Ectopic expression of the oncogenic transcription factor HoxA9 is a major cause of acute myeloid leukemia (AML). Here, we demonstrate that HoxA9 is a specific substrate of granule proteases. Protease knockout allowed the comprehensive determination of genome-wide HoxA9 binding sites by chromatin immunoprecipitation sequencing in primary murine cells and a human AML cell line. The kinetics of enhancer activity and transcription rates in response to alterations of an inducible HoxA9 were determined. This permitted identification of HoxA9-controlled enhancers and promoters, allocation to their respective transcription units, and discrimination against HoxA9-bound, but unresponsive, elements. HoxA9 triggered an elaborate positive-feedback loop that drove expression of the complete Hox-A locus. In addition, it controlled key oncogenic transcription factors Myc and Myb and directly induced the cell cycle regulators Cdk6 and CyclinD1, as well as telomerase, drawing the essential blueprint for perturbation of proliferation by leukemogenic HoxA9 expression.

Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. Objective The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. Methods We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. Results The proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. Conclusions This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.

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Cardiovascular problems are evolving as the chief cause of death worldwide. Heart Rate, Blood Pressure, Respiratory Rate, Oxygen Saturation, Systolic Upstroke Time, Heart Beat duration, Diastolic time, RR intervalare some important physiological parameters that help to monitor our daily health condition. Those parameters are very useful to determine if a person is suffering from any cardiovascular problems or not based on daily data collection and monitoring over a certain period of time and in this context machine learning algorithms will be very helpful for developing a smart cardiovascular tele-monitoring & recommendation system for better lifestyle. Irregularities in the heart signal can pop up a serious indication for upcoming cardiac problem. Here, we have concentrated on intensity variation based heart rate calculation process from PPG with major analysis on captured contact video. Here we have used normal handy smart phone camera which is available to everyone and able to capture fingertip videos of flowing blood in the vessels with visible light wavelength. In this paper, we have performed analysis on captured videos for accurate health parameter capturing and compared it with standard devices (FDA approved).

Enterprise Service Bus (ESB) is proposed to address the application integration problem by facilitating communication among different systems in a loosely coupled, standardbased, and protocol independent manner. Data sources are maintained out of the ESB's control and there should be a mechanism to select the most suitable data source among all available data sources. Especially, when two or more data sources are about the same object. For instance, it is normal to use more than one sensor to measure pressure or temperature at a particular point. Data quality can play an important role in selecting data sources in ESB since quality of data is an essential factor in the success of organizations. There is no built-in component in the current ESB platforms to handle data quality. In this paper, we present a flexible and comprehensive data quality framework for managing resources in ESB. The framework is independent of concrete ESB platforms. We evaluate our framework using four different scenarios within the wind energy domain.