CXCL14 in prostate cancer: complex interactions in the tumor microenvironment and future prospects.

CXCL14 is a highly conserved chemokine expressed in various cell types, playing crucial roles in both physiological and pathological processes, including immune regulation and tumorigenesis. Recently, the role of CXCL14 in tumors has attracted considerable attention. However, previous pan-cancer studies have reported inconsistencies regarding the effects of CXCL14 on tumors, particularly concerning its expression levels in tumor tissues and its influence on various phenotypes of cancer cells.

Stress-induced self-assembly of hierarchically twisted stripe arrays.

Hierarchical organization is prevalent in nature, yet the artificial construction of hierarchical materials featuring asymmetric structures remains a big challenge. Herein, we report a stress-induced self-assembly strategy for the synthesis of hierarchically twisted stripe arrays (HTSAs) with mesoporous structures. A soft and thin mesostructured film assembled by micelles and TiO oligomers is the prerequisite.

Ribosome profiling reveals dynamic translational landscape in HEK293T cells following X-ray irradiation.

X-ray irradiation induces widespread changes in gene expression. Positioned at the bottom of the central dogma, translational regulation responds swiftly to environmental stimuli, fine-tuning protein levels. However, the global view of mRNA translation following X-ray exposure remains unclear.

Proteomic analysis of wanxi white goose testicles in different reproductive stages by data-independent acquisition (DIA) strategy.

Wanxi white goose is an important male parent in crossbreeding of Chinese geese, but its short reproductive cycle restricts its application in Northeast China. Therefore, understanding the potential mechanism of breeding period regulation in Wanxi white goose will help to provide more options for crossbreeding. In this study, the reproductive period was divided into prophase (T1), metaphase (T2) and anaphase (T3) according to the laying rhythm of geese.

Simulating fish autonomous swimming behaviours using deep reinforcement learning based on Kolmogorov-Arnold Networks.

The study of fish swimming behaviours and locomotion mechanisms holds significant scientific and engineering value. With the rapid advancements in artificial intelligence, a new method combining deep reinforcement learning (DRL) with computational fluid dynamics (CFD) has emerged and been applied to simulate the autonomous behavior of higher organisms like fish. However, the scale of this cross-disciplinary method is directly affected by the efficiency of the DRL model.