Search Results (269)

Advanced manufacturing places stringent demands on measurement technologies, requiring ultra-high precision, non-contact operation, high throughput, and real-time adaptability. Optical metrology, with its distinct advantages, has become a key enabler in this context. This paper reviews optical metrology techniques from the perspective of precision manufacturing applications, emphasizing precision positioning and surface topography measurement while noting the limitations of traditional contact-based methods. For positioning, interferometers, optical encoders, and time-of-flight methods enable accurate linear and angular measurements. For surface characterization, techniques such as interferometry, structured light profilometry, and confocal microscopy provide reliable evaluation across scales, from large structures to micro- and nano-scale features. By integrating these approaches, optical metrology is shown to play a central role in bridging macroscopic and nano-scale characterization, supporting both structural assessment and process optimization. This review highlights its essential contribution to advanced manufacturing, and offers a concise reference for future progress in high-precision and intelligent production. Full article

The Chinese hamster ovary K1 cell line (CHO-K1) constitutively produces retroviral-like particles (RVLPs) containing reverse transcriptase (RT) activity, which, thus far, have not been shown to be infectious. Since infectious retroviruses have been reported in other rodent species, this study was undertaken to investigate the presence of latent, infectious, endogenous retroviruses (ERVs) in CHO-K1 cells by using chemical induction assays and detection of activated virus using the highly sensitive, product-enhanced RT (PERT) assay, with subsequent infectivity analysis in cell lines of different species, including human. The results demonstrated activation of A-type and C-type retroviral particles based on transmission electron microscopy and increased production of cell-free RT-particles after treatment of the cells with 5-iodo-2′-deoxyuridine and 5-azacytidine, which was greater with dual treatment than with each inducer alone. Induction of A- and C-type particles was confirmed in dual-drug-treated CHO-K1 cells by long-read high-throughput sequence (HTS) analysis. Infectivity studies performed by inoculating human A549, HEK-293, and MRC-5 cells; African green monkey Vero cells; Mus dunni cells; and CHO-K1 cells with supernatant containing RT-particles from dual-treated CHO-K1 cells indicated the absence of a replicating retrovirus in supernatant from extended cell culture using the PERT assay. Furthermore, short-read HTS analysis did not show evidence of integration of retroviral sequences in inoculated A549 and 293 cells. The overall results showed no evidence for latent, infectious, endogenous RVLPs in CHO-K1 cells. Full article

Rammed earth walls in traditional villages in the humid and hot climate of Lingnan are susceptible to microbial damage and disease. Paishan Village in Zhuhai, which is the largest extant rammed earth building complex in the Pearl River Delta with rammed earth walls dating from the Ming (1368–1644) and Qing (1644–1912) Dynasties to the Republic of China (1912–1949) period, faces weathering and hollowing issues, yet targeted microbial research is lacking. This study, with a focus on the village’s rammed earth walls, aimed to reveal microbial diversity and its relationship to the environment, providing a basis for heritage conservation. We used SEM (scanning electron microscopy) to analyze the microstructure of walls facing different orientations. High-throughput sequencing (based on the 16S rRNA gene V3–V4 region) was combined with microbial community analysis. Species annotation and differential analysis were performed using QIIME2 and R. The results indicated that the west wall had the highest microbial diversity (45 at the phylum level and 2969 at the genus level), while the south wall exhibited the lowest. Different orientations shaped distinct community structures, with the north wall harboring a higher concentration of hygrophilous microorganisms, while the south wall was dominated by thermotolerant bacteria. All four walls shared only 0.29% of the core microorganisms. This study elucidates the distribution patterns of microorganisms in rammed earth walls in humid and hot areas, offering scientific support for their ecological restoration. Full article

Background: The automated analysis of intestinal organoids in microscopy images are essential for high-throughput morphological studies, enabling precision and scalability. Traditional manual analysis is time-consuming and subject to observer bias, whereas Machine Learning (ML) approaches have recently demonstrated superior performance. Purpose: This study aims to evaluate YOLO (You Only Look Once) for organoid segmentation and classification, comparing its standalone performance with a hybrid pipeline that integrates DL-based feature extraction and ML classifiers. Methods: The dataset, consisting of 840 light microscopy images and over 23,000 annotated intestinal organoids, was divided into training (756 images) and validation (84 images) sets. Organoids were categorized into four morphological classes: cystic non-budding organoids (Org0), early organoids (Org1), late organoids (Org3), and Spheroids (Sph). YOLO version 10 (YOLOv10) was trained as a segmenter-classifier for the detection and classification of organoids. Performance metrics for YOLOv10 as a standalone model included Average Precision (AP), mean AP at 50% overlap (mAP50), and confusion matrix evaluated on the validation set. In the hybrid pipeline, trained YOLOv10 segmented bounding boxes, and features extracted from these regions using YOLOv10 and ResNet50 were classified with ML algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Random Forest, eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptrons (MLP). The performance of these classifiers was assessed using the Receiver Operating Characteristic (ROC) curve and its corresponding Area Under the Curve (AUC), precision, F1 score, and confusion matrix metrics. Principal Component Analysis (PCA) was applied to reduce feature dimensionality while retaining 95% of cumulative variance. To optimize the classification results, an ensemble approach based on AUC-weighted probability fusion was implemented to combine predictions across classifiers. Results: YOLOv10 as a standalone model achieved an overall mAP50 of 0.845, with high AP across all four classes (range 0.797–0.901). In the hybrid pipeline, features extracted with ResNet50 outperformed those extracted with YOLO, with multiple classifiers achieving AUC scores ranging from 0.71 to 0.98 on the validation set. Among all classifiers, Logistic Regression emerged as the best-performing model, achieving the highest AUC scores across multiple classes (range 0.93–0.98). Feature selection using PCA did not improve classification performance. The AUC-weighted ensemble method further enhanced performance, leveraging the strengths of multiple classifiers to optimize prediction, as demonstrated by improved ROC-AUC scores across all organoid classes (range 0.92–0.98). Conclusions: This study demonstrates the effectiveness of YOLOv10 as a standalone model and the robustness of hybrid pipelines combining ResNet50 feature extraction and ML classifiers. Logistic Regression emerged as the best-performing classifier, achieving the highest ROC-AUC across multiple classes. This approach ensures reproducible, automated, and precise morphological analysis, with significant potential for high-throughput organoid studies and live imaging applications. Full article

High variability in stem cell research is a well-known limiting phenomenon, with technical variation across experiments and laboratories often surpassing variation caused by genotypic effects of induced pluripotent stem cell (iPSC) lines. Evaluation of kidney organoid protocols and culture conditions across laboratories remains scarce in the literature. We used the original air-medium interface protocol to evaluate kidney organoid success rate and reproducibility with several human iPSC lines, including a novel patient-derived GRACILE syndrome iPSC line. Organoid morphology was assessed with light microscopy and immunofluorescence-stained maturing glomerular and tubular structures. The protocol was further adapted to four microplate-based high-throughput approaches utilizing spheroid culture steps. Quantitative high-content screening analysis of the nephrin-positive podocytes and ECAD-positive tubular cells revealed that the choice of approach and culture conditions were significantly associated with structure development. The culture approach, iPSC line, experimental replication, and initial cell number explained 35–77% of the variability in the logit-transformed proportion of nephrin and ECAD-positive area, when fitted into multiple linear models. Our study highlights the benefits of high-throughput culture and multivariate techniques to better distinguish sources of technical and biological variation in morphological analysis of organoids. Our microplate-based high-throughput approach is easily adaptable for other laboratories to combat organoid size variability. Full article

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. Full article

In recent years, symptoms suggestive of viral infection have commonly occurred in Gynura bicolor in China. However, no viral genome infecting G. bicolor has been reported. This study applied high-throughput sequencing to plant samples with chlorotic spots in Sanya, Hainan. Viral sequences were confirmed using RT-PCR and RACE. Complete genomes of vanilla distortion mosaic virus (VDMV, Potyvirus vanillae) and an unknown virus were obtained. Sequence analysis indicated that the VDMV isolate from the G. bicolor is a novel variant. It shares 81.13% identity with its closest known strain. The unknown virus is phylogenetically related to maculaviruses but shares less than 76% nucleotide identity with other tymovirids. According to the ICTV, it should be classified as a new member of the genus Maculavirus. In this study, we provisionally designated the virus as gynura bicolor maculavirus (GBMV). Transmission electron microscopy revealed both filamentous and icosahedral virions in stems, but only filamentous virions in leaves. Quantitative RT-PCR showed high RNA accumulation of both viruses in the stems. GBMV levels were significantly lower in leaves. Dodder-mediated mechanical transmission successfully transferred VDMV and GBMV to Nicotiana occidentalis, Oenothera biennis, and Chenopodium amaranticolor. O. biennis developed chlorotic symptoms 15 days after dual infection. Full article

Biodegradable mulching films are promoted as alternatives to traditional polyethylene films, but their environmental impacts remain controversial. This study investigates how biodegradable films affect microplastic pollution of soil, fungal community structure, and ecological network stability. We conducted a maize field experiment comparing conventional polyethylene (CF, PE) and biodegradable (BF, PLA + PBAT) film residues. We used scanning electron microscopy and high-throughput sequencing of fungal ITS genes. We assessed soil properties, microplastic release, fungal communities, and network stability through co-occurrence analysis. BF degraded rapidly, releasing microplastic concentrations much higher than CF. BF increased soil carbon and nitrogen and substantially enhanced maize biomass. However, it significantly reduced soil pH and decreased key functional fungi (saprotrophs and symbionts) abundance. The fungal ecological network complexity and stability declined significantly. Correlation analysis revealed positive associations between saprotrophic and symbiotic fungi abundance and network stability. In contrast, CF reduced some nutrient levels but improved fungal network complexity and stability. This study reveals that biodegradable films create an “ecological trap.” Short-term nutrient benefits mask systematic damage to soil microbial network stability. Our findings challenge the notion that “biodegradable equals environmentally friendly.” Environmental assessments of agricultural materials must extend beyond degradability to include microplastic release, functional microbial responses, and ecological network stability. Full article

Porcine Teschovirus (PTV) is a highly prevalent pathogen within swine populations, primarily associated with encephalitis, diarrhea, pneumonia, and reproductive disorders in pigs, thereby posing a significant threat to the sustainable development of the pig farming industry. In this study, a novel strain of PTV was isolated from the feces of a pig exhibiting symptoms of diarrhea, utilizing PK-15 cell lines. The structural integrity of the viral particles was confirmed via transmission electron microscopy, and the viral growth kinetics and characteristics were evaluated in PK-15 cells. High-throughput sequencing facilitated the acquisition of the complete viral genome, and subsequent phylogenetic analysis and full-genome alignment identified the strain as belonging to the PTV 2 genotype. Further investigation revealed that infection with the PTV-GXLZ2024 strain induces phosphorylation of the eukaryotic translation initiation factor 2α (eIF2α) in PK-15 cells, indicating activation of the unfolded protein response (UPR) through the PERK pathway, with minimal involvement of the IRE1 or ATF6 pathways. Notably, ATF4 protein expression was progressively downregulated throughout the infection, while downstream CHOP protein levels remained unchanged, indicating an incomplete UPR induced by PTV-GXLZ2024. Furthermore, PERK knockdown was found to enhance the replication of PTV-GXLZ2024. This study provides critical insights into the molecular mechanisms underlying PTV pathogenesis and establishes a foundation for future research into its evolutionary dynamics and interactions with host organisms. Full article

Red blood cell (RBC), accounting for approximately 45% of total blood volume, are essential for oxygen delivery and carbon dioxide removal. Their unique biconcave morphology, high surface area-to-volume ratio, and remarkable deformability enable them to navigate microvessels narrower than their resting diameter, ensuring efficient microcirculation. RBC deformability is primarily determined by membrane viscoelasticity, cytoplasmic viscosity, and cell geometry, all of which can be altered under various physiological and pathological conditions. Reduced deformability is a hallmark of numerous diseases, including sickle cell disease, malaria, diabetes mellitus, sepsis, ischemia–reperfusion injury, and storage lesions in transfused blood. As these mechanical changes often precede overt clinical symptoms, RBC deformability is increasingly recognized as a sensitive biomarker for disease diagnosis, prognosis, and treatment monitoring. Over the past decades, diverse techniques have been developed to measure RBC deformability. These include single-cell methods such as micropipette aspiration, optical tweezers, atomic force microscopy, magnetic twisting cytometry, and quantitative phase imaging; bulk approaches like blood viscometry, ektacytometry, filtration assays, and erythrocyte sedimentation rate; and emerging microfluidic platforms capable of high-throughput, physiologically relevant measurements. Each method captures distinct aspects of RBC mechanics, offering unique advantages and limitations. This review synthesizes current knowledge on the pathophysiological significance of RBC deformability and the methods for its measurement. We discuss disease contexts in which deformability is altered, outline mechanical models describing RBC viscoelasticity, and provide a comparative analysis of measurement techniques. Our aim is to guide the selection of appropriate approaches for research and clinical applications, and to highlight opportunities for developing robust, clinically translatable diagnostic tools. Full article

Background/Objectives: Digital morphology analysers have been developed to overcome the limitations of manual microscopy. This study aimed to evaluate the performance of the DC-1 on leukaemia samples, determining if it is a suitable for the identification of leukaemia in low-throughput or remote laboratories. To the best of our knowledge, there is no current published literature evaluating the performance of the DC-1 with leukaemia samples. Methods: This study utilised 88 leukaemia peripheral blood smears donated from various anonymous hospitals and medical laboratories in collaboration with RMIT university. DC-1 pre-classification was compared with post-classification using Cohen’s kappa, sensitivity, and specificity calculations. Pre- and post-classification was compared with manual microscopy using Passing–Bablok regression, Pearson’s r correlation, and Bland–Altman analysis. Results: DC-1 pre-classification results showed a moderate agreement with post-classification (k = 0.52), a very high specificity for most leukocytes (>94%) and variable sensitivity (21–86%). Pre- and post-classification displayed a higher accuracy and correlation with manual results for segmented neutrophils and lymphocytes, compared to other leukocyte classes. Additionally, there was an improvement in the post-classification of immature granulocytes, band neutrophils, and blast cells compared to pre-classification. Conclusions: The results indicate that the DC-1 displayed a better performance for the classification of segmented neutrophils and lymphocytes compared to other cell classes, indicating that the DC-1 is more acceptable for use in infection or normal samples, as opposed to leukaemia. The gold standard therefore remains with the morphologist who can distinguish leukaemia samples. Full article

Digital holographic microscopy (DHM) provides numerous advantages, such as noninvasive sample analysis, real-time dynamic detection, and three-dimensional (3D) reconstruction, making it a valuable tool in fields such as biomedical research, cell mechanics, and environmental monitoring. To achieve more accurate and comprehensive imaging, it is crucial to capture detailed information on the microstructure and 3D morphology of samples. Phase processing of holograms is essential for recovering phase information, thus making it a core component of DHM. Traditional phase processing techniques often face challenges, such as low accuracy, limited robustness, and poor generalization. Recently, with the ongoing advancements in deep learning, addressing phase processing challenges in DHM has become a key research focus. This paper provides an overview of the principles behind DHM and the characteristics of each phase processing step. It offers a thorough analysis of the progress and challenges of deep learning methods in areas such as phase retrieval, filtering, phase unwrapping, and distortion compensation. The paper concludes by exploring trends, such as ultrafast 3D holographic reconstruction, high-throughput holographic data analysis, multimodal data fusion, and precise quantitative phase analysis. Full article

Group A rotavirus (RVA) is a leading causative agent of diarrhea in both young animals and humans. In China, multiple genotypes are commonly found within the bovine population. In this study, we investigated 1917 fecal samples from calves with diarrhea between 2022 and 2025, with 695 testing positive for RVA, yielding an overall detection rate of 36.25%. The highest positivity rate was observed in Hohhot (38.98%), and annual detection rates ranged from 26.75% in 2022 to 42.22% in 2025. A bovine rotavirus (BRV) strain, designated 0205HG, was successfully isolated from a fecal sample of a newborn calf. Its presence was confirmed through cytopathic effects (CPEs), the indirect immunofluorescence assay (IFA), electron microscopy (EM), and high-throughput sequencing. Genomic characterization identified the strain as having the G6-P[1]-I2-R2-C2-M2-A3-N2-T6-E2-H3 genotype constellation. The structural proteins VP2 and VP7, along with nonstructural genes NSP1–NSP4, shared high sequence identity with Chinese bovine strains, whereas VP1, VP4, and NSP5 clustered more closely with human rotaviruses, and VP3 was related to feline strains. These findings highlight the genetic diversity and interspecies reassortment of BRVs in China, underlining the importance of continued surveillance and evolutionary analysis. Full article

Lung organoids play a crucial role in modeling drug responses in pulmonary diseases. However, their morphological analysis remains hindered by manual detection inefficiencies and the high computational cost of existing algorithms. To overcome these challenges, this study proposes Orga-Dete—a lightweight, high-precision detection model based on YOLOv11n—which first employs data augmentation to mitigate the small-scale dataset and class imbalance issues, then optimizes via a triple co-optimization strategy: a bi-directional feature pyramid network for enhanced multi-scale feature fusion, MPCA for stronger micro-organoid feature response, and EMASlideLoss to address class imbalance. Validated on a lung organoid microscopy dataset, Orga-Dete achieves 81.4% mAP@0.5 with only 2.25 M parameters and 6.3 GFLOPs, surpassing the baseline model YOLOv11n by 3.5%. Ablation experiments confirm the synergistic effects of these modules in enhancing morphological feature extraction. With its balance of precision and efficiency, Orga-Dete offers a scalable solution for high-throughput organoid analysis, underscoring its potential for personalized medicine and drug screening. Full article

This study presents a U-Net-based automatic segmentation framework for quantitative analysis of surface morphology in a PEEK-based composite following tribological testing. Controlled Pin-on-Disc tests were conducted to characterize tribological performance, worn surfaces were captured by laser scanning microscopy to acquire optical images and height maps, and the model produced pixel-level segmentation masks distinguishing different regions, enabling high-throughput, objective analysis of worn surface morphology. Sixty-three manually annotated image sets—with labels for fiber, third-body patch, and matrix regions—formed the training corpus. A 70-layer U-Net architecture with four-channel input was developed and rigorously evaluated using five-fold cross-validation. To enhance performance on the challenging patch and fiber classes, the top five model instances were ensembled through Bayesian-optimized weighted voting, achieving significant improvements in class-specific F1 metrics. Segmentation outputs on unseen data confirmed the method’s robustness and generalizability across complex surface topographies. This approach establishes a scalable, accurate tool for automated morphological analysis, with potential extensions to real-time monitoring and other composite systems. Full article