Search Results (15)

This paper focuses on the two-block composite convex optimization problems prevalent in signal recovery, image processing and related fields. Although Alternating Block Coordinate Descent (ABCD) is simple to implement and low in computational cost, its objective function often oscillates during iterations. We propose monotone ABCD (MABCD) to guarantee the monotonic descent of the objective. On this basis, an inexact variant iMABCD is presented, which allows inexact subproblem solutions to lower computational overhead and adaptation to realistic application scenarios. Rigorous theoretical analysis of numerical experiments demonstrates that both algorithms attain the $O(1/k^2)$ convergence rate. The monotonic constraint effectively removes undesirable iterative fluctuations and stabilizes the optimization process, while the inexact updating rule greatly improves practical applicability. Full article

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset $t_{\mathrm{pred}}$ at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5 $= 73.4\%$, MAE$= 1.357$ turns); the full system achieves OW@5 $= 70.7\%$ with MAE $= 2.442$ turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

High-modulus asphalt binders are increasingly used to improve rutting resistance and enable pavement thickness reduction. Conventional binder indices do not always capture the stress-dependent response of high-modulus systems under heavy loading, and quantitative rules for selecting a high-modulus additive dosage are still limited. This study develops a full-temperature-range evaluation and dosage determination framework for high-modulus additive-modified asphalt binders (HMABs) produced on an SBS-modified base binder. Four binders were prepared with high-modulus additive dosages of 0%, 17%, 22% and 28% with a binder mass basis. High-temperature performance was evaluated by PG grading and an enhanced MSCR protocol that included 0.1, 3.2, 6.4 and 12.8 kPa. MSCR temperatures were selected based on PG results. Intermediate-temperature performance was evaluated using LAS at 25 °C with VECD-based fatigue analysis on RTFO + PAV-aged binders. Low-temperature cracking was evaluated using ABCD on PAV-aged binders at −36 °C. The results show that the high-temperature PG increased with dosage, but the 22% and 28% binders fell into the same grade, indicating limited dosage discrimination by the PG test. The enhanced MSCR test captured clearer dosage differences under higher stresses. Non-recoverable compliance decreased markedly with dosage, and stress sensitivity showed an overall decreasing trend; 6.4 kPa provided higher dosage sensitivity and lower variability than 3.2 kPa. LAS test shows a non-monotonic fatigue response in which peak shear stress and predicted fatigue life increased up to 22% and then declined at 28%. At 2.5% and 5% strain, the 22% binder increased predicted fatigue life by about 273% and 83% relative to the base binder, while at 10% strain, it was about 11% lower. ABCD results show an upward shift in critical cracking temperature and a clear reduction in fracture stress at high dosages, indicating increasing low-temperature fracture risk. Therefore, high-modulus additives markedly improve high-temperature stability but introduce full-temperature trade-offs. The proposed full-temperature-range examined framework improves performance discrimination and supports dosage selection. A target dosage of 22% is recommended, and 17~22% is suggested as an engineering-controllable range for a balanced full-temperature performance, while 28% should be treated as an upper-bound option, primarily for warm regions where rutting dominates. Full article

Accurate and timely classification of skin lesions is critical for early cancer detection, yet current deep learning approaches suffer from high computational costs, limited interpretability, and poor transparency for clinical deployment. This study presents HCHS-Net, a lightweight and interpretable multimodal framework for six-class skin lesion classification on the PAD-UFES-20 dataset. The proposed framework extracts a 116-dimensional visual feature vector through three complementary handcrafted modules: a Color Module employing multi-channel histogram analysis to capture chromatic diagnostic patterns, a Haralick Module deriving texture descriptors from the gray-level co-occurrence matrix (GLCM) that quantify surface characteristics correlated with malignancy, and a Shape Module encoding morphological properties via Hu moment invariants aligned with the clinical ABCD rule. The architectural design of HCHS-Net adopts a biomimetic approach by emulating the hierarchical information processing of the human visual system and the cognitive diagnostic workflows of expert dermatologists. Unlike conventional black-box deep learning models, this framework employs parallel processing branches that simulate the selective attention mechanisms of the human eye by focusing on biologically significant visual cues such as chromatic variance, textural entropy, and morphological asymmetry. These visual features are concatenated with a 12-dimensional clinical metadata vector encompassing patient demographics and lesion characteristics, yielding a compact 128-dimensional multimodal representation. Classification is performed through an ensemble of three gradient boosting algorithms (XGBoost, LightGBM, CatBoost) with majority voting. HCHS-Net achieves 97.76% classification accuracy with only 0.25 M parameters, outperforming deep learning baselines, including VGG-16 (94.60%), ResNet-50 (94.80%), and EfficientNet-B2 (95.16%), which require 60–97× more parameters. The framework delivers an inference time of 0.11 ms per image, enabling real-time classification on standard CPUs without GPU acceleration. Ablation analysis confirms the complementary contribution of each feature module, with metadata integration providing a 2.53% accuracy gain. The model achieves perfect melanoma and nevus recall (100%) with 99.55% specificity, maintaining reliable discrimination at safety-critical diagnostic boundaries. Comprehensive benchmarking against 13 published methods demonstrates that domain-informed handcrafted features combined with clinical metadata can match or exceed deep learning fusion approaches while offering superior interpretability and computational efficiency for point-of-care deployment. Full article

Accurate skin lesion classification is crucial for the early detection of malignant lesions, including melanoma, as well as improved patient outcomes. While convolutional neural networks (CNNs) excel at capturing local morphological features, they struggle with global context modeling essential for comprehensive lesion assessment. Vision transformers address this limitation but suffer from quadratic computational complexity O(n²), hindering deployment in resource-constrained clinical environments. We propose DermaMamba, a novel dual-branch fusion architecture that integrates CNN-based local feature extraction with Vision Mamba (VMamba) for efficient global context modeling with linear complexity O(n). Our approach introduces a state space fusion mechanism with adaptive weighting that dynamically balances local and global features based on lesion characteristics. We incorporate medical domain knowledge through multi-directional scanning strategies and ABCDE (Asymmetry, Border irregularity, Color variation, Diameter, Evolution) rule feature integration. Extensive experiments on the ISIC dataset show that DermaMamba achieves 92.1% accuracy, 91.7% precision, 91.3% recall, and 91.5% mac-F1 score, which outperforms the best baseline by 2.0% accuracy with 2.3× inference speedup and 40% memory reduction. The improvements are statistically significant based on a significance test (p < 0.001, Cohen’s d > 0.8), with greater than 79% confidence also preserved on challenging boundary cases. These results establish DermaMamba as an effective solution bridging diagnostic accuracy and computational efficiency for clinical deployment. Full article

Skin cancer is one of the most easily developed cancers and is continuously seeing an increased incidence rate. In this study, we propose a novel ABC ensemble model for skin lesion classification by leveraging the ABCD rule, which is commonly used in dermatology to evaluate lesion features such as asymmetry, border, color, and diameter. Our model consists of five distinct blocks, two of which focus on learning general image characteristics, while the remaining three focus on specialized features related to the ABCD rule. The final classification results are achieved through a weighted soft voting approach, where the generalization blocks are assigned higher weights to optimize performance. Through 15 experiments using various model configurations, we show that the weighted ABC ensemble model outperforms the baseline models, achieving the best performance with an accuracy of 0.9326 and an F1-score of 0.9302. Additionally, Grad-CAM analysis is employed to assess how each block in the ensemble focuses on distinct lesion features, further enhancing the interpretability and reliability of the model. Our findings demonstrate that integrating general image features with specific lesion characteristics improves classification performance, and that adjusting the soft voting weights yields optimal results. This novel model offers a reliable tool for early skin lesion diagnosis. Full article

Introduction: The incidence of melanoma increased considerably in recent decades, representing a significant public health problem. We aimed to evaluate the ability of non-specialists for the preliminary screening of skin lesions to identify melanoma-suspect lesions. Materials and Methods: A medical student and a dermatologist specialist examined the total body scans of 50 patients. Results: The agreement between the expert and the non-specialist was 87.75% (κ = 0.65) regarding the assessment of clinical significance. The four parameters of the ABCD rule were evaluated on the 129 lesions rated as clinically significant by both observers. Asymmetry was evaluated similarly in 79.9% (κ = 0.59), irregular borders in 74.4% (κ = 0.50), color in 81.4% (κ = 0.57), and diameter in 89.9% (κ = 0.77) of the cases. The concordance of the two groups was 96.9% (κ = 0.83) in the case of the detection of the Ugly Duckling Sign. Conclusions: Although the involvement of GPs is part of routine care worldwide, emphasizing the importance of educating medical students and general practitioners is crucial, as many European countries lack structured melanoma screening training programs targeting non-dermatologists. Full article

SHIP2 is a multi-domain inositol 5-phosphatase binding to a variety of phosphotyrosine (pY)-containing proteins through its SH2 domain, so as to regulate various cell signaling pathways by modulating the phosphatidylinositol level in the plasma membrane. Unfavorably, Helicobacter pylori can hijack SHIP2 through the CagA protein to induce gastric cell carcinogenesis. To date, the interaction between SHIP2 and CagA was not analyzed from a structural point of view. Here, the binding of SHIP2-SH2 with Tyr-phosphorylated peptides from four EPIYA motifs (A/B/C/D) in CagA was studied using NMR spectroscopy. The results showed that EPIYA-C and -D bind to a similar interface of SHIP2-SH2, including a pY-binding pocket and a hydrophobic pocket, to achieve high affinity, while EPIYA-A and -B bind to a smaller interface of SHIP2-SH2 with weak affinity. By summarizing the interface and affinity of SHIP2-SH2 for CagA EPIYA-A/B/C/D, c-MET and FcgR2B ITIM, it was proposed that, potentially, SHIP2-SH2 has a selective preference for L > I > V for the aliphatic residues at the pY+3 position in its ligand. This study reveals the rule of the ligand sequence bound by SHIP2-SH2 and the mechanism by which CagA protein hijacks SHIP2, which will help design a peptide inhibitor against SHIP2-SH2. Full article

Feline calicivirus (FCV) is a common pathogen in domestic cats that is highly contagious, resistant to many disinfectants and demonstrates a high genetic variability. FCV infection can lead to serious or even fatal diseases. In this review, the European Advisory Board on Cat Diseases (ABCD), a scientifically independent board of experts in feline medicine from 11 European countries, presents the current knowledge of FCV infection and fills gaps with expert opinions. FCV infections are particularly problematic in multicat environments. FCV-infected cats often show painful erosions in the mouth and mild upper respiratory disease and, particularly in kittens, even fatal pneumonia. However, infection can be associated with chronic gingivostomatitis. Rarely, highly virulent FCV variants can induce severe systemic disease with epizootic spread and high mortality. FCV can best be detected by reverse-transcriptase PCR. However, a negative result does not rule out FCV infection and healthy cats can test positive. All cats should be vaccinated against FCV (core vaccine); however, vaccination protects cats from disease but not from infection. Considering the high variability of FCV, changing to different vaccine strain(s) may be of benefit if disease occurs in fully vaccinated cats. Infection-induced immunity is not life-long and does not protect against all strains; therefore, vaccination of cats that have recovered from caliciviral disease is recommended. Full article

Skin moles and lesions can be the first signs of severe skin diseases such as cancer. This paper presents the development of an end-user device capable of capturing images, segmentation and diagnosis of moles by using the ABCD rule, which stands for analyzing moles’ parameters as: asymmetry, border, color, and diameter. These are the main mole characteristics that doctors look at, each of them having a different factor of importance, and depending on these an accurate diagnosis can be given. For the hardware, we developed a small and compact device that can be manipulated easily by anyone without knowledge of medicine, in which we considered a custom-designed 3D enclosure with two white LEDs to control the light. The device has the role of facilitating analysis of the suspicious moles regularly at home, even if only from an indicative and not from a medical point of view. The developed PC software permits the storage of the images in a local database for easy tracking and analysis in time. The image processing developed for the ABCD rule is incorporated into the PC software and tested extensively on the international PH2 database with skin melanoma images to validate our segmentation and criteria evaluation. Using the developed device, we captured mole images for patients, who also took a medical examination by a specialist using the standard dermatoscope. Therefore, we obtained our own database containing 26 images for which we have also the specialists’ diagnosis. The performance evaluation measures obtained using our device are—Accuracy: 0.92, Precision: 1.0, Recall: 0.92, F1-score: 0.96. Full article

Breast cancer is the most common cancer in women in the world. The role of germline genetic variability in ATP-binding cassette (ABC) transporters in cancer chemoresistance and prognosis still needs to be elucidated. We used next-generation sequencing to assess associations of germline variants in coding and regulatory sequences of all human ABC genes with response of the patients to the neoadjuvant cytotoxic chemotherapy and disease-free survival (n = 105). A total of 43 prioritized variants associating with response or survival in the above testing phase were then analyzed by allelic discrimination in the large validation set (n = 802). Variants in ABCA4, ABCA9, ABCA12, ABCB5, ABCC5, ABCC8, ABCC11, and ABCD4 associated with response and variants in ABCA7, ABCA13, ABCC4, and ABCG8 with survival of the patients. No association passed a false discovery rate test, however, the rs17822931 (Gly180Arg) in ABCC11, associating with response, and the synonymous rs17548783 in ABCA13 (survival) have a strong support in the literature and are, thus, interesting for further research. Although replicated associations have not reached robust statistical significance, the role of ABC transporters in breast cancer should not be ruled out. Future research and careful validation of findings will be essential for assessment of genetic variation which was not in the focus of this study, e.g., non-coding sequences, copy numbers, and structural variations together with somatic mutations. Full article

Aim: To compare racial groups for the effect of parental educational attainment on adolescents’ social, emotional, and behavioral problems. Methods: In this cross-sectional study, 10,762 youth from the Adolescent Brain Cognitive Development (ABCD) study were included. The independent variable was parental educational attainment. The main outcomes were (1) anxious and depressed mood, (2) withdrawn and depressed affect, (3) somatic complaints, (4) social and interpersonal problems, (5) thought problems, (6) rule-breaking behaviors, (7) attention problems, and (8) violent and aggressive behaviors. These scores were generated based on parent-reported behavioral problems measured using the Child Behavior Checklist (CBCL). Race and ethnicity were the moderators. Linear regression was used to analyze the ABCD data. Results: Overall, high parental educational attainment was associated with lower scores across all domains. Race and ethnicity showed statistically significant interactions with parental educational attainment on adolescents’ fewer social, emotional, and behavioral problems (all domains), net of all confounders, indicating smaller tangible gains from their parental educational attainment for Black and Hispanic compared to non-Hispanic White adolescents. Conclusions: The protective effects of parental education against social, emotional, and behavioral problems are systematically diminished for Hispanic and Black than non-Hispanic White adolescents. Full article

In this paper, a new Computer-Aided Detection (CAD) system for the detection and classification of dangerous skin lesions (melanoma type) is presented, through a fusion of handcraft features related to the medical algorithm ABCD rule (Asymmetry Borders-Colors-Dermatoscopic Structures) and deep learning features employing Mutual Information (MI) measurements. The steps of a CAD system can be summarized as preprocessing, feature extraction, feature fusion, and classification. During the preprocessing step, a lesion image is enhanced, filtered, and segmented, with the aim to obtain the Region of Interest (ROI); in the next step, the feature extraction is performed. Handcraft features such as shape, color, and texture are used as the representation of the ABCD rule, and deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet (an ILSVRC Imagenet task). MI measurement is used as a fusion rule, gathering the most important information from both types of features. Finally, at the Classification step, several methods are employed such as Linear Regression (LR), Support Vector Machines (SVMs), and Relevant Vector Machines (RVMs). The designed framework was tested using the ISIC 2018 public dataset. The proposed framework appears to demonstrate an improved performance in comparison with other state-of-the-art methods in terms of the accuracy, specificity, and sensibility obtained in the training and test stages. Additionally, we propose and justify a novel procedure that should be used in adjusting the evaluation metrics for imbalanced datasets that are common for different kinds of skin lesions. Full article

We present the results from a theoretical investigation of laser beam propagation in relay imaging multi-pass layouts, which recently found application in high-energy laser amplifiers. Using a method based on the well-known ABCD-matrix formalism and proven by ray tracing, it was possible to derive a categorization of such systems. Furthermore, basic rules for the setup of such systems and the compensation for low order aberrations are derived. Due to the introduced generalization and parametrization, the presented results can immediately be applied to any system of the investigated kinds for a wide range of parameters, such as number of round-trips, focal lengths and optics sizes. It is shown that appropriate setups allow a close-to-perfect compensation of defocus caused by a thermal lens and astigmatism caused by non-normal incidence on the imaging optics, as well. Both are important to avoid intensity spikes leading to damages of optics in multi-pass laser amplifiers. Full article

Melanoma diagnosis depends on the experience of doctors. Symmetry is one of the most important factors to measure, since asymmetry shows an uncontrolled growth of cells, leading to melanoma cancer. A system for melanoma detection in diagnosing melanocytic diseases with high sensitivity is proposed here. Two different sets of features are extracted based on the importance of the ABCD rule and symmetry evaluation to develop a new architecture. Support Vector Machines are used to classify the extracted sets by using both an alternative labeling method and a structure divided into two different classifiers which prioritize sensitivity. Although feature extraction is based on former works, the novelty lies in the importance given to symmetry and the proposed architecture, which combines two different feature sets to obtain a high sensitivity, prioritizing the medical aspect of diagnosis. In particular, a database provided by Hospital Universitario de Gran Canaria Doctor Negrín was tested, obtaining a sensitivity of 100% and a specificity of 66.66% using a leave-one-out validation method. These results show that 66.66% of biopsies would be avoided if this system is applied to lesions which are difficult to classify by doctors. Full article