Search Results (1,118)

Unmanned aerial vehicles (UAVs) are becoming an important platform for precision agriculture, supporting both high-throughput sensing and active field operations such as spraying, monitoring, and phenotyping. However, unlike general UAV applications, agricultural environments impose distinctive challenges due to heterogeneous field structures, canopy occlusion, terrain variation, dynamic disturbances, and strong coupling between navigation performance and task quality. To address this gap, this review presents a systematic analysis of UAV navigation in agricultural environments from a system-level perspective. The review first summarizes the core technical components of agricultural UAV navigation, including sensing, localization, mapping, planning, and control. It then discusses how navigation requirements vary across representative scenarios such as open fields, orchards, and terraced farmland, and examines their roles in key applications including aerial mapping, field monitoring, precision spraying, and close-range orchard operations. In addition, datasets, simulation platforms, and evaluation protocols relevant to agricultural UAV navigation are reviewed. Finally, major challenges are identified, including scene heterogeneity, perception degradation, insufficient task-semantic integration, limited control robustness, and the lack of standardized benchmarks. Future research should move toward robust, task-aware, and modular navigation architectures that support reliable and scalable agricultural UAV deployment. Full article

Genetic diversity and antifungal susceptibility profiles of Aspergillus fumigatus are critical for understanding the evolution of resistance in clinical and environmental settings. We performed comprehensive genomic characterization of A. fumigatus isolates using whole-genome sequencing combined with phenotypic susceptibility assays. SnpEff-based variant annotation identified 76,079 single-nucleotide polymorphisms, revealing a high proportion of mutations (78.8%) in upstream and downstream regulatory regions, whereas high-impact coding variants remained rare (0.083%). Several key mutations were identified, including the well-established cyp51A M220V and HMG1 S212P/Y564H mutations. Moreover, a diverse array of peripheral cyp51A polymorphisms (M39I, E402D, N248K, and K372N) was detected, although these variants did not correlate with the resistant phenotypes. Our comparative genomic analysis identified a novel A586T substitution in the FKS1 gene in an isolate with an elevated minimum effective concentration of caspofungin, suggesting its possible association with reduced susceptibility, although functional validation is required. In isolates lacking canonical target-site mutations, the high frequency of regulatory-region variants indicated the involvement of non–target-site mechanisms. This study provides a detailed map of the genomic landscape of A. fumigatus and identifies candidate loci for future functional validation. Our results demonstrate the utility of high-throughput genomic surveillance for monitoring emerging resistance trends and characterizing the genetic background of clinical fungal pathogens. Full article

Reproducible morphological profiling, particularly for drug discovery, has become an important tool for compound evaluation. Established workflows such as CellProfiler provide a widely adopted foundation for Cell Painting analysis. However, conventional pipelines often require substantial manual configuration and technical expertise, which can limit scalability and accessibility. In this study, a fully automated deep learning-based workflow is presented for segmentation-driven morphological profiling from raw microscopy data. Using a curated subset of the JUMP Cell Painting pilot dataset, ground-truth masks were generated and used to train a U-net–based segmentation model in the IKOSA platform. Post-processing strategies were introduced to improve instance separation and reduce segmentation artifacts. The final model achieved strong segmentation performance (precision/recall/AP up to 0.98/0.94/0.92 for nuclei), with an average runtime of 2.2 s per 1080 × 1080 image. Segmentation outputs enabled large-scale feature extraction, yielding 3664 morphological descriptors that showed high correlation with CellProfiler-derived measurements (normalized MAE: 0.0298). Feature prioritization further reduced redundancy to 1145 informative descriptors. These results demonstrate that automated deep learning pipelines can complement established Cell Painting workflows by reducing configuration overhead while maintaining compatibility with validated morphological profiling standards. The proposed workflow may help improve resource efficiency in drug discovery and personalized medicine. Full article

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R² = 0.90) and weedy yield (MAE = 258, R² = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials. Full article

High-throughput plant phenotyping (HTPP) is increasingly limited by the mismatch between the need for field-relevant, fine-grained phenotypic information and the restricted capability of conventional observation platforms under complex agricultural conditions. Ground mobile robots are emerging as the key carrier for resolving this gap because they combine close-range sensing, autonomous mobility, and physical interaction within real field environments. In this paper, a structured scoping review is presented using a closed-loop perception–decision–action pipeline as the organizing principle. Within this framework, recent advances are synthesized from the perspectives of multimodal fusion, localization-aware sensing, motion planning, deep-learning-based phenotypic analysis, active observation, robotic intervention, and edge deployment. The review further clarifies the complementary roles of Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), and air–ground collaboration in multiscale phenotyping workflows. Beyond summarizing technologies, the article provides three concrete deliverables: a structured taxonomy of mobile phenotyping systems; comparative tables covering sensing modalities, localization/navigation methods, and AI models; and a research agenda linking technical progress to field deployability. The synthesis highlights four persistent bottlenecks, namely environmental generalization, annotation scarcity, limited standardization and reproducibility, and the gap between advanced models and agricultural edge hardware. Overall, ground robots are identified not merely as sensing platforms, but as the central system architecture for advancing mobile phenotyping toward autonomous, fine-grained, and field-deployable operation. Full article

Holy basil (Ocimum tenuiflorum L.) is an extensively utilized herb, encompassing numerous bioactive compounds that hold significant interest in the food and pharmaceutical industries. High-throughput phenotyping is a rapid and non-invasive technique, providing diverse phenotypic trait observation and measurement. However, basic knowledge regarding the diversity among varieties beneficial for large-scale production in terms of yield and secondary metabolites under a controlled greenhouse environment is limited. Hence, we assessed and classified 12 Thai accessions and two commercial cultivars by evaluating growth, yield, and secondary metabolites at each harvesting time using an advanced NSTDA-Plant Phenomics platform. Notably, accessions OC130, OC141, OC072, and OC059 demonstrated stable metabolite production and antioxidant activity, highlighting their potential as superior accessions for further cultivation and utilization. These findings underscore the potential for tailored cultivation practices to manipulate secondary metabolite synthesis, thereby enhancing the medicinal properties and market value of Thai holy basil. The implications of this study extend to farmers, providing valuable insights into the phenotypic variation and practical avenues under consistent environmental conditions. Breeders can observe genetic diversity to improve basil varieties with desirable traits for specific environmental niches. Moreover, modern agricultural practices can benefit from understanding the impact of controlled environments on secondary metabolite synthesis. Full article

Genetic liver diseases encompass a heterogeneous group of conditions that disrupt hepatic development, structure, or function. Advances in high-throughput sequencing have revealed the molecular basis of many disorders previously defined only by clinical or biochemical features, transforming diagnostic and therapeutic approaches. This review proposes a mechanistic framework that distinguishes diseases arising from developmental abnormalities from those caused by functional impairments in hepatocellular or biliary physiology. It outlines how defects in transporters, enzymes, signaling pathways, intracellular trafficking, and mitochondrial function converge to produce diverse hepatic phenotypes. Moreover, translational aspects are discussed such as how the growing integration of genetic testing into clinical practice enables precise diagnosis, informs prognosis and therapy, and refines disease classification. Finally, the review discusses future directions in the field, emphasizing the role of multi-omic approaches, organoid modeling, and data sharing in elucidating unresolved pathogenic mechanisms and advancing precision hepatology. Full article

Low temperature critically influences cellular metabolism by impairing processes such as membrane fluidity, enzyme activity, and protein folding. However, the comprehensive genetic landscape and regulatory mechanisms governing cold acclimation remain poorly understood. Here, we performed high-throughput, pooled genetic screening in the model alga Chlamydomonas reinhardtii (C. reinhardtii) to identify genes essential for cold acclimation. Our screening revealed numerous candidate genes implicated not only in early cold response pathways but also in core cellular processes, including DNA dynamics, protein homeostasis, metabolic regulation, and substrate transport. Notably, we identified a member of the RUS (ROOT UVB SENSITIVE) family, encoding a conserved DUF647 domain protein, designated CrRUS1. CRISPR-generated rus1 mutant alleles in C. reinhardtii display a phenotype consistent with our screening: the mutants did not exhibit any visible growth defects, but show severe growth defects at low temperature. Interestingly, the cold-induced phenotypic changes in rus1 can be reversed by dark conditions, suggesting that CrRUS1 likely promotes cold acclimation in C. reinhardtii through a light-dependent pathway. Our work provides novel genetic resources and mechanistic insights into cold acclimation in C. reinhardtii, with potential translational relevance for enhancing cold tolerance in crop species. Full article

This study investigates the impact of inoculating seeds with bacterial endophytes isolated from Vitis amurensis Rupr. on endophytic community composition in Arabidopsis thaliana (L.) Heynh. Ten bacterial isolates of the genera Agrobacterium, Bacillus, Curtobacterium, Erwinia, Frondihabitans, Gordonia, Pantoea, Pseudomonas, Sphingomonas, and Xanthomonas were applied to seeds and some visible phenotypic effects were observed on plant growth after two weeks. High-throughput sequencing of 16S rRNA revealed that the native endophytic microbiome of A. thaliana was dominated by Gammaproteobacteria, Actinomycetes, Bacteroidia, and Alphaproteobacteria. The key families were Microscillaceae, Chitinophagaceae, Rhizobiaceae, Rhodanobacteraceae, Nocardioi-daceae, Nocardiaceae, Xanthomonadaceae, Devosiaceae, Microbacteriaceae, Crocinitomi-caceae, Pseudomonadaceae, Solimonadaceae, Comamonadaceae, Caulobacteraceae, and Micrococcaceae. Arabidopsis seed inoculation with Agrobacterium sp. R8SCh-B12, Curtobacterium sp. P7SA-B3, and Gordonia aichiensis P6PL2 significantly reduced alpha diversity (Shannon index) and altered beta diversity relative to controls, indicating strong community restructuring. These three isolates, along with Pseudomonas sp. R8SCh-B2, Sphingomonas sp. RA62c-B5, Xanthomonas sp. R7SCh-B6, and Bacillus velezensis AMR25, successfully colonized the plant tissues, as evidenced by significant increases in genus-specific amplicon sequence variants, ASVs (up to 17,820-fold for Curtobacterium sp. ASV33). In contrast, Pantoea sp. P7SCH-B5, Erwinia sp. R8SCh-B3, and Frondihabitans sp. RA62c-B2 failed to colonize A. thaliana, despite being applied to the seeds, suggesting the existence of mechanisms restraining colonization. These findings demonstrate that only a subset of grapevine-derived endophytes can effectively colonize A. thaliana, and that successful colonization correlates with significant shifts in the native microbiome, even in the absence of overt phenotypic changes. This emphasizes the importance of strain-specific compatibility in plant–endophyte interactions. Thus, we report the first descriptions of several novel endophytes that colonized Arabidopsis plants and establish a convenient model to investigate plant–bacterial interactions. Full article

The anticancer ruthenium complex indazolium trans-[tetrachlorobis(1H-indazole) ruthenate (III)—also known as KP1019—inhibits cancer cell proliferation in vitro, causes tumor regression in animal models, and showed no dose-limiting toxicity in a phase I clinical trial. Previous studies found that KP1019 damages DNA in both cancer cells and the budding yeast Saccharomyces cerevisiae. To identify other potential targets of KP1019 along with pathways that modulate the drug’s cellular effects, we screened the yeast gene deletion strain library by quantitative high-throughput cell array phenotyping (Q-HTCP). Fitness differences, as judged by growth curve analysis, identified genes for which loss of function (gene deletion) interacts with (enhances or suppresses) KP1019 effects. Drug-enhancing deletions were enriched for DNA repair functions, consistent with DNA damage being a primary target of KP1019 in yeast. pH homeostasis also modified the effects of KP1019. Drug-suppressing deletions prominently involved ribosomal proteins. A mechanistic link between ribosomal protein function and KP1019 toxicity was supported by dose-dependent accumulation of Rpl7a-GFP in the nucleolus, which is a hallmark of ribosomal biogenesis stress. Furthermore, KP1019 acted synergistically with the TOR pathway inhibitor everolimus to inhibit cell proliferation. The resulting model, wherein KP1019 perturbs ribosome assembly, can inform the design of future combination therapies. Full article

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R² = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R² = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R² = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices. Full article

Chronic kidney disease (CKD) and kidney aging share many pathological and molecular features, with cellular senescence emerging as a potentially important contributor to disease progression. Senescent cells accumulate in the kidneys due to persistent stressors, contributing to chronic inflammation and fibrosis via the senescence-associated secretory phenotype (SASP). This review explores the intersection between CKD and renal aging, focusing on the mechanisms driving senescence, its impact on kidney function, and potential therapeutic interventions. We explore various senotherapeutic approaches, such as senolytics, senomorphics, and rejuvenating agents, and highlight the increasing role of artificial intelligence (AI) and machine learning (ML) in detecting and monitoring senescent cells, enabling high-throughput and precise assessment across experimental and clinical settings. Understanding these mechanisms offers new avenues for developing targeted treatments to slow CKD progression and improve patient outcomes. Full article

A comprehensive understanding of plant diversity is essential for ecological research, conservation planning, and sustainable resource management. Advances in genetic technologies have transformed the assessment of plant biodiversity, enabling more precise and efficient characterization of genetic variation. Early molecular markers, widely used in the late 2000s, have largely been replaced by polymerase chain reaction (PCR)-based tools that require less DNA, are easier to use, and are supported by accessible commercial kits. The 2020s have seen the emergence of new, more accessible tools driven by cost reduction and efficiency improvements. High-throughput sequencing (HTS) technologies have further revolutionized the field by providing genome-wide insights into allelic diversity, structural polymorphisms, and epigenetic modifications. These innovations enhance the detection of adaptive variation, improve understanding of spatial genetic structure, and support the evaluation of environmental impacts on plant populations. Marker-assisted selection, now common in modern breeding, leverages genomic data to develop cultivars with enhanced resistance and desirable agronomic traits. Emerging tools such as environmental DNA (eDNA) analysis, high-throughput phenotyping, and advanced bioinformatics workflows expand the capacity to monitor species, assess population viability, and identify key traits linked to adaptation. The present review aims to highlight these technological advancements and the more recent and useful tools available from Next-Generation Sequencing to genotyping-by-sequencing, discussing their role for conserving plant genetic resources, improving breeding programs, and deepening knowledge of plant biodiversity within changing ecosystems. Full article

Introduction and Aim: Assessment of antitumor activity in preclinical models remains challenging when relying solely on conventional size-based imaging, particularly for complex agents such as cannabinoids, whose biological effects may not translate into early volumetric tumor changes. Cannabinoid formulations, including the synthetic cannabinoid JWH-182, Cannabixir^® Medium dried flowers, and Cannabixir^® THC full extract, exhibit diverse and potentially subtle effects on tumor biology. Radiomics enables high-throughput extraction of quantitative imaging features that capture intratumoral heterogeneity beyond gross tumor volume. The primary aim of this study was to evaluate the utility of MRI-based radiomics as a sensitive tool for detecting cannabinoid-associated tumor phenotypic modulation in a preclinical breast cancer model. Methods: Orthotopic breast tumors were induced in mice using the 4T1 cell line. Animals received cannabinoid formulations in combination with chemotherapy according to a predefined protocol. Tumor burden was assessed at baseline and post-treatment using ultrasonography and whole-body MRI to calculate tumor doubling time. T1- and T2-weighted MRI datasets were segmented and analyzed using radiomics to extract morphometric and signal-based features. Results: Conventional imaging revealed no significant differences in tumor doubling time between most cannabinoid-treated groups and controls, except for accelerated growth in animals treated with Cannabixir^® THC full extract. In contrast, radiomics identified distinct, compound-specific tumor phenotypes, including structural features consistent with reduced aggressiveness, in JWH-182-treated tumors, despite similar volumetric growth patterns. Conclusion: MRI-based radiomics sensitively captures cannabinoid-associated tumor phenotype alterations beyond volumetric assessment, supporting its value as a pharmaco-imaging tool for characterizing treatment-related tumor biology in preclinical oncology. Full article

Tomato (Solanum lycopersicum L.) is a globally important vegetable crop and a key model species for studying reproductive development in other Solanaceae members with edible fleshy fruits, such as eggplant, sweet and hot peppers, and Physalis spp. The morphogenesis and patterning of tomato floral organs fundamentally determine fruit yield and quality. Recent advances in high-throughput sequencing and gene editing have significantly deepened our understanding of the molecular network regulating tomato reproductive development. This process, from the transition of vegetative shoot apical meristem to the inflorescence meristem, forming floral meristems with primordia of sepals, petals, stamens, carpels, and fruits, is precisely coordinated by a genetic network involving homeobox and other types of transcription factors, along with signaling pathways. This review systematically outlines the core regulatory network, with an emphasis on the MADS-domain transcription factor family and its associated ABCDE model. Integrating insights from hormone signaling and mutant phenotypes, we summarize the maintenance of inflorescence meristem identity, the specification of floral meristems, and the morphogenetic patterns and core gene regulatory mechanisms for each floral whorl in tomato. We further extend this framework to the flower–fruit continuum, examining how carpel development, floral meristem termination, and ovule differentiation influence fruit morphology, locule number, pericarp structure, and metabolic traits. Finally, we discuss the integration of floral organ development with molecular design breeding and formulate a forward-looking research agenda that translates floral regulatory mechanisms to breeding strategies for yield, uniformity, and fruit quality. This synthesis provides a theoretical foundation and genetic resources for the genetic improvement of tomato flower architecture and its underlying regulatory mechanisms. Full article