Assembly and Functionality of 2D Protein Arrays.

Among the unique classes of 2D nanomaterials, 2D protein arrays garner increasing attention due to their remarkable structural stability, exceptional physiochemical properties, and tunable electronic and mechanical attributes. The interest in mimicking and surpassing the precise architecture and advanced functionality of natural protein systems drives the field of 2D protein assembly toward the development of sophisticated functional materials. Recent advancements deepen the understanding of the fundamental principles governing 2D protein self-assembly, accelerating the creation of novel functional biomaterials.

Zinc-Mediated Deacetylation of FXR Activates the ATGL Pathway to Reduce Hepatic Lipid Accumulation and Enhance Lipolysis in Yellow Catfish.

Background: High-fat diets (HFD) can lead to excessive accumulation of lipids in the liver, leading to liver injury. Dietary zinc (Zn) has been shown to reduce high-fat diets-induced lipid accumulation and improve lipid profiles in mammals, yet it remains unclear whether waterborne Zn maintains its lipid-lowering effects in osteichthyes.

Objective: This study aimed to elucidate the regulatory role of Zn in high fat diet-induced hepatic lipid accumulation in yellow catfish (Pelteobagrus fulvidraco) and its potential mechanisms.

Artificial base mismatches-mediated PCR (ABM-PCR) for detecting clinically relevant single-base mutations.

Objectives: Detecting point mutations with high sensitivity and specificity can be technically very challenging, but it is crucial for early diagnosis and effective drug treatment of cancers. To enable ultrasensitive and ultraspecific detection of single-base mutations in simple and economical ways, we have developed an artificial base mismatches-mediated PCR (ABM-PCR) detection approach.

Methods: ABM-PCR was applied to quantitative PCR (qPCR) and droplet digital PCR (ddPCR) detection platforms.

Neural networks trained by weight permutation are universal approximators.

The universal approximation property is fundamental to the success of neural networks, and has traditionally been achieved by training networks without any constraints on their parameters. However, recent experimental research proposed a novel permutation-based training method, which exhibited a desired classification performance without modifying the exact weight values. In this paper, we provide a theoretical guarantee of this permutation training method by proving its ability to guide a ReLU network to approximate one-dimensional continuous functions.

Whole-genome resequencing reveals the population structure and domestication processes of endemic endangered goose breeds (Anser cygnoides).

In recent years, the dwindling population of these endangered geese has hindered our understanding of their phenotypic variations and the genes associated with important traits. To investigate the population structure and genetic diversity of this breed, the whole-genome data of 90 individuals from a conservation farm were obtained using the Illumina 6000 paired-end platform. The research results indicate that each locally endangered goose variety has formed a monophyletic population.