Hasil untuk "Microscopy"

Enrico Lattuada, Fabian Krautgasser, Maxime Lavaud et al.

Over nearly two decades, Differential Dynamic Microscopy (DDM) has become a standard technique for extracting dynamic correlation functions from time-lapse microscopy data, with applications spanning colloidal suspensions, polymer solutions, active fluids, and biological systems. In its most common implementation, DDM analyzes image sequences acquired with a conventional microscope equipped with a digital camera, yielding time- and wavevector-resolved information analogous to that obtained in multi-angle Dynamic Light Scattering (DLS). With a widening array of applications and a growing, heterogeneous user base, lowering the technical barrier to performing DDM has become a central objective. In this tutorial article, we provide a step-by-step guide to conducting DDM experiments -- from planning and acquisition to data analysis -- and introduce the open-source software package fastDDM, designed to efficiently process large image datasets. fastDDM employs optimized, parallel algorithms that reduce analysis times by up to four orders of magnitude on typical datasets (e.g., 10,000 frames), thereby enabling high-throughput workflows and making DDM more broadly accessible across disciplines.

Naoki Fukutake, Shuichi Makita, Yoshiaki Yasuno

We construct an image formation theory that covers the majority of optical microscopy techniques that use diverse coherent or incoherent light-matter interactions. The theories of individual microscopy methods could not previously be connected with other systems because of the absence of a common theoretical framework. Using the general principles of quantum physics and applying a four-dimensional representation, we unify the image formation theories of optical systems ranging from classical microscopy to cutting-edge instruments into a single framework in which light is replaced with quantum fields and the interactions are represented using double-sided Feynman diagrams. Our universal methodology requires a four-dimensional aperture that enables sufficient understanding of the associations between the different imaging types and interprets image formation appropriately for all systems, including optical coherence tomography.

Himanshu Buckchash, Gyanendra Kumar Verma, Dilip K. Prasad

The complexity of human biology and its intricate systems holds immense potential for advancing human health, disease treatment, and scientific discovery. However, traditional manual methods for studying biological interactions are often constrained by the sheer volume and complexity of biological data. Artificial Intelligence (AI), with its proven ability to analyze vast datasets, offers a transformative approach to addressing these challenges. This paper explores the intersection of AI and microscopy in life sciences, emphasizing their potential applications and associated challenges. We provide a detailed review of how various biological systems can benefit from AI, highlighting the types of data and labeling requirements unique to this domain. Particular attention is given to microscopy data, exploring the specific AI techniques required to process and interpret this information. By addressing challenges such as data heterogeneity and annotation scarcity, we outline potential solutions and emerging trends in the field. Written primarily from an AI perspective, this paper aims to serve as a valuable resource for researchers working at the intersection of AI, microscopy, and biology. It summarizes current advancements, key insights, and open problems, fostering an understanding that encourages interdisciplinary collaborations. By offering a comprehensive yet concise synthesis of the field, this paper aspires to catalyze innovation, promote cross-disciplinary engagement, and accelerate the adoption of AI in life science research.

Jaret J. Vasquez-Lozano, Qiang Sun, Shuo Li et al.

Quantum correlation microscopy is an emerging technique for improving optical resolution. By taking advantage of the quantum statistics from single-photon fluorophores, more information about the emitters (including number and location) is obtained compared with classical microscopy. Although it is known that the resolution can be improved by increasing detector numbers, as well as using quantum correlation, the quantitative relationship between these two approaches is not immediately clear. Here we explore widefield quantum correlation microscopy using arrays of single-photon detectors. We explicitly compare the use of $N$ detectors used in photon counting mode vs $N/2$ detectors used to measure quantum correlations. i.e., where there are $N/2$ Hanbury Brown and Twiss systems, using the same $N$ detectors, on randomly generated two-emitter systems. We find regimes where $N/2$ Hanbury Brown and Twiss detectors provide improved localisation compared to $N$ photon counting detectors, as a function of emitter position and number of photons sampled.

A. Yu. Aladyshkin, R. A. Hovhannisyan, S. Yu. Grebenchuk et al.

We provide an overview of the methodology and fundamental principles associated with newly developed experimental technique -- scanning quantum-vortex microscopy [Hovhannisyan et al., Commun. Mater., vol. 6, 42 (2025)]. This approach appears promising for experimental studies of vortex pinning phenomena in superconducting films and nanodevices. In particular, we studied the magnetic properties of magnetron-sputtered niobium (Nb) films by low-temperature magnetic force microscopy. As the temperature approaches the superconducting critical temperature, the pinning potential caused by structural defects weakens; consequently, the attractive interaction between the magnetic tip of the cantilever and a single-quantum vortex begins to dominate. In this scenario the magnetic probe is capable of trapping a vortex during the scanning process. Because the dragged vortex continues interacting with structural defects, it serves as an efficient nano-probe to explore pinning potentials and visualize grain boundaries in granular Nb films, achieving resolutions (30 nm) comparable to the superconducting coherence length.

Xiaofeng Xu, Weipeng Yu, Xingduo Fan et al.

Abstract Currently, bamboo fibers (BFs) are commonly processed through alkali boiling softening pretreatment, which generates wastewater that poses environmental pollution risks. This process is also complex and requires significant human and material resources. In contrast, the saturated steam softening pretreatment method studied in this study is environmentally friendly and significantly simplifies the post-processing of bamboo fiber preparation. Additionally, it provides methods and parameters for the hygrothermal-mechanical extraction of bamboo fibers. In this study, three-year-old bitter bamboo (Pleioblastus amarus) growing in Zhongtai town, Yuhang district, Hangzhou city, China was selected as the raw material. Firstly, bamboo fibers were prepared by crushing and mechanical extraction after softening through alkaline boiling and saturated steam pretreatment, respectively. The yield, mechanical properties, and other indicators of the fibers were then tested and compared. Subsequently, Scanning electron microscopy (SEM) was employed to observe and comparatively analyze the microstructural morphology of the two types of fibers. Infrared spectroscopy (IR) was performed on the functional groups of bamboo after alkali boiling and saturated steam softening to investigate changes in cellulose, hemicellulose, and lignin. Finally, the mechanism of mechanical extraction of bitter bamboo (Pleioblastus amarus) fibers by saturated steam pretreatment was further analyzed.

WANG Baoyi, HU Xuefang, PEI Haisheng, ZHAI Xiaona, LIANG Liang, LI Yuanyuan

In order to make efficient use of wheat bran and to improve its quality characteristics, wheat bran was physically modified by ultrafine grinding combined with gradient glutenin addition in this study. The changes in physicochemical and functional characteristics such as particle size distribution, color, hydration characteristics, filling characteristics and flow characteristics of wheat bran ultrafine powder were analyzed. The structural changes were analyzed by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The results showed that the addition of glutenin during the ultrafine grinding of wheat bran significantly increased the whiteness of wheat bran ultrafine powder, significantly reduced the particle size of wheat bran and enhanced its uniformity (P < 0.05), and improved the agglomeration behavior of wheat bran. The oil holding capacity, solubility and swelling capacity of wheat bran were significantly increased by ultrafine grinding combined with gradient glutenin addition (P < 0.05). With increasing glutenin addition, the bulk density and tap density of wheat bran ultrafine powder increased, and the angle of repose and slip angle decreased, reflecting improved filling and fluidity of wheat bran. Ultrafine grinding combined with moderate glutenin addition (2%–4%) resulted in the breakage of the cell wall matrix of wheat bran, promoted the complete deconstruction of amorphous cellulose and the partial deconstruction of crystalline cellulose in wheat bran ultrafine powder, broke the intramolecular glycosidic bonds of cellulose and hemicellulose in wheat bran ultrafine powder, led to enhanced hydrogen bonding, and improved the quality characteristics of wheat bran ultrafine powder and the tensile properties of whole wheat flour dough. Therefore, ultrafine grinding combined with moderate glutenin holds great application potential as an important technical means for wheat bran modification.

Illia Tsiporenko, Pavel Chizhov, Dmytro Fishman

Segmentation is a crucial step in microscopy image analysis. Numerous approaches have been developed over the past years, ranging from classical segmentation algorithms to advanced deep learning models. While U-Net remains one of the most popular and well-established models for biomedical segmentation tasks, recently developed transformer-based models promise to enhance the segmentation process of microscopy images. In this work, we assess the efficacy of transformers, including UNETR, the Segment Anything Model, and Swin-UPerNet, and compare them with the well-established U-Net model across various image modalities such as electron microscopy, brightfield, histopathology, and phase-contrast. Our evaluation identifies several limitations in the original Swin Transformer model, which we address through architectural modifications to optimise its performance. The results demonstrate that these modifications improve segmentation performance compared to the classical U-Net model and the unmodified Swin-UPerNet. This comparative analysis highlights the promise of transformer models for advancing biomedical image segmentation. It demonstrates that their efficiency and applicability can be improved with careful modifications, facilitating their future use in microscopy image analysis tools.

Gesa Sarnighausen, Tram Thi Ngoc Nguyen, Thorsten Hohage et al.

Traction force microscopy is a method widely used in biophysics and cell biology to determine forces that biological cells apply to their environment. In the experiment, the cells adhere to a soft elastic substrate, which is then deformed in response to cellular traction forces. The inverse problem consists in computing the traction stress applied by the cell from microscopy measurements of the substrate deformations. In this work, we consider a linear model, in which 3D forces are applied at a 2D interface, called 2.5D traction force microscopy, and a nonlinear pure 2D model, from which we directly obtain a linear pure 2D model. All models lead to a linear resp. nonlinear parameter identification problem for a boundary value problem of elasticity. We analyze the respective forward operators and conclude with some numerical experiments for simulated and experimental data.

Sofiene Ben Kaab, Juan Antonio Fernández Pierna, Berenice Foncoux et al.

The use of chemical herbicides induces negative impacts on the environment, animals, and human health. It also leads to the development of herbicide-resistant weeds. In this context, natural and efficacious herbicides are highly sought after. Essential oils are natural compounds with antibacterial, fungicidal, and phytotoxic properties. For this reason, we studied the post-emergence phytotoxic effect of cinnamon essential oil (cinnamon EO) from <i>Cinnamomum cassia</i> under greenhouse conditions, testing it against <i>Trifolium incarnatum</i> (<i>T. incarnatum</i>) and <i>Lolium perenne</i> (<i>L. perenne</i>). The content of malondialdehyde (MDA), percentage of water loss, electrolyte leakage, and the fluorescence of treated leaves by cinnamon EO were determined in order to understand the physiological and biochemical responses. In addition, transmission electron microscopy (TEM) was used to study the effect of cinnamon EO on cellular organelles in different tissues of <i>T. incarnatum</i> leaves. Results showed that cinnamon EO quickly induced oxidative stress in treated leaves by increasing MDA content, impacting membrane integrity and causing water loss. TEM observations confirmed the cell desiccation by cellular plasmolysis and showed an alteration of the membrane integrity and chloroplast damages. Moreover, Raman analysis confirms the disturbance of the plant metabolism by the disappearance of some scattering bands which correspond to primary metabolites. Through our finding, we confirm that cinnamon essential oil (EO) could be proposed in the future as a potential bioherbicide and a suitable source of natural phytotoxic compounds with a multisite action on weeds.

Binayak Rimal, James D. Chang, Chengyin Liu et al.

Rui Li, Gabriel della Maggiora, Vardan Andriasyan et al.

Light microscopy is a widespread and inexpensive imaging technique facilitating biomedical discovery and diagnostics. However, light diffraction barrier and imperfections in optics limit the level of detail of the acquired images. The details lost can be reconstructed among others by deep learning models. Yet, deep learning models are prone to introduce artefacts and hallucinations into the reconstruction. Recent state-of-the-art image synthesis models like the denoising diffusion probabilistic models (DDPMs) are no exception to this. We propose to address this by incorporating the physical problem of microscopy image formation into the model's loss function. To overcome the lack of microscopy data, we train this model with synthetic data. We simulate the effects of the microscope optics through the theoretical point spread function and varying the noise levels to obtain synthetic data. Furthermore, we incorporate the physical model of a light microscope into the reverse process of a conditioned DDPM proposing a physics-informed DDPM (PI-DDPM). We show consistent improvement and artefact reductions when compared to model-based methods, deep-learning regression methods and regular conditioned DDPMs.

Lauri Kurki, Niko Oinonen, Adam S. Foster

Scanning tunnelling microscopy (STM) with a functionalized tip apex reveals the geometric and electronic structure of a sample within the same experiment. However, the complex nature of the signal makes images difficult to interpret and has so far limited most research to planar samples with a known chemical composition. Here, we present automated structure discovery for STM (ASD-STM), a machine learning tool for predicting the atomic structure directly from an STM image, by building upon successful methods for structure discovery in non-contact atomic force microscopy (nc-AFM). We apply the method on various organic molecules and achieve good accuracy on structure predictions and chemical identification on a qualitative level, while highlighting future development requirements to ASD-STM. This method is directly applicable to experimental STM images of organic molecules, making structure discovery available for a wider SPM audience outside of nc-AFM. This work also opens doors for more advanced machine learning methods to be developed for STM discovery.

Philipp Johannes Schubert, Rangoli Saxena, Joergen Kornfeld

High-throughput 2D and 3D scanning electron microscopy, which relies on automation and dependable control algorithms, requires high image quality with minimal human intervention. Classical focus and astigmatism correction algorithms attempt to explicitly model image formation and subsequently aberration correction. Such models often require parameter adjustments by experts when deployed to new microscopes, challenging samples, or imaging conditions to prevent unstable convergence, making them hard to use in practice or unreliable. Here, we introduce DeepFocus, a purely data-driven method for aberration correction in scanning electron microscopy. DeepFocus works under very low signal-to-noise ratio conditions, reduces processing times by more than an order of magnitude compared to the state-of-the-art method, rapidly converges within a large aberration range, and is easily recalibrated to different microscopes or challenging samples.

Miguel Tavares, Álvaro Torrinha, Raquel Queirós et al.

Electrochemical portable sensing systems can offer viable support in the analysis of environmental contaminants due to the compactness of their electronic components and overall simplicity of their detection principles. In the present work, a new electrochemical portable platform (EPP) with miniaturized chip-integrated gold electrodes was developed and applied in the detection of the drug acetaminophen (APAP) as a model analyte. The produced miniaturized chip-integrated gold electrodes were first characterized via atomic force and scanning electron microscopy and integrated into the EPP, and subsequently the complete set-up was tested for electrochemical detection of APAP. The results showed adequate performance of the developed EPP when compared to a traditional electrochemical system under optimal conditions (pH 8, deposition potential 0.1 V, deposition time 240 s and scan rate of 50 mV.s⁻¹), with a sensitivity of 1.6 μA.mM⁻¹ and limit of detection of 67 µM. The EPP was validated in river and wastewater samples, achieving recoveries ranging from 93.0 to 96.6%.

Mircea Nicolaescu, Cosmin Codrean, Corina Orha et al.

The synthesis of nanoporous copper oxide (NP-CuO) materials via the dealloying and thermal oxidation of amorphous CuZrAl ribbons, representing the novelty of this research and previously achieved via a melt-spinning process, was carried out in an aqueous hydrofluoric acid (HF) solution by varying the holding time. These nanoporous copper (NPC) structures were used as a template to achieve a 3D-NP-CuO materials with different surface morphologies. To investigate the structural and morphological properties of the obtained sandwich-type materials, X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive x-ray spectroscopy (SEM/EDX), and ultraviolet–visible spectroscopy (UV-VIS) techniques were used. In summary, the dealloying and thermal oxidation of amorphous ribbons is an interesting approach to achieving a three-dimensional (3D) network of NP-CuO with different morphologies and with a low production cost. These sandwich-type structures, consisting of NPC and copper oxide nanowires (CuO/Cu₂O), combine the good electrical properties of NPC with the catalytic properties of copper oxide semiconductors, making them suitable materials for photocatalysis, photoelectrodes in solar cells, battery applications, and electrochemical sensors.

Han-Wei Jiang, Hsiang-Yi Wu, Chun-Hsiung Wang et al.

Abstract Phycobilisomes (PBS) are antenna megacomplexes that transfer energy to photosystems II and I in thylakoids. PBS likely evolved from a basic, inefficient form into the predominant hemidiscoidal shape with radiating peripheral rods. However, it has been challenging to test this hypothesis because ancestral species are generally inaccessible. Here we use spectroscopy and cryo-electron microscopy to reveal a structure of a “paddle-shaped” PBS from a thylakoid-free cyanobacterium that likely retains ancestral traits. This PBS lacks rods and specialized ApcD and ApcF subunits, indicating relict characteristics. Other features include linkers connecting two chains of five phycocyanin hexamers (CpcN) and two core subdomains (ApcH), resulting in a paddle-shaped configuration. Energy transfer calculations demonstrate that chains are less efficient than rods. These features may nevertheless have increased light absorption by elongating PBS before multilayered thylakoids with hemidiscoidal PBS evolved. Our results provide insights into the evolution and diversification of light-harvesting strategies before the origin of thylakoids.

Iva Ganeva, Koini Lim, Jerome Boulanger et al.

Summary: Lipid droplets (LDs) are intracellular organelles responsible for storing surplus energy as neutral lipids. Their size and number vary enormously. In white adipocytes, LDs can reach 100 μm in diameter, occupying >90% of the cell. Cidec, which is strictly required for the formation of large LDs, is concentrated at interfaces between adjacent LDs and facilitates directional flux of neutral lipids from the smaller to the larger LD. The mechanism of lipid transfer is unclear, in part because the architecture of interfaces between LDs remains elusive. Here we visualize interfaces between LDs by electron cryo-tomography and analyze the kinetics of lipid transfer by quantitative live fluorescence microscopy. We show that transfer occurs through closely apposed monolayers, is slowed down by increasing the distance between the monolayers, and follows exponential kinetics. Our data corroborate the notion that Cidec facilitates pressure-driven transfer of neutral lipids through two “leaky” monolayers between LDs.

Khalid S. Hashim, Abdul Kareem K. Alsaffar, Rasha Salah Alkizwini et al.

The excessive concentration of fluoride (F−) in water represents a grave problem for several countries, especially those that depend on groundwater as a main source of drinking water. Therefore, many treatment methods, such as chemical precipitation and membrane, were practised to remove F− from water. However, the traditional methods suffer from many limitations, such as the high cost and the slowness. Hence, many studies have been directed towards developing novel and effective water defluoridation methods. In this context, the current study investigates the development of an eco-friendly adsorbent by extracting Ca, Al, and Fe from industrial by-products, precipitating them on sand particles, and using this new adsorbent to remove F− from water. The removal experiments were commenced under different pH levels (3-10), contact times (0–240 minutes) and concentrations of F− (7.5–37.5 mg/L). X-ray fluorescence (XRF), X-ray diffraction Investigator (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) methods were used to characterise the green adsorbent. Adsorption isotherm and kinetic studies were also conducted to define the adsorption type. The results confirmed that the new adsorbent could remove as high as 86% of F− at pH, contact time, agitation speed and adsorbent dose of 10, 180 minutes, 200 rpm and 15 mg/L, respectively. The characterisation studies prove the occurrence of the sorption process and the suitability of the morphology of the adsorbent for F− removal. Adsorption kinetics follow better with a pseudo-first-order model that indicates the predominance of physisorption, which agrees with the FTIR results. The isotherm study indicated that Langmuir isotherm is more suitable for representing data with an R2 value of 0.992, which means the adsorption of F− occurs as monolayer adsorption on homogeneous sites on the surface of the new adsorbent. In summary, it can be concluded that the developed adsorbent in this study could be a promising alternative to the traditional F− removal methods.

Florian Ströhl, Daniel Henry Hansen, Mireia Nager Grifo et al.

Multifocus microscopy enables recording of entire volumes in a single camera exposure. In dense samples, multifocus microscopy is severely hampered by background haze. Here, we introduce a scalable multifocus method that incorporates optical sectioning and offers axial superresolution capabilities. In our method, a dithered oblique light-sheet scans the sample volume during a single exposure, while generated fluorescence is linearised onto the camera with a multifocus optical element. A synchronised rolling shutter readout realised optical sectioning. We describe the technique theoretically and verify its optical sectioning and superresolution capabilities. We demonstrate a prototype system with a multifocus beam splitter cascade and record monolayers of endothelial cells at 35 volumes per second. We furthermore image uncleared engineered human heart tissue and visualise the distribution of mitochondria at axial superresolution. Our method manages to capture sub-diffraction sized mitochondria-derived vesicles up to 30 um deep into the tissue.