Search Results (596)

Background: Next-generation sequencing (NGS) analysis of viral samples generates results dispersed across multiple files—genome assembly, variant calling, and functional annotations—making integrated interpretation challenging. Variants often yield numerous low-frequency or non-significant variants, yet only a small fraction are biologically relevant. Virologists must manually sift through extensive data to identify meaningful mutations, a time-consuming and error-prone process. To address these practical challenges, we developed vvv2_display, a dedicated summarization and visualization tool, integrated within comprehensive Galaxy workflows. Results: vvv2_display streamlines variant interpretation by consolidating key results into two concise and interoperable outputs. The first output is a PNG image showing alignment coverage depth and genomic annotations, with significant variants displayed along the genome as symbols whose height reflects frequency and shape indicates the affected protein. At a glance, this enables virologists to identify all deviations from a reference viral genome. Each significant variant is assigned a unique identifier that directly links to the second output: a tab-separated (TSV) text file listing only high-confidence variants, with frequencies, flanking nucleotides, and impacted genes and proteins. This cross-referenced design supports rapid, accurate, and intuitive data exploration. Availability: vvv2_display is open source, available on Github and installable via Mamba. Full article

Background: Hereditary spastic paraplegia (HSP) is a clinically and genetically heterogeneous group of neurodegenerative disorders characterized by progressive lower-limb spasticity and weakness. SPAST mutations are the most common cause of autosomal dominant HSP (SPG4). However, many pathogenic SPAST variants are unique and genetic data from underrepresented communities remain limited. Methods: Whole-exome sequencing (WES) was performed on the index patient with HSP. Variant annotation tools included Ensembl VEP, LOFTEE, CADD, SIFT, PolyPhen-2, MutationTaster, and SpliceAI. Variant interpretation followed ACMG/AMP guidelines. Clinical evaluation and family history supported phenotypic correlation and segregation. Results: A novel heterozygous frameshift variant in SPAST (c.339delG; p.Glu114Serfs*47) was identified. The variant was predicted to cause nonsense-mediated decay, resulting in loss of the microtubule-interacting and AAA ATPase domains of spastin. It was absent from population databases (gnomAD, TOPMed, 1000 Genomes) and public variant repositories (ClinVar, HGMD). The variant segregated with disease in two affected siblings and could be classified as likely pathogenic. Conclusions: This novel SPAST frameshift variant expands the mutational spectrum of SPG4-HSP and highlights the importance of including isolated or minority communities in genomic research to improve variant interpretation. Full article

Image matching plays a critical role in a wide range of computer vision applications, including object recognition, 3D reconstruction, aiming-point and six-degree-of-freedom detection for aiming devices, and video surveillance. Over the past three decades, image-matching algorithms and techniques have evolved significantly, from handcrafted feature extraction algorithms to modern approaches powered by deep learning neural networks and attention mechanisms. This paper provides a comprehensive review of image-matching techniques, aiming to offer researchers valuable insights into the evolving landscape of this field. It traces the historical development of feature-based methods and examines the transition to neural network-based approaches that leverage large-scale data and learned representations. Additionally, this paper discusses the current state of the field, highlighting key algorithms, benchmarks, and real-world applications. Furthermore, this study introduces some recent contributions to this area and outlines promising directions for future research, including H-matrix optimization, LoFTR model speedup, and performance improvements. It also identifies persistent challenges such as robustness to viewpoint and illumination changes, scalability, and matching under extreme conditions. Finally, this paper summarizes future trends for research and development in this field. Full article

The study presents a comparative analysis of five distinct image processing methodologies for roadside wild animal detection using thermal imagery, aiming to identify an optimal approach for embedded system implementation to mitigate wildlife–vehicle collisions. The evaluated techniques included the following: bilateral filtering followed by thresholding and SIFT feature matching; Gaussian filtering combined with Canny edge detection and contour analysis; color quantization via the nearest average algorithm followed by contour identification; motion detection based on absolute inter-frame differencing, object dilation, thresholding, and contour comparison; and animal detection based on a YOLOv8n neural network. These algorithms were applied to sequential thermal images captured by a custom roadside surveillance system incorporating a thermal camera and a Raspberry Pi processing unit. Performance evaluation utilized a dataset of consecutive frames, assessing average execution time, sensitivity, specificity, and accuracy. The results revealed performance trade-offs: the motion detection method achieved the highest sensitivity (92.31%) and overall accuracy (87.50%), critical for minimizing missed detections, despite exhibiting the near lowest specificity (66.67%) and a moderate execution time (0.126 s) compared to the fastest bilateral filter approach (0.093 s) and the high-specificity Canny edge method (90.00%). Consequently, considering the paramount importance of detection reliability (sensitivity and accuracy) in this application, the motion-based methodology was selected for further development and implementation within the target embedded system framework. Subsequent testing on diverse datasets validated its general robustness while highlighting potential performance variations depending on dataset characteristics, particularly the duration of animal presence within the monitored frame. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

Face recognition technology utilizes unique facial features to analyze and compare individuals for identification and verification purposes. This technology is crucial for several reasons, such as improving security and authentication, effectively verifying identities, providing personalized user experiences, and automating various operations, including attendance monitoring, access management, and law enforcement activities. In this paper, comprehensive evaluations are conducted using different face detection and modality segmentation methods, feature extraction methods, and classifiers to improve system performance. As for face detection, four methods are proposed: OpenCV’s Haar Cascade classifier, Dlib’s HOG + SVM frontal face detector, Dlib’s CNN face detector, and Mediapipe’s face detector. Additionally, two types of feature extraction techniques are proposed: hand-crafted features (traditional methods: global local features) and deep learning features. Three global features were extracted, Scale-Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Global Image Structure (GIST). Likewise, the following local feature methods are utilized: Local Binary Pattern (LBP), Weber local descriptor (WLD), and Histogram of Oriented Gradients (HOG). On the other hand, the deep learning-based features fall into two categories: convolutional neural networks (CNNs), including VGG16, VGG19, and VGG-Face, and Siamese neural networks (SNNs), which generate face embeddings. For classification, three methods are employed: Support Vector Machine (SVM), a one-class SVM variant, and Multilayer Perceptron (MLP). The system is evaluated on three datasets: in-house, Labelled Faces in the Wild (LFW), and the Pins dataset (sourced from Pinterest) providing comprehensive benchmark comparisons for facial recognition research. The best performance accuracy for the proposed ten-feature extraction methods applied to the in-house database in the context of the facial recognition task achieved 99.8% accuracy by using the VGG16 model combined with the SVM classifier. Full article

Background: RNF114 gene encodes an E3 ubiquitin ligase involved in immune signaling and regulation of inflammation. Genetic variants, particularly nonsynonymous single-nucleotide polymorphisms (nsSNPs), may interfere with protein function and cause immune diseases such as psoriasis. Although significant, the structural and functional impact of RNF114 nsSNPs is not well understood. Methods: We used comprehensive bioinformatics analyses to predict the functional impact of RNF114 nsSNPs. Deleterious variants were predicted by SIFT, PolyPhen-2, PROVEAN, META-SNP, ESNP&GO, PANTHER, and Alpha-Missense. Protein stability was examined by I-Mutant2.0, and MUpro further contextualized variant effects. Structural modeling was performed by AlphaFold and visualized using UCSF ChimeraX 1.10.1. Additionally, we studied the Conservation using ConSurf and protein-protein interaction by STRING tools. Results: Among 252 available nsSNPs, three mutations—C49R (rs1600868749), R68C (rs745318334), and R68H (rs758000156)—were predicted to have a deleterious and destabilizing effects on the protein structure by all the tools. All three variants were located in extremely conserved residues and were predicted to significantly destabilize the protein structure. Structural modeling demonstrated disruptions in the RNF114 domain structure. STRING analysis revealed interactions of RNF114 with key immune regulators, and pathway enrichment pointed to roles in NF-κB signaling, ubiquitin-mediated proteolysis, and autoimmune disease pathways. Conclusions: In the current study, we predicted three novel, potentially pathogenic RNF114 variants with protein-destabilizing effect that could lead to immune dysregulation. Full article

Polymer matrix composites have been used extensively in the aerospace and automotive industries. Nevertheless, the growing demand for composites raises concerns about the thermal stability, cost, and environmental impacts of synthetic fillers like graphene and carbon nanotubes. Hence, this study investigates the possibility of enhancing the thermomechanical properties of polymer composites through the incorporation of agricultural waste as fillers. Particles from walnut, coffee, and coconut shells were used as fillers to create particulate composites. Bio-based composites with 10 to 30 wt.% filler were created by sifting these particles into various mesh sizes and dispersing them in an epoxy matrix. In comparison to the pure polymer, DSC results indicated that the inclusion of 50 mesh 30 wt.% agricultural waste fillers increased the glass transition temperature by 8.5%, from 55.6 °C to 60.33 °C. Also, the TGA data showed improved thermal stability. Subsequently, the agricultural wastes were employed as reinforcement for laminated composites containing woven glass fiber with a 50% fiber volume fraction, eight plies, and varying particle filler weight percentages from 0% to 6% with respect to the laminated composite. The hybrid laminated composite demonstrated improved impact resistance of 142% in low-velocity impact testing. These results demonstrate that fillers made of agricultural wastes can enhance the thermomechanical properties of sustainable composites, creating new environmentally friendly prospects for the automotive and aerospace industries. Full article

Capillaroscopy imaging is a non-invasive technique used to examine the microcirculation of the oral mucosa. However, the acquired video sequences are often affected by motion noise and shaking, which can compromise diagnostic accuracy and hinder the development of automated systems for capillary identification and segmentation. To address these challenges, we implemented a comprehensive video stabilization model, structured as a multi-phase pipeline and visually represented through a flow-chart. The proposed method integrates keypoint extraction, optical flow estimation, and affine transformation-based frame alignment to enhance video stability. Within this framework, we evaluated the performance of three keypoint extraction algorithms—Scale-Invariant Feature Transform (SIFT), Oriented FAST and Rotated BRIEF (ORB) and Good Features to Track (GFTT)—on a curated dataset of oral capillaroscopy videos. To simulate real-world acquisition conditions, synthetic tremors were introduced via Gaussian affine transformations. Experimental results demonstrate that all three algorithms yield comparable stabilization performance, with GFTT offering slightly higher structural fidelity and ORB excelling in computational efficiency. These findings validate the effectiveness of the proposed model and highlight its potential for improving the quality and reliability of oral videocapillaroscopy imaging. Experimental evaluation showed that the proposed pipeline achieved an average SSIM of 0.789 and reduced jitter to 25.8, compared to the perturbed input sequences. In addition, path smoothness and RMS errors (translation and rotation) consistently indicated improved stabilization across all tested feature extractors. Compared to previous stabilization approaches in nailfold capillaroscopy, our method achieved comparable or superior structural fidelity while maintaining computational efficiency. Full article

This study investigates the potential of hemp fiber reinforcement in wood–sodium silicate composites for additive manufacturing. It focuses on the impact of hemp fiber length and content on the rheological, flexural, compression properties, and extrudability of the composite. Composites contained varying amounts of sodium silicate (45, 50, 55 wt%) and hemp fibers of varying lengths (1, 3, 5 mm) and amounts (2.5, 5, 10 wt%) along with wood fibers sifted through a 40-mesh sieve. The study shows that higher sodium silicate content significantly increases viscosity while reducing the motor power needed to extrude the composite. Hemp fiber amount positively affects flexural and compression strength, increasing by 31.2% and 35.6%, respectively, with 5 wt% hemp fiber. This improvement in mechanical properties significantly increases the thermoset-based composite’s potential for various applications. This study also demonstrates for the first time, the feasibility of using the hemp fiber-reinforced wood–sodium silicate composite for additive manufacturing by successfully depositing a multi-layer sample print and determining its bending strength. Full article

To address the high-precision navigation requirements of urban low-altitude electric vertical take-off and landing (eVTOL) aircraft in environments where global navigation satellite systems (GNSSs) are denied and under complex urban terrain conditions, a terrain-matching optimization algorithm based on light detection and ranging (LiDAR) is proposed. Given the issues of GNSS signal susceptibility to occlusion and interference in urban low-altitude environments, as well as the error accumulation in inertial navigation systems (INSs), this algorithm leverages LiDAR point cloud data to assist in constructing a digital elevation model (DEM). A terrain-matching optimization algorithm is then designed, incorporating enhanced feature description for key regions and an adaptive random sample consensus (RANSAC)-based misalignment detection mechanism. This approach enables efficient and robust terrain feature matching and dynamic correction of INS positioning errors. The simulation results demonstrate that the proposed algorithm achieves a positioning accuracy better than 2 m in complex scenarios such as typical urban canyons, representing a significant improvement of 25.0% and 31.4% compared to the traditional SIFT-RANSAC and SURF-RANSAC methods, respectively. It also elevates the feature matching accuracy rate to 90.4%; meanwhile, at a 95% confidence level, the proposed method significantly increases the localization success rate to 96.8%, substantially enhancing the navigation and localization accuracy and robustness of eVTOLs in complex low-altitude environments. Full article

Contact-force perception is a critical component of safe robotic grasping. With the rapid advances in embodied intelligence technology, humanoid robots have enhanced their multimodal perception capabilities. Conventional force sensors face limitations, such as complex spatial arrangements, installation challenges at multiple nodes, and potential interference with robotic flexibility. Consequently, these conventional sensors are unsuitable for biomimetic robot requirements in object perception, natural interaction, and agile movement. Therefore, this study proposes a sensorless external force detection method that integrates SuperPoint-Scale Invariant Feature Transform (SIFT) feature extraction with finite element analysis to address force perception challenges. A visual analysis method based on the SuperPoint-SIFT feature fusion algorithm was implemented to reconstruct a three-dimensional displacement field of the target object. Subsequently, the displacement field was mapped to the contact force distribution using finite element modeling. Experimental results demonstrate a mean force estimation error of 7.60% (isotropic) and 8.15% (anisotropic), with RMSE < 8%, validated by flexible pressure sensors. To enhance the model’s reliability, a dual-channel video comparison framework was developed. By analyzing the consistency of the deformation patterns and mechanical responses between the actual compression and finite element simulation video keyframes, the proposed approach provides a novel solution for real-time force perception in robotic interactions. The proposed solution is suitable for applications such as precision assembly and medical robotics, where sensorless force feedback is crucial. Full article

Fish condition detection, including the identification of feeding desire, biological attachments, fence breaches, and dead fishes, has become an important research frontier in fishery aquaculture. However, perception in underwater conditions is less satisfactory and remains a tricky problem. Firstly, we have developed a multimodal dataset based on Neuromorphic vision (NeuroVI) and RGB images, encompassing challenging fishery aquaculture scenarios. Within the fishery aquaculture dataset, a spike neural network (SNN) method is designed to filter NeuroVI images, and the sift feature points are leveraged to select the optimal image. Next, we propose a dual-image cross-attention learning network that achieves scene segmentation in a fishery aquaculture cage. This network comprises double-channels feature extraction and guided attention learning modules. In detail, the feature matrix of NeuroVI images serves as the query matrix for RGB images, generating attention for calculating key and value matrices. Then, to alleviate the computational burden of the dual-channel network, we replace dot-product multiplication with element-wise multiplication, thereby reducing the computational load among different matrices. Finally, our experimental results from the fishery cage demonstrate that the proposed method achieves the state-of-the-art segmentation performance in the management process of fishery aquaculture. Full article

The traditional process for kinematic synthesis of planar mechanisms involves setting a few prescribed positions, then solving a set of equations to identify a vector chain that exactly reproduces those positions. In evaluating these equations, designers often must sift through multiple “infinities” of solutions corresponding to some number of free-choice variables that each have an infinite number of possible values. In this vast solution space, some combination of those variables will produce the most optimal solution, but finding that optimal solution is not trivial. There are two extremes for addressing the impossibility of sifting through infinite possible values. First, one could use analytical techniques to make educated estimates of the optimal values. Or, alternatively, a designer could completely remove their perspective from the process, passing the problem into a computer and programming it to sift through millions (or orders of magnitude more) possible solutions. The present work proposes a novel intermediate step in the analytical synthesis process that functions as a middle ground between these extremes. Optimizing solution density involves a designer manually manipulating the problem definition to increase the percentage of solutions that have pivots in acceptable locations. This is accomplished by changing the values of δ_j and α_j (prescribed translation and rotation of the moving plane, respectively) to manipulate the position of the poles. A physical example, designing a 7-bar parallel-motion generator, shows that applying this method yields more passing solutions when comparing over the same search depth. Specifically, 0.008% of solutions pass the design criteria without applying the method, and 3.154% pass after optimizing. This approach can reduce the computational load placed on a computer running a search script, as designers can use larger increments on the free choices without skipping over a family of solutions. Full article

Acorus calamus, a traditional Tibetan medicine with potential antiviral activity but undefined mechanisms, was studied for its anti-respiratory syncytial virus (RSV) mechanisms using network pharmacology and molecular docking, given RSV’s substantial disease burden and lack of specific therapies. The primary active compounds were identified and analyzed through a literature search, the PubChem database, and the SwissADME. Relevant targets were sifted through the SwissTargetPrediction platform, OMIM, and GeneCards databases. Common targets underwent enrichment analysis using Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Molecular docking and GEO datasets were used for further analysis. Among the screened data, 268 targets were associated with Acorus calamus compounds and 1633 with RSV. KEGG analysis of the shared targets revealed potential therapeutic roles via the PI3K–Akt and JAK–STAT signaling pathways. Molecular docking results demonstrated that CCND1, EGFR, and SRC exhibited relatively lower binding energies with compounds in comparison to other proteins, suggesting better interactions, and GEO-derived RSV datasets further validated CCND1’s significance. This study demonstrates Acorus calamus’s anti-RSV activity and its potential mechanism, providing a theoretical foundation for the effective active ingredients of Acorus calamus targeting CCND1 as a strategy to combat RSV infection. Full article