Modifiable chromatophore proteins in photosynthetic bacteria.

The chromatophores of Chromatium vinosum, as well as six other photosynthetic bacteria, contained two or more proteins which were insoluble when heated in the presence of sodium dodecyl sulfate (SDS) and 2-mercaptoethanol (beta-ME). When the chromatophores were dissolved at room temperature in SDS-beta-ME, these proteins were present in the SDS-polyacrylamide gel electrophoresis profiles, but when the samples were dissolved at 100 degrees C, they were absent or considerably diminished. When one-dimensional gels of chromatophores solubilized at room temperature were soaked in the SDS-beta-ME solution and heated to 100 degrees C and the gels were run in a second dimension, the proteins became immobilized in the original first-dimension gel, where they could be detected by staining. The two major proteins so affected in C. vinosum had apparent molecular weights of 28,000 and 21,000. The chromatophores of several other photosynthetic bacteria also contained predominant proteins between 30,000 and 19,000 molecular weight, which became insoluble when heated in the presence of SDS and beta-ME. In at least two of the species examined, these appeared to be reaction center proteins. The conditions causing the proteins to become insoluble were complex and involved temperature, SDS concentration, and the presence of sulfhydryl reagents. The chromatophores of four of the Chromatiaceae species and two strains of one of the Rhodospirillaceae species examined had a protein-pigment complex that was visible in SDS-polyacrylamide gel profiles of samples dissolved at room temperature but was absent in samples dissolved at 100 degrees C.     

Cellular automata and ecology

Many indices for measuring species diversity have been proposed. In this article, a link is noted between a common family of diversity indices and non-additive statistical mechanics. This makes the Shannon index and the Simpson diversity (or Gini coefficient) special cases of a more general index. The general index includes a parameter q that can be interpreted from a statistical mechanics perspective for systems with an underlying (multi)fractal structure. A q - generalised version of the Zipf–Mandelbrot distribution sometimes used to characterise rank–abundance relationships may be obtained by maximising this entropy.     

Designing convergent cellular automata

Cellular automata (CA) have been used by biologists to study dynamic non-linear systems where the interaction between cell behaviour and end-pattern is investigated. It is difficult to achieve convergence of a CA towards a specific static pattern and a common solution is to use genetic algorithms and evolve a ruleset that describes cell behaviour. This paper presents an alternative means of designing CA to converge to specific static patterns. A matrix model is introduced and analysed then a design algorithm is demonstrated. The algorithm is significantly less computationally intensive than equivalent evolutionary algorithms, and not limited in scale, complexity or number of dimensions.     

Modifiable chemically crosslinked poli(κ-carrageenan) particles

Micron size κ-carrageenan hydrogel particles, p(CRN) from linear κ-carrageenan, were prepared via microemulsion polymerization using divinyl sulfone (DVS) as chemical crosslinker in a sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse miceller system. Magnetic field responsive (m-p(CRN)) composite particles were also synthesized by encapsulating magnetic ferrite (Fe₃O₄) nanoparticles together with linear κ-carrageenan within the AOT reverse micelle before the crosslinking reaction. The synthesized bare p(CRN) particles were further modified to produce positive charges on the particles (q-p(CRN)) by a quaternization reaction with an 3-chloro-2-hydroxypropyl trimethyl ammonium chloride aqueous solution. Scanning electron microscopy (SEM), dynamic light scattering (DLS), zeta potential measurements and FT-IR analysis confirmed that particle sizes and charges were altered by chemical modification. Furthermore, a model drug, phenylephrine HCl was used for in vitro drug delivery studies to compare the effectiveness of modification of p(CRN) microgels by comparing bare p(CRN), m-p(CRN) and q-p(CRN) particles drug release capabilities in phosphate buffer solution (PBS) at pH 7.4.     

Asthma Trajectories in Early Childhood: Identifying Modifiable Factors

Background

There are conflicting views as to whether childhood wheezing represents several discreet entities or a single but variable disease. Classification has centered on phenotypes often derived using subjective criteria, small samples, and/or with little data for young children. This is particularly problematic as asthmatic features appear to be entrenched by age 6/7. In this paper we aim to: identify longitudinal trajectories of wheeze and other atopic symptoms in early childhood; characterize the resulting trajectories by the socio-economic background of children; and identify potentially modifiable processes in infancy correlated with these trajectories.

Data and Methods

The Millennium Cohort Study is a large, representative birth cohort of British children born in 2000–2002. Our analytical sample includes 11,632 children with data on key variables (wheeze in the last year; ever hay-fever and/or eczema) reported by the main carers at age 3, 5 and 7 using a validated tool, the International Study of Asthma and Allergies in Childhood module. We employ longitudinal Latent Class Analysis, a clustering methodology which identifies classes underlying the observed population heterogeneity.

Results

Our model distinguished four latent trajectories: a trajectory with both low levels of wheeze and other atopic symptoms (54% of the sample); a trajectory with low levels of wheeze but high prevalence of other atopic symptoms (29%); a trajectory with high prevalence of both wheeze and other atopic symptoms (9%); and a trajectory with high levels of wheeze but low levels of other atopic symptoms (8%). These groups differed in terms of socio-economic markers and potential intervenable factors, including household damp and breastfeeding initiation.

Conclusion

Using data-driven techniques, we derived four trajectories of asthmatic symptoms in early childhood in a large, population based sample. These groups differ in terms of their socio-economic profiles. We identified correlated intervenable pathways in infancy, including household damp and breastfeeding initiation.     

Modifiable Risk Factors for Increased Arterial Stiffness in Outpatient Nephrology

Arterial stiffness, as measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and mortality. Arterial stiffness increases with age. However, modifiable risk factors such as smoking, BP and salt intake also impact on PWV. The finding of modifiable risk factors may lead to the identification of treatable factors, and, thus, is of interest to practicing nephrologist. We have now studied the prevalence and correlates of arterial stiffness, assessed by PWV, in 191 patients from nephrology outpatient clinics in order to identify modifiable risk factors for arterial stiffness that may in the future guide therapeutic decision-making. PWV was above normal levels for age in 85/191 (44.5%) patients. Multivariate analysis showed that advanced age, systolic BP, diabetes mellitus, serum uric acid and calcium polystyrene sulfonate therapy or calcium-containing medication were independent predictors of PWV. A new parameter, Delta above upper limit of normal PWV (Delta PWV) was defined to decrease the weight of age on PWV values. Delta PWV was calculated as (measured PWV) - (upper limit of the age-adjusted PWV values for the general population). Mean±SD Delta PWV was 0.76±1.60 m/sec. In multivariate analysis, systolic blood pressure, active smoking and calcium polystyrene sulfonate therapy remained independent predictors of higher delta PWV, while age, urinary potassium and beta blocker therapy were independent predictors of lower delta PWV. In conclusion, arterial stiffness was frequent in nephrology outpatients. Systolic blood pressure, smoking, serum uric acid, calcium-containing medications, potassium metabolism and non-use of beta blockers are modifiable factors associated with increased arterial stiffness in Nephrology outpatients.     

Modifiable Prognostic Factors of Hepatocellular Carcinoma in Patients with Non-Surgical Treatment

Background & aims

Current hepatocellular carcinoma (HCC) staging systems only use baseline characteristics to predict outcome. We aimed to explore modifiable factors of the prognosis in HCC cases had undergone non-surgical treatment.

Methods

All HCC cases in Kaohsiung Chang Gung Memorial hospital in southern Taiwan from 2002 to 2012 must met all below criteria: (1) met international diagnostic guidelines, (2) underwent the initial treatments in our hospital (3) treated by non-surgical treatment modalities and (4) survived more than two years, with follow-up time longer than five years.

Results

A total 698 patients were enrolled: 451 (24.6%, group A) survivied between 2 to 5 years, and 247 (13.5%, group B) had survived > 5 years. Aside from liver function reserve and BCLC stages, four interventional factors: initial treatment modality, outcomes of 1^st or 2^nd treatment, and anti-viral therapy to chronic viral hepatitis were associated with prognosis. After propensity score matching, multiple logistic regression of 223 well-matched pairs showed that recurrence within one year after 1^st treatment (OR: 2.17, 95% CI: 1.35–3.48), incomplete 2^nd treatment (2.01, 1.27–3.17) and absence of anti-viral agents (1.68, 1.09–2.59) were independent poor prognostic factors.

Conclusion

Complete treatment and anti-viral agents to chronic hepatitis were both independent modifiable prognostic factors of HCC patients had undergone non-surgical treatment. Based on these findings, timely treatment to achieve maximal locoregional control and anti-viral treatment should be provided as possible.     

Modifiable reporting unit problems and time series of long-term human activity

This paper responds to a resurgence of interest in constructing long-term time proxies of human activity, especially but not limited to models of population change over the Pleistocene and/or Holocene. While very much agreeing with the need for this increased attention, we emphasize three important issues that can all be thought of as modifiable reporting unit problems: the impact of (i) archaeological periodization, (ii) uneven event durations and (iii) geographical nucleation-dispersal phenomena. Drawing inspiration from real-world examples from prehistoric Britain, Greece and Japan, we explore their consequences and possible mitigation via a reproducible set of tactical simulations.This article is part of the theme issue ‘Cross-disciplinary approaches to prehistoric demography’.     

Seroprevalence and Modifiable Risk Factors for Toxocara spp. in Brazilian Schoolchildren

Background

Toxocariasis is a worldwide helminthic zoonosis caused by infection with the larvae of the ascarid worms that comprise the Toxocara spp. Children are particularly prone to infection because they are exposed to the eggs in sandboxes and playgrounds contaminated with dog and cat feces. Certain behaviors, such as a geophagy habit, poor personal hygiene, a lack of parental supervision, close contact with young dogs, and ingestion of raw meat, as well as gender, age, and socioeconomic status, affect the prevalence of the disease. However, previous studies of the risk factors for toxocariasis have generally produced inconsistent results. An epidemiological cross-sectional study was conducted to evaluate the seroprevalence of IgG anti-Toxocara spp. antibodies and associated factors in schoolchildren from a region in the southeast of Brazil.

Methodology/Principal Findings

A total of 252 schoolchildren aged 1 to 12 years (120 males and 132 females) were assessed. An enzyme-linked immunosorbent assay based on Toxocara canis larval excretory-secretory antigens was used to determine outcomes. A questionnaire was used to collect information on children, family, and home characteristics. Clinical and laboratory data completed the dataset investigated in this study. Seroprevalence was 15.5% (95%CI 11.5–19.8). Geophagy (aPR 2.38 [95%CI 1.36–4.18], p-value 0.029) and the habit of hand washing before meals (aPR 0.04 [95%CI 0.01–0.11], p-value ≤0.001) were factors associated with increased and decreased seroprevalence, respectively. The income factor and its related variables lost statistical significance after adjustment with a multiple Poisson regression model.

Conclusions/Significance

The current study confirms that toxocariasis is a public health problem in the evaluated area; modifiable factors such as soil contact and personal hygiene appear to have a greater influence on the acquisition of infection than sociodemographic attributes, thus representing direct targets for disease prevention and control.     

Modelling fungus dispersal scenarios using cellular automata

This study focused on representing spatio-temporal patterns of fungal dispersal using cellular automata. Square lattices were used, with each site representing a host for a hypothetical fungus population. Four possible host states were allowed: resistant, permissive, latent or infectious. In this model, the probability of infection for each of the healthy states (permissive or resistant) in a time step was determined as a function of the host's susceptibility, seasonality, and the number of infectious sites and the distance between them. It was also assumed that infected sites become infectious after a pre-specified latency period, and that recovery is not possible. Several scenarios were simulated to understand the contribution of the model's parameters and the spatial structure on the dynamic behaviour of the modelling system. The model showed good capability for representing the spatio-temporal pattern of fungus dispersal over planar surfaces. With a specific problem in mind, the model can be easily modified and used to describe field behaviour, which can contribute to the conservation and development of management strategies for both natural and agricultural systems.     

Simulation of vessel morphogenesis using cellular automata

We present a cellular automaton model, including lateral inhibition of an autocatalytic morphogen, as well as a genetic switch that differentiates tissue into substrate-depleting vessels. This model yields isotropic morphogenesis, including: dichotomous and lateral branching, blind vessel ends, and closed loops due to anastosmosis. The algorithm consists of a list of simple rules describing the essential biophysical features, permitting comfortable programming and fast computations. Depending on the choice of the substrate s, the model is applicable to leaf veins (s is auxin), insect trachea (s is CO2) or neovascularization (s is an angiogenesis factor). Sequential addition of rules can be correlated to evolutionary steps in leaf morphogenesis.     

A cellular automata model of enzyme kinetics

We have developed a cellular automata model of an enzyme reaction with a substrate in water. The model produces Michaelis-Menten kinetics with good Lineweaver-Burk plots. The variation in affinity parameters predicts that, in general, hydrophobic substrates are more reactive with enzymes, this attribute being more important than the relationship between enzyme and substrate. The ease of generation and the illustrative value of the model lead us to believe that cellular automata models have a useful role in the study of dynamic phenomena such as enzyme kinetics.     

Backpropagation algorithm adaptation parameters using learning automata

Despite of the many successful applications of backpropagation for training multi-layer neural networks, it has many drawbocks. For complex problems it may require a long time to train the networks, and it may not train at all. Long training time can be the result of the non-optimal parameters. It is not easy to choose appropriate value of the parameters for a particular problem. In this paper, by interconnection of fixed structure learning automata (FSLA) to the feedforward neural networks, we apply learning automata (LA) scheme for adjusting these parameters based on the observation of random response of neural networks. The main motivation in using learning automata as an adaptation algorithm is to use its capability of global optimization when dealing with multi-modal surface. The feasibility of proposed method is shown through simulations on three learning problems: exclusive-or, encoding problem, and digit recognition. The simulation results show that the adaptation of these parameters using this method not only increases the convergence rate of learning but it increases the likelihood of escaping from the local minima.     

Predictive landscapes hidden beneath biological cellular automata

To celebrate Hans Frauenfelder’s achievements, we examine energy(-like) “landscapes” for complex living systems. Energy landscapes summarize all possible dynamics of some physical systems. Energy(-like) landscapes can explain some biomolecular processes, including gene expression and, as Frauenfelder showed, protein folding. But energy-like landscapes and existing frameworks like statistical mechanics seem impractical for describing many living systems. Difficulties stem from living systems being high dimensional, nonlinear, and governed by many, tightly coupled constituents that are noisy. The predominant modeling approach is devising differential equations that are tailored to each living system. This ad hoc approach faces the notorious “parameter problem”: models have numerous nonlinear, mathematical functions with unknown parameter values, even for describing just a few intracellular processes. One cannot measure many intracellular parameters or can only measure them as snapshots in time. Another modeling approach uses cellular automata to represent living systems as discrete dynamical systems with binary variables. Quantitative (Hamiltonian-based) rules can dictate cellular automata (e.g., Cellular Potts Model). But numerous biological features, in current practice, are qualitatively described rather than quantitatively (e.g., gene is (highly) expressed or not (highly) expressed). Cellular automata governed by verbal rules are useful representations for living systems and can mitigate the parameter problem. However, they can yield complex dynamics that are difficult to understand because the automata-governing rules are not quantitative and much of the existing mathematical tools and theorems apply to continuous but not discrete dynamical systems. Recent studies found ways to overcome this challenge. These studies either discovered or suggest an existence of predictive “landscapes” whose shapes are described by Lyapunov functions and yield “equations of motion” for a “pseudo-particle.” The pseudo-particle represents the entire cellular lattice and moves on the landscape, thereby giving a low-dimensional representation of the cellular automata dynamics. We outline this promising modeling strategy.

     

Synchronous and asynchronous updating in cellular automata.

We analyze the properties of a synchronous and of various asynchronous methods to iterate cellular automata. Asynchronous methods in which the time variable is not explicitly defined, operate by specifying an updating order of the cells. The statistical properties of this order have significant consequences for the dynamics and the patterns generated by the cellular automata. Stronger correlations between consecutive steps in the updating order result in more, artificial structure in the patterns. Among these step-driven methods, using random choice with replacement to pick the next cell for updating, yields results that are least influenced by the updating method. We also analyse a time-driven method in which the state transitions of single cells are governed by a probability per unit time that determines an exponential distribution of the waiting time until the next transition. The statistical properties of this method are completely independent of the size of the grid. Consecutive updating steps therefore show no correlation at all. The stationary states of a cellular automaton do not depend on whether a synchronous or asynchronous updating method is used. Their basins of attraction might, however, be vastly different under synchronous and asynchronous iteration. Cyclic dynamics occur only with synchronous updating.     

A life-like virtual cell membrane using discrete automata

A framework is presented that captures the discrete and probabilistic nature of molecular transport and reaction kinetics found in a living cell as well as formally representing the spatial distribution of these phenomena. This particle or agent-based approach is computationally robust and complements established methods. Namely it provides a higher level of spatial resolution than formulations based on ordinary differential equations (ODE) while offering significant advantages in computational efficiency over molecular dynamics (MD). Using this framework, a model cell membrane has been constructed with discrete particle agents that respond to local component interactions that resemble flocking or herding behavioural cues in animals. Results from simulation experiments are presented where this model cell exhibits many of the characteristic behaviours associated with its biological counterpart such as lateral diffusion, response to osmotic pressure gradients, membrane growth and cell division. Lateral diffusion rates and estimates for the membrane modulus of elasticity derived from these simple experiments fall well within a biologically relevant range of values. More importantly, these estimates were obtained by applying a simple qualitative tuning of the model membrane. Membrane growth was simulated by injecting precursor molecules into the proto-cell at different rates and produced a variety of morphologies ranging from a single large cell to a cluster of cells. The computational scalability of this methodology has been tested and results from benchmarking experiments indicate that real-time simulation of a complete bacterial cell will be possible within 10 years.