Search Results (10,050)

Tooth development (odontogenesis) is regulated by interactions between epithelial and mesenchymal tissues through signaling pathways such as Bone Morphogenetic Protein (BMP), Wingless-related integration site (Wnt), Sonic Hedgehog (SHH), and Fibroblast Growth Factor (FGF). Mesenchymal stem cells (MSCs) derived from dental tissues—including dental pulp stem cells (DPSCs), periodontal ligament stem cells (PDLSCs), and dental follicle progenitor cells (DFPCs)—show promise for regenerative dentistry due to their multilineage differentiation potential. Epigenetic regulation, particularly DNA methylation, is hypothesized to underpin their distinct regenerative capacities. This study reanalyzed publicly available DNA methylation data generated with Illumina Infinium HumanMethylation450 BeadChip arrays (450K arrays) from DPSCs, PDLSCs, and DFPCs. High-confidence CpG sites were selected based on detection p-values, probe variance, and genomic annotation. Principal Component Analysis (PCA) and hierarchical clustering identified distinct methylation profiles. Functional enrichment analyses highlighted biological processes and pathways associated with specific methylation clusters. Noncoding RNA analysis was integrated to construct regulatory networks linking DNA methylation patterns with key developmental genes. Distinct epigenetic signatures were identified for DPSCs, PDLSCs, and DFPCs, characterized by differential methylation across specific genomic contexts. Functional enrichment revealed pathways involved in odontogenesis, osteogenesis, and neurodevelopment. Network analysis identified central regulatory nodes—including genes, such as PAX6, FOXC2, NR2F2, SALL1, BMP7, and JAG1—highlighting their roles in tooth development. Several noncoding RNAs were also identified, sharing promoter methylation patterns with developmental genes and being implicated in regulatory networks associated with stem cell differentiation and tissue-specific function. Altogether, DNA methylation profiling revealed that distinct epigenetic landscapes underlie the developmental identity and differentiation potential of dental-derived mesenchymal stem cells. This integrative analysis highlights the relevance of noncoding RNAs and regulatory networks, suggesting novel biomarkers and potential therapeutic targets in regenerative dentistry and orthodontics. Full article

In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor–critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

 Porcine epidemic diarrhea virus (PEDV) continues to circulate globally, causing substantial economic losses to the swine industry. Historically, PEDV strains are classified into the classical G1, epidemic G2, and S-INDEL genotypes. Among these genotypes, the highly virulent and prevalent G2 genotype has been extensively studied. However, recent clinical outbreaks in China necessitate a reevaluation of the epidemiological and evolutionary dynamics of circulating strains. This study analyzed 37 newly sequenced S genes and public sequences to characterize the genetic variations of S-INDEL strains. Our analysis revealed that S-INDEL strains are endemic throughout China, with a phylogenetic analysis identifying two distinct clades: clade 1, comprising early endemic strains, and clade 2, representing a recently dominant, geographically restricted lineage in China. While inter-genotypic recombination has been documented, our findings also demonstrate that intra-genotypic and intra-clade recombination events contributed significantly to the emergence of clade 2, distinguishing its evolutionary pattern from clade 1. A comparative analysis identified 22 clade-specific amino acid changes, 11 of which occurred in the D0 domain. Notably, mutations at positively selected sites—113 and 114 within the D0 domain, a domain associated with pathogenicity—were specific to clade 2. A phylodynamic analysis indicated Germany as the epicenter of S-INDEL dispersal, with China acting as a sink population characterized by localized transmission networks and frequent recombination events. These results demonstrate that contemporary S-INDEL strains, specifically clade 2, exhibit unique recombination patterns and mutations potentially impacting virulence. Continuous surveillance is essential to assess the pathogenic potential of these evolving recombinant variants and the efficacy of vaccines against them.  Full article

Moyamoya disease (MMD) is primarily associated with genetic variants in RNF213. RNF213 p.R4810K (c.14429G>A, p.Arg4810Lys) is a founder variant predominantly found in East Asian populations and is strongly associated with MMD, a rare cerebrovascular condition characterized by progressive stenosis of intracranial arteries and the development of abnormal collateral networks. Recent evidence suggests that RNF213 variants are also enriched in non-moyamoya intracranial arteriopathies, such as large-artery atherosclerotic stroke and intracranial arterial stenosis/occlusion (ICASO), particularly in east Asian individuals with early-onset or cryptogenic stroke. This expanded phenotypic spectrum, termed RNF213-related vasculopathy (RRV), represents a distinct pathogenic entity that may involve unique pathogenic processes separate from traditional atherosclerosis. In this review, we synthesize current genetic, clinical, radiological, and experimental findings that delineate the unique features of RRV. Patients with RRV typically exhibit a lower burden of traditional vascular risk factors, negative vascular remodeling in the absence of atheromatous plaques, and an increased propensity for disease progression. RNF213 variants may compromise vascular resilience by impairing adaptive responses to hemodynamic stress. Furthermore, emerging cellular and animal model data indicate that RNF213 influences angiogenesis, lipid metabolism, and stress responses, offering mechanistic insights into its role in maintaining vascular integrity. Recognizing RRV as a distinct clinical entity has important implications for diagnosis, risk stratification, and the development of genome-informed therapeutic strategies. Full article

Sex-based immunological differences significantly influence the outcome of vaccination, yet the molecular mediators underpinning these differences remain largely elusive. MicroRNAs (miRNAs), key post-transcriptional regulators of gene expression, have emerged as critical modulators of innate and adaptive immune responses. In this study, we investigated the expression profile of selected circulating miRNAs as potential biomarkers of sex-specific humoral responses to the mRNA COVID-19 vaccine in a cohort of health care workers. Plasma samples were collected longitudinally at a defined time point (average 71 days) post-vaccination and analyzed using RT-qPCR to quantify a panel of immune-relevant miRNAs. Anti-spike (anti-S) IgG titers were measured by chemiluminescent immunoassays. Our results revealed sex-dependent differences in miRNA expression dynamics, with miR-221-3p and miR-148a-3p significantly overexpressed in vaccinated female HCWs and miR-155-5p overexpressed in vaccinated males. MiR-148a-3p showed a significant association with anti-S/RBD (RBD: receptor binding domain) IgG levels in a sex-specific manner. Bioinformatic analysis for miRNA targets indicated distinct regulatory networks and pathways involved in innate and adaptive immune responses, potentially underlying the differential immune activation observed between males and females. These findings support the utility of circulating miRNAs as minimally invasive biomarkers for monitoring and predicting sex-specific vaccine-induced immune responses and provide mechanistic insights that may inform tailored vaccination strategies. Full article

Black gay and bisexual older men face numerous barriers across the life course that can contribute to negative health and well-being as they age. Drawing on strengths-based social determinants discussed in the health literature and literature on intersectionality, justice, and critical consciousness, this study examines qualitative data from seventeen Black gay and bisexual older men about sources and strategies of resilience and thriving amidst intersecting systems of power and oppression that shape health inequities. The findings revealed an evolution of positive support networks across their life courses, including biological family and families of choice such as “houses” and support groups. Early and ongoing negative experiences relating to intersecting positionalities (e.g., race, gender, sexual orientation) also provided sources of strength and resilience. Participants identified three strategies for building resilience and thriving: naming external ignorance, acknowledging common struggles, and reconciling contradictions. These strategies reflected various levels of critical consciousness that helped them navigate complex and intersecting systems of power that they encountered as Black gay men across the life course. Overall, the findings underscore the importance of considering intersecting systems of power and critical consciousness when examining resilience and social determinants of health and contribute new insights on a vastly understudied population. Full article

The accurate and effective prediction of PM2.5 concentrations is crucial for mitigating air pollution, improving environmental quality, and safeguarding public health. To address the challenge of strong temporal correlations in PM2.5 concentration forecasting, this paper proposes a novel hybrid model that integrates the Particle Optimization Algorithm (POA) and Variational Mode Decomposition (VMD) with the Long Short-Term Memory (LSTM) network. First, POA is employed to optimize VMD by adaptively determining the optimal parameter combination [k, α], enabling the decomposition of the original PM2.5 time series into subcomponents while reducing data noise. Subsequently, an LSTM model is constructed to predict each subcomponent individually, and the predictions are aggregated to derive hourly PM2.5 concentration forecasts. Empirical analysis using datasets from Beijing, Tianjin, and Tangshan demonstrates the following key findings: (1) LSTM outperforms traditional machine learning models in time series forecasting. (2) The proposed model exhibits superior effectiveness and robustness, achieving optimal performance metrics (e.g., MAE: 0.7183, RMSE: 0.8807, MAPE: 4.01%, R²: 99.78%) in comparative experiments, as exemplified by the Beijing dataset. (3) The integration of POA with serial decomposition techniques effectively handles highly volatile and nonlinear data. This model provides a novel and reliable tool for PM2.5 concentration prediction, offering significant benefits for governmental decision-making and public awareness. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Multimodal sentiment analysis (MSA) benefits from integrating diverse modalities (e.g., text, video, and audio). However, challenges remain in effectively aligning non-text features and mitigating redundant information, which may limit potential performance improvements. To address these challenges, we propose a Hierarchical Text-Guided Refinement Network (HTRN), a novel framework that refines and aligns non-text modalities using hierarchical textual representations. We introduce Shuffle-Insert Fusion (SIF) and the Text-Guided Alignment Layer (TAL) to enhance crossmodal interactions and suppress irrelevant signals. In SIF, empty tokens are inserted at fixed intervals in unimodal feature sequences, disrupting local correlations and promoting more generalized representations with improved feature diversity. The TAL guides the refinement of audio and visual representations by leveraging textual semantics and dynamically adjusting their contributions through learnable gating factors, ensuring that non-text modalities remain semantically coherent while retaining essential crossmodal interactions. Experiments demonstrate that the HTRN achieves state-of-the-art performance with accuracies of 86.3% (Acc-2) on CMU-MOSI, 86.7% (Acc-2) on CMU-MOSEI, and 80.3% (Acc-2) on CH-SIMS, outperforming existing methods by 0.8–3.45%. Ablation studies validate the contributions of SIF and the TAL, showing 1.9–2.1% performance gains over baselines. By integrating these components, the HTRN establishes a robust multimodal representation learning framework. Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

Early blight, caused by the pathogen Alternaria solani, is a major fungal disease impacting potato production globally, with reported yield losses of up to 40% in susceptible varieties. As one of the most common diseases affecting potatoes, its incidence has been steadily increasing year after year. This study aimed to elucidate the molecular mechanisms underlying resistance to early blight by comparing gene expression profiles in resistant (B1) and susceptible (D30) potato seedlings. Transcriptome sequencing was conducted at three time points post-infection (3, 7, and 10 dpi) to identify differentially expressed genes (DEGs). Weighted Gene Co-expression Network Analysis (WGCNA) and pathway enrichment analyses were performed to explore resistance-associated pathways and hub genes. Over 11,537 DEGs were identified, with the highest number observed at 10 dpi. Genes such as LOC102603761 and LOC102573998 were significantly differentially expressed across multiple comparisons. In the resistant B1 variety, upregulated genes were enriched in plant–pathogen interaction, MAPK signaling, hormonal signaling, and secondary metabolite biosynthesis pathways, particularly flavonoid biosynthesis, which likely contributes to biochemical defense against A. solani. WGCNA identified 24 distinct modules, with hub transcription factors (e.g., WRKY33, MYB, and NAC) as key regulators of resistance. These findings highlight critical molecular pathways and candidate genes involved in early blight resistance, providing a foundation for further functional studies and breeding strategies to enhance potato resilience. Full article

Alopecia, a prevalent dermatological disorder affecting over half of the global population, is strongly associated with psychological distress. Extracts from Periplaneta americana (L. PA), a medicinal insect resource, exhibit pharmacological activities (e.g., antioxidant, anti-inflammatory, microcirculation improvement) that align with core therapeutic targets for alopecia. This study aimed to systematically investigate the efficacy and mechanisms of PA extracts in promoting hair regeneration. A strategy combining network pharmacology prediction and in vivo experiments was adopted. The efficacy of a Periplaneta americana extract was validated by evaluating hair regrowth status and skin pathological staining in C57BL/6J mice. Transcriptomics, metabolomics, RT-qPCR, and 16s rRNA techniques were integrated to dissect the underlying mechanisms of its hair-growth-promoting effects. PA-011 significantly promoted hair regeneration in depilated mice via multiple mechanisms: enhanced skin superoxide dismutase activity and upregulated vascular endothelial growth factor expression; modulated FOXO/PI3K/AKT signaling pathway and restored skin microbiota homeostasis; and accelerated transition of hair follicles from the telogen to anagen phase. PA-011 exerts hair-promoting effects through synergistic modulation of FOXO/PI3K/AKT signaling and the skin microbiome. As a novel therapeutic candidate, it warrants further systematic investigation for clinical translation. Full article

Perfluoroalkyl substances (PFASs) possess immunosuppressive properties. However, their association with rheumatoid arthritis (RA) risk remains inconclusive across epidemiological studies. This study integrates population-based and mechanistic evidence to clarify the relationship between PFAS exposure and RA. We analyzed 8743 U.S. adults from the NHANES (2005–2018), assessing individual and mixed exposures to PFOA, PFOS, PFNA, and PFHxS using multivariable logistic regression, Bayesian kernel machine regression, quantile g-computation, and weighted quantile sum models. Network toxicology and molecular docking were utilized to identify core targets mediating immune disruption. The results showed that elevated PFOA (OR = 1.63, 95% CI: 1.41–1.89), PFOS (OR = 1.41, 1.25–1.58), and PFNA (OR = 1.40, 1.20–1.63) levels significantly increased RA risk. Mixture analyses indicated a positive joint effect (WQS OR = 1.06, 1.02–1.10; qgcomp OR = 1.26, 1.16–1.38), with PFOA as the primary contributor. Stratified analyses revealed stronger effects in females (PFOA Q4 OR = 3.75, 2.36–5.97) and older adults (≥60 years). Core targets included EGFR, SRC, TP53, and CTNNB1. PFAS mixtures increase RA risk, dominated by PFOA and modulated by sex/age. These findings help reconcile prior contradictions by identifying key molecular targets and vulnerable subpopulations, supporting regulatory attention to PFAS mixture exposure. Full article