Search Results (153)

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

Robust visual perception of Aids to Navigation (AtoN) is essential for Maritime Autonomous Surface Ships (MASS) operating in restricted navigational waters, where estuarine clutter, fog, glare, and dense traffic can severely degrade detection reliability. Existing maritime vision datasets largely emphasize open-sea targets or coarse AtoN categories, leaving a granularity gap for IALA-compliant fine-grained understanding in river–sea transition and port-approach channels. The River–Sea AtoN Navigation Dataset (RSAND) is introduced, a large-scale benchmark collected along the Yangtze River Deepwater Channel from inland corridors to open estuarine waters. RSAND contains 39,926 images with expert-verified bounding-box annotations and a hierarchical taxonomy that jointly captures AtoN location, shape, and functional semantics across 29 categories. To support both realistic long-tailed evaluation and standardized model comparison, two protocols are provided: RSAND-Full (29 categories) and RSAND-Balanced (10 critical categories). All quantitative benchmarking results in this paper are reported on RSAND-Balanced, while RSAND-Full is released for future large-scale long-tailed robustness studies. Benchmarking experiments on 14 state-of-the-art detectors demonstrate that YOLOv12x achieves superior performance with an mAP50-95 of 80.7%, significantly outperforming previous baselines. However, the analysis reveals persistent challenges in detecting small, distant targets and distinguishing visually similar functional markers. RSAND and the accompanying evaluation toolkit are released to facilitate reproducible research toward safer and smarter marine and coastal navigation. Full article

Aspergillus fijiensis is an industrially important filamentous fungus, whose genetic analysis has been limited by the absence of species-specific tools. This study establishes an optimized CRISPR–Cas9 genome editing platform for A. fijiensis, from protoplast preparation to DNA repair pathway engineering. Antibiotic screening first identified hygromycin B and 5-FOA (5-fluoroorotic acid) as effective positive and counter-selection markers. A high-efficiency protoplast regeneration protocol was developed depending on specific osmotic stabilization and mycelial competence. Evaluation of a plasmid-based CRISPR system revealed that while autonomous replication was feasible, gene editing was constrained by low efficiency and a predominant bias toward NHEJ (non-homologous end joining). We implemented a Cas9–sgRNA RNP (ribonucleoprotein) delivery approach, with RNP delivery alone producing frequent indels. However, targeted integration remained inefficient when using conventional MMEJ (Microhomology-mediated end joining) donors. By employing donors containing short (5 bp) microhomology arms between cleavage sites, we effectively engaged the MMEJ pathway, enabling precise insertions and large-fragment deletions in 92% of the analyzed transformants. Donor templates containing minimal 5 bp microhomology sequences could effectively shift the predominant repair pathway from NHEJ to MMEJ. These findings demonstrate that MMEJ is the superior pathway with a unique mechanism for genome engineering in A. fijiensis, providing a versatile toolkit for unlocking the biotechnological potential of this recalcitrant species and a successful paradigm for establishing genetic systems in other species. Full article

The high costs and time-consuming nature of space exploration missions are among the major barriers to studying deep space. The lack of samples and limited information make such studies challenging, highlighting the need for innovative solutions, including advanced data-mining techniques and tools such as geochemical modeling, as strategies for overcoming challenges in data scarcity. Geochemical modeling is a powerful tool for understanding the processes that govern the composition and distribution of elements and compounds in a system. In cosmology, space geochemical modeling could support cosmochemistry by simulating the evolution of the atmospheres, crusts, and interiors of astronomical objects and predicting the geochemical conditions of their surfaces or subsurfaces. This study uniquely focuses on the geochemical modeling of celestial bodies beyond Mars, fills a significant gap in the literature, and provides a vision of what has been done by analyzing, categorizing, and providing the critical points of these research objectives, exploring geochemical modeling aspects, and outcomes. To systematically trace the intellectual structure of this field, this study follows the PRISMA guidelines for systematic reviews. It includes a structured screening process that uses bibliographic methods to identify relevant studies. To this end, we developed the Custom Bibliometric Analyses Toolkit (CBAT), which includes modules for keyword extraction, targeted thematic mapping, and visual network representation. This toolkit enables the precise identification and analysis of relevant studies, providing a robust methodological framework for future research. Europa, Titan, and Enceladus are among the most studied celestial bodies, with spectrometry and thermodynamic models as the most prevalent methods, supported by tools such as FREZCHEM, PHREEQC, and CHNOSZ. By exploring geochemical modeling solutions, our systematic review serves to inform future exploration of distant celestial bodies and assist in ambitious questions such as habitability and the potential for extraterrestrial life in the outer solar system. Full article

Effective drug delivery in oncology is challenged by a hierarchy of biological barriers—from abnormal vasculature and dense stroma to cellular immunosuppression and specialized interfaces like the blood–brain barrier. This review provides a contemporary analysis of smart nanoformulations through the lens of a rational, stage-gated design pipeline. We first deconstruct the solid tumor microenvironment as a multi-tiered obstacle (systemic, stromal, cellular), establishing a barrier-specific foundation for nanocarrier design. The core of the review articulates an architectural toolkit, detailing how intrinsic nanoparticle properties precondition in vivo identity via the protein corona, which in turn informs the selection of advanced ligands for cellular targeting and programmed intracellular trafficking. This integrated framework sets the stage for exploring sophisticated applications, including endogenous and externally triggered responsive systems, bio-orthogonal activation, immuno-nanoformulations, and combination strategies aimed at overcoming multidrug resistance. By synthesizing these components into a cohesive design philosophy, this review moves beyond a catalog of advances to offer a blueprint for engineering next-generation nanotherapeutics. We critically assess the translational landscape and contend that this hierarchical design approach is essential for developing more effective, personalized, and clinically viable cancer treatments. Full article

A compact, in situ beta-spectroscopy approach for real-time monitoring of Strontium-90 (Sr-90) in contaminated groundwater has been investigated. Two inorganic scintillators, CaF₂(Eu) and ZnSe(Al,O), were coupled to silicon photomultipliers (SiPMs) and evaluated experimentally using custom front-end electronics. This was also modelled with Monte Carlo simulations using the Geant4 toolkit. Although simulations correctly predicted ZnSe(Al,O) has an advantage due to its higher light yield and optical transport, experimental measurements additionally revealed practical limitations of the readout electronics which were not captured in the simulation model. ZnSe(Al,O) showed excellent agreement with the simulated detector response (R² ≈ 0.86; ${\chi }^2$/NDF ≈ 27). It also attains a higher relative detection efficiency (∼61.5%), yielding faithful capture of the composite Sr-90/Y-90 spectrum with only minor suppression at the extreme high-energy tail. CaF₂(Eu) exhibits a deficit at low-mid energies and an apparent enhancement in the high-energy tail. This is consistent with threshold and photon-statistics losses and leads to poorer agreement with simulation (${\chi }^2$/NDF ≈ 179) and lower overall efficiency (∼22.7%). These findings identify ZnSe(Al,O) as the stronger candidate for an underwater, in situ Sr-90 beta-spectroscopy system and motivate targeted optimisation of SiPM coupling and crystal-edge reflectivity in future designs. Full article

Cystic fibrosis (CF) is caused by loss-of-function variants in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride and bicarbonate channel and affects multiple organs, with pancreatic involvement showing very high penetrance. In pancreatic ducts, CFTR drives secretion of alkaline, bicarbonate-rich fluid that maintains intraductal patency, neutralises gastric acid and permits safe delivery of digestive enzymes. Selective impairment of CFTR-dependent bicarbonate transport, even in the presence of residual chloride conductance, is strongly associated with exocrine pancreatic insufficiency, recurrent pancreatitis and cystic-fibrosis-related diabetes. These clinical manifestations are captured by pharmacodynamic anchors such as faecal elastase-1, steatorrhoea, pancreatitis burden and glycaemic control, providing clinically meaningful benchmarks for CFTR-targeted therapies. In this review, we summarise the principal mechanisms underlying pancreatic pathophysiology and the current approaches to clinical management. We then examine in vitro pancreatic duct models that are used to evaluate small molecules and emerging therapeutics targeting CFTR. These experimental systems include native tissue, primary cultures, organoids, co-cultures and microfluidic devices, each of which has its own advantages and limitations. Intact micro-perfused ducts provide the physiological benchmark for studying luminal pH control and bicarbonate (HCO₃⁻) secretion. Primary pancreatic duct epithelial cells (PDECs) and pancreatic ductal organoids (PDO) preserve ductal identity, patient-specific genotype and key regulatory networks. Immortalised ductal cell lines grown on permeable supports enable scalable screening and structure activity analyses. Co-culture models and organ-on-chip devices incorporate inflammatory, stromal and endocrine components together with flow and shear and provide system-level readouts, including duct-islet communication. Across this complementary toolkit, we prioritise bicarbonate-relevant endpoints, including luminal and intracellular pH and direct measures of HCO₃⁻ flux, to improve alignment between in vitro pharmacology and clinical pancreatic outcomes. The systematic use of complementary models should facilitate the discovery of next-generation CFTR modulators and adjunctive strategies with the greatest potential to protect both exocrine and endocrine pancreatic function in people with CF. Full article

Manipulating the mitochondrial genome remains a significant challenge in genetic engineering, primarily due to the mitochondrial double-membrane structure. While recent advances have expanded the genetic toolkit for nuclear and cytoplasmic targets, precise editing of mitochondrial DNA (mtDNA) has remained elusive. Here we report the first successful mitochondrial import of a catalytically active RNA-guided prokaryotic Argonaute protein from the mesophilic bacterium Alteromonas macleodii (AmAgo). By guiding AmAgo to the single-stranded D- or R-loop region of mtDNA using synthetic RNA guides, we observed a nearly threefold reduction in mtDNA copy number in human cell lines. This proof of concept study demonstrates that a bacterial Argonaute can remain active within the mitochondrial environment and influence mtDNA levels. These findings establish a foundational framework for further development of programmable systems for mitochondrial genome manipulation. Full article

The rapid evolution of CRISPR/Cas technology has transformed genome editing across biological systems in which zebrafish have emerged as a powerful vertebrate model for functional genomics and disease research. Due to its transparency, genetic similarity to humans, and suitability for large-scale screening, zebrafish is an appropriate system for translating molecular discoveries into biomedical and environmental applications. Thereby, this review highlights the recent progress in zebrafish gene editing, targeting innovations in ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors. These approaches have greatly improved editing accuracy, reduced mosaicism, and enabled efficient F0 phenotyping. In the near future, automated microinjections, optimized guide RNA design, and multi-omics validation pipelines are expected to enhance reproducibility and scalability. Although recent innovations such as ribonucleoprotein delivery, PAM-flexible Cas variants, and precision editors have expanded the zebrafish genome-editing toolkit, their benefits are often incremental and context-dependent. Mosaicism, allele complexity, and variable germline transmission remain common, particularly in F0 embryos. Precision editors enable defined nucleotide changes but typically exhibit modest efficiencies and locus-specific constraints in zebrafish. Consequently, rigorous validation, standardized workflows, and careful interpretation of F0 phenotypes remain essential. This review critically examines both the capabilities and limitations of current zebrafish gene-editing technologies, emphasizing experimental trade-offs, reproducibility challenges, and realistic use cases. Full article

Modernizing public security risk governance demands a paradigm shift from reactive response to proactive, systems-oriented prevention. Prevailing governance models, with their focus on institutions and technology, often neglect the micro-foundational mechanisms of risk generation: the internal psychological processes of individuals. To address this gap, this study develops a novel theoretical model—the F(T, P, C, R) framework—which integrates self-organization theory with a psychological gaming perspective. We conceptualize an individual’s behavioral choice (F_behavior) as an emergent outcome of the dynamic interplay among four constitutive factors: the situational context of Time (T) and Place (P), and the cognitive assessments of perceived Risk Control power (C) and perceived Risk Destructive power (R). Employing automotive driving behavior—specifically decisions regarding safe distance maintenance and the adoption of autonomous driving technologies—as our primary analytical scenario, we derive a dynamic risk-decision matrix. This matrix categorizes behavioral outcomes into four distinct quadrants (Confirm, Tend-to-Confirm, Tend-to-Deny, Deny) based on the subjective calculus between C and R, thereby elucidating the internal logic of risk-related choices. The study’s main contribution is constituted by this novel micro-behavioral analytical framework that integrates cognitive science with systems-based governance principles. It offers theoretical insights for behavioral public policy and provides a structured toolkit for diagnosing and designing targeted interventions, ultimately aiming to enhance proactive risk management and systemic resilience. Full article

Recent advancements in molecular tools for plant genetic engineering, particularly CRISPR-based technologies, have created new opportunities for targeted genome editing. However, applying these tools remains challenging in crop species such as sunflower (Helianthus annuus) that lack established and effective transformation pipelines, including transient reagent delivery methods for functional screening and validation of genetic engineering tools. To address this gap, three major reagent delivery platforms, namely protoplast transfection, leaf infiltration, and Agrobacterium-mediated tissue co-culture, were systematically adapted and assessed for use in sunflower seedlings. While each method enabled successful reagent delivery, they differed in their levels of scalability and efficiency. With these platforms, delivery by different Agrobacterium strains and the effectiveness of various reporter gene expression cassettes were compared to define the most experimentally suitable components for different applications in sunflowers. Together, these results establish a foundational toolkit for transient functional testing in sunflower and pave the way for more sophisticated genetic engineering approaches in this agriculturally important oilseed, confectionary seed, and horticultural crop. Full article

Background: Understanding how Regional Innovation Systems (RISs) drive innovation outputs remains a central question in innovation studies. Most existing empirical research relies on linear or single-indicator models, which fail to capture nonlinear interactions among the key RIS dimensions—Firms, Knowledge, Government, and Economy. Methodology: This study proposes an integrated analytical framework that combines Confirmatory Factor Analysis (CFA), CatBoost machine learning, and SHAP-based explainability to bridge theory-driven modeling with data-driven prediction. Using provincial panel data from China spanning 2011–2023, CFA is first employed to construct and validate four latent RIS dimensions. These latent constructs are then used as inputs in a CatBoost model to predict regional patent outputs, followed by SHAP analysis to quantify the marginal and interactive contributions of each dimension. Results: The CFA results confirm the reliability and validity of the four latent dimensions, establishing a robust structural foundation for the RIS. The CatBoost model achieves high predictive accuracy (log-transformed $R^2$ = 0.975, RMSE = 0.206), substantially outperforming traditional linear benchmarks. SHAP analysis indicates that the Firm dimension is the primary driver of innovation output, while Knowledge, Government, and Economy dimensions exhibit context-dependent moderating effects characterized by diminishing returns, threshold effects, and nonlinear synergies. Conclusions: By integrating latent-variable modeling with interpretable machine learning, this study develops a “CFA-CatBoost-SHAP” closed-loop paradigm for transparent and high-precision analysis of innovation mechanisms. This approach advances RIS theory by empirically validating its multidimensional structure, enriches the methodological toolkit for innovation research, and provides actionable insights for the design of targeted R&D and innovation policies. Full article

Breast cancer is one of the most prevalent malignant tumors worldwide, with the highest incidence and mortality among women. Early precise diagnosis and the development of efficient treatment regimens remain major clinical challenges. Harnessing the programmable size, surface chemistry, and tumor microenvironment (TME) responsiveness of nanomaterials, there is tremendous potential for their applications in breast cancer diagnosis and therapy. In the diagnostic arena, nanomaterials serve as core components of novel contrast agents (e.g., gold nanorods, quantum dots, superparamagnetic iron oxide nanoparticles) and biosensing platforms, substantially enhancing the sensitivity and specificity of molecular imaging modalities—such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging (FLI)—and enabling high-sensitivity detection of circulating tumor cells and tumor-derived exosomes, among various liquid biopsy biomarkers. In therapy, nanoscale carriers (e.g., liposomes, polymeric micelles) improve tumor targeting and accumulation efficiency through passive and active targeting strategies, thereby augmenting anticancer efficacy while effectively reducing systemic toxicity. Furthermore, nanotechnology has spurred the rapid advancement of emerging modalities, including photothermal therapy (PTT), photodynamic therapy (PDT), and immunotherapy. Notably, the construction of theranostic platforms that integrate diagnostic and therapeutic units within a single nanosystem enables in vivo, real-time visualization of drug delivery, treatment monitoring, and therapeutic response feedback, providing a powerful toolkit for advancing breast cancer toward personalized, precision medicine. Despite challenges that remain before clinical translation—such as biocompatibility, scalable manufacturing, and standardized evaluation—nanomaterials are undoubtedly reshaping the paradigm of breast cancer diagnosis and treatment. Full article

Cyclin-dependent kinase 5 (Cdk5) is an atypical serine/threonine kinase distinct from classical cell cycle regulators. Its activity is highest in the nervous system and essential for development, but its functions in other tissues, particularly in cancer, are increasingly being elucidated. This review explores the functional duality of Cdk5 by comparing its constructive role in neurodevelopment with its repurposed oncogenic function in cancer. In neurodevelopment, Cdk5 orchestrates nearly every stage of brain construction, including neuronal differentiation, migration, and synaptic plasticity. However, in many cancers, this neurodevelopmental toolkit is repurposed, and aberrantly activated Cdk5 promotes proliferation, metastasis, and therapeutic resistance in diverse solid tumors. Cdk5 also actively shapes the tumor microenvironment by promoting angiogenesis and modulating immunity. Notably, this oncogenic function is not universal, as Cdk5 exhibits its duality even within the context of cancer; it acts as a tumor suppressor in gastric cancer upon nuclear localization. Taken together, these lines of evidence underscore that Cdk5 is a context-dependent kinase whose output is determined by upstream regulation, subcellular localization, and the cellular environment. This review discusses the molecular basis of its dual role and highlights both the potential and complexity of Cdk5 as a therapeutic target in oncology. Full article

RNA interference (RNAi) offers programmable, sequence-specific silencing via small interfering RNA (siRNA) and microRNA (miRNA), but clinical translation hinges on overcoming instability, immunogenicity, and inefficient endosomal escape. This review synthesizes advances in non-viral nanocarriers—liposomes, polymeric nanoparticles, and extracellular vesicles (EVs)—that stabilize nucleic acids, tune biodistribution, and enable organ- and cell-selective delivery. We highlight design levers that now define the field: ligand-guided targeting, stimuli-responsive release, biomimicry and endogenous carriers, and rational co-delivery with small molecules. Across major disease areas—cancer and cardiovascular, respiratory, and urological disorders—these platforms achieve tissue-selective uptake (e.g., macrophages, endothelium, and myocardium), traverse physiological barriers (including the blood–brain barrier and fibrotic stroma), and remodel hostile microenvironments or immune programs to enhance efficacy while maintaining favorable safety profiles. Early clinical studies reflect this diversity, spanning targeted nanoparticles, local drug depots, exosome and cellular carriers, and inhaled formulations, e.g., and converge on core phase-I endpoints (safety, maximum tolerated dose, pharmacokinetics/pharmacodynamics, and early activity). Looking ahead, priorities include good manufacturing practice scale, consistent manufacture—especially for EVs; more efficient loading and cargo control; improved endosomal escape and biodistribution; and rigorous, long-term safety evaluation with standardized, head-to-head benchmarking. Emerging directions such as in vivo EVs biogenesis, theragnostic integration, and data-driven formulation discovery are poised to accelerate translation. Collectively, nanoparticle-enabled RNAi has matured into a versatile, clinically relevant toolkit for precise gene silencing, positioning the field to deliver next-generation therapies across diverse indications. Full article