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Accurately predicting the trajectories of mesoscale eddies is essential for comprehending the distribution of marine resources and the multiscale energy cascade in the ocean. Nevertheless, current approaches for predicting mesoscale eddy trajectories frequently exhibit inadequate examination of the intrinsic multiscale temporal data, resulting in diminished predictive precision. To address this challenge, our research introduces an enhanced transformer-based framework for predicting mesoscale eddy trajectories. Initially, a multivariate dataset of mesoscale eddy trajectories is constructed and expanded, encompassing eddy properties and pertinent ocean environmental information. Additionally, novel feature factors are delineated based on the physical attributes of eddies. Subsequently, a multi-head attention mechanism is introduced to bolster the modeling of the multiscale time-varying connections within eddy trajectories. Furthermore, the original positional encoding is substituted with Time-Absolute Position Encoding, which considers the dimensions and durations of the sequence mapping, thereby improving the distinguishability of embedded vectors. Ultimately, the Soft-DTW loss function is integrated to more accurately assess the overall discrepancies among mesoscale eddy trajectories, thereby improving the model’s resilience to erratic and diverse trajectory sequences. The effectiveness of the proposed framework is assessed using the eddy-abundant South China Sea. Our framework exhibits exceptional predictive accuracy, achieving a minimum central error of 8.507 km over a seven-day period, surpassing existing state-of-the-art models.

Rice B03S mutants with intermittent leaf discoloration were developed from the photoperiod- and thermosensitive genic male sterile (PTGMS) rice line Efeng 1S. After these plants were deeply transplanted, the new leaves manifested typical stripe patterns. In this study, deep and shallow transplantation of B03S was carried out, and aluminum shading was performed directly on the leaf sheath. It was determined that the reason for the appearance of the striped leaf trait was that the base of leaf sheath lacked light, at which time the sheath transformed from the source organ to the sink organ in rice. To elucidate the related metabolic changes in glycometabolism and abscisic acid (ABA) biosynthesis and transcriptional regulation in the leaf sheath, ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) combined with transcriptome and real-time quantitative PCR (qPCR) validation were used for analysis after deep and shallow transplantation. The result indicates that the leaf sheath may need to compete with the new leaves for sucrose produced by the photosynthesis of old leaves in response to lacking light at the base of sheath. Moreover, the ABA content increases in the leaf sheath when the gene expression of ABA2 and AAO1 is upregulated at the same time, enhancing the plant’s resistance to the adverse condition of shading at the leaf sheath. Furthermore, exogenous spraying of B03S with ABA solution was carried out to help recovery under shading stress. The result indicates that the synthesis of endogenous ABA in the leaf sheath is reduced by spraying ABA. At the same time, ABA regulates sucrose metabolism by inhibiting the expression of the SUS gene. This allows for more sucrose synthesized by the old leaves to be transported to the new leaves, resulting an obvious recovery effect of the strip leaf character due to the re-balance of sugar supply and demand in B03S. These findings improve the understanding of the physiological function and metabolic mechanism of the rice leaf sheath, provide a theoretical basis for uneven leaf coloration in nature, and provide theoretical guidance for rice production via seedling transplantation or direct seeding.

Lower urinary tract dysfunction often requires tissue repair or replacement to restore physiological functions. Current clinical treatments involving autologous tissues or synthetic materials inevitably bring in situ complications and immune rejection. Advances in therapies using stem cells offer new insights into treating lower urinary tract dysfunction. One of the most frequently used stem cell sources is adipose tissue because of its easy access, abundant source, low risk of severe complications, and lack of ethical issues. The regenerative capabilities of adipose-derived stem cells (ASCs) in vivo are primarily orchestrated by their paracrine activities, strong regenerative potential, multi-differentiation potential, and cell–matrix interactions. Moreover, biomaterial scaffolds conjugated with ASCs result in an extremely effective tissue engineering modality for replacing or repairing diseased or damaged tissues. Thus, ASC-based therapy holds promise as having a tremendous impact on reconstructive urology of the lower urinary tract.

Ni3S2 nanowires were synthesized in situ using a one-pot hydrothermal reaction on Ni foam (NF) for use in supercapacitors as a positive electrode, and various contents (0.3−0.6 mmol) of Co(OH)2 shells were coated onto the surfaces of the Ni3S2 nanowire cores to improve the electrochemical properties. The Ni3S2 nanowires were uniformly formed on the smooth NF surface, and the Co(OH)2 shell was formed on the Ni3S2 nanowire surface. By direct NF participation as a reactant without adding any other Ni source, Ni3S2 was formed more closely to the NF surface, and the Co(OH)2 shell suppressed the loss of active material during charging–discharging, yielding excellent electrochemical properties. The Co(OH)2–Ni3S2/Ni electrode produced using 0.5 mmol Co(OH)2 (Co0.5–Ni3S2/Ni) exhibited a high specific capacitance of 1837 F g−1 (16.07 F cm−2) at a current density of 5 mA cm−2, and maintained a capacitance of 583 F g−1 (16.07 F cm−2) at a much higher current density of 50 mA cm−2. An asymmetric supercapacitor (ASC) with Co(OH)2–Ni3S2 and active carbon displayed a high-power density of 1036 kW kg−1 at an energy density of 43 W h kg−1 with good cycling stability, indicating its suitability for use in energy storage applications. Thus, the newly developed core–shell structure, Co(OH)2–Ni3S2, was shown to be efficient at improving the electrochemical performance.

Benggang is a typical erosional landform in southern and southeastern China. Since benggang poses significant risks to local ecological environments and economic infrastructure, it is vital to accurately detect benggang-eroded areas. Relying only on remote sensing imagery for benggang detection cannot produce satisfactory results. In this study, we propose integrating high-resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) data for efficient and automatic benggang discovery. The fusion of complementary rich information hidden in both DOM and DSM data is realized by a two-stream convolutional neural network (CNN), which integrates aggregated terrain and activation image features that are both extracted by supervised deep learning. We aggregate local low-level geomorphic features via a supervised diffusion-convolutional embedding branch for expressive representations of benggang terrain variations. Activation image features are obtained from an image-oriented convolutional neural network branch. The two sources of information (DOM and DSM) are fused via a gated neural network, which learns the most discriminative features for the detection of benggang. The evaluation of a challenging benggang dataset demonstrates that our method exceeds several baselines, even with limited training examples. The results show that the fusion of DOM and DSM data is beneficial for benggang detection via supervised convolutional and deep fusion networks.