Insulin release: synchronizing beta cells in the pancreas

The secretion of insulin from the pancreas relies on both gap junctions and subpopulations of beta cells with specific intrinsic properties

Software reliability cases: the bridge between hardware, software and system safety and reliability

High integrity/high consequence systems must be safe and reliable; hence it is only logical that both software safety and software reliability cases should be developed. Risk assessments in safety cases evaluate the severity of the consequences of a hazard and the likelihood of it occurring. The likelihood is directly related to system and software reliability predictions. Software reliability cases, as promoted by SAE JA 1002 and 1003, provide a practical approach to bridge the gap between hardware reliability, software reliability, and system safety and reliability by using a common methodology and information structure. They also facilitate early insight into whether or not a project is on track for meeting stated safety and reliability goals, while facilitating an informed assessment by regulatory and/or contractual authorities

Software Reliability Cases: The Bridge Between Hardware, Software and System Safety and Reliability

High integrity/high consequence systems must be safe and reliable; hence it is only logical that both software safety and software reliability cases should be developed. Risk assessments in safety cases evaluate the severity of the consequences of a hazard and the likelihood of it occurring. The likelihood is directly related to system and software reliability predictions. Software reliability cases, as promoted by SAE JA 1002 and 1003, provide a practical approach to bridge the gap between hardware reliability, software reliability, and system safety and reliability by using a common methodology and information structure. They also facilitate early insight into whether or not a project is on track for meeting stated safety and reliability goals, while facilitating an informed assessment by regulatory and/or contractual authorities

Engineered swift equilibration of a Brownian particle

A fundamental and intrinsic property of any device or natural system is its relaxation time relax, which is the time it takes to return to equilibrium after the sudden change of a control parameter [1]. Reducing $tau$ relax , is frequently necessary, and is often obtained by a complex feedback process. To overcome the limitations of such an approach, alternative methods based on driving have been recently demonstrated [2, 3], for isolated quantum and classical systems [4--9]. Their extension to open systems in contact with a thermostat is a stumbling block for applications. Here, we design a protocol,named Engineered Swift Equilibration (ESE), that shortcuts time-consuming relaxations, and we apply it to a Brownian particle trapped in an optical potential whose properties can be controlled in time. We implement the process experimentally, showing that it allows the system to reach equilibrium times faster than the natural equilibration rate. We also estimate the increase of the dissipated energy needed to get such a time reduction. The method paves the way for applications in micro and nano devices, where the reduction of operation time represents as substantial a challenge as miniaturization [10]. The concepts of equilibrium and of transformations from an equilibrium state to another, are cornerstones of thermodynamics. A textbook illustration is provided by the expansion of a gas, starting at equilibrium and expanding to reach a new equilibrium in a larger vessel. This operation can be performed either very slowly by a piston, without dissipating energy into the environment, or alternatively quickly, letting the piston freely move to reach the new volume

Synchronizing beta cells in the pancreas

The secretion of insulin from the pancreas relies on both gap junctions and subpopulations of beta cells with specific intrinsic properties

Risk Management Plan

This document describes a proactive plan for assessing and controlling sources of risk for the ASCI (Accelerated Strategic Computing Initiative) V&V program at Sandia National Laboratories. It offers a graded approach for identifying, analyzing, prioritizing, responding to, and monitoring risks

A Mathematical Model of Collective Cell Migration in a Three-Dimensional, Heterogeneous Environment

<div><p>Cell migration is essential in animal development, homeostasis, and disease progression, but many questions remain unanswered about how this process is controlled. While many kinds of individual cell movements have been characterized, less effort has been directed towards understanding how clusters of cells migrate collectively through heterogeneous, cellular environments. To explore this, we have focused on the migration of the border cells during Drosophila egg development. In this case, a cluster of different cell types coalesce and traverse as a group between large cells, called nurse cells, in the center of the egg chamber. We have developed a new model for this collective cell migration based on the forces of adhesion, repulsion, migration and stochastic fluctuation to generate the movement of discrete cells. We implement the model using Identical Math Cells, or IMCs. IMCs can each represent one biological cell of the system, or can be aggregated using increased adhesion forces to model the dynamics of larger biological cells. The domain of interest is filled with IMCs, each assigned specific biophysical properties to mimic a diversity of cell types. Using this system, we have successfully simulated the migration of the border cell cluster through an environment filled with larger cells, which represent nurse cells. Interestingly, our simulations suggest that the forces utilized in this model are sufficient to produce behaviors of the cluster that are observed <i>in vivo</i>, such as rotation. Our framework was developed to capture a heterogeneous cell population, and our implementation strategy allows for diverse, but precise, initial position specification over a three- dimensional domain. Therefore, we believe that this model will be useful for not only examining aspects of <i>Drosophila</i> oogenesis, but also for modeling other two or three-dimensional systems that have multiple cell types and where investigating the forces between cells is of interest.</p></div

Simulating the three dimensional model results in collective migration.

<p>A simulation showing six border cells (green), two polar cells (red), the epithelium (transparent green), and the surface of the oocyte (black, right) at three time points during the migration. Fifteen nurse cells are situated inside the egg chamber, but are not plotted so as to maintain clarity of this three dimensional structure. Polar cells are surrounded by border cells, making them hard to distinguish. (A) At 2 minutes, cells are beginning to invade between nurse cells. (B) At 2.4 hours, the cluster is about halfway to its destination. (C) At 5.6 hours, the border cell cluster has reached the edge of the oocyte. See also Supplemental <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122799#pone.0122799.s003" target="_blank">S3 Movie</a>.</p

Polar cell positions along main axis of migration.

<p>(A) The distance of the polar cells from the anterior of the egg chamber versus time. (B) The relative positions of the two polar cells to one another, along the axis that runs from anterior to posterior through the egg chamber. Each line corresponds to one of the polar cells. As the cluster moves forward, we observe that the polar cells are changing position with respect to one another along this axis, including a complete switch at 0.8 hours. This simulation modeled six border cells.</p