A chalcone derivative reactivates latent HIV-1 transcription through activating P-TEFb and promoting Tat-SEC interaction on viral promoter.

The principal barrier to the eradication of HIV/AIDS is the existence of latent viral reservoirs. One strategy to overcome this barrier is to use latency-reversing agents (LRAs) to reactivate the latent proviruses, which can then be eliminated by effective anti-retroviral therapy. Although a number of LRAs have been found to reactivate latent HIV, they have not been used clinically due to high toxicity and poor efficacy. In this study, we report the identification of a chalcone analogue called Amt-87 that can significantly reactivate the transcription of latent HIV provirses and act synergistically with known LRAs such as prostratin and JQ1 to reverse latency. Amt-87 works by activating the human transcriptional elongation factor P-TEFb, a CDK9-cyclin T1 heterodimer that is part of the super elongation complex (SEC) used by the viral encoded Tat protein to activate HIV transcription. Amt-87 does so by promoting the phosphorylation of CDK9 at the T-loop, liberating P-TEFb from the inactive 7SK snRNP, and inducing the formation of the Tat-SEC complex at the viral promoter. Together, our data reveal chalcones as a promising category of compounds that should be further explored to identify effective LRAs for targeted reversal of HIV latency

Electrospun Poly(Ethylene Oxide) Fibers Reinforced with Poly (Vinylpyrrolidone) Polymer and Cellulose Nanocrystals

Green poly(ethylene oxide) (PEO)/cellulose nanocrystals (CNCs)/poly(vinylpyrrolidone) (PVP) composites were prepared via electrospinning technique. The use of PVP and/or CNCs improved the overall thermal stability and mechanical properties of the PEO fibers. A strong synergistic reinforcing effect was achieved when PVP polymer and CNCs were combined in the composite. This synergistic reinforcement was accompanied with the formation of unique fiber-bead-fiber morphology. The beads were elongated and orientated along the applied force direction during tensile testing, providing an energy dissipation mechanism and a positive reinforcement effect. The combination of CNCs with PVP induced special chemical interactions, and distracted the interactions between PVP and PEO. As a result, the crystallinity of PEO was increased in the system, which also helped enhance fiber properties. The approach developed in this work offers a new way for reinforcing electrospun PEO-based composite fibers for sustainable green composite development

Optical molecular imaging and theranostics in neurological diseases based on aggregation-induced emission luminogens

Optical molecular imaging and image-guided theranostics benefit from special and specific imaging agents, for which aggregation-induced emission luminogens (AIEgens) have been regarded as good candidates in many biomedical applications. They display a large Stokes shift, high quantum yield, good biocompatibility, and resistance to photobleaching. Neurological diseases are becoming a substantial burden on individuals and society that affect over 50 million people worldwide. It is urgently needed to explore in more detail the brain structure and function, learn more about pathological processes of neurological diseases, and develop more efficient approaches for theranostics. Many AIEgens have been successfully designed, synthesized, and further applied for molecular imaging and image-guided theranostics in neurological diseases such as cerebrovascular disease, neurodegenerative disease, and brain tumor, which help us understand more about the pathophysiological state of brain through noninvasive optical imaging approaches. Herein, we focus on representative AIEgens investigated on brain vasculature imaging and theranostics in neurological diseases including cerebrovascular disease, neurodegenerative disease, and brain tumor. Considering different imaging modalities and various therapeutic functions, AIEgens have great potential to broaden neurological research and meet urgent needs in clinical practice. It will be inspiring to develop more practical and versatile AIEgens as molecular imaging agents for preclinical and clinical use on neurological diseases

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

Federated learning (FL) has been recognized as a viable distributed learning paradigm for training a machine learning model across distributed clients without uploading raw data. However, FL in wireless networks still faces two major challenges, i.e., large communication overhead and high energy consumption, which are exacerbated by client heterogeneity in dataset sizes and wireless channels. While model quantization is effective for energy reduction, existing works ignore adapting quantization to heterogeneous clients and FL convergence. To address these challenges, this paper develops an energy optimization problem of jointly designing quantization levels, scheduling clients, allocating channels, and controlling computation frequencies (QCCF) in wireless FL. Specifically, we derive an upper bound identifying the influence of client scheduling and quantization errors on FL convergence. Under the longterm convergence constraints and wireless constraints, the problem is established and transformed into an instantaneous problem with Lyapunov optimization. Solving Karush-Kuhn-Tucker conditions, our closed-form solution indicates that the doubly adaptive quantization level rises with the training process and correlates negatively with dataset sizes. Experiment results validate our theoretical results, showing that QCCF consumes less energy with faster convergence compared with state-of-the-art baselines