Identification of a novel D-amino acid oxidase and its application in deracemization of D, L-phosphinothricin.

DAAO is applied as a potential catalyst in the biosynthesis of L-PPT. However, its low solubility expression constrains its broader industrial application. Herein, a novel DAAO derived from Cladophialophora carrionii (CcDAAO) was identified, which demonstrated superior catalytic performance toward D-Ala (specific activity: 106.

Road of Large Language Model: Source, Challenge, and Future Perspectives.

Language model (LM), a foundational algorithm in the development of capable artificial intelligence, has been widely explored, achieving remarkable attainment. As research advances, large language models (LLMs) have emerged by pretraining transformer-based models on large-scale corpora. These models showed great zero-shot and few-shot learning capabilities across a variety of tasks, attracting widespread attention from both academia and industry.

High loading of atomically exposed edge nickel sites embedded in hollow porous carbon nanofibers for enhanced methanol electrooxidation in direct methanol fuel cells.

The enhancement of catalytic activity and durability for atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts in methanol oxidation reaction (MOR) anodes within direct methanol fuel cells presents a significant challenge. Here, we developed hollow porous nanofiber catalysts featuring edge Ni-N atomic sites through coaxial electrostatic spinning with domain-restricted Ni atoms embedded within a zeolitic imidazolium ester backbone, thereby increasing the exposure of accessible active sites (Ni: 4.96 %).

Learning From Target-Level Incomplete Annotation: A Novel Perspective for Weakly-Supervised Multi-Lesion Segmentation.

Accurately segmenting various clinically significant lesion areas from whole-body computed tomography (CT) scans is crucial for automated diagnosis and treatment planning. Training an automatic segmentation model effectively is desirable, but it heavily relies on a large scale of pixel-wise labeled data, which is laborious, time-consuming, and expensive to obtain. Existing weakly-supervised segmentation approaches often struggle with regions nearby the lesion boundaries.

Cross-modal Guided Visual Representation Learning for Social Image Retrieval.

Social images are often associated with rich but noisy tags from community contributions. Although social tags can potentially provide valuable semantic training information for image retrieval, existing studies all fail to effectively filter noises by exploiting the cross-modal correlation between image content and tags. The current cross-modal vision-and-language representation learning methods, which selectively attend to the relevant parts of the image and text, show a promising direction.

SAAF: Self-Adaptive Attention Factor-Based Taylor-Pruning on Convolutional Neural Networks.

Nowadays, pruning techniques have drawn attention to convolutional neural networks (CNNs) for reducing the consumption of computation resources. In particular, the Taylor-based method simplifies the evaluation of importance for each filter as the product of the gradient and weight value of the output features, which outperforms other methods in reductions of parameters and floating point operations (FLOPs). However, the Taylor-based method sacrifices too much accuracy when the overall pruning rate is relatively large compared with other pruning algorithms.

CDI-NSTSEG: A Clinical Diagnosis-Inspired Effective and Efficient Framework for Non-Salient Small Tumor Segmentation.

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors).

NTSM: a non-salient target segmentation model for oral mucosal diseases.

Background: Oral mucosal diseases are similar to the surrounding normal tissues, i.e., their many non-salient features, which poses a challenge for accurate segmentation lesions.

A practical key recovery attack on the lightweight WG-5 stream cipher.

WG-5 is a lightweight stream cipher proposed for usage in the resource-constrained devices, e.g., passive RFID tags, industrial controllers, contactless smart cards and sensors.

A General Global and Local Pre-Training Framework for 3D Medical Image Segmentation.

Accurate target segmentation from computed tomography (CT) scans is crucial for surgical robots to perform clinical surgeries successfully. However, the lack of medical image data and annotations has been the biggest obstacle to learning robust medical image segmentation models. Self-supervised learning can effectively address this problem by providing a strategy to pre-train a model with unlabeled data, and then fine-tune downstream tasks with limited labeled data.

Sensory gamma entrainment: Impact on amyloid protein and therapeutic mechanism.

The deposition of amyloid β peptide (Aβ) is one of the main pathological features of AD. The much-talked sensory gamma entrainment may be a new treatment for Aβ load. Here we reviewed the generation and clearance pathways of Aβ, aberrant gamma oscillation in AD, and the therapeutic effect of sensory gamma entrainment on AD.

Evaluation of Sufu Fermented Using : Biochemical, Textural, Structural and Microbiological Properties.

The quality and safety of sufu fermented using was studied and compared with naturally fermented sufu. After 90 days post-fermentation, both naturally fermented and inoculated fermented sufu reached the maturity standard of sufu, and the degree of protein hydrolysis of natural sufu (WP/TP: 34% ± 1%; AAN/TN: 33% ± 1%) was slightly higher than that of the inoculated sufu (WP/TP: 28.2% ± 0.

Effect of Coagulant and Treatment Conditions on the Gelation and Textural Properties of Acidic Whey Tofu.

This study aimed to investigate the properties of acidic whey tofu gelatin generated from two acidic whey coagulants by pure fermentation of and , as well as the characteristics of acidic whey tofu. The optimal holding temperature and the amount of coagulants added were determined based on the pH, water-holding capacity, texture, microstructure, and rheological properties of tofu gelation. Then, the differences in quality between tofu produced by pure bacterial fermentation and by natural fermentation were investigated under optimal tofu gelatin preparation conditions.

Deep Multiple Instance Hashing for Fast Multi-Object Image Search.

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately.

TelecomNet: Tag-Based Weakly-Supervised Modally Cooperative Hashing Network for Image Retrieval.

We are concerned with using user-tagged images to learn proper hashing functions for image retrieval. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images. However, tagging data suffers from noises, vagueness and incompleteness.

Comparative Genomics of Reveals What Makes a Cosmopolitan Bacterium.

Although the strategies used by bacteria to adapt to specific environmental conditions are widely reported, fewer studies have addressed how microbes with a cosmopolitan distribution can survive in diverse ecosystems. is a versatile genus whose members are commonly found in various habitats. To better understand the mechanisms underlying the universality of , we collected 105 strains from diverse environments and performed large-scale metabolic and adaptive ability tests.

Adversarial Incomplete Multiview Subspace Clustering Networks.

Multiview clustering aims to leverage information from multiple views to improve the clustering performance. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the problem of incomplete multiview clustering (IMC).

Query-biased Self-attentive Network for Query-focused Video Summarization.

This paper addresses the task of query-focused video summarization, which takes user queries and long videos as inputs and generates query-focused video summaries. Compared to video summarization, which mainly concentrates on finding the most diverse and representative visual contents as a summary, the task of query-focused video summarization considers the user's intent and the semantic meaning of generated summary. In this paper, we propose a method, named query-biased self-attentive network (QSAN) to tackle this challenge.

Multiview Concept Learning Via Deep Matrix Factorization.

Multiview representation learning (MVRL) leverages information from multiple views to obtain a common representation summarizing the consistency and complementarity in multiview data. Most previous matrix factorization-based MVRL methods are shallow models that neglect the complex hierarchical information. The recently proposed deep multiview factorization models cannot explicitly capture consistency and complementarity in multiview data.

Learning to Map Social Network Users by Unified Manifold Alignment on Hypergraph.

Nowadays, a lot of people possess accounts on multiple online social networks, e.g., Facebook and Twitter.

The complete genome sequence of Alcaligenes faecalis ZD02, a novel potential bionematocide.

Root-knot nematodes (RKNs) can infect almost all crops, and result in huge economic losses in agriculture. There is no effective and environmentally safe means available to control RKNs. Alcaligenes faecalis ZD02 isolated from free living nematode Caenorhabditis elegans cadavers shows toxicity against RKN Meloidogyne incognita, that makes this strain to be a good bionematicide candidate for controlling of RKNs.

Plasmids are vectors for redundant chromosomal genes in the Bacillus cereus group.

Background: Prokaryotic plasmids have played significant roles in the evolution of bacterial genomes and have a great impact on the metabolic functions of the host cell. Many bacterial strains contain multiple plasmids, but the relationships between bacterial plasmids and chromosomes are unclear. We focused on plasmids from the Bacillus cereus group because most strains contain several plasmids.