HD Physiology Project-Japanese efforts to promote multilevel integrative systems biology and physiome research.

The HD Physiology Project is a Japanese research consortium that aimed to develop methods and a computational platform in which physiological and pathological information can be described in high-level definitions across multiple scales of time and size. During the 5 years of this project, an appropriate software platform for multilevel functional simulation was developed and a whole-heart model including pharmacokinetics for the assessment of the proarrhythmic risk of drugs was developed. In this article, we outline the description and scientific strategy of this project and present the achievements and influence on multilevel integrative systems biology and physiome research

Bartering integer commodities with exogenous prices

The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed by Nasini et al. (accepted 2014) to study pure exchange economies where discrete amounts of commodities are exchanged at fixed prices. Barter processes, consisting in sequences of elementary reallocations of couple of commodities among couples of agents, are formalized as local searches converging to equilibrium allocations. A direct application of the analyzed processes in the context of computational economics is provided, along with a Java implementation of the approaches described in this research report.Comment: 30 pages, 5 sections, 10 figures, 3 table

Using numerical plant models and phenotypic correlation space to design achievable ideotypes

Numerical plant models can predict the outcome of plant traits modifications resulting from genetic variations, on plant performance, by simulating physiological processes and their interaction with the environment. Optimization methods complement those models to design ideotypes, i.e. ideal values of a set of plant traits resulting in optimal adaptation for given combinations of environment and management, mainly through the maximization of a performance criteria (e.g. yield, light interception). As use of simulation models gains momentum in plant breeding, numerical experiments must be carefully engineered to provide accurate and attainable results, rooting them in biological reality. Here, we propose a multi-objective optimization formulation that includes a metric of performance, returned by the numerical model, and a metric of feasibility, accounting for correlations between traits based on field observations. We applied this approach to two contrasting models: a process-based crop model of sunflower and a functional-structural plant model of apple trees. In both cases, the method successfully characterized key plant traits and identified a continuum of optimal solutions, ranging from the most feasible to the most efficient. The present study thus provides successful proof of concept for this enhanced modeling approach, which identified paths for desirable trait modification, including direction and intensity.Comment: 25 pages, 5 figures, 2017, Plant, Cell and Environmen

BeWith: A Between-Within Method to Discover Relationships between Cancer Modules via Integrated Analysis of Mutual Exclusivity, Co-occurrence and Functional Interactions

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun in which the relations between modules are enriched with mutual exclusivity while genes within each module are functionally related; (ii) BeME-WithCo which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun which ensures co-occurrence between modules while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to the importance of the interaction between NCOR and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergetic effects, including a potential synergy between mutations in TP53 and metastasis related DCC gene

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

Addressing barriers to learning: In the classroom and schoolwide.

IntroductionPublic education is at a crossroads. Moving in new directions is imperative. Just tweaking and tinkering with old ideas is a recipe for disaster.Continuing challenges confronting public education highlight why moving school improvement policy and practice in new directions is imperative. With a view to enhancing graduation rates and successful transitions to post-secondary opportunities and well-being, pressing challenges include:Increasing equity of opportunity for every student to succeed, narrowing the achievement gap, and countering the school to prison pipeline Reducing unnecessary referrals for special assistance and special education; Improving school climate and retaining good teachers Reducing the number of low performing schools.As education leaders well know, meeting these challenges requires making sustainable progress inimproving supports for specific subgroups (e.g., English Learners, immigrant newcomers, lagging minorities, homeless students, students with disabilities) increasing the number of disconnected students who re-engage in classroom learning and thus improving attendance, reducing disruptive behaviors (e.g., including bullying and sexual harassment), and decreasing suspensions and dropouts increasing family and community engagement with schools responding effectively when schools experience crises events and preventing crises whenever possible.In some schools, continuous progress related to these concerns is being made. For many districts, however, sustainable progress remains elusive – and will continue to be so as long as the focus of school improvement policy and practice is mainly on improving instruction. Efforts to expand the use of instructional technology, develop new curriculum standards, make teachers more accountable, and improve teacher preparation and licensing all have merit; but they are insufficient for addressing the many everyday barriers to learning and teaching that interfere with effective student engagement in classroom instruction.Most policy makers and administrators know that good instruction delivered by highly qualified teachers cannot ensure that all students have an equal opportunity to succeed at school.Even the best teacher can’t do the job alone. Teachers need student and learning supports in the classroom and schoolwide in order to personalize instruction and provide special assistance when students manifest learning, behavior, and emotional problems. Unfortunately, school improvement plans continue to give short shrift to these critical matters.We recognize, as did a Carnegie Task Force on Education, that school systems are not responsible for meeting every need of their students. But as the task force stressed: when the need directly affects learning, the school must meet the challenge.The most pressing challenge is to enhance equity of opportunity by fundamentally improving how schools address barriers to learning and teaching. The future of public education depends on moving in new directions to accomplish this.Now is the time to fundamentally transform how schools address factors that keep too many students from doing well at school. And while transformation is never easy, pioneering work across the country is showing the way. Trailblazers are redeploying existing funds allocated for addressing barriers to learning and weaving these together with the invaluable resources that can be garnered by collaboration with other agencies and with community stakeholders, family members, and students themselves.The first step in moving forward is to escape old ideas. The second step is to incorporate a new vision in school improvement planning for addressing barriers to learning and teaching and re-engaging disconnected students. Our analyses envision a plan that designs and develops a unified, comprehensive, and equitable system of student and learning supports. The third step is to develop a strategic plan for systemic change, scale-up, and sustainability.This book highlights each of these matters. We invite you to join us in the quest to enhance equity of opportunity for all students to succeed at school and beyond. And we look forward to hearing from you about moving schools forward to make the rhetoric of the Every Student Succeeds Act a reality

Implementation of a fixing strategy and parallelization in a recent global optimization method

Electromagnetism-like Mechanism (EM) heuristic is a population-based stochastic global optimization method inspired by the attraction-repulsion mechanism of the electromagnetism theory. EM was originally proposed for solving continuous global optimization problems with bound constraints and it has been shown that the algorithm performs quite well compared to some other global optimization methods. In this work, we propose two extensions to improve the performance of the original algorithm: First, we introduce a fixing strategy that provides a mechanism for not being trapped in local minima, and thus, improves the effectiveness of the search. Second, we use the proposed fixing strategy to parallelize the algorithm and utilize a cooperative parallel search on the solution space. We then evaluate the performance of our study under three criteria: the quality of the solutions, the number of function evaluations and the number of local minima obtained. Test problems are generated by an algorithm suggested in the literature that builds test problems with varying degrees of difficulty. Finally, we benchmark our results with that of the Knitro solver with the multistart option set