Search

Fungal α/β-glucans have significant importance in cellular functions including cell wall structure, host-pathogen interactions and energy storage, and wide application in high-profile fields, including food, nutrition, and pharmaceuticals. Fungal species and their growth/developmental stages result in a diversity of glucan contents, structures and bioactivities. Substantial progresses have been made to elucidate the fine structures and functions, and reveal the potential molecular synthesis pathway of fungal α/β-glucans. Herein, we review the current knowledge about the biosynthetic machineries, including: precursor UDP-glucose synthesis, initiation, elongation/termination and remodeling of α/β-glucan chains, and molecular regulation to maximally produce glucans in edible fungi. This review would provide future perspectives to biosynthesize the targeted glucans and reveal the catalytic mechanism of enzymes associated with glucan synthesis, including: UDP-glucose pyrophosphate phosphorylases (UGP), glucan synthases, and glucanosyltransferases in edible fungi. [ABSTRACT FROM AUTHOR]

Genus Pseudomonas bacteria mainly consume glucose through the Entner–Doudoroff (ED) route due to a lack of a functional Embden–Meyerhof–Parnas (EMP) pathway. In the present study, a 6-phosphogluconate dehydratase (edd) operon in the ED route was well investigated to find its structural characteristics and roles in the regulation of glucose consumption and 2-ketogluconic acid (2KGA) metabolism in the industrial 2KGA-producer P. plecoglossicida JUIM01. The edd operon contained four structural genes of edd, glk, gltR, and gtrS, encoding 6-PG dehydratase Edd, glucokinase Glk, response regulatory factor GltR, and histidine kinase GtrS, respectively. A promoter region was observed in the 5′-upstream of the edd gene, with a transcriptional start site located 129 bp upstream of the edd gene and in a pseudo-palindromic sequence of 5′-TTGTN7ACAA-3′ specifically binding to the transcription factor HexR. The knockout of the edd gene showed a remarkably negative effect on cell growth and re-growth using 2KGA as a substrate, beneficial to 2KGA production, with an increase of 8%. The deletion of glk had no significant effect on the cell growth or glucose metabolism, while showing an adverse impact on the 2KGA production, with a decrease of 5%. The outputs of the present study would provide a theoretical basis for 2KGA-producer improvement with metabolic engineering strategies and the development and optimization of P. plecoglossicida as the chassis cells. [ABSTRACT FROM AUTHOR]

Urban road networks have complex spatial and temporal correlations, driving a surge of research interest in spatial-temporal traffic flow prediction. However, prior approaches often overlook the temporal-scale differentiation of spatial-temporal features, limiting their ability to extract complex structural information. In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. Besides, we design the spatial-temporal feature fusion (STFF) module to refine the prediction results. Based on the multibranch complementary features, the module adopts a coarse-to-fine fusion strategy, incorporating different spatial-temporal scale features to obtain recalibrated prediction results. Finally, we evaluate the MBAF-GCN using two real-world traffic datasets. Experimentally, the newly designed multibranch can efficaciously utilize the prior information of different temporal scales. Our MBAF-GCN achieved better performance in the comparative model, indicating its potential and validity., Author(s): Xin Zan [1]; Jasmine Siu Lee Lam (corresponding author) [2] 1. Introduction The rise in vehicle numbers has resulted in increased pressure on urban traffic and travelers. To improve [...]

Since 2002, with the SARS outbreak, infectious diseases, including the ongoing COVID-19 pandemic, have continued to be a major global public health threat. It is critical to develop effective data science methods to quickly detect disease outbreaks and contain their rapid globalized spread. However, in practice, limited testing availability, and thus insufficient testing data poses significant challenges in effective analysis and real-time monitoring of infectious diseases, especially during early stages of a novel disease outbreak. To tackle these challenges, this article proposes adaptive testing resource allocation strategies integrated with a physics-informed model to dynamically allocate limited testing resources across different communities. The physics-informed model accounts for transmission dynamics and health disparities, enabling effective health risk assessment despite limited data. By integrating nonstationary Multi-Armed Bandit techniques that strike superior balance between exploring the communities with high uncertain risks and exploiting those with high risk levels, the proposed methodology facilitates test allocation to collect high-quality testing data for early outbreak detection. Theoretical analysis is carried out to evaluate the performance of the proposed allocation strategies, ensuring either sublinear or linear dynamic pseudo-regret under regularity assumptions. A comprehensive simulation study is conducted under three transmission scenarios to thoroughly evaluate the proposed methodology. [ABSTRACT FROM AUTHOR]