Transgenic overexpression of UDP glycosyltransferase gene UGT41A3 induces resistance to nucleopolyhedrovirus in Bombyx mori.

Bombyx mori nuclear polyhedrosis, caused by B. mori nucleopolyhedrovirus (BmNPV), threatens sericulture seriously. To explore strategies for controlling it, the UDP glycosyltransferase gene UGT41A3 (BmUGT41A3) was targeted.

VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset.

In this paper, we propose the Vision-Audio-Language Omni-peRception pretraining model (VALOR) for multimodal understanding and generation. Unlike widely-studied vision-language pretraining models, VALOR jointly models the relationships among vision, audio, and language in an end-to-end manner. It consists of three separate encoders for single modality representations and a decoder for multimodal conditional text generation.

Single-Atom-Based Nanoenzyme in Tissue Repair.

Since the discovery of ferromagnetic nanoparticles FeO that exhibit enzyme-like activity in 2007, the research on nanoenzymes has made significant progress. With the in-depth study of various nanoenzymes and the rapid development of related nanotechnology, nanoenzymes have emerged as a promising alternative to natural enzymes. Within nanozymes, there is a category of metal-based single-atom nanozymes that has been rapidly developed due to low cast, convenient preparation, long storage, less immunogenicity, and especially higher efficiency.

Stark Localization as a Resource for Weak-Field Sensing with Super-Heisenberg Precision.

Gradient fields can effectively suppress particle tunneling in a lattice and localize the wave function at all energy scales, a phenomenon known as Stark localization. Here, we show that Stark systems can be used as a probe for the precise measurement of gradient fields, particularly in the weak-field regime where most sensors do not operate optimally. In the extended phase, Stark probes achieve super-Heisenberg precision, which is well beyond most of the known quantum sensing schemes.

An Efficient Sampling-Based Attention Network for Semantic Segmentation.

Self-attention is widely explored to model long-range dependencies in semantic segmentation. However, this operation computes pair-wise relationships between the query point and all other points, leading to prohibitive complexity. In this paper, we propose an efficient Sampling-based Attention Network which combines a novel sample method with an attention mechanism for semantic segmentation.