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.

Classification of instability modes in a model of aluminium reduction cells with a uniform magnetic field

A unified view on the interfacial instability in a model of aluminium reduction cells in the presence of a uniform, vertical, background magnetic field is presented. The classification of instability modes is based on the asymptotic theory for high values of parameter β, which characterises the ratio of the Lorentz force based on the disturbance current, and gravity. It is shown that the spectrum of the travelling waves consists of two parts independent of the horizontal cross-section of the cell: highly unstable wall modes and stable or weakly unstable centre, or Sele’s modes. The wall modes with the disturbance of the interface being localised at the sidewalls of the cell dominate the dynamics of instability. Sele’s modes are characterised by a distributed disturbance over the whole horizontal extent of the cell. As β increases these modes are stabilized by the field.

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.

Composition and cure temperature: The influence on properties of final flexible PU cold cure foam parts

Foams are cellular structures, produced by gas bubbles formed during the polyurethane polymerization mixture. Flexible PU foams meet the following two criteria: have a limited resistance to an applied load, being both permeable to air and reversibly deformable. There are two main types of flexible foams, hot and cold cure foams differing in composition and processing temperatures. The hot cure foams are widely applied and represent the main composition of actual foams, while cold cure foams present several processing and property advantages, e.g, faster demoulding time, better humid aging properties and more versatility, as hardness variation with index changes are greater than with hot cure foams. The processing of cold cure foams also is attractive due to the low energy consumption (mould temperature from 30 degrees to 65 degrees C) comparatively to hot cure foams (mould temperature from 30 degrees to 250 degrees C). Another advantage is the high variety of soft materials for low temperature processing moulds. Cold cure foams are diphenylmethane diisocyanate (MDI) based while hot cure foams are toluene diisocyanate (TDI) based. This study is concerned with Viscoelastic flexible foams MDI based for medical applications. Differential Scanning Calorimetry (DSC) was used to characterize the cure kinetics and Dynamical Mechanical Analisys to collect mechanical data. The data obtained from these two experimental procedures were analyzed and associated to establish processing/properties/operation conditions relationships. These maps for the selection of optimized processing/properties/operation conditions are important to achieve better final part properties at lower costs and lead times.

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.

Implicit and explicit knowledge bases in artificial grammar learning

Two experiments examined the claim for distinct implicit and explicit learning modes in the artificial grammar-learning task (Reber, 1967, 1989). Subjects initially attempted to memorize strings of letters generated by a finite-state grammar and then classified new grammatical and nongrammatical strings. Experiment 1 showed that subjects' assessment of isolated parts of strings was sufficient to account for their classification performance but that the rules elicited in free report were not sufficient. Experiment 2 showed that performing a concurrent random number generation task under different priorities interfered with free report and classification performance equally. Furthermore, giving different groups of subjects incidental or intentional learning instructions did not affect classification or free report.

Chemical composition and fermentation characteristics of grain and different parts of the stover from maize land races harvested at different growing periods in two zones of central Mexico

The objective of this work was to determine the rumen fermentation characteristics of maize land races used as forage in central Mexico. In vitro gas production (ml per 200 mg dry matter (DM)) incubations were carried out, and cumulative gas volumes were fitted to the Krishnamoorthy et al. (1991) model. The trial used a split-plot design with cultivation practices associated with maize colour (COL) as the main plot with three levels: white, yellow and black maize; growing periods (PER) were the split plots where PER1, PER2 and PER3 represented the first, second and third periods, respectively and two contrasting zones (Z1 = valley and Z2 = mountain) were used as blocking factors. The principal effects observed were associated with the maturity of the plants and potential gas production increased (P < 0.05) in stems (PER 1 = 51.8, PER2 = 56.3, PER3 = 58.4 ml per 200 mg DM) and in whole plant (PER 1 = 60.9, PER2 = 60.8, PER3= 70.9 ml per 200 mg DM). An inverse effect was observed with fermentation rates in leaves (P < 0.01) with 0.061, 0.053 and 0.0509 (per h) and in whole plant (P < 0.05) with 0.068, 0.057, 0.050 (per h) in PER1, PER2 and PER3 respectively. The digestibility of the neutral-detergent fibre (NDF) decreased with maturity especially in leaves (P < 0.05) with values of 0.71, 0.67 and 0.66 g/kg; in rachis (P < 0.01) 0.75, 0.72, and 0.65 in PER1, PER2 and PER3 respectively. The NDF content in leaves in leaves (668, 705 and 713 g/kg DM for PER1, PER2 and PER3, respectively), stems (580, 594 and 644 g/kg DM) and, husk (663, 774 and, 808 g/kg DM) increased (P < 0.05) with increasing plant maturity, rachis were significantly different between periods (P < 0.01). The structure with-the best nutritive characteristics was the husk, because it had the lowest fibre contents, especially in acid-detergent lignin, with values of 22.6, 28.6 and 37.6 g/kg DM in PER1, PER2 and PER3, respectively.

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.