k-NN Boosting Prototype Learning for Object Classification

Object classification is a challenging task in computer vision. Many approaches have been proposed to extract meaningful descriptors from images and classifying them in a supervised learning framework. In this paper, we revisit the classic k-nearest neighbors (k-NN) classification rule, which has shown to be very effective when dealing with local image descriptors. However, k-NN still features some major drawbacks, mainly due to the uniform voting among the nearest prototypes in the feature space. In this paper, we propose a generalization of the classic k-NN rule in a supervised learning (boosting) framework. Namely, we redefine the voting rule as a strong classifier that linearly combines predictions from the k closest prototypes. To induce this classifier, we propose a novel learning algorithm, MLNN (Multiclass Leveraged Nearest Neighbors), which gives a simple procedure for performing prototype selection very efficiently. We tested our method on 12 categories of objects, and observed significant improvement over classic k-NN in terms of classification performances

Biological cells classification using bio-inspired descriptor in a boosting k-NN framework

International audienceHigh-content imaging is an emerging technology for the analysis and quantification of biological phenomena. Thus, classifying a huge number of cells or quantifying markers from large sets of images by experts is a very time-consuming and poorly reproducible task. In order to overcome such limitations, we propose a supervised method for automatic cell classification. Our approach consists of two steps: the first one is an indexing stage based on specific bio-inspired features relying on the distribution of contrast information on segmented cells. The second one is a supervised learning stage that selects the prototypical samples best representing the cell categories. These prototypes are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of pathologies. In order to evaluate the automatic classification performances, we tested our algorithm on the HEp2 Cells dataset of (Foggia et al, CBMS 2010). Results are very promising, showing classification precision larger than 96% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications

Triggers for atrial fibrillation. the role of anxiety

Atrial fibrillation (AF) is the most widely recognized arrhythmia. Systemic arterial hypertension, diabetes, obesity, heart failure, and valvular heart diseases are major risk factors for the onset and progression of AF. Various studies have emphasized the augmented anxiety rate among AF patients due to the poor quality of life; however, little information is known about the possibility of triggering atrial fibrillation by anxiety. +e present review sought to underline the possible pathophysiological association between AF and anxiety disorders and suggests that anxiety can be an independent risk factor for AF, acting as atrigger, creating an arrhythmogenic substrate, and modulating the autonomic nervous system.+e awareness of the role of anxietydisorders as a risk factor for AF may lead to the development of new clinical strategies for the management of AF

Classification of biological cells using bio-inspired descriptors

International audienceThis paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis

Malondialdehyde–Deoxyguanosine Adducts among Workers of a Thai Industrial Estate and Nearby Residents

Background: Humans living near industrial point emissions can experience high levels of exposures to air pollutants. Map Ta Phut Industrial Estate in Thailand is the location of the largest steel, oil refinery, and petrochemical factory complexes in Southeast Asia. Air pollution is an important source of oxidative stress and reactive oxygen species, which interact with DNA and lipids, leading to oxidative damage and lipid peroxidation, respectively. Objective: We measured the levels of malondialdehyde-deoxyguanosine (dG) adducts, a biomarker of oxidative stress and lipid peroxidation, in petrochemical workers, nearby residents, and subjects living in a control district without proximity to industrial sources. Design: We conducted a cross-sectional study to compare the prevalence of malondialdehyde-dG adducts in groups of subjects experiencing various degrees of air pollution. Results: The multivariate regression analysis shows that the adduct levels were associated with occupational and environmental exposures to air pollution. The highest adduct level was observed in the steel factory workers. In addition, the formation of DNA damage tended to be associated with tobacco smoking, but without reaching statistical significance. A non significant increase in DNA adducts was observed after 4-6 years of employment among the petrochemical complexes. Conclusions: Air pollution emitted from the Map Ta Phut Industrial Estate complexes was associated with increased adduct levels in petrochemical workers and nearby residents. Considering the mutagenic potential of DNA lesions in the carcinogenic process, we recommend measures aimed at reducing the levels of air pollution

A Bio-inspired Learning and Classification Method for Subcellular Localization of a Plasma Membrane Protein

International audienceHigh-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automatic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology

Analysis of the phytochemical composition of pomegranate fruit juices, peels and kernels: A comparative study on four cultivars grown in Southern Italy

The increasing popularity of pomegranate (Punica granatum L.), driven by the awareness of its nutraceutical properties and excellent environmental adaptability, is promoting a global expansion of its production area. This investigation reports the variability in the weight, moisture, pH, total soluble solids, carbohydrates, organic acids, phenolic compounds, fatty acids, antioxidant activities, and element composition of different fruit parts (juices, peels, and kernels) from four (Ako, Emek, Kamel, and Wonderful One) of the most widely cultivated Israeli pomegranate varieties in Salento (South Italy). To the best of our knowledge, this is the first systematic characterization of different fruit parts from pomegranate cultivars grown simultaneously in the same orchard and subjected to identical agronomic and environmental conditions. Significant genotype-dependent variability was observed for many of the investigated parameters, though without any correlation among fruit parts. The levels of phenols, flavonoids, anthocyanins, and ascorbic and dehydroascorbic acids of all samples were higher than the literature-reported data, as was the antioxidant activity. This is likely due to positive interactions among genotypes, the environment, and good agricultural practices. This study also confirms that pomegranate kernels and peels are, respectively, rich sources of punicic acid and phenols together, with several other bioactive molecules. However, the variability in their levels emphasizes the need for further research to better exploit their agro-industrial potential and thereby increase juice-production chain sustainability. This study will help to assist breeders and growers to respond to consumer and industrial preferences and encourage the development of biorefinery strategies for the utilization of pomegranate by-products as nutraceuticals or value-added ingredients for custom-tailored supplemented foods

Electrocardiographic features, mapping and ablation of idiopathic outflow tract ventricular arrhythmias

Idiopathic outflow tract ventricular arrhythmias are ventricular tachycardias or premature ventricular contractions presumably not related to myocardial scar or disorders of ion channels. These arrhythmias have focal origin and display characteristic electrocardiographic features. The purpose of this article is to review the state of the art of diagnosis and treatment of idiopathic outflow tract ventricular arrhythmias