Search Results (326)

Anthocyanins, a subclass of flavonoid pigments, impart vivid red, purple, and blue coloration to horticultural plants, playing essential roles in ornamental enhancement, stress resistance, and pollinator attraction. Recent studies have identified B-box (BBX) proteins as a critical class of transcription factors (TFs) involved in anthocyanin biosynthesis. Despite these advances, comprehensive reviews systematically addressing BBX proteins are urgently needed, especially given the complexity and diversity of their roles in regulating anthocyanin production. In this paper, we provide an in-depth overview of the fundamental structures, biological functions, and classification of BBX TFs, along with a detailed description of anthocyanin biosynthetic pathways and bioactivities. Furthermore, we emphasize the diverse molecular mechanisms through which BBX TFs regulate anthocyanin accumulation, including direct activation or repression of target genes, indirect modulation via interacting protein complexes, and co-regulation with other transcriptional regulators. Additionally, we summarize the known upstream regulatory signals and downstream target genes of BBX TFs, highlighting their significance in shaping anthocyanin biosynthesis pathways. Understanding these regulatory networks mediated by BBX proteins will not only advance fundamental horticultural science but also provide valuable insights for enhancing the aesthetic quality, nutritional benefits, and stress adaptability of horticultural crops. Full article

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

Variants in ELAC2, a gene encoding the mitochondrial RNase Z enzyme essential for mitochondrial tRNA processing, have been associated with severe pediatric-onset mitochondrial dysfunction, primarily presenting with developmental delay, hypertrophic cardiomyopathy (HCM), and lactic-acidosis. We hereby report the case of a 25-year-old young woman presenting with dilated cardiomyopathy (DCM) and peripheral sensorimotor polyneuropathy, harboring a homozygous variant in ELAC2. The same variant has been reported only once so far in a case of severe infantile-onset form of HCM and mitochondrial respiratory chain dysfunction, with in vitro data showing a moderate reduction in the RNase Z activity and supporting the current classification as C4 according to the American College of Medical Genetics (ACMG) criteria (PS3, PM2, PM3, PP4). Our extensive clinical, imaging, histological, and genetic investigations support a causal link between the identified variant and the patient’s phenotype, despite the fact that the latter might be considered atypical according to the current state of knowledge. A detailed review of the existing literature on ELAC2-related disease is also provided, highlighting the molecular mechanisms underlying tRNA maturation, mitochondrial dysfunction, and the variable phenotypic expression. Our case further expands the clinical spectrum of ELAC2-related cardiomyopathies to include a relatively late onset in young adulthood and underscores the importance of comprehensive genetic testing in unexplained cardiomyopathies with multisystem involvement. Full article

Indoor navigation poses persistent challenges due to the limitations of traditional positioning systems within buildings. In this study, we propose a novel approach to address this issue—not by continuously tracking the user’s location, but by estimating their position based on how closely their observed behavior matches the expected progression along a predefined route. This concept, while not universally applicable, is well-suited for specific indoor navigation scenarios, such as guiding couriers or delivery personnel through complex residential buildings. We explore this idea in detail in our paper. To implement this behavior-based localization, we introduce an LSTM-based method for classifying user behavior patterns, including standing, walking, and using stairs or elevators, by analyzing velocity sequences derived from smartphone sensors’ data. The developed model achieved 75% accuracy for individual activity type classification within one-second time windows, and 98.6% for full-sequence classification through majority voting. These results confirm the viability of real-time activity recognition as the foundation for a navigation system that aligns live user behavior with pre-recorded patterns, offering a cost-effective alternative to infrastructure-heavy indoor positioning systems. Full article

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article

Municipal solid waste (MSW) and refuse-derived fuel (RDF) incineration generate over 20 million tons of residues annually in the EU. These include bottom ash (IBA), fly ash (FA), and air pollution control residues (APCr), which pose significant environmental challenges due to their leaching potential and hazardous properties. While these residues contain valuable metals and reactive mineral phases suitable for carbonation or alkaline activation, chemical, techno-economic, and policy barriers have hindered the implementation of sustainable, full-scale management solutions. Accelerated carbonation technology (ACT) offers a promising approach to simultaneously sequester CO₂ and enhance residue stability. This review provides a comprehensive assessment of waste incineration residue carbonation, covering 227 documents ranging from laboratory studies to field applications. The analysis examines reactor designs and process layouts, with a detailed classification based on material characteristics, operating conditions, investigated parameters, and the resulting pollutant stabilization, CO₂ uptake, or product performance. In conclusion, carbonation-based approaches must be seamlessly integrated into broader waste management strategies, including metal recovery and material repurposing. Carbonation should be recognized not only as a CO₂ sequestration process, but also as a binding and stabilization strategy. The most critical barrier remains chemical: the persistent leaching of sulfates, chromium(VI), and antimony(V). We highlight what we refer to as the antimony problem, as this element can become mobilized by up to three orders of magnitude in leachate concentrations. The most pressing research gap hindering industrial deployment is the need to design stabilization approaches specifically tailored to critical anionic species, particularly Sb(V), Cr(VI), and SO₄²⁻. Full article

Compliant force control is a fundamental capability for enabling robots to interact safely and effectively with dynamic and uncertain environments. This paper presents a comprehensive survey of compliant force control strategies, intending to enhance safety, adaptability, and precision in applications such as physical human–robot interaction, robotic manipulation, and collaborative tasks. The review begins with a classification of compliant control methods into passive and active approaches, followed by a detailed examination of direct force control techniques—including hybrid and parallel force/position control—and indirect methods such as impedance and admittance control. Special emphasis is placed on advanced compliant control strategies applied to structurally complex robotic systems, including aerial, mobile, cable-driven, and bionic robots. In addition, intelligent compliant control approaches are systematically analyzed, encompassing neural networks, fuzzy logic, sliding mode control, and reinforcement learning. Sensorless compliance techniques are also discussed, along with emerging trends in hardware design and intelligent control methodologies. This survey provides a holistic view of the current landscape, identifies key technical challenges, and outlines future research directions for achieving more robust, intelligent, and adaptive compliant force control in robotic systems. Full article

Graphitic carbon nitride (g-C₃N₄) is emerging as one of the most promising non-metallic semiconductors for the degradation of pollutants in water by photocatalytic processes. Its exceptional reduction–oxidation (redox) potentials and adequate band gap of approximately 2.7 eV give it the ability to absorb in the visible light range. However, the characteristic sensitivity to light absorption is limited, leading to rapid recombination of electron–hole pairs. Therefore, different strategies have been explored to optimize this charge separation, among which the formation of heterostructures based on g-C₃N₄ is highlighted. This review addresses recent advances in photocatalysis mediated by g-C₃N₄ heterostructures, considering the synthesis methods enabling the optimization of the morphology and active interface of these materials. Next, the mechanisms of charge transfer are discussed in detail, with special emphasis on type II, type S, and type Z classifications and their influence on the efficiency of photodegradation. Subsequently, the progress in the application of these photocatalysts for the degradation of water pollutants, such as toxic organic dyes, pharmaceutical pollutants, pesticides, and per- and polyfluoroalkyl substances (PFAS), are analyzed, highlighting both experimental advances and remaining challenges. Finally, future perspectives oriented towards the optimization of heterostructures, the efficiency of synthesis methods, and the practical application of these in photocatalytic processes for environmental remediation. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

Mesenchymal dendritic cell neoplasms represent a distinct category of hematologic malignancies that challenge traditional classifications of histiocytic and classical dendritic/Langerhans cell neoplasms. Historically grouped under the broader umbrella of dendritic cell neoplasms, these entities differ significantly in their ontogeny, histopathologic features, molecular alterations, and clinical behavior. They are categorized into three main subtypes including follicular dendritic cell sarcoma, fibroblastic reticular cell tumor, and EBV-positive inflammatory follicular dendritic cell sarcoma/fibroblastic reticular cell tumor. They originate from mesenchymal stromal cells, and genetic alterations activating the NF- κβ pathway are frequent in follicular dendritic cell sarcomas. Immunophenotypic characterization is critical to distinguish these from other hematologic malignancies including histiocytic and classical dendritic/Langerhans cell neoplasms and other solid (non-hematopoietic) cancers. This review recapitulates current knowledge on existing classifications, details their diverse ontogeny from classical dendritic cell neoplasms, and provides insights into their clinicopathologic characteristics to improve diagnostic accuracy. We detail two case studies that demonstrate the challenges involved in the histopathologic diagnosis of these rare tumors, necessitating a comprehensive workup. Integrating developmental biology into practical diagnostic algorithms is essential to improve recognition and classification of these underdiagnosed neoplasms, ultimately guiding timely management. Full article

The SLC4A11 gene encodes a membrane transporter implicated in congenital hereditary endothelial dystrophy, Harboyan syndrome, and certain cancers. Despite its clinical importance, current data on SLC4A11 expression patterns, transcript variants, and functional roles remain inconsistent and sometimes contradictory. We have systematized existing data, identified areas of consensus, and highlighted discrepancies. This review addresses SLC4A11 transcript and isoform diversity and how this complexity influences both the interpretation of its tissue expression patterns (particularly in the corneal endothelium) and the investigation of its functional roles in health and disease. Our review also untangles the evolving understanding of SLC4A11 function, from its initial classification as a bicarbonate transporter to its established roles in NH₃- and pH-regulated H⁺/OH⁻ transport, lactate efflux, cellular stress responses, and adhesion. The review details how pathogenic mutations disrupt protein maturation, membrane localization, or transport activity, contributing to corneal fluid imbalance and disease. We also discuss the emerging role of SLC4A11 in cancer metabolism and the common metabolic features of dystrophic corneas and tumors. Methodological challenges are appraised, encouraging caution in interpretation and the need for isoform-specific studies. Overall, this review provides a comprehensive update on SLC4A11 biology and identifies key gaps for future research. Full article

Artificial intelligence (AI) is revolutionising neuroimaging by enabling automated analysis, predictive analytics, and the discovery of biomarkers for neurological disorders. However, traditional artificial neural networks (ANNs) face challenges in processing spatiotemporal neuroimaging data due to their limited temporal memory and high computational demands. Spiking neural networks (SNNs), inspired by the brain’s biological processes, offer a promising alternative. SNNs use discrete spikes for event-driven communication, making them energy-efficient and well suited for the real-time processing of dynamic brain data. Among SNN architectures, NeuCube stands out as a powerful framework for analysing spatiotemporal neuroimaging data. It employs a 3D brain-like structure to model neural activity, enabling personalised modelling, disease classification, and biomarker discovery. This paper explores the advantages of SNNs and NeuCube for multimodal neuroimaging analysis, including their ability to handle complex spatiotemporal patterns, adapt to evolving data, and provide interpretable insights. We discuss applications in disease diagnosis, brain–computer interfaces, and predictive modelling, as well as challenges such as training complexity, data encoding, and hardware limitations. Finally, we highlight future directions, including hybrid ANN-SNN models, neuromorphic hardware, and personalised medicine. Our contributions in this work are as follows: (i) we give a comprehensive review of an SNN applied to neuroimaging analysis; (ii) we present current software and hardware platforms, which have been studied in neuroscience; (iii) we provide a detailed comparison of performance and timing of SNN software simulators with a curated ADNI and other datasets; (iv) we provide a roadmap to select a hardware/software platform based on specific cases; and (v) finally, we highlight a project where NeuCube has been successfully used in neuroscience. The paper concludes with discussions of challenges and future perspectives. Full article

Accurate fish species identification is crucial for marine biodiversity conservation, environmental monitoring, and sustainable fishery management, particularly as marine ecosystems face increasing pressures from human activities and climate change. Traditional morphological identification methods are inherently labor-intensive and resource-demanding, while contemporary automated approaches, particularly deep learning models, often suffer from significant computational overhead and struggle with the pervasive issue of class imbalance inherent in ecological datasets. Addressing these limitations, this research introduces a novel computationally parsimonious fish classification framework leveraging the hybrid Mobile–Former neural network architecture. This architecture strategically combines the local feature extraction strengths of convolutional layers with the global context modeling capabilities of transformers, optimized for efficiency. To specifically mitigate the detrimental effects of the skewed data distributions frequently observed in real-world fish surveys, the framework incorporates a sophisticated robust asymmetric loss function designed to enhance model focus on under-represented categories and improve resilience against noisy labels. The proposed system was rigorously evaluated using the comprehensive FishNet dataset, comprising 74,935 images distributed across a detailed taxonomic hierarchy including eight classes, seventy-two orders, and three-hundred-forty-eight families, reflecting realistic ecological diversity. Our model demonstrates superior classification accuracy, achieving 93.97 percent at the class level, 88.28 percent at the order level, and 84.02 percent at the family level. Crucially, these high accuracies are attained with remarkable computational efficiency, requiring merely 508 million floating-point operations, significantly outperforming comparable state-of-the-art models in balancing performance and resource utilization. This advancement provides a streamlined, effective, and resource-conscious methodology for automated fish species identification, thereby strengthening ecological monitoring capabilities and contributing significantly to the informed conservation and management of vital marine ecosystems. Full article

First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring the fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize the neutron flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5 × 10⁵ cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive data set with the potential for machine learning applications. The neutron flux throughout the pile is measured using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistically accurate Monte Carlo model using OpenMC 0.15.0 and MCNP 6.3. The experimental measurements reveal that the testbed has a neutron environment with a total neutron flux approaching 9.5 × 10³ n/cm² × s and a thermal flux of 6.5 × 10³ n/cm² × s. This work establishes that the modified Purdue subcritical pile can provide fair neutron and gamma fluxes within a large volume to enable the radiation testing of integral electronic components and can be a versatile research instrument with the potential to support material testing and limited isotope activation, while generating valuable training data sets for machine learning algorithms in nuclear applications. Full article

In this report, we document and analyze a case in which the Irkut virus (IRKV) (Mononegavirales: Rhabdoviridae) caused a fatal human case following a bat bite in June 2021. Unfortunately, the available data did not permit a detailed taxonomic classification of the carrier bat (Chiroptera). The event occurred in the southwestern part of the Sikhote-Alin mountain region (Russian Far East) covered by the Ussuri taiga forest. The symptoms of the illness began with the following: fever; pronounced psychomotor and motor agitation; tremor of the lower jaw and tongue; aphasia; dyslexia; and dysphagia. These rapidly developed, leading to a severe and fatal encephalitis. The patient was not vaccinated for rabies and did not receive rabies immunoglobulin. Using brain sections prepared from the deceased, molecular diagnostics were performed: immunofluorescence (polyclonal anti-rabies immunoglobulin) indicating the presence of the lyssavirus antigen; and RT-PCR indicating traces of viral RNA. Sectional material (brain) was used for whole-genome sequencing, resulting in a near-complete sequence of the lyssavirus genome. The obtained genomic sequence was identified as the Irkut virus. A comparative analysis of the new sequence and other currently available IRKV sequences (NCBI) revealed differences. Specifically, amino acid differences between antigenic sites in the isolate and those of the rabies vaccine strain used regionally were noted. The patient history and subsequent analysis confirm human IRKV infection following bat contact. Like other fatal cases of IRKV infection described earlier, this case occurred in the southern part of the Russian Far East. Two have occurred in the southwestern part of the Sikhote-Alin mountain region. This indicates the possible existence of an active, natural viral focus. Full article