\u3ci\u3eMedicine Meets Virtual Reality 17\u3c/i\u3e

Chapter, A Virtual Reality Training Program for Improvement of Robotic Surgical Skills, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. Chapter, Consistency of Performance of Robot-Assisted Surgical Tasks in Virtual Reality, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. The 17th annual Medicine Meets Virtual Reality (MMVR17) was held January 19-22, 2009, in Long Beach, CA, USA. The conference is well established as a forum for emerging data-centered technologies for medical care and education. Each year, it brings together an international community of computer scientists and engineers, physicians and surgeons, medical educators and students, military medicine specialists and biomedical futurists. MMVR emphasizes inter-disciplinary collaboration in the development of more efficient and effective physician training and patient care. The MMVR17 proceedings collect 108 papers by conference lecture and poster presenters. These papers cover recent developments in biomedical simulation and modeling, visualization and data fusion, haptics, robotics, sensors and other related information-based technologies. Key applications include medical education and surgical training, clinical diagnosis and therapy, physical rehabilitation, psychological assessment, telemedicine and more. From initial vision and prototypes, through assessment and validation, to clinical and academic utilization and commercialization - MMVR explores the state-of-the-art and looks toward healthcare’s future. The proceedings volume will interest physicians, surgeons and other medical professionals interested in emerging and future tools for diagnosis and therapy; educators responsible for training the next generation of doctors and scientists; IT and medical device engineers creating state-of-the-art and next-generation simulation, imaging, robotics and communication systems; data technologists creating systems for gathering, processing and distributing medical intelligence; military medicine specialists addressing the challenges of warfare and defense health needs; and biomedical futurists and investors who want to understand where the field is headed.https://digitalcommons.unomaha.edu/facultybooks/1233/thumbnail.jp

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Plug & Test at System Level via Testable TLM Primitives

With the evolution of Electronic System Level (ESL) design methodologies, we are experiencing an extensive use of Transaction-Level Modeling (TLM). TLM is a high-level approach to modeling digital systems where details of the communication among modules are separated from the those of the implementation of functional units. This paper represents a first step toward the automatic insertion of testing capabilities at the transaction level by definition of testable TLM primitives. The use of testable TLM primitives should help designers to easily get testable transaction level descriptions implementing what we call a "Plug & Test" design methodology. The proposed approach is intended to work both with hardware and software implementations. In particular, in this paper we will focus on the design of a testable FIFO communication channel to show how designers are given the freedom of trading-off complexity, testability levels, and cos