Search Results (4,201)

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research. Full article

With the rapid expansion of the global timber trade, accurate wood identification has become essential for regulating ecosystems and combating illegal logging. Traditional methods, largely reliant on manual analysis, are inadequate for large-scale, high-precision demands. A multi-architecture fusion network model that combines generative adversarial networks and one-dimensional convolutional neural networks aims to solve the problems in data quality and the challenges in classification accuracy existing in the classification process of pine tree species. The generative adversarial network is used to improve the data, which effectively expands the scale of the training set. Moreover, the one-dimensional convolutional neural network is utilized to extract local and global features from the spectral data, which improves the classification accuracy of the model and also makes the model more stable. The results obtained from the experiment show that MAFNet can achieve an accuracy rate of 99.63% in the classification of pine species. The model performed best on cross-sectional data. The research finds that MAFNet, relying on the strategy of integrating data enhancement and deep feature extraction, provides strong technical support for the rapid, accurate and non-destructive identification of pine species. Full article

Detecting citrus Huanglongbing (HLB) in orchard environments is particularly challenging due to multi-scale targets and occlusions due to clustering, which manifest as complex and variable backgrounds, targets ranging from distant single leaves to nearby full canopies, and frequent instances where symptomatic leaves are hidden behind others, all significantly hindering accurate detection. To overcome these challenges, this study introduces a novel citrus object detection model, Multi-Scale Huanglongbing DETR (MSHLB-DETR), developed on the basis of an improved Real-Time DEtection TRansformer (RT-DETR). The model significantly enhances detection accuracy and efficiency for HLB under complex orchard conditions. To address the issue of small target feature loss in leaf detection, a new efficient transformer module called Smart Disease Recognition for Citrus Huanglongbing with Multi-scale (SDRM) is introduced. SDRM includes a space-to-depth (SPD) module and inverted residual mobile block (IRMB), which facilitate deep interaction between local and global features and significantly improve the computational efficiency of the transformer. Additionally, the transformer encoder incorporates a Context-Guided Block (CGBlock) for contextual feature learning. To evaluate the proposed model under complex background conditions, a dataset of 4367 images was collected from diverse orchard scenes, preprocessed, and divided into training, validation, and testing subsets. The experimental results demonstrate that the proposed MSHLB-DETR achieved the best detection performance on the test set, with an ${\mathrm{mAP}}_{50}$ of 96.0%, surpassing other state-of-the-art models of similar scale. Compared to the original RT-DETR, the proposed model increased ${\mathrm{mAP}}_{50}$ by 15.8%, reduced Params by 7.5%, and decreased GFLOPs by 5.2%. This study reveals the critical importance of developing efficient multi-scale detection techniques for the accurate identification of citrus Huanglongbing in complex real-time monitoring scenarios. The proposed algorithm is expected to provide valuable references and new insights for the precise and timely detection of citrus Huanglongbing. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition, contributing significantly to global morbidity and mortality. Traditional diagnostic tools are effective in diagnosing COPD. However, these tools demand specialized equipment and expertise. Advances in artificial intelligence (AI) provide a platform for enhancing COPD diagnosis by leveraging diverse data modalities. The existing reviews primarily focus on single modalities and lack information on interpretability and explainability. Thus, this review intends to synthesize the AI-powered frameworks for COPD identification, focusing on data modalities, methodological innovation, evaluation strategies, and reporting limitations and potential biases. By adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across multiple repositories. From an initial pool of 1978 records, 22 studies were included in this review. The included studies demonstrated exceptional performance in specific settings. Most studies were retrospective and limited in diversity, lacking generalizability and external or prospective validation. This review presents a roadmap for advancing AI-assisted COPD detection. By highlighting the strengths and limitations of existing studies, it supports the development of future research. Future studies can utilize the findings to build models using prospective, multicenter, and multi-ethnic validations, ensuring generalizability and fairness. Full article

Coccidioides immitis and Coccidioides posadasii, the causative agents of coccidioidomycosis, represent a major public health concern in endemic regions of North and South America. The disease spectrum ranges from mild respiratory illness to severe disseminated infections, with thousands of cases reported annually in the United States and an increasing recognition of its global impact. Despite existing antifungal therapies, treatment remains challenging due to toxicity, drug resistance, and limited therapeutic options. High-throughput screening platforms have revolutionized drug discovery for infectious diseases; however, progress in antifungal screening for Coccidioides spp. has been hampered by the requirement for Biosafety Level 3 (BSL-3) containment. To overcome these barriers, we leveraged an attenuated C. posadasii strain that can be safely handled under BSL-2 conditions. Here, we describe the development and optimization of 96-well and 384-well plate screening methodologies, providing a safer and more efficient platform for antifungal discovery. This approach enhances the feasibility of large-scale screening efforts and may facilitate the identification of novel therapeutics for coccidioidomycosis. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

Background: Leishmaniasis is a zoonotic vector-borne disease and a significant global public health concern worldwide and in Algeria. In this study, we investigated the potential role of ticks and fleas as carriers of Leishmania in endemic regions of Algeria. Methods: Adult ectoparasites were collected from reservoir dogs and cohabiting animals across three provinces: Tizi-Ouzou (northeast), M’Sila (southeast), and Tébessa (extreme east). A subset of 247 ectoparasites was randomly selected for Leishmania DNA screening using ITS1-PCR. Results: Morphological identification revealed two tick species, Rhipicephalus turanicus (378 specimens) and Rhipicephalus sanguineus s.l (127 specimens), and one flea species, Ctenocephalides felis (94 specimens). Dogs were the most heavily infested hosts (74.12%), followed by sheep (9.51%) and cats (9.34%). Leishmania DNA was detected in 36.43% (90/247) of the tested specimens, with higher positivity in ticks (41.32%) compared to fleas (17.64%). Infection rates varied by host species, with dogs harboring the majority of positive ectoparasites (62/90), primarily R. sanguineus s.l (19/30) and R. turanicus (40/115). Leishmania DNA was also detected in ectoparasites collected from cats and sheep, whereas goats and rabbits were free from Leishmania DNA. Conclusions: This investigation highlights the high detection rate of Leishmania DNA in ticks and fleas from animals in Algerian endemic regions, indicating exposure to infected hosts. Together with previous reports, these findings support the view that ticks and fleas may act as incidental hosts or mechanical carriers of the parasite. However, their role in parasite transmission remains unconfirmed and warrant further investigation, particularly through studies assessing vector competence. These results emphasize the need for additional research to clarify the contribution of these ectoparasites to Leishmania transmission and multi-host dynamics. Full article

Glazed components in ancient Chinese architecture hold profound historical and cultural value. However, over time, environmental erosion, physical impacts, and human disturbances gradually lead to various forms of damage, severely impacting the durability and stability of the buildings. Therefore, preventive protection of glazed components is crucial. The key to preventive protection lies in the early detection and repair of damage, thereby extending the component’s service life and preventing significant structural damage. To address this challenge, this study proposes a Restoration-Scale Identification (RSI) method that integrates depth information. By combining RGB-D images acquired from a depth camera with intrinsic camera parameters, and embedding a Convolutional Block Attention Module (CBAM) into the backbone network, the method dynamically enhances critical feature regions. It then employs a scale restoration strategy to accurately identify damage areas and recover the physical dimensions of glazed components from a global perspective. In addition, we constructed a dedicated semantic segmentation dataset for glazed tile damage, focusing on cracks and spalling. Both qualitative and quantitative evaluation results demonstrate that, compared with various high-performance semantic segmentation methods, our approach significantly improves the accuracy and robustness of damage detection in glazed components. The achieved accuracy deviates by only ±10 mm from high-precision laser scanning, a level of precision that is essential for reliably identifying and assessing subtle damages in complex glazed architectural elements. By integrating depth information, real scale information can be effectively obtained during the intelligent recognition process, thereby efficiently and accurately identifying the type of damage and size information of glazed components, and realizing the conversion from two-dimensional (2D) pixel coordinates to local three-dimensional (3D) coordinates, providing a scientific basis for the protection and restoration of ancient buildings, and ensuring the long-term stability of cultural heritage and the inheritance of historical value. Full article

Lower respiratory tract infections (LRTIs) represent a significant global health concern, and the accurate identification of pathogens is crucial for patient care. Culture-based methods are the gold standard, but their detection abilities are limited. Next-generation sequencing (NGS) offers a promising method for comprehensive microbial detection, providing valuable information for clinical practice. In this study, 144 bronchoalveolar lavage fluid samples were collected, culture-based diagnostics were performed, and bacterial microbiome profiles were generated by short-read sequencing of the V4 region of the 16S rRNA gene using Illumina technologies and long-read sequencing with Oxford Nanopore Technologies (ONT) to determine the full-length 16S rRNA gene. The most common genera detected by NGS included Streptococcus, Staphylococcus, Veillonella, Prevotella, Rothia, Enterococcus, and Haemophilus. Short-read sequencing detected cultured bacteria at the genus level in ~85% of cases, while long-read sequencing demonstrated agreement with cultured species in ~62% of cases. In three cases, long-read sequencing identified the uncommon potential lung pathogen Tropheryma whipplei not detected with traditional culturing techniques. The NGS results showed a partial overlap with culture as the current diagnostic gold standard in LRTI. Additionally, NGS detected a broader spectrum of bacteria, revealed fastidious potential pathogens, and offered deeper insights into the complex microbial ecosystem of the lungs. Full article

Understanding the intricate dynamics of precipitation patterns is essential for effective water resource management and climate adaptation in Missouri. Existing analyses of Missouri’s climate variability lack the spatial granularity needed to capture nuanced variations across climate divisions. The Missouri historical agricultural weather database, an open-source tool that contains key weather measurements gathered at Mesonet stations across the state, is beginning to fill in the data sparsity gaps. The aim of this study is to identify core patterns associated with ENSO in the global wavelet output. Using a continuous wavelet transform analysis on data from 32 stations (2000–2024), we identified significant precipitation cycles. Where previous studies used just four Automated Surface Observing Systems (ASOSs) located at airports across Missouri to characterize climate variability, this study uses an additional 28 from the Missouri Mesonet. The use of a global wavelet power spectrum analysis reveals that precipitation patterns, with the exception of southeast Missouri, have a distinct annual cycle. Furthermore, separating the stations based on the significance of their ENSO (El Niño–Southern Oscillation) signal results in the identification of three precipitation zones: an annual, ENSO, and residual zone. This spatial data analysis reveals that the Missouri climate division boundaries broadly capture the three precipitation zones found in this study. Additionally, the results suggest a corridor in central Missouri where precipitation is particularly sensitive to an ENSO signal. These findings provide critical insights for improved water resource management and climate adaptation strategies. Full article

This paper presents the first exploration of young carers in early childhood (YCEC), in the context of whole family support, through the application of the Power Threat Meaning Framework (PTMF). Existing contributions to young carer research have shaped social policy, legislation, and practice concerned with whole family approaches to the identification, assessment, and support for young carers globally. To date, the literature has predominantly focused on young carers in middle childhood to young adulthood, contributing to socially constructed Eurocentric ideologies of who young carers are likely to be. As such, YCEC remain disempowered in broader young carer and family science discourse. This qualitative exploration centers upon the experiences of three families. Primary data collated retrospective accounts of two adult siblings supported by documentary data obtained by participants through a subject access request (SAR). Secondary data derived from two case studies, drawn from the lead author’s master’s dissertation, offering experiences of families each with a young carer aged four years old. Participants lived in England, United Kingdom (UK). Deductive analysis utilized dual methodological approaches, offering nuanced insight. Thematic codes were synthesized into predetermined themes. ‘Power,’ ‘threat,’ ‘meaning,’ ‘threat responses,’ and ‘strengths’ to explore the application of the PTMF beyond individual experience. Findings show systemic and structural powers held within the lives of YCEC. This disempowers the ethos of whole family support, which should serve to endorse integrated working and foster the autonomous functioning of family life. Findings consider threats, worsening vulnerabilities, and exposure to harm. Meaning is deduced from findings offering recommendations for future research, practice, and policy decisions. In conclusion, opportunities for the prevention of inappropriate caring roles, early identification, and intervention have been missed. This study adds to the growing exploration of the PTMF. It harnesses its potential application as a holistic assessment tool and qualitative data analysis framework, helping to bridge structural and developmental viewpoints which typically frame the current understanding of family functioning and related social policy. Full article

Background/Objectives: Forensic dental determination plays a central role in human identification, age estimation, and trauma analysis in medico-legal contexts. Traditional approaches—including clinical examination, odontometric analysis, and radiographic comparison—remain essential but are constrained by examiner subjectivity, population variability, and reduced applicability in fragmented or degraded remains. Recent advances in cone-beam computed tomography (CBCT), three-dimensional surface scanning, intraoral imaging, and artificial intelligence (AI) offer promising opportunities to enhance accuracy, reproducibility, and integration with multidisciplinary forensic evidence. The aim of this review is to synthesize conventional and emerging approaches in forensic odontology, critically evaluate their strengths and limitations, and highlight areas requiring validation. Methods: A structured literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2015 and 2025. Search terms combined forensic odontology, dental identification, CBCT, 3D scanning, intraoral imaging, and AI methodologies. From 108 records identified, 81 peer-reviewed articles met eligibility criteria and were included for analysis. Results: Digital methods such as CBCT, 3D scanning, and intraoral imaging demonstrated improved diagnostic consistency compared with conventional techniques. AI-driven tools—including automated age and sex estimation, bite mark analysis, and restorative pattern recognition—showed potential to enhance objectivity and efficiency, particularly in disaster victim identification. Persistent challenges include methodological heterogeneity, limited dataset diversity, ethical concerns, and issues of legal admissibility. Conclusions: Digital and AI-based approaches should complement, not replace, the expertise of forensic odontologists. Standardization, validation across diverse populations, ethical safeguards, and supportive legal frameworks are necessary to ensure global reliability and medico-legal applicability. Full article

The 3D chromatin architecture establishes a complex network of genes and regulatory elements necessary for transcriptomic regulation in development and disease. This network can be modeled by cis-regulatory hubs (CRH), which underscore the local functional interactions between enhancers and promoter regions and differ from other higher-order chromatin structures such as topologically associated domains (TAD). The activity-by-contact (ABC) model of enhancer–promoter regulation has been recently used in the identification of these CRHs, but little is known about the role of transcription factor CCTC binding factor (CTCF) on ABC scores and their consequent impact on CRHs. Here, we show that the loss of CTCF leads to a reorganization of the ABC-derived rankings of putative enhancers in the mouse heart, a global reduction in the total number of CRHs and an increase in the size of CRHs. Furthermore, CTCF loss leads to a higher percentage of CRHs that cross TAD boundaries. These results provide additional evidence to support the importance of CTCF in forming the regulatory networks necessary for gene regulation. Full article

To address the issue of hysteresis nonlinearity adversely affecting the tracking accuracy of piezoelectric actuators, an improved particle swarm optimization (PSO) algorithm is proposed to improve the accuracy of hysteresis model parameter identification. Additionally, a discrete-time linear quadratic optimal tracking (DLQT) control strategy incorporating hysteresis compensation is developed to improve tracking performance. This study employs the Hammerstein model to characterize the nonlinear hysteresis behavior of piezoelectric actuators. Regarding parameter identification, the conventional PSO algorithm tends to suffer from premature convergence and being trapped in local optima. To address this, a cross-variation mechanism is introduced to enhance population diversity and improve global search ability. Furthermore, adaptive and dynamically adjustable inertia weights are designed based on evolutionary factors to balance exploration and exploitation, thereby enhancing convergence and identification accuracy. The inertia weights and learning factors are adaptively adjusted based on the evolutionary factor to balance local and global search capabilities and accelerate convergence. Benchmark function tests and model identification experiments demonstrate the improved algorithm’s superior convergence speed and accuracy. In terms of control strategy, a hysteresis compensator based on an asymmetric hysteresis model is designed to improve system linearity. To address the issues of incomplete hysteresis compensation and low tracking accuracy, a DLQT controller is developed based on hysteresis compensation. Hardware-in-the-loop tracking control experiments using single and composite frequency reference signals show that the relative error is below 3.3% in the no-load case and below 4.5% in the loaded case. Compared with the baseline method, the proposed control strategy achieves lower root-mean-square error and maximum steady-state error, demonstrating its effectiveness. Full article