event-related potentials (ERPs) are modulated by facial expressions. The goal of the current study was to investigate individual differences in the recognition of facial expressions by examining the relationship between ERP components and the discrimination of facial expressions. Pictures of 3 facial expressions (angry, happy, and neutral) were presented to 36 young adults during ERP recording. Participants were asked to respond with a button press as soon as they recognized the expression depicted. A multiple regression analysis, where ERP components were set as predictor variables, assessed hits and reaction times in response to the facial expressions as dependent variables. The N170 amplitudes significantly predicted for accuracy of angry and happy expressions, and the N170 latencies were predictive for accuracy of neutral expressions. The P2 amplitudes significantly predicted reaction time. The P2 latencies significantly predicted reaction times only for neutral faces. These results suggest that individual differences in the recognition of facial expressions emerge from early components in visual processing.     

The matrix representation of pharmacokinetic models.

An equation is developed from the matrix of rate constants which describes the behaviour of linear pharmacokinetic models for any initial condition as a function of time. This general matrix equation is then used to derive analogous expressions for drug distribution after a period of infusion, at the steady state, or during a multiple constant-dosage regimen. Matrix expressions are also derived for areas under drug concentration curves for any compartment after single doses or during multiple dosing. General matrix equations are shown to yield loading dosage schedules to achieve plateau concentrations throughout any open system.It is suggested that matrix methods have advantages over previously used mathematical techniques in pharmacokinetics in the simplicity of the algebraic expressions, and their ease of manipulation. An algebraic example of an open two-compartment model is worked to indicate the applicability of the general expressions.     

Holistic gaze strategy to categorize facial expression of varying intensities

Using faces representing exaggerated emotional expressions, recent behaviour and eye-tracking studies have suggested a dominant role of individual facial features in transmitting diagnostic cues for decoding facial expressions. Considering that in everyday life we frequently view low-intensity expressive faces in which local facial cues are more ambiguous, we probably need to combine expressive cues from more than one facial feature to reliably decode naturalistic facial affects. In this study we applied a morphing technique to systematically vary intensities of six basic facial expressions of emotion, and employed a self-paced expression categorization task to measure participants' categorization performance and associated gaze patterns. The analysis of pooled data from all expressions showed that increasing expression intensity would improve categorization accuracy, shorten reaction time and reduce number of fixations directed at faces. The proportion of fixations and viewing time directed at internal facial features (eyes, nose and mouth region), however, was not affected by varying levels of intensity. Further comparison between individual facial expressions revealed that although proportional gaze allocation at individual facial features was quantitatively modulated by the viewed expressions, the overall gaze distribution in face viewing was qualitatively similar across different facial expressions and different intensities. It seems that we adopt a holistic viewing strategy to extract expressive cues from all internal facial features in processing of naturalistic facial expressions.     

Novel algorithm for coexpression detection in time-varying microarray data sets

When analyzing the results of microarray experiments, biologists generally use unsupervised categorization tools. However, such tools regard each time point as an independent dimension and utilize the Euclidean distance to compute the similarities between expressions. Furthermore, some of these methods require the number of clusters to be determined in advance, which is clearly impossible in the case of a new dataset. Therefore, this study proposes a novel scheme, designated as the Variation-based Coexpression Detection (VCD) algorithm, to analyze the trends of expressions based on their variation over time. The proposed algorithm has two advantages. First, it is unnecessary to determine the number of clusters in advance since the algorithm automatically detects those genes whose profiles are grouped together and creates patterns for these groups. Second, the algorithm features a new measurement criterion for calculating the degree of change of the expressions between adjacent time points and evaluating their trend similarities. Three real-world microarray datasets are employed to evaluate the performance of the proposed algorithm.     

The influence of diffusion on the rate of a reversible quasi-unimolecular reaction in one, two or three dimensions

The role of diffusion in the kinetics of a reversible quasi-unimolecular reaction is considered. Equations that couple diffusion and reversible reaction are defined. From these equations are derived expressions for the concentrations of the reacting species, as a function of time, after a perturbation from their equilibrium concentrations. These expressions demonstrate how the time-dependent approach by a concentration to its equilibrium value is determined by the binding rate of adjacent molecules, the dissociation rate, the diffusion coefficients, the distance of closest approach of the reactants, the concentrations of the reactants, and the dimensionality. The expressions are applicable to perturbation-relaxation experiments in one, two, and three dimensions. The formalism is compared with previously existing theories.     

Analytic approximations for the broadening of the spectral lines of hydrogen-like ions

Broadband approximate expressions for calculating the broadening of the spectral lines of hydrogen-like ions in a multicomponent plasma are derived taking into account both the influence of the interaction between plasma particles on the distribution function of the plasma microfield and the effect of the microfield dynamics on the broadening of the central component of the spectral line. With the approximate expressions proposed, the calculation of the shape of a given spectral line of a certain ion in a plasma with a given ion composition requires only a few seconds of computer time. The approximate expressions provide a good computational accuracy not only for the central component of the spectral line but also for the spectral line wings.     

Stochastic epidemics: major outbreaks and the duration of the endemic period

A study is made of a two-dimensional stochastic system that models the spread of an infectious disease in a population. An asymptotic expression is derived for the probability that a major outbreak of the disease will occur in case the number of infectives is small. For the case that a major outbreak has occurred, an asymptotic approximation is derived for the expected time that the disease is in the population. The analytical expressions are obtained by asymptotically solving Dirichlet problems based on the Fokker-Planck equation for the stochastic system. Results of numerical calculations for the analytical expressions are compared with simulation results.     

Derivation of some parameters of myoelectric signals recorded during sustained constant force isometric contractions.

Mathematical expressions are derived for some parameters of the myoelectric (ME) signal recorded during a constant force isometric contraction. The expressions are developed from a stochastic model for the motor-unit action-potential trains obtained from empirical results. The following parameters: (a) the mean rectified value, (b) the mean integrated rectified value, (c) the root-mean-square value, and (d) the power density spectrum are described as functions of contraction time and constant force of an isometric muscle contraction. The calculated parameters are compared to their corresponding empirically obtained measurements which have been reported in the literature. A discussion on the behavior of the parameters during increasing contraction time is presented. Synchronization of the motor-unit action-potential trains is shown to have a pronounced effect on the parameters of the myoelectric signal. This result should be considered when analyzing long records of myoelectric signals.     

Dynamic model of CHO cell metabolism

Fed-batch cultures are extensively used for the production of therapeutic proteins. However, process optimization is hampered by lack of quantitative models of mammalian cellular metabolism in these cultures. This paper presents a new kinetic model of CHO cell metabolism and a novel framework for simulating the dynamics of metabolic and biosynthetic pathways of these cells grown in fed-batch culture. The model defines a subset of the intracellular reactions with kinetic rate expressions based on extracellular metabolite concentrations and temperature- and redox-dependent regulatory variables. The simulation uses the rate expressions to calculate pseudo-steady state flux distributions and extracellular metabolite concentrations at discrete time points. Experimental data collected in this study for several different CHO cell fed-batch cultures are used to derive the rate expressions, fit the parameters, and validate the model. The simulations accurately predicted the effects of process variables, including temperature shift, seed density, specific productivity, and nutrient concentrations.     

Population dynamics with age- and time-dependent birth and death rates

A continous, deterministic mathematical model is used to predict population distributions by age at any time, given the initial distribution and the variation of birth and death rates with age and time. Solutions are obtained on a computer using a semi-discretization algorithm in which time derivatives in the partial differential equations are replaced by finite-difference expressions. The resulting sets of ordinary differential equations are solved by a predictor-corrector method. Graphical results are shown for some examples.     

Analytical theory of the control equations for protein synthesis in the Goodwin model

The analytical expressions, and their convergent series expansions, for the time period and various time averages of the dynamical quantities are derived for the Goodwin model. The radius of convergence of the series expansions is also calculated. The changes necessary to fully take into account the restrictions on dynamical variable, following from the positivity of physical quantities of the model, for the statistical mechanical treatment of the system are also discussed.     

Modeling observation error and its effects in a random walk/extinction model

This paper examines the consequences of observation errors for the "random walk with drift", a model that incorporates density independence and is frequently used in population viability analysis. Exact expressions are given for biases in estimates of the mean, variance and growth parameters under very general models for the observation errors. For other quantities, such as the finite rate of increase, and probabilities about population size in the future we provide and evaluate approximate expressions. These expressions explain the biases induced by observation error without relying exclusively on simulations, and also suggest ways to correct for observation error. A secondary contribution is a careful discussion of observation error models, presented in terms of either log-abundance or abundance. This discussion recognizes that the bias and variance in observation errors may change over time, the result of changing sampling effort or dependence on the underlying population being sampled.     

Translocation of Rodlike Polymers through Membrane Channels

A theory of channel-facilitated transport of long rodlike macromolecules through thin membranes under the influence of a driving force of arbitrary strength is developed. Analytic expressions are derived for the translocation probability and the Laplace transform of the probability density of time that a macromolecule spends in the channel. We also derive expressions for the (conditional) probability densities of time spent in the channel by translocating and nontranslocating (returning back) macromolecules. These results are used to study how the distribution of the macromolecule lifetime in the channel depends on a polymer chain length and the driving force. It is shown that depending on the values of the parameters, the lifetime probability density may have one or two peaks. Our theory is a generalization of the theory developed by Lubensky and Nelson, who were inspired by recent experiments on driven translocation of single-stranded RNA and DNA molecules through single channels in narrow membranes.     

Calculation of the Second-Order Polarizability of a Warm Magnetized Plasma

An analysis is made of the general expression for the density of a nonlinear charge induced in a magnetized plasma in the interaction between two arbitrary waves. Asymptotic expressions for the nonlinear induced charge density are derived for the first time in the case where both of the interacting waves are short-scale.     

Identifying subset of genes that have influential impacts on cancer progression: a new approach to analyze cancer microarray data

Cancer is a complex genetic disease, resulting from defects of multiple genes. Development of microarray techniques makes it possible to survey the whole genome and detect genes that have influential impacts on the progression of cancer. Statistical analysis of cancer microarray data is challenging because of the high dimensionality and cluster nature of gene expressions. Here, clusters are composed of genes with coordinated pathological functions and/or correlated expressions. In this article, we consider cancer studies where censored survival endpoint is measured along with microarray gene expressions. We propose a hybrid clustering approach, which uses both pathological pathway information retrieved from KEGG and statistical correlations of gene expressions, to construct gene clusters. Cancer survival time is modeled as a linear function of gene expressions. We adopt the clustering threshold gradient directed regularization (CTGDR) method for simultaneous gene cluster selection, within-cluster gene selection, and predictive model building. Analysis of two lymphoma studies shows that the proposed approach - which is composed of the hybrid gene clustering, linear regression model for survival, and clustering regularized estimation with CTGDR - can effectively identify gene clusters and genes within selected clusters that have satisfactory predictive power for censored cancer survival outcomes.