Role of the habenulo-interpeduncular system in the neurodevelopmental basis of susceptibility and resilience to stress-induced anxiety

Experiencing stress during sensitive periods of brain development has a major impact on how individuals cope with later stress. Although many become more prone to develop anxiety or depression, some appear resilient. The mechanisms underlying these differences are unknown. Key answers may lie in how genetic and environmental stressors interact to shape the circuits controlling emotions. Here we studied the role of the habenulo-interpeducuncular system (HIPS), a critical node of reward circuits, in early stress-induced anxiety. We found that a subcircuit of this system, characterized by Otx2 expression, is particularly responsive to chronic stress during puberty, which induces HIPS hypersensitivity to later stress and susceptibility to develop anxiety. We further show that Otx2 deletion restricted to the HIPS counteracts these effects of stress. Together, these results demonstrate that Otx2 and stress interact, around puberty, to shape the HIPS stress-response, revealed here as a key modulator of susceptibility/resilience to develop anxiety.

Role of the habenulo-interpeduncular system in the neurodevelopmental basis of susceptibility and resilience to stressinduced anxiety

Experiencing stress during sensitive periods of brain development has a major impact on how individuals cope with later stress. Although many become more prone to develop anxiety or depression, some appear resilient. The mechanisms underlying these differences are unknown. Key answers may lie in how genetic and environmental stressors interact to shape the circuits controlling emotions. Here we studied the role of the habenulo-interpeducuncular system (HIPS), a critical node of reward circuits, in early stress-induced anxiety. We found that a subcircuit of this system, characterized by Otx2 expression, is particularly responsive to chronic stress during puberty, which induces HIPS hypersensitivity to later stress and susceptibility to develop anxiety. We further show that Otx2 deletion restricted to the HIPS counteracts these effects of stress. Together, these results demonstrate that Otx2 and stress interact, around puberty, to shape the HIPS stress-response, revealed here as a key modulator of susceptibility/resilience to develop anxiety.

MEA-CNDP: A Membrane Evolutionary Algorithm for Solving Biobjective Critical Node Detection Problem

The critical node detection problem (CNDP) refers to the identification of one or more nodes that have a significant impact on the entire complex network according to the importance of each node in a complex network. Most methods consider the CNDP as a single-objective optimization problem, which requires more prior knowledge to a certain extent. This paper proposes a membrane evolution algorithm MEA-CNDP to solve biobjective CNDP. MEA-CNDP includes a population initialization strategy based on the evaluation of decision variables, a strategy to transform the main objective, a strategy to update the membrane inherited pool, and four membrane evolutionary operators. The numerical experiments on 16 benchmark problems with random and logarithmic weights show that MEA-CNDP outperforms other algorithms in most cases. In particular, MEA-CNDP has unique advantages in dealing with large-scale sparse bi-CNDP.

From molecule to behavior: hypocretin/orexin revisited from a sex-dependent perspective

The hypocretin/orexin (Hcrt/Orx) system in the perifornical lateral hypothalamus has been recognized as a critical node in a complex network of neuronal systems controlling both physiology and behavior in vertebrates. Our understanding of the Hcrt/Orx system and its array of functions and actions have grown exponentially in merely two decades. This review will examine the latest progress in discerning the roles played by the Hcrt/Orx system in the regulation of homeostatic functions and in the execution of instinctive and learned behaviors. Furthermore, the gaps that currently exist in our knowledge of sex-related differences in this field of study are discussed.

Effective Routing in Vehicular Adhoc Network (VANET) using an Bio-inspired Algorithm: Enhanced Deep Reinforcement Learning (EDRL) for Secure Wireless Communication

For improving the performance of city wide-ranging lane networks through the optimized control signal, we proposed a framework in Vehicular Adhoc Network (VANET). Node which reduces the traffic efficiency drastically is identified as critical node, with the help of defined framework. Tripartite graph is used for identifying critical node through vehicle trajectory in the over-all viewpoint. Enhanced Deep Reinforcement Learning (EDRL) method is introduced to control the traffic signal and gives appropriate decision for routing the data from Road Side Unit (RSU) to intermediate or destination node. Various experiments were done with proposed model and the result shows considerable efficiency in delay and travelling time of the node in VANET.

Approaching the bi-objective critical node detection problem with a smart initialization-based evolutionary algorithm

Determining the critical nodes in a complex network is an essential computation problem. Several variants of this problem have emerged due to its wide applicability in network analysis. In this article we study the bi-objective critical node detection problem (BOCNDP), which is a new variant of the well-known critical node detection problem, optimizing two objectives at the same time: maximizing the number of connected components and minimizing the variance of their cardinalities. Evolutionary multi-objective algorithms (EMOA) are a straightforward choice to solve this type of problem. We propose three different smart initialization strategies which can be incorporated into any EMOA. These initialization strategies take into account the basic properties of the networks. They are based on the highest degree, random walk (RW) and depth-first search. Numerical experiments were conducted on synthetic and real-world network data. The three different initialization types significantly improve the performance of the EMOA.

PINC: Pickup Non-Critical Node Based k-Connectivity Restoration in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is connected if a communication path exists among each pair of sensor nodes (motes). Maintaining reliable connectivity in WSNs is a complicated task, since any failure in the nodes can cause the data transmission paths to break. In a k-connected WSN, the connectivity survives after failure in any k-1 nodes; hence, preserving the k-connectivity ensures that the WSN can permit k-1 node failures without wasting the connectivity. Higher k values will increase the reliability of a WSN against node failures. We propose a simple and efficient algorithm (PINC) to accomplish movement-based k-connectivity restoration that divides the nodes into the critical, which are the nodes whose failure reduces k, and non-critical groups. The PINC algorithm pickups and moves the non-critical nodes when a critical node stops working. This algorithm moves a non-critical node with minimum movement cost to the position of the failed mote. The measurements obtained from the testbed of real IRIS motes and Kobuki robots, along with extensive simulations, revealed that the PINC restores the k-connectivity by generating optimum movements faster than its competitors.