Small interfering RNA (siRNA) has shown promising therapeutic prospects in many major diseases. However, two main reasons limit the application of siRNA: poor endocytosis efficiency and weak endosomal escape ability. Therefore, the development of efficient and safe delivery vectors has always been an important study aspect of RNAi technology. Herein, we designed a self-assembled nanoparticle based on functionalized peptides to deliver siRNA to the down-regulated polo-like kinase 1 (PLK1) gene, which can inhibit tumor cells in the G2 phase. The functional polypeptide consists of cell membrane-penetrating peptide (CPP44) and p16 minimal inhibitory sequence (p16MIS). CPP44 can effectively mediate endocytosis, while p16MIS can inhibit tumor growth in the G1 phase and synergistically promote the apoptosis of tumor cells with siPLK1. In vitro and in vivo studies demonstrate that the developed nanoparticle exhibits high levels of silencing efficiency, antitumor activity, and therapeutic efficacy. Consequently, this study provides a novel approach to cancer treatment by simultaneously disrupting two stages of tumor cell division.

Knowing the correct phase connectivity information plays a significant role in maintaining high-quality power and reliable electricity supply to end-consumers. However, managing the consumer-phase connectivity of a low-voltage distribution network is often costly, prone to human errors, and time-intensive, as it involves either installing expensive high-precision devices or employing field-based methods. Besides, the ever-increasing electricity demand and the proliferation of behind-the-meter resources have also increased the complexity of leveraging the phase connectivity problem. To overcome the above challenges, this paper develops a data-driven model to identify the phase connectivity of end-consumers using advanced metering infrastructure voltage and current measurements. Initially, a preprocessing method that employs linear interpolation and singular value decomposition is adopted to improve the quality of the smart meter data. Then, using Kirchoff’s current law and correlation analysis, a discrete convolution optimization model is built to uniquely identify the phase to which each end-consumer is connected. The data sets utilized are obtained by performing power flow simulations on a modified IEEE-906 test system using OpenDSS software. The robustness of the model is tested against data set size, missing smart meter data, measurement errors, and the influence of prosumers. The results show that the method proposed correctly identifies the phase connections of end-consumers with an accuracy of about 98%.

A speech study by a research team from The University of Texas at El Paso has identified an underappreciated aspect of speech in English and Spanish speakers that could lead to improvements in artificial intelligence spoken dialogue systems.

SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two multilingual datasets via joint KGC and alignment loops.

Research team cuts shadow-tomography sample cost to ε^(−3) by seeding FTRL with a rough state guess ϱ, halving measurements when ϱ sits within trace distance Θ(ε) of the true ρ.

PyABV injects high-performance assertion engines into PyRTL, doubling verification coverage on complex HADM designs while sustaining RTL simulation speed for both academia and industry.

 Recent survey delivers the first systematic map of LLM tool-learning, dissecting why tools supercharge models and how four-stage workflows (plan→select→call→respond) perform, while cataloging benchmarks and open challenges to jump-start newcomers.