Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

PERVASIVE computing 75 80 PERVASIVE computing Presentation Input sensor Coordinator Model Figure 4. Application framework infrastructure. The coordinator oversees the composition of the model, presentation, and controller components.82 PERVASIVE computing PERVASIVE computing 83 the AUTHORS Manuel Román is a PhD candidate at the University of Illinois at Urbana-Champaign. His research interests include ubiquitous computing, middleware, operating systems, and interactive and programmable active spaces. He received his BS and MS in computer science from the La Salle School of Engineering (Ramon Llull Univ.).

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases, hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth Type 2 (CMT2). In contrast, ALS associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss of function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.

Abstract. Ubiquitous Computing advocates the construction of massively distributed systems that help transform physical spaces into computationally active and intelligent environments. The design of systems and applications in these environments needs to take account of heterogeneous devices, mobile users and rapidly changing contexts. Most importantly, agents in ubiquitous and mobile environments need to be context-aware so that they can adapt themselves to different situations. In this paper, we argue that ubiquitous computing environments must provide middleware support for context-awareness. We also propose a middleware that facilitates the development of context-aware agents. The middleware allows agents to acquire contextual information easily, reason about it using different logics and then adapt themselves to changing contexts. Another key issue in these environments is allowing autonomous, heterogeneous agents to have a common semantic understanding of contextual information. Our middleware tackles this problem by using ontologies to define different types of contextual information. This middleware is part of Gaia, our infrastructure for enabling Smart Spaces.

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R ecent advances in distributed, mobile, and ubiquitous systems demand new computing environments characterized by a high degree of dynamism. Variations in resource availability, network connectivity, and hardware and software platforms influence the performance of the related user applications. The expected growth of ubiquitous computing over the next five years will further alter the nature of the computational infrastructure, bringing a plethora of small devices and requiring customized protocols and policies to fulfill users' evolving quality of service (QoS) requirements.During the past 10 years, software developers created middleware technology to facilitate development of software systems, most notably distributed and Internet-based, to support activities as diverse as scientific computation, information discovery and dissemination, and e-commerce. Middleware mediates interaction between the application and the operating system (hence its name). Related technologies, including the Object Management Group's CORBA, Sun Microsystems' Java-based J2EE, and Microsoft's .NET, hide from the pro-grammer the complicated details of network communication, remote method invocation, naming, and service instantiation, easing construction of complex distributed systems. CORBA, or Common Object Request Broker Architecture, and Java also hide the differences in the underlying software and hardware platforms, increasing portability and facilitating maintenance, as new versions of operating systems are released. Despite aiding development of distributed applications, conventional middleware technology lacks support for the dynamic aspects of the new computational infrastructure. Next-generation applications require middleware that can be adapted to changes in the environment and customized to fit into devices, from PDAs and sensors to powerful desktops and multicomputers [1,5]. Here, we reflect on how two independent research projects might influence the evolution of next-generation middleware. The reflective middleware model is a principled and efficient way of dealing with highly dynamic environments yet supports development of flexible and adaptive systems and applications.It's flexible and reconfigurable yet simple for programmers to use, notably for building dynamic distributed applications operating on the Net.

Background Unplanned readmission of a hospitalized patient is an indicator of patients’ exposure to risk and an avoidable waste of medical resources. In addition to hospital readmission, intensive care unit (ICU) readmission brings further financial risk, along with morbidity and mortality risks. Identification of high-risk patients who are likely to be readmitted can provide significant benefits for both patients and medical providers. The emergence of machine learning solutions to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities for developing an efficient discharge decision-making support system for physicians and ICU specialists. Methods and findings We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We incorporate multiple types of features including chart events, demographic, and ICD-9 embeddings. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able to incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate). We show that our LSTM-based solution can better capture high volatility and unstable status in ICU patients, an important factor in ICU readmission. Our machine learning models identify ICU readmissions at a higher sensitivity rate of 0.742 (95% CI, 0.718–0.766) and an improved Area Under the Curve of 0.791 (95% CI, 0.782–0.800) compared with traditional methods. We perform in-depth deep learning performance analysis, as well as the analysis of each feature contribution to the predictive model. Conclusion Our manuscript highlights the ability of machine learning models to improve our ICU decision-making accuracy and is a real-world example of precision medicine in hospitals. These data-driven solutions hold the potential for substantial clinical impact by augmenting clinical decision-making for physicians and ICU specialists. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.