Model-based vehicle detection and classification using orthographic approximations

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a real-time system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing to be carried out by low-cost auxiliary hardware, (ii) all 3-D reasoning to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) & (iii) have radically different computing performance and computational costs, and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site-specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (ECa) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and ECa. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.

A review and classification of the existing models of cyanobacteria

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting bloom occurrence in lakes and rivers. In this paper existing key models of cyanobacteria are reviewed, evaluated and classified. Two major groups emerge: deterministic mathematical and artificial neural network models. Mathematical models can be further subcategorized into those models concerned with impounded water bodies and those concerned with rivers. Most existing models focus on a single aspect such as the growth of transport mechanisms, but there are a few models which couple both.

Soft topographic map for clustering and classification of bacteria

In this work a new method for clustering and building a topographic representation of a bacteria taxonomy is presented. The method is based on the analysis of stable parts of the genome, the so-called “housekeeping genes”. The proposed method generates topographic maps of the bacteria taxonomy, where relations among different type strains can be visually inspected and verified. Two well known DNA alignement algorithms are applied to the genomic sequences. Topographic maps are optimized to represent the similarity among the sequences according to their evolutionary distances. The experimental analysis is carried out on 147 type strains of the Gammaprotebacteria class by means of the 16S rRNA housekeeping gene. Complete sequences of the gene have been retrieved from the NCBI public database. In the experimental tests the maps show clusters of homologous type strains and present some singular cases potentially due to incorrect classification or erroneous annotations in the database.

Rapid radiation in spiny lobsters (Palinurus spp) as revealed by classic and ABC methods using mtDNA and microsatellite data

Background: Molecular tools may help to uncover closely related and still diverging species from a wide variety of taxa and provide insight into the mechanisms, pace and geography of marine speciation. There is a certain controversy on the phylogeography and speciation modes of species-groups with an Eastern Atlantic-Western Indian Ocean distribution, with previous studies suggesting that older events (Miocene) and/or more recent (Pleistocene) oceanographic processes could have influenced the phylogeny of marine taxa. The spiny lobster genus Palinurus allows for testing among speciation hypotheses, since it has a particular distribution with two groups of three species each in the Northeastern Atlantic (P. elephas, P. mauritanicus and P. charlestoni) and Southeastern Atlantic and Southwestern Indian Oceans (P. gilchristi, P. delagoae and P. barbarae). In the present study, we obtain a more complete understanding of the phylogenetic relationships among these species through a combined dataset with both nuclear and mitochondrial markers, by testing alternative hypotheses on both the mutation rate and tree topology under the recently developed approximate Bayesian computation (ABC) methods. Results: Our analyses support a North-to-South speciation pattern in Palinurus with all the South-African species forming a monophyletic clade nested within the Northern Hemisphere species. Coalescent-based ABC methods allowed us to reject the previously proposed hypothesis of a Middle Miocene speciation event related with the closure of the Tethyan Seaway. Instead, divergence times obtained for Palinurus species using the combined mtDNA-microsatellite dataset and standard mutation rates for mtDNA agree with known glaciation-related processes occurring during the last 2 my. Conclusion: The Palinurus speciation pattern is a typical example of a series of rapid speciation events occurring within a group, with very short branches separating different species. Our results support the hypothesis that recent climate change-related oceanographic processes have influenced the phylogeny of marine taxa, with most Palinurus species originating during the last two million years. The present study highlights the value of new coalescent-based statistical methods such as ABC for testing different speciation hypotheses using molecular data.

Interaction of pesticides with p-glycoprotein and other ABC proteins: A survey of the possible importance to insecticide, herbicide and fungicide resistance

P-glycoproteins (p-gps) are ubiquitous membrane proteins from the ABC (ATP-binding cassette) family. They have been found in many animals, bacteria, plants and fungi and are extremely important in regulating a wide range of xenobiotics including pesticides. P-gps have been linked to xenobiotic resistance, most famously in resistance to cancer drug treatments. Their wide substrate range has led to what is known as "multidrug resistance", where resistance developed to one type of xenobiotic gives resistance to a different classes of xenobiotic. P-gps are a major contributor to drug resistance in mammalian tumours and infections of protozoan parasites such as Plasmodium and Leishmania. There is a growing body of literature suggesting that p-gps, and other ABC proteins, are important in regulating pesticide toxicity and represent potential control failure through the development of pesticide resistance, in both agricultural and medical pests. At the same time, aspects of their biochemistry offer new hope in pest control, in particular in furthering our understanding of toxicity and offering insights into how we can improve control without recourse to new chemical discovery. (c) 2008 Elsevier Inc. All rights reserved.

Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation

Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC.

Osmotic upshift transiently inhibits uptake via ABC transporters in gram-negative bacteria

ATP-binding cassette transporters from several rhizobia and Salmonella enterica serovar Typhimurium, but not secondarily coupled systems, were inhibited by high concentrations (100 to 500 mM) of various osmolytes, an effect reversed by the removal of the osmolyte. ABC systems were also inactivated in isolated pea bacteroids, probably due to the obligatory use of high-osmolarity isolation media. Measurement of nutrient cycling in isolated pea bacteroids is impeded by this effect.

Synthesis and characterization of biocompatible, thermoresponsive ABC and ABA triblock copolymer gelators

The synthesis of doubly thermoresponsive PPO-PMPC-PNIPAM triblock copolymer gelators by atom transfer radical polymerization using a PPO-based macroinitiator is described. Provided that the PPO block is sufficiently long, dynamic light scattering and differential scanning calorimetry studies confirm the presence of two separate thermal transitions corresponding to micellization and gelation, as expected. However, these ABC-type triblock copolymers proved to be rather inefficient gelators: free-standing gels at 37 degrees C required a triblock copolymer concentration of around 20 wt%. This gelator performance should be compared with copolymer concentrations of 6-7 wt% required for the PNIPAM-PMPC-PNIPAM triblock copolymers reported previously. Clearly, the separation of micellar self-assembly from gel network formation does not lead to enhanced gelator efficiencies, at least for this particular system. Nevertheless, there are some features of interest in the present study. In particular, close inspection of the viscosity vs temperature plot obtained for a PPO43-PMPC160-PNIPAM(81) triblock copolymer revealed a local minimum in viscosity. This is consistent with intramicelle collapse of the outer PNIPAM blocks prior to the development of the intermicelle hydrophobic interactions that are a prerequisite for macroscopic gelation.

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.