Familial aggregation, candidate genes and genome scans: Analyzing the role of genetics in stroke

The genetic etiology of stroke likely reflects the influence of multiple loci with small effects, each modulating different pathophysiological processes. This research project utilized three analytical strategies to address the paucity of information related to the identification and characterization of genetic variation associated with stroke in the general population. ^ First, the general contribution of familial factors to stroke susceptibility was evaluated in a population-based sample of unrelated individuals. Increased risk of subclinical cerebral infarction was observed among individuals with a positive parental history of stroke. This association did not appear to be mediated by established stroke risk factors, specifically blood pressure levels or hypertension status. ^ The need to identify specific gene variation associated with stroke in the general population was addressed by evaluating seven candidate gene polymorphisms in a population-based sample of unrelated individuals. Three polymorphisms were significantly associated with increased subclinical cerebral infarction or incident clinical ischemic stroke risk. These relationships include the G-protein β3 subunit 825C/T polymorphism and clinical stroke in Whites, the lipoprotein lipase S/X447 polymorphism and subclinical and clinical stroke in men, and the angiotensin I-converting enzyme Ins/Del polymorphism and subclinical stroke in White men. These associations did not appear to be obfuscated by the stroke risk factors adjusted for in the analysis models specifically blood pressure levels or anti-hypertensive medication use. ^ The final research strategy considered, on a genome-wide scale, the idea that genetic variation may contribute to the occurrence of hypertension or stroke through a common etiologic pathway. Genomic regions were identified for which significant evidence of heterogeneity was observed among hypertensive sibpairs stratified by family history of stroke information. Regions identified on chromosome 15 in African Americans, and chromosome 13 in Whites and African Americans, suggest the presence of genes influencing hypertension and stroke susceptibility. ^ Insight into the role of genetics in stroke is useful for the potential early identification of individuals at increased risk for stroke and improved understanding of the etiology of the disease. The ultimate goal of these endeavors is to guide the development of therapeutic intervention and informed prevention to provide a lasting and positive impact on public health. ^

Forecasting and Analyzing Economic Activity with Coincident and Leading Indexes: The Case of Connecticut

We develop coincident and leading employment indexes for the Connecticut economy. Four employment-related variables enter the coincident index while five employment-related variables enter the leading index. The peaks and troughs in the leading index lead the peaks and troughs in the coincident index by an average of 3 and 9 months. Finally, we use the leading index in vector-autoregressive (VAR) and Bayesian vector-autoregressive (BVAR) models to forecast the coincident index, nonfarm employment, and the unemployment rate.

Analyzing the function of myogenin in adult satellite cells through the construction of a myogenin conditional allele

Although mechanisms regulating the formation of embryonic skeletal muscle are well characterized, less is known about muscle formation in postnatal life. This disparity is unfortunate because the largest increases in skeletal muscle mass occur after birth. Adult muscle stem cells (satellite cells) appear to recapitulate the events that occur in embryonic myoblasts. In particular, the myogenic basic helix-loop-helix factors, which have crucial functions in embryonic muscle development, are assumed to have similar roles in postnatal muscle formation. Here, I test this assumption by determining the role of the myogenic regulator myogenin in postnatal life. Myogenin-null mice die at birth, necessitating the generation of floxed alleles of myogenin and the use of cre-recombinase lines to delete myogenin. Removing myogenin before embryonic muscle development resulted in myofiber deficiencies identical to those observed in myogenin-null mice. However, mice in which myogenin was deleted following embryonic muscle development had normal skeletal muscle, except for modest alterations in MRF4 and MyoD expression. Notably, myogenin-deleted mice were 30% smaller than controls, suggesting that myogenin's absence disrupted general body growth. These results suggest that skeletal muscle growth in postnatal life is controlled by mechanisms distinct from those occurring in embryonic muscle development. ^

Analyzing the effectiveness of Kinderworld's food program on lowering & controlling childhood obesity

This is an analysis of previously collected data at Kinderworld Child Care and Early Learning Center, as part of an evaluation done to determine the program's effectiveness in reducing/controlling childhood obesity. Kinderworld is a private, for-profit organization that provides healthy, nutritious meals and snacks to children under the guidelines of the Child and Adult Care Food Program, and is reimbursed by the Texas Department of Agriculture on a fixed price-per-meal basis. The primary goal of the program is to reduce childhood obesity. Previous studies have shown a link between obesity and diet. Other, similar programs have shown success in reducing obesity among children with a healthy diet and exercise. The results from the outcome evaluation indicated that time spent in the center was positively related to higher proportions of healthy-weight children, and inversely related to BMI levels. ^

Analyzing left-truncated right-censored data with uncertain onset time with parametric models

Prevalent sampling is an efficient and focused approach to the study of the natural history of disease. Right-censored time-to-event data observed from prospective prevalent cohort studies are often subject to left-truncated sampling. Left-truncated samples are not randomly selected from the population of interest and have a selection bias. Extensive studies have focused on estimating the unbiased distribution given left-truncated samples. However, in many applications, the exact date of disease onset was not observed. For example, in an HIV infection study, the exact HIV infection time is not observable. However, it is known that the HIV infection date occurred between two observable dates. Meeting these challenges motivated our study. We propose parametric models to estimate the unbiased distribution of left-truncated, right-censored time-to-event data with uncertain onset times. We first consider data from a length-biased sampling, a specific case in left-truncated samplings. Then we extend the proposed method to general left-truncated sampling. With a parametric model, we construct the full likelihood, given a biased sample with unobservable onset of disease. The parameters are estimated through the maximization of the constructed likelihood by adjusting the selection bias and unobservable exact onset. Simulations are conducted to evaluate the finite sample performance of the proposed methods. We apply the proposed method to an HIV infection study, estimating the unbiased survival function and covariance coefficients. ^

Capability Matrix : A Framework for Analyzing Capabilities in Value Chains

This paper develops a Capability Matrix for analyzing capabilities of developing country firms that participate in global and national value chains. This is a generic framework to capture firm-level knowledge accumulation in the context of global and local industrial constellations, by integrating key elements of the global value chain (GVC) and technological capabilities (TC) approaches. The framework can visually portray characteristics of firms’ capabilities, and highlight a relatively overlooked factor in the GVC approach: local firms’ endogenous learning efforts in varieties of relationship with lead firms.

Analyzing Business Models for Mobile Augmented Reality

Nowadays, a wide offer of mobile augmented reality (mAR) applications is available at the market, and the user base of mobile AR-capable devices -smartphones- is rapidly increasing. Nevertheless, likewise to what happens in other mobile segments, business models to put mAR in value are not clearly defined yet. In this paper, we focus on sketching the big picture of the commercial offer of mAR applications, in order to inspire a posterior analysis of business models that may successfully support the evolution of mAR. We have gathered more than 400 mAR applications from Android Market, and analyzed the offer as a whole, taking into account some technology aspects, pricing schemes and user adoption factors. Results show, for example, that application providers are not expecting to generate revenues per direct download, although they are producing high-quality applications, well rated by the users.

Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech

We present a novel approach using both sustained vowels and connected speech, to detect obstructive sleep apnea (OSA) cases within a homogeneous group of speakers. The proposed scheme is based on state-of-the-art GMM-based classifiers, and acknowledges specifically the way in which acoustic models are trained on standard databases, as well as the complexity of the resulting models and their adaptation to specific data. Our experimental database contains a suitable number of utterances and sustained speech from healthy (i.e control) and OSA Spanish speakers. Finally, a 25.1% relative reduction in classification error is achieved when fusing continuous and sustained speech classifiers. Index Terms: obstructive sleep apnea (OSA), gaussian mixture models (GMMs), background model (BM), classifier fusion.

Analyzing Query Success and User Context

This paper describes the participation of DAEDALUS at the LogCLEF lab in CLEF 2011. This year, the objectives of our participation are twofold. The first topic is to analyze if there is any measurable effect on the success of the search queries if the native language and the interface language chosen by the user are different. The idea is to determine if this difference may condition the way in which the user interacts with the search application. The second topic is to analyze the user context and his/her interaction with the system in the case of successful queries, to discover out any relation among the user native language, the language of the resource involved and the interaction strategy adopted by the user to find out such resource. Only 6.89% of queries are successful out of the 628,607 queries in the 320,001 sessions with at least one search query in the log. The main conclusion that can be drawn is that, in general for all languages, whether the native language matches the interface language or not does not seem to affect the success rate of the search queries. On the other hand, the analysis of the strategy adopted by users when looking for a particular resource shows that people tend to use the simple search tool, frequently first running short queries build up of just one specific term and then browsing through the results to locate the expected resource

Analyzing logic programs with dynamic scheduling

Traditional logic programming languages, such as Prolog, use a fixed left-to-right atom scheduling rule. Recent logic programming languages, however, usually provide more flexible scheduling in which computation generally proceeds leftto- right but in which some calis are dynamically "delayed" until their arguments are sufRciently instantiated to allow the cali to run efficiently. Such dynamic scheduling has a significant cost. We give a framework for the global analysis of logic programming languages with dynamic scheduling and show that program analysis based on this framework supports optimizations which remove much of the overhead of dynamic scheduling.