Search Results (1,415)

The response of plants to elevated atmospheric [CO₂] is highly dynamic and influenced by developmental stage, yet its role in photosynthetic acclimation remains underexplored. This study examines the physiological and molecular responses of wheat (Triticum durum, var. Amilcar) to elevated [CO₂] (700 ppm vs. 400 ppm) at two distinct developmental stages: the vegetative stage at the end of the elongation stage and the reproductive stage at the beginning of ear emergence (Z39 and Z51, respectively). Wheat plants at the developmental stage Z39, cultivated under elevated [CO₂], maintained photosynthetic rates despite a carbohydrate build-up. However, at Z51, photosynthetic acclimation became more evident as the decline in Rubisco carboxylation capacity (Vc_max) persisted, but also stomatal conductance and diffusion were decreased. This was accompanied by the up-regulation of the CA1 and CA2 genes, likely as a compensatory mechanism to maintain CO₂ supply. Additionally, hormonal adjustments under elevated [CO₂], including increased auxin and bioactive cytokinins (zeatin and isopentenyl adenine), may have contributed to delayed senescence and nitrogen remobilization, sustaining carbon assimilation despite biochemical constraints. These findings highlight the developmental regulation of photosynthetic acclimation, emphasizing the need for the stage-specific assessments of crop responses to future atmospheric conditions. Full article

There is a lack of studies on the effectiveness of polar-transformed spectrograms in the visualization and prediction of cardiac arrhythmias from electrocardiogram (ECG) data. In this study, single-lead ECG waveforms were converted into two-dimensional rectangular time–frequency spectrograms and polar time–frequency spectrograms. Three pre-trained convolutional neural network (CNN) models (ResNet50, MobileNet, and DenseNet121) served as baseline networks for model development and testing. Prediction performance and visualization quality were evaluated across various image resolutions. The trade-offs between image resolution and model capacity were quantitatively analyzed. Polar-transformed spectrograms demonstrated superior delineation of R-R intervals at lower image resolutions (e.g., 96 × 96 pixels) compared to conventional spectrograms. For deep-learning-based classification of cardiac arrhythmias, polar-transformed spectrograms achieved comparable accuracy to conventional spectrograms across all evaluated resolutions. The results suggest that polar-transformed spectrograms are particularly advantageous for deep CNN predictions at lower resolutions, making them suitable for edge computing applications where the reduced use of computing resources, such as memory and power consumption, is desirable. Full article

This paper proposes a hybrid method for skin lesion classification combining deep learning features with conventional descriptors such as HOG, Gabor, SIFT, and LBP. Feature extraction was performed by extracting features of interest within the tumor area using suggested fusion methods. We tested and compared features obtained from different deep learning models coupled to HOG-based features. Dimensionality reduction and performance improvement were achieved by Principal Component Analysis, after which SVM was used for classification. The compared methods were tested on the reference database skin cancer-malignant-vs-benign. The results show a significant improvement in terms of accuracy due to complementarity between the conventional and deep learning-based methods. Specifically, the addition of HOG descriptors led to an accuracy increase of 5% for EfficientNetB0, 7% for ResNet50, 5% for ResNet101, 1% for NASNetMobile, 1% for DenseNet201, and 1% for MobileNetV2. These findings confirm that feature fusion significantly enhances performance compared to the individual application of each method. Full article

The rocks of the Burpala alkaline intrusion contain a wide range of rare minerals that concentrate rare earth elements (REEs), Nb, Th, Li, and other incompatible elements. One of the examples of the occurrence of such mineralization is albite–aegirine rocks located at the contact zone between the intrusion and the host terrigenous–sedimentary rock. In albite–aegirine rocks, cubic crystals of “metaloparite”, partially or completely substituted by bastnäsite-(Ce) and polymorphic TiO₂ phases (anatase and rutile) mainly represent the rare metal minerals. In albite–aegirine rocks, trace element minerals are predominantly represented by cubic crystals of “metaloparite”, which are partially or completely replaced by bastnäsite-(Ce) and polymorphic TiO₂ phases such as anatase and rutile. Additionally, Th-bearing zircon (up to 17.7 wt% ThO₂) and a variety of unidentified minerals containing REEs, Th, and Nb were detected. The obtained data indicate that bastnäsite-(Ce) is the result of the recrystallization of “metaloparite” accompanied by the formation of Th-bearing zircon and Nb-bearing rutile (up to 9.9 wt% Nb₂O₅) and the separation of various undiagnosed, unidentified LREE phases. Our studies show that remobilization of LREEs, HFSEs, and local enrichment of rocks in these elements occurred due to the effects of residual fluid enriched in fluorine and carbon dioxide. Full article

Daoist robes in the Ming Dynasty literature underwent a marked transformation from exclusive religious vestments to widespread secular attire. Originally confined to Daoist priests and sacred rites, these garments began to appear in everyday work, entertainment, and ceremonies across social strata. Drawing on a hand-coded corpus of novels that yields robe related passages, and by analyzing textual references from Ming novels, Daoist canonical works, and visual artifacts, and applying clothing psychology and semiotic theory, this study elucidates how Daoist robes were re-coded as secular fashion symbols. For example, scholar-officials donned Daoist robes to convey moral prestige, laborers adopted them to signal upward mobility, and merchants donned them to impersonate the educated elite for commercial gain. By integrating close textual reading with cultural theory, the article advances a three-stage model, sacred uniform, ritual costume, and secular fashion, that clarifies the semantic flow of Daoist robes. In weddings and funerals, many commoners flaunted Daoist robes despite sumptuary laws, using them to assert honor and status. These adaptations reflect both the erosion of Daoist institutional authority and the dynamic process of identity construction through dress in late Ming society. Our interdisciplinary analysis highlights an East Asian perspective on the interaction of religion and fashion, offering historical insight into the interplay between religious symbolism and sociocultural identity formation. Full article

Topological insulators (TIs) hold considerable promise for the advancement of optoelectronic technologies, including spectroscopy, imaging, and communication, owing to their remarkable optical and electrical characteristics. This study proposes a novel combination of Bi${}_2$Se${}_3$ TIs and ReSe${}_2$ for self-powered broadband photodetectors with high sensitivity and fast response time. The Bi${}_2$Se${}_3$/ReSe${}_2$ heterojunction photodetector achieves broadband response spectra ranging for 375 nm to 1 $\mathsf{\mu }$m. It demonstrates a significant responsivity of 64 mA/W at a wavelength of 600 nm (1 mW/cm${}^2$), exhibits a rapid response speed of 345 $\mathsf{\mu }$s rise/336 $\mathsf{\mu }$s fall time, and has a 3 dB bandwidth of 1.4 kHz under zero-bias conditions. The high performance can be attributed to the suitable energy band structure of Bi${}_2$Se${}_3$/ReSe${}_2$ and high carrier mobility in surface states of Bi${}_2$Se${}_3$. Excitingly, self-powered TIs photodetectors allow for high-quality signal transmission. The TIs employed in photodetectors can stimulate the production of new optoelectronic features, but they could also be used for highly integrated photonic circuits in the future. Full article

Side-scan sonar (SSS) image classification is crucial for underwater applications, but the trade-off between the accuracy afforded by high-resolution images and the associated computational cost challenges deployment, particularly on resource-constrained platforms like AUVs. This study systematically investigates and quantifies this accuracy–efficiency trade-off in SSS image classification by varying input resolution. Using two distinct SSS datasets and a resolution-adaptive deep learning strategy employing MobileNetV2 and ResNet variants across six resolutions, we evaluated classification accuracy and computational metrics. Results demonstrate a clear inverse relationship: decreasing resolution significantly reduces computational load and processing times but lowers classification accuracy, with the degradation being more pronounced for the more complex four-class dataset. Notably, model test accuracy did not necessarily increase monotonically with resolution. Importantly, acceptable accuracy levels above 90% or 80% could be maintained at significantly lower resolutions, offering substantial efficiency gains. In conclusion, strategically reducing SSS image resolution based on application-specific accuracy requirements is a viable approach for optimizing computational resources. This work provides a quantitative framework for navigating this trade-off and underscores the need for developing SSS-specific architectures for future advancements. Full article

Objective: The classification of skin cancer is very helpful in its early diagnosis and treatment, considering the complexity involved in differentiating AK from BCC and SK. These conditions are generally not easily detectable due to their comparable clinical presentations. Method: This paper presents a new approach to hyperspectral imaging for enhancing the visualization of skin lesions called the Spectrum-Aided Vision Enhancer (SAVE), which has the ability to convert any RGB image into a narrow-band image (NBI) by combining hyperspectral imaging (HSI) to increase the contrast of the area of the cancerous lesions when compared with the normal tissue, thereby increasing the accuracy of classification. The current study investigates the use of ten different machine learning algorithms for the purpose of classification of AK, BCC, and SK, including convolutional neural network (CNN), random forest (RF), you only look once (YOLO) version 8, support vector machine (SVM), ResNet50, MobileNetV2, Logistic Regression, SVM with stochastic gradient descent (SGD) Classifier, SVM with logarithmic (LOG) Classifier and SVM- Polynomial Classifier, in assessing the capability of the system to differentiate AK from BCC and SK with heightened accuracy. Results: The results demonstrated that SAVE enhanced classification performance and increased its accuracy, sensitivity, and specificity compared to a traditional RGB imaging approach. Conclusions: This advanced method offers dermatologists a tool for early and accurate diagnosis, reducing the likelihood of misclassification and improving patient outcomes. Full article

Recently, there has been a rise in skin cancer cases, for which early detection is highly relevant, as it increases the likelihood of a cure. In this context, this work presents a benchmarking study of standard Convolutional Neural Network (CNN) architectures for automated skin lesion classification. A total of 38 CNN architectures from ten families (ConvNeXt, DenseNet, EfficientNet, Inception, InceptionResNet, MobileNet, NASNet, ResNet, VGG, and Xception) were evaluated using transfer learning on the HAM10000 dataset for seven-class skin lesion classification, namely, actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi, and vascular lesions. The comparative analysis used standardized training conditions, with all models utilizing frozen pre-trained weights. Cross-database validation was then conducted using the ISIC 2019 dataset to assess generalizability across different data distributions. The ConvNeXtXLarge architecture achieved the best performance, despite having one of the lowest performance-to-number-of-parameters ratios, with 87.62% overall accuracy and 76.15% F1 score on the test set, demonstrating competitive results within the established performance range of existing HAM10000-based studies. A proof-of-concept multiplatform mobile application was also implemented using a client–server architecture with encrypted image transmission, demonstrating the viability of integrating high-performing models into healthcare screening tools. Full article

Background/Objectives: In recent years, there has been a significant increase in the number of women with breast cancer. Breast cancer prediction is defined as a medical data analysis and image processing problem. Experts may need artificial intelligence technologies to distinguish between benign and malignant tumors in order to make decisions. When the studies in the literature are examined, it can be seen that applications of deep learning algorithms in the field of medicine have achieved very successful results. Methods: In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. Images of pathological specimens from breast biopsies consisting of two classes, benign and malignant, were used for classification analysis. To limit the computational time and speed up the analysis process, 10,000 images, 6172 IDC-negative and 3828 IDC-positive, were selected. Of the images, 80% were used for training, 10% were used for validation, and 10% were used for testing the trained model. Results: The results demonstrate that DenseNet201 achieved the highest classification accuracy of 89.4%, with a precision of 88.2%, a recall of 84.1%, an F1 score of 86.1%, and an AUC score of 95.8%. Conclusions: In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability. Full article

The aim of this study was to improve the water–gas shift efficiency of Co/CeO₂ catalyst by incorporating praseodymium and rhenium. The catalysts were synthesized via combustion method and characterized using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, Scanning Electron Microscope (SEM), H₂-temperature programmed reduction (H₂-TPR), NH₃-temperature programmed desorption (NH₃-TPD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). These characterization techniques evaluate the increase of the surface acidity and oxygen vacancies in Co-based catalysts, which leads to an increase in water–gas shift performance because CO molecules prefer to react with surface oxygen, then followed by the production of CO₂ and oxygen vacancies which act as active sites for H₂O dissociation. The 1%Re4%Co/Ce-5%Pr-O catalyst exhibited a maximum CO conversion of 86% at 450 °C, substantially outperforming the 5%Co/Ce-5%Pr-O catalyst, which showed only 62% CO conversion at 600 °C. In addition, 1%Re4%Co/Ce-5%Pr-O catalyst is more resistant towards deactivation than 5%Co/Ce-5%Pr-O. The result presented that the catalytic activity of 1%Re4%Co/Ce-5%Pr-O catalyst was kept constant for the whole period of 50 h, while a 6% decrease in water–gas shift activity was found for the 5%Co/Ce-5%Pr-O catalyst. Moreover, the addition of rhenium into the Co/Ce-Pr-O catalyst reveals that the enhancement of oxygen vacancy concentration, oxygen mobility, and surface acidity, thereby enhances CO conversion efficiency. Full article

Mobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals. Full article

CK2α is a kinase important for essential cellular and biological processes. CK2α is ubiquitously expressed, albeit at different tissue levels, and its transcript levels are dysregulated in disease. However, there is limited knowledge on the regulation of CK2α gene expression. The best one studied, the human CSNK2A1 (CK2α) gene promoter, contains uncharacterized binding motifs for NF-κB. Our goal was to investigate the role of NF-κB in Csnk2a1 promoter regulation. We cloned the mouse Csnk2a1 promoter which had significant sequence homology with the human CSNK2A1 promoter. Using promoter deletions, we identified a minimal promoter region containing transcription factor motifs (NF-κB, Ets-1, Sp1) consistent with those published for the CSNK2A1 promoter. Electrophoretic mobility shift assays demonstrated specific NF-κB subunit binding to the minimal promoter. NF-κB subunit transfection and extracellular NF-κB stimulation in non-tumor cell lines led to increased transactivation of the mouse minimal promoter. These data, together with data on the regulation of NF-κB by CK2 kinase activity, suggest a positive-feedback loop between CK2α and NF-κB. Non-tumor cell line re-plating and increased percent confluence upregulated Csnk2a1 transcript levels which differed from tumor cell line published data. In summary, Csnk2a1 promoter is regulated by NF-κB signaling and during cellular proliferation. Full article

Clay minerals contain significant amounts of Fe in their alumosilicate framework, and this structural Fe can be reduced and re-oxidized, constituting a potentially renewable source of reduction equivalents in sedimentary environments. However, dissolution and/or clay mineral transformations during microbial Fe reduction contradict this concept. Here, we investigate how Fe reduction and re-oxidation affect the propensity of Fe to be released from the clay mineral structure and use selective sequential extractions in combination with Mössbauer spectroscopy. Negligible amounts of Fe were released in the sequential extraction of high Fe content clay minerals NAu-1 and NAu-2. Once aqueous Fe(II) was added as a reductant, the extraction procedure recovered the initially added Fe amount and up to 30% of the Fe from the clay mineral structure as both Fe(II) and Fe(III). Similar extents of Fe mobilization were found for clay minerals partly reduced (7%–20%) with dithionite, suggesting that mobilization was reduction-induced and independent of the source of reduction equivalents (Fe(II), dithionite). Although higher Fe reduction extents mobilized more structural Fe, i.e., >90% in fully reduced clay minerals, re-oxidation largely reverted the reduction-induced Fe mobilization in clay minerals. Our finding of reduction-driven Fe mobilization provides a plausible explanation for conflicting reports on Fe release from clay minerals and how extensive Fe atom exchange between aqueous and clay mineral Fe occurs. Full article

Background/Objectives: Achilles tendon pathologies, such as Achilles tendinitis, tendinosis, ruptures, and equinus contracture, cause pain and functional impairment. While surgical intervention is indicated in some cases, many patients are successfully managed with nonoperative treatment. The goal of this review was to evaluate the current evidence-based treatments for the nonoperative management of Achilles tendon disorders, focusing on indications and clinical outcomes. Methods: A scoping review of the literature was conducted from 2015 to 2025 from the PubMed database. Research published in the last ten years was included if it addressed nonoperative treatments for Achilles tendinopathy, acute ruptures, and/or equinus contracture. The outcome measures of interest included functional outcomes, re-rupture rates, and overall patient satisfaction. Results: Nonoperative management results in favorable outcomes for a wide range of Achilles tendon pathologies. Eccentric loading is supported for chronic tendinopathy, and functional rehabilitation programs with early mobilization have shown comparable outcomes to surgical repair for acute tendon ruptures. Combination therapy for the nonoperative management of equinus is favored. These therapies include stretching protocols, casting, and the botulinum toxin. Conclusions: The literature supports the notion that nonoperative management strategies for Achilles tendon pathologies provide symptom relief and functional improvement in patients. However, these treatment plans should be individualized and tailored to patient-specific goals. Full article