Search Results (3,320)

Blue light (BL) is a key environmental signal influencing plant flowering, yet its role in floral development beyond the transition phase remains underexplored. This review provides a comprehensive synthesis of current research on BL-mediated floral development, with a particular emphasis on horticultural crops grown in a controlled environment. Unlike prior reviews that focus primarily on floral induction, this article systematically examines BL’s effects on later stages of flowering, including floral organ morphogenesis, sex expression, bud abortion, flower opening, scent emission, coloration, pollination, and senescence. Drawing on evidence from both model plants (e.g., Arabidopsis thaliana) and crop species, this review identifies key photoreceptors, hormonal regulators, and signaling components involved in BL responses. It also highlights species-specific and context-dependent outcomes of BL manipulation, proposes mechanistic hypotheses to explain conflicting findings, and outlines critical knowledge gaps. By integrating molecular, physiological, and environmental perspectives, this review offers a framework for optimizing BL applications to improve flowering traits and postharvest quality in horticultural production systems. Full article

In recent years, the increasing frequency of global seismic events has imposed severe impacts on human society. Timely and accurate assessment of post-earthquake damage and recovery is essential for developing effective emergency response strategies and enhancing urban resilience. This study investigates 11 provinces in Turkey affected by the February 2023 Turkey–Syria earthquake, conducting a multidimensional evaluation of disaster loss and recovery. For loss assessment, existing studies typically focus on changes in the total value of nighttime lights at the regional level, overlooking variations at the pixel scale. In this study, we introduce a pixel-level NTL loss metric, which provides finer-grained insights and helps interpret outcomes driven by spatial heterogeneity. For recovery assessment, we propose a Composite Nighttime Light Index (CNLI) that integrates multiple recovery-phase indicators into a single quantitative measure, thus capturing more information than a one-dimensional metric. To account for complex interrelationships among indicators, a Bayesian network is employed, which moves beyond the conventional independence assumption. Moreover, an information gain (IG) approach is applied to optimize indicator weights, minimizing subjectivity and avoiding abnormal weight distributions compared with traditional methods, thereby ensuring a more objective construction of the Resilience Index (RI). Results show that Sanliurfa, Kilis, and Hatay suffered the most severe damage; Kahramanmaras and Malatya exhibited the lowest CNLI values, while Hatay, Kilis, and Gaziantep showed higher CNLI values. In contrast, Gaziantep and Adana obtained the highest RI values. Since CNLI reflects actual recovery performance while RI characterizes inherent resilience, accordingly, effectively linking CNLI and RI establishes a dual-perspective and novel framework, the 11 provinces are classified into four categories, and differentiated recovery strategies are suggested. This study contributes a refined quantitative framework for post-earthquake loss and recovery assessment and provides scientific evidence to support emergency response and targeted reconstruction. Full article

Transcription factors (TFs) are important gene transcription regulators involved in myriad functions such as development, metabolism, and stress response. TFs are found in all eukaryotes, with many families of TFs unique to plants and algae. Algae are of interest due to a wide range of novel metabolites, of which TFs play an important role in regulating their biosynthesis. In particular, the red algae (phylum Rhodophyta) are a source of several important metabolites that are a current focus of further research. However, to date, investigations of TF families in rhodophytes have been limited due to the relative lack of genomic resources available and the small number of in silico analyses of their TFs. In this study, we used genomic and transcriptomic data to identify rhodophyte TFs. We found that the general proportion of TFs in rhodophytes was overall consistent with previous research. However, for the first time in the rhodophyte class Florideophyceae, we report the presence of a putative TF within the trihelix TF (TTF) family, which are light-sensitive TFs associated with growth and stress response. In particular, we demonstrate evidence suggesting the presence of putative TTFs in three Asparagopsis taxiformis genomes, as well as in several other florideophyte assemblies. This was supported by analyses including Neighbour-Joining phylogeny, protein structure prediction, and motif analysis. In summary, this research reported the repertoire of TFs in rhodophyte algae across a much greater range than previously reported and identified putative TTFs in several algae from the class Florideophyceae. This opens an avenue for further research into the evolution of various TFs in early plants, as well as key regulatory factors in rhodophyte metabolism, though future research, such as functional characterisation, will be required to confirm these findings. Full article

In recent years, Mamba has gained increasing importance in the field of image restoration, gradually outperforming traditional convolutional neural networks (CNNs) and Transformers. However, the existing Mamba-based networks mainly focus on capturing global contextual relationships and neglect the crucial impact of local feature interactions on restoration performance in low-light environments. These environments inherently require the joint optimization of multi-scale spatial dependencies and frequency-domain characteristics. The traditional CNNs and Transformers face challenges in modeling long-range dependencies, while State Space Models (SSMs) in Mamba demonstrate proficiency in sequential modeling yet exhibit limitations in fine-grained feature extraction. To address the limitations of existing methods in capturing global degradation patterns, this paper proposes a novel global-to-local feature extraction framework through systematic Mamba integration. The Low-Frequency Mamba Block (LFMBlock) is introduced first to perform refined feature extraction in the low-frequency domain. The High-Frequency Guided Enhancement Block (HFGBlock) is used, which utilizes low-frequency priors to compensate for texture distortions in high-frequency components. Comprehensive experiments on multiple benchmark datasets show that the Global-to-Local Mamba architecture achieves superior performance in low-light restoration and image enhancement. It significantly outperforms state-of-the-art methods in both quantitative metrics and visual quality preservation, especially in recovering edge details and suppressing noise amplification under extreme illumination conditions. The hierarchical design effectively bridges global structural recovery with local texture refinement, setting a new paradigm for frequency-aware image restoration. Full article

Structured-light 3D reconstruction is an active measurement technique that extracts spatial geometric information of objects by projecting fringe patterns and analyzing their distortions. It has been widely applied in industrial inspection, cultural heritage digitization, virtual reality, and other related fields. This review presents a comprehensive analysis of mainstream fringe-based reconstruction methods, including Fringe Projection Profilometry (FPP) for diffuse surfaces and Phase Measuring Deflectometry (PMD) for specular surfaces. While existing reviews typically focus on individual techniques or specific applications, they often lack a systematic comparison between these two major approaches. In particular, the influence of different projection schemes such as Digital Light Processing (DLP) and MEMS scanning mirror–based laser scanning on system performance has not yet been fully clarified. To fill this gap, the review analyzes and compares FPP and PMD with respect to measurement principles, system implementation, calibration and modeling strategies, error control mechanisms, and integration with deep learning methods. Special focus is placed on the potential of MEMS projection technology in achieving lightweight and high-dynamic-range measurement scenarios, as well as the emerging role of deep learning in enhancing phase retrieval and 3D reconstruction accuracy. This review concludes by identifying key technical challenges and offering insights into future research directions in system modeling, intelligent reconstruction, and comprehensive performance evaluation. Full article

Intrusion Detection Systems (IDS) play a vital role in safeguarding networks, yet their effectiveness is often challenged, as cyberattacks evolve in new and unexpected ways. Machine learning models, although very powerful, usually perform well only on data that closely resembles what they were trained on. When faced with unfamiliar traffic, they often misclassify. In this work, we examine this generalization gap by training IDS models on one Denial-of-Service (DoS) variant, DoS Hulk, and testing them against other variants such as Goldeneye, Slowloris, and Slowhttptest. Our approach combines careful preprocessing, dimensionality reduction with Principal Component Analysis (PCA), and model training using Random Forests and Deep Neural Networks. To better understand model behavior, we tuned decision thresholds beyond the default 0.5 and found that small adjustments can significantly affect results. We also applied Shapley Additive Explanations (SHAP) to shed light on which features the models rely on, revealing a tendency to focus on fixed components that do not generalize well. Finally, using Uniform Manifold Approximation and Projection (UMAP), we visualized feature distributions and observed overlaps between training and testing datasets, but these did not translate into improved detection performance. Our findings highlight an important lesson: visual or apparent similarity between datasets does not guarantee generalization, and building robust IDS requires exposure to diverse attack patterns during training. Full article

The electromagnetic density of states (EM-DOS) plays a crucial role in understanding light–matter interactions, especially at metal–dielectric interfaces. This study explores the impact of interface geometry, material properties, and nanostructures on EM-DOS, with a focus on surface plasmon polaritons (SPPs) and evanescent waves. Using a combination of analytical and numerical methods, the behavior of EM-DOS is analyzed as a function of distance from metal–dielectric interfaces, showing exponential decay with penetration depth. The influence of different metals, including copper, gold, and silver, on EM-DOS is examined. Additionally, the effects of dielectric materials, such as Ti${\mathrm{O}}_2$, PMMA, and ${\mathrm{Al}}_2$${\mathrm{O}}_3$, on the enhancement of electromagnetic field confinement are discussed. The study also investigates the effect of nanostructures, like nanohole and nanopillar arrays, on EM-DOS by calculating effective permittivity and analyzing the interaction of quantum emitters with these structures. Results show that nanopillar arrays enhance EM-DOS more effectively than nanohole arrays, especially in the visible spectrum. The findings provide insights into optimizing plasmonic devices for applications in sensing, quantum technologies, and energy conversion. Full article

Optogenetics leverages light to control neural circuits, but traditional systems are often bulky and tethered, limiting their use. This work introduces OptoBrain, a novel, portable wireless system for optogenetics designed to overcome these challenges. The system integrates modules for multichannel data acquisition, smart neurostimulation, and continuous processing, with a focus on low-power and low-voltage operation. OptoBrain features up to eight neuronal acquisition channels with a low input-referred noise (e.g., 0.99 µVRMS at 250 sps with 1 V/V gain), and reliably streams data via a Bluetooth 5.0 link at a measured throughput of up to 400 kbps. Experimental results demonstrate robust performance, highlighting its potential as a simple, practical, and low-cost solution for emerging optogenetics research centers and enabling new avenues in neuroscience. Full article

Lipids obtained from microalgae have recently received significant attention from the energy and food industries. Microalgae are promising alternatives and are more sustainable sources of lipids for the food industry, which faces a growing demand for food and increased environmental awareness among consumers. This study provides a bibliometric review of research articles published between 2019 and 2024 with the aim of understanding the future trends and tendencies of the applications of microalgal lipids in the food industry. A thorough assessment of 255 articles retrieved from the Scopus database showed an apparent decrease in the number of publications per year within the analyzed timeframe. The predominant focus has been basic research conducted on a lab-scale using chlorophytes (green algae) to optimize lipid production by modulating physicochemical cultivation parameters (i.e., nutrient availability, temperature, light, and pH). Lipids were mainly extracted using the Bligh and Dyer or Folch methods, quantified gravimetrically, and characterized using gas chromatography coupled to mass spectrometry. Publications referring to polyunsaturated fatty acids, such as omega-3 and omega-6, were the most abundant. The results emphasized the significance of microalgae as a promising biotechnological platform for the production of lipids within the food industry. Full article

Outdoor substations present complex conditions such as uneven terrain, strong illumination variations, and frequent occlusions, which pose significant challenges for autonomous robotic inspection. To address these issues, we develop an embedded inspection robot that integrates attention-enhanced semantic segmentation with GPS-assisted navigation for reliable operation. A lightweight DeepLabV3+ model is improved with ECA-SimAM and CBAM attention modules and further extended with a GPS-guided attention component that incorporates coarse location priors to refine feature focus and improve boundary recognition under challenging lighting and occlusion. The segmentation outputs are used to generate real-time road masks and navigation lines via center-of-mass and least-squares fitting, while RTK-GPS provides global positioning and triggers waypoint-based behaviors such as turning and stopping. Experimental results show that the proposed method achieves 85.26% mean IoU and 89.45% mean pixel accuracy, outperforming U-Net, PSPNet, HRNet, and standard DeepLabV3+. Deployed on an embedded platform and validated in real substations, the system demonstrates both robustness and scalability for practical infrastructure inspection tasks. Full article

The optical microscope is an indispensable observation instrument that has fundamentally contributed to progress in science and technology. Dark-field microscopy and scattered light imaging techniques enable high-contrast observation of nanoparticles in water. However, the scattered light is focused by the optical lenses, resulting in a blurred image of the nanoparticle structure. Here, we developed a projection optical microscope (PROM), which directly observes the scattered light from the nanoparticles without optical lenses. In this method, the sample is placed below the focus position of the microscope’s objective lens and the projected light is detected by an image sensor. This enables direct observation of the sample with a spatial resolution of approximately 20 nm. Using this method, changes in the aggregation state of nanoparticles in solution can be observed at a speed faster than the video frame rate. Moreover, the mechanism of such high-resolution observation may be related to the quantum properties of light, making it an interesting phenomenon from the perspective of optical engineering. We expect this method to be applicable to the observation and analysis of samples in materials science, biology and applied physics, and thus to contribute to a wide range of scientific, technological and industrial fields. Full article

Cardiovascular diseases (CVDs) are increasingly being defined not only in terms of metabolic or purely vascular disorders, but also as complex immunometabolic disorders. One of the most groundbreaking discoveries in recent years is the role of neutrophil extracellular networks (NETs/NENs) as a key link between chronic vascular wall inflammation and thrombotic processes. In this article, we present a synthetic overview of the latest data on the biology of NETs/NENs and their impact on the development of atherosclerosis, endothelial dysfunction, and the mechanisms of immunothrombosis. We highlight how these structures contribute to the weakening of atherosclerotic plaque stability, impaired endothelial barrier integrity, platelet activation, and the initiation of the coagulation cascade. We also discuss the modulating role of classic risk factors such as hypertension, dyslipidemia, and exposure to tobacco smoke, which may increase the formation or hinder the elimination of NETs/NENs. We also focus on the practical application of this knowledge: we present biomarkers associated with the presence of NETs/NENs (cfDNA, MPO–DNA complexes, CitH3, NE), which may be useful in diagnostics and risk stratification, and we discuss innovative therapeutic strategies. In addition to classic methods for indirectly inhibiting NET/NEN formation (antiplatelet, anti-inflammatory, and immunometabolic agents), we present experimental approaches aimed at their neutralization and removal (e.g., DNase I, elastase, and myeloperoxidase inhibitors). We pay particular attention to the context of cardiac and cardiac surgical procedures (Percutaneous Coronary Intervention-PCI, coronary artery bypass grafting-CABG), where rapid NET/NEN bursts can increase the risk of acute thrombotic complications. The overall evidence indicates that NETs/NENs represent an innovative and promising research and therapeutic target, allowing us to view cardiovascular diseases in a new light—as a dynamic interaction of inflammatory, atherosclerotic, and thrombotic processes. This opens up new possibilities in diagnostics, combination treatment and personalisation of therapy, although further research and standardization of detection methods remain necessary. Full article

The global need for tissue and organ transplantation paved the way for plant-based scaffolds as cheap, ethical, and valuable alternatives to synthetic and animal-derived matrices for tissue regeneration. Over the years, the field has outgrown its initial scope, including the development of tissue models, platforms for drug testing and delivery, biosensors, and laboratory-grown meat. In this scoping review, we aimed to shed light on the frequency of the use of different plant matrices, the main techniques for decellularization, the functionalization methods for stimulating mammalian cell attachment, and the main results. To that purpose, we searched the keywords “decellularized” AND “scaffold” AND (“plant” OR “vegetable”) in online-available databases (Science Direct, Scopus, PubMed, and Sage Journals). From the selection and study of 71 articles, we observed a multitude of plant sources and tissues, along with a large and inhomogeneous body of protocols used for decellularization, functionalization and recellularization of plant matrices, which all led to variable results, with different extents of success (mostly in vitro). Since the field of plant-based scaffolds shows high potential for growth in the next few years, driven by emerging biotechnological applications, we conclude that future research should focus on plant sources with low economic and environmental impacts while also pursuing the standardization of the methods involved and a much deeper characterization of the scaffold performance in vivo. Full article

The Dark Triad (Machiavellianism, narcissism, and psychopathy) has been traditionally associated with externalizing behaviors and a lack of empathy. However, emerging evidence suggests that these traits also relate to internal emotional vulnerabilities (such as depression, alexithymia, and anhedonia). This study aimed to examine the association between Dark Triad traits and emotional variabilities (alexithymia and anhedonia) in the general population, with a particular focus on sex differences. A total of 492 French-speaking adults completed a battery of validated self-report measures online, including the SD3fr, BDI-II, TAS-20, and PAS. Comparative and multivariate analyses were conducted separately for males and females. High SD3 scores were associated with greater depression, alexithymia (especially difficulty in identifying feelings), and anhedonia in the overall sample. These associations remained significant after adjusting for depression severity. Sex differences emerged: in females, high Dark Triad traits were strongly linked to emotional dysfunction, while no such associations were observed in males. These findings support the presence of sex-specific emotional correlates of the Dark Triad, particularly in females, where Machiavellianism and psychopathy were linked to emotional distress. Clinical implications are discussed in light of hormonal, sociocultural, and emotional regulation differences. Full article

Sphagneticola trilobata (L.) Pruski has been introduced to many countries due to its ornamental and economic value. However, it has been listed in the world’s 100 worst alien invasive species due to its invasive nature. This species easily escapes cultivation and forms dense ground covers. It reproduces asexually through ramet formation from stem fragments. It also produces a large number of viable seeds that establish extensive seed banks. The movement of stem fragments and the dispersal of seeds, coupled with human activity, contribute to its short- and long-distance distribution. S. trilobata grows rapidly due to its high nutrient absorption and photosynthetic abilities. It exhibits high genetic and epigenetic variation. It can adapt to the different habitats and tolerate various adverse environmental conditions, including cold and high temperatures, low and high light irradiation, low nutrient levels, waterlogging, drought, salinity and global warming. S. trilobata has powerful defense systems against herbivory and pathogen infection. These systems activate the jasmonic acid signaling pathway, producing several defensive compounds. This species may also acquire more resources through allelopathy, which suppresses the germination and growth of neighboring plants. These life history traits and defensive abilities likely contribute to its invasive nature. This is the first review to focus on the mechanisms of its invasiveness in terms of growth, and reproduction, as well as its ability to adapt to different environmental conditions and defend itself. Full article