Search Results (9,030)

Chroococcidiopsis sp. was isolated from the endolithic habitat of the Atacama Desert (northern Chile), one of the most challenging-to-life polyextreme environments on Earth. The photosynthetic machinery of microorganisms inhabiting this environment is supposed to be highly adapted to cope with the intense solar radiation of the area. Thus, PAR-red light ranging from 100 to 900 µmol photon·m⁻²·s⁻¹ has been investigated as a strategy to enhance culture productivity and stimulate the synthesis of bioactive molecules in Chroococcidiopsis sp. A control culture was maintained under white light at 100 µmol photon·m⁻²·s⁻¹. The results revealed that red light was utilized more efficiently than white light of similar irradiance, and its modulation enhanced both growth and photosynthetic activity of the cyanobacterium. Furthermore, Chroococcidiopsis sp. appeared to activate mechanisms to mitigate photooxidative stress produced by excess light energy. Specifically, increasing light irradiance induced photoacclimation responses, characterized by a decrease in chlorophyll content and a concomitant increase in carotenoid accumulation, likely aimed at reducing photon flux transduced to photosynthesis. Additionally, scytonemin synthesis was enhanced under high irradiances, contributing to dissipating excess light energy. Overall, this study demonstrates that modulation of red-light irradiance effectively improves the growth of Chroococcidiopsis sp. while promoting the accumulation of antioxidant compounds—primarily carotenoids and, to a lesser extent, scytonemin. Full article

Conventional multispectral photodetectors typically rely on multiple electrical terminals to discriminate different wavelengths, which inevitably increases structural complexity. Here, we break this paradigm by demonstrating a dual-color visible–infrared photodetector based on a simple two-terminal Au/MoS₂/Te heterostructure. The device operates through a bias-switching mechanism: reversing the voltage polarity selectively activates either the MoS₂/Au Schottky junction for visible-light detection (520 nm) or the Te/MoS₂ heterojunction for infrared detection (1550 nm). This bias-controlled wavelength selectivity is unambiguously verified by scanning photocurrent mapping. Beyond dual-color discrimination, an adaptive convolutional neural network is employed to decode the nonlinear current–voltage characteristics and enable precise spectral identification, achieving a reconstruction error of approximately 4.5%. Furthermore, high-fidelity dual-color imaging is demonstrated at room temperature. These results establish a hardware–algorithm co-design strategy based on a minimalist two-terminal architecture, providing a viable route toward compact and intelligent spectral-sensing systems. Full article

Infrared and visible image fusion aims to combine the complementary advantages of thermal radiation information and rich texture details to generate more informative images for downstream perception tasks. However, existing deep learning-based methods usually assume ideal imaging conditions and often suffer from performance degradation in complex environments such as low illumination, rain interference, and strong lighting disturbances. To address this problem, this paper proposes an adaptive prompting-driven degradation-aware fusion framework. Specifically, a degradation-aware prompt generation module is introduced to automatically perceive degradation patterns from the input images and generate structured conditional prompts. These prompts guide the network to adaptively adjust feature representations through learnable affine modulation. Furthermore, a semantic-aligned feature learning strategy is designed to ensure consistent cross-modal representation in the latent space. Extensive experiments demonstrate that the proposed method achieves superior performance compared with several state-of-the-art fusion approaches under both normal and degraded conditions. Full article

Bovine spongiform encephalopathy (BSE) is a fatal transmissible spongiform encephalopathy caused by the misfolding of the host prion protein (PrP), representing a unique intersection between molecular pathology, neuroanatomy, and public health regulation. Although historically framed as a single feedborne epizootic, BSE is now recognized as a spectrum of strain-defined prion disorders encompassing classical and atypical forms with distinct origins, neuroanatomical trajectories, and surveillance implications. This review integrates advances in prion biology, neurodegenerative mechanisms, and anatomical pathways of neuroinvasion to reframe BSE as a heterogeneous disease entity. We synthesize evidence on PrP^C structure, trafficking, and proteolytic processing to explain how normal cellular physiology enables strain-specific conversion to pathogenic PrP^Sc and subsequent neurotoxicity. Distinct patterns of neuroinvasion and regional vulnerability are discussed for classical versus atypical (H- and L-type) BSE, highlighting differences in lymphoid involvement, brainstem targeting, and cortical or cerebellar tropism. We further examine how these biological differences translate into diagnostic sensitivity, surveillance design, and zoonotic risk assessment. By integrating molecular strain diversity with neuroanatomical connectivity, this review underscores the limitations of obex-centered surveillance for atypical BSE and emphasizes the need for proportionate yet precautionary monitoring strategies. These considerations should be interpreted in light of surveillance-dependent detection biases, which influence the apparent distribution of BSE forms. Ultimately, BSE emerges as a critical model for understanding how protein misfolding disorders bridge cellular mechanisms, animal health, and human public health policy. Full article

Hydrophobic carbon quantum dots (hbCQDs) with tunable photoluminescence were synthesized via a solvothermal approach and further hybridized with Rhodamine B (RhB) to extend emission into the visible range. The hbCQDs exhibit quasi-spherical morphology with an average particle size of 8 nm and predominantly disordered graphitic structure, as confirmed by TEM and XRD analyses. FTIR and XPS characterizations reveal surface functional groups including C–N, C=O/C–O, and S–H, which govern the photoluminescence properties. Pure hbCQDs display blue emission at 453 nm under excitation, with a quantum yield (QY) of 6.2%. Incorporation of RhB leads to dual-emission behavior: the surface-state emission remains in the blue region, while molecular-state emission from RhB appears in the orange-red region. The 0.2 mL RhB–CQD composite exhibits optimal properties, including a QY of 13% and a production yield of 82%, emitting white light under 365 nm UV excitation. Increasing RhB loading to 0.4 mL results in a shift in emission peaks and a reduced QY (<9%), with weaker orange fluorescence. These findings demonstrate that controlled RhB hybridization effectively tunes the emission spectrum of hbCQDs, offering a simple and reproducible strategy to achieve dual-color and white-light emission. The optimized hbCQDs/RhB composites hold significant potential for applications in hydrophobic media-compatible organic optoelectronics, light-emitting devices, and bioimaging. Full article

As modern urban systems advance, video surveillance has become indispensable for ensuring high-quality urban development. Nighttime images acquired in urban monitoring scenarios are often degraded by haze and non-uniform illumination, resulting in reduced visibility, color distortion, and blurred structural boundaries. To address these issues, this paper proposes a nighttime image dehazing framework that combines mixed-norm variational atmospheric-light estimation with adaptive boundary-constrained transmission refinement. Specifically, an  L₂ − L_p mixed-norm regularization model is introduced to improve atmospheric-light estimation under complex nighttime illumination and suppress halo diffusion and color distortion around strong light sources. In addition, an adaptive boundary-constrained transmission refinement strategy with weighted soft-threshold shrinkage is developed to reduce residual artifacts while preserving structural edges. Experimental results on synthetic and real nighttime haze datasets demonstrate that the proposed method consistently outperforms representative state-of-the-art methods in both visual quality and quantitative metrics, showing superior robustness and restoration performance for nighttime urban monitoring applications. Full article

Background/Objectives: Cancer remains one of the leading global health burdens, mainly because of the lack of specificity and off-target toxicity associated with conventional therapeutic approaches. To move toward more efficient anticancer drug discovery, we have developed an advanced machine-learning-based architecture that allows for predictive modeling of anticancer small molecules. Methods: A total of 3600 compounds with experimentally validated IC₅₀ values were systematically processed to derive a comprehensive suite of molecular representations comprising 2D physicochemical descriptors, structural fingerprints, and hybrid descriptor sets generated via the Mordred and PaDEL frameworks. A total of six machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), Extra-Trees classifier (ET), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM)—were trained and benchmarked via a rigorous model evaluation protocol incorporating 10-fold cross-validation along with multiple performance metrics. Ensemble voting strategies were also examined to assess potential performance. Result: Of all configurations, the XGB-Hybrid architecture emerged as the most robust and generalizable classifier with an AUC of 0.88 and accuracy of 79.11% on the independent test set. To ensure interpretability and mechanistic insight, SHAP-based feature analysis was conducted, by which feature contributions could be quantified and the molecular determinants most influential for anticancer activity discrimination were revealed. Altogether, the current study establishes an XGB-Hybrid framework as technically rigorous, interpretable, and high-performance predictive modeling with the ability to accelerate early-stage anticancer small molecule identification. Conclusions: The study has brought into focus the transformational effect of machine learning in modern computational oncology and rational drug design pipelines. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article

Recently, lead-free metal halide perovskite variant nanocrystals (NCs) have emerged as promising alternatives to their lead-based counterparts, with tunable optoelectronic properties achievable through structural and compositional engineering. Their tunable bandgaps, near-unity quantum yields, solution-processable synthesis routes, and intrinsic environmental benignity render them attractive candidates for a broad range of optoelectronic applications. This review comprehensively summarizes recent advances in perovskite-derived NCs, including diverse synthetic strategies, as well as structural and compositional engineering approaches for optimizing their photophysical properties. Additionally, this review critically discusses the emerging applications of lead-free metal halide perovskite variants, such as solid-state lighting, high-sensitivity photodetection, and advanced radiation imaging. This review aims to provide in-depth insight into the structure–composition–performance relationship of lead-free perovskite variant NCs and pave the way for next-generation eco-friendly optoelectronic materials and devices. Full article

The development of effective water disinfection treatment processes will be crucial to help food producers save water and cope with the inevitable challenges resulting from increases in human population and climate change, while promoting sustainable agriculture. The inactivation efficiency of UV-C light emitting diodes (LEDs) that emit light at 280 nm was tested as a disinfection method. Water samples from a horticulture industry were collected and characterized in terms of total microorganisms, total coliforms, Escherichia coli and enterococci as well as parameters that influence photolysis such as the percent transmittance of the irrigation water (that, due to the nutrients added for plant growth, was extremely low and varied between 40 and 55%). Nevertheless, laboratory scale results showed that three single small UV LEDs that emit light at 280 nm were extremely efficient for the inactivation of microorganisms present at occurrence levels in the irrigation water samples, as well as Phytophthora capsici and Escherichia coli spiked in sterile distilled water and filtered irrigation water samples. Overall, the findings demonstrate that UV-C LEDs operating at 280 nm represent a promising sustainable disinfection strategy for modern food production systems facing tightening environmental and public-health pressures. Full article

Background: Complex regional pain syndrome (CRPS) is a disabling post-traumatic pain condition that may occur after distal radius fracture (DRF), potentially impairing recovery and upper-limb function. Identifying effective preventive strategies after DRF is therefore clinically important. Objective: To synthesize and critically appraise interventions intended to prevent CRPS after DRF, including rehabilitation protocols and clinical prophylaxis strategies. Methods: This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA and was registered in the International Prospective Register of Systematic Reviews PROSPERO (CRD42023408499). Five databases (PubMed, Web of Science, Scopus, ScienceDirect, and B-on) were searched for studies published from January 2013 to 22 September 2023 in English, Portuguese, or Spanish. The primary outcome was CRPS incidence after DRF. Findings were synthesized narratively due to heterogeneity in interventions and diagnostic criteria, and risk of bias was assessed using design-appropriate tools. Results: Nine studies were included (total N = 7075; CRPS cases n = 127). Interventions comprised vitamin C supplementation (2 studies), probiotics, aspirin, polarized/polychromatic light therapy plus conventional treatment, early rehabilitation/home-exercise programs, and general CRPS-prevention protocols after DRF. Probiotics and aspirin did not reduce CRPS incidence. Vitamin C showed mixed findings across the included studies and remains debated in the broader literature. Light therapy was associated with reduced CRPS occurrence in a single study, while early active home-exercise programs appeared promising but were supported by a limited number of studies. Study designs and CRPS diagnostic criteria varied, and risk of bias was moderate-to-serious in several non-randomized studies. Conclusions: Evidence remains insufficient to support a single standardized prevention protocol for CRPS after distal radius fracture. Early active rehabilitation and progressive mobilization appear promising, but the available evidence is still limited and heterogeneous. Adjunctive strategies such as vitamin C and light therapy should be interpreted with caution, as findings for vitamin C remain debated in the literature and the evidence for light therapy is currently based on a single study. Other approaches, including probiotics and aspirin, have shown inconclusive results. Full article

The millisecond-level volatile fluctuations in workloads in large-scale intelligent computing clusters pose significant challenges to traditional electricity markets. Constrained by optical–electrical–optical conversion bottlenecks, these markets struggle to achieve real-time response and risk substantial social welfare loss. Leveraging existing fiber-optic infrastructure to build All-Optical Networks (AONs) presents a cost-effective evolutionary path. This paper develops a distributed energy trading strategy based on all-optical multicast. By utilizing the physical multicast properties of the underlying light-tree architecture instead of traditional protocols, the proposed strategy bypasses end-to-end latency constraints. This enables rapid transaction synchronization and dynamic tracking of social welfare optima within millisecond-level time-slots. Simulation results demonstrate that the proposed scheme elevates the transaction saturation threshold by two orders of magnitude compared with traditional strategies, effectively breaking the physical locking effect of latency on system throughput. Across various topologies, the social welfare gains exceed those of conventional schemes by more than 20 times. This study validates the engineering value of all-optical architectures for high-frequency trading and provides critical technical support for ultra-dynamic power trading algorithms. Full article

The increasing prevalence of antimicrobial resistance, together with recurring infectious disease outbreaks, has intensified the need for alternative strategies to control microbial infections beyond conventional antibiotic therapies. Antimicrobial photodynamic therapy has emerged as a promising non-antibiotic approach in which light-activated photosensitising compounds generate reactive oxygen species that induce oxidative damage to microbial cells. Plant-derived photosensitisers have attracted increasing attention due to their structural diversity, biocompatibility, natural abundance, and potential for sustainability. Natural compounds such as curcumin, hypericin, chlorophyll derivatives, flavonoids, anthraquinones, and riboflavin exhibit favourable photochemical properties that enable efficient production of reactive oxygen species upon irradiation with visible light. Through radical- and singlet-oxygen-mediated photochemical pathways, these molecules exhibit broad-spectrum antimicrobial activity against bacteria, fungi, viruses, and biofilm-associated microorganisms. This review examines the photophysical properties and mechanisms of reactive oxygen species generation associated with plant-derived photosensitisers, together with key factors influencing their antimicrobial performance. Recent advances in nanocarrier-based delivery systems, dual-wavelength activation strategies, and synergistic combination therapies are also discussed for their potential to improve photostability, enhance reactive oxygen species generation, and increase microbial inactivation efficiency. Finally, current progress, challenges, and future research directions for advancing plant-derived photosensitisers in antimicrobial photodynamic therapy are discussed. Full article

This study falls within the field of lifelong education and investigates the role of aesthetic-sensory experiences—particularly those mediated by digital visual arts—in the development of critical thinking, a key competence identified in the European LifeComp Framework. In light of contemporary social transformations characterised by complexity, uncertainty, and change, critical thinking emerges as a transversal skill of fundamental importance for the education of conscious and autonomous citizens. The objective of this research is to analyse how digital sensory experiences, integrated with innovative teaching methodologies—such as laboratory-based teaching and collaborative learning—can foster the activation and enhancement of critical thinking in educational contexts. The work is based on a narrative analysis of the literature, conducted through the consultation of academic databases (e.g., Scopus, ERIC, and Google Scholar), and is developed within a theoretical framework encompassing digital education, laboratory didactics, and collaborative learning strategies. Studies published in recent years were selected according to their relevance to digital aesthetic experiences and critical thinking in educational contexts and were analysed through a thematic synthesis of the main conceptual contributions. The knowledge activities include the selection, categorisation, and discussion of recent studies that relate aesthetic experiences to the development of soft skills, in line with the principles of visual education understood as aesthetic and critical literacy in visual languages. The results of the review indicate that the intentional use of aesthetic digital environments, in combination with active and reflective teaching approaches, can stimulate complex cognitive processes and significantly contribute to the formation of critical thinking. The contribution implements an interdisciplinary approach among visual education, digital education, and experiential aesthetics, emphasising the need for further empirical research to consolidate the evidence and guide the implementation of innovative educational practices based on this approach. Full article