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Key message: The content of flavonoids could increase in A. canescens under saline conditions. Overexpression of AcCHI in transgenic A. thaliana promotes flavonoid biosynthesis, thereby functioning in the tolerance of transgenic plants to salt and osmotic stress by maintaining ROS homeostasis. Atriplex canescens is a halophytic forage shrub with excellent adaptation to saline environment. Our previous study showed that a large number of genes related to the biosynthesis of flavonoids in A. canescens were significantly up-regulated by NaCl treatments. However, it remains unclear whether flavonoids are involved in A. canescens response to salinity. In this study, we found that the accumulation of flavonoids significantly increased in either the leaves or roots of A. canescens seedling under 100 and 300 mM NaCl treatments. Correspondingly, AcCHS, AcCHI and AcF3H, which encode three key enzymes (chalcone synthases (CHS), chalcone isomerase (CHI), and flavanone 3-hydroxylase (F3H), respectively) of flavonoids biosynthesis, were significantly induced in the roots or leaves of A. canescens by 100 or 300 mM NaCl. Then, we generated the transgenic Arabidopsis thaliana overexpressing AcCHI and found that transgenic plants accumulated more flavonoids through enhancing the pathway of flavonoids biosynthesis. Furthermore, overexpression of AcCHI conferred salt and osmotic stress tolerance in transgenic A. thaliana. Contrasted with wild-type A. thaliana, transgenic lines grew better with greater biomass, less H2O2 content as well as lower relative plasma permeability in either salt or osmotic stress conditions. In conclusion, our results indicate that flavonoids play an important role in A. canescens response to salt stress through reactive oxygen species (ROS) scavenging and the key enzyme gene AcCHI in flavonoids biosynthesis pathway of A. canescens has the potential to improve the stress tolerance of forages and crops. [ABSTRACT FROM AUTHOR]

Purpose: Medulloblastoma is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer. Methods: In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%. Results: The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott's NanoString assay were highly comparable with a concordance rate of 96.4%. Conclusions: In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical. [ABSTRACT FROM AUTHOR]

Purpose: Medulloblastoma is the most common childhood malignant brain tumor and is a leading cause of cancer-related death in children. Recent transcriptional studies have shown that medulloblastomas comprise at least four molecular subgroups, each with distinct demographics, genetics, and clinical outcomes. Medulloblastoma subtyping has become critical for subgroup-specific therapies. The use of gene expression assays to determine the molecular subgroup of clinical specimens is a long-awaited application of molecular biology for this pediatric cancer. Methods: In the current study, we established a medulloblastoma transcriptome database of 460 samples retrieved from three published datasets (GSE21140, GSE37382, and GSE37418). With this database, we identified a 23-gene signature that is significantly associated with the medulloblastoma subgroups and achieved a classification accuracy of 95.2%. Results: The 23-gene signature was further validated in a long-term cohort of 142 Chinese medulloblastoma patients. The 23-gene signature classified 21 patients as WNT (15%), 41 as SHH (29%), 16 as Group 3 (11%), and 64 as Group 4 (45%). For patients of WNT, SHH, Group 3, and Group 4, 5-year overall-survival rate reached 80%, 62%, 27%, and 47%, respectively (p < 0.0001), meanwhile 5-year progression-free survival reached 80%, 52%, 27%, and 45%, respectively (p < 0.0001). Besides, SHH/TP53-mutant tumors were associated with worse prognosis compared with SHH/TP53 wild-type tumors and other subgroups. We demonstrated that subgroup assignments by the 23-gene signature and Northcott’s NanoString assay were highly comparable with a concordance rate of 96.4%. Conclusions: In conclusion, we present a novel gene signature that is capable of accurately and reliably assigning FFPE medulloblastoma samples to their molecular subgroup, which may serve as an auxiliary tool for medulloblastoma subtyping in the clinic. Future incorporation of this gene signature into prospective clinical trials is warranted to further evaluate its clinical. [ABSTRACT FROM AUTHOR]

This study explores the synergistic use of the mixotrophic flagellate Poterioroochromonas malhamensis and octanoic acid to effectively control harmful Microcystis sp. and prevent the resurgence of resistant strains. Poterioroochromonas malhamensis efficiently grazes on Microcystis sp., yet predation pressure can drive the evolution of resistant strains, which often re-emerge and predominate within approximately 20 days. Importantly, octanoic acid maintains its inhibitory and lethal effects against these strains, preventing their comeback. The study's key findings are as follows: (1) The concurrent application of P. malhamensis and 100 mg L-1 octanoic acid rapidly eliminates Microcystis sp., utilizing the acid's resistance-suppressing properties to prevent regrowth. (2) A preliminary reduction of Microcystis sp. density by P. malhamensis, succeeded by a low dose of octanoic acid (12.5 mg L-1 or higher), postpones the resurgence, presenting a cost-effective strategy. (3) In instances where P. malhamensis density is inadequate for controlling Microcystis sp. alone, a supplementary application of octanoic acid at 25 mg L-1 daily for four days (25 mg L-1× 4) aids P. malhamensis in eradicating Microcystis sp. The study concludes that octanoic acid fortifies the biological control by P. malhamensis and, by inhibiting and eliminating resistant Microcystis strains, introduces an innovative strategy for managing Microcystis blooms. [ABSTRACT FROM AUTHOR]

The openness and interconnectedness of industrial control systems (ICSs) is increasing, leading to a heightened risk of network-based attacks. Although research on industrial intrusion detection is ongoing, current methods often overlook the unique characteristics of industrial control flows. This study introduced an industrial network intrusion detection algorithm based on the improved gray wolf optimizer (IGWO) gated recurrent unit (GRU) model. Starting with the temporal aspects of industrial control network traffic, a simple GRU was chosen as the network model. By integrating the gray wolf optimizer (GWO) with autonomous learning methods, the algorithm could address the slow convergence caused by large volumes of industrial control network traffic. In response to the slow convergence of the GWO and its low optimization accuracy, this study developed the improved gray wolf optimizer (IGWO). By simulating an intrusion detection system (IDS) using datasets from the Natural Gas Pipeline Control System and Secure Water Treatment (SWaT) datasets, the experimental results demonstrated that the IGWO-GRU algorithm exhibited considerable advantages in terms of accuracy, false alarm rate, and false report rate, thereby enhancing the security capabilities of ICSs. [ABSTRACT FROM AUTHOR]