Intersection between metabolic dysfunction, high fat diet consumption, and brain aging

Deleterious neurochemical, structural, and behavioral alterations are a seemingly unavoidable aspect of brain aging. However, the basis for these alterations, as well as the basis for the tremendous variability in regards to the degree to which these aspects are altered in aging individuals, remains to be elucidated. An increasing number of individuals regularly consume a diet high in fat, with high‐fat diet consumption known to be sufficient to promote metabolic dysfunction, although the links between high‐fat diet consumption and aging are only now beginning to be elucidated. In this review we discuss the potential role for age‐related metabolic disturbances serving as an important basis for deleterious perturbations in the aging brain. These data not only have important implications for understanding the basis of brain aging, but also may be important to the development of therapeutic interventions which promote successful brain aging.Fil: Uranga, Romina Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Bioquímicas de Bahía Blanca. Universidad Nacional del Sur. Instituto de Investigaciones Bioquímicas de Bahía Blanca; ArgentinaFil: Bruce Keller, Annadora J.. State University of Louisiana; Estados UnidosFil: Morrison, Christopher D.. State University of Louisiana; Estados UnidosFil: Fernandez Kim, Sun Ok. State University of Louisiana; Estados UnidosFil: Ebenezer, Philip J.. State University of Louisiana; Estados UnidosFil: Zhang, Le. State University of Louisiana; Estados UnidosFil: Dasuri, Kalavathi. State University of Louisiana; Estados UnidosFil: Keller, Jeffrey N.. State University of Louisiana; Estados Unido

Stackelberg Security Game for Optimizing Security of Federated Internet of Things Platform Instances

This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model

Data Spine: A Federated Interoperability Enabler for Heterogeneous IoT Platform Ecosystems

Today, the Internet of Things (IoT) is pervasive and characterized by the rapid growth of IoT platforms across different application domains, enabling a variety of business models and revenue streams. This opens new opportunities for companies to extend their collaborative networks and develop innovative cross-platform and cross-domain applications. However, the heterogeneity of today’s platforms is a major roadblock for mass creation of IoT platform ecosystems, pointing at the current absence of technology enablers for an easy and innovative composition of tools/services from the existing platforms. In this paper, we present the Data Spine, a federated platform enabler that bridges IoT interoperability gaps and enables the creation of an ecosystem of heterogeneous IoT platforms in the manufacturing domain. The Data Spine allows the ecosystem to be extensible to meet the need for incorporating new tools/services and platforms. We present a reference implementation of the Data Spine and a quantitative evaluation to demonstrate adequate performance of the system. The evaluation suggests that the Data Spine provides a multitude of advantages (single sign-on, provision of a low-code development environment to support interoperability and an easy and intuitive creation of cross-platform applications, etc.) over the traditional approach of users joining multiple platforms separately