Evolution and emergence: higher order information structure in protein interactomes across the tree of life

The internal workings of biological systems are notoriously difficult to understand. Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein–protein interactome networks remain black boxes. One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. We analyzed the protein interactomes of over 1800 species, containing in total 8 782 166 protein–protein interactions, at different scales. We show the emergence of higher order ‘macroscales’ in these interactomes and that these biological macroscales are associated with lower noise and degeneracy and therefore lower uncertainty. Moreover, the nodes in the interactomes that make up the macroscale are more resilient compared with nodes that do not participate in the macroscale. These effects are more pronounced in interactomes of eukaryota, as compared with prokaryota; these results hold even after sensitivity tests where we recalculate the emergent macroscales under network simulations where we add different edge weights to the interactomes. This points to plausible evolutionary adaptation for macroscales: biological networks evolve informative macroscales to gain benefits of both being uncertain at lower scales to boost their resilience, and also being ‘certain’ at higher scales to increase their effectiveness at information transmission. Our work explains some of the difficulty in understanding the workings of biological networks, since they are often most informative at a hidden higher scale, and demonstrates the tools to make these informative higher scales explicit.

Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

Wealth Inequality, Network Topology and Financial Instability

This paper asks if two, otherwise identical, economies were distinguished only by their distributions of wealth, are they equally stable in response to a random shock? A theoretical financial network model is proposed to understand the relationship between wealth inequality and financial crises. In a financial network, financial assets link individual asset and liability holders to form a web of economic connections. The total connectivity of an individual is described by their degree, and the overall distribution of connections in the network is imposed through a degree distribution--equivalent to the wealth distribution as incoming connections represent assets and outgoing connections liabilities. A network's topology varies with the level of wealth inequality and total wealth and together, simulations show, they determine network contagion in the event of a random negative income shock to some individual. Random network simulations, whereby each financial connection is randomly placed, reveal that increasing wealth inequality makes a wealthy network less stable--as measured by the share of individuals failing financially or the decline in financial asset values. These results suggest a unique architectural role for accumulated assets and their distribution in macro-financial stability.

Design and Simulation of Smart Communication System for Unmanned Arial Vehicles

In recent years, the use of drones has drastically increased as the evolution of drone use in commercial sectors and reduced costs of the hardware. Earlier drone services were mostly used for military operations but nowadays the Unmanned Arial Vehicles (UAV) system is very advanced and its applications are not limited to military operations. The recent years have also witnessed a network evolution of UAVs from single ground to air network to multi-UAV network systems along with usage of wireless public networks like LTE which can act as UAV communication channel. In the proposed project, a communication system used in the UAS system is simulated to analyze the UAV behavior under different conditions with respect to mission planning and the communication networks used. A comprehensive study is done on communication networks used in controlling UAVs. For a safer approach, the proposed model is simulated using available software instead of hardware implementations. ArduPilot SITL, MAVProxy and Mission Planner are used to simulate the UAV system virtually. Whereas network simulations of Wi-Fi and LTE network are done with the help of NS-3 on a separate platform. Various network parameters like network delay, throughput, etc., are graphically represented.

Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach

Maintenance of solar tower power plants (STPP) is very important to ensure production continuity. However, random and non-optimal maintenance can increase the intervention cost. In this paper, a new procedure, based on the criticality analysis, was proposed to improve the maintenance of the STPP. This procedure is the combination of three methods, which are failure mode effects and criticality analysis (FMECA), Bayesian network and artificial intelligence. The FMECA is used to estimate the criticality index of the different elements of STPP. Moreover, corrections and improvements were introduced on the criticality index values based on the expert advice method. The modeling and the simulation of the FMECA estimations incorporating the expert advice method corrections were performed using the Bayesian network. The artificial neural network is used to predicate the criticality index of the STPP exploiting the database obtained from the Bayesian network simulations. The results showed a good agreement comparing predicted and actual criticality index values. In order to reduce the criticality index value of the critical elements of STPP, some maintenance recommendations were suggested.