Novel 8-Port Network

The proposed 8-port network which consists of 4-coupler combined in unique way to produce 8-port network. This network with the proper terminations will find many applications in microwave and millimeter wave networks. This proposed 8-port network (with the proper termination) can be used as power combiner divider, six-port reflect meter in order to measure both the amplitude and the phase of the network under test. The proposed 8-port also can be used as phase comparator in a monopulse radar system for determining both the azimuth and the elevation of the target (K. Chang, et al, 1987). In addition, the circuit can be utilized as a 4-way power combiner/divider with the sub-arms isolated from one another. Generally, this 8-port network enjoys a compact structure which contains slots in sections the ground plane and between the coupled lines in order to improve the performance of this networks. Also this circuit can be utilized with proper termination as a network synthesize (G. Matthaei, et al, 1980). In the theoretical analysis of the proposed 8-port network, unique signal flow has been developed for this proposed. The purpose of the 8-port has been fabricated on Roger substrate of dielectric constant (ɛṛ = 3.38) and (thickness of the substrate is =0.2).

Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms, Version 1.1

The design of complex systems often requires engineers from multiple disciplines (mechanical, electrical, production, and so on) to communicate with each other and exchange system design information. Systems engineering models are a cross-disciplinary foundation for this process, but are not well-integrated with specialized engineering information, leading to redundant and inconsistent system specifications. The software provided here translates system models in the Systems Modeling Language (SysML) to physical interaction and signal flow (also known as lumped-parameter, one-dimensional, or network) models on two simulation platforms used in many engineering domains.

Akt phosphorylates insulin receptor substrate to limit PI3K-mediated PIP3 synthesis

The phosphoinositide 3-kinase (PI3K)-Akt network is tightly controlled by feedback mechanisms that regulate signal flow and ensure signal fidelity. A rapid overshoot in insulin-stimulated recruitment of Akt to the plasma membrane has previously been reported, which is indicative of negative feedback operating on acute timescales. Here, we show that Akt itself engages this negative feedback by phosphorylating insulin receptor substrate (IRS) 1 and 2 on a number of residues. Phosphorylation results in the depletion of plasma membrane-localised IRS1/2, reducing the pool available for interaction with the insulin receptor. Together these events limit plasma membrane-associated PI3K and phosphatidylinositol (3,4,5)-trisphosphate (PIP3) synthesis. We identified two Akt-dependent phosphorylation sites in IRS2 at S306 (S303 in mouse) and S577 (S573 in mouse) that are key drivers of this negative feedback. These findings establish a novel mechanism by which the kinase Akt acutely controls PIP3 abundance, through post-translational modification of the IRS scaffold.

Analysis of Liquid Crystal Tunable Thin-Film Optical Filters Using Signal Flow Graph Technique

In this paper, a liquid crystal tunable thin-film optical bandpass filter is studied and analyzed using the signal flow graph technique. This paper investigates an exact form for calculating the transmission coefficients, reflection coefficients, and the transmission intensity of the filter. The simulation results show the filter performance and the channel shape profile. In addition, the results show the tuning capability of the filter. The signal flow graph technique provides an attractive method for analyzing the thin-film optical filters since it overcomes the difficulty of the refractive index concept in extending to optical applications. Moreover, it simplifies the filter analysis and design process.

Identifying molecular targets for reverse aging using integrated network analysis of transcriptomic and epigenomic changes during aging

Aging is associated with widespread physiological changes, including skeletal muscle weakening, neuron system degeneration, hair loss, and skin wrinkling. Previous studies have identified numerous molecular biomarkers involved in these changes, but their regulatory mechanisms and functional repercussions remain elusive. In this study, we conducted next-generation sequencing of DNA methylation and RNA sequencing of blood samples from 51 healthy adults between 20 and 74 years of age and identified aging-related epigenetic and transcriptomic biomarkers. We also identified candidate molecular targets that can reversely regulate the transcriptomic biomarkers of aging by reconstructing a gene regulatory network model and performing signal flow analysis. For validation, we screened public experimental data including gene expression profiles in response to thousands of chemical perturbagens. Despite insufficient data on the binding targets of perturbagens and their modes of action, curcumin, which reversely regulated the biomarkers in the experimental dataset, was found to bind and inhibit JUN, which was identified as a candidate target via signal flow analysis. Collectively, our results demonstrate the utility of a network model for integrative analysis of omics data, which can help elucidate inter-omics regulatory mechanisms and develop therapeutic strategies against aging.

Robustness of Collective Scenting in the Presence of Physical Obstacles

Honey bees (Apis mellifera L.) aggregate around the queen by collectively organizing a communication network to propagate volatile pheromone signals. Our previous study shows that individual bees “scent” to emit pheromones and fan their wings to direct the signal flow, creating an efficient search and aggregation process. In this work, we introduce environmental stressors in the form of physical obstacles that partially block pheromone signals and prevent a wide open path to the queen. We employ machine learning methods to extract data from the experimental recordings, and show that in the presence of an obstacle that blocks most of the path to the queen, the bees need more time but can still effectively employ the collective scenting strategy to overcome the obstacle and aggregate around the queen. Further, we increase the complexity of the environment by presenting the bees with a maze to navigate to the queen. The bees require more time and exploration to form a more populated communication network. Overall, we show that given volatile pheromone signals and only local communication, the bees can collectively solve the swarming process in a complex unstructured environment with physical obstacles.

Probability-based mechanisms in biological networks with parameter uncertainty

Mathematical models of biomolecular networks are commonly used to study mechanisms of cellular processes, but their usefulness is often questioned due to parameter uncertainty. Here, we employ Bayesian parameter inference and dynamic network analysis to study dominant reaction fluxes in models of extrinsic apoptosis. Although a simplified model yields thousands of parameter vectors with equally good fits to data, execution modes based on reaction fluxes clusters to three dominant execution modes. A larger model with increased parameter uncertainty shows that signal flow is constrained to eleven execution modes that use 53 out of 2067 possible signal subnetworks. Each execution mode exhibits different behaviors to in silico perturbations, due to different signal execution mechanisms. Machine learning identifies informative parameters to guide experimental validation. Our work introduces a probability-based paradigm of signaling mechanisms, highlights systems-level interactions that modulate signal flow, and provides a methodology to understand mechanistic model predictions with uncertain parameters.