Effects of natural isoflavones on the structural competition of human telomeric G-quadruplex d[AG ₃ (T ₂ AG ₃ ) ₃ ] and its related Watson–Crick duplex d[AG ₃ (T ₂ AG ₃ ) ₃ -(C ₃ TA ₂ ) ₃ C ₃ T] are investigated by using circular dichroism (CD), ESI-MS, fluorescence quenching measurement, CD stopped-flow kinetic experiment, UV spectroscopy and molecular modeling methods. It is intriguing to find out that isoflavones can stabilize the G-quadruplex structure but destabilize its corresponding Watson–Crick duplex and this discriminated interaction is intensified by molecular crowding environments. Kinetic experiments indicate that the dissociation rate of quadruplex ( k_{obs290 nm} ) is decreased by 40.3% at the daidzin/DNA molar ratio of 1.0 in K ⁺ , whereas in Na ⁺ the observed rate constant is reduced by about 12.0%. Furthermore, glycosidic daidzin significantly induces a structural transition of the polymorphic G-quadruplex into the antiparallel conformation in K ⁺ . This is the first report on the recognition of isoflavones with conformational polymorphism of G-quadruplex, which suggests that natural isoflavone constituents potentially exhibit distinct regulation on the structural competition of quadruplex versus duplex in human telomeric DNA.

Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance.

The design of a new-type of inductive magnetic energy harvesting system with high permeability magnetic material combined with a dual polarity boost converter is presented. By using the strong magnetostatic interaction between the coils and permanent magnets, the output power and power density of the energy harvester is enhanced compared to conventional air cored inductive harvesters. In the proposed dual polarity boost converter, the coil leakage inductor of the harvester is utilized to form the input inductor of the boost converter to minimize the converter size. Therefore, the magnetic energy harvester and the power converter share the same magnetic core. Optimum system design becomes a balance of core size, required leakage inductance and required magnetizing inductance in order to extract maximum energy.

In hyperspectral image (HSI) classification, each pixel is assigned to a specific land cover type, which is critical for applications in environmental monitoring, agriculture, and urban planning. Convolutional neural network (CNN) and Transformers have become widely adopted due to their exceptional feature extraction capabilities. However, the local receptive field of CNN limits their ability to capture global context, while Transformers, though effective in modeling long-range dependencies, introduce computational overhead. To address these challenges, we propose a frequency-domain and spatial-domain feature fusion network (FSFF-Net) for HSI classification, which reduces computational complexity while capturing global features. The FSFF-Net consists of a frequency-domain transformer (FDformer) and a deepwise convolution-based parallel encoder structure. The FDformer replaces the self-attention mechanism in traditional Visual Transformers with a three-step process: two-dimensional discrete Fourier transform (2D-DFT), adaptive filter, and two-dimensional inverse Fourier transform (2D-IDFT). 2D DFT and 2D-IDFT convert images between the spatial and frequency domains. Adaptive filter can adaptively retain important frequency components, remove redundant components, and assign weights to different frequency components. This module not only can reduce computational overhead by decreasing the number of parameters, but also mitigates the limitations of CNN by capturing complementary frequency-domain features, which enhance the spatial-domain features for improved classification. In parallel, deepwise convolution is employed to capture spatial-domain features. The network then integrates the frequency-domain features from FDformer and the spatial-domain features from deepwise convolution through a feature fusion module. The experimental results demonstrate that our method is efficient and robust for HSIs classification, achieving overall accuracies of 98.03%, 99.57%, 97.05%, and 98.40% on Indian Pines, Pavia University, Salinas, and Houston 2013 datasets, respectively.

Metaverse applications often require many new 3D point cloud models that are unlabeled and that have never been seen before; this limited information results in difficulties for data-driven model analyses. In this paper, we propose a novel data-driven 3D point cloud analysis network GN-CNN that is suitable for such scenarios. We tackle the difficulties with a few-shot learning (FSL) approach by proposing an unsupervised generative adversarial network GN-GAN to generate prior knowledge and perform warm start pre-training for GN-CNN. Furthermore, the 3D models in the Metaverse are mostly acquired with a focus on the models’ visual appearances instead of the exact positions. Thus, conceptually, we also propose to augment the information by unleashing and incorporating local variance information, which conveys the appearance of the model. This is realized by introducing a graph convolution-enhanced combined multilayer perceptron operation (CMLP), namely GCMLP, to capture the local geometric relationship as well as a local normal-aware GeoConv, namely GNConv. The GN-GAN adopts an encoder–decoder structure and the GCMLP is used as the core operation of the encoder. It can perform the reconstruction task. The GNConv is used as the convolution-like operation in GN-CNN. The classification performance of GN-CNN is evaluated on ModelNet10 with an overall accuracy of 95.9%. Its few-shot learning performance is evaluated on ModelNet40, when the training set size is reduced to 30%, the overall classification accuracy can reach 91.8%, which is 2.5% higher than Geo-CNN. Experiments show that the proposed method could improve the accuracy in 3D point cloud classification tasks and under few-shot learning scenarios, compared with existing methods such as PointNet, PointNet++, DGCNN, and Geo-CNN, making it a beneficial method for Metaverse applications.

There have been considerable efforts in generating tree crown maps from satellite images. However, tree localization in urban environments using satellite imagery remains a challenging task. One of the difficulties in complex urban tree detection tasks lies in the segmentation of dense tree crowns. Currently, methods based on semantic segmentation algorithms have made significant progress. We propose to split the tree localization problem into two parts, dense clusters and single trees, and combine the target detection method with a procedural generation method based on planting rules for the complex urban tree detection task, which improves the accuracy of single tree detection. Specifically, we propose a two-stage urban tree localization pipeline that leverages deep learning and planting strategy algorithms along with region discrimination methods. This approach ensures the precise localization of individual trees while also facilitating distribution inference within dense tree canopies. Additionally, our method estimates the radius and height of trees, which provides significant advantages for three-dimensional reconstruction tasks from remote sensing images. We compare our results with other existing methods, achieving an 82.3% accuracy in individual tree localization. This method can be seamlessly integrated with the three-dimensional reconstruction of urban trees. We visualized the three-dimensional reconstruction of urban trees generated by this method, which demonstrates the diversity of tree heights and provides a more realistic solution for tree distribution generation.