Search Results (1,072)

Understanding how HIV-1 pathogenesis affects systemic and TB specific immunity in the setting of latent (LTBI+) compared to active TB infection could provide actionable insights for the prevention of reactivation. Fifty HIV-seronegative and 112 HIV-1-positive anti-retroviral therapy (ART)-naïve participants were stratified as LTBI+ (n = 35), active TB+ (n = 22) and non-coinfected (n = 55) based on an interferon gamma release assay (IGRA) and clinical confirmation prior to receiving TB therapy. Systemic and TB-specific (DosR and Rpf) immune monitoring of cellular subsets, together with multi-analyte plasma analysis, was carried out. Pursuant to isoniazid prophylaxis therapy (IPT) and ART initiation, HIV-1-positive LTBI+ participants (HLTBI+) were followed for up to two years. Before ART initiation, HLTBI+ individuals exhibited the lowest levels of circulating intermediate monocytes, T-cell activation and PD-1 expression, with a decreased frequency of T-regulatory cells and higher circulating IL-10 and IL-17A. PD-1 expression on CD4+ T cell memory subsets, together with opposing anamnestic TNF-α responses to DosR and Rpf, was a discriminatory signature for the HLTBI+ group, as was preserved (following ART) TB-specific TNF-α production, which positively correlated with the CD4/CD8 ratio. Our results highlight an immunomodulatory phenotype conferred by latent TB infection in PLHIV, whose preservation may provide strategies to mitigate TB reactivation. Full article

This paper examines the state-of-the-art in fire suppression technologies based on computer vision applications in the subject areas of computer science and engineering. The study involves a two-stage analysis of publications using keywords. This paper presents a bibliographic analysis of scientific literature from the Scopus database using VOSviewer software and the author’s methodological approach. General keywords were used for the initial analysis of the dataset, followed by a more detailed study with additional criteria and specific keywords. The categories considered in the article are as follows: Firefighting Robots, Fire Detection, Fire Suppression, Aerial Vehicles, and Computer Vision. It is shown that the research includes technical aspects of fire robots and systems, as well as the improvement of their software and hardware. The subsequent review highlights the important role of computer vision in improving the efficiency and effectiveness of fire suppression systems. It is noted that key advances include the development of sophisticated fire detection algorithms and the implementation of automated fire suppression systems. The study also discusses the challenges and future directions in this field, emphasizing the need for continuous innovation and interdisciplinary collaboration. This review provides valuable information for researchers, engineers, and practitioners in the field of fire safety by offering a comprehensive overview of state-of-the-art technologies and their applications in fire suppression. Full article

Background: Women living with HIV (WLHIV) in low-income urban settings face multiple intersecting nutritional risks from food insecurity, poor dietary quality, and mental health problems. We evaluated the prevalence of household food insecurity and inadequate dietary diversity, examining their associations with depressive symptoms, antiretroviral therapy (ART)-related factors, and body mass index (BMI) among WLHIV attending routine ART clinics in Kinshasa, The Democratic Republic of Congo. This study addresses critical gaps in understanding the interplay between mental health and nutrition in the context of HIV care, with significant implications for improving health outcomes among vulnerable populations. Methods: In this clinic-based cross-sectional study (February–April 2024), we enrolled 571 women on ART in Masina 2, Kinshasa. Household food insecurity was measured using the Household Food Insecurity Access Scale (HFIAS), dietary diversity was assessed using the Minimum Dietary Diversity for Women (MDD_W; inadequate ≤ 5 food groups in 24 h), and probable depression was assessed using the Hopkins Symptom Checklist-10 (HSCL-10), which is a validated screening tool. We obtained baseline BMIs from clinic records at ART induction, which we measured again upon survey completion. We used analysis of covariance (ANCOVA) to model follow-up BMI, adjusting for baseline values, age, ART duration, self-reported adherence, household food insecurity, dietary diversity, and probable depression. Sensitivity analyses included change-score and mixed-effects models. Results: The prevalence of any household food insecurity was high (75%; 95% CI:71.5–78.6), with 57.6% (95% CI:53.5–61.6) of the participants experiencing inadequate dietary diversity (MDD_W < 5). Furthermore, forty-two per cent (95% CI:38.4–46.5) experienced depressive symptoms and sixty-eight percent (95% CI: 64.4–72.0) adhered to antiretroviral therapy (ART). The mean MDD_W was 4.3, with a low consumption rate of animal-source foods. Baseline BMI was associated with follow-up values (adjusted β_{unstandardized}, 0.48 kg/m² per 1 kg/m² baseline, 95% CI 0.38–0.59; p < 0.001). Probable depression was independently associated with a lower follow-up BMI (adjusted β_{unstandardized}, −0.99 kg/m²; 95% CI −1.72 to −0.26; p = 0.008). Time since ART initiation showed a slight positive association with BMI (adjusted β_{unstandardized}, 0.10 kg/m² per year). Self-reported ART adherence, household food insecurity, and dietary diversity were not independently associated with follow-up BMI in fully adjusted models. The interaction between age and probable depression did not suggest heterogeneity between age groups (p = 0.503). Conclusions: In our cohort, food insecurity and poor dietary diversity were widespread but did not significantly correlate with BMI, while probable depression, a potentially modifiable factor, was independently associated with lower BMI after accounting for baseline nutritional status. These findings highlight the need for HIV care programs integrating mental health screening and services with nutrition-sensitive interventions to support recovery and long-term health among WLHIV. Full article

Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance. Full article

We present the findings of a case showing an improvement in severe, chronic mid-back pain (MBP) and disability following sagittal correction of the thoracic spine using Chiropractic BioPhysics^® (CBP^®) spinal rehabilitation with a nine-month long-term follow-up. A 40-year-old female had suffered for years and was referred for spinal rehabilitation by her physicians and physical therapist to treat her severe, chronic MBP. The symptoms had not improved despite several months of physical therapy, traditional chiropractic spinal manipulation, and pain management trigger point injections. The pain was reported as severe and rated as 8/10 at worst on the numerical rating scale. The pain was severe enough to interfere with her normal activities including martial arts training. Postural analysis revealed increased thoracic flexion and spine hyperkyphosis. Lateral thoracic radiography showed a previously undiagnosed wedged vertebral body at T6. Mensuration of the radiograph found an increase in overall posterior tangent angulation from T3–T10 measuring 66.2°. Negative sagittal balance measured from a vertical of T3 above T10 was −16.3 mm. Treatment included Chiropractic Biophysics^® (CBP^®) orthopedic rehabilitation protocols including postural and radiographic based Mirror Image^® (MI^®) exercises, spinal manipulation, and traction. The patient was treated in-office 37 times over the course of 3 months and all initial subjective and objective outcomes were re-assessed. It was reported that the initial average pain of 8/10 for the mid-back had nearly resolved and was rated as 2/10. All ADLs were reported as pain free, including intense exercise and martial arts. Post-treatment radiography was taken following a 24 h “rest-period” and found reduction in the overall hyperkyphosis from T3–T10 now measured 45.2°. Due to the presence of the wedge vertebra, it was recommended that the patient continue home traction and exercises, and long-term follow-up was assessed at 9 months including a repeat of all initial examinations, for subjective and objective outcomes. Thoracic kyphosis was maintained at 47.7° and VAS was 0/10 at 9-month follow-up and symptoms remained nearly resolved. Full article

Learning and understanding of art are increasingly understood as dynamic processes in which emotion and cognition unfold over time. However, classroom-based evidence on how structured temporal intervals and guided prompts reshape students’ emotional experience remains limited. This study addresses these gaps by quantitatively examining changes in emotion over time in a higher education institution. Employing a comparative experimental design, third-year undergraduate art students participated in two structured courses, where emotional responses were captured using an emotion recognition approach (facial expression and self-reported text) during two sessions: initial impression and delayed impression (three days later). The findings reveal a high consistency in dominant facial expressions and substantial agreement in self-reported emotions across both settings. However, the delayed impression elicited greater emotional diversity and intensity, reflecting deeper cognitive engagement and emotional processing over time. These results reveal a longitudinal trajectory of emotion influenced by guided reflective re-view over time. Emotional dynamics extend medium theory by embedding temporal and affective dimensions into TBMA course settings. This study proposes an ethically grounded and technically feasible framework for emotion recognition that supports reflective learning rather than mere measurement. Together, these contributions redefine TBMA education as a temporal and emotional ecosystem and provide an empirical foundation for future research on how emotion fosters understanding, interest, and appreciation in higher media art education. Full article

To explore the potential of Large Language Models (LLMs) as AI Annotators in the domain of sustainability reporting, this study establishes a systematic evaluation methodology. We use the specific case of European football clubs, quantifying their sustainability reports based on the sport Positive matrix as a benchmark to compare the performance of three state-of-the-art models (i.e., GPT-4o, Qwen-2-72b-instruct, and Llama-3-70b-instruct) against human expert scores. The evaluation is benchmarked on dimensions including accuracy, mean absolute error (MAE), and hallucination rates. The results indicate that GPT-4o is the top performer, yet its average accuracy of approximately 56% shows it cannot fully replace human experts at present. The study also reveals significant issues with overconfidence and factual hallucinations in models like Qwen-2-72b-instructon. Critically, we find that by implementing further data processing, specifically a Chain-of-Verification (CoVe) self-correction method, GPT-4o’s initial hallucination rate is successfully reduced from 16% to 10%, while accuracy improved to 58%. In conclusion, while LLMs demonstrate immense potential to streamline and democratize sustainability ratings, inherent risks like hallucinations remain a primary obstacle. Adopting verification strategies such as CoVe is a crucial pathway to enhancing model reliability and advancing their effective application in this field. Full article

This paper addresses the fundamental problem of seed selection in semi-supervised clustering, where the quality of initial seeds has a significant impact on clustering performance and stability. Existing methods often rely on randomly or heuristically selected seeds, which can propagate errors and increase dependence on expert labeling. To overcome these limitations, we propose FA-Seed, a flexible and adaptive model that integrates active querying with self-guided adaptation within the framework of fuzzy hyperboxes. FA-Seed partitions the data into hyperboxes, evaluates seed reliability through measures of membership and association density, and propagates labels with an emphasis on label purity. The model demonstrates strong adaptability to complex and ambiguous data distributions in which cluster boundaries are vague or overlapping. The main contributions of FA-Seed include: (1) automatic estimation and selection of candidate seeds that provide auxiliary supervision, (2) dynamic cluster expansion without retraining, (3) automatic detection and identification of structurally complex regions based on cluster characteristics, and (4) the ability to capture intrinsic cluster structures even when clusters vary in density and shape. Empirical evaluations on benchmark datasets, specifically the UCI and Computer Science collections, show that our approach consistently outperforms several state-of-the-art semi-supervised clustering methods. Full article

Existing point cloud registration methods can effectively handle large-scale and partially overlapping point cloud pairs. However, registering unbalanced point cloud pairs with significant disparities in spatial extent and point density remains a challenging problem that has received limited research attention. This challenge primarily arises from the difficulty in achieving accurate local registration when the point clouds exhibit substantial scale variations and uneven density distributions. This paper presents a novel registration method for unbalanced point cloud pairs that utilizes the local point cluster structure feature for effective outlier rejection. The fundamental principle underlying our method is that the internal structure of a local cluster comprising a point and its K-nearest neighbors maintains rigidity-preserved invariance across different point clouds. The proposed pipeline operates through four sequential stages. First, keypoints are detected in both the source and target point clouds. Second, local feature descriptors are employed to establish initial one-to-many correspondences, which is a strategy that increases correspondences redundancy to enhance the pool of potential inliers. Third, the proposed Local Point Cluster Structure Feature is applied to filter outliers from the initial correspondences. Finally, the transformation hypothesis is generated and evaluated through the RANSAC method. To validate the efficacy of the proposed method, we construct a carefully designed benchmark named KITTI-UPP (KITTI-Unbalanced Point cloud Pairs) based on the KITTI odometry dataset. We further evaluate our method on the real-world TIESY Dataset which is a LiDAR-scanned dataset collected by the Third Railway Survey and Design Institute Group Co. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods in terms of both registration success rate and computational efficiency on the KITTI-UPP benchmark. Moreover, it achieves competitive results on the real-world TIESY dataset, confirming its applicability and generalizability across diverse real-world scenarios. Full article

Neurodegenerative diseases (NDDs) are a group of progressive diseases affecting neuronal cells in specific areas of the brain, causing cognitive decline and movement impairment. Nowadays, NDDs play a significant role in the global burden of disease, and their incidence is increasing, particularly due to population aging. NDD onset is multi-factorial; based on the current knowledge, genetic, environmental, and cellular factors are believed to contribute to their occurrence and progression. Taking into account that at an early stage, the symptoms are not clearly defined, and diagnosis may be delayed, the development of innovative and non-invasive methodological approaches for early diagnosis of NDDs is strategic for timely and tailored disease management, as well as for the overall improvement of patients’ quality of life. The present review aims to provide, in the first part, an overview based on the current level of knowledge on the environmental risk factors that can explicate a role in the onset of the most common NDDs and on the main pathogenic mechanisms involved in disease initiation and progression. The second part aims to define the current state of the art regarding the significance of Volatile Organic Compounds (VOCs) in the volatome of different human biological matrices (exhaled breath, feces, and skin sebum) as candidate biomarkers of specific NDDs, with the aim of developing non-invasive diagnostic approaches for the early diagnosis and personalized management of the patients. A critical synthesis and discussion on the applied methodological approaches and on the relevant outcomes obtained across the studies is reported. Full article

Accurate prediction of the flow field is crucial to evaluating the aerodynamic performance of an aircraft. While traditional computational fluid dynamics (CFD) methods solve the governing equations to capture both global flow structures and localized gradients, they are computationally intensive. Deep learning-based surrogate models offer a promising alternative, yet often struggle to simultaneously model long-range dependencies and near-wall flow gradients with sufficient fidelity. To address this challenge, this paper introduces the Message-passing And Global-attention block (MAG-BLOCK), a graph neural network module that combines local message passing with global self-attention mechanisms to jointly learn fine-scale features and large-scale flow patterns. Building on MAG-BLOCK, we propose FLOW-GLIDE, a cross-architecture deep learning framework that learns a mapping from initial conditions to steady-state flow fields in a latent space. Evaluated on the AirfRANS dataset, FLOW-GLIDE outperforms existing models on key performance metrics. Specifically, it reduces the error in the volumetric flow field by 62% and surface pressure prediction by 82% compared to the state-of-the-art. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Toxins are naturally poisonous small compounds, peptides and proteins that are produced in all three kingdoms of life. Venoms are animal toxins and can contain even hundreds of different compounds. Numerous approaches have been used to detect toxins, including prediction methods. We developed a novel machine learning-based predictor for detecting protein toxins from their sequences. The gradient boosting method was trained on carefully selected training data. Initially, we tested 2614 features, which were reduced to 88 after a comprehensive feature selection procedure. Out of the four tested algorithms, XGBoost was chosen to train the final predictor. Comparison to available predictors indicated that ProToxin showed significant improvement compared to state-of-the-art predictors. On a blind test dataset, the accuracy was 0.906, the Matthews correlation coefficient was 0.796, and the overall performance measure was 0.796. ProToxin is a fast and efficient method and is freely available. It can be used for small and large numbers of sequences. Full article

Addressing the planetary crisis associated with climate change, biodiversity loss, global pollution, and public health requires novel and holistic approaches. Here, we present the methodology and initial results of an experiment conducted in Rome within the framework of the National Biodiversity Future Center (NBFC) project, Spoke 6. The major objective of this study was to outline the planetary health approach as a lens to assess urban health. This transdisciplinary case study explored the relationship between urban traffic-related external exposome and pro-oxidative responses in humans and plants. This methodology is based on the integration of atmospheric dynamics modeling, state-of-the-art aerosol measurements, biomonitoring in human cohorts, in vitro cellular assays, and the assessment of functional trait markers in urban trees. The results indicate that short-term exposure to urban aerosols, even at low concentrations, triggers rapid oxidative and inflammatory responses in bronchial epithelial cells, modulates gene and miRNA expression, alters gut microbiota diversity, and induces functional trait changes in urban trees. This study also highlights the feedback mechanisms between vegetation and atmospheric conditions, emphasizing the role of urban greenery in modulating microclimate and exposure. The methodology and initial results presented here will be further analyzed in future studies to explore proof of a cause–effect relationship between short-term exposure to traffic-related environmental stressors in urban areas and oxidative stress in humans and plants, with implications for chronic responses. In a highly urbanized world, this evidence could be pivotal in motivating the widespread implementation of planetary health approaches for assessing urban health. Full article

In order to enhance leakage detection accuracy in water distribution networks (WDNs) while reducing sensor deployment costs, an intelligent algorithm for the optimal deployment of water network monitoring sensors based on the automatic labelling and graph neural network (ALGN) was proposed for the optimal deployment of WDN monitoring sensors. The research aims to develop a data-driven, topology-aware sensor deployment strategy that achieves high leakage detection performance with minimal hardware requirements. The methodology consisted of three main steps: first, the dung beetle optimization algorithm (DBO) was employed to automatically determine optimal parameters for the DBSCAN clustering algorithm, which generated initial cluster labels; second, a customized graph neural network architecture was used to perform topology-aware node clustering, integrating network structure information; finally, optimal pressure sensor locations were selected based on minimum distance criteria within identified clusters. The key innovation lies in the integration of metaheuristic optimization with graph-based learning to fully automate the sensor placement process while explicitly incorporating the hydraulic network topology. The proposed approach was validated on real-world WDN infrastructure, demonstrating superior performance with 93% node coverage and 99.77% leakage detection accuracy, surpassing state-of-the-art methods by 2% and 0.7%, respectively. These results indicate that the ALGN framework provides municipal water utilities with a robust, automated solution for designing efficient pressure monitoring systems that balance detection performance with implementation cost. Full article