Liquid-liquid phase separation (LLPS) plays a central role in intracellular compartmentalization and gene regulation. Although optogenetic optoDroplet systems are widely used to study LLPS in living cells, the mechanisms underlying the transition from dynamic liquid-like condensates to operationally defined gel-like assemblies remain poorly understood because intermediate assembly states are difficult to monitor in situ. Here, we developed a confocal laser microspectroscopy-based approach to characterize the assembly dynamics of the fused sarcomas (FUS)n-mCherry-CRY2 optoDroplet system in living NIH3T3 cells. Quantitative fluorescence analysis enabled the identification and discrimination of three distinct assembly states: monomeric, dimer/oligomer intermediate, and gel-like. Passive micro-rheology, fluorescence recovery after photobleaching (FRAP), and automated morphological analyses further demonstrated progressive reductions in molecular mobility and increasing structural rigidity during condensate maturation. Three-dimensional spatial analysis revealed that mature condensates exhibit a distinct core-shell organization consisting of an operationally defined gel-like core, a dimer/oligomer-rich intermediate layer, and an outer monomer-rich boundary layer with liquid-like properties. Upon cessation of blue-light stimulation, the outer layer rapidly dissolved, whereas the central gel-like core remained intact, indicating the acquisition of physical irreversibility. These findings establish fluorescence microspectroscopy as a quantitative approach for resolving molecular assembly states during condensate maturation and demonstrate that gelation proceeds from the condensate center toward the periphery. This framework provides new insights into pathological liquid-to-gel phase transitions associated with neurodegenerative diseases.
This study employed scanning electron microscopy (SEM) to conduct a morphological observation of vessel elements from 10 Gentianaceae species collected on the Qinghai-Xizang Plateau. We systematically compared vessel type, end-wall and lateral-wall characteristics, and vessel diameter to explore their adaptive relationship to the extreme plateau environment and the evolutionary trends of vessels. The results indicated that the vessel end walls of all 10 species featured simple perforation plates, while annular, spiral, and scalariform thickenings were observed on the lateral walls of all species, with scalariform vessels constituting the highest proportion in most. The vessel types were primarily annular, spiral, and scalariform; pitted vessels were found only in Gentiana farreri and G. waltonii. The vessel diameters were generally small (mostly < 20 μm). The vessel morphology exhibited significant adaptive characteristics to plateau adversities such as low temperature, drought, and intense radiation. Based on the analysis of vessel type, end wall features, and lateral wall characteristics, the vessels of G. farreri and G. waltonii exhibited the highest proportions of morphologically specialized types (pitted and scalariform vessels) according to the classical Bailey-Frost sequence, whereas those of G. aristata and G. hexaphylla were dominated by the more generalized annular and spiral types. This research provides new anatomical evidence for understanding the structural adaptation mechanisms of Gentianaceae plants in high-altitude environments and offers a theoretical basis for the conservation and utilization of these plant resources.
Bimetallic structures (BMSs) are being used more frequently in industries like automotive, aerospace, and shipbuilding because they combine different material properties in a single component. Traditional methods like welding and brazing have their limits, but additive manufacturing (AM) offers a more flexible and cost-effective way to create complex multimaterial parts. This study focuses on the microstructure and mechanical properties of BMSs made by depositing stainless steel (SS) on high-strength low-alloy (LA) steel using the Wire Arc Additive Manufacturing (WAAM) process with Surface Tension Transfer (STT). The results show that defect-free BMSs can be successfully fabricated, improving design possibilities. Microstructural analysis revealed a clear Ferrite-Austenite (FA) boundary at the SS-LA interface. Key findings include an 81.81% increase in hardness at the interface relative to the LA steel side, along with an interface tensile strength of 621 ± 7 MPa and elongation of 19.28% ± 1.76%. Electron backscatter diffraction (EBSD) showed fine, randomly oriented grains in the SS region and elongated, directionally aligned grains in the LA region, with no dominant texture, indicating near-isotropic behavior. The presence of both low-angle and high-angle grain boundaries confirmed dynamic recrystallization during deposition. Fracture analysis showed strong mechanical bonding, with failure mainly occurring on the LA side. This demonstrates that WAAM with STT is a viable method for creating reliable, high-performance multimaterial components.
As a rare and precious traditional Chinese medicine (TCM), Ophiocordyceps sinensis (O. sinensis) is plagued by various adulterants in the market due to factors such as resource scarcity, which seriously affects medication safety and clinical efficacy. Existing identification methods generally have problems including strong subjectivity, high sample destructiveness, long detection cycle, and complex operation, and it is difficult to identify different types of adulterants using the same method. Innovatively, this study adopted scanning electron microscopy-energy dispersive x-ray spectroscopy (SEM-EDS) technology to establish a rapid, accurate, and widely applicable method for identifying adulterated O. sinensis. Authentic O. sinensis samples from major producing areas such as Xizang, Qinghai, Sichuan, and Gansu, as well as different types of adulterated samples, were collected. Using SEM-EDS technology, combined with micromorphology and elemental composition, the differences between authentic and adulterated samples were systematically revealed through indicators including morphological differences, mass fraction of characteristic elements, and surface area coverage (SAC) of characteristic elements in surface scanning. This study confirmed that SEM-EDS technology could simultaneously obtain the micromorphological and elemental distribution information of samples, realizing rapid and accurate identification of multiple types of adulterated O. sinensis. The proposed method provided new technical support for the quality control and market supervision of O. sinensis, which could be extended to the adulteration inspection of other precious and delicate TCM.
Histological image classification plays a critical role in biomedical research and diagnostic processes. Advances in the field of deep learning present significant opportunities for enhancing diagnostic accuracy and developing automated decision support systems. This study aims to comparatively evaluate the out-of-distribution generalization and cross-domain classification performance of different deep neural network encoders. In this study, models were trained on an internal dataset of 4307 hematoxylin and eosin (H&E) stained images of male rat lung, cerebellum, and adipose tissues, obtained under ethical committee approval (Ege University, HADYEK 2026-06). To evaluate genuine generalization, testing was conducted on a separate, diverse external cohort of 600 mixed human-and-animal images. Nine different deep learning architectures-MobileNetV3-S, MobileNetV3-L, DenseNet121, DenseNet201, ConvNeXt-S, ConvNeXt-B, DINOv3 ViT-S, DINOv3 ViT-H+, and the pathology foundation model UNI2-h-were evaluated using frozen feature extraction combined with a linear probe. For performance evaluation, accuracy, sensitivity, specificity, F1 score, Cohen's κ, and ROC-AUC metrics were analyzed along with per-image inference times. While all models achieved near-perfect results during internal cross-validation, their performance diverged significantly on the external dataset, confirming that the observed differences reflect genuine cross-domain generalization capabilities rather than under-fitting. All competitive models demonstrated high performance in classifying adipose and cerebellum tissues, achieving an F1 score of 95% or higher for these tissues. In distinguishing the lung tissue, which has the most diverse structure and is the most difficult to classify, the UNI2-h model emerged as the most successful, achieving an F1 score of 97.4% and a recall of 95.0%. When evaluated in terms of computational efficiency, the MobileNetV3-Small model stood out as having the lowest processing time among all scenarios, demonstrating an inference time of 9.4 ms on the CPU and 12.5 ms on the GPU. In conclusion, while the pathology foundation model UNI2-h achieved the highest accuracy values across all metrics, the ConvNeXt-Small model was identified as the system providing the optimal balance between speed and accuracy. These findings indicate that although UNI2-h stands out for the most precise results in cross-domain classifications, the ConvNeXt-Small architecture emerges as an ideal alternative, particularly in practical applications where computational efficiency is critical and resources are constrained.
The low electronic conductivity and rigid structure make sample preparation for in situ transmission electron microscopy (TEM) of oxide nanofibers challenging. This work evaluates three sample preparation approaches for mounting single oxide nanofibers on MEMS chips: (i) drop casting combined with focused ion beam (FIB) cutting and metal deposition, (ii) single-probe micromanipulation, and (iii) double-probe micromanipulation inside a FIB-SEM. Advantages and limitations of each method are discussed, including issues of fiber agglomeration, positioning/orientation, and charging in the electron or ion beam. Drop casting offers simplicity but poor positional control. The probe-based methods enable precise positioning; charging however remains a major challenge which is best compensated for by using more than one microprobe. Strategies for improving electrical contact and minimizing contamination during metal deposition are also addressed. The findings provide practical guidelines for preparing oxide nanofibers for operando TEM experiments involving electrical and thermal stimuli.
With the increasing adoption of 3D cell cultures and bioprinting in biomedical research, there is a growing demand for reliable and accurate monitoring methods. While fluorescence microscopy is widely accepted for 2D cell cultures, when introducing the third dimension it often suffers from signal attenuation and out-of-focus interference, making automated image analysis challenging. In this study, a hybrid pipeline that integrates deep learning and traditional image processing techniques is presented with the aim of establishing an automated tool for cell segmentation and viability assessment in 3D from fluorescence microscopy images. A U2-Net architecture was trained on 2D cell cultures for cell segmentation, while watershed-based separation and intensity-based classification were employed for viability detection. The model demonstrated accurate segmentation with minimal overfitting. Quantitative comparisons with manual counts and ImageJ-based results confirmed its accuracy. We then demonstrated the possibility of extending this model to 3D constructs by tracking cell density and viability during time in the case of human umbilical vein endothelial cells bioprinted in gelatin methacrylate at different seeding densities, taken as case study. Automated counts closely matched manual ones, highlighting the method reliability and minimal invasiveness avoiding the need for sacrificing the construct. Hence, this approach provides a scalable, reproducible, and efficient alternative to manual counting in 3D environments, enabling high-throughput analysis of complex fluorescence microscopy data.
Cell membrane rupture plays a pivotal role in drug delivery, physical tumor therapies, and cellular mechanobiology. Despite its importance, the quantitative mechanical mechanisms governing membrane failure remain poorly understood. A375 human melanoma cells were employed as a representative model to investigate membrane rupture behavior through the integration of atomic force microscopy (AFM) puncture experiments and finite element simulations. The AFM measurements yielded a membrane rupture force of 27.09 ± 0.446 nN, an indentation depth of 3.46 ± 0.429 μm, and a rupture energy of 16.77 ± 0.902 fJ. Based on these experimentally obtained parameters, a finite element model was developed to reproduce the stress evolution and failure process associated with membrane puncture. The predicted equivalent stress, equivalent strain, and strain energy density differed from the experimentally derived values by only 4.4%, 4.9%, and 8.1%, respectively, demonstrating good agreement between simulation and experiment. Comparative analyses of linear elastic, elastoplastic, and hyperelastic constitutive formulations further revealed that the hyperelastic model provided the most accurate representation of membrane puncture behavior. This finding highlights the dominant role of large deformation nonlinear mechanics in governing membrane failure during AFM puncture. Overall, the combined experimental and computational framework established in this work offers quantitative insights into the mechanics of cell membrane rupture and provides a useful platform for future investigations of membrane damage mechanisms and biomechanical modeling of living cells.
Authentication of high-value honey, particularly from the biodiverse Anzer Valley, relies on melissopalynology to verify botanical origin. However, traditional manual analysis is time-consuming and subject to observer bias. This study developed an automated system for the simultaneous detection and classification of 53 pollen grains belonging to ecologically and apiculturally important taxa distributed in Anzer Valley using advanced deep learning architectures. A novel dataset was constructed from approximately 2095 light microscopy images of the pollen grains, enhanced to 19,860 images via geometric and photometric data augmentation to improve model robustness. Two state-of-the-art object detection models, YOLOv11 and YOLOv12, were trained and compared. The YOLOv11 architecture outperformed YOLOv12, demonstrating superior stability and precision with a mean Average Precision (mAP@0.5) of 93.2% and an F1-score of 0.896. The model showed exceptional generalization on unseen data (recall 90.6%), successfully handling the visual complexity of raw microscopic samples. Misclassifications were minimal, primarily occurring among pollen grains belonging to morphologically similar taxa of Geraniaceae and Fabaceae. These findings demonstrate that YOLOv11 provides a reliable, high-throughput framework for automated pollen analysis. By significantly reducing processing time while maintaining high accuracy, this system offers a scalable tool for the identification of pollen grains, overcoming the bottlenecks of manual microscopy. Beyond its technical contribution, this approach may support more objective, standardized, and scalable regulatory practices for honey authentication and geographical origin verification.
Although the f-ratio method effectively eliminates the influence of beam current fluctuations in scanning electron microscope/energy dispersive spectroscopy (SEM-EDS) quantitative analysis, its practical application remains strictly limited by the reliance on time-consuming Monte Carlo (MC) simulations. To overcome this fundamental computational bottleneck, a novel fast f-ratio quantification method combining the XPP analytical model with a dynamic iterative numerical algorithm is proposed in this work. By substituting the MC simulation with the XPP model, the computation time for theoretical intensities is drastically reduced from hours to milliseconds. Experimental validations performed on binary systems (CdSe, InAs, and ZnSe) and a ternary mineral (FeAsS) demonstrate that the proposed method achieves a remarkably low quantification error ranging from 0.6% to 2.8% after merely a few iterations, completely circumventing the requirement of a preset standard database. In summary, this study provides a new framework for real-time, high-precision standardless or standard-limited EDS quantitative analysis.
ABSTRACT Current platforms for drug screening typically do not account for the tumor microenvironment. Microfluidic shear flow assays provide a highly sensitive tool for studying tumor cell communication at the single cell level with the microenvironment. Adhesion thereby serves as a functional readout that reflects cellular state, including loss of viability. However, previous platforms required extensive manual handling and time‐consuming post‐assay analysis. We developed a bright‐field microscopy‐enabled, semi‐automated shear flow platform that combines hardware operation with a machine‐learning‐based analysis pipeline. The algorithm delivers consistent, high‐quality results within minutes with a precision of 98.3% and a recall of 99.1%, indicative of high tracking specificity and object discrimination.
ABSTRACT To evaluate whether nanoscale configurational changes in cytoskeletal filaments can be detected, we imaged AZDye532‐labeled actin filaments bound to various myosin fragments in vitro using a home‐built, low‐cost direct stochastic optical reconstruction microscopy (dSTORM) system. The microscope, constructed by modifying a conventional fluorescence setup with a multimode 532‐nm laser, achieved a spatial resolution of 25 nm. In the absence of ATP, actin filaments tethered to myosin subfragment‐1 (S1) immobilized on a collodion‐coated glass surface exhibited an average full width at half maximum (FWHM) of 38 nm. Replacing S1 with full‐length myosin increased the FWHM to approximately 68 nm, indicating that the super‐resolution images capture size‐dependent configurational differences arising from thermal fluctuations of the tethering molecules. Desmin intermediate filaments labeled at their single cysteine residue with Alexa Fluor 532 maleimide were also imaged at super‐resolution. Their transverse FWHM averaged 65 nm, and partially repeating axial intensity peaks with a spacing of approximately 45 nm were detected. These results suggest that periodic structural elements within intermediate filaments can be visualized under appropriate conditions. Overall, these findings demonstrate that an accessible dSTORM setup provides a practical platform for detecting nanoscale configurational changes in cytoskeletal filaments interacting with related proteins.
This study investigates the effects of rare‐earth (La and Ce) and alkali (Mg and K) promoters on the distribution profile and properties of spherical γ‐Al 2 O 3 supported Ni catalysts. The catalysts were synthesized via sequential wet impregnation, targeting 10 wt.% Ni, with individual and co‐promotion of promoters. The distribution profiles of Ni within the spherical support revealed that promoter addition caused a NiO concentration gradient along the spherical catalysts. Characterization techniques such as X‐ray diffraction (XRD), BET (Brunauer–Emmet–Teller), scanning electron microscope & energy dispersive X‐ray spectroscopy (SEM–EDX), scanning/transmission electron microscope (S/TEM), and temperature programmed reduction (TPR) demonstrated that promoter addition significantly influenced the distribution profile, particle size, and reducibility of NiO. According to the obtained results, catalysts co‐promoted with La and Mg achieved a homogeneous distribution profile and the lowest reduction temperature, indicating enhanced Ni reducibility and potentially improved catalytic performance. These findings highlight the critical role of promoters as well as co‐promotion in modifying the active metal distribution and properties of Ni/γ‐Al 2 O 3 catalysts.
Liquid-liquid phase separation (LLPS) has been shown to compartmentalize transcriptional condensates , thereby regulating gene expression. The optoDroplet method is frequently employed to elucidate the regulation of this process; however, the intricacies of the method remain poorly understood. The optoDroplet system, an optogenetics-based platform that uses light to activate phase transitions mediated by intrinsic disordered regions (IDRs) in living cells (NIH3T3 cells), was used to create condensed phases of fused sarcomas (FUS) driven by IDRs. The process of droplet formation and gelation of live NIH3T3 cells upon light irradiation was investigated through confocal laser microscopic spectroscopy. After exposure to light, the IDRs of FUS underwent a process of droplet formation at low light intensities, which was subsequently followed by gelation. The spectroscopic separation method enabled discrimination between monomers, dimers, and trimers, as well as gelation of the photolyase homology region (pHR)-FUS-mCherry-Cry2WT. Through the implementation of the optoDroplet method, we were able to discern disparities in the molecular morphology of the fluorescent probes. This approach may provide a novel method for elucidating the intricacies of intracellular aggregate formation.
In situ scanning electron microscopy is crucial for investigating materials' properties. High-temperature in situ experiments are important for revealing the phase transitions and thermal deformations of materials. However, thermal electrons and noise in the high-temperature environment can cause image distortion. Reducing the influence of thermal electrons and noise is a key means to improve the quality of high-temperature imaging. Rapid scanning emerges as a key strategy to address this challenge, as it minimizes the duration of thermal electron interference on images. This study presents an externally controlled in situ image acquisition system that mitigates thermal electron accumulation through multi-rate scanning, incorporates a dual-stage signal regulator to compensate for signal attenuation during high-speed scanning, and integrates an image fusion algorithm to effectively balance noise suppression and detail preservation. Together, these components constitute a coordinated hardware-algorithm solution for high-temperature imaging. The results show that the acquisition system can obtain high-quality images at 1050°C, with a total image acquisition time of only 1.5 s, which is significantly faster than the 3.5 s of traditional systems. Compared to the conventional acquisition speed of 3.2 μs/pixel, the synthesized images exhibit an increase in SSIM and a decrease in NIQE within the temperature range of 900°C~1050°C. This method provides reliable technical support for the dynamic microstructure characterization of high-temperature materials and promotes the application of in situ SEM in extreme environments.
Excessive proliferation of white blood cells (WBCs) in the bone marrow leads to a type of blood cancer known as leukemia. This blood cancer impairs the immune response, and timely detection and diagnosis are crucial for human health. Several manual and automated methods for leukemia diagnosis have emerged recently, with the latter still requiring medical practitioners' attention for leukemia treatment. Microscopy is an essential technique in the diagnosis of leukemia, as it allows examination of blood cells in detail and accurate identification of cancerous cells. But artificial intelligence (AI), especially deep learning, has recently been explored to enhance leukemia detection and classification. This study presents an approach to detect leukemia from microscopic images using a convolutional neural network (CNN). The approach starts with image pre-processing, then enhances the training dataset through data augmentation strategies, increasing the number of image samples from 270 to 1268. The U-Net model is used to segment leukemia and normal cells, allowing for efficient feature extraction from high resolution microscopic images. Then, the ASH dataset is classified using a CNN-based architecture, differentiating between the different subtypes of leukemia. Through 10-fold cross-validation, the proposed model achieves an accuracy rate of 99.06% for binary classification and 98.68% for multi-class classification, with a recall of 96.74%, a precision of 96.83%, and an F1-score of 96.77% ± 1.09%. These results suggest that the proposed model performs similarly to other methods. The framework of microscopic imaging and deep learning in our model shows promise as computer-aided diagnosis of leukemia. But more work is needed to train it on a larger and more diverse dataset. This process can be extended to detect other blood disorders through the inclusion of other deep learning models or potentially investigate robust data augmentation techniques in the future.
Electron beam irradiation is well known to induce damage to materials during transmission electron microscope (TEM) characterization, especially for insulating materials. Phase transformation is an important damage phenomenon caused by beam irradiation. Here, we employed in situ TEM combined with electron-energy loss spectroscopy (EELS) to dynamically track the phase transformation of synthetic aragonite. The evolution process and underlying mechanism of the phase transformation in aragonite are systematically discussed. This work reveals that the near-surface region of aragonite undergoes a phase transformation induced by electron beam irradiation, prior to the bulk of aragonite. It provides more possibilities for searching the multiple near-surface properties of materials.
Primulaceae are distributed all over the world, encompassing around 2590 species. The genus Lysimachia is also part of this family. Lysimachia nummularia L., popularly known as moneywort or creeping Jenny, has been used in Europe to treat many diseases. However, there are no reports of a complete morpho-anatomical description for this species. The aim of this study was to characterize the morpho-anatomical features of the plant, as well as to establish High-Performance Thin-Layer Chromatography (HPTLC)-fingerprints of extracts prepared from the plant by decoction and infusion as well as the organic fractions obtained from these extracts. Standard techniques of light and scanning electron microscopy, as well as histochemical tests, were used; a standard method of HPTLC was applied. The species shows simple, opposite, membranaceous leaves, with rounded roots containing a central medulla and a ribbed stem with endoderm. Amphistomatic leaves bear glandular trichomes on the abaxial surface, also found on the petiole, midrib, petal, and filament. Histochemical analyses confirmed the presence of lipophilic compounds, proteins, starch, phenolics, tannins, and lignin. HPTLC analysis showed some differences in the fingerprints of the organic fractions examined, yet it highlighted the need for further studies regarding the chemical composition of the plant.
This study provides a detailed characterization of the macroscopic anatomy and microscopic features (light and scanning electron microscopy) of the lingual papillae in the adult Anatolian wild boar (Sus scrofa libycus). Tissue samples were taken from two adult wild boars found dead in their natural habitat within an average of 1.5 h. Following macroscopic observation, samples underwent routine histological processing, histochemical staining, and high-resolution surface morphology analysis via scanning electron microscopy (SEM). Morphological analysis revealed five distinct types of papillae: filiform, fungiform, conical, foliate, and vallate. Filiform papillae, exhibiting either sharp or blunt apices, showed structural adaptations specialized for mechanical function. Fungiform papillae were identified in circular and oval forms; notably, taste buds were restricted to the circular type. Foliate papillae, comprising 3-5 distinct folds, lacked taste buds. A pair of vallate papillae, each demarcated by a prominent trench and housing lateral taste buds, was observed on the caudal tongue. Histochemical evaluation using Periodic Acid-Schiff (PAS) and Alcian Blue (AB) confirmed the presence of both neutral and acidic mucins within the lingual glands. These findings suggest that the lingual morphology of the Anatolian wild boar is closely adapted to its specific dietary habits and ecological niche. This data serves as a critical baseline for comparative anatomy, forensic veterinary medicine, and future morphological research.
The pink bollworm, Pectinophora gossypiella Saunders is a major pest of cotton, notorious for its high reproductive potential and rapid evolution of resistance to Bacillus thuringiensis (Bt) toxins. Despite its economic significance, detailed knowledge of its reproductive anatomy and egg ultrastructure has remained limited, constraining the development of advanced molecular control strategies such as CRISPR/Cas9-based genome editing. The present study provides the first comprehensive characterization of the reproductive system and egg surface morphology of P. gossypiella using stereomicroscopy and scanning electron microscopy (SEM) techniques. The male reproductive system consists of fused, bean-shaped testes, seminal vesicles, duplex and simplex ejaculatory ducts, and paired accessory glands. The female reproductive system comprises paired ovaries with four polytrophic ovarioles per ovary, lateral and common oviducts, accessory glands, corpus bursae, and spermathecal glands. Eggs are oval, dorsoventrally flattened, exhibit a reticulated chorion with distinct micropylar and aeropylar regions. SEM images revealed 6-9 rosette cells encircling a circular micropylar plate, 14-19 first order and 17-23 s order ribs, and 250-291 polygonal surface cells. The structural features of P. gossypiella eggs reveal key sites for sperm entry, aeropylar respiration, and candidate zones for microinjection in gene editing applications. These findings establish a morphological baseline critical for optimizing embryo manipulation and ribonucleoprotein (RNP) delivery in lepidopteran genome editing. This study represents a pioneering effort to integrate classical egg morphology with molecular entomology, thereby advancing precision genetic interventions aimed at resistance management and population suppression in P. gossypiella.
