MNPmApp: An image analysis tool to quantify mononuclear phagocyte distribution in mucosal tissuesa, b

Mononuclear phagocytes (MNPs) such as dendritic cells and macrophages perform key sentinel functions in mucosal tissues and are responsible for inducing and maintaining adaptive immune responses to mucosal pathogens. Positioning of MNPs at the mucosal epithelial interface facilitates their access to luminally-derived antigens and may regulate MNP function through soluble mediators or surface receptor interactions. Therefore, accurately quantifying the distribution of MNPs within mucosal tissues as well as their spatial relationship with other cells is important to infer functional cellular interactions in health and disease. In this study, we developed and validated a MATLAB-based tissue cytometry platform, termed “MNP mapping application” (MNPmApp), that performs high throughput analyses of MNP density and distribution in the gastrointestinal mucosa based on digital multicolor fluorescence microscopy images and that integrates a Monte Carlo modeling feature to assess randomness of MNP distribution. MNPmApp identified MNPs in tissue sections of the human gastric mucosa with a specificity of 98.3 ± 1.6% and a sensitivity of 76.4 ± 15.1%. Monte Carlo modeling revealed that mean MNP-MNP distances were significantly lower than anticipated based on random cell placement, whereas MNP-epithelial distances did not significantly differ from those of randomly placed cells. Interestingly, H. pylori infection had no significant impact on MNP density or distribution with regards to MNP-epithelial distances or MNP-MNP distances in gastric tissue. Overall, our analysis demonstrates that MNPmApp is a useful tool for unbiased quantitation of MNPs and their distribution at mucosal sites.

A New Physical Design Flow for a Selective State Retention Based Approach

This research presents a novel approach for physical design implementation aimed for a System on Chip (SoC) based on Selective State Retention techniques. Leakage current has become a dominant factor in Very Large Scale Integration (VLSI) design. Power Gating (PG) techniques were first developed to mitigate these leakage currents, but they result in longer SoC wake-up periods due to loss of state. The common State Retention Power Gating (SRPG) approach was developed to overcome the PG technique’s loss of state drawback. However, SRPG resulted in a costly expense of die area overhead due to the additional state retention logic required to keep the design state when power is gated. Moreover, the physical design implementation of SRPG presents additional wiring due to the extra power supply network and power-gating controls for the state retention logic. This results in increased implementation complexity for the physical design tools, and therefore increases runtime and limits the ability to handle large designs. Recently published works on Selective State Retention Power Gating (SSRPG) techniques allow reducing the total amount of retention logic and their leakage currents. Although the SSRPG approach mitigates the overhead area and power limitations of the conventional SRPG technique, still both SRPG and SSRPG approaches require a similar extra power grid network for the retention cells, and the effect of the selective approach on the complexity of the physical design has not been yet investigated. Therefore, this paper introduces further analysis of the physical design flow for the SSRPG design, which is required for optimal cell placement and power grid allocation. This significantly increases the potential routing area, which directly improves the convergence time of the Place and Route tools.

Scaffold modelling captures the structure-function-dynamics relationship in brain microcircuits

Modelling brain networks with complex configuration and cellular properties requires a set of neuroinformatic tools and an organized staged workflow. We have therefore developed the Brain Scaffold Builder (BSB), a new modeling framework embedding multiple strategies for cell placement and connectivity and a flexible management of cellular and network mechanisms. With BSB, for the first time, the mouse cerebellar cortex was reconstructed and simulated at cellular resolution, using morphologically realistic multi-compartmental single-neuron models. Embedded connection rules allowed BSB to generate the cerebellar connectome, unifying a collection of scattered experimental data into a coherent construct. Naturalistic background and sensory-burst stimulation were used for functional validation against recordings in vivo, monitoring the impact of subcellular mechanisms on signal propagation and spatio-temporal processing and providing a new ground-truth about circuit organization for the prediction of neural dynamics.

Local Search with a SAT Oracle for Combinatorial Optimization

NP-hard combinatorial optimization problems are pivotal in science and business. There exists a variety of approaches for solving such problems, but for problems with complex constraints and objective functions, local search algorithms scale the best. Such algorithms usually assume that finding a non-optimal solution with no other requirements is easy. However, what if it is NP-hard? In such case, a SAT solver can be used for finding the initial solution, but how can one continue solving the optimization problem? We offer a generic methodology, called Local Search with SAT Oracle (), to solve such problems. facilitates implementation of advanced local search methods, such as variable neighbourhood search, hill climbing and iterated local search, while using a SAT solver as an oracle. We have successfully applied our approach to solve a critical industrial problem of cell placement and productized our solution at Intel.

Design and Simulation of Optimal Number of Small Cells Deployment in Fourth-Generation Cellular Networks

In next-generation of wireless communication systems, Fifth-Generation (5G), small cells deployment is one of the most important issues that must be taking in the account. This paper discusses this issue in three aspects. First, it aims to derive the Critical Handover Location (CHL) point for neighbouring wireless stations which in turn is considered an entrance to the second aspect of this work that decides the small cell placement in one network. Finally, the work proposed a new approach to evaluating the Number of Small Cells (NRS) deployment mathematically. The proposed approach provides the balance in resources allocation in the network in terms of transmitted power of each small cell and their placement in order to provide maximum capacity and coverage area with a lower level of interference between nearest wireless stations thus decreasing the total cost of network insulation.