Search Results (100)

Bats are recognized as reservoirs for diverse paramyxoviruses, some of which are closely related to known human pathogens or directly implicated in zoonotic transmission. The emergence of the zoonotic Sosuga virus (SOSV) from Egyptian rousette bats (ERBs), which caused an acute febrile illness in a reported human case in Africa, has increased the focus on the zoonotic potential of the Rubulavirinae subfamily. Previous studies identified human parainfluenza virus 2 (HPIV2)- and mumps (MuV)-related viruses in ERBs from South Africa, with HPIV2-related viruses restricted to gastrointestinal samples, an underexplored target for rubulavirus biosurveillance, suggesting that sample-type bias may have led to their oversight. To address this, we performed a longitudinal analysis of population-level fecal samples from an ERB maternity roost for rubulavirus RNA, employing a broadly reactive hemi-nested RT-PCR assay targeting the polymerase gene. We detected HPIV2- and MuV-related viruses in addition to numerous pararubulaviruses, highlighting significant viral diversity. Temporal analysis of three major clades revealed peaks in rubulavirus shedding that correlated with seasonal environmental changes and host reproductive cycles, although shedding patterns varied between clades. These findings identify specific periods of increased risk for the spillover of bat-associated rubulaviruses to humans, providing critical information for developing targeted mitigation strategies to minimize zoonotic transmission risk within the local community. Full article

In this study, GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI) and Lightning NETwork (LINET) data were used for lightning detection. A total of 20 lightning cases from the summer of 2020–2021 were selected, with 14 cases for training and 6 for validation to develop lightning detection algorithms. Since these two datasets have different spatiotemporal resolutions, spatiotemporal matching was performed to use them together. To find the optimal lightning detection algorithm, we designed 25 experiments and selected the best experiment by evaluating the detection level. Although the best experiment had a high POD (>0.9) before post-processing, it also showed over-detection of lightning. To minimize the over-detection problem, statistical and Region-Growing post-processing methods were applied, improving the detection performance (FAR: −19.14~−24.32%; HSS: +76.92~+86.41%; Bias: −59.3~−66.9%). Also, a sensitivity analysis of the collocation criterion between the two datasets showed that the detection level improved when the spatial criterion was relaxed. These results suggest that detecting lightning in mid-latitude regions, including the Korean Peninsula, is possible by using GK2A/AMI data. However, reducing the variability in detection performance and the high FAR associated with anvil clouds and addressing the parallax problem of thunderstorms in mid-latitude regions are necessary to improve the detection performance. Full article

Modified vaccinia Ankara (MVA) virus is a widely used vaccine platform, making accurate titration essential for vaccination studies. However, the current plaque forming unit (PFU) assay, the standard for MVA titration, is prone to observer bias and other limitations that affect accuracy and precision. To address these challenges, we developed a new flow cytometry-based quantification method using a highly specific monoclonal antibody (mAb) for the detection of MVA-infected cells, as a more accurate titration assay. Through previous work, we serendipitously identified three MVA-specific hybridoma antibody clones, which we characterized through ELISA, immunoblot, and flow cytometry, confirming their specificity for MVA. Sequencing confirmed that each antibody was monoclonal, and mass spectrometry results revealed that all mAbs target the MVA cell surface binding protein (CSBP, MVA105L). We next optimized the titration protocol using the most effective mAb, 33C7 by refining culture conditions and staining protocols to enhance sensitivity and minimize background. Our optimized method demonstrated superior sensitivity, reliability, and reduced processing time when compared with the traditional PFU assay, establishing it as a more accurate and efficient approach for MVA titration. Full article

This paper presents a novel underwater image enhancement method addressing the challenges of low contrast, color distortion, and detail loss prevalent in underwater photography. Unlike existing methods that may introduce color bias or blur during enhancement, our approach leverages a two-pronged strategy. First, an Efficient Fusion Edge Detection (EFED) module preserves crucial edge information, ensuring detail clarity even in challenging turbidity and illumination conditions. Second, a Multi-scale Color Parallel Frequency-division Attention (MCPFA) module integrates multi-color space data with edge information. This module dynamically weights features based on their frequency domain positions, prioritizing high-frequency details and areas affected by light attenuation. Our method further incorporates a dual multi-color space structural loss function, optimizing the performance of the network across RGB, Lab, and HSV color spaces. This approach enhances structural alignment and minimizes color distortion, edge artifacts, and detail loss often observed in existing techniques. Comprehensive quantitative and qualitative evaluations using both full-reference and no-reference image quality metrics demonstrate that our proposed method effectively suppresses scattering noise, corrects color deviations, and significantly enhances image details. In terms of objective evaluation metrics, our method achieves the best performance in the test dataset of EUVP with a PSNR of 23.45, SSIM of 0.821, and UIQM of 3.211, indicating that it outperforms state-of-the-art methods in improving image quality. Full article

This study presents a theoretical investigation into the voltammetric behavior of bipolar interfacial nanopores due to the effect of potential scan rate (1–1000 V/s). Finite element method (FEM) is utilized to explore the current–voltage (I–V) properties of bipolar interfacial nanopores at different bulk salt concentrations. The results demonstrate a strong impact of the scan rate on the I–V response of bipolar interfacial nanopores, particularly at relatively low concentrations. Hysteresis loops are observed in bipolar interfacial nanopores under specific scan rates and potential ranges and divided by a cross-point potential that remains unaffected by the scan rate employed. This indicates that the current in bipolar interfacial nanopores is not just reliant on the bias potential that is imposed but also on the previous conditions within the nanopore, exhibiting history-dependent or memory effects. This scan-rate-dependent current–voltage response is found to be significantly influenced by the length of the nanopore (membrane thickness). Thicker membranes exhibit a more pronounced scan-rate-dependent phenomenon, as the mass transfer of ionic species is slower relative to the potential scan rate. Additionally, unlike conventional bipolar nanopores, the ion current passing through bipolar interfacial nanopores is minimally affected by the membrane thickness, making it easier to detect. Full article

Carbapenemase-producing organisms (CPOs) present a major threat to public health, demanding precise diagnostic techniques for their detection. Discrepancies among the CPO tests have raised concerns, partly due to limitations in detecting bacterial diversity within host specimens. We explored the impact of an unbiased colony selection on carbapenemase testing and assessed its relevance to various tests. Using the FirstAll method for unbiased colony selection to reduce bias, we compared the results from different methods, namely the modified carbapenem inactivation method/EDTA-modified carbapenem inactivation method (mCIM/eCIM), the Carba5, the CPO panel, and the multiplex PCR (MPCR). We compared the FirstAll method to the conventional colony selection for MPCR with seven CPO species. In addition, we evaluated the test performance on seven CPO species using MPCR as a reference and the FirstAll method as the colony-selection method. The results revealed that the selections from the FirstAll method have improved rates of carbapenemase detection, in comparison to approximately 11.2% of the CPO isolates that were noted to be false negatives in the conventional colony-selection methods. Both the Carba5 test and the CPO panel showed suboptimal performance (sensitivity/specificity: Carba5 74.6%/89.5%, CPO panel 77.2%/74.4%) in comparison to the FirstAll method. The Carba5 test provided specific carbapenemase class assignments, but the CPO panel failed in 18.7% of the cases. The Carba5 test and the CPO panel results correlated well with ceftazidime–avibactam minimal inhibitory concentrations (MICs). The concordance for Class A/D with MICs was 94.7% for Carba5 and 92.7% for the CPO panel; whereas for Class B, it was 86.5% for Carba5 and 75.9% for the CPO panel. In conclusion, FirstAll, as the unbiased colony-selection method, was shown to impact carbapenemase testing. With FirstAll, the diagnostic performance of both the Carba5 and the CPO panel was found to be lower. Furthermore, the utilization of ceftazidime–avibactam guided by either the CPO panel or Carba5 was appropriate. Full article

Power transformers are vital and costly components in power systems, essential for ensuring a reliable and uninterrupted supply of electrical energy. Their protection is crucial for improving reliability, maintaining network stability, and minimizing operational costs. Previous studies have introduced differential protection schemes with harmonic restraint to detect internal transformer faults. However, these schemes often struggle with computational inaccuracies in fault detection due to neglecting current transformer (CT) saturation and associated uncertainties. CT saturation during internal faults can produce even harmonics, disrupting relay operations. Additionally, CT saturation during transformer energization can introduce a DC component, leading to incorrect relay activation. This paper introduces a novel feature extracted through advanced wavelet transform analysis of differential current. This feature, combined with differential current amplitude and bias current, is used to train a deep learning system based on long short-term memory (LSTM) networks. By accounting for existing uncertainties, this system accurately identifies internal transformer faults under various CT saturation and measurement uncertainty conditions. Test and validation results demonstrate the proposed method’s effectiveness and superiority in detecting internal faults in power transformers, even in the presence of CT saturation, outperforming other recent modern techniques. Full article

Background: The gross motor function measurement is considered as the gold standard for the motor assessment of children with cerebral palsy. The aim was to carry out the cross-cultural adaptation and investigate psychometric properties. Methods: A total of 150 children with cerebral palsy aged 2–16 (mean 8.82 ± 3.78 years; 54.7% male) included. The Gross Motor Function Measurement was adapted into Turkish. Two physiotherapies independently administered the gross motor function measurement. Internal consistency and intra/inter-rater reliability were assessed using Cronbach’s alpha, intraclass-correlation-coefficient. Standard-error-of-measurement, minimal-detectible-change calculated. The Bland–Altman method was applied to estimate the measurement bias in reliability analysis. Construct validity assessed with Spearman’s correlation coefficient between the gross motor function measurement and the gross motor function classification system, pediatric-evaluation-of-disability-inventory—mobility; confirmatory-factor-analysis was carried. Results: Internal-consistency (α: 0.997–1.00); reliability indices were excellent for total scale (intraclass-correlation-coefficient for intra-rater reliability 0.994–0.999, inter-rater reliability 0.997–0.999) and for each sub-dimension and total score. Standard-error-of-measurement was ranging 1.044–1.677, minimal-detectible-change was 2.435–5.520. Construct validity was supported by strong to excellent negative significant correlations (p < 0.05). Full article

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation ($R^2$ = 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation. Full article

Introduction: Lumbar foraminal stenosis (LFS) occurs primarily due to degenerative changes in older adults, affecting the spinal foramina and leading to nerve compression. Characterized by pain, numbness, and muscle weakness, LFS arises from structural changes in discs, joints, and ligaments, further complicated by factors like inflammation and spondylolisthesis. Diagnosis combines patient history, physical examination, and imaging, while management ranges from conservative treatment to surgical intervention, underscoring the need for a tailored approach. Materials and Methods: This multicenter study, conducted over six years at a tertiary hospital, analyzed the volumetric dimensions of lumbar foramina and their correlation with nerve structures in 500 patients without lumbar pathology. Utilizing high-resolution MRI with a standardized imaging protocol, eight experienced researchers independently reviewed the images for accurate measurements. The study emphasized quality control through the calibration of measurement tools, double data entry, validation checks, and comprehensive training for researchers. To ensure reliability, interobserver and intraobserver agreements were analyzed, with statistical significance determined by kappa statistics and the Student’s t-test. Efforts to minimize bias included blinding observers to patient information and employing broad inclusion criteria to mitigate referral and selection biases. The methodology and findings aim to enhance the understanding of normal lumbar foramina anatomy and its implications for diagnosing and treating lumbar conditions. Results: The study’s volumetric analysis of lumbar foramina in 500 patients showed a progressive increase in foraminal volume from the L1/L2 to the L5/S1 levels, with significant enlargement at L5/S1 indicating anatomical and biomechanical complexity in the lumbar spine. Lateral asymmetry suggested further exploration. High interobserver and intraobserver agreement levels (ICC values of 0.91 and 0.95, respectively) demonstrated the reliability and reproducibility of measurements. The patient cohort comprised 58% males and 42% females, highlighting a balanced gender distribution. These findings underscore the importance of understanding foraminal volume variations for lumbar spinal health and pathology. Conclusion: Our study significantly advances spinal research by quantifying lumbar foraminal volumes, revealing a clear increase from the L1/L2 to the L5/S1 levels, indicative of the spine’s adaptation to biomechanical stresses. This provides clinicians with a precise tool to differentiate between pathological narrowing and normal variations, enhancing the detection and treatment of lumbar foraminal stenosis. Despite limitations like its cross-sectional design, the strong agreement in measurements underscores the method’s reliability, encouraging future research to further explore these findings’ clinical implications. Full article

(1) Background: Head and neck cancer (HNC) is a major public health challenge worldwide, with oral squamous cell carcinoma (OSCC) being the predominant form. Despite advances in treatment, OSCC remains a major cause of morbidity and mortality due to delayed diagnosis and limited therapeutic efficacy. This study reviews omics technologies to assess new salivary biomarkers for the early detection of OSCC. (2) Methods: A comprehensive literature search in the last 20 years identified four relevant studies focusing on salivary biomarkers in OSCC. (3) Results: Proteomic and genomic analyses revealed significant changes in salivary composition between OSCC patients and healthy controls, suggesting promising diagnostic and prognostic biomarkers. However, studies showed varying degrees of bias, indicating the need for further research and improved standardization. (4) Conclusions: Saliva, with its advantages of ease of collection, minimal invasiveness, and potential for large-scale screening, is an emerging promising substrate for non-invasive biomarker research. Nonetheless, there is a need for improved biomarker sensitivity and specificity; currently, histological examination remains the golden standard. Full article

Vital signs are crucial for assessing the condition of a patient and detecting early symptom deterioration. Noncontact sensor technology has been developed to take vital measurements with minimal burden. This study evaluated the accuracy of a mat-type noncontact sensor in measuring respiratory and pulse rates in patients with cardiovascular diseases compared to conventional methods. Forty-eight hospitalized patients were included; a mat-type sensor was used to measure their respiratory and pulse rates during bed rest. Differences between mat-type sensors and conventional methods were assessed using the Bland–Altman analysis. The mean difference in respiratory rate was 1.9 breaths/min (limits of agreement (LOA): −4.5 to 8.3 breaths/min), and proportional bias existed with significance (r = 0.63, p < 0.05). For pulse rate, the mean difference was −2.0 beats/min (LOA: −23.0 to 19.0 beats/min) when compared to blood pressure devices and 0.01 beats/min (LOA: −11.4 to 11.4 beats/min) when compared to 24-h Holter electrocardiography. The proportional bias was significant for both comparisons (r = 0.49, p < 0.05; r = 0.52, p < 0.05). These were considered clinically acceptable because there was no tendency to misjudge abnormal values as normal. The mat-type noncontact sensor demonstrated sufficient accuracy to serve as an alternative to conventional assessments, providing long-term monitoring of vital signs in clinical settings. Full article

Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios. Full article

Throwing performance is a critical aspect of sports, particularly in overhead activities, necessitating reliable assessment methods. This study explores the test–retest reliability of throwing performance metrics measured by the 1080 Sprint, a robotic device integrating linear position technology and an electric motor. Specifically focusing on professional volleyball athletes with scapular dyskinesis, the study draws data from a previously published investigation on the impact of mirror cross exercise. Thirty-nine athletes were recruited, aged 21.9 ± 3.6 years, height 1.79 ± 0.3 m weight 68.5 ± 19.8 kg, and body mass index 21.3 ± 3.2 kg/m², meeting stringent inclusion criteria. One-sample t-tests indicated no statistically significant differences between test–retest trials. The study revealed excellent reliability of the 1080 Sprint, with intraclass correlation coefficient (ICC) values exceeding 0.99 for all metrics, including speed, force, and power. The standard error of measurement (SEM) calculation revealed that the Sprint 1080 motorized resistance device demonstrates high precision in measuring throwing performance. Bland and Altman plots indicated minimal systematic bias across all metrics, encompassing speed, force, and power. The provision of the minimum detectable change (MDC) for each variable of the Sprint 1080 motorized resistance device offers coaches a valuable tool to identify performance improvements in volleyball athletes. In conclusion, the present study shows that the 1080 Sprint is valid and reliable for measuring throwing performance in volleyball athletes for monitoring purposes. Full article

Effective fault detection in chemical processes is of utmost importance to ensure operational safety, minimize environmental impact, and optimize production efficiency. To enhance the monitoring of chemical processes under noisy conditions, an innovative statistical approach has been introduced in this study. The proposed approach, called Multiscale Principal Component Analysis (PCA), combines the dimensionality reduction capabilities of PCA with the noise reduction capabilities of wavelet-based filtering. The integrated approach focuses on extracting features from the multiscale representation, balancing the need to retain important process information while minimizing the impact of noise. For fault detection, the Kantorovich distance (KD)-driven monitoring scheme is employed based on features extracted from Multiscale PCA to efficiently detect anomalies in multivariate data. Moreover, a nonparametric decision threshold is employed through kernel density estimation to enhance the flexibility of the proposed approach. The detection performance of the proposed approach is investigated using data collected from distillation columns and continuously stirred tank reactors (CSTRs) under various noisy conditions. Different types of faults, including bias, intermittent, and drift faults, are considered. The results reveal the superior performance of the proposed multiscale PCA-KD based approach compared to conventional PCA and multiscale PCA-based monitoring methods. Full article