Parameter estimation for the distribution of single cell lag times

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level.We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.     

Correlations between the psychological peculiarities of an individual and the efficacy of a single neurofeedback session (by the EEG characteristics)

Modifications of different EEG rhythms induced by a single neurofeedback session (by the EEG characteristics) directed toward an increase in the ratio of the spectral powers (SPs) of the α vs θ oscillations were compared with the psychological characteristics of the tested subjects (the group included 30 persons). A generally accepted neurofeedback technique was used; the intensity of acoustic white noise served as the feedback signal. EEG potentials were recorded from the C3 and C4 leads. Psychological testing was carried out using Eysenck’s (EPQ), Rusalov’s (OST), and (16 PF) questionnaires. The directions of changes in the SPs of EEG frequency components were found to significantly correlate with some individuality-related peculiarities of the tested subjects. The SP of the δ rhythm correlated with the EPQ scale “neuroticism,” OST scale “social plasticity,” and 16 PF factors H (“parmia”), I (“premsia”), and Q₃ (“self-control of behavior”). The SP of the θ component demonstrated correlations with the OST scales “ergisity,” “plasticity,” and “social temp” and with 16 PF factors M (“autia”), Q₄ (“frustration”), and Q1 (“exvia”). The SP of the α rhythm correlated with 16 PF factors Q₃ (“self-control of behavior”), G (“strength of superEgo”), O (“hypothymia”), L (“protension”), and N (“shrewdness”). The SP of the β rhythm correlated with the OST scale “emotionality,” while that of the γ rhythm showed correlations with the 16 PF indices L (“protension”) and M (“autia”). Changes in the ratio of the α vs θ SPs correlated with the EPQ scale “neuroticism.” Thus, our data confirm the statement that a high individual variability of the results of a single (first in the series) neurofeedback session is to a great extent related to peculiarities of the individual psychological pattern of the subject. Neirofiziologiya/Neurophysiology, Vol. 38, No. 3, pp. 239–247, May–June, 2006.     

Some Common Themes for Enzymes and Verbs

Enzymes are remarkable molecules which make metabolism possible. Their processing powers are considerable for not only are they catalysts they also contribute to information processing, integration, coherence and memory in the cell. This complex of attributes suggests that a complementary perspective to enzyme nature and activity is needed related to what enzymes and verbs have in common. The value of this kind of thinking is that it shifts the focus from objects and mechanisms to processes and information. In order to support this idea a number of features which enzymes and verbs share are discussed including, context-dependence, occurrence, cases, voice, mood and glue/integrative capacities. The paper concludes with some reflections on the utility of a view of enzymes as verbs.     

Establishment of the introduced kelp Undaria pinnatifida in Tasmania depends on disturbance to native algal assemblages

Despite recent rapid increases in the occurrence of nonindigenous marine organisms in the marine environment, few studies have critically examined the invasion process for a marine species. Here we use manipulative experiments to examine processes of invasion for the Asian kelp Undaria pinnatifida (Harvey) Suringar at two sites on the east coast of Tasmania. Disturbance to reduce cover of the native algal canopy was found to be critical in the establishment of U. pinnatifida, while the presence of a stable native algal canopy inhibited invasion. In the first sporophyte growth season following disturbance of the canopy, U. pinnatifida recruited in high densities (up to 19 plants m⁻²) while remaining rare or absent in un-manipulated plots. The timing of disturbance was also important. U. pinnatifida recruited in higher densities in plots where the native canopy was removed immediately prior to the sporophyte growth season (winter 2000), compared with plots where the canopy was removed 6 months earlier during the period of spore release (spring 1999). Removal of the native canopy also resulted in a significant increase in cover of sediment on the substratum. In the second year following canopy removal, U. pinnatifida abundance declined significantly, associated with a substantial recovery of native canopy-forming species. A feature of the recovery of the native algal canopy was a significant shift in species composition. Species dominant prior to canopy removal showed little if any signs of recovery. The recovery was instead dominated by canopy-forming species that were either rare or absent in the study areas prior to manipulation of the canopy.     

Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities

Summary .  We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.     

Algal Population Growth Model Integrated with Toxicokinetics for Ecological Risk Assessment under Time-Varying Pesticide Exposure

An algal population growth model integrated with toxicokinetics was developed for assessing the effect of pesticides on population dynamics. This model is a simple one-compartment, first-order kinetic model in which toxicity (growth inhibition and mortality) depends on the intracellular effective concentration of a pesticide at a target site. The model's parameters were derived using an experimental study that investigated the effects of pretilachlor, bensulfuron-methyl, pentoxazone, and quinoclamine on the growth, mortality, and subsequent population recovery of the green alga Pseudokirchneriella subcapitata. Modeled and measured trajectories of algal population dynamics agreed well. The effect on population recovery was underestimated by the model that ignored the toxicokinetics. The four tested herbicides had a variety of toxicity characteristics and physicochemical properties, indicating the wide range of the model's applicability. Moreover, the developed model and the obtained model's parameters were extrapolated to predict long-term algal population dynamics under time-varying herbicide exposure. The calculated integral biomass lost compared with the control was considered a quantitative index of the population-level ecological risk. The model's prediction showed that the same exposure level (peak concentration is equivalent to EC50) indicated much different population-level effect depending on the herbicide.