Li Jiang

This article presents a new optimization method for complex power distribution networks (PDNs) with irregular shapes and multilayer structures using deep reinforcement learning (DRL), which has not been considered before. A fast boundary integration method is applied to compute the impedance matrix of a PDN structure. Subsequently, a new DRL algorithm based on proximal policy optimization (PPO) is proposed to optimize the decoupling capacitor (decap) placement by minimizing the number of decaps while satisfying the desired target impedance. In the proposed approach, the PDN structure information is encoded into matrices and serves as the input of the DRL algorithm, which increases the flexibility of the method to be extended and generalized to different PDN configurations. Also, the output of the algorithm determines the selection of decap types and locations collaboratively, making it easier to find the optimal solution in a huge search space. The proposed method is compared with the state-of-the-art approaches and shows consistent advantages in reducing the number of decaps in different testing cases.

This article presents a novel extraction method for parasitic parameters of on-wafer calibration standards using an optimization strategy. Based on the physical models of the calibration standards, the parasitic parameters of all calibration standards can be extracted using the Bayesian optimization method with a fast calculation speed and high accuracy. Compared with the conventional thru-reflect-line (TRL) extraction method, the proposed approach can work well in systems with high crosstalk. The methodology is validated through on-wafer measurements using a ground–signal–ground (GSG) probe with low crosstalk and a ground–signal (GS) probe with high crosstalk. The proposed method shows a comparable accuracy with the TRL method in the low-crosstalk case, while it has higher accuracy in the high-crosstalk scenario.

The cell membrane system comprises the plasma membrane, endoplasmic reticulum, Golgi apparatus, lysosome, mitochondria, and nuclear membrane, which are essential for maintaining normal physiological functions of cells. The proteins associated with these membrane-organelles are frequently modified to regulate their functions, the most common of which is ubiquitin modification. So far, many ubiquitin E3 ligases anchored in the membrane system have been identified as critical players facilitating intracellular biofunctions whose dysfunction is highly related to cancer. In this review, we summarized membrane-associated E3 ligases and revealed their relationship with cancer, which is of great significance for discovering novel drug targets of cancer and may open up new avenues for inducing ubiquitination-mediated degradation of cancer-associated membrane proteins via small chemicals such as PROTAC and molecular glue.

Organoids are in vitro simplified and miniature microcosms of internal organs, which have aroused great interest in tissue development, multiple disease models, clinical diagnosis, as well as high-throughput drug screening and personalized medicine and so on. The success of physiology-related organoid culture has greatly advanced the translational medicine research in the field of cancer treatment, which was once troubled by the inconsistency between two-dimensional (2D) cell culture and in vivo studies. Especially in recent years, the success rate of establish an organoid has been greatly improved, and the types of organoids have been gradually enriched. Moreover, the utilizing of some the cutting-edge technologies, including gene editing technology such as CRISPR-Cas9, the scope of application of organoid is broadened. In this review, we discuss the latest progress and applications of organoids, and also outline the potential challenges of organoids for future improvement.