Bicaudal D2 facilitates the cytoplasmic trafficking and nuclear import of HIV-1 genomes during infection.

Numerous viruses, including HIV-1, exploit the microtubule network to traffic toward the nucleus during infection. Although numerous studies have observed a role for the minus-end microtubule motor dynein in HIV-1 infection, the mechanism by which the viral core containing the viral genome associates with dynein and induces its perinuclear trafficking has remained unclear. Here, we report that the dynein adapter protein bicaudal D2 (BICD2) is able to interact with HIV-1 viral cores in target cells. We also observe that BICD2 can bind in vitro-assembled capsid tubes through its CC3 domain. We observe that BICD2 facilitates infection by promoting the trafficking of viral cores to the nucleus, thereby promoting nuclear entry of the viral genome and infection. Finally, we observe that depletion of BICD2 in the monocytic cell line THP-1 results in an induction of IFN-stimulated genes in these cells. Collectively, these results identify a microtubule adapter protein critical for trafficking of HIV-1 in the cytoplasm of target cells and evasion of innate sensing mechanisms in macrophages.

Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants.

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

Development of low-cost micro-fabrication procedures for planar micro-thermoelectric generators based on thin-film technology for energy harvesting applications.

With the rapid proliferation of portable and wearable electronics, energy autonomy through efficient energy harvesting has become paramount. Thermoelectric generators (TEGs) stand out as promising candidates due to their silent operation, high reliability, and maintenance-free nature. This paper presents the design, fabrication, and analysis of a micro-scale TEG for powering such devices. A planar configuration was employed for its inherent miniaturization advantages. Finite element analysis using ANSYS reveals that a double-layer device under a 50 K temperature gradient generates an impressive open-circuit voltage of 1417 mV and a power output of 2.4 µW, significantly exceeding its single-layer counterpart (226 mV, 0.12 µW). Validation against the analytical model results yields errors within 2.44% and 2.03% for voltage and power, respectively. Furthermore, a single-layer prototype fabricated using paper shadow masks and sputtering deposition exhibits a voltage of 131 mV for a 50 K temperature difference, thus confirming the feasibility of the proposed design. This work establishes a foundation for developing highly efficient micro-TEGs for powering next-generation portable and wearable electronics.