Search Results (61,031)

Background: This study aimed to evaluate the influence of scleral contact lens (SCL) wear on optical coherence tomography (OCT) scan quality and structural measurements in patients with keratoconus. Methods: This retrospective observational study included 28 eyes of 28 keratoconus patients. All participants underwent a comprehensive ophthalmologic evaluation, including corneal topography and spectral-domain OCT (Optopol REVO 60). Two OCT measurement sessions were performed on the same day: one without SCLs and one after a 30–75 min adaptation period with Mini Misa^® scleral lenses. Recorded parameters included corneal and epithelial thicknesses, ganglion cell–inner plexiform layer (GCIPL) thickness, retinal nerve fiber layer (RNFL) thickness, and device-reported quality index (QI). Correlation analyses between topographic values, age, and OCT parameters were also conducted. Results: The mean age of participants was 32.96 ± 13.72 years. SCL wear significantly decreased anterior segment QI (6.76 ± 1.73 vs. 5.57 ± 2.34, p = 0.019) but improved posterior segment QI in both the ganglion (2.52 ± 1.03 vs. 5.76 ± 2.17, p < 0.001) and disc (2.82 ± 0.94 vs. 4.39 ± 1.87, p < 0.001) modules. Central corneal thickness remained stable, while central epithelial thickness decreased slightly (50.53 ± 6.66 µm vs. 47.59 ± 7.20 µm, p = 0.007). RNFL and GCIPL thicknesses showed no significant changes, except for minor sectoral variations. Steeper keratometry values correlated with lower QI in both conditions. Conclusions: SCLs enhanced posterior OCT scan quality while reducing anterior segment image clarity. These findings suggest that SCLs not only provide visual rehabilitation but also facilitate more reliable posterior segment imaging in keratoconus patients, despite mild interference with anterior segment OCT metrics. Further prospective studies are warranted to validate these results. Full article

Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity, while local-descriptor approaches can lose context and suffer from inter-class local-pattern overlap. To address these limitations, we propose a Global–Local Feature Fusion network that combines a frozen, pretrained global feature branch with a self-attention based multi-local feature fusion branch. Multiple random crops are encoded by a shared backbone (ResNet-12), projected to Query/Key/Value embeddings, and fused via scaled dot-product self-attention to suppress background noise and highlight discriminative local cues. The fused local representation is concatenated with the global feature to form robust class prototypes used in a prototypical-network style classifier. On four benchmarks, our method achieves strong improvements: Mini-ImageNet 70.31% ± 0.20 (1-shot)/85.91% ± 0.13 (5-shot), Tiered-ImageNet 73.37% ± 0.22/87.62% ± 0.14, FC-100 47.01% ± 0.20/64.13% ± 0.19, and CUB-200-2011 82.80% ± 0.18/93.19% ± 0.09, demonstrating consistent gains over competitive baselines. Ablation studies show that (1) naive local averaging improves over global-only baselines, (2) self-attention fusion yields a large additional gain (e.g., +4.50% in 1-shot on Mini-ImageNet), and (3) concatenating global and fused local features gives the best overall performance. These results indicate that explicitly modeling inter-patch relations and fusing multi-granularity cues produces markedly more discriminative prototypes in few-shot regimes. Full article

Addressing the challenges of multi-scene precision pesticide application for field broccoli crops and computational limitations of edge devices, this study proposes a lightweight broccoli detection method named DWG-YOLOv8, based on an improved YOLOv8s architecture. Firstly, Ghost Convolution is introduced into the C2f module, and the standard CBS module is replaced with Depthwise Separable Convolution (DWConv) to reduce model parameters and computational load during feature extraction. Secondly, a CDSL module is designed to enhance the model’s feature extraction capability. The CBAM attention mechanism is incorporated into the Neck network to strengthen the extraction of channel and spatial features, enhancing the model’s focus on the target. Experimental results indicate that compared to the original YOLOv8s, the DWG-YOLOv8 model has a size decreased by 35.6%, a processing time reduced by 1.9 ms, while its precision, recall, and mean Average Precision (mAP) have increased by 1.9%, 0.9%, and 3.4%, respectively. In comparative tests on complex background images, DWG-YOLOv8 showed reductions of 1.4% and 16.6% in miss rate and false positive rate compared to YOLOv8s. Deployed on edge devices using field-collected data, the DWG-YOLOv8 model achieved a comprehensive recognition accuracy of 96.53%, representing a 5.6% improvement over YOLOv8s. DWG-YOLOv8 effectively meets the lightweight requirements for accurate broccoli recognition in complex field backgrounds, providing technical support for object detection in intelligent precision pesticide application processes for broccoli. Full article

Background: Type 2 diabetes mellitus (DM) is a major cardiovascular risk factor that induces monocyte dysfunction and contributes to their accumulation in atherosclerotic lesions. Monocyte recruitment and accumulation in the tissues contribute to chronic inflammation and are essential to the pathobiology of diabetes-induced atherosclerosis. However, the mechanisms that drive the accumulation of monocytes in the diabetic environment are not clearly understood. Methods: Primary monocytes from type 2 (T2) DM and non-T2DM individuals were isolated using magnet-assisted cell sorting. To examine the influence of a diabetic milieu on monocyte function, monocytes from T2DM patients, db/db mice, or human monocytes subjected to hyperglycaemia were analysed for their responses to pro-atherogenic cytokines using Boyden chamber assays. Furthermore, the interactions of non-diabetic and diabetic monocytes with TNFα-inflamed endothelium were studied using live-cell imaging under physiological flow conditions. RT-qPCR and FACS were used to study the expression of relevant molecules involved in monocyte-endothelium interaction. Results: CD14⁺CD16⁻ monocytes isolated from T2DM patients or monocytes exposed to hyperglycaemic conditions showed reduced chemotactic responses towards atherosclerosis-promoting cytokines, CCL2 and CX3CL1, indicating monocyte dysfunction. Under flow conditions, the transendothelial migration (TEM) capacity of T2DM monocytes was significantly reduced. Even though these monocytes adhered to the endothelial monolayer, only a few transmigrated. Interestingly, the T2DM monocytes and monocytes exposed to hyperglycaemic conditions accumulated in the ablumen following transendothelial migration. The time period in the ablumen of T2DM cells was prolonged, as there was a significant impairment of the reverse transendothelial migration (rTEM). Mechanistically, the T2DM milieu specifically induced the activation of monocyte integrins, Macrophage-1 antigen (Mac-1; integrin αMβ2 consisting of CD11b and CD18), and Lymphocyte function-associated antigen 1 (LFA-1; αLβ2 consisting of CD11a and CD18). Furthermore, elevated levels of CD18 transcripts were detected in T2DM monocytes. Junctional Adhesion Molecule 3 (JAM-3)–MAC-1 interactions are known to impede rTEM and T2DM milieu-potentiated JAM-3 expression in human coronary artery endothelial cells (HCAEC). Finally, the overexpression of JAM-3 on HCAEC was sufficient to completely recapitulate the impaired rTEM phenotype. Conclusions: Our results revealed for the first time that the enhanced T2DM monocyte accumulation in the ablumen is not secondary to the elevated transmigration through the endothelium. Instead, the accumulation of monocytes is due to the direct consequence of a dysfunctional rTEM, potentially due to enhanced JAM3-MAC1 engagement. Our results highlight the importance of restoring the rTEM capacity of monocytes to reduce monocyte accumulation-dependent inflammation induction and atherogenesis in the T2DM environment. Full article

Obstructive sleep apnea (OSA) is a chronic disorder associated with serious health consequences, yet many cases remain undiagnosed due to limited access to standard diagnostic tools such as polysomnography. Recent advances in artificial intelligence (AI) have enabled the development of deep convolutional neural networks that analyze craniofacial radiographs, particularly lateral cephalograms, to detect anatomical risk factors for OSA. The goal of this approach is not to replace polysomnography but to identify individuals with a high suspicion of OSA at the primary care or dental level and to guide them toward timely and appropriate diagnostic evaluation. Current studies have demonstrated that AI can recognize patterns of oropharyngeal crowding and anatomical imbalance of the upper airway with high accuracy, often exceeding manual assessment. Furthermore, interpretability analyses suggest that AI focuses on clinically meaningful regions, including the tongue, mandible, and upper airway. Unexpected findings such as predictive signals from outside the airway also suggest AI may detect subtle features associated with age or obesity. Ultimately, integrating AI with cephalometric imaging may support early screening and referral for polysomnography, improving care pathways and reducing delays in OSA treatment. Full article

Jets and outflows are key components of low-mass star formation, regulating accretion and shaping the surrounding molecular clouds. These flows, traced by molecular species at (sub)millimeter wavelengths (e.g., CO, SiO, SO, H₂CO, and CH₃OH) and by atomic, ionized, and molecular lines in the infrared (e.g., H₂, [Fe II], [S I]), originate from protostellar accretion disks deeply embedded within dusty envelopes. Jets play a crucial role in removing angular momentum from the disk, thereby enabling continued mass accretion, while directly preserving a record of the protostar’s outflow history and potentially providing indirect insights into its accretion history. Recent advances in high-resolution, high-sensitivity observations, particularly with the James Webb Space Telescope (JWST) in the infrared and the Atacama Large Millimeter/submillimeter Array (ALMA) at (sub)millimeter wavelengths, have revolutionized studies of protostellar jets and outflows. These instruments provide complementary views of warm, shock-excited gas and cold molecular component of the jet–outflow system. In this review, we discuss the current status of observational studies that reveal detailed structures, kinematics, and chemical compositions of protostellar jets and outflows. Recent analyses of mass-loss rates, velocities, rotation, molecular abundances, and magnetic fields provide critical insights into jet launching mechanisms, disk evolution, and the potential formation of binary systems and planets. The synergy of JWST’s infrared sensitivity and ALMA’s high-resolution imaging is advancing our understanding of jets and outflows. Future large-scale, high-resolution surveys with these facilities are expected to drive major breakthroughs in outflow research. Full article

The high spatiotemporal resolution of optically pumped magnetometers (OPMs) makes them an essential tool for functional brain imaging, enabling accurate recordings of neuronal activity. However, physiological signals such as eye blinks and cardiac activity overlap with neural magnetic signals in the frequency domain, resulting in contamination and creating challenges for the observation of brain activity and the study of neurological disorders. To address this problem, an automatic physiological artifact removal method based on OPM magnetic reference signals and a channel attention mechanism is proposed. The randomized dependence coefficient (RDC) is employed to evaluate the correlation between independent components and reference signals, enabling reliable recognition of artifact components and the construction of training and testing datasets. A channel attention mechanism is subsequently introduced, which fuses features from global average pooling (GAP) and global max pooling (GMP) layers through convolution to establish a data-driven automatic recognition model. The backbone network is further optimized to enhance performance. Experimental results demonstrate a strong correlation between the magnetic reference signals and artifact components, confirming the reliability of magnetic signals as artifact references for OPM-MEG. The proposed model achieves an artifact recognition accuracy of 98.52% and a macro-average score of 98.15%. After artifact removal, both the event-related field (ERF) responses and the signal-to-noise ratio (SNR) are significantly improved. Leveraging the flexible and modular characteristics of OPM-MEG, this study introduces an artifact recognition framework that integrates magnetic reference signals with an attention mechanism. This approach enables highly accurate automatic recognition and removal of OPM-MEG artifacts, paving the way for real-time, automated data analysis in both scientific research and clinical applications. Full article

Indonesia boasts diverse tropical fruits like ciplukan, harendong, and kecapi, which are nutrient-rich but underutilized. To address this, an automated fruit recognition system was developed using Convolutional Neural Network (CNN) with MobileNet architecture, leveraging Depthwise Separable Convolution (DSC) for efficiency. The model was trained on 5000 images of five local fruits (224 × 224 pixels), split into 70% training, 20% validation, and 10% testing. Optimized for Android, the system enables real-time identification with minimal hardware requirements (e.g., 2 GB RAM for low-end devices). Evaluation metrics (accuracy, precision, recall) achieved 97.43% accuracy, demonstrating MobileNet’s effectiveness. This study highlights deep learning’s potential in preserving and promoting Indonesia’s indigenous fruits. Full article

The background of the article refers to the diagnosis of focal liver lesions (FLLs) through contrast-enhanced ultrasound (CEUS) based on the integration of spatial and temporal information. Traditional computer-aided diagnosis (CAD) systems predominantly rely on static images, which limits the characterization of lesion dynamics. This study aims to assess the effectiveness of Transformer-based architectures in enhancing CAD performance within the realm of liver pathology. The methodology involved a systematic comparison of deep learning models for the analysis of CEUS images and videos. For image-based classification, a Hybrid Transformer Neural Network (HTNN) was employed. It combines Vision Transformer (ViT) modules with lightweight convolutional features. For video-based tasks, we evaluated a custom spatio-temporal Convolutional Neural Network (CNN), a CNN with Long Short-Term Memory (LSTM), and a Video Vision Transformer (ViViT). The experimental results show that the HTNN achieved an outstanding accuracy of 97.77% in classifying various types of FLLs, although it required manual selection of the region of interest (ROI). The video-based models produced accuracies of 83%, 88%, and 88%, respectively, without the need for ROI selection. In conclusion, the findings indicate that Transformer-based models exhibit high accuracy in CEUS-based liver diagnosis. This study highlights the potential of attention mechanisms to identify subtle inter-class differences, thereby reducing the reliance on manual intervention. Full article

To investigate the failure characteristics and high-strain-rate mechanical response of polyvinyl alcohol-modified alkali-activated materials (PAAMs) under static and dynamic impact loads, quasi-static and uniaxial impact compression tests were performed on AAMs with varying PVA content. These tests employed a universal testing machine and an 80 mm diameter split Hopkinson pressure bar (SHPB). Digital image correlation (DIC) was then utilized to study the surface strain field of the composite material, and the crack propagation process during sample failure was analyzed. The experimental results demonstrate that the compressive strength of AAMs diminishes with higher PVA content, while the flexural strength initially increases before decreasing. It is suggested that the optimal PVA content should not exceed 5%. When the strain rate varies from 25.22 to 130.08 s⁻¹, the dynamic compressive strength, dissipated energy, and dynamic compressive increase factor (DCIF) of the samples all exhibit significant strain rate effects. Furthermore, the logarithmic function model effectively fits the dynamic strength evolution pattern of AAMs. DIC observations reveal that, under high strain rates, the crack mode of the samples gradually transitions from tensile failure to a combined tensile–shear multi-crack pattern. Furthermore, the crack propagation rate rises as the strain rate increases, which demonstrates the toughening effect of PVA on AAMs. Full article

Background: Non-Hodgkin lymphomas (NHLs) are a heterogeneous group of indolent or aggressive lymphoproliferative neoplasms arising from lymph nodes or in extranodal locations. Computed tomography (CT) is the imaging modality of choice, while the definitive diagnosis is confirmed by analyzing tissue samples. The aim of this study was to determine the correlation between CT characteristics and histopathological types of abdominal lymphomas. Methods: A retrospective cross-sectional study included 119 patients with histopathologically confirmed abdominal lymphomas who underwent CT of the abdomen and pelvis prior to treatment. The following CT parameters were extracted: morphological presentation (enlarged lymph nodes/conglomerates, solid mass/masses, gastrointestinal wall thickening, abdominal organ involvement, intra- and extraperitoneal infiltrates), location, two-dimensional size, propagation if present, and postcontrast enhancement. Results: Enlarged lymph nodes were a slightly more common CT morphological appearance in the indolent B NHL group, while gastrointestinal (GI) wall thickening, solid masses, and infiltrates were more frequent in the aggressive B NHL group (p = 0.0256). Aggressive B-cell lymphomas had larger size at time of diagnosis compared to other types (p = 0.0436). CT postcontrast enhancement showed lymphomas originating from the gastrointestinal tract, which presented as wall thickening, had the highest enhancement (p = 0.0065 and p = 0.0485). Conclusions: Observed differences in abdominal lymphomas’ histopathological and imaging characteristics including location/origin, CT morphological appearance, and postcontrast enhancement revealed that extranodal lymphomas were more often of the aggressive B-cell type, aggressive B-cell types were larger, and GI tract lymphomas showed the most prominent enhancement. These findings can help in the diagnostic process and enable better management of lymphomas. Full article

The Auxin-Regulated Gene Involved in Organ Size (ARGOS) proteins have crucial regulatory effects on organ size and responses to environmental stresses. Despite their importance, Brassica oleracea ARGOS gene members and their functions in response to abiotic stresses have not been thoroughly investigated. In this study, we identified 40 ARGOS genes via a genome wide analysis of cauliflower and two other B. oleracea morphotypes as well as Brassica rapa, Brassica nigra, and Raphanus sativus. Expression pattern analyses indicated that these genes are responsive to multiple abiotic stresses, including salinity, heat, cold, and diverse hormones. Notably, the expression of an ARGOS-like gene (BobARL2) was upregulated in cauliflower treated with 1-aminocyclopropane-1-carboxylic acid (ACC). Moreover, the overexpression of BobARL2 decreased ethylene sensitivity, resulting in less inhibition of root elongation compared to the wild-type. Additionally, the overexpression lines exhibited enhanced salt tolerance. A yeast two-hybrid assay and luciferase complementation imaging (LCI) assay confirmed that BobARL2 can interact with Reversion-to-ethylene sensitivity Like4 (BobRTL4), which negatively regulates ethylene signal transduction. These findings advance our understanding of the evolution and functional roles of ARGOS genes in cauliflower and other Brassicaceae species, particularly in relation to abiotic stress responses, while also offering valuable insights relevant to the genetic improvement and breeding of novel varieties. Full article

This study introduces an Encyclopedic Agent for children’s learning that integrates multimodal retrieval with retrieval-augmented generation (RAG). To support this framework, we construct a dataset of 9524 Wikipedia pages covering 935 encyclopedia topics, each converted into images with associated topical queries and explanations. Based on this dataset, we fine-tune SigLIP, a vision–language retrieval model, using LoRA adaptation on 8484 training pairs, with 1040 reserved for testing. Experimental results show that the fine-tuned SigLIP significantly outperforms baseline models such as ColPali in both accuracy and latency, enabling efficient and precise document-image retrieval. Combined with GPT-5 for response generation, the Encyclopedic Agent delivers illustrated, interactive Q&A that is more accessible and engaging for children compared to traditional text-only methods. These findings highlight the feasibility of applying multimodal retrieval and RAG to educational agents, offering new possibilities for personalized, child-centered learning in domains such as science, history, and the arts. Full article

Background: Microwave ablation (MWA) is an effective, minimally invasive therapy for benign thyroid nodules; however, the treatment response varies considerably. Identifying imaging biomarkers that can predict volumetric outcomes may optimize patient selection. Diffusion-weighted MRI (DW-MRI) offers a noninvasive assessment of tissue microstructure through apparent diffusion coefficient (ADC) measurements, which may correlate with ablation efficacy. Methods: In this prospective study, 48 patients with 50 cytologically confirmed benign thyroid nodules underwent diffusion-weighted magnetic resonance imaging (DW-MRI) before minimally invasive ablation (MWA). Baseline ADC values were measured, and nodule volumes were assessed by ultrasound at baseline and 1, 3, and 6 months postprocedure. The volume reduction ratio (VRR) was calculated, and associations with baseline variables were analyzed via Pearson correlation and multivariable linear regression. ROC curve analysis was used to evaluate the diagnostic performance of ADC in predicting significant volume reduction (VRR ≥ 50%). Results: Lower baseline ADC values were strongly correlated with greater VRR at 3 months (r = −0.525, p < 0.001) and 6 months (r = −0.564, p < 0.001). Multivariable regression revealed that the baseline ADC was the sole independent predictor of the 6-month VRR (β = −19.52, p = 0.0004). ROC analysis demonstrated excellent discriminative performance (AUC = 0.915; 95% CI: 0.847–0.971), with an ADC cutoff of 2.20 × 10⁻³ mm²/s yielding 90.9% sensitivity and 83.3% specificity for predicting a favorable volumetric response. Conclusions: Baseline ADC values derived from DW-MRI strongly predict volumetric response following microwave ablation of benign thyroid nodules. Incorporating ADC assessment into preprocedural evaluation may enhance patient selection and improve therapeutic outcomes. Full article

Background/Objectives: Qualitative child-centered research on pediatric obesity treatment and body image remains limited. This study aimed to explore children’s and adolescents’ experiences with hospital-based obesity treatment and how these experiences relate to body image. Methods: A full-day workshop including three main participatory tasks was conducted in two groups of children (9–13 years) and adolescents (14–18 years), focusing on their experiences with obesity treatment and body image. Data were audiotaped, transcribed verbatim, and analyzed using reflexive thematic analysis. Results: Four main themes emerged, reflecting different aspects of participants’ experiences. The first theme, Talk with me and not my parents, encompassed participants’ desire for greater agency, as children described lacking information and feeling excluded from consultations. The second theme, Experiences of communication with healthcare professionals (HCPs) about obesity, concerned participants’ perceptions of trust, support, and non-judgmental communication, with some adolescents expressing a need for additional psychological support. The third theme, Internalization of lifestyle advice, indicated that healthy diet was viewed as the primary focus of obesity treatment, while physical activity received less attention. The final theme, Perceptions of the body, conveyed mixed experiences with weighing and most participants perceived weight loss as success in treatment and weight gain as failure. The participants shared experiences of weight-based bullying, perceived stigma, and challenges with maintaining a positive body image in a society with stereotypical thin and muscular body ideals. Conclusions: Body image was influenced by HCPs’ emphasis on health and body size, and by their own internalized perceptions, influenced by societal ideals and experiences of stigma. Full article