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Received β€” 27 May 2026 ⏭

Ginkgolic Acids Degradation by the <em>Ginkgo biloba</em> Endophytic Fungus <em>Fusarium</em> sp. DLT-118

βœ‡Omics In Lung
By: Lu-Ting Dai Β· Zhi-Fang Yu Β· You-Xing Zhao Β· Yi Zheng
14 April 2026 at 18:00

Foods. 2026 Apr 6;15(7):1247. doi: 10.3390/foods15071247.

ABSTRACT

Ginkgolic acids (GAs), the principal toxic constituents in Ginkgo biloba, pose health risks including cytotoxicity, allergenicity, and pro-inflammatory effects, limiting the application of Ginkgo resources in the food and health product industries. Developing efficient and environmentally friendly removal methods is essential. The endophytic fungus Fusarium sp. DLT-118, isolated from Ginkgo biloba, degraded 96.47% of GAs in Ginkgo biloba leaf extract (GE) at an initial concentration of 1 mg/mL within 7 days at 28 Β°C, while concurrently enhancing the antioxidant activity of GE, as indicated by a reduction in the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging IC50 from 755.7 ΞΌg/mL to 544.6 ΞΌg/mL. Morphological and oxidative stress analyses showed critical cellular adaptations and stress responses under degradation conditions. Integrated multi-omics analysis indicated that GE stress induced the remodeling of fungal amino acid, lipid, and energy metabolism, as well as the adjustment of membrane and transport functions, to facilitate GAs detoxification. Cytotoxicity assays indicated no significant cytotoxicity of the degradation products towards human normal lung epithelial cells (Beas-2B) and gastric mucosal epithelial cells (GES-1). These findings highlight Fusarium sp. DLT-118 as a promising agent for the efficient removal of GAs, offering a potential strategy for the production of GA-reduced Ginkgo-based food and health products.

PMID:41976541 | PMC:PMC13073844 | DOI:10.3390/foods15071247

Received β€” 8 April 2026 ⏭

General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Genwei Ma Β· Ting Luo Β· Ping Yang Β· Xing Zhao
7 April 2026 at 12:00
arXiv:2604.03321v1 Announce Type: cross Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving partial differential equations (PDEs). The successful deployment of such methods beyond academic research remains limited. For example, PINN methods primarily consider discrete point-to-point fitting and fail to account for the potential properties of real solutions. The adoption of continuous activation functions in these approaches leads to local characteristics that align with the equation solutions while resulting in poor extensibility and robustness. A general explicit network (GEN) that implements point-to-function PDE solving is proposed in this paper. The "function" component can be constructed based on our prior knowledge of the original PDEs through corresponding basis functions for fitting. The experimental results demonstrate that this approach enables solutions with high robustness and strong extensibility to be obtained.
Received β€” 11 March 2026 ⏭

SketchGraphNet: A Memory-Efficient Hybrid Graph Transformer for Large-Scale Sketch Corpora Recognition

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Shilong Chen Β· Mingyuan Li Β· Zhaoyang Wang Β· Zhonglin Ye Β· Haixing Zhao
10 March 2026 at 12:00
arXiv:2603.07521v1 Announce Type: cross Abstract: This work investigates large-scale sketch recognition from a graph-native perspective, where free-hand sketches are directly modeled as structured graphs rather than raster images or stroke sequences. We propose SketchGraphNet, a hybrid graph neural architecture that integrates local message passing with a memory-efficient global attention mechanism, without relying on auxiliary positional or structural encodings. To support systematic evaluation, we construct SketchGraph, a large-scale benchmark comprising 3.44 million graph-structured sketches across 344 categories, with two variants (A and R) to reflect different noise conditions. Each sketch is represented as a spatiotemporal graph with normalized stroke-order attributes. On SketchGraph-A and SketchGraph-R, SketchGraphNet achieves Top-1 accuracies of 83.62% and 87.61%, respectively, under a unified training configuration. MemEffAttn further reduces peak GPU memory by over 40% and training time by more than 30% compared with Performer-based global attention, while maintaining comparable accuracy.
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