Multidisciplinary Digital Publishing Institute (Switzerland)
Multidisciplinary Digital Publishing InstituteNot a member yet
1861300 research outputs found
Sort by
SOX11 Is Regulated by EGFR-STAT3 and Promotes Epithelial–Mesenchymal Transition in Head and Neck Squamous Cell Carcinoma
No full textThe transcription factor SOX11 is implicated in tumor progression across multiple types of cancers, including head and neck squamous cell carcinoma (HNSCC). However, its mechanistic role in HNSCC remains elusive. In this study, we found that the expression of SOX11 was induced by epidermal growth factor (EGF) but suppressed by an epidermal growth factor receptor (EGFR) inhibitor in HNSCC cells. The signal transducer and activator of transcription 3 (STAT3) bound to the Sox11 gene promoter and transcriptionally upregulated the expression of Sox11 in HNSCC cells. Meanwhile, analyses of The Cancer Genome Atlas (TCGA) gene expression datasets indicated that Sox11 gene expression was significantly overexpressed in HNSCC versus adjacent normal tissues and correlated with those of most epithelial–mesenchymal transition transcription factors (EMT-TFs) and marker genes. Knockdown of SOX11 significantly downregulated the expression of EMT-related genes, including EMT-TFs, vimentin, fibronectin, and N-cadherin, but significantly upregulated E-cadherin and vice versa when SOX11 was overexpressed. Collectively, our studies demonstrated that SOX11 was regulated by EGF-EGFR-STAT3 signals, promoting EMT in HNSCC
Impact of Helicopter Vibrations on In-Ear PPG Monitoring for Vital Signs—Mountain Rescue Technology Study (MoReTech)
No full textPulsoximeters are widely used in the medical care of preclinical patients to evaluate the cardiorespiratory status and monitor basic vital signs, such as pulse rate (PR) and oxygen saturation (SpO2). In many preclinical situations, air transport of the patient by helicopter is necessary. Conventional pulse oximeters, mostly used on the patient’s finger, are prone to motion artifacts during transportation. Therefore, this study aims to determine whether simulated helicopter vibration has an impact on the photoplethysmogram (PPG) derived from an in-ear sensor at the external ear canal and whether the vibration influences the calculation of vital signs PR and SpO2. The in-ear PPG signals of 17 participants were measured at rest and under exposure to vibration generated by a helicopter simulator. Several signal quality indicators (SQI), including perfusion index, skewness, entropy, kurtosis, omega, quality index, and valid pulse detection, were extracted from the in-ear PPG recordings during rest and vibration. An intra-subject comparison was performed to evaluate signal quality changes under exposure to vibration. The analysis revealed no significant difference in any SQI between vibration and rest (all p > 0.05). Furthermore, the vital signs PR and SpO2 calculated using the in-ear PPG signal were compared to reference measurements by a clinical monitoring system (ECG and SpO2 finger sensor). The results for the PR showed substantial agreement (CCCrest = 0.96; CCCvibration = 0.96) and poor agreement for SpO2 (CCCrest = 0.41; CCCvibration = 0.19). The results of our study indicate that simulated helicopter vibration had no significant impact on the calculation of the SQIs, and the calculation of vital signs PR and SpO2 did not differ between rest and vibration conditions
Finger Unit Design for Hybrid-Driven Dexterous Hands
No full textDexterous hands are the core end-effectors of humanoid robots, and their design is a key research focus in this field. With multiple independent finger units, the units’ dexterity directly determines the hand’s operational performance, yet achieving three-degree-of-freedom (3-DOF) anthropomorphic motion remains a key design challenge. To address this, this paper proposes a hybrid-driven index finger unit: combining linkage and tendon–cable drive advantages to realize 3-DOF anthropomorphic motion, and adopting independent drive/transmission modules to simplify manufacturing and boost parameter optimization flexibility. Validated via motion dynamics, DOF, and operational force assessments, this design offers key unit tech for dexterous hand development and serves as a reference for optimizing multi-DOF anthropomorphic finger designs
Investigation of Ensemble Machine Learning Models for Estimating the Ultimate Strain of FRP-Confined Concrete Columns
No full textAccurately predicting the ultimate strain of fiber-reinforced polymer (FRP)-confined concrete columns is essential for the widespread application of FRP in strengthening reinforced concrete (RC) columns. This study comprehensively investigates the performance of ensemble machine learning (ML) models in estimating the ultimate strain of FRP-confined concrete (FRP-CC) columns. A dataset of 547 test results of the ultimate strain of FRP-CC columns was collected from the literature for training and testing ML models. The four best single ML models were used to develop ensemble models employing voting, stacking and bagging techniques. The performance of the ensemble models was compared with 10 single ML and 11 empirical strain models. The study results revealed that the single ML models yielded good agreement between the estimated ultimate strain and the test results, with the best single ML models being the K-Star, k-Nearest Neighbor (k-NN) and Decision Table (DT) models. The three best ensemble models, a stacking-based ensemble model comprising K-Star, k-NN and DT models; a stacking-based ensemble model comprising K-Star and k-NN models and a voting-based ensemble model comprising K-Star and k-NN models, achieved higher estimation accuracy than the best single ML model in estimating the strain capacity of FRP-CC columns
Safety in Smart Cities—Automatic Recognition of Dangerous Driving Styles
No full textRoad safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics
The Role of Ceramides in Metabolic and Cardiovascular Diseases
No full textCeramides are bioactive sphingolipids increasingly recognized as mediators of cardiometabolic disease and residual cardiovascular risk. Accumulating evidence from experimental and clinical studies indicates that specific ceramide species contribute to insulin resistance, endothelial dysfunction, myocardial injury, and adverse cardiovascular outcomes. In particular, long-chain ceramides (C16:0, C18:0, C20:0 Cer) are consistently associated with myocardial infarction, heart failure, and cardiovascular mortality, whereas very-long-chain ceramides (C22:0, C24:0 Cer) exhibit neutral or potentially protective associations. This narrative review integrates biochemical, experimental, and clinical evidence to examine ceramide metabolism, molecular diversity, and their emerging role as biomarkers for cardiovascular risk stratification. We also discuss ceramide-based risk scores and their potential clinical utility beyond traditional lipid parameters. Understanding the structure–function relationships of ceramides may support the development of novel diagnostic and therapeutic strategies in cardiovascular prevention
Emerging Roles of Tubulin Isoforms and Their Post-Translational Modifications in Microtubule-Based Transport and Cellular Functions
No full textMicrotubules are hollow cylindrical polymers made up of tubulin. This heterodimeric protein, tubulin, exists in multiple forms: tubulin isotypes and tubulin isoforms. Distinct α- and β-tubulin genes give rise to tubulin isotypes, which differ in their amino acid sequences and cellular expression patterns. The tubulin post-translational modifications (PTMs) encode regulatory information within the microtubule lattice, modifying its biophysical characteristics and shaping interactions with motor proteins and microtubule-associated proteins. Different tubulin isotype compositions and post-translational modification patterns generate distinct tubulin isoforms. These isoforms are tissue-specific and regulate the functions of microtubules in specialized cells and cellular components such as cilia. Tubulin isoforms control cellular transport, regulate mechanosensitivity and shape the cytoskeleton, impacting the cellular functions and homeostasis. This review discusses the tubulin PTMs, including acetylation, methylation, palmitoylation, polyamination, glutamylation, glycylation, tyrosination, phosphorylation, SUMOylation, and ubiquitination, with emphasis on how isotype diversity and PTM-driven regulation together modulate microtubule behaviour, intracellular transport, and cellular functions
Propagation of Emerging and Re-Emerging Infectious Disease Pathogens in Africa: The Role of Migratory Birds
No full textMigratory birds have been implicated in the spread of diverse emerging infectious pathogens, including West Nile virus, Usutu virus, Avian influenza viruses, Salmonella, Campylobacter, antimicrobial-resistant (AMR) bacteria, and antibiotic resistance genes (ARGs). Beyond their roles as vectors and reservoirs, migratory birds are also susceptible hosts whose own health may be compromised by these infections, reflecting their dual position in the ecology of pathogens. As facilitators of pathogen transmission during their long-distance migrations, often spanning thousands of kilometres and connecting ecosystems across continents, these birds can easily cross-national borders and circumvent traditional biosecurity measures, thereby acting as primary or secondary vectors in the transmission of cross-species diseases among wildlife, livestock, and humans. Africa occupies a pivotal position in global migratory bird networks, yet comprehensive data on pathogen carriage remain limited. Gaps in knowledge of pathogen diversity constrain current surveillance systems, resulting in insufficient genomic monitoring of pathogen evolution and a weak integration of avian ecology with veterinary and human health. These limitations hinder early detection of novel pathogens and reduce the continent’s preparedness to manage outbreaks. Therefore, this review provides a holistic assessment of these challenges by consolidating existing knowledge concerning the pathogens transmitted by migratory birds in Africa, while recognizing the adverse effect of pathogens, which potentiates population decline, extinction, and ecological imbalance. It further advocates for the adoption of a comprehensive One Health-omics approach that not only strengthens surveillance and technological capacity but also prioritizes the protection of avian health as an integral component of ecosystem and public health
Linear-Region-Based Contour Tracking for Edge Images
No full textThis work presents the Linear-Region-Based Contour Tracking (LRCT) method for extracting external contours in images, designed to achieve an accurate and efficient description of shapes, particularly useful for archaeological materials with irregular geometries. The approach treats the contour as a discrete signal and analyzes image regions containing edge segments. From these regions, a local linear model is estimated to guide the selection and chaining of representative pixels, yielding a continuous perimeter trajectory. This strategy reduces the amount of data required to describe the contour without compromising shape fidelity. As a case study, the method was applied to images of replicas of archaeological materials exhibiting substantial variations in color and morphology. The results show that the obtained trajectories are comparable in quality to those obtained using classical pipelines based on Canny edge detection followed by Moore tracing, while providing more compact representations well suited for subsequent analyses. Consequently, the method offers an efficient and reproducible alternative for documentation, recording, and morphological comparison, strengthening data-driven approaches in archaeological research
Text-Injected Discriminative Model for Remote Sensing Visual Grounding
No full textRemote Sensing Visual Grounding (RSVG) requires fine-grained understanding of language descriptions to localize the specific image regions. Conventional methods typically employ a pipeline of separate visual and textual encoders and a fusion module. However, as visual and textual features are extracted independently, they tend to lack semantic focus on object features during extraction, leading to suboptimal object focus. While some recent attempts have incorporated textual cues into visual feature extraction, they often design complex fusion modules. To address this, we introduce a simple fusion strategy to integrate textual information into visual backbone networks with minimal architectural changes. Moreover, most of the current works use common object detection losses, which only focus on the features inside the bounding box and neglect the background features. In remote sensing images, the high visual similarity between objects can confuse models, making it difficult to locate the correct target accurately. To this end, we design a novel attention regularization strategy to enhance the model’s ability to distinguish similar features outside bounding box regions. Experiments on three benchmark datasets demonstrate the promising performance of our approach