Search Results (178)

Background/Objectives: mRNA vaccines introduced during the COVID-19 pandemic were a significant step forward in the rapid development and deployment of vaccines in a global pandemic context. These vaccines showed good protective efficacy, but—due to limited breadth of the immune response—they required frequent boosters with manufactured spike sequences that often lagged behind the circulating strains. In order to enhance the breadth, durability, and magnitude of immune responses, we studied the effect of combining priming with an RNA vaccine technology with boosting with protein/adjuvant using a TLR4-agonist based adjuvant. Methods: Specifically, four proprietary adjuvants (EmT4^TM, LiT4Q^TM, MiT4^TM, and AlT4^TM) were investigated in combination with multiple modes of SARS-CoV-2 vaccination (protein, peptide, RNA) for their effectiveness in boosting antibody responses to SARS-CoV-2 spike protein in murine models. Results: Results showed significant improvement in immune response strength and breadth—especially against more distant SARS-CoV-2 variants such as Omicron—when adjuvants were used in combination with boosters following an RNA vaccine prime. Conclusions: The use of novel TLR4 adjuvants in combination with protein or RNA vaccinations presents a promising strategy for improving the efficacy of vaccines in the event of future pandemics, by leveraging rapid response using an RNA vaccine prime and following up with protein/adjuvant-based vaccines to enhance the breadth of immunity. Full article

Virtual reality (VR) can enrich neuropsychological testing, yet the ergonomic trade-offs of its input modes remain under-examined. Seventy-seven healthy volunteers—young (19–29 y) and middle-aged (35–56 y)—completed a VR Trail Making Test with three pointing methods: eye-tracking, head-gaze, and a six-degree-of-freedom hand controller. Completion time, spatial accuracy, and error counts for the simple (Trail A) and alternating (Trail B) sequences were analysed in 3 × 2 × 2 mixed-model ANOVAs; post-trial scales captured usability (SUS), user experience (UEQ-S), and acceptability. Age dominated behaviour: younger adults were reliably faster, more precise, and less error-prone. Against this backdrop, input modality mattered. Eye-tracking yielded the best spatial accuracy and shortened Trail A time relative to manual control; head-gaze matched eye-tracking on Trail A speed and became the quickest, least error-prone option on Trail B. Controllers lagged on every metric. Subjective ratings were high across the board, with only a small usability dip in middle-aged low-gamers. Overall, gaze-based ray-casting clearly outperformed manual pointing, but optimal choice depended on task demands: eye-tracking maximised spatial precision, whereas head-gaze offered calibration-free enhanced speed and error-avoidance under heavier cognitive load. TMT-VR appears to be accurate, engaging, and ergonomically adaptable assessment, yet it requires age-specific–stratified norms. Full article

As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices ($\rho$) demonstrate how sparsity balances information propagation efficiency ($\eta$) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy ($H$) rises 18% (2000–2024), while redundancy ($R$) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation ($Moran’sI>0.16$, $p<0.05$), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering ($C$) rising 16% as the path length ($L$) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

Protein function prediction plays a crucial role in uncovering the molecular mechanisms underlying life processes in the post-genomic era. However, with the widespread adoption of high-throughput sequencing technologies, the pace of protein function annotation significantly lags behind that of sequence discovery, highlighting the urgent need for more efficient and reliable predictive methods. To address the problem of existing methods ignoring the hierarchical structure of gene ontology terms and making it challenging to dynamically associate protein features with functional contexts, we propose a novel protein function prediction framework, termed Partial Order-Based Self-Attention for Gene Ontology (POSA-GO). This cross-modal collaborative modelling approach fuses GO terms with protein sequences. The model leverages the pre-trained language model ESM-2 to extract deep semantic features from protein sequences. Meanwhile, it transforms the partial order relationships among Gene Ontology (GO) terms into topological embeddings to capture their biological hierarchical dependencies. Furthermore, a multi-head self-attention mechanism is employed to dynamically model the association weights between proteins and GO terms, thereby enabling context-aware functional annotation. Comparative experiments on the CAFA3 and SwissProt datasets demonstrate that POSA-GO outperforms existing state-of-the-art methods in terms of Fmax and AUPR metrics, offering a promising solution for protein functional studies. Full article

Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch–Tung–Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R² = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human–robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics. Full article

Soil-transmitted helminths (STHs) are parasitic nematodes that infect humans, particularly in tropical and subtropical regions, where they contribute substantially to neglected tropical diseases (NTDs). Among them, hookworms (Ancylostoma duodenale, Necator americanus and Ancylostoma ceylanicum) cause substantial morbidity, leading to anaemia, malnutrition, and developmental impairment. Despite the global impact of hookworm disease, genomic research on A. duodenale has lagged behind that of other hookworms, limiting comparative and molecular biological investigations. Here, we report the first chromosome-level reference genome of A. duodenale, assembled from a single adult specimen archived in ethanol at −20 °C for more than 27 years. Using third-generation sequencing (PacBio Revio, Menlo Park, CA, USA, Oxford Nanopore, Oxford, UK), Hi-C scaffolding, and advanced computational tools, we produced a high-quality 319 Mb genome, filling a critical gap in hookworm genomics. Comparative analyses with N. americanus and the related, free-living nematode Caenorhabditis elegans provided new insights into genome organisation, synteny, and specific adaptations. While A. duodenale exhibited strong chromosomal synteny with N. americanus, its limited synteny with C. elegans highlights its distinct parasitic adaptations. We identified 20,015 protein-coding genes, including conserved single-copy orthologues (SCOs) linked to host–pathogen interactions, immune evasion and essential biological processes. The first comprehensive secretome analysis of A. duodenale revealed a diverse repertoire of excretory/secretory (ES) proteins, including immunomodulatory candidates predicted to interact with host structural and immune-related proteins. This study advances hookworm genomics, establishes a basis for the sequencing of archival specimens, and provides fundamental insights into the molecular biology of A. duodenale. The genomic resource for this hookworm species creates new opportunities for diagnostic, therapeutic, and vaccine development within a One Health framework. It complements recent epidemiological work and aligns with the WHO NTD roadmap (2021–2030) and Sustainable Development Goal 3.3. Full article

Background: In the era of precision medicine, Next-Generation Sequencing (NGS) has emerged as an important tool for identifying targetable mutations and tailoring treatment options. Yet the Middle East and North Africa (MENA) lags behind in adopting this technology. This study aims to demonstrate the transformative potential of molecular profiling in the region. Methods: This retrospective study reviewed cancer patients at the American University of Beirut Medical Centre, comparing outcomes between those who received NGS-based treatment adjustments (NBTAs) and those who did not. Results: The study enrolled 180 patients, including those with non-small-cell lung cancer (21.2%), sarcomas (20%), gastrointestinal malignancies (23.3%), breast cancer (10.6%), and other cancers (24.9%); 58.3% had stage 4 cancer at diagnosis. Before molecular profiling, 20.6% had stable disease, 21.7% showed partial response, and 57.8% had progressive disease. Most (96%) had received treatment, mainly systemic (90%), with chemotherapy (89%) being the most common. Forty patients (22.2%) underwent NGS-based treatment adjustments (NBTAs). Post-NGS, targeted therapies increased from 35% to 43% and immunotherapies from 14% to 18%. Mutations were detected in 98% of patients, with a median of four mutations per patient. NBTA patients had a median overall survival of 59 months, compared to 23 months for non-NBTA patients (p = 0.096), and significantly improved progression-free survival (5.32 vs. 3.28 months, p = 0.023). Conclusions: The use of large-scale molecular profiling to guide treatment adjustments promises advancements in patient care. Integrating NGS into clinical practice correlates with improved PFS, calling for a broader adoption of its use in the MENA region. Full article

The root-associated microbiome significantly influences plant health and pest resistance, yet the temporal dynamics of its compositional and functional change in response to Ectropis grisescens Warren (Lepidoptera: Geometridae) infestation remain largely unexplored. The study took samples of leaves, roots, and rhizosphere soil at different times after the plants were attacked by E. grisescens. These samples were analyzed using transcriptomic and high-throughput sequencing of 16S rRNA techniques. The goal was to understand how the plant’s defense mechanisms and the microbial community around the roots changed after the attack. Additionally, bacterial feedback assays were conducted to evaluate the effects of selected microbial strains on plant growth and pest defense responses. By conducting 16S rRNA sequencing on the collected soil samples, we found significant shifts in bacterial communities by the seventh day, suggesting a lag in community adaptation. Transcriptomic analysis revealed that E. grisescens attack induced reprogramming of the tea root transcriptome, upregulating genes related to defensive pathways such as phenylpropanoid and flavonoid biosynthesis. Metagenomic data indicated functional changes in the rhizosphere microbiome, with enrichment in genes linked to metabolic pathways and nitrogen cycling. Network analysis showed a reorganization of core microbial members, favoring nitrogen-fixing bacteria like Burkholderia species. Bacterial feedback assays confirmed that selected strains, notably Burkholderia cepacia strain ABC4 (T1) and a nine-strain consortium (T5), enhanced plant growth and defense responses, including elevated levels of flavonoids, polyphenols, caffeine, jasmonic acid, and increased peroxidase (POD) and superoxide dismutase (SOD) activities. This study emphasizes the potential of utilizing root-associated microbial communities for sustainable pest management in tea cultivation, thereby enhancing resilience in tea crops while maintaining ecosystem balance. Full article

As a common food-borne pathogen, Vibrio parahaemolyticus comes into direct or indirect contact with gastric acid after ingestion. However, the mechanisms by which Vibrio parahaemolyticus passes through the gastric acid barrier, recovers, and causes pathogenicity remain unclear. In this study, static in vitro digestion simulation experiments showed that some strains can pass through the gastric acid barrier by utilizing microacid tolerance mechanisms and altering their survival state. Food digestion simulation experiments showed that food matrices could help bacteria escape gastric acid stress, with significantly different survival rates observed for bacteria in various food matrices after exposure to gastric acid. Interestingly, surviving Vibrio parahaemolyticus showed a significantly shorter growth lag time (LT) during recovery. Transcriptome sequencing (RNA-seq) analyses indicated that the bacteria adapted to gastric acid stress by regulating the two-component system through stress proteins secreted via the ribosomal pathway. Pathogenic Vibrio parahaemolyticus that successfully passes through the gastric acid barrier potentially exhibits enhanced pathogenicity during recovery due to the significant upregulation of virulence genes such as tdh and yscF. This study provides a scientific basis for revealing the tolerance mechanisms of food-borne pathogens represented by Vibrio parahaemolyticus in the human body. Full article

Genome-editing technology has advanced significantly since the 2020 Nobel Prize in Chemistry was awarded for the development of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). While CRISPR–Cas9 has become widely used in academic research, its social implementation has lagged due to unresolved patent disputes and slower progress in gene function analysis. To address this, new approaches bypassing direct gene function analysis are needed, with bioinformatics and next-generation sequencing (NGS) playing crucial roles. NGS is essential for sequencing the genome of target species, but challenges such as data quality, genome heterogeneity, ploidy, and small individual sizes persist. Despite these issues, advancements in sequencing technologies, like PacBio high-fidelity (HiFi) long reads and high-throughput chromosome conformation capture (Hi-C), have improved genome sequencing. Bioinformatics contributes to genome editing through off-target prediction and target gene selection, both of which require accurate genome sequence information. In this review, I will give updates on the development of genome editing and bioinformatics technologies with a focus on the rapid progress in genome sequencing. Full article

Accurate wind power forecasting (WPF) is crucial to enhance availability and reap the benefits of integration into power grids. The time lag of wind power generation lags the time of wind speed changes, especially in ultra-short-term forecasting. The prediction model is sensitive to outliers and sudden changes in input historical meteorological data, which may significantly affect the robustness of the WPF model. To address this issue, this paper proposes a novel hybrid machine learning model for highly accurate forecasting of wind power generation in ultra-short-term forecasting. The raw wind power data were filtered and classified with the local outlier factor (LOF) and the voting tree (VT) model to obtain a subset of inputs with the best relevance. The time-varying properties of the fluctuating sub-signals of the wind power sequences were analyzed with the optimized variational mode decomposition (OVMD) algorithm. The Northern Goshawk optimization (NGO) algorithm was improved by incorporating a logical chaotic initialization strategy and chaotic adaptive inertia weights. The improved NGO algorithm was used to optimize the least squares support vector regression (LSSVR) prediction model to improve the computational speed and prediction results. The proposed model was compared with traditional machine learning models, deep learning models, and other hybrid models. The experimental results show that the proposed model has an average R² of 0.9998. The average MSE, average MAE, and average MAPE are as low as 0.0244, 0.1073, and 0.3587, which displayed the best results in ultra-short-term WPF. Full article

The paper presents a methodology for determining the minimum split DC link capacitance for a family of three-phase, three-level grid-connected bidirectional AC-DC converters operating under arbitrary power factor under restriction of DC-only zero-sequence injection. The approach is based on the recently revealed generalized behavior of split DC link voltages in the above-mentioned converters family while distinguishing between leading and lagging power factors in order to highlight different impacts on split DC link capacitor voltages pulsating components. The minimum capacitance value is derived from the boundary condition, ensuring the mains voltage remains below or equal to the capacitor voltage at all times. It is revealed that operation with the lowest expected leading power factor should be employed as the design operating point. The accuracy of the proposed methodology is validated by simulations and experiments carried out employing a 10 kVA grid-connected T-type converter prototype. The results demonstrate close agreement between theoretical predictions and experiments, confirming the practical applicability of the proposed method. Full article

Using Generative Artificial Intelligence (GAI) in education has opened new avenues for innovation, yet its role as an interactive tool with learners remains underexplored. Research in this domain faces challenges from pedagogical complexities and the variability of AI tools. To address these gaps, this study developed an online project-based learning (PBL) platform incorporating a GAI plug-in and conducted a year-long experiment to analyze its impact. Three sets of experimental analyses were performed to examine learners’ methods, cognitive processes, and learning effectiveness. The findings reveal that GAI significantly influenced students’ learning approaches, cognitive engagement, and learning effectiveness. Additionally, the study demonstrates that PBL offers an effective framework for investigating the educational implications of GAI, providing new insights for future research in this evolving field. Full article

Accurate micro-kick detection is crucial for blowout accident preventions. The more drilling parameters that change due to kicks, the more accurate the warning results become. However, when the micro-kick occurs, there is a significant time lag between these parameter changes. Dominant kick detection methods based on long short-term memory (LSTM) forget early parameter trends when dealing with long time series. To improve the recognition accuracy of micro-kicks and avoid potential blowout accidents by memorizing the early or long-term trends in drilling parameters, an intelligent micro-kick detection method based on a multi-head self-attention network is proposed. First, a novel multi-head structure is designed to separate various types of features due to different monitoring parameter changes at different speeds or trends. Second, a self-attention mechanism is employed to focus on parameter changes in separated monitoring data sequences. Then, a feed-forward network with parallel computation capability is utilized to analyze long-range correlations, thus avoiding the loss of early or long-term trend information. Finally, an artificial neural network is used to establish nonlinear relationship models between the trend features of each monitoring parameter and kick accidents. The experiment results demonstrate that the recognition accuracy of the proposed micro-kick detection method is 7.9% higher than that of the LSTM-based method. Full article