Search Results (59,111)

The described features of dinoflagellate gene expression indicate the predominance of post-transcriptional and translational regulation over transcriptional control. These microorganisms also exhibit extensive RNA editing and distinctive splicing characteristics. This regulatory landscape underscores the central role of RNA-binding proteins in dinoflagellate biology. In this review, we summarize current knowledge on major RNA-binding protein groups identified or bioinformatically annotated in dinoflagellates, including RNA recognition motif domain-containing proteins, Sm and Sm-like family, KH domain-containing proteins, zinc-finger proteins, and Pumilio family proteins, S1 domain-containing and cold shock domain-containing proteins, DEAD/DEAH-box RNA helicases, and pentatricopeptide repeat proteins. We focus on the features of their conserved domains, their functions in eukaryotes, and available data on their presence and putative roles in dinoflagellate cells. Integrating genomic, transcriptomic, and proteomic evidence, and where possible experimental data, we highlight both their overall conservation and potential lineage-specific traits. Our aim is to provide a concise synthesis of current knowledge, identify key uncertainties, and outline promising directions for future research into the evolution and cellular roles of RNA-binding proteins in this ecologically and biologically remarkable group. Full article

Background: Overweight and obesity in childhood and adolescence represent one of the most significant public health challenges of our century. Affected children and adolescents often face psychosocial maladaptation, including low self-esteem, depressive and anxiety symptoms, and behavioral problems, many of which may persist till later in life. The aim of our study was to evaluate the impact of a multidisciplinary, personalized lifestyle intervention program on psychosocial and behavioral symptoms, assessed through standardized psychometric questionnaires, and to investigate their relation with cardiometabolic parameters in children and adolescents with overweight and obesity. Methods: In this prospective cohort study, 537 children and adolescents (6–18 years; females: 52.9%; pubertal: 43.6%) with obesity (n = 44.3%), overweight (n = 33.7%), or normal BMI (n = 22%) participated in a personalized lifestyle intervention program for one year. Clinical and laboratory evaluations, including anthropometric, cardiometabolic, and endocrinologic parameters, as well as psychosocial functioning assessed by the Child Behavior Checklist (CBCL) and Youth Self-Report (YSR), were performed at the beginning and the end of the study. Linear regression analyses identified predictors of psychometric change. Results: At initial evaluation, children and adolescents with obesity displayed a less favorable cardiometabolic profile and greater emotional/conduct difficulties compared to their overweight and normal-BMI counterparts. Following the intervention, significant improvements were observed in BMI, anthropometric and cardiometabolic parameters, as well as reductions in internalizing, externalizing, and total problem scores across multiple CBCL and YSR domains (p < 0.05). The improvements in psychosocial functioning were partly independent of BMI reduction. Linear regression analyses identified cardiometabolic and endocrine markers as significant predictors of psychometric change (p < 0.05), highlighting interactions between metabolic recovery, pubertal hormones, and stress physiology. Conclusions: A personalized, multidisciplinary lifestyle intervention program implemented for 1 year led to parallel improvements in psychosocial and cardiometabolic health in children and adolescents with overweight and obesity. Identification of specific metabolic and endocrine predictors provides novel insights into potential biological mechanisms associated with adiposity, emotional well-being, and neurodevelopment. Full article

With the extensive application of unmanned aerial vehicles (UAVs) in both military and civilian domains, the significance of UAV identification technology has become increasingly prominent. Among various recognition methods, voice recognition has garnered considerable attention due to its advantages of low cost and easy deployment. However, most existing research primarily focuses on isolating UAV sounds from noise signals in complex environments, with limited studies on long-distance UAV sound recognition. Based on this, this paper proposes a frequency domain feature extraction method based on harmonic features. By analyzing the harmonic features of UAV sounds, we select stable parameters with strong robustness against interference capabilities as the main features to minimize information redundancy and feature fluctuation. The experimental results indicate that this method achieves a recognition accuracy of 78.03% for the DJI Phantom 4 Pro V2.0 UAV at a distance of 120 m. To validate the proposed method, comprehensive comparisons against traditional MFCC, Log-Mel Spectrogram, and modern Raw Waveform CNN (M5) baselines demonstrate the superior robustness of the proposed approach. While these comparative methods exhibited significant performance drops in challenging long-distance scenarios (e.g., accuracies falling below 24% for the DJI Mavic Pro), the proposed method maintained consistent identification capabilities, validating its effectiveness in low-signal environments. Full article

Fruit softening in Carica papaya L. is a significant postharvest limitation, primarily driven by the dynamic remodeling of cell wall polysaccharides. In this study, we conducted a genome-wide identification and in silico characterization of gene families involved in cell wall assembly and disassembly in papaya. A total of 181 genes were identified and classified into metabolic pathways: hemicellulose (58), pectin (69), extensin (24), expansin (13), and cellulose (17). These genes encode 176 predicted proteins, ranging in size from 100 to 1093 amino acids, featuring family-specific catalytic domains, including glycosyl hydrolases, transferases, and serine/threonine kinases. Phylogenetic analyses revealed strong conservation within the expansin-A and pectin polygalacturonase subfamilies, while hemicellulose-related XTH genes exhibited significant diversification. Experimental validation of nine XTH members confirmed this diversification, with amplicons ranging from 322 to 1370 bp, consistent with computational predictions. Notably, CpXTH1 and CpXTH32 produced bands of approximately 1200 and 1400 bp, respectively. These findings underscore the complexity of papaya cell wall gene families and provide a molecular framework for understanding fruit softening. Given that postharvest losses of papaya in Mexico exceed 34.7% of production (approximately 150,000 tons annually), our results offer valuable genomic resources for biotechnological strategies aimed at extending shelf life and reducing economic losses. Full article

This paper presents the rationale and design of a study of growth and development in young track and field athletes: the EXcellence and PERformance in Track and field (EXPERT) study, and details the methodologies used. Background: Longitudinal research examining individual-environment interactions in youth athletic development is scarce for track and field. Objectives: The EXPERT study investigates how individual (anthropometry, maturation, motivation) and environmental (family, coach, club) characteristics influence developmental trajectories in youth track and field athletes. Methods: A mixed-longitudinal design will follow 400 athletes (200♂, 200♀; aged 10–14 years) from 40 Portuguese clubs across five cohorts assessed biannually over three years. Guided by Bronfenbrenner’s bioecological model, assessments encompass individual, performance, health, and environmental domains. Data quality control will consist of rigorous training of all research team members, implementation of standardized protocols, a pilot study, and an in-field reliability study. Multilevel growth models will examine trajectories and predictor effects of predictors. Conclusions: EXPERT will provide evidence to optimize training and support holistic youth athlete development. Full article

High concentrations of tropospheric ozone (${\mathrm{O}}_3$) in urban areas pose a significant risk to human health. This study proposes an evaluation framework based on the XGBoost algorithm to predict ${\mathrm{O}}_3$ concentration, assessing the model’s capacity for seasonal extrapolation and spatial transferability. The experiment uses hourly air pollution data (${\mathrm{O}}_3$, NO, ${\mathrm{NO}}_2$, and NOx) and meteorological factors (temperature, relative humidity, barometric pressure, wind speed, and wind direction) from six monitoring stations in the Monterrey Metropolitan Area, Mexico (from 22 September 2022 to 21 September 2023). In the preprocessing phase, the datasets were extended via feature engineering, including cyclic variables, rolling windows, and lag features, to capture temporal dynamics. The prediction models were optimized using a random search, with time-series cross-validation to prevent data leakage. The models were evaluated across a concentration range of 0.001 to 0.122 ppm, demonstrating high predictive accuracy, with a coefficient of determination (${\mathrm{R}}^2$) of up to 0.96 and a root-mean-square error (RMSE) of 0.0034 ppm when predicting summer (${\mathrm{O}}_3$) concentrations without prior knowledge. Spatial generalization was robust in residential areas (${\mathrm{R}}^2$ > 0.90), but performance decreased in the industrial corridor (AQMS-NL03). We identified that this decrease is related to local complexity through the quantification of domain shift (Kolmogorov–Smirnov test) and Shapley additive explanations (SHAP) diagnostics, since the model effectively learns atmospheric inertia in stable areas but struggles with the stochastic effects of NOx titration driven by industrial emissions. These findings position the proposed approach as a reliable tool for “virtual detection” while highlighting the crucial role of environmental topology in model implementation. Full article

Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly improving risk management within the banking industry. This paper investigates the mediating role of AI in the relationship between Fintech and financial stability in the context of Islamic and conventional banks across selected countries in the Organization of Islamic Cooperation (OIC). It employs structural equation modeling (SEM) to explore the causal linkages across time domains. The results of this research identify that AI is a significant mediator, playing a critical role between Fintech and stability. It either mitigates or amplifies risks, depending on the regulatory framework and implementation practices in place. The analysis indicates that AI has a weak mediating effect in the short run, but a strong mediating effect in the long run between Fintech and stability. This research paper emphasizes the importance of developing robust, forward-thinking policies to leverage the benefits of AI. It also addresses the risks to financial stability in both Islamic and conventional banking systems. Full article

The development of cardiovascular tissue engineering is a promising area of study in regenerative medicine, offering innovative solutions for restoring damaged cardiac structures. However, traditional methods face multiple limitations, including the complexity of scaffolds, optimal recellularization, and functional tissue maturation. At the same time, deep learning has demonstrated significant potential in biomedicine and is increasingly being explored to optimize processes. This review examines recent benefits in cardiovascular tissue engineering and the applicability of deep learning in this domain, highlighting the benefits of artificial intelligence (AI) algorithms in scaffold modeling, cellular interaction analysis, and tissue regeneration prediction. Additionally, we discuss major challenges in integrating AI, such as the lack of large, standardized datasets; the need for interpretable models for clinical use; and ethical and regulatory constraints. Despite these limitations, recent progress in AI and the availability of advanced machine learning techniques provide promising perspectives for transforming regenerative medicine. Future research should focus on improving access to relevant data, developing explainable AI models, and integrating these technologies into personalized medicine, ultimately accelerating the progression of cardiovascular tissue engineering from an experimental stage to clinical utilization. Full article

To address the path planning challenges for land–air amphibious biomimetic robots in unstructured environments, this study proposes a global path planning algorithm based on an Improved Proximal Policy Optimization (IPPO) framework. Unlike traditional single-domain navigation, amphibious robots face significant kinematic discontinuities when switching between terrestrial and aerial modes. To mitigate this, we integrate a Gated Recurrent Unit (GRU) module into the policy network, enabling the agent to capture temporal dependencies and make smoother decisions during mode transitions. Furthermore, to enhance exploration efficiency and stability, we replace the standard Gaussian noise with Ornstein–Uhlenbeck (OU) noise, which generates temporally correlated actions aligned with the robot’s physical inertia. Additionally, a Multi-Head Self-Attention mechanism is introduced to the value network, allowing the agent to dynamically prioritize critical environmental features—such as narrow obstacles—over irrelevant background noise. The simulation results demonstrate that the proposed IPPO algorithm significantly outperforms standard PPO baselines, achieving higher convergence speed, improved path smoothness, and greater success rates in complex amphibious scenarios. Full article

Weak-signal fluorescence channels (e.g., 4′,6-diamidino-2-phenylindole (DAPI)) often fail to provide reliable structural details due to low signal-to-noise ratio (SNR) and insufficient high-frequency information, limiting the ability of single-channel super-resolution methods to restore edge continuity and texture. This study proposes a multi-channel guided super-resolution method based on SwinIR, utilizing the high-SNR fluorescein isothiocyanate (FITC) channel as a structural reference. Dual-channel adaptation is implemented at the model input layer, enabling the window attention mechanism to fuse cross-channel correlation information and enhance the structural recovery capability of weak-signal channels. To address the loss of high-frequency information in weak-signal imaging, we introduce a frequency-domain consistency loss: this mechanism constrains spectral consistency between the predicted and true images in the Fourier domain, improving the clarity of fine-structure reconstruction. Experimental results on the DAPI channel demonstrate significant improvements: PSNR increases from 27.05 dB to 44.98 dB, and SSIM rises from 0.763 to 0.960. Visual analysis indicates that this method restores more continuous nuclear edges and weak textural details while suppressing background noise; frequency-domain results reduce the minimum resolvable feature size from approximately 1.5 μm to 0.8 μm. In summary, multi-channel structural information provides an effective and physically interpretable deep learning approach for super-resolution reconstruction of weak-signal fluorescence images. Full article

The development of multifunctional polymer composites capable of both heat conduction and latent heat storage is of great interest for advanced thermal management applications. In this work, thermoplastic polyurethane (TPU) nanocomposites containing microencapsulated paraffin-based phase change materials (PCMs) and multi-walled carbon nanotubes (MWCNTs) were systematically investigated. The microstructure, thermal stability, specific heat capacity, thermal diffusivity and conductivity of these composites were analyzed as a function of the PCM and MWCNTs content. SEM observations revealed the homogeneous dispersion of PCM microcapsules and the presence of localized MWCNT aggregates in PCM-rich domains. Thermal diffusivity measurements indicated a monotonic decrease with increasing temperature for all compositions, from 0.097 mm²·s⁻¹ at 5 °C to 0.091 mm²·s⁻¹ at 25 °C for neat TPU, and from 0.186 mm²·s⁻¹ to 0.173 mm²·s⁻¹ for TPU with 5 vol.% MWCNTs. Distinct non-linear behavior was observed around 25 °C, i.e., in correspondence to the paraffin melting, where the apparent diffusivity temporarily decreased due to latent heat absorption. The trend of the thermal conductivity (λ) was determined by the competing effects of PCM and MWCNTs: PCM addition reduced λ at 25 °C from 0.162 W·m⁻¹·K⁻¹ (neat TPU) to 0.128 W·m⁻¹·K⁻¹ at 30 vol.% PCM, whereas the incorporation of 5 vol.% of MWCNTs increased λ up to 0.309 W·m⁻¹·K⁻¹. In PCM-containing nanocomposites, MWCNT networks efficiently bridged the polymer–microcapsule interfaces, creating continuous conductive pathways that mitigated the insulating effect of the encapsulated paraffin and ensured stable heat transfer even across the solid–liquid transition. A one-dimensional transient heat-transfer model confirmed that increasing the matrix thermal conductivity accelerates the melting of the PCM, improving the dynamic thermal buffering capacity of these materials. Therefore, these results underlined the potential of TPU/MWCNT/PCM composites as versatile materials for applications requiring both rapid heat dissipation and effective thermal management. Full article

Free live sports streaming (FLS) services attract millions of users who, driven by the excitement of live events, often engage with these high-risk platforms. Although these platforms are widely perceived as risky, the specific threats they pose have lacked large-scale empirical analysis. This paper addresses this gap through a comprehensive study of the FLS ecosystem, conducted during two major international sporting events (UCL playoffs and NHL Stanley Cup Playoffs, 2024–2025 season). We analyze the infrastructure, security threats, and privacy violations that define this space. Analysis of 260 unique domains uncovers systemic security risks, including drive-by downloads delivering persistent malware, and widespread privacy violations, such as invasive device fingerprinting that disregards regulations like the General Data Protection Regulation (GDPR). Furthermore, we map the ecosystem’s resilient infrastructure, identifying eight clusters of co-owned domains. These findings imply that effective countermeasures must target the centralized infrastructure and ephemeral nature of the FLS ecosystem beyond traditional blocking. Full article

Visual large language models (VLLMs) are discussed as potential tools for assisting radiologists in image interpretation, yet their clinical value remains unclear. This study provides a systematic and comprehensive comparison of general-purpose and biomedical VLLMs in radiology. We evaluated 180 representative clinical images with validated reference diagnoses (radiography, CT, MRI; 60 each) using seven VLLMs (ChatGPT-4o, Gemini 2.0, Claude Sonnet 3.7, Perplexity AI, Google Vision AI, LLaVA-1.6, LLaVA-Med-v1.5). Each model interpreted the image without and with clinical context. Mixed-effects logistic regression models assessed the influence of model, modality, and context on diagnostic performance and hallucinations (fabricated findings or misidentifications). Diagnostic accuracy varied significantly across all dimensions (p ≤ 0.001), ranging from 8.1% to 29.2% across models, with Gemini 2.0 performing best and LLaVA performing weakest. CT achieved the best overall accuracy (20.7%), followed by radiography (17.3%) and MRI (13.9%). Clinical context improved accuracy from 10.6% to 24.0% (p < 0.001) but shifted the model to rely more on textual information. Hallucinations were frequent (74.4% overall) and model-dependent (51.7–82.8% across models; p ≤ 0.004). Current VLLMs remain diagnostically unreliable, heavily context-biased, and prone to generating false findings, which limits their clinical suitability. Domain-specific training and rigorous validation are required before clinical integration can be considered. Full article

In recent years, behavioral economics has revolutionized various fields, including finance, marketing, and public policy. Its application in people management, however, remains an emerging area of exploration. By integrating psychological insights into economic decision-making, behavioral economics offers a nuanced understanding of human behavior, essential for designing effective HR practices. While many of the concepts are not new in organizational behavior research and related fields, thanks to the incorporation of formalized models of choice, behavioral economics brings analytical clarity to domains traditionally studied through descriptive or qualitative methods in the behavioral sciences. This review article delves into how behavioral economics can shed light on key aspects of people management, focusing on five domains: incentives, decision-making, leadership, personalization, and organizational change. We offer a critical overview integrating some of the most well-known findings with applicability in these areas as well as promising avenues for future research. One of the main conclusions is that behavioral economics offers a powerful lens to approach people management, but also that behavioral principles need to be understood in depth (beyond average effects, for example) as generalization is often flawed, claiming for personalized solutions and interventions grounded on comprehensive perspectives. Full article

In high-precision positioning applications, lightly damped structural resonances fundamentally limit the achievable performance of precision linear motion stages, enforcing a stringent trade-off between control bandwidth and transient vibration suppression. This paper investigates a unified synergistic control architecture integrating input shaping (IS), feedforward control (FF), and notch filtering (NF) within a feedback framework to jointly mitigate command-induced excitation, compensate predictive dynamics, and suppress narrowband resonant modes. Four representative schemes—FB, FB+NF, FF+FB+NF, and IS+FF+FB+NF—are systematically evaluated through step/ramp responses, sinusoidal tracking, and industrial S-curve trajectory under aggressive operating conditions (up to 2.8 m/s and 160 m/s²). Simulation results show that incorporating IS into the FF+FB+NF baseline effectively eliminates overshoot and suppresses residual vibration, yielding a 22.0% reduction in the ±2 μm settling time. Complementary frequency-domain analyses demonstrate that the proposed IS+FF+FB+NF architecture achieves a superior balance between tracking agility and stability, maintaining robust gain/phase margins while attenuating resonant sensitivity peaks. Robustness studies further indicate that the proposed IS+FF+FB+NF architecture preserves bandwidth consistency despite resonant frequency drifts. Overall, this coordinated integration provides a practically deployable and industrially compatible solution for enhancing vibration suppression and positioning consistency in precision motion systems. Full article