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Two male patients aged in their 20s, who had no relationship with each other, separately presented with a three-month history of multiple asymptomatic papules on the genitalia. They were both sexually active and had no history of sexually transmissible infections (STIs). Physical examination showed multiple uniform, pinhead-sized, discrete, skin-coloured papules with a shiny surface on the glans penis and foreskin (Figure 1A-C). The biopsies of both patients showed a distinctive histology with a dense, well-circumscribed lymphohistiocytic infiltrate confined to the dermal papillae surrounded by hyperplastic rete ridges (Figure 1D).

In this study, high-performance flexible strain sensors based on carbon nanotube (CNT) and graphene nanoplatelet (GNP) filled thermoplastic polyurethane (TPU) composites were fabricated via Fused Filament Fabrication (FFF) 3D printing. The introduction of GNPs generated a more complete conductive network of the composites due to the improved nanofiller dispersion. Due to the synergy of CNTs and GNPs, the printed CNT/GNP(3:1)/TPU sensor shows higher sensitivity (GF = 136327.4 at 250% strain), larger detectable range (0–250% strain), and better stability (3000 cycles) compared with the CNT/TPU and GNP/TPU sensors with a nanofiller content of 2 wt%. Furthermore, the printed sensors can accurately detect strains at different frequencies (0.01–1 Hz). A modelling study based on tunneling theory was conducted to analysis the strain sensing mechanism, and the theoretical results agreed well with the experimental data. The capability of the sensors in monitoring physiological activities and speech recognition has also been demonstrated.

Background Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. Results GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. Conclusions The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, and some of them exceed safe ranges. Multi-exposure models reveal significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provides a comprehensive human serum exposome atlas and disease risk information, which can guide subsequent in-depth cause-and-effect studies between environmental exposures and human health. Current studies have provided limited knowledge on real-world chemical exposures and related risks. Here, the authors show serum exposure characteristics of humans in different regions and age groups, revealing diverse risk relationships with multiple chronic diseases.

In recent years, application of recommendation algorithm in real life such as Amazon, Taobao is getting universal, but it is not perfect yet. A few problems need to be solved such as sparse data and low recommended accuracy. Collaborative filtering is a mature algorithm in the recommended systems, but there are still some problems. In this paper, a novel collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering is presented. Firstly, score matrix is pre-processed with normalization and dimension reduction, to obtain denser score data. Based on these processed data, clustering principle is generated and dynamic evolutionary clustering is implemented. Secondly, the search for the nearest neighbors with highest similar interest is considered. A measurement about the relationship between users is proposed, called user correlation, which applies the satisfaction of users and the potential information. In each user group, user correlation is applied to choose the nearest neighbors to predict ratings. The proposed method is evaluated using the Movielens dataset. Diversity experimental results demonstrate that the proposed method has outstanding performance in predicted accuracy and recommended precision.

The joint classification of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data holds significant importance for various practical uses, including urban mapping, mineral prospecting, and ecological observation. Achieving robust and transferable feature representations is essential to fully leverage the complementary properties of HSI and LiDAR modalities. However, existing methods are often constrained to scene-specific training and lack generalizability across datasets, limiting their discriminative power. To tackle this challenge, we introduce a new dual-phase approach for the combined classification of HSI and LiDAR data. Initially, a transformer-driven network is trained on various HSI-only datasets to extract universal spatial–spectral features. In the second stage, LiDAR data is incorporated as a task-specific prompt to adapt the model to HSI-LiDAR scenes and enable effective multimodal fusion. Through extensive testing on three benchmark datasets, our framework proves highly effective, outperforming all competing approaches.

To alleviate CaF2 scaling on reverse osmosis membranes, polyepoxysuccinic acid (PESA) was modified with 2,5-dihydroxyterephthalic acid (DHTA) to obtain DHTA-PESA. Its structure and thermal stability were confirmed through characterization. Scale inhibition performance was evaluated using static and dynamic experiments. Results showed that in static tests, at a dosage of 200 mg/L, DHTA-PESA achieved a CaF2 scale inhibition rate of nearly 100%, demonstrating Ca2+ chelation ability and the capacity to prolong crystallization induction time. In dynamic experiments, indicating superior CaF2 dispersion performance and effective mitigation of membrane fouling. X-ray diffraction and scanning electron microscopy analysis revealed that DHTA-PESA induces irregular growth of CaF2 crystals, disrupting their formation and altering crystal morphology. The primary scale inhibition mechanisms include dispersion, lattice distortion, and chelation.

Superhydrophobic and magnetic materials with multiple functions (e.g., continuous oil collection, remote oil removal in confined spaces under magnetic driven, separation of oil/water emulsions, and good oil absorption capacity with robust stability) are highly required for practical oily wastewater remediation, but still a challenge to be realized. For this purpose, superhydrophobic Fe3O4 nanoparticles/polystyrene (PS) composite sponge has been fabricated via high internal phase emulsion template method. The as-prepared sponge exhibits high water-repellence and superoleophilicity with water/oil contact angles are 155º and 0º, respectively. Given the magnetic properties of Fe3O4, our sponge displays the capacity for remote oil capture under magnetic driven. Additionally, continuous oil collection has been also realized with the equipment of pumper. Different from some previous reported sponges, our sponge also possesses the unique ability to separate surfactant stabilized oil/water emulsion. Besides, our sponge can also act as a high-efficiency oil absorbent with robust cycling stability (oil recovery rate can reach 92%, even after 10 absorption-centrifugation cycles). These outstanding functions make our sponge hold great potential for the purification of oily wastewater.This work reported a simple and environmentally-friendly approach to prepare multi-functional magnetic and superhydrophobic sponges via high internal phase emulsion (HIPE) method. The PS/Fe3O4 composite sponges exhibited unique properties: (i) high water-repellence and good oleophilicity; (ii) the function as a high-efficiency oil absorbent with robust cycling stability; (iii) ability to remove oil remotely under magnetic driven; (iv) continuous oil collection; (v) the capacity to separate surfactant stabilized oil/water emulsion. These outstanding performances make our sponge is of great importance for practical oily wastewater remediation.

Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model’s robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment.

Transformer has shown its effectiveness and advantage in many computer vision tasks, for example, image classification and object re‐identification (ReID). However, existing vision transformers are stacked layer by layer, lacking direct information exchange among every layer. Inspired by DenseNet, we propose a dense transformer framework (termed Denseformer) that connects each layer to every other layer through class tokens. We demonstrate that Denseformer can consistently achieve better performance on person ReID tasks across datasets (Market‐1501, DukeMTMC, MSMT17, and Occluded‐Duke), only at a negligible increase of computation. We show that Denseformer has several compelling advantages: it pays more attention to the main parts of human bodies and obtains discriminative global features.