Toxics Release Inventory facilities and childhood cancer: Geographic information systems based approach

Purpose. To examine the association between living in proximity to Toxics Release Inventory (TRI) facilities and the incidence of childhood cancer in the State of Texas. ^ Design. This is a secondary data analysis utilizing the publicly available Toxics release inventory (TRI), maintained by the U.S. Environmental protection agency that lists the facilities that release any of the 650 TRI chemicals. Total childhood cancer cases and childhood cancer rate (age 0-14 years) by county, for the years 1995-2003 were used from the Texas cancer registry, available at the Texas department of State Health Services website. Setting: This study was limited to the children population of the State of Texas. ^ Method. Analysis was done using Stata version 9 and SPSS version 15.0. Satscan was used for geographical spatial clustering of childhood cancer cases based on county centroids using the Poisson clustering algorithm which adjusts for population density. Pictorial maps were created using MapInfo professional version 8.0. ^ Results. One hundred and twenty five counties had no TRI facilities in their region, while 129 facilities had at least one TRI facility. An increasing trend for number of facilities and total disposal was observed except for the highest category based on cancer rate quartiles. Linear regression analysis using log transformation for number of facilities and total disposal in predicting cancer rates was computed, however both these variables were not found to be significant predictors. Seven significant geographical spatial clusters of counties for high childhood cancer rates (p<0.05) were indicated. Binomial logistic regression by categorizing the cancer rate in to two groups (<=150 and >150) indicated an odds ratio of 1.58 (CI 1.127, 2.222) for the natural log of number of facilities. ^ Conclusion. We have used a unique methodology by combining GIS and spatial clustering techniques with existing statistical approaches in examining the association between living in proximity to TRI facilities and the incidence of childhood cancer in the State of Texas. Although a concrete association was not indicated, further studies are required examining specific TRI chemicals. Use of this information can enable the researchers and public to identify potential concerns, gain a better understanding of potential risks, and work with industry and government to reduce toxic chemical use, disposal or other releases and the risks associated with them. TRI data, in conjunction with other information, can be used as a starting point in evaluating exposures and risks. ^

Quantitative comparison of late effects following photon versus proton external-beam radiation therapies: Toward an evidence-based approach to selecting a treatment modality

Radiation therapy has been used as an effective treatment for malignancies in pediatric patients. However, in many cases, the side effects of radiation diminish these patients’ quality of life. In order to develop strategies to minimize radiogenic complications, one must first quantitatively estimate pediatric patients’ relative risk for radiogenic late effects, which has not become feasible till recently because of the calculational complexity. The goals of this work were to calculate the dose delivered to tissues and organs in pediatric patients during contemporary photon and proton radiotherapies; to estimate the corresponding risk of radiogenic second cancer and cardiac toxicity based on the calculated doses and on dose-risk models from the literature; to test for the statistical significance of the difference between predicted risks after photon versus proton radiotherapies; and to provide a prototype of an evidence-based approach to selecting treatment modalities for pediatric patients, taking second cancer and cardiac toxicity into account. The results showed that proton therapy confers a lower predicted risk of radiogenic second cancer, and lower risks of radiogenic cardiac toxicities, compared to photon therapy. An uncertainty analysis revealed that the qualitative findings of this study are insensitive to changes in a wide variety of host and treatment related factors.

Building an Evidence-Based Approach to Address Dating Violence Prevention

Purpose: To discuss the approach and recommendations related to the adoption of school based curriculum for violence prevention. Findings: Preliminary assessments suggest that middle and high school youth experience a variety of forms of violence in social and dating relationships. Such experiences have negative academic, behavioral and emotional consequences. Conclusions: The authors have clearly illuminated the need for addressing the phenomenon of dating violence. The field could benefit from more robust evidenced-based investigations that substantiate that interventions have an impact beyond attitudinal changes toward the behavior. Such academic endeavors will provide a platform to validate the inclusion of such information in a school based curriculum as act as a call for action for broad based interventions.

A graph-based approach for latency modeling and optimization in multiview video encoding

We present a novel framework for encoding latency analysis of arbitrary multiview video coding prediction structures. This framework avoids the need to consider an specific encoder architecture for encoding latency analysis by assuming an unlimited processing capacity on the multiview encoder. Under this assumption, only the influence of the prediction structure and the processing times have to be considered, and the encoding latency is solved systematically by means of a graph model. The results obtained with this model are valid for a multiview encoder with sufficient processing capacity and serve as a lower bound otherwise. Furthermore, with the objective of low latency encoder design with low penalty on rate-distortion performance, the graph model allows us to identify the prediction relationships that add higher encoding latency to the encoder. Experimental results for JMVM prediction structures illustrate how low latency prediction structures with a low rate-distortion penalty can be derived in a systematic manner using the new model.

Non-linear gain - look-up table based approach for modeling a family of DC to DC converters based on transient response analysis

The purpose of this work is to propose a structure for simulating power systems using behavioral models of nonlinear DC to DC converters implemented through a look-up table of gains. This structure is specially designed for converters whose output impedance depends on the load current level, e.g. quasi-resonant converters. The proposed model is a generic one whose parameters can be obtained by direct measuring the transient response at different operating points. It also includes optional functionalities for modeling converters with current limitation and current sharing in paralleling characteristics. The pusposed structured also allows including aditional characteristics of the DC to DC converter as the efficency as a function of the input voltage and the output current or overvoltage and undervoltage protections. In addition, this proposed model is valid for overdamped and underdamped situations.

A region based approach to background modeling in a wavelet multi-resolution framework

In the field of detection and monitoring of dynamic objects in quasi-static scenes, background subtraction techniques where background is modeled at pixel-level, although showing very significant limitations, are extensively used. In this work we propose a novel approach to background modeling that operates at region-level in a wavelet based multi-resolution framework. Based on a segmentation of the background, characterization is made for each region independently as a mixture of K Gaussian modes, considering the model of the approximation and detail coefficients at the different wavelet decomposition levels. Background region characterization is updated along time, and the detection of elements of interest is carried out computing the distance between background region models and those of each incoming image in the sequence. The inclusion of the context in the modeling scheme through each region characterization makes the model robust, being able to support not only gradual illumination and long-term changes, but also sudden illumination changes and the presence of strong shadows in the scene

Generating multimedia presentations that summarize the behavior of dynamic systems using a model-based approach

This article describes a knowledge-based method for generating multimedia descriptions that summarize the behavior of dynamic systems. We designed this method for users who monitor the behavior of a dynamic system with the help of sensor networks and make decisions according to prefixed management goals. Our method generates presentations using different modes such as text in natural language, 2D graphics and 3D animations. The method uses a qualitative representation of the dynamic system based on hierarchies of components and causal influences. The method includes an abstraction generator that uses the system representation to find and aggregate relevant data at an appropriate level of abstraction. In addition, the method includes a hierarchical planner to generate a presentation using a model with dis- course patterns. Our method provides an efficient and flexible solution to generate concise and adapted multimedia presentations that summarize thousands of time series. It is general to be adapted to differ- ent dynamic systems with acceptable knowledge acquisition effort by reusing and adapting intuitive rep- resentations. We validated our method and evaluated its practical utility by developing several models for an application that worked in continuous real time operation for more than 1 year, summarizing sen- sor data of a national hydrologic information system in Spain.

And-or parallel prolog: a recomputation based approach

We argüe that in order to exploit both Independent And- and Or-parallelism in Prolog programs there is advantage in recomputing some of the independent goals, as opposed to all their solutions being reused. We present an abstract model, called the Composition-Tree, for representing and-or parallelism in Prolog Programs. The Composition-tree closely mirrors sequential Prolog execution by recomputing some independent goals rather than fully re-using them. We also outline two environment representation techniques for And-Or parallel execution of full Prolog based on the Composition-tree model abstraction. We argüe that these techniques have advantages over earlier proposals for exploiting and-or parallelism in Prolog.

A flexible (C)LP-based approach to the analysis of object-oriented programs

Static analyses of object-oriented programs usually rely on intermediate representations that respect the original semantics while having a more uniform and basic syntax. Most of the work involving object-oriented languages and abstract interpretation usually omits the description of that language or just refers to the Control Flow Graph(CFG) it represents. However, this lack of formalization on one hand results in an absence of assurances regarding the correctness of the transformation and on the other it typically strongly couples the analysis to the source language. In this work we present a framework for analysis of object-oriented languages in which in a first phase we transform the input program into a representation based on Horn clauses. This allows on one hand proving the transformation correct attending to a simple condition and on the other being able to apply an existing analyzer for (constraint) logic programming to automatically derive a safe approximation of the semantics of the original program. The approach is flexible in the sense that the first phase decouples the analyzer from most languagedependent features, and correct because the set of Horn clauses returned by the transformation phase safely approximates the standard semantics of the input program. The resulting analysis is also reasonably scalable due to the use of mature, modular (C)LP-based analyzers. The overall approach allows us to report results for medium-sized programs.

An abstract interpretation-based approach to mobile code safety

Recent approaches to mobile code safety, like proof- arrying code, involve associating safety information to programs. The code supplier provides a program and also includes with it a certifícate (or proof) whose validity entails compliance with a predefined safety policy. The intended benefit is that the program consumer can locally validate the certifícate w.r.t. the "untrusted" program by means of a certifícate checker—a process which should be much simpler, eflicient, and automatic than generating the original proof. We herein introduce a novel approach to mobile code safety which follows a similar scheme, but which is based throughout on the use of abstract interpretation techniques. In our framework the safety policy is specified by using an expressive assertion language defined over abstract domains. We identify a particular slice of the abstract interpretation-based static analysis results which is especially useful as a certifícate. We propose an algorithm for checking the validity of the certifícate on the consumer side which is itself in fact a very simplified and eflicient specialized abstract-interpreter. Our ideas are illustrated through an example implemented in the CiaoPP system. Though further experimentation is still required, we believe the proposed approach is of interest for bringing the automation and expressiveness which is inherent in the abstract interpretation techniques to the área of mobile code safety.

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)

Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.

knOWLearn: a reuse-based approach for building ontologies in a semi-automatic way

In this poster paper we present an overview of knOWLearn, a novel approach for building domain ontologies in a semi-automatic fashion.

Towards a lightweight mobile semantic-based approach for enhancing interaction with smart objects

This work describes a semantic extension for a user-smart object interaction model based on the ECA paradigm (Event-Condition-Action). In this approach, smart objects publish their sensing (event) and action capabilities in the cloud and mobile devices are prepared to retrieve them and act as mediators to configure personalized behaviours for the objects. In this paper, the information handled by this interaction system has been shaped according several semantic models that, together with the integration of an embedded ontological and rule-based reasoner, are exploited in order to (i) automatically detect incompatible ECA rules configurations and to (ii) support complex ECA rules definitions and execution. This semantic extension may significantly improve the management of smart spaces populated with numerous smart objects from mobile personal devices, as it facilitates the configuration of coherent ECA rules.

Dental Implants Success Prediction: Tomography Based and Data Mining Based Approach.

There are a number of factors that contribute to the success of dental implant operations. Among others, is the choice of location in which the prosthetic tooth is to be implanted. This project offers a new approach to analyse jaw tissue for the purpose of selecting suitable locations for teeth implant operations. The application developed takes as input jaw computed tomography stack of slices and trims data outside the jaw area, which is the point of interest. It then reconstructs a three dimensional model of the jaw highlighting points of interest on the reconstructed model. On another hand, data mining techniques have been utilised in order to construct a prediction model based on an information dataset of previous dental implant operations with observed stability values. The goal is to find patterns within the dataset that would help predicting the success likelihood of an implant.