Biodegradation of mango kernel by Syncephalastrum racemosum and its biological control

Poor germination of mango seed due to fungal decay is a common problem in mango nurseries. The main causal fungus behind this problem is a black spore producing fungus, Syncephalastrum racemosum. This fungus exhibited high amylolytic activity and hence utilised the starch present in mango kernel for its own growth, thereby resulting in the death of emerging radical and plumule, which ultimately causes the death of emerging seedlings. A simple biocontrol device has been worked out by modifying the storage conditions of the mango seeds from aerobic to facultative. This change resulted in yeast growth on the pulp sticking to the mango seed, which in turn produced alcohol and prevented the growth of this fungus. The germination of such seeds has been found to be about 85–90%.     

Comparing vector–host and SIR models for dengue transmission

Various simple mathematical models have been used to investigate dengue transmission. Some of these models explicitly model the mosquito population, while others model the mosquitoes implicitly in the transmission term. We study the impact of modeling assumptions on the dynamics of dengue in Thailand by fitting dengue hemorrhagic fever (DHF) data to simple vector–host and SIR models using Bayesian Markov chain Monte Carlo estimation. The parameter estimates obtained for both models were consistent with previous studies. Most importantly, model selection found that the SIR model was substantially better than the vector–host model for the DHF data from Thailand. Therefore, explicitly incorporating the mosquito population may not be necessary in modeling dengue transmission for some populations.     

PRASA: An integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.     

Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

Population variability and uncertainty are important features of biological systems that must be considered when developing mathematical models for these systems. In this paper we present probability-based parameter estimation methods that account for such variability and uncertainty. Theoretical results that establish well-posedness and stability for these methods are discussed. A probabilistic parameter estimation technique is then applied to a toxicokinetic model for trichloroethylene using several types of simulated data. Comparison with results obtained using a standard, deterministic parameter estimation method suggests that the probabilistic methods are better able to capture population variability and uncertainty in model parameters.     

Computational tools for isotopically instationary 13C labeling experiments under metabolic steady state conditions

(13)C metabolic flux analysis (MFA) has become an important and powerful tool for the quantitative analysis of metabolic networks in the framework of metabolic engineering. Isotopically instationary (13)C MFA under metabolic stationary conditions is a promising refinement of classical stationary MFA. It accounts for the experimental requirements of non-steady-state cultures as well as for the shortening of the experimental duration. This contribution extends all computational methods developed for classical stationary (13)C MFA to the instationary situation by using high-performance computing methods. The developed tools allow for the simulation of instationary carbon labeling experiments (CLEs), sensitivity calculation with respect to unknown parameters, fitting of the model to the measured data, statistical identifiability analysis and an optimal experimental design facility. To explore the potential of the new approach all these tools are applied to the central metabolism of Escherichia coli. The achieved results are compared to the outcome of the stationary counterpart, especially focusing on statistical properties. This demonstrates the specific strengths of the instationary method. A new ranking method is proposed making both an a priori and an a posteriori design of the sampling times available. It will be shown that although still not all fluxes are identifiable, the quality of flux estimates can be strongly improved in the instationary case. Moreover, statements about the size of some immeasurable pool sizes can be made.     

Modeling the Expansion of an Introduced Tree Disease

Pine wilt disease is caused by the introduced pinewood nematode, Bursaphelenchus xylophilus, for which the vector is the pine sawyer beetle, Monochamus alternatus. Native Japanese pines, black pine (Pinus thunbergii) and red pine (P. densiflora), are extremely sensitive to the nematode's infection, and the parasite has been expanding nationwide in the last few decades, despite intensive control efforts. To understand the parasite's range expansion in Japan, we modeled the dynamics of the pines and the beetle that disperses the nematode, using an integro-difference equation in a one-dimensional space. Based on field data collected in Japan, we investigated the dependence of the parasite's rate of range expansion on the eradication rate of the beetle, the initial pine density, and the beetle dispersal ability. Our model predicts several results. (1) The Allee Effect operates on beetle reproduction, and consequently the parasite cannot invade a pine stand, once the beetle density decreases below a threshold. (2) The distribution of the dispersal distance of the beetles critically affects the expansion rate of the disease. As the fraction of the beetles that travel over long distance increases from zero, the range expansion accelerates sharply. (3) However, too frequent long-range dispersal results in a failure of the parasite invasion due to the Allee Effect, suggesting the importance of correctly assessing the beetle's mobility to predict the speed of range expansion of the parasite. (4) As the eradication rate is increased, the range expansion speed decreases gradually at first and suddenly drops to zero at a specific value of the eradication rate. This revised version was published online in July 2006 with corrections to the Cover Date.     

Using a Strategic Environmental Assessment framework to quantify the environmental impact of bioenergy plans

Renewable energy and greenhouse gas (GHG) reduction targets are driving an acceleration in the use of bioenergy resources. The environmental impact of national and regional development plans must be assessed in compliance with the EU Strategic Environmental Assessment (SEA) Directive (2001/42/EC). Here, we quantify the environmental impact of an Irish Government bioenergy plan to replace 30% of peat used in three peat‐burning power stations, located within the midlands region, with biomass. Four plan alternatives for supplying biomass to the power plant were considered in this study: (1) importation of palm kernel shell from south‐east Asia, (2) importation of olive cake pellets from Spain and (3) growing either willow or (4) Miscanthus in the vicinity of the power stations. The impact of each alternative on each of the environmental receptors proposed in the SEA Directive was first quantified before the data were normalized on either an Irish, regional or global scale. Positive environmental impacts were very small compared to the negative environmental impacts for each of the plan alternatives considered. Comparison of normalized indicator values confirmed that the adverse environmental consequences of each plan alternative are concentrated at the location where the biomass is produced. The analysis showed that the adverse environmental consequences of biomass importation are substantially greater than those associated with the use of willow and Miscanthus grown on former grassland. The use of olive cake pellets had a greater adverse environmental effect compared to the use of peat whereas replacement of peat with either willow or Miscanthus feedstocks led to a substantial reduction in environmental pressure. The proposed assessment framework combines the scope of SEA with the quantitative benefits of life cycle assessment and can be used to evaluate the environmental consequences of bioenergy plans.     

Estimating the accuracy of polymerase chain reaction-based tests using endpoint dilution

Polymerase chain reaction (PCR)-based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly "sensitive," yet the concept of sensitivity is ill-defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity, when the target number is 0). However, estimating this "sensitivity curve" is problematic, since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric, and semiparametric (spline-based) models for the sensitivity curve. The methods are compared on a new test for M. genitalium.     

Application of neem seed kernel extracts result in mouthpart deformities and subsequent mortality in Nezara viridula (L.) (Hem: Pentatomidae)

Abstract:  The effect of topical application of neem seed kernel aqueous suspension (NSKAS) and neem seed kernel hexane extract (NSKHE) on 2-day-old fourth instar nymphs of Nezara viridula (L.) was studied under laboratory conditions. The applications resulted in loosening of the proboscis, along with separation and deformation of mouthparts, predominantly in the stylets in fifth instar nymphs, adults and in inter-stadial moults. This rendered the insects incapable of feeding and led them to death. Application of NSKAS at 2.5% concentration led to 100% mortality, of which 40% was due to mouthpart deformities. Application of NSKHE at 2.5% concentration led to only 70% mortality, of which 3.33% was due to mouthpart deformities. At 5% concentration, mortality was again 100% for NSKAS and reached close to 100% for the NSKHE application. Dose-dependent mortality was observed for both the applications. However, dose-dependent mouthpart deformities were observed only for NSKAS, the highest being 60% at the 5% concentration. Moreover, for both the applications, more than 50% of the total mortality was observed up to the fifth instar at almost all the concentrations. The LC₅₀ values for NSKAS and NSKHE applications, for mortality up to the fifth instar were 0.36% and 5.22%, and for mortality to the adult stage (10 days old) they were 0.23% and 1.32% respectively. The percentage of bugs showing mouthpart deformities and toxicity was higher for NSKAS than NSKHE, and indicated that NSKAS was more potent than NSKHE.     

Semiparametric estimation of tag loss and reporting rates for tag-recovery experiments using exact time-at-liberty data

We present a semiparametric likelihood approach to estimating reporting rates and tag-loss rates from the tags returned from capture-recapture studies. Such studies are commonly used to estimate critical population parameters. Tag loss rates are estimated using double-tagged animals, while reporting rates are estimated using information from high-reward tags. A likelihood function is constructed based on the conditional distribution of the type of tag returned (low or high reward, single or double tag), given that a tag has been returned. This involves many sparse 5 x 1 tag-return contingency tables, and choosing a good functional form for the tag loss rate is difficult with such data. We model tag-loss rates using monotone-smoothing splines, and use these nonparametric estimates to diagnose the parametric form of the tag-loss rate. The nonparametric methods can also be used directly to model tag-loss rates.