Increasing in vitro microrhizome production of ginger (Zingiber officinale Roscoe)

In this study, using the quadratic saturation 310 D-optimal design method, we examined the effect of kinetin (KT), gibberellic acid (GA), and naphthalene acetic acid (NAA) on microrhizome production in ginger. The effect of GA on rhizome induction was larger than that of KT or NAA. Using simulation and optimality selection for tissue culture, we found that concentrations of GA, KT, and NAA of 1.33–2.35, 0.49–0.66, and 0.62 g/l, respectively, gave a microrhizome weight of over 0.25 g. The optimal conditions for microrhizome production were 80 g/l sucrose, 2 × MS macro-elements, and 1 × MS micro-elements, with a photoperiod of 24L:0D (light/dark). At the same time, 100% survival could be achieved on transfer of the in vitro ginger plantlets with microrhizomes to soil.     

Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Protein Sequence Modules

Abstract

Conserved protein sequence segments are commonly believed to correspond to functional sites in the protein sequence. A novel approach is proposed to profile the changing degree of conservation along the protein sequence, by evaluating the occurrence frequencies of all short oligopeptides of the given sequence in a large proteome database. Thus, a protein sequence conservation profile can be plotted for every protein. The profile indicates where along the sequences the potential functional (conserved) sites are located. The corresponding oligopeptides belonging to the sites are very frequent across many prokaryotic species. Analysis of a representative set of such profiles reveals a common feature of all examined proteins: they consist of sequence modules represented by the peaks of conservation. Typical size of the modules (peak-to-peak distance) is 25–30 amino acid residues.     

Simulated beetle defoliation on willow genotypes in mixture and monotype plantations

The effect of simulated beetle damage (0%, 25%, 50% and 75% mechanical defoliation) on 12 willow genotypes, grown in short‐rotation coppice, was studied in a modified criss‐cross experimental design. The design enabled the above‐ground effects of monoculture and mixed planting to be assessed. Repeated measurements were modelled to produce derived variables in terms of time or, more appropriately, in terms of accumulated day length (i.e. ‘developmental time’) units. These derived variables were then analysed using the REsidual Maximum Likelihood (REML) method implemented in GenStat? (2001) . No significant competition effect between the genotypes due to planting regime was detected. Genotypes Salix viminalis × Salix schwerinii‘Beagle’ and S. viminalis × S. schwerinii‘Torhild’ were found to have the greatest rate of increase in leaves regardless of defoliation and also the greatest height prior to defoliation. Genotype Salix dasyclados‘Loden’ showed the highest rate of growth under the stress of defoliation. When assessing height at the end of the growing season, S. viminalis × S. schwerinii‘Olof’ was the highest genotype for 25% and 75% levels of defoliation, but genotypes Salix aurita × Salix cinerea‘Delamere’, Loden and S. viminalis × Salix burjatica‘Ashton Parfitt’ appeared to be most tolerant by having consecutively lower base day lengths (i.e. increasing the accumulation of developmental units and the length of the growing season) for increasing defoliation. Shorter genotypes tended to be more tolerant, but of the higher genotypes reaching a control height of greater than 3 m by the end of the growing season, S. viminalis × S. schwerinii‘Tora’ and Beagle performed best to 50% defoliation.     

Persistence of Dispersal-limited Species in Structured Dynamic Landscapes

Dynamic landscape models have generally assumed random distributions of habitat although real landscapes show spatial organization at many scales. To explore the role of spatial structure in determining the frequency of dispersal-limited forest species, we used a cellular landscape model divided into two zones. Zones were distributed in a random, clustered, or regular spatial pattern. Within each zone habitat cells were randomly destroyed and regenerated, and habitat density and turnover rate were systematically varied. A hypothetical habitat-limited species dispersed between adjacent habitat cells. All trials showed a reduced species frequency relative to a static landscape. Reduction was greater at low habitat density (P = 0.30) than at high density (0.90) suggesting the importance of habitat connectivity in controlling species frequency. The greatest reduction occurred when habitat was concentrated in a small, regularly distributed zone at low habitat density reflecting the enforced isolation of individual habitat cells. Very little reduction was observed when habitat cells were packed into a small clustered zone, a situation promoting connectivity between cells. Moderate–severe frequency reduction occurred when habitat turnover was concentrated in a clustered zone at high habitat density, but little was observed when turnover was widely distributed in a regular or random pattern. These results can be interpreted in terms of a source-sink function in which spatial pattern controlled the degree of contact between landscape zones and determined opportunities for dispersal between habitat cells. We conclude that clustering of forest habitat has the potential to maintain herb species frequency in sparsely forested landscapes. Conversely, clustering of forest disturbance in heavily forested regions, or regular distribution of forest stands (as often occurs in agricultural regions) creates areas which are difficult to colonize, and should be avoided.     

Differential susceptibility epidemic models

We formulate compartmental differential susceptibility (DS) susceptible-infective-removed (SIR) models by dividing the susceptible population into multiple subgroups according to the susceptibility of individuals in each group. We analyze the impact of disease-induced mortality in the situations where the number of contacts per individual is either constant or proportional to the total population. We derive an explicit formula for the reproductive number of infection for each model by investigating the local stability of the infection-free equilibrium. We further prove that the infection-free equilibrium of each model is globally asymptotically stable by qualitative analysis of the dynamics of the model system and by utilizing an appropriately chosen Liapunov function. We show that if the reproductive number is greater than one, then there exists a unique endemic equilibrium for all of the DS models studied in this paper. We prove that the endemic equilibrium is locally asymptotically stable for the models with no disease-induced mortality and the models with contact numbers proportional to the total population. We also provide sufficient conditions for the stability of the endemic equilibrium for other situations. We briefly discuss applications of the DS models to optimal vaccine strategies and the connections between the DS models and predator-prey models with multiple prey populations or host-parasitic interaction models with multiple hosts are also given.This research was partially supported by the Department of Energy under contracts W-7405-ENG-36 and the Applied Mathematical Sciences Program KC-07-01-01.     

Markov modelling of immunological and virological states in HIV-1 infected patients

The purpose of this study was to evaluate the evolution of HIV infected patients and to bring out some significant factors associated with this pathology. The main criteria revealing the State of illness is viral load measurement (VL). However the CD4 lymphocytes also represent an important marker as these reflect the State of the immune reservoir. Many studies have been carried out in this field and different models have been proposed with a view to a better understanding of this disease. Multi State Markov models defined in terms of CD4 counts, or in terms of viral load, have proved to be very useful tools for modelling HIV disease progression. The model we have developed in this study is based on both the CD4 lymphocytes counts and VL. Markov models are characterized by transition intensities. In this paper we explored several structures in succession. First, we used a homogeneous continuous time Markov process with four states defined by crossed values of CD4 and VL in a given patient at a given time. Then, the effect of certain covariates on the infection process was introduced into the model via the transition intensity functions, as with a Cox regression model. Since the hypothesis of homogeneity may be unrealistic in certain cases, we also considered piecewise homogeneous Markov models. Finally, the effects of covariates and time were combined in a piecewise homogeneous model with a covariate. We applied these methods to data from 1313 HIV-infected patients included in the NADIS cohort.     

Structural and spectroscopic studies of a model for catechol oxidase

A binuclear copper complex, [Cu₂(BPMP)(OAc)₂][ClO₄]·H₂O, has been prepared using the binucleating ligand 2,6-bis[bis(pyridin-2-ylmethylamino)methyl]-4-methylphenol (H-BPMP). The X-ray crystal structure reveals the copper centers to have a five-coordinate square pyramidal geometry, with the acetate ligands bound terminally. The bridging phenolate occupies the apical position of the square-based pyramids and magnetic susceptibility, electron paramagnetic resonance (EPR) and variable-temperature variable-field magnetic circular dichroism (MCD) measurements indicate that the two centers are very weakly antiferromagnetically coupled (J = −0.6 cm⁻¹). Simulation of the dipole–dipole-coupled EPR spectrum showed that in solution the Cu–O–Cu angle was increased from 126° to 160° and that the internuclear distance was larger than that observed crystallographically. The high-resolution spectroscopic information obtained has been correlated with a detailed ligand-field analysis to gain insight into the electronic structure of the complex. Symmetry arguments have been used to demonstrate that the sign of the MCD is characteristic of the tetragonally elongated environment. The complex also displays catecholase activity (k _cat = 15 ± 1.5 min⁻¹, K _M = 6.4 ± 1.8 mM), which is compared with other dicopper catechol oxidase models. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Confidence bands for cumulative incidence curves under the additive risk model

In the context of competing risks, the cumulative incidence function is often used to summarize the cause-specific failure-time data. As an alternative to the proportional hazards model, the additive risk model is used to investigate covariate effects by specifying that the subject-specific hazard function is the sum of a baseline hazard function and a regression function of covariates. Based on such a formulation, we present an approach to constructing simultaneous confidence intervals for the cause-specific cumulative incidence function of patients with given risk factors. A melanoma data set is used for the purpose of illustration.