Search Results (157)

Infection with Burkholderia pseudomallei, the causative agent of melioidosis, is uncommon in the United States (U.S.), leading to delays in pathogen identification and clinical diagnosis which can often lead to laboratory exposures. The indirect hemagglutination assay (IHA) is the primary serological test for confirming exposure to B. pseudomallei. In the U.S., a titer of ≥1:40 suggests exposure to B. pseudomallei or a closely related species, and a 4-fold rise in IHA titer ≥1:40 with clinically compatible illness is considered diagnostically probable. A retrospective analysis of 160 voluntarily reported laboratory exposure events to B. pseudomallei across 29 U.S. jurisdictions and 5 countries between 2008–2024 was conducted. This analysis included post-exposure management data and IHA results for 855 exposed laboratory personnel who had serological monitoring performed at the U.S. Centers for Disease Control and Prevention (CDC). Among exposed laboratory personnel, 105 (12%) had a seropositive titer. Of these, ninety-one (87%) laboratory personnel remained seropositive (≥1:40) at their last IHA test. Five (1%) people had a 4-fold rise in titers, though none developed melioidosis. This report underscores the need for prospective studies to evaluate seropositive laboratory personnel and to update risk guidance for laboratory exposures in non-endemic areas. Full article

Background: Breast cancer remains a critical public health problem worldwide and is a leading cause of cancer-related mortality. Optimizing clinical outcomes is contingent upon the early and precise detection of malignancies. Advances in medical imaging and artificial intelligence (AI), particularly in the fields of radiomics and deep learning (DL), have contributed to improvements in early detection methodologies. Nonetheless, persistent challenges, including limited data availability, model overfitting, and restricted generalization, continue to hinder performance. Methods: This study aims to overcome existing challenges by improving model accuracy and robustness through enhanced data augmentation and the integration of radiomics and deep learning features from the CBIS-DDSM dataset. To mitigate overfitting and improve model generalization, data augmentation techniques were applied. The PyRadiomics library was used to extract radiomics features, while transfer learning models were employed to derive deep learning features from the augmented training dataset. For radiomics feature selection, we compared multiple supervised feature selection methods, including RFE with random forest and logistic regression, ANOVA F-test, LASSO, and mutual information. Embedded methods with XGBoost, LightGBM, and CatBoost for GPUs were also explored. Finally, we integrated radiomics and deep features to build a unified multimodal feature space for improved classification performance. Based on this integrated set of radiomics and deep learning features, 13 pre-trained transfer learning models were trained and evaluated, including various versions of ResNet (50, 50V2, 101, 101V2, 152, 152V2), DenseNet (121, 169, 201), InceptionV3, MobileNet, and VGG (16, 19). Results: Among the evaluated models, ResNet152 achieved the highest classification accuracy of 97%, demonstrating the potential of this approach to enhance diagnostic precision. Other models, including VGG19, ResNet101V2, and ResNet101, achieved 96% accuracy, emphasizing the importance of the selected feature set in achieving robust detection. Conclusions: Future research could build on this work by incorporating Vision Transformer (ViT) architectures and leveraging multimodal data (e.g., clinical data, genomic information, and patient history). This could improve predictive performance and make the model more robust and adaptable to diverse data types. Ultimately, this approach has the potential to transform breast cancer detection, making it more accurate and interpretable. Full article

To address the semantic mismatch between limited textual descriptions in image captioning training datasets and the multi-semantic nature of images, as well as the underutilized external commonsense knowledge, this article proposes a novel image captioning model based on multi-step cross-attention cross-modal alignment and external commonsense knowledge enhancement. The model employs a backbone architecture comprising CLIP’s ViT visual encoder, Faster R-CNN, BERT text encoder, and GPT-2 text decoder. It incorporates two core mechanisms: a multi-step cross-attention mechanism that iteratively aligns image and text features across multiple rounds, progressively enhancing inter-modal semantic consistency for more accurate cross-modal representation fusion. Moreover, the model employs Faster R-CNN to extract region-based object features. These features are mapped to corresponding entities within the dataset through entity probability calculation and entity linking. External commonsense knowledge associated with these entities is then retrieved from the ConceptNet knowledge graph, followed by knowledge embedding via TransE and multi-hop reasoning. Finally, the fused multimodal features are fed into the GPT-2 decoder to steer caption generation, enhancing the lexical richness, factual accuracy, and cognitive plausibility of the generated descriptions. In the experiments, the model achieves CIDEr scores of 142.6 on MSCOCO and 78.4 on Flickr30k. Ablations confirm both modules enhance caption quality. Full article

This paper presents a comparative analysis of bearing and rotating component fault classification based on different time–frequency representations using vision transformer (ViT). Four different time–frequency transformation techniques—short-time Fourier transform (STFT), continuous wavelet transform (CWT), Hilbert–Huang transform (HHT), and Wigner–Ville distribution (WVD)—were applied to convert the signals into 2D images. A pretrained ViT-Base architecture was fine-tuned on the resulting images for classification tasks. The model was evaluated on two separate scenarios: (i) eight-class rotating component fault classification and (ii) four-class bearing fault classification. Importantly, in each task, the samples were collected under varying conditions of the other component (i.e., different rotating conditions in bearing classification and vice versa). This design allowed for an independent assessment of the model’s ability to generalize across fault domains. The experimental results demonstrate that the ViT-based approach achieves high classification performance across various time–frequency representations, highlighting its potential for mechanical fault diagnosis in rotating machinery. Notably, the model achieved higher accuracy in bearing fault classification compared to rotating component faults, suggesting higher sensitivity to bearing-related anomalies. Full article

Background/Objectives: Dietary supplements are sources of nutrients or other substances that added to a healthy lifestyle help to preserve human homeostasis. Since inflammation is one of the major contributors to the alteration of homeostasis, this work investigated the effects of a multi-ingredient dietary supplement on human macrophages, cells involved in the inflammatory response. Methods: THP-1 cells were differentiated into macrophage-like cells and polarized in M₁ or M₂ phenotypes. Cell migration was evaluated by Boyden chamber assay; phenotypic markers by qRT-PCR; cytokine release by ELISA and LPS/ATP-induced pyroptosis by LDH assay. The antioxidant properties of the supplement were evaluated in human and mouse fibroblasts by DCF-DA assay. After supplement treatment, cell extracts were analyzed by HPLC-PDA-MS/MS and GC-MS to evaluate the presence of the ingredients. Results: Our results showed that the dietary supplement promoted M₂ migration and polarization and significantly reduced migration of M₁. In a model of LPS-induced inflammation in M₀, it significantly reduced NF-κB activation, COX-2 expression, and cytokine release. The supplement was not a specific inhibitor of NLRP-3, but it was able to modulate LPS priming. In addition, the supplement decreased granulocyte adhesion to HUVEC and reduced the oxidative stress in fibroblasts. The analysis of cell extracts showed the presence of the following ingredients of the formulation inside the cells: CoQ10, spermidine, resveratrol, 5-hydroxytryptophan from Griffonia simplicifolia (Vahl ex DC.) Baill., bacosides from Bacopa monnieri (L.) Wettst, vit B₂, B₅, E acetate. Conclusions: Our results demonstrate how a combination of natural active ingredients may contribute to the maintenance of homeostasis in human cells. Full article

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

Specific venom immunotherapy (VIT) in patients with hymenoptera venom allergy (HVA) represents a well-studied approach to reduce the severity of a possible anaphylactic reaction. Currently, data on mechanisms of tolerance induction at the cellular level within the first hours of therapy are lacking. To address this, total and unoccupied high-affinity IgE receptor (FcεRI) numbers per basophil, soluble FcεRI (sFcεRI) and serum tryptase levels were measured before and after the first day of VIT induction in HVA patients. Additionally, basophil activation tests (BATs) were performed at those time points. In the early phase of VIT induction, no significant change in total FcεRI receptor density on basophils was observed, but a significant increase in unoccupied FcεRI was noticeable, predominantly in patients with high total IgE and low baseline unoccupied FcεRI density. No meaningful difference in serum tryptase levels or sFcεRI levels was observed after VIT induction. BATs showed heterogeneous results, often unchanged before and after VIT (in 47% of the cases), sometimes increased (in 40%) and only rarely decreased EC₅₀ sensitivity (in 13%). Changes in the BAT EC₅₀ correlated with FcεRI receptor density changes in basophils. In summary, VIT induction led to an increased ratio of unoccupied-to-total FcεRI without notable tryptase or sFcεRI serum elevation, pointing towards subthreshold cell activation with receptor internalization and recycling. However, the mostly unchanged or even increased basophil sensitivity in EC₅₀ calls for further research to clarify the clinical relevance of these rapid receptor modulations. Full article

Deep neural network (DNN) hardware (HW) accelerators have achieved great success in improving DNNs’ performance and efficiency. One key reason is the dataflow in executing a DNN layer, including on-chip data partitioning, computation parallelism, and scheduling policy, which have large impacts on latency and energy consumption. Unlike prior works that required considerable efforts from HW engineers to design suitable dataflows for different DNNs, this work proposes an efficient data-centric approach, named Dataflow Code Propagation (DCP), to automatically find the optimal dataflow for DNN layers in seconds without human effort. It has several attractive benefits that prior studies lack, including the following: (i) We translate the HW dataflow configuration into a code representation in a unified dataflow coding space, which can be optimized by back-propagating gradients given a DNN layer or network. (ii) DCP learns a neural predictor to efficiently update the dataflow codes towards the desired gradient directions to minimize various optimization objectives, e.g., latency and energy. (iii) It can be easily generalized to unseen HW configurations in a zero-shot or few-shot learning manner. For example, without using additional training data, Extensive experiments on several representative models such as MobileNet, ResNet, and ViT show that DCP outperforms its counterparts in various settings. Full article

Deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) perform well in histological image classification, but often lack interpretability. We introduce a unified framework that adds an attention branch and CAM Fostering, an entropy-based regularizer, to improve Grad-CAM visualizations. Six backbone architectures (ResNet-50, DenseNet-201, EfficientNet-b0, ResNeXt-50, ConvNeXt, CoatNet-small) were trained, with and without our modifications, on five H&E-stained datasets. We measured explanation quality using coherence, complexity, confidence drop, and their harmonic mean (ADCC). Our method increased the ADCC in five of the six backbones; ResNet-50 saw the largest gain (+15.65%), and CoatNet-small achieved the highest overall score (+2.69%), peaking at 77.90% on the non-Hodgkin lymphoma set. The classification accuracy remained stable or improved in four models. These results show that combining attention and entropy produces clearer, more informative heatmaps without degrading performance. Our contributions include a modular architecture for both convolutional and hybrid models and a comprehensive, quantitative explainability evaluation suite. Full article

A transdermal drug delivery system based on hydrogel patches was explored, leveraging their sustained release properties and biocompatibility. Despite these advantages, conventional hydrogels often lack proper adhesion to the skin, limiting their practical application. To address this issue, we designed a skin-adhesive hydrogel using a polyacrylamide (PAM)/polydopamine (PDA) dual-network structure. The matrix combines the mechanical toughness of PAM with the strong adhesive properties of PDA, derived from mussel foot proteins, enabling firm tissue attachment and robust performance under physiological conditions. To demonstrate its applicability, the hydrogel was integrated with poly(lactic-co-glycolic acid) (PLGA) nanoparticles encapsulating the hydrophobic antioxidant vitamin E as a model compound. The resulting PAM/PDA@VitE hydrogel system exhibited improved swelling behavior, high water retention, and prolonged release of α-tocopherol. These results suggest that the PAM/PDA hydrogel platform is a versatile vehicle not only for vitamin E, but also for the transdermal delivery of various cosmetic and therapeutic agents. Full article

This study focuses on the issue of visual sustainability of colors in commercial historical districts, taking the historical area of Xueshi Street–Wuzoufang Street in Suzhou, China as a case study. It explores how to balance modern commercial development with the protection of historical culture. Due to the impact of commercialization and the introduction of various immature protection policies, historical districts often face the dilemma of coexisting “color conflict” and “color poverty”. Traditional color protection methods are either overly subjective or excessively quantitative, making it difficult to balance scientific rigor and adaptability. Therefore, this study provides a detailed literature review, compares and selects current quantitative color research methods, and proposes a comprehensive color analysis framework based on ViT (Vision Transformer), the CIEDE2000 color difference model, and K-means clustering (V-C-K framework). Using this framework, we conducted an in-depth analysis of the color-harmony situation in the studied area, aiming to accurately identify color issues in the district and provide optimization strategies. The experimental results show that the commercial colors of the Xueshi Street–Wuzoufang Street historical district exhibit a clear phenomenon of polarization: some areas have colors that are overly bright, leading to visual conflict, while others have colors that are too dull, lacking vitality and energy; furthermore, some areas display a mix of both conditions. Based on this situation, we then compared the extracted negative colors to the prohibited colors in the mainstream Munsell color system’s urban-color management guidelines. We found that colors with “high lightness and high saturation”, which are strictly limited by traditional color criteria, are not necessarily disharmonious, while “low lightness and low saturation” colors that are not restricted may not guarantee harmony either and could exacerbate the area’s “dilapidated feeling”. In other words, traditional color-protection standards often emphasize the safety of “low saturation and low lightness” colors unilaterally, ignoring that they can also cause dullness and discordance in certain environments. Under the $\Delta E$ (color difference value) threshold framework, color recognition is relatively more sensitive, balancing the inclusivity of “vibrant” colors and the caution against “dull” colors. Based on the above experimental results, this study proposes the following recommendations: (1) use the $\Delta E00$ threshold to control the commercial colors in the district, ensuring that the colors align with the historical atmosphere while possessing commercial vitality; (2) in protection practices, comprehensively utilize the ViT, CIEDE2000, and K-means quantitative methods (i.e., the V-C-K framework) to reduce subjective errors; (3) based on the above quantitative framework, while referencing the reasonable parts of existing protection guidelines, combine cooperative collaboration, cultural group color preference surveys, policy incentives, and continuous monitoring and feedback to construct an operable plan for the entire “recognition–analysis–control” process. Full article

Timely and accurate detection of agricultural disasters is crucial for ensuring food security and enhancing post-disaster response efficiency. This paper proposes a deployable UAV-based multimodal agricultural disaster detection framework that integrates multispectral and RGB imagery to simultaneously capture the spectral responses and spatial structural features of affected crop regions. To this end, we design an innovative stride–cross-attention mechanism, in which stride attention is utilized for efficient spatial feature extraction, while cross-attention facilitates semantic fusion between heterogeneous modalities. The experimental data were collected from representative wheat and maize fields in Inner Mongolia, using UAVs equipped with synchronized multispectral (red, green, blue, red edge, near-infrared) and high-resolution RGB sensors. Through a combination of image preprocessing, geometric correction, and various augmentation strategies (e.g., MixUp, CutMix, GridMask, RandAugment), the quality and diversity of the training samples were significantly enhanced. The model trained on the constructed dataset achieved an accuracy of 93.2%, an F1 score of 92.7%, a precision of 93.5%, and a recall of 92.4%, substantially outperforming mainstream models such as ResNet50, EfficientNet-B0, and ViT across multiple evaluation metrics. Ablation studies further validated the critical role of the stride attention and cross-attention modules in performance improvement. This study demonstrates that the integration of lightweight attention mechanisms with multimodal UAV remote sensing imagery enables efficient, accurate, and scalable agricultural disaster detection under complex field conditions. Full article

Storm surge forecasting presents a significant challenge for coastal resilience, particularly in typhoon-prone regions such as southeastern China, where compound flooding events lead to substantial socioeconomic losses. Although artificial intelligence (AI) models have shown strong potential in storm surge prediction, their inherent “black-box” nature limits both their interpretability and operational trust. In this study, we integrate a Vision Transformer (ViT) model with an explainable AI (XAI) method—specifically, Shapley value analysis (SHAP)—to develop an interpretable, high-performance storm surge forecasting framework. The baseline ViT model demonstrates excellent predictive skill, achieving spatiotemporal correlation coefficients exceeding 0.90 over a 12 h lead time. However, it exhibits systematic underestimations in topographically complex regions, such as semi-enclosed bays (e.g., up to 0.06 m). SHAP analysis reveals that the model primarily relies on the autocorrelation of historical surge levels rather than external wind forcing—contrary to the conventional physical understanding of storm surge dynamics. Guided by these insights, we introduce the surge time difference (ΔZ/Δt) as an explicit input feature to enhance the model’s physical representation. This modification yields substantial improvements: during the critical first hour of forecasting—a key window for disaster mitigation—the RMSE is reduced from 0.01 m to 0.005 m, while the correlation coefficient increases from 0.92 to 0.98. This study bridges the gap between data-driven forecasting and physical interpretability, offering a transparent and trustworthy framework for next-generation intelligent storm surge prediction. Full article

Accurate and efficient crop maps are essential for decision-makers to improve agricultural monitoring and management, thereby ensuring food security. The integration of advanced artificial intelligence (AI) models with hyperspectral remote sensing data, which provide richer spectral information than multispectral imaging, has proven highly effective in the precise discrimination of crop types. This systematic review examines the evolution of hyperspectral platforms, from Unmanned Aerial Vehicle (UAV)-mounted sensors to space-borne satellites (e.g., EnMAP, PRISMA), and explores recent scientific advances in AI methodologies for crop mapping. A review protocol was applied to identify 47 studies from databases of peer-reviewed scientific publications, focusing on hyperspectral sensors, input features, and classification architectures. The analysis highlights the significant contributions of Deep Learning (DL) models, particularly Vision Transformers (ViTs) and hybrid architectures, in improving classification accuracy. However, the review also identifies critical gaps, including the under-utilization of hyperspectral space-borne imaging, the limited integration of multi-sensor data, and the need for advanced modeling approaches such as Graph Neural Networks (GNNs)-based methods and geospatial foundation models (GFMs) for large-scale crop type mapping. Furthermore, the findings highlight the importance of developing scalable, interpretable, and transparent models to maximize the potential of hyperspectral imaging (HSI), particularly in underrepresented regions such as Africa, where research remains limited. This review provides valuable insights to guide future researchers in adopting HSI and advanced AI models for reliable large-scale crop mapping, contributing to sustainable agriculture and global food security. Full article

Background: Micelles have attracted significant interest in nanomedicine as drug delivery systems. This study investigates the morphology of micelles formed by the D-α-tocopherol polyethylene glycol 1000 succinate (VitE-TPGS) surfactant in the presence and absence of, respectively, a poorly soluble pharmaceutical compound (PSC), i.e., Eltrombopag (0.08 wt%) and CaCl₂ (0.03 wt%). The aim was to assess the micelles’ ability to solubilize the PSC and potentially shield it from Ca²⁺ ions, simulating in vivo conditions. Methods: For this purpose, we have developed a novel theoretical approach for analyzing Pair Distribution Function (PDF) data derived from Small-Angle X-ray Scattering (SAXS) measurements, based on the use of PDF’s moments. Results: Our spheroid-based model was able to characterize successfully the micellar morphology and their interactions with PSC and CaCl₂, providing detailed insights into their size, shape, and electron density contrasts. The presence of PSC significantly affected the shape and integral of the PDF curves, indicating incorporation into the micelles. This also resulted in a decrease in the micelle size, regardless of the presence of CaCl₂. When this salt was added, it reduced the amount of PSC within the micelles. This is likely due to a decrease in the overall PSC availability in solution, induced by Ca²⁺ ions. Conclusions: This advanced yet straightforward analytical model represents a powerful tool for characterizing and optimizing micelle-based drug delivery systems. Full article