Value: a framework for radiation oncology

In the current health care system, high costs without proportional improvements in quality or outcome have prompted widespread calls for change in how we deliver and pay for care. Value-based health care delivery models have been proposed. Multiple impediments exist to achieving value, including misaligned patient and provider incentives, information asymmetries, convoluted and opaque cost structures, and cultural attitudes toward cancer treatment. Radiation oncology as a specialty has recently become a focus of the value discussion. Escalating costs secondary to rapidly evolving technologies, safety breaches, and variable, nonstandardized structures and processes of delivering care have garnered attention. In response, we present a framework for the value discussion in radiation oncology and identify approaches for attaining value, including economic and structural models, process improvements, outcome measurement, and cost assessment

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy

Pseudorehearsal in value function approximation

Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple problems, given proper initialization of the rehearsal parameters

Adding New Tasks to a Single Network with Weight Transformations using Binary Masks

Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic forgetting issues. Recent work has shown that masking the internal weights of a given original conv-net through learned binary variables is a promising strategy. We build upon this intuition and take into account more elaborated affine transformations of the convolutional weights that include learned binary masks. We show that with our generalization it is possible to achieve significantly higher levels of adaptation to new tasks, enabling the approach to compete with fine tuning strategies by requiring slightly more than 1 bit per network parameter per additional task. Experiments on two popular benchmarks showcase the power of our approach, that achieves the new state of the art on the Visual Decathlon Challenge

Identity dynamics as a barrier to organizational change

This article seeks to explore the construction of group and professional identities in situations of organizational change. It considers empirical material drawn from a health demonstration project funded by the Scottish Executive Health Department, and uses insights from this project to discuss issues that arise from identity construction(s) and organizational change. In the course of the project studied here, a new organizational form was developed which involved a network arrangement with a voluntary sector organization and the employment of “lay-workers” in what had traditionally been a professional setting. Our analysis of the way actors made sense of their identities reveals that characterizations of both self and other became barriers to the change process. These identity dynamics were significant in determining the way people interpreted and responded to change within this project and which may relate to other change-oriented situations

Nurse Engagement and Contributions to the Clinical and Translational Science Awards Initiative

CTSAs are mandated to follow a multidisciplinary model. Requests for applications direct responsive applications to “integrate clinical and translational science across multiple departments, schools,” listing disciplines in addition to medicine such as engineering, nursing, and public health. This inventory of nurse engagement in CTSAs describes the extent of nursing's CTSA engagement from the perspective of participating nurse scientists within individual CTSAs, including institutional/national contributions and best practices that foster a multidisciplinary model. Of the 50 CTSAs affiliated with a nursing school, 44 responded (88% response rate). Of the ten CTSAs not affiliated with a nursing school, four responded (40% response rate). Overall funding success rates of nurse applicants are: TL1 fellowships 81%, KL2 fellowships 54%, and nurse‐led pilots 58%. At most CTSAs nursing is contributing to the accomplishment of the CTSA mandate. The strongest categories of contribution are community engagement, implementation science, and training. Best practices to enhance multidisciplinary collaboration are: (1) inclusion of multiple disciplines on key committees who meet regularly to guide individual core and overall CTSA strategic planning and implementation; (2) required multidisciplinary co‐mentors (ideally from different schools within the CTSA) on training grants and as co‐investigators on pilot projects; and (3) documentation of multidisciplinary activity in annual reports.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98323/1/cts12020.pd

Spatial heterogeneity enhances and modulates excitability in a mathematical model of the myometrium

The muscular layer of the uterus (myometrium) undergoes profound changes in global excitability prior to parturition. Here, a mathematical model of the myocyte network is developed to investigate the hypothesis that spatial heterogeneity is essential to the transition from local to global excitation which the myometrium undergoes just prior to birth. Each myometrial smooth muscle cell is represented by an element with FitzHugh–Nagumo dynamics. The cells are coupled through resistors that represent gap junctions. Spatial heterogeneity is introduced by means of stochastic variation in coupling strengths, with parameters derived from physiological data. Numerical simulations indicate that even modest increases in the heterogeneity of the system can amplify the ability of locally applied stimuli to elicit global excitation. Moreover, in networks driven by a pacemaker cell, global oscillations of excitation are impeded in fully connected and strongly coupled networks. The ability of a locally stimulated cell or pacemaker cell to excite the network is shown to be strongly dependent on the local spatial correlation structure of the couplings. In summary, spatial heterogeneity is a key factor in enhancing and modulating global excitability

Reinventing College Physics for Biologists: Explicating an epistemological curriculum

The University of Maryland Physics Education Research Group (UMd-PERG) carried out a five-year research project to rethink, observe, and reform introductory algebra-based (college) physics. This class is one of the Maryland Physics Department's large service courses, serving primarily life-science majors. After consultation with biologists, we re-focused the class on helping the students learn to think scientifically -- to build coherence, think in terms of mechanism, and to follow the implications of assumptions. We designed the course to tap into students' productive conceptual and epistemological resources, based on a theoretical framework from research on learning. The reformed class retains its traditional structure in terms of time and instructional personnel, but we modified existing best-practices curricular materials, including Peer Instruction, Interactive Lecture Demonstrations, and Tutorials. We provided class-controlled spaces for student collaboration, which allowed us to observe and record students learning directly. We also scanned all written homework and examinations, and we administered pre-post conceptual and epistemological surveys. The reformed class enhanced the strong gains on pre-post conceptual tests produced by the best-practices materials while obtaining unprecedented pre-post gains on epistemological surveys instead of the traditional losses.Comment: 35 pages including a 15 page appendix of supplementary material