Search Results (52)

Unmanned aerial vehicle (UAV) visual navigation relies critically on robust image matching between UAV-acquired aerial imagery and pre-existing satellite reference maps. However, extreme cross-domain heterogeneity—encompassing temporal, radiometric, viewpoint, and sensor variations—causes severe performance degradation in existing deep learning-based matchers trained on conventional benchmarks. Furthermore, manual annotation of ground-truth correspondences is prohibitively expensive. This paper proposes a semi-supervised saliency-aware image matching framework with bi-level meta-learning. Our approach comprises two synergistic stages: (1) automated dense correspondence generation via parameterized geometric synthesis, which constructs a large-scale coarse dataset ${\mathcal{D}}_c$ (approximately 50,000 pairs) without dense manual point annotation, serving as the primary training corpus for the feature matching network; (2) expert-validated meta-data curation producing a high-quality meta-dataset ${\mathcal{D}}_m$ (500 pairs) that supervises the training of a Saliency Judgment Network through bi-level meta-optimization, enabling the network to identify and prioritize geometrically reliable correspondences. Experimental results on the proposed RS-Hetero-50K benchmark and cross-domain FuJian-Mountain dataset demonstrate substantial improvements over representative sparse and detector-free matchers, including LoFTR, SuperGlue, and LightGlue. The complete CNN-attention and saliency-aware framework achieves 95.4% matching precision, which is consistent with the best result reported in the experimental section. The plug-and-play experiments further confirm that the proposed saliency module consistently improves representative sparse and detector-free matchers, indicating that the performance gain stems from both stronger feature representation and saliency-guided correspondence selection. The largest terrain-specific gain is observed in gobi scenes, where the AUC@5 px improves by 16.8% relative to the LoFTR baseline, demonstrating improved robustness in weakly textured remote sensing environments. Full article

The global rise in the incidence and severity of invasive fungal infections, particularly among immunocompromised and immunodeficient patients, has created an urgent need for rapid and accurate diagnostic techniques. Therefore, fungal-specific positron emission tomography imaging agents are increasingly in demand, as they offer the potential for early-stage detection of fungal infections. Recently, 2-deoxy-2-[¹⁸F]fluorocellobiose ([¹⁸F]FCB), a fluorine-18-labeled analog of cellobiose that is selectively metabolized by fungal pathogens possessing cellulose-degrading mechanisms (cellulolytic), was developed for the targeted imaging of Aspergillus infections. However, the final [¹⁸F]FCB contained less than 2% unreacted 2-deoxy-2-[¹⁸F]fluoroglucose ([¹⁸F]FDG), which can potentially interfere with image interpretation. Accordingly, this study aims to eliminate residual [¹⁸F]FDG from the final product by enzymatically converting it to [¹⁸F]FDG-6-phosphate through hexokinase-mediated phosphorylation. A Trasis AllInOne (Trasis AIO) module was used to automate the radiolabeling procedure. The reagent vials contain [¹⁸F]FDG, glucose-1-phosphate, cellobiose phosphorylase, adenosine triphosphate (ATP), and hexokinase. A Sep-Pak cartridge was used to purify the tracer. The overall radiochemical yield was 45–50% (n = 3, decay-corrected) in a 40 min synthesis time, with a radiochemical purity of >99% (no detectable [¹⁸F]FDG). This is a highly reliable protocol to produce current good manufacturing practice (cGMP)-compliant [¹⁸F]FCB for clinical PET imaging. Full article

Background/Objectives: Fibroblast activation protein (FAP) has emerged as a promising target for oncologic molecular imaging due to its high expression in cancer-associated fibroblasts and low presence in healthy tissues. Among available FAP ligands, [⁶⁸Ga]Ga-FAPi-46 has shown rapid tumor accumulation, low background uptake, and broad tumor applicability. This study reports the successful translation of [⁶⁸Ga]Ga-FAPi-46 from preclinical development to routine clinical radiopharmacy practice, detailing automated synthesis, quality control performance, radiochemical stability, and the first clinical imaging results. Methods: Automated radiolabeling of FAPi-46 with gallium-68 was performed using a synthesis module. Quality control included radiochemical purity assessments by iTLC, SPE, and RP-HPLC (pH, appearance, endotoxin levels, and membrane integrity testing). Radiochemical stability was evaluated in saline (up to 6 h) and human serum (up to 90 min). In vitro characterization included the partition coefficient and serum protein binding determination. A clinical evaluation was conducted in a woman with newly diagnosed lung adenocarcinoma who underwent both [¹⁸F]FDG PET/CT and [⁶⁸Ga]Ga-FAPi-46 PET/CT. Results: Automated synthesis of [⁶⁸Ga]Ga-FAPi-46 achieved a high radiochemical yield (87.9 ± 1.3%) and radiochemical purity greater than 98%. All batches met release specifications for sterility, apyrogenicity, and physicochemical parameters. The radiotracer demonstrated high stability in saline and human serum, with radiochemical purity consistently above 95% at all evaluated time points. The compound showed a hydrophilic profile (LogP = −3.32 ± 0.14) and 40–60% serum protein binding. Clinically, [⁶⁸Ga]Ga-FAPi-46 PET/CT provided superior lesion delineation compared to [¹⁸F]FDG, revealing additional mediastinal, supraclavicular, and brain metastases. Conclusions: [⁶⁸Ga]Ga-FAPi-46 can be reliably synthesized using automated procedures under routine radiopharmacy conditions, meeting regulatory quality standards and demonstrating excellent stability. Its enhanced lesion detectability compared with [¹⁸F]FDG in the first patient case supports its potential value for oncological staging and clinical implementation. Full article

Plant-parasitic nematodes (PPNs) cause big crop losses globally. Safe/reliable methods for their durable management strategies can harness various beneficial relationships among the plant immune system and related microbiomes. Molecular mechanisms basic to these relations reveal wide arrays of significant roles for plant-healthy growth. This review focuses on such relations of microbiomes to prime and immunize plants against PPNs. It also highlights molecular issues facing PPN-resistant varieties with possible solutions such as genetic breeding/engineering, grafting, PPN-antagonistic root exudates, and novel resistant cultivars. These issues call for optimal uses of various widespread groups of microbiomes. Related plant signaling hormones and transcription factors that regulate gene expression and modulate nematode-responsive genes to ease positive/negative adaptation are presented. Exploring PPN-resistance genes, their activation mechanisms, and signaling networks offers a holistic grasp of plant defense related to biotic/abiotic factors. Such factors relevant to systemic acquired resistance (SAR) via plant–microbe interactions to manage PPNs are stressed. The microbiomes can be added as inoculants and/or steering the indigenous rhizosphere ones. Consequently, SAR is mediated by the accumulation of salicylic acid and the subsequent expression of pathogenesis-related genes. To activate SAR, adequate priming and induction of plant defense against PPNs would rely on closely linked factors. They mainly include the engaged microbiome species/strains, plant genotypes, existing fauna/flora, compatibility with other involved biologicals, and methods/rates of the inoculants. To operationalize improved plant resistance and the microbiome’s usage, novel actionable insights for research and field applications are necessary. Synthesis of adequate screening techniques in plant breeding would better use multiple parameters (molecular and classical ones)-based ratings for PPN-host suitability designation. Sound statistical analyses and interpretation approaches can better identify genotypes with high-level, stable resistance to PPNs than the commonly used ones. Linking molecular mechanisms to consistent field relevance can be progressed via dissemination of many advanced techniques. The CRISPR/Cas9 system has been effective in knocking out both the OsHPP04 gene in rice to confer resistance against Meloidogyne graminicola and the GhiMLO3 gene in cotton to minimize the Rotylenchulus reniformis reproduction. Its genetic modifications in crops synthesized “transgene-free” PPN-resistant plants without decreased growth/yield. Characterizing microbiome species/strains needed to prime and immunize plants requires better molecular tools for fine-scale taxonomic resolution than the common ones used. The former can distinguish closely related ones that exhibit divergent phenotypes for key attributes like stability and production of enzymes and secondary metabolites. As PPN-control strategies via tritrophic interactions are more sensitive to the relevant settings than chemical nematicides, it is suggested herein to test these settings on a case-by-case basis to avoid erratic/contradictory results. Moreover, expanding the use of automated systems to expedite detection/count processes of PPN and related microbes with objectivity/accuracy is discussed. When PPNs and their related microbial distribution patterns were modeled, more aspects of their field distributions were discovered in order to optimize their integrated management. Hence, the feasibility of site-specific microbiome application in PPN–hotspot infections can be evaluated. The main technical challenges and controversies in the field are also addressed herein. Their conceptual revision based on harnessing novel techniques/tools is direly needed for future clear trends. This review also engages raising growers’ awareness to leverage such strategies for enhancing plant resistance and advancing the microbiome role. Microbiomes enjoy wide spectrum efficacy, low fitness cost, and inheritance to next generations in durable agriculture. Full article

In the era of the digital food economy, high-fidelity food images are critical for applications ranging from visual e-commerce presentation to automated dietary assessment. However, developing robust computer vision systems for food analysis is often hindered by data scarcity for long-tail or regional dishes. To address this challenge, we propose a novel high-fidelity food image synthesis framework as an effective data augmentation tool. Unlike generic generative models, our method introduces an Ingredient-Aware Diffusion Model based on the Masked Diffusion Transformer (MaskDiT) architecture. Specifically, we design a Label and Ingredients Encoding (LIE) module and a Cross-Attention (CA) mechanism to explicitly model the relationship between food composition and visual appearance, simulating the “cooking” process digitally. Furthermore, to stabilize training on limited data samples, we incorporate a linear interpolation strategy into the diffusion process. Extensive experiments on the Food-101 and VireoFood-172 datasets demonstrate that our method achieves state-of-the-art generation quality even in data-scarce scenarios. Crucially, we validate the practical utility of our synthetic images: utilizing them for data augmentation improved the accuracy of downstream food classification tasks from 95.65% to 96.20%. This study provides a cost-effective solution for generating diverse, controllable, and realistic food data to advance smart food systems. Full article

The accelerating industrial demand for high-speed manipulation has necessitated the development of robotic architectures that effectively balance dynamic performance with workspace size. While serial SCARA robots offer large workspaces and parallel Delta robots provide high acceleration, existing architectures fail to combine these benefits effectively for specific four-degree-of-freedom (4-DoF) Schoenflies motion tasks. This study introduces and characterizes a novel 4-DoF parallel topology, having a symmetrical build-up, which is distinguished by its use of modular 2-DoF linear drive units. The research methodology entails the structural synthesis of the kinematic chain followed by kinematic analysis using vector algebra to derive closed-form inverse geometric models. Additionally, the Jacobian matrix is formulated to evaluate velocity transmission and systematically classify singular configurations, while the dexterity index is defined to assess the rotational capabilities of the mechanism. Numerical simulations of pick-and-place trajectory were also conducted, varying trajectory curvature to analyze kinematic behavior. The results demonstrate that the proposed modular architecture yields a highly symmetric and homogeneous workspace that can be scaled simply by adjusting the drive module lengths. Furthermore, the singularity and dexterity analyses reveal a substantial, singularity-free operational workspace, although tighter trajectory curvatures were found to impose higher velocity demands on the joints. In conclusion, the proposed mechanism successfully achieves the targeted Schoenflies motion, offering a solution for automated industrial tasks. Full article

Background/Objectives: Overactivation of the HGF/c-MET pathway is implicated in various cancers, making its inhibition a promising therapeutic strategy. While several MET-targeting agents are currently approved or in advanced clinical development, patient selection often relies on invasive tissue-based assays. The development of a specific c-MET radioligand for PET imaging and radioligand therapy represents a non-invasive alternative, enabling real-time monitoring of target expression and offering a pathway to personalized treatment. Methods: Radiosynthesis of [⁶⁸Ga]Ga-DOTA-EMP100 was performed using a GMP-certified ⁶⁸Ge/⁶⁸Ga generator connected to an automated synthesis module. The radiopharmaceutical production was optimized by scaling down the amount of DOTA-EMP-100 from 50 to 20 μg. Synthesis efficiency and release criteria were assessed according to Ph. Eur. for all the final products by evaluating radiochemical yield (RY%), radiochemical purity, presence of free gallium (by Radio-UV-HPLC) and gallium colloids (by Radio-TLC), molar activity (Am), chemical purity, pH, and LAL test results. Results: An optimized formulation of [⁶⁸Ga]Ga-DOTA-EMP-100, using 40 μg of precursor, provided the best outcome in terms of radiochemical performance. Process validation across three independent productions confirmed a consistent radiochemical yield of 64.5% ± 0.5, high radiochemical purity (>99.99%), and a molar activity of 53.41 GBq/µmol ± 0.8. Conclusions: [⁶⁸Ga]Ga-DOTA-EMP-100 was successfully synthesized with high purity and reproducibility, supporting its potential for multi-dose application in clinical PET imaging and targeted radioligand therapy. Full article

To develop a modifier based on diethanolamine, a corresponding phosphoramidite for automated solid-phase deoxyribonucleic acid synthesis was synthesized. The influence of this modifier on the thermal stability of the terminally modified nucleic acids showed a dependence on the neighboring nucleobases and could be attributed to the fraying of the DNA ends. The potential for modification with dioxazaborocanes was first investigated using a small molecule model, and the formation of the dioxazaborocane was confirmed both in solution and in the solid state. Such a modification could expand the scope of xenonucleic acids in the future and modulate the properties of nucleic acids in solution. The influence on the thermal stability of the modified nucleic acids was minimal. In the future, this modification will be extended to internal incorporation and the potential of dioxazaborocanes in the nucleic acid context will be further exploited. Full article

The growing use of experimental radiopharmaceuticals for targeted radionuclide therapy (TRT) highlights the need for robust “in house” radiolabeling protocols. Among these, PSMA-ALB-56 is a PSMA ligand incorporating an albumin-binding moiety to enhance pharmacokinetics, which showed promise for prostate cancer treatment. This study investigated manual radiolabeling conditions of this vector molecule with lutetium-177 and developed a corresponding automated synthesis protocol. Manual experiments on low activities explored buffer systems and antioxidants, identifying sodium acetate buffer and L-methionine as optimal, achieving radiochemical purities above 97% with excellent stability over 48 h. However, when these conditions were transposed directly to an automated process on a GAIA^® module with activities > 2 GBq, radiochemical purity dropped below 70% due to significant radiolysis. This result emphasized that conditions optimized at low activities are not directly transferable to high-activity automated production, and highlighted the crucial role of antioxidant concentration. An optimized automated method was subsequently developed, integrating a solid-phase extraction purification step, higher antioxidant levels during radiolabeling and formulation, and a larger final product volume. These changes led to radiochemical purities above 98.9% and excellent product stability over 120 h for 3 test batches. The presence of high concentrations of methionine and ascorbic acid was essential to protect against radiolysis. This work underscores the importance of adjusting radiolabeling strategies during process scale-up and confirmed that antioxidant concentration is essential for successful ¹⁷⁷Lu radiolabeling. The optimized automated method developed here for [¹⁷⁷Lu]Lu-PSMA-ALB-56 may also be adapted to other radiopharmaceuticals in development for TRT. Full article

Lattice-based cryptography (LBC) is an essential direction in the fields of homomorphic encryption (HE), zero-knowledge proofs (ZK), and post-quantum cryptography (PQC), while number theoretic transformations (NTT) are a performance bottleneck that affects the promotion and deployment of LBC applications. Field-programmable gate arrays (FPGAs) are an ideal platform for accelerating NTT due to their reconfigurability and parallel capabilities. High-level synthesis (HLS) can shorten the FPGA development cycle, but for algorithms such as NTT, the synthesizer struggles to handle the inherent memory dependencies, often resulting in suboptimal synthesis outcomes for direct designs. This paper proposes a systematic HLS co-design to progressively guide the synthesis of NTT accelerators. The approach integrates several key techniques: arithmetic module resource optimization, conflict-free butterfly scheduling, memory partitioning, and template-based automated design fusion. It reveals how to resolve pipeline bottlenecks in HLS-based designs and expand parallel processing, guiding microarchitecture iterations to achieve an efficient design space. Compared to existing HLS-based designs, the area-latency product achieves a performance improvement of 1.93 to 191 times, and compared to existing HDL-based designs, the area-cycle product achieves a performance improvement of 1.7 to 10.6 times. Full article

Background/Objectives: Fibroblast activation protein (FAP) is highly expressed in tumor stroma and selected inflammatory conditions, offering a promising target for molecular imaging. [⁶⁸Ga]Ga-FAPI-46 is a DOTA-based FAP inhibitor with excellent tumor-to-background ratio and potential advantages over [¹⁸F]FDG in low-glycolytic tumors. This article aims to highlight the quality elements that are relevant to clinical practice and to describe the development of an investigational medicinal product dossier for a bicentric clinical trial involving [⁶⁸Ga]Ga-FAPI-46. Methods: The radiolabeling was performed by the two facilities using different automated synthesizers, but with the same specifications and acceptance criteria Results: Three validation batches per site were analyzed for radiochemical/radionuclidic purity, pH, endotoxin, sterility, and bioburden according to European Pharmacopoeia standards. Stability was as sessed up to 2 h post-synthesis. All batches met predefined acceptance criteria. Conclusions: Despite differences in radiosynthesizer modules, product quality and process reproducibility were maintained across both centers. [⁶⁸Ga]Ga-FAPI-46 can be reliably produced in academic settings under GMP-like conditions, enabling multicenter clinical research and facilitating IMPD submissions for broader adoption of FAP-targeted PET imaging. Full article

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

Accurate segmentation of brain tumors in magnetic resonance imaging (MRI) remains a challenging task due to heterogeneous tumor structures, varying intensities across modalities, and limited annotated data. Deep learning has significantly advanced segmentation accuracy; however, it often suffers from sensitivity to hyperparameter settings and limited generalization. To overcome these challenges, bio-inspired metaheuristic algorithms have been increasingly employed to optimize various stages of the deep learning pipeline—including hyperparameter tuning, preprocessing, architectural design, and attention modulation. This review systematically examines developments from 2015 to 2025, focusing on the integration of nature-inspired optimization methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and novel hybrids including CJHBA and BioSwarmNet into deep learning-based brain tumor segmentation frameworks. A structured multi-query search strategy was executed using Publish or Perish across Google Scholar and Scopus databases. Following PRISMA guidelines, 3895 records were screened through automated filtering and manual eligibility checks, yielding a curated set of 106 primary studies. Through bibliometric mapping, methodological synthesis, and performance analysis, we highlight trends in algorithm usage, application domains (e.g., preprocessing, architecture search), and segmentation outcomes measured by metrics such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), Hausdorff Distance (HD), and ASSD. Our findings demonstrate that bio-inspired optimization significantly enhances segmentation accuracy and robustness, particularly in multimodal settings involving FLAIR and T1CE modalities. The review concludes by identifying emerging research directions in hybrid optimization, real-time clinical applicability, and explainable AI, providing a roadmap for future exploration in this interdisciplinary domain. Full article

Traumatic brain injury (TBI) triggers a cascade of molecular and cellular disturbances, including apoptosis, inflammation, and destabilization of neuronal connections. The transcription factor p53 plays a pivotal role in regulating cell fate following brain injury by initiating pro-apoptotic signaling cascades. Hydrogen sulfide (H₂S) may significantly contribute to the regulation of p53. Using scanning laser confocal microscopy, we found that after TBI, p53 accumulates extensively in the damaged cerebral cortex, showing distinct subcellular localization in neurons and astrocytes. In neurons, p53 predominantly localizes to the cytoplasm, suggesting involvement in mitochondria-dependent apoptosis, whereas in astrocytes, p53 is found in both the nucleus and cytoplasm, indicating possible activation of transcription-dependent apoptotic pathways. Quantitative analysis confirmed a correlation between p53 localization and morphological signs of cell death, as revealed by Sytox Green and Hoechst nuclear staining. Modulating H₂S levels exerted a marked influence on p53 expression and distribution. Administration of the H₂S donor sodium thiosulfate (Na₂S₂O₃) reduced the overall number of p53-positive cells, decreased nuclear localization, and lowered the level of apoptosis. Conversely, inhibition of H₂S synthesis using aminooxyacetic acid (AOAA) led to enhanced p53 expression, increased numbers of cells exhibiting nuclear fragmentation, and a more pronounced apoptotic response. These findings highlight a neuroprotective role for H₂S, likely mediated through the suppression of p53-dependent cell death pathways. To improve analytical accuracy, we developed a YOLO-based deep-learning model for the automated detection of fragmented nuclei. Additionally, evolutionary and molecular dynamics analysis revealed a high degree of p53 conservation among vertebrates and indicated that, although H₂S does not form stable complexes with p53, it may modulate its conformational dynamics. Full article

Background/Objectives: Gliomas, including the most aggressive subtype—glioblastoma multiforme, are brain tumors with an unfavorable prognosis and high mortality. Early diagnosis is essential to improve treatment efficacy. Positron emission tomography PET with O-(2-[¹⁸F] fluoroethyl)-L-tyrosine ([¹⁸F]FET) has been supported by clinical studies for its role in diagnosis and monitoring the disease. However, the low availability of [¹⁸F]FET in Italy has limited its use in clinical praxis. This study describes the technological transfer of the radiopharmaceutical IASOglio^® (the commercial [¹⁸F]FET developed by Curium Pharma in Italy), with the aim of improving national access to this advanced diagnostic technology. Methods: Three consecutive batches were produced using the automated Trasis AllinOne module, and quality control was performed, including chemical and microbiological tests, to successfully validate the production process. Additionally, the stability of the radiopharmaceutical for its entire shelf life has been demonstrated with stability testing at 14 h after end of synthesis (EOS). Results: The production of [¹⁸F]FET achieved a non-corrected yield between 49% and 52%, with a corrected decay rate ranging from 73% to 79%. The process met the required quality specifications, including bio-burden control and filter integrity. The technological transfer was successfully completed, and production authorization was obtained from the Italian Medicines Agency (AIFA) for the Officina Farmaceutica of Institute of Clinical Physiology of the National Research Council (CNR-IFC) located in Pisa. Conclusions: Local production of [¹⁸F]FET in Italy marks a milestone in glioma diagnosis, thereby contributing to timely treatment and improved clinical outcomes. Full article