Sharp Bounds for Sequential Federated Learning on Heterogeneous Data

Enzyme Immunoassays (EIA) 

#Assay, #EIA, #Heterogeneous, #Homogenous #Immunology Enzymes labelled conjugates were first introduced in 1966 for localization of antigens in tissues, as an alternative to fluorescent conjugates.…

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

In the context of the Edge-Cloud computing continuum, containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better …

Personal Knowledge Graphs. Semantic Entity Persistence in Relational Model

The graph is a mathematical abstraction that could be useful for various transport, topology, networks, and even social studies tasks. So, sometimes, the graph itself has a value . The next step is…

Effective Connectivity and Bias Entropy Improve Prediction of Dynamical Regime in Automata Networks

Biomolecular network dynamics are thought to operate near the critical boundary between ordered and disordered regimes, where large perturbations to a small set of elements neither die out nor spread on average. A biomolecular automaton (e.g., gene, protein) typically has high regulatory redundancy, where small subsets of regulators determine activation via collective canalization. Previous work has shown that effective connectivity, a measure of collective canalization, leads to improved dynamical regime prediction for homogeneous automata networks. We expand this by (i) studying random Boolean networks (RBNs) with heterogeneous in-degree distributions, (ii) considering additional experimentally validated automata network models of biomolecular processes, and (iii) considering new measures of heterogeneity in automata network logic. We found that effective connectivity improves dynamical regime prediction in the models considered; in RBNs, combining effective connectivity with bias entropy further improves the prediction. Our work yields a new understanding of criticality in biomolecular networks that accounts for collective canalization, redundancy, and heterogeneity in the connectivity and logic of their automata models. The strong link we demonstrate between criticality and regulatory redundancy provides a means to modulate the dynamical regime of biochemical networks.