Optimization of Solutions for the One Plant Protection Problem

Plant protection problems are simulated by a system of ordinary differential equations with given initial conditions. The sensitivity and resistance of pathogen subpopulations to fungicide mixtures, fungicide weathering, plant growth, etc. are taken into consideration. The system of equations is solved numerically for each set of initial conditions and parameters of the disease and fungicide applications. Optimization algorithms were investigated and a computer program was developed for optimization of these solutions. 14 typical cases of the disease were simulated and optimized in order to determine optimal fungicide treatments. The optimized strategy for fungicide application differs considerably from the commonly used method and seems to be an important new principle in plant protection. The approach developed in this study may be useful for a wide spectrum of purposes in the simulation of leaf diseases. It may also help the biologist to decrease or pinpoint experimental work and analyze its results and is perspective for plant disease control.     

A \sqrt N Dynamic Load Distribution Algorithm Using Anti-Tasks and Load State Vectors

One of the fundamental issues to ensure maximal performance improvement in a cluster computing environment is load distribution, which is commonly achieved by using polling-based load distribution algorithms. Such algorithms suffer from two weaknesses: (1) Load information exchanged during a polling session is confined to the two negotiating nodes only. (2) Such algorithms are not scalable in that growth of the distributed system is accompanied with increasing amount of polling sessions.In this paper, we proposed a LD algorithm which is based on anti-tasks and load state vectors. Anti-tasks travel around the distributed system for pairing up task senders and receivers. As an anti-task travels, timed load information is collected and disseminated over the entire system via the load state vector bundled with the anti-task. Guided by load state vectors, anti-tasks are spontaneously directed towards processing nodes having high transient workload, thus allowing their surplus workload to be relocated soonest possible. No peer-to-peer negotiations between senders and receivers are needed.To reduce the network bandwidth consumption caused by the anti-task algorithm, the number of hosts that an anti-task needs to travel to must be carefully limited. The algorithm achieves this by employing the mathematical notion of Finite Projective Plane (FPP). By employing FPP, the number of nodes that each anti-task has to travel is at most , where N is the number of nodes in the system, without sacrifying the spread of load information.     

Isolation by distance and sharp discontinuities in gene frequencies: implications for the phylogeography of an alpine insect species, Carabus solieri

Analysis of genetic isolation by distance (IBD) is of prime importance for the study of processes responsible for spatial population genetic structure and is thus frequently used in case studies. However, the identification of a significant IBD pattern does not necessarily imply the absence of sharp discontinuities in gene frequencies. Therefore, identifying barriers to gene flow and/or secondary contact between differentiated entities remains a major challenge in population biology. Geographical genetic structure of 41 populations (1080 individuals) of an alpine insect species, Carabus solieri, was studied using 10 microsatellite loci. All populations were significantly differentiated and spatially structured according to IBD over the entire range. However, clustering analyses clearly identified three main clusters of populations, which correspond to geographical entities. Whereas IBD also occurs within each cluster, population structure was different according to which group of populations was considered. The southernmost cluster corresponds to the most fragmented part of the range. Consistently, it was characterized by relatively high levels of differentiation associated with low genetic diversity, and the slope of the regression of genetic differentiation against geographical distances was threefold those of the two other clusters. Comparisons of within-cluster and between-cluster IBD patterns revealed barriers to gene flow. A comparison of the two approaches, IBD and clustering analyses, provided us with valuable information with which to infer the phylogeography of the species, and in particular to propose postglacial colonization routes from two potential refugia located in Italy and in southeastern France. Our study highlights strongly the possible confounding contribution of barriers to gene flow to IBD pattern and emphasizes the utility of the model-based clustering analysis to identify such barriers.     

Protein fragment clustering and canonical local shapes

A novel clustering method is used to cluster protein fragments by shape. The centroids (mean fragments from each cluster) form a basis set of structural motifs. A database of 156,643 seven-residue fragments is used, and eight different basis sets with varying levels of resolution are generated. Coarse basis sets contain tens of centroids and provide meaningful local shapes, which are more detailed than the traditional secondary structure categories. High-resolution basis sets contain thousands of centroids and can be used to model tertiary structure of longer segments. The basis sets generated fit nontraining set proteins with the expected accuracy.     

Parameterization and classification of the protein universe via geometric techniques

We present a scheme for the classification of 3487 non-redundant protein structures into 1207 non-hierarchical clusters by using recurring structural patterns of three to six amino acids as keys of classification. This results in several signature patterns, which seem to decide membership of a protein in a functional category. The patterns provide clues to the key residues involved in functional sites as well as in protein-protein interaction. The discovered patterns include a "glutamate double bridge" of superoxide dismutase, the functional interface of the serine protease and inhibitor, interface of homo/hetero dimers, and functional sites of several enzyme families. We use geometric invariants to decide superimposability of structural patterns. This allows the parameterization of patterns and discovery of recurring patterns via clustering. The geometric invariant-based approach eliminates the computationally explosive step of pair-wise comparison of structures. The results provide a vast resource for the biologists for experimental validation of the proposed functional sites, and for the design of synthetic enzymes, inhibitors and drugs.     

Combining inference from evolution and geometric probability in protein structure evaluation

Starting from the hypothesis that evolutionarily important residues form a spatially limited cluster in a protein's native fold, we discuss the possibility of detecting a non-native structure based on the absence of such clustering. The relevant residues are determined using the Evolutionary Trace method. We propose a quantity to measure clustering of the selected residues on the structure and show that the exact values for its average and variance over several ensembles of interest can be found. This enables us to study the behavior of the associated z-scores. Since our approach rests on an analytic result, it proves to be general, customizable, and computationally fast. We find that clustering is indeed detectable in a large representative protein set. Furthermore, we show that non-native structures tend to achieve lower residue-clustering z-scores than those attained by the native folds. The most important conclusion that we draw from this work is that consistency between structural and evolutionary information, manifested in clustering of key residues, imposes powerful constraints on the conformational space of a protein.