Virulence Potential And Antibiotic Susceptibility Pattern Of Motile Aeromonads Associated With Freshwater Ornamental Fish Culture Systems: A Possible Threat To public Health

Aeromonas spp. are ubiquitous aquatic organisms, associated with multitude of diseases in several species of animals, including fishes and humans. In the present study, water samples from two ornamental fish culture systems were analyzed for the presence of Aeromonas. Nutrient agar was used for Aeromonas isolation, and colonies (60 No) were identified through biochemical characterization. Seven clusters could be generated based on phenotypic characters, analyzed by the programme NTSYSpc, Version 2.02i, and identified as: Aeromonas caviae (33.3%), A. jandaei (38.3%) and A. veronii biovar sobria (28.3%). The strains isolated produced highly active hydrolytic enzymes, haemolytic activity and slime formation in varying proportions. The isolates were also tested for the enterotoxin genes (act, alt and ast), haemolytic toxins (hlyA and aerA), involved in type 3 secretion system (TTSS: ascV, aexT, aopP, aopO, ascF–ascG, and aopH), and glycerophospholipid-cholesterol acyltransferase (gcat). All isolates were found to be associated with at least one virulent gene. Moreover, they were resistant to frequently used antibiotics for human infections. The study demonstrates the pathogenic potential of Aeromonas, associated with ornamental fish culture systems suggesting the emerging threat to public health

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering

Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Pattern Analysis Techniques for the Recognition of Unconstrained Handwritten Malayalam Character Images

The objective of the study is to develop a hand written character recognition system that could recognisze all the characters in the mordern script of malayalam language at a high recognition rate

Temporal pattern of fish production in a microtidal tropical estuary in the south-west coast of India

The status of fisheries and seasonal variation in fish diversity in the Kodungallur-Azhikode Estuary (KAE) were investigated. Total annual average fish production in the estuary declined significantly to 908.6 t with average yield of 5.4 kg ha-1 day-1, when compared to earlier study; where 2747 t was reported. During the present study, 60 species of finfishes (belonging to 34 finfish families), 6 species of penaeid shrimps, 2 species of palaemonid prawns, 2 species of crabs (4 crustacean families), 6 species of bivalves and 2 species of edible oysters (3 molluscan families) were noticed. Finfishes were the major group that contributed 69.62% of total fishery in the estuary and crustaceans (23.47%), bivalves (6.84%) and oysters (0.07%) also formed good fishery. Many of the fish species in the estuary were observed as threatened (Horabagrus brachysoma, Channa striatus, Channa marulius, Clarias batrachus, Heteropneustes fossilis and Wallago attu). The major fishing gears employed in the estuary were gillnets, cast nets, stake nets, scoop nets, ring nets, traps and Chinese dip nets. Gillnets contributed 45% of the total fish catch. Gillnets also showed highest catch per unit effort (CPUE) of 6.91 kg h -1 followed by cast nets (1.85 kg h -1), Chinese dip nets (3.20 kg h -1), stake nets (3.05 kg h -1), ring nets (1.27 kg h -1), hooks and lines (1.35 kg h -1) and scoop nets (0.92 kg h -1). The study implies that temporal changes in fish landing pattern of the KAE was mainly due to environmental variability, habitat modification and fish migration; under the influence of south-west monsoon and anthropogenic activities in the KAE. Results of the study suggest that spatio-temporal variations in the fish community structure could be an indicator for anthropogenic stress and it should be considered for restoration programmes.

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features

Resource use pattern and agroecosystem functioning in Rawanganga micro-watershed in Garhwal Himalaya, India

Agro-ecological resource use pattern in a traditional hill agricultural watershed in Garhwal Himalaya was analysed along an altitudinal transect. Thirty one food crops were found, although only 0.5% agriculture land is under irrigation in the area. Fifteen different tree species within agroforestry systems were located and their density varied from 30-90 trees/ha. Grain yield, fodder from agroforest trees and crop residue were observed to be highest between 1200 and 1600 m a.s.l. Also the annual energy input- output ratio per hectare was highest between 1200 and 1600 m a.s.l. (1.46). This higher input- output ratio between 1200-1600 m a.s.l. was attributed to the fact that green fodder, obtained from agroforestry trees, was considered as farm produce. The energy budget across altitudinal zones revealed 95% contribution of the farmyard manure and the maximum output was in terms of either crop residue (35%) or fodder (55%) from the agroforestry component. Presently on average 23%, 29% and 41% cattle were dependent on stall feeding in villages located at higher, lower and middle altitudes respectively. Similarly, fuel wood consumption was greatly influenced by altitude and family size. The efficiency and sustainability of the hill agroecosystem can be restored by strengthening of the agroforestry component. The approach will be appreciated by the local communities and will readily find their acceptance and can ensure their effective participation in the programme.

Derivation of an Efficient Rule System Pattern Matcher

Formalizing algorithm derivations is a necessary prerequisite for developing automated algorithm design systems. This report describes a derivation of an algorithm for incrementally matching conjunctive patterns against a growing database. This algorithm, which is modeled on the Rete matcher used in the OPS5 production system, forms a basis for efficiently implementing a rule system. The highlights of this derivation are: (1) a formal specification for the rule system matching problem, (2) derivation of an algorithm for this task using a lattice-theoretic model of conjunctive and disjunctive variable substitutions, and (3) optimization of this algorithm, using finite differencing, for incrementally processing new data.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

Developmental pattern of digit span in Spanish population

Resumen tomado de la publicaci??n

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal