Search Results (2,266)

Flaviviruses such as dengue virus (DENV) and Zika virus (ZIKV) co-circulate widely and cause significant morbidity, yet effective broad-spectrum antivirals are limited. This study evaluated the antiviral efficacy, cytotoxicity, and host transcriptional responses to the nucleic acid–hydrolyzing antibody fragment 3D8 scFv in mono- and co-infection models. RNA sequencing of A549 cells treated with 3D8 scFv revealed a dose-dependent activation of the MAPK–HSP70 stress response, with minimal transcriptomic disruption at antiviral concentrations. Comparative transcriptomic analysis identified distinct host signatures for ZIKV and DENV2, and machine learning classifiers accurately distinguished infection states (AUC > 0.95). In Vero E6 cells, prophylactic treatment with 3D8 scFv significantly reduced viral RNA, protein expression, and infectious particle production for both viruses, including during co-infection. Optimized post-entry treatment also demonstrated antiviral activity. Cytotoxicity assays confirmed good tolerability at effective concentrations. These findings indicate that 3D8 scFv inhibits viral replication through early cleavage of viral nucleic acids while inducing a limited protective stress response, supporting its development as a broad-spectrum antiviral candidate. Full article

The development of a universal SARS-CoV-2 vaccine holds great promise for achieving broad and durable protection against existing and future coronavirus variants. The identification, selection, and rational redesign of conserved viral epitopes constitute the direct immunological foundation of universal SARS-CoV-2 vaccine development. The breadth and durability of protection are therefore primarily determined at the level of antigen and epitope design, whereas adjuvants, delivery platforms, and routes of administration serve as enabling and amplifying components rather than primary drivers of universality. Accordingly, this review discusses key determinants of universal vaccine design, including antigen selection, adjuvant utilization, and route of administration. The spike protein, particularly its receptor-binding domain, is a major antigenic target, but its high mutation rate challenges long-term vaccine efficacy. Strategies focusing on conserved epitopes in antigen designs show potential to elicit cross-neutralizing immune responses. Nanoparticle-based vaccines capable of presenting multiple homologous or heterologous antigens have demonstrated enhanced immunogenicity, broad protection in preclinical models and safety in clinical trials. The addition of next-generation adjuvants further amplifies humoral and cellular immunity beyond the capabilities of traditional aluminum-based adjuvants. Moreover, mucosal vaccine delivery may provide superior local protection at viral entry sites and limit transmission. Importantly, integrating these technological advances with epitope-centered antigen design and immunological data from vaccinated individuals will accelerate the identification of conserved epitopes and inform future vaccine design. A multidisciplinary approach combining optimized antigen engineering, novel adjuvant systems, and innovative delivery strategies is essential for the realization of a broadly protective universal SARS-CoV-2 vaccine. Full article

In Algebraic Statistics, M.A. Cueto, J. Morton and B. Sturmfels introduced a statistical model, the Restricted Boltzmann Machine, which introduced the Hadamard product of two or more vectors of an affine or projective space, i.e., the componentwise product of their entries, forcing Algebraic Geometry to enter. The Hadamard product $X\star Y$ of two subvarieties $X,Y\subset {\mathbb{P}}^n$ is defined as the Zariski closure of the Hadamard product of its elements. Recently, D. Antolini and A. Oneto introduced and studied the definition of Hadamard rank, and we prove some results on it. Moreover, we prove some theorems on the dimension and shape of the Hadamard powers of X. The aim is to describe the images of the Hadamard products without taking the Zariski closure. We also discuss several scenarios describing the case in which some of the data, i.e., the variety X, is wrong or it is not possible to recover it. Full article

The acoustic design of learning spaces is commonly carried out using geometrical acoustics simulations or analytical calculations. While 3D simulations provide high accuracy, they are time-consuming and resource-intensive, whereas analytical calculations are limited to reverberation time. This study proposes a set of empirical regression formulas for estimating the speech clarity index $C_{50}$ from the reverberation time $T_{30}$ and the source–receiver distance r. The models are intended for design verification, allowing a quick assessment of whether a proposed acoustic solution meets speech clarity criteria without using numerical simulations. A total of 455 measurement entries from 28 rooms were analyzed, representing three categories of acoustic conditions. Polynomial and logarithmic regression models were developed and evaluated using three statistical criteria: the adjusted coefficient of determination ($R_{\mathrm{adj}}^2$), the Akaike Information Criterion (AIC), and the root mean square error (RMSE). The results show that logarithmic models generally provide better fit consistency across room types, whereas polynomial models describe lower frequency bands more accurately. The proposed relationships demonstrate practical potential for predicting $C_{50}$ for mid–high frequencies in real rooms using analytically obtained $T_{30}$ values and geometric distances. The proposed models are intended for early-stage building and classroom design, where numerical simulations are not yet available. Full article

Background/Objectives: Coxsackievirus B4 (CVB4), a member of the Enterovirus genus and the Picornaviridae family, is a significant pathogen causing several human diseases such as pancreatitis, myocarditis, cardiomyopathy and type 1 diabetes. Despite its clinical impact, no vaccines or specific antiviral therapies are currently available. This study investigates the attenuation of CVB4 virulence through targeted mutations in the domain V of the IRES (Internal Ribosome Entry Segment) sequence present in the 5′ UTR (Untranslated Region) of the viral genome. Materials and Methods: We engineered six CVB4E2 mutants by introducing single nucleotide mutations in domain V of the IRES sequence using PCR-based site-directed mutagenesis assays. Mutants were rigorously evaluated in vitro for their replicative capacities on HeLa cell culture and for their in vitro translation efficiencies in standard rabbit reticulocyte lysates supplemented with HeLa cell S10 extracts. Using different strategies of immunization and lethal challenges in a Balb/c mice model, we evaluated the immune responses elicited by the most attenuated C488A mutant strain. Results: The obtained results demonstrated that the live-attenuated C488A mutant with the single mutation C to A at nucleotide position 488 of the viral IRES sequence exhibited a significant reduction in vitro of both viral productivity and translation efficiency. The oral immunization with the live-attenuated C488A mutant induced a potent immune response and protected Balb/c mice against lethal infection challenge with a pathogenic strain. Conclusions: These findings underscored the critical role of IRES in CVB4 virulence and highlighted the use of the live-attenuated C488A mutant strain as a promising candidate for developing a live-attenuated vaccine against CVB4 infections. Full article

Underwater noise pollution, driven by human activities, is an emerging environmental concern, yet its effects on fish behavior and physiology remain poorly understood. As a vertebrate model with conserved stress pathways, zebrafish (Danio rerio) is well-suited for investigating the mechanistic basis of such impacts. We hypothesized that daytime and nighttime noise exposure would differentially induce anxiety-like behavior and associated neuroendocrine disruptions in zebrafish, with effects varying by sex. To evaluate this hypothesis, adult zebrafish were exposed to anthropogenic noise (100–1000 Hz, 130 dB) for seven days, specifically during daytime (08:00–20:00) and nighttime (20:00–08:00) periods. Behavioral assays revealed that noise exposure delayed the first entry of females into the top zone during daytime, while both sexes exhibited prolonged bottom-dwelling and reduced exploratory behavior under nighttime noise. Physiological analyses showed elevated plasma cortisol levels in females, accompanied by up-regulated HPI-axis genes, whereas males displayed a non-significant cortisol increase. Neurotransmitter profiling indicated a sex-specific response to nighttime noise: In females, brain 5-hydroxytryptamine (5-HT) showed a non-significant increasing trend, whereas in males it was significantly elevated, while dopamine (DA) decreased in both sexes. Gene expression analysis further revealed disruptions in 5-HT and DA pathways. These findings demonstrate that underwater noise induces anxiety-like behavior in zebrafish by dysregulating endocrine and neurotransmitter systems, with nighttime noise exhibiting more pronounced effects, suggesting that chronic exposure to anthropogenic noise may impair natural behavior and stress regulation in aquatic species, particularly during nighttime periods. Full article

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the word level using only a single RGB camera, under a fixed single-user and single-camera configuration. We propose a word-level contactless typing framework that models each word as a distinctive spatiotemporal finger motion pattern derived from hand joint trajectories. Typing motions are temporally segmented, and direction-aware finger displacements are accumulated to construct compact motion representations that are relatively insensitive to absolute hand position and typing duration within the evaluated setup. Each word is represented by multiple motion prototypes that are incrementally updated through online learning with a trial-delayed adaptation protocol. Experiments with vocabularies of up to 200 words show that the proposed approach progressively learns and recalls word-level motion patterns through repeated interaction, achieving stable recognition performance within the tested configuration at realistic typing speeds. Additional evaluations demonstrate that learned motion representations can transfer from physical keyboards to flat-surface typing within the same experimental setting, even when tactile feedback and visual layout cues are reduced. These results support the feasibility of reframing contactless typing as a word-level motion recall problem, and suggest its potential role as a complementary component to character-centric camera-based input methods under constrained monocular sensing. Full article

Background/Objectives: Safe placement of sacral vertebra 1 (S1) screws is essential in lumbosacral instrumentation and iliosacral fixation. Existing anatomical safe zones are largely based on averaged geometry and do not provide quantitative probability estimates for permissible deviations from an ideal entry point. This study aims to develop a probabilistic, computed tomography–based (CT-based) safe zone model for S1 screw placement. Methods: This retrospective imaging-based anatomical modeling study will analyze 1000 anonymized lumbosacral CT scans. A reproducible reference entry point will be defined on the lateral S1 projection, and bilateral offset-based virtual screw trajectories will be evaluated. Two independent raters will classify each trajectory as intraosseous or extraosseous. Probabilistic safety maps will be generated by aggregating binary classifications across offsets and directions. Interobserver reliability will be assessed using Cohen’s kappa, and anatomical influences will be analyzed using multivariable regression models. Results: The study is expected to generate continuous probabilistic safety maps illustrating the likelihood of intraosseous S1 screw placement across predefined offset distances and directions from the reference entry point. These maps are anticipated to demonstrate a gradual transition from high to low safety probabilities rather than a binary safe–unsafe boundary, and to identify anatomical factors influencing screw containment. Conclusions: This protocol describes a CT-based probabilistic modeling approach to S1 screw placement that aims to provide a more nuanced and quantitative definition of anatomical safe zones. If successful, the proposed method may improve preoperative planning and intraoperative decision-making by moving beyond averaged geometric constraints toward probability-informed screw placement. Full article

Background/Objectives: Dental diseases represent a great problem for oral health care, and early diagnosis is essential to reduce the risk of complications. Panoramic radiographs provide a detailed perspective of dental structures that is suitable for automated diagnostic methods. This paper aims to investigate the use of an advanced deep learning (DL) model for the multiclass classification of diseases at the sub-diagnosis level using panoramic radiographs to resolve the inconsistencies and skewed classes in the dataset. Methods: To classify and test the models, rich data of 10,580 high-quality panoramic radiographs, initially annotated in 93 classes and subsequently improved to 35 consolidated classes, was used. We applied extensive preprocessing techniques like class consolidation, mislabeled entry correction, redundancy removal and augmentation to reduce the ratio of class imbalance from 2560:1 to 61:1. Five modern convolutional neural network (CNN) architectures—InceptionV3, EfficientNetV2, DenseNet121, ResNet50, and VGG16—were assessed with respect to five metrics: accuracy, mean average precision (mAP), precision, recall, and F1-score. Results: InceptionV3 achieved the best performance with a 97.51% accuracy rate and a mAP of 96.61%, thus confirming its superior ability for diagnosing a wide range of dental conditions. The EfficientNetV2 and DenseNet121 models achieved accuracies of 97.04% and 96.70%, respectively, indicating strong classification performance. ResNet50 and VGG16 also yielded competitive accuracy values comparable to these models. Conclusions: Overall, the results show that deep learning models are successful in dental disease classification, especially the model with the highest accuracy, InceptionV3. New insights and clinical applications will be realized from a further study into dataset expansion, ensemble learning strategies, and the application of explainable artificial intelligence techniques. The findings provide a starting point for implementing automated diagnostic systems for dental diagnosis with greater efficiency, accuracy, and clinical utility in the deployment of oral healthcare. Full article

The lateral abutment stress and damage range of the coal seam are prerequisites for the layout of gob-side entries and surrounding rock control. They are influenced by the structure and mechanical properties of the coal seam and the overlying strata. To address this issue, this study establishes a mechanical analysis model for the lateral abutment stress and damage range under coupled conditions between the coal seam and overlying strata. This model systematically investigates the influence of various factors, including the fracture height and break angle of the overlying strata, the rotation angle and subsidence of key blocks, the burial depth and thickness of the coal seam, as well as the cohesion and internal friction angle of the coal mass. The study reveals that the weight and overburden load of the triangular hanging roof zone, along with the subsidence and rotation of the key blocks, are the key factors influencing the lateral abutment stress and damage range. Meanwhile, the reliability of the mechanical model has been substantiated through a combination of numerical simulation and in situ monitoring results. Full article

To address the “silo effect” in construction waste management and the inefficiency of resource allocation in large-scale, multi-section engineering projects, this study developed a cloud-based regional collaborative platform for construction waste management. The platform adopts a technical framework based on Java 1.8.0, Spring Boot 2.4.4, and MySQL 8.0.16, and integrates a visual interactive interface. It supports dynamic access, data entry, quality review, and scheduling of construction waste information across multiple sections and projects. Validated through a case study on the Changhu section of the Guangdong Guanshen–Changhu Expressway expansion project, the platform successfully achieved spatial–temporal optimization of 740 thousand cubic meters of diversified construction waste across seven sections. The comprehensive utilization rate of construction waste increased by more than 25%. Practice has shown that the platform effectively promotes carbon emission reduction in earthworks, enhances resource circularity, and provides digital support for construction quality control. This platform presents an innovative informatics-driven approach to construction waste management, serving as a replicable model. Full article

Pseudorabies (PR) is an acute and highly contagious disease caused by the pseudorabies virus (PRV). This virus has a wide range of susceptible hosts and has caused major economic losses to the global swine industry. While rosmarinic acid possesses broad antioxidant and antiviral properties, its efficacy against PRV has remained unexplored. Therefore, this study aimed to evaluate the anti-PRV activity of rosmarinic acid and to elucidate its underlying mechanism, with a focus on the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. The results revealed that rosmarinic acid exhibited potent, concentration-dependent antiviral activity in vitro, with a half-maximal inhibitory concentration (IC50) of 0.02654 mg/mL, a half-maximal cytotoxic concentration (CC50) of 0.1043 mg/mL, and a selectivity index (SI) of 3.9. Rosmarinic acid inhibited virus adsorption, entry, and intracellular replication. It also significantly suppressed the expression of the gB protein. In a mouse model, rosmarinic acid treatment (200 mg/kg) significantly enhanced the survival rate to 28.5%. This treatment reduced the viral load in the brain, lungs, kidneys, heart, and spleen. It also alleviated the tissue damage caused by PRV infection. Furthermore, rosmarinic acid counteracted PRV-induced oxidative stress by elevating the activity of the antioxidant factors SOD and CAT and reducing the level of the oxidative factor MDA. Network pharmacology analyses predicted the Nrf2 signaling pathway as a key target for rosmarinic acid. Subsequent mechanistic studies confirmed that rosmarinic acid upregulated the expression of the Nrf2, HO-1, GPX, SOD, and CAT genes, as well as Nrf2 and HO-1 proteins, thereby promoting the nuclear translocation of Nrf2. These results identify rosmarinic acid as a promising anti-PRV agent that acts through multi-phase viral inhibition and activation of the Nrf2-mediated antioxidant defense, suggesting its potential as a novel pharmacological strategy against PRV. Full article

Unlike classical multi-layered micro-perforated panels (MPPs), which rely on sub-millimeter orifices for sound dissipation, we propose a multi-layered porous Helmholtz resonators absorber. It consists of alternately layered perforated porous material panels and perforated rigid panels with millimeter- to centimeter-scale orifices, primarily relying on porous materials for sound energy dissipation. Theoretically, perforated porous material panels are modeled as homogeneous fluid layers using double porosity theory, and the total surface impedance is derived through bottom-to-top impedance translation. A double-layered prototype was tested to validate the theoretical and numerical models, achieving near-perfect absorption peaks at 262 Hz and 774 Hz, with a subwavelength total thickness of 11 cm and a broadband absorption above an absorption coefficient of 0.7 from 202 Hz to 1076 Hz. Simulations of sound pressure, particle velocity, power dissipation, and sound intensity flow confirm that Helmholtz resonances in each layer enhance sound entry into resistive porous materials, causing absorption peaks. Parameter studies show this absorber maintains high absorption peaks across wide ranges of orifice diameters and panel thicknesses. Finally, an optimized triple-layer porous Helmholtz resonators absorber achieves an ultra-broadband absorption above a coefficient of 0.95 from 280 Hz to 1349 Hz with only 16.5 mm thickness. Compared with conventional MPPs, this design features significantly larger orifices that are easier to fabricate and less susceptible to blockage in harsh environments, offering an alternative solution for low-frequency and broadband sound absorption. Full article

Higher education is in a period of change driven by increasing demands for student-centred learning, flexible delivery, and stronger industry relevance. While innovation in course design is widely recognised as essential, academics often face barriers such as limited time, institutional constraints, budget and financial constraints and risk aversion. Building on previous pedagogical and innovation models, this paper presents the enabling innovation framework, developed through an iterative, design-thinking process and grounded in Rogers’ Diffusion of Innovation theory. The framework conceptualises three interconnected modes of innovation: everyday, strategic, and radical. The development of each mode highlights the importance of time and scholarly activity as underpinning concepts of the framework. Everyday innovation involves small, often spontaneous adjustments to teaching practice; strategic innovation is collaborative and aligns with institutional or program-level goals; and radical innovation is transformative, disrupting existing practices to create new cultures of learning. Together, these modes offer multiple entry points into innovation, encouraging academics to engage meaningfully with course design regardless of their level of risk appetite or institutional positioning. By framing innovation as a continuum supported by scholarship, the framework provides educators with a practical scaffold to initiate and sustain pedagogical change. This work argues that enabling innovation at different levels fosters a stronger culture of creativity, adaptability, and quality in higher education teaching and learning. Full article

In the face of accelerating climate change and increasingly complex disturbance regimes, enhancing forest ecosystem resilience has become a core priority in forest ecology and management. This paper argues that long-term resilience in hemiboreal forests depends fundamentally on the management of ecosystem legacies—structural, compositional, and functional remnants that persist following past disturbances and land use. Organized under the resilience framework, this perspective emphasizes that resilience is not solely a matter of response or effect, but an emergent property shaped by abiotic and biotic legacies, including life history traits, landscape heterogeneity, and both anthropogenic and natural disturbance. In this paper, drawing from disturbance ecology, resilience theory, and regional empirical studies, a conceptual model is presented that integrates legacy attributes, environmental filters, and management objectives to support adaptive restoration strategies. It helps design restoration pathways that are ecologically meaningful, operationally realistic, and robust to novel disturbance regimes. By operationalizing legacy–action linkages, the model offers practitioners concrete entry points for retention, disturbance use, and landscape design to enhance resilience. Full article