Food Insecurity and Cardiovascular Risk Factors in U.S. Adolescents

Introduction: Disparities in cardiovascular diseases are one of today’s most important public health challenges. Pathological processes related to modifiable cardiovascular risk factors have shown to begin in childhood and disparities in these risk factors have been reported in adolescence. Food insecurity is significantly associated with cardiovascular risk factors in adults; however, little is known about cardiovascular risk in food insecure adolescents. Objective: The objective of this study was to examine the relationship between food insecurity and cardiovascular risk factors in U.S. adolescents aged 12-17 years. Methods: Using cross-sectional data on 1,853 adolescents aged 12-17 years from the National Health and Nutrition Examination Survey 2007-2012, we examined the association between food insecurity and cardiovascular risk factors. Food security status was measured using the validated 18-item Household Food Security Survey Module. Cardiovascular risk was measured based on American Heart Association’s Life’s Simple 7 factors (LS7; tobacco smoke exposure, diet quality, physical activity, body mass index, blood pressure, total cholesterol, blood glucose levels). Results: Nearly 10.0% of U.S. adolescents were food insecure. A total of 26.1% of adolescents failed to attain ideal scores on more than 5 LS7 components. In bivariate analyses, food secure, in comparison to food insecure adolescents, were more likely to have ideal scores on 5-7 LS7 components (75.1% vs. 63.0%, p = 0.0089). In multivariate models adjusted for demographic, socioeconomic, health, and health care access factors, food insecurity was not significantly associated with cardiovascular risk in adolescents. However, food insecure adolescents had significantly lower odds of attaining ideal levels of tobacco smoke exposure ([OR] = 0.54 [95% CI 0.31, 0. 94]) than food secure adolescents. Adolescents living in families with incomes below the Federal Poverty Level (Odds Ratio [OR] = 0.59 [95% CI 0.40,0.86]) had significantly lower odds of having ideal LS7 scores and lower odds of attaining ideal scores on tobacco smoke exposure ([OR] = 0.25 [95% CI 0.13, 0.49]) and physical activity ([OR] = 0.60 [95% CI 0.38, 0.95]). Conclusion: Although cardiovascular risk is not more pronounced in food insecure adolescents than their counterparts, adolescents from low SES households may be at particular risk of developing cardiovascular diseases. Multifaceted and tailored strategies inclusive of nutrition assistance are needed to facilitate effective cardiovascular risk prevention as these vulnerable populations transition into early adulthood

Similarity Based Ranking of Query Results from Real Web Databases

The information available in the World Wide Web is stored using many real Web databases (e.g. vehicle database). Accessing the information from these real Web databases has become increasingly important for the users to find the desired information. Web users search for the desired information by querying these Web databases, when the number of query results generated is large, it is very difficult for the Web user to select the most relevant information from the large result set generated. Users today, have become more and more demanding in terms of the quality of information that is provided to them while searching the Web databases. The most common solution to solve the problem involves ranking the query results returned by the Web databases. Earlier approaches have used query logs, user profiles and frequencies of database values. The problem in all of these techniques is that ranking is performed in a user and query independent manner. This paper, proposes an automated ranking of query results returned by Web databases by analyzing user, query and workload similarity. The effectiveness of this approach is discussed considering a vehicle Web database as an example

YY1 Is Required for Germinal Center B Cell Development.

YY1 has been implicated as a master regulator of germinal center B cell development as YY1 binding sites are frequently present in promoters of germinal center-expressed genes. YY1 is known to be important for other stages of B cell development including the pro-B and pre-B cells stages. To determine if YY1 plays a critical role in germinal center development, we evaluated YY1 expression during B cell development, and used a YY1 conditional knock-out approach for deletion of YY1 in germinal center B cells (CRE driven by the immunoglobulin heavy chain γ1 switch region promoter; γ1-CRE). We found that YY1 is most highly expressed in germinal center B cells and is increased 3 fold in splenic B cells activated by treatment with anti-IgM and anti-CD40. In addition, deletion of the yy1 gene by action of γ1-CRE recombinase resulted in significant loss of GC cells in both un-immunized and immunized contexts with corresponding loss of serum IgG1. Our results show a crucial role for YY1 in the germinal center reaction

Personalized Recommendation of Web Pages Using Group Average Agglomerative Hierarchical Clustering (GAAHC)

Entrepreneurs are investing heavily on marketing and promoting business through the websites to enhance their online reputation and draw the attention of the web users. Website structure plays the vital role in attracting the web users. Creating personalized website structure for individual user by restructuring the web site structure is a tedious and endless job. If the users do not find the required information easily in the websites, then users abandon such websites. Hence, personalized recommendation of web pages to the web users increases the user’s interest and the time they spend in the website. Personalization is the process of creating customized participation of users to a website, rather than providing a broad participation. Personalization allows the website to present the users with the unique participation bespoke to their demands and passion. Personalized recommendation is a challenging task, which has drawn the focus of many researchers. Personalization has to trace the behavior of individual users. Usage behavior can be traced by observing the individual navigation patterns using web log file of the specific website. This method requires session identification, clustering sessions into similar clusters and building a model for personalized recommendations using access time length and frequency of access. Most of the existing works on this topic have used K-Means clustering with Euclidean distance. K-Means suffers from choosing the initial random center and sequence of page visits is not considered. The proposed research work uses Group Average Agglomerative Hierarchical Clustering (GAAHC), with Modified Levenshtein

IMPROVED AUTOMATIC DETECTION OF GLAUCOMA USING CUP-TO-DISK RATIO AND HYBRID CLASSIFIERS.

Glaucoma is one of the most complicated disorder in human eye that causes permanent vision loss gradually if not detect in early stage. It can damage the optic nerve without any symptoms and warnings. Different automated glaucoma detection systems were developed for analyzing glaucoma at early stage but lacked good accuracy of detection. This paper proposes a novel automated glaucoma detection system which effectively process with digital colour fundus images using hybrid classifiers. The proposed system concentrates on both Cup-to Disk Ratio (CDR) and different features to improve the accuracy of glaucoma. Morphological Hough Transform Algorithm (MHTA) is designed for optic disc segmentation. Intensity based elliptic curve method is used for separation of optic cup effectively. Further feature extraction and CDR value can be estimated. Finally, classification is performed with combination of Naive Bayes Classifier and K Nearest Neighbour (KNN). The proposed system is evaluated by using High Resolution Fundus (HRF) database which outperforms the earlier methods in literature in various performance metrics

Web page access prediction using hierarchical clustering based on modified levenshtein distance and higher order Markov model

Web Page access prediction is a challenging task in the current scenario, which draws the attention of many researchers. Predictions need to keep track of history data to analyze the usage behavior of the users. Web Usage behavior of a user can be analyzed using the web log file of a specific website. User behavior can be analyzed by observing the navigation patterns. This approach requires user session identification, clustering the sessions into similar clusters and developing a model for prediction using the current and earlier accesses. Most of the previous works in this field have used K-Means clustering technique with Euclidean distance for computation. The drawbacks of K-Means is that deciding on the number of clusters, choosing the initial random center are difficult and the order of page visits are not considered. The proposed research work uses hierarchical clustering technique with modified Levenshtein distance, Page Rank using access time length, frequency and higher order Markov model for prediction. Experimental results prove that the proposed approach for prediction gives better accuracy over the existing techniques

Moving Vehicle Identification using Background Registration Technique for Traffic Surveillance

Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance and human-machine interface. In this paper we present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The present algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles at intersection, rejecting background and tracks vehicles over a specific period of time. Real-life traffic video sequences are used to illustrate the effectiveness of the proposed algorithm

MULTISTAGE CLASSIFICATION OF DIABETIC RETINOPATHY USING FUZZY-NEURAL NETWORK CLASSIFIER

Diabetic Retinopathy (DR) is complicated disorder in human retina which is affected due to an increasing amount of insulin in blood that results in vision impairment. Early detection of DR is used to support the patients to prevent blindness and to be aware of this disease. This paper proposes a novel technique for detecting DR using hybrid classifiers. It includes pre-processing of the image, segmentation of region of interest, feature extraction and classification. Retinal structures like microaneurysms, exudates, hemorrhages and blood vessels are segmented. Classification is performed with integration of Fuzzy logical System and Neural Network (NN) which improves the accuracy of classification. Experimentation is carried out with the MESSIDOR data set. Results are compared against various performance metrics like accuracy, sensitivity and specificity. An accuracy close to 100 percent and low average error rate of 0.012 are obtained using the proposed method. The results obtained are encouraging

Cell-associated bacteria in the human lung microbiome

Abstract Background Recent studies have revealed that bronchoalveolar lavage (BAL) fluid contains previously unappreciated communities of bacteria. In vitro and in vivo studies have shown that host inflammatory signals prompt bacteria to disperse from cell-associated biofilms and adopt a virulent free-living phenotype. The proportion of the lung microbiota that is cell-associated is unknown. Results Forty-six BAL specimens were obtained from lung transplant recipients and divided into two aliquots: ‘whole BAL’ and ‘acellular BAL,’ the latter processed with a low-speed, short-duration centrifugation step. Both aliquots were analyzed via bacterial 16S rRNA gene pyrosequencing. The BAL specimens represented a wide spectrum of lung health, ranging from healthy and asymptomatic to acutely infected. Bacterial signal was detected in 52% of acellular BAL aliquots, fewer than were detected in whole BAL (96%, p ≤ 0.0001). Detection of bacteria in acellular BAL was associated with indices of acute infection [BAL neutrophilia, high total bacterial (16S) DNA, low community diversity, p < 0.01 for all] and, independently, with low relative abundance of specific taxonomic groups (p < 0.05). When whole and acellular aliquots from the same bronchoscopy were directly compared, acellular BAL contained fewer bacterial species (p < 0.05); whole and acellular BAL similarity was positively associated with evidence of infection and negatively associated with relative abundance of several prominent taxa (p < 0.001). Acellular BAL contained decreased relative abundance of Prevotella spp. (p < 0.05) and Pseudomonas fluorescens (p < 0.05). Conclusions We present a novel methodological and analytical approach to the localization of lung microbiota and show that prominent members of the lung microbiome are cell-associated, potentially via biofilms, cell adhesion, or intracellularity.http://deepblue.lib.umich.edu/bitstream/2027.42/111056/1/40168_2014_Article_75.pd

Dynamic object detection, tracking and counting in video streams for multimedia mining

Video Segmentation is one of the most challenging areas in Multimedia Mining. It deals with identifying an object of interest. It has wide application in the fields like Traffic surveillance, Security, Criminology etc. This paper initially proposes a technique for identifying a moving object in a video clip of stationary background for real time content based multimedia communication systems and discusses one application like traffic surveillance. We present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. Dynamic objects are identified using both background elimination and background registration techniques. Post processing techniques are applied to reduce the noise. The background elimination method uses concept of least squares to compare the accuracies of the current algorithm with the already existing algorithms. The background registration method uses background subtraction which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments