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The hardness properties of unwashed surimi gel are considered as the qualities of gelation defect. This research investigated the effect of ultrasound-assisted first-stage thermal treatment (UATT) on the physicochemical properties of unwashed Silver Carp surimi gel, and the enhancement mechanism. UATT could reduce protein particle size, which significantly reduced from 142.22 μm to 106.70 μm after 30 min of UATT compared with the nature protein. This phenomenon can promote the protein crosslinking, resulting in the hardness of surimi gel increased by 15.08 %. Partially unfolded structure of myofibrillar protein and exposures of tryptophan to water, lead to the increase in the zeta potential absolute value, driven by UATT. The reduced SH group level and the conformational conversion of proteins from random coiling to α-helix and β-sheet, which was in support of intermolecular interaction and gel network construction. The results are valuable for processing protein gels and other food products.

IntroductionAcute myeloid leukemia (AML) is an aggressive blood cancer with high heterogeneity and poor prognosis. Although the metabolic reprogramming of nicotinamide adenine dinucleotide (NAD) has been reported to play a pivotal role in the pathogenesis of acute myeloid leukemia (AML), the prognostic value of NAD metabolism and its correlation with the immune microenvironment in AML remains unclear.MethodsWe utilized our large-scale RNA-seq data on 655 patients with AML and the NAD metabolism-related genes to establish a prognostic NAD metabolism score based on the sparse regression analysis. The signature was validated across three independent datasets including a total of 1,215 AML patients. ssGSEA and ESTIMATE algorithms were employed to dissect the tumor immune microenvironment. Ex vivo drug screening and in vitro experimental validation were performed to identify potential therapeutic approaches for the high-risk patients. In vitro knockdown and functional experiments were employed to investigate the role of SLC25A51, a mitochondrial NAD+ transporter gene implicated in the signature.ResultsAn 8-gene NAD metabolism signature (NADM8) was generated and demonstrated a robust prognostic value in more than 1,800 patients with AML. High NADM8 score could efficiently discriminate AML patients with adverse clinical characteristics and genetic lesions and serve as an independent factor predicting a poor prognosis. Immune microenvironment analysis revealed significant enrichment of distinct tumor-infiltrating immune cells and activation of immune checkpoints in patients with high NADM8 scores, acting as a potential biomarker for immune response evaluation in AML. Furthermore, ex vivo drug screening and in vitro experimental validation in a panel of 9 AML cell lines demonstrated that the patients with high NADM8 scores were more sensitive to the PI3K inhibitor, GDC-0914. Finally, functional experiments also substantiated the critical pathogenic role of the SLC25A51 in AML, which could be a promising therapeutic target.ConclusionOur study demonstrated that NAD metabolism-related signature can facilitate risk stratification and prognosis prediction in AML and guide therapeutic decisions including both immunotherapy and targeted therapies.

Bacillus cereus (B. cereus), a prevalent foodborne pathogen, constitutes a substantial risk to food safety due to its pronounced resilience under adverse environmental conditions such as elevated temperatures and ultraviolet radiation. This resilience can be attributed to its capacity for biofilm synthesis and sustained high viability. Our research aimed to elucidate the mechanisms governing biofilm biosynthesis in B. cereus. To this end, we constructed a 5088-mutant library of the B. cereus strain BC1 utilizing the transposon TnYLB-1. Systematic screening of this library yielded mutants exhibiting diminished biofilm formation capabilities. Twenty-four genes associated with the biofilm synthesis were identified by reverse PCR in these mutants, notably revealing a significant reduction in biofilm synthesis upon disruption of the orbF gene in B. cereus BC1. Comparative analysis between the wild type and orbF-deficient BC1 strains (BC1ΔorbF) indicated a marked downregulation (decreased by 11.7% to 96.7%) in the expression of genes implicated in biofilm formation, flagellar assembly, and bacterial chemotaxis in the BC1ΔorbF. Electrophoretic mobility shift assay (EMSA) further corroborated the role of OrbF, demonstrating its binding to the promoter region of the biofilm gene cluster, subsequently leading to the suppression of transcriptional activity of biofilm-associated genes in B. cereus BC1. Our findings underscore the pivotal role of orbF in biofilm biosynthesis in B. cereus, highlighting its potential as a target for strategies aimed at mitigating biofilm formation in this pathogen.

With the rapid development of Earth observation technology, how to effectively and efficiently detect changes in multi-temporal images has become an important but challenging problem. Relying on the advantages of high performance and robustness, object-based change detection (CD) has become increasingly popular. By analyzing the similarity of local pixels, object-based CD aggregates similar pixels into one object and takes it as the basic processing unit. However, object-based approaches often have difficulty capturing discriminative features, as irregular objects make processing difficult. To address this problem, in this paper, we propose a novel superpixel-based multi-scale Siamese graph attention network (MSGATN) which can process unstructured data natively and extract valuable features. First, a difference image (DI) is generated by Euclidean distance between bitemporal images. Second, superpixel segmentation is employed based on DI to divide each image into many homogeneous regions. Then, these superpixels are used to model the problem by graph theory to construct a series of nodes with the adjacency between them. Subsequently, the multi-scale neighborhood features of the nodes are extracted through applying a graph convolutional network and concatenated by an attention mechanism. Finally, the binary change map can be obtained by classifying each node by some fully connected layers. The novel features of MSGATN can be summarized as follows: (1) Training in multi-scale constructed graphs improves the recognition over changed land cover of varied sizes and shapes. (2) Spectral and spatial self-attention mechanisms are exploited for a better change detection performance. The experimental results on several real datasets show the effectiveness and superiority of the proposed method. In addition, compared to other recent methods, the proposed can demonstrate very high processing efficiency and greatly reduce the dependence on labeled training samples in a semisupervised training fashion.