Underwater objects often suffer from severe visual degradation, including strong background noise induced by water impurities, blurred boundaries caused by optical scattering, and small object suppression in complex environments. These factors collectively pose significant challenges to underwater object detection (UOD). To address these issues, we propose the Multi-Scale Inverted Pyramid Network (MIP-Net) tailored for UOD. Moving beyond standard feature fusion paradigms, MIP-Net introduces explicit conceptual shifts through two key components: the Local Adaptive Contrast module (LAC) and the Multi-Scale Inverted Feature Pyramid Network (MSIFPN). Unlike conventional global attention mechanisms, LAC selectively calibrates intra-feature contrast layer-by-layer, establishing a theoretical basis for dynamic feature modulation that prevents high-frequency detail dilution. Simultaneously, MSIFPN transcends traditional FPN architectures by coupling a dual-pyramid bidirectional flow with a strict mathematical foreground-background separation strategy, effectively isolating target semantics from ambiguous water impurities. Experiments on the official DUO benchmark demonstrate that MIP-Net achieves an AP of 70.1%, surpassing state-of-the-art single-stage, two-stage, and Transformer-based methods, while maintaining a highly competitive trade-off between computational efficiency (Params/FLOPs) and accuracy. Furthermore, evaluations on the terrestrial COCO dataset yield an AP of 45.6%, confirming the strong cross-domain generalization capability of our framework. The code is publicly available at: https://github.com/YitengGuo/MIP-Net
VwMYB1 directly activates VwF3′5'H to drive delphinidin accumulation, establishing a core regulatory module controlling pansy petal blotch formation. Anthocyanins play a pivotal role in determining the visual characteristics of ornamental plants. To elucidate the molecular basis of the distinctive "blotch" formation in pansy (Viola × wittrockiana), this study employed an integrated approach that combines targeted metabolomics and comparative transcriptomics. Comparative analysis of blotched versus non-blotched petal regions identified VwMYB1, a differentially expressed R2R3-MYB transcription factor, as a key candidate regulator. Functional validation revealed that the heterologous overexpression of VwMYB1 in tobacco enhanced floral pigmentation, while its transient overexpression in pansy petals induced localized deepened blotches, thereby confirming its role in promoting pigmentation. Targeted metabolomic profiling demonstrated that the blotch phenotype is primarily attributed to the substantial and specific accumulation of delphinidin-type anthocyanins. Given that F3′5'H is the pivotal branch-point enzyme for delphinidin biosynthesis, the regulatory interaction between VwMYB1 and VwF3′5'H was further examined. Yeast one-hybrid and dual-luciferase reporter assays confirmed that VwMYB1 directly binds to the promoter of VwF3′5'H and activates its expression. This study not only illustrates a streamlined pipeline from transcriptome-based gene discovery to functional and metabolic validation but also uncovers the core regulatory mechanism by which VwMYB1 governs delphinidin-based blotch formation through the direct transcriptional activation of VwF3′5'H, thereby offering both mechanistic insight and a practical genetic tool for precision breeding in flower color modification.
As electric transportation and energy storage systems continue to scale up, lithium-ion battery health management imposes increasingly stringent requirements on cross-temperature State of Health (SOH) Estimation. However, temperature variations alter charging kinetics and the statistical scale of health features, inducing a systematic bias in the feature-to-SOH mapping learned in the Source temperature domain when applied to a Target temperature domain. Meanwhile, full-lifetime labeled data are often scarce in the Target temperature domain, making costly retraining-from-scratch impractical for rapid deployment. To address these challenges, a Temporal Graph Network–Long Short-Term Memory Network (TGL-Net) is developed for cross-temperature SOH Estimation, together with a transfer learning paradigm of Source-domain Pre-training followed by Few-Cell Fine-Tuning in the Target temperature domain for fast adaptation. TGL-Net takes a cycle-level 16-dimensional health-feature sequence as input. A Conv1d module first extracts short-horizon degradation patterns while suppressing local fluctuations; a chain-structured temporal Subgraph is then constructed via a Sliding Window, and 2 TimeGNN layers encode neighboring-cycle dependencies through message passing; subsequently, 2 stacked LSTM layers model mid-to-long-term degradation trends. A Linear output head produces the SOH estimate for the end-of-window Cycle, enabling full-curve Estimation over the battery lifetime. Experiments on 25 °C test batteries show that TGL-Net achieves a Mean Absolute Error of 0.0075, a Root Mean Squared Error of 0.0106, and an R-squared of 0.9961, with an inference time of approximately 0.033 s per Battery and a model size of about 0.737 MB. Moreover, after Fine-Tuning with only a small number of Batteries in the 15 °C/35°C Target temperature domain, Estimation accuracy is markedly improved compared with zero-shot direct transfer. Systematic evaluation using a 1008-run hyperparameter sweep, ablation studies, and comparisons with multiple baseline methods indicates that stable performance can be maintained over a broad hyperparameter range, together with favorable cross-temperature Transferability and Data efficiency.
Although existing research on fruit powder-fortified cereal products has primarily focused on antioxidant activity, systematic studies investigating the in vitro xanthine oxidase (XOD) inhibitory effects related to hyperuricaemia intervention remain scarce. Furthermore, no studies have yet determined how to balance maintaining steamed bread quality with their uric acid-lowering functional activity following cherry powder addition. This study is the first to apply cherry powder to steamed bread production, providing a comprehensive analysis of its textural quality, antioxidant activity and in vitro XOD inhibitory activity. This study therefore fills a critical research gap. We combined CP with gluten flour and coix seed powder to make steamed bread and found that replacing 10
Fluorescent labeling of long-chain inulin (FXL) is essential for tracking its spatiotemporal dynamics in gastrointestinal systems, yet conventional methods risk altering its native properties. This study systematically compared three labeling strategies: FITC-DB (dibutyltin dilaurate-catalyzed hydroxyl conjugation), FITC-RA (reductive amination), and FA (fluoresceinamine coupling). Multimodal characterization revealed that FITC-DB achieved the highest substitution degree (DS = 0.42 at 50 mg FITC), though fluorescence quenching occurred at elevated loadings. FITC-RA induced significant aggregation (peak size at 50 mg), while FA labeling exhibited minimal structural perturbation but low efficiency (DS < 0.20). Spectroscopic analyses confirmed covalent thiourea bond formation (1720 cm⁻¹ FTIR peak) and aggregation-dependent optical shifts. Crucially, FITC-DB at 50 mg balanced optimal labeling efficiency, colloidal stability (highest zeta potential), and preserved macromolecular architecture. These findings establish design principles for high-fidelity FXL probes in biological tracking applications.
