Conventional Microscopy Imaging Research Articles

On The Preparation Of Training Data For 3D Tracking Of Position And Orientation Of Microswimmers Using Defocusing And Deep Learning

The use of image analysis algorithms based on deep convolutional neural networks (CNNs) is opening new possibilities in experimental techniques for fluid dynamics. In the field of microscopic 3D particle tracking based on defocusing, it has been shown on synthetic images that CNNs can be used to determine not only the depth position but also the orientation of non-spherical microscopic particles or specimens. However, the translation of such approaches to experimental setups is challenging, especially due to the difficulty of obtaining reliable labeled images to train the neural networks. In this work, we propose a method to obtain labeled images of microswimmers from conventional microscopy images, exploiting the properties of the swimming motion at low Reynolds numbers, where inertial effects are negligible. This method allows us to obtain ground truth values of orientation and depth position of a microorganism swimming at a regular pace that can be used as training data for CNNs. The proposed methodology was used to obtain labeled images from experimental videos of the micro-organism Euplotes Vannus. The labeled images were used to train a VGG16 network, providing an estimated uncertainty in the determination of the orientation angles psi, theta, phi, and the depth position Z of 2.4%, 1.4%, 2.3%, and 3.5%, respectively.

Fungal and bacterial species richness in biodeteriorated seventeenth century Venetian manuscripts

Historical paper documents are susceptible to complex degradation processes, including biodeterioration, which can progressively compromise their aesthetic and structural integrity. This study analyses seventeenth century handwritten historical letters stored at the Correr Museum Library in Venice, Italy, exhibiting pronounced signs of biodeterioration. The techniques used encompassed traditional colony isolation on agar plates and proteomics analyses, employing nanoscale liquid chromatography coupled with high-resolution mass spectrometry (nano-LC–MS). Fluorescence microscopy was used for the first time in the historical paper biodeterioration context to supplement the conventional stereoscopic, optical, and scanning electron microscopic imaging techniques. This method enables the visualisation of microorganisms beyond and beneath the paper’s surface through their natural intrinsic autofluorescence in a non-invasive and non-destructive way. The results demonstrate a diverse, complex, and abundant microbiota composed of coexisting fungal and bacterial species (Ascomycota, Mucoromycota, Basidiomycota, Proteobacteria, and Actinobacteria), along with mite carcasses, insects, parasites, and possibly protists. Furthermore, this study reveals certain species that were not previously documented in the biodeterioration of historical paper, including human pathogens, such as Histoplasma capsulatum, Brucella, Candida albicans, and species of Aspergillus (A. flavus, A. fumigatus, A. oryzae, A. terreus, A. niger) known to cause infections or produce mycotoxins, posing substantial risk to both artefacts and humans.

Sub-50 nm optical imaging in ambient air with 10× objective lens enabled by hyper-hemi-microsphere

Optical microsphere nanoscope has great potential in the inspection of integrated circuit chips for semiconductor industry and morphological characterization in biology due to its superior resolving power and label-free characteristics. However, its resolution in ambient air is restricted by the magnification and numerical aperture (NA) of microsphere. High magnification objective lens is required to be coupled with microsphere for nano-imaging beyond the diffraction limit. To overcome these challenges, in this work, high refractive index hyper-hemi-microspheres with tunable magnification up to 10× are proposed and realized by accurately tailoring their thickness with focused ion beam (FIB) milling. The effective refractive index is put forward to guide the design of hyper-hemi-microspheres. Experiments demonstrate that the imaging resolution and contrast of a hyper-hemi-microsphere with a higher magnification and larger NA excel those of a microsphere in air. Besides, the hyper-hemi-microsphere could resolve ~50 nm feature with higher image fidelity and contrast compared with liquid immersed high refractive index microspheres. With a hyper-hemi-microsphere composed microscale compound lens configuration, sub-50 nm optical imaging in ambient air is realized by only coupling with a 10× objective lens (NA = 0.3), which enhances a conventional microscope imaging power about an order of magnitude.

Metasurface-Enabled 3-in-1 Microscopy.

Edge enhancement and polarization detection are critical to image transparent or low-contrast samples. However, currently available systems are limited to performing only a single functionality. To meet the requirement of system integration, there is a pressing need for a microscope with multiple functionalities. Here, we propose and develop a microscope with three different functionalities based on spatial multiplexing and polarization splitting. A novel geometric metasurface (MS) is used to realize a spiral phase profile and two phase gradient profiles along two vertical directions, which can perform such an extremely challenging optical task. This is the first demonstration of a 3-in-1 microscope that can simultaneously obtain five images with different optical properties in an imaging plane for the same sample. Imaging experiments with different samples verify its capability to simultaneously perform edge imaging, polarimetric imaging, and conventional microscope imaging. Benefiting from the compactness and multifunctionality of the optical MS device, the integration does not increase the volume of the microscope. This approach can enable users to visualize the multiple facets of samples in real-time.

FOLDOSCOPE SCOPE IN NEUROSURGERY A FEASIBLE AND NOVEL TECHNIQUE

Background: Intra operative squash cytology in identifying CNS lesions and availability of conventional microscope and trained pathologist remains a global health problem especially in remote areas of developing countries which have limited resources .In this study, we evaluated the performance of a low- cost, smartphone attachable paper-based microscope when used for classifying images of Squash cytology of CNS lesions in remote areas and it impact on intraoperative consultation. Samples Methodology: collected during operating CNS pathology From Month Of March,2018 To September 2020 Were Included. Total 22 Cases Were Included but 15 cases were operated in our institute. Intraoperative Squash is done at operating table with the sample tissue and images through foldscope were sent to pathologist electronically and time noted to get the result. Same tissue slides images acquired through conventional microscope are compared with images acquired by foldscope. Mobile phone-based microscope acquired ima Results: ges were observed and compared with a conventional microscope and found the morphology of the tissue sections was signicantly similar to that of conventional light microscope images. The intraoperative time to get the report signicantly reduced in remote settings. With its clear limitations, improvements Conclusion: can be made to transform it into a cost-effective use device exclusively in the diagnosis of cancer in remote and emergency situations.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis.

In bio-medical mobile workstations, e.g., the prevention of epidemic viruses/bacteria, outdoor field medical treatment and bio-chemical pollution monitoring, the conventional bench-top microscopic imaging equipment is limited. The comprehensive multi-mode (bright/dark field imaging, fluorescence excitation imaging, polarized light imaging, and differential interference microscopy imaging, etc.) biomedical microscopy imaging systems are generally large in size and expensive. They also require professional operation, which means high labor-cost, money-cost and time-cost. These characteristics prevent them from being applied in bio-medical mobile workstations. The bio-medical mobile workstations need microscopy systems which are inexpensive and able to handle fast, timely and large-scale deployment. The development of lightweight, low-cost and portable microscopic imaging devices can meet these demands. Presently, for the increasing needs of point-of-care-test and tele-diagnosis, high-performance computational portable microscopes are widely developed. Bluetooth modules, WLAN modules and 3G/4G/5G modules generally feature very small sizes and low prices. And industrial imaging lens, microscopy objective lens, and CMOS/CCD photoelectric image sensors are also available in small sizes and at low prices. Here we review and discuss these typical computational, portable and low-cost microscopes by refined specifications and schematics, from the aspect of optics, electronic, algorithms principle and typical bio-medical applications.

PLA Renewable Bio Polymer Based Solid-State Gamma Radiation Detector-Dosimeter for Biomedical and Nuclear Industry Applications.

Polylactic acid (PLA) as a "green," renewable corn-soy based polymer resin was assessed as a novel solid-state detector for rapid-turnaround gamma radiation dosimetry in the 1-100 kGy range-of significant interest in biomedical and general nuclear industry applications. Co-60 was used as the source of gamma photons. It was found that PLA resin responds well in terms of rheology and porosity metrics with an absorbed gamma dose (Dg). In this work, rheological changes were ascertained via measuring the differential mass loss ratio (MLR) of irradiated PLA placed within PTFE-framed (40 mm × 20 mm × 0.77 mm) cavities bearing ~0.9 g of PLA resin and pressed for 12-16 min in a controlled force hot press under ~6.6 kN loading and platens heated to 227 °C for the low Dg range: 0-11 kGy; and to 193 °C for the extended Dg range: 11-120 kGy. MLR varied quadratically from 0.05 to ~0.2 (1σ ~ 0.007) in the 0-11 kGy experiments, and from 0.05 to ~0.5 (1σ ~0.01) in the 0-120 kGy experiments. Rheological changes from gamma irradiation were modeled and simultaneously correlated with void-pocket formations, which increase with Dg. A single PLA resin bead (~0.04 g) was compressed 5 min at 216 °C in 0-16 kGy experiments, and compressed 2 min at 232 °C in the 16-110 kGy experiments, to form sturdy ~100 µm thick wafers in the same press. Aggregate coupon porosity was then readily measurable with conventional optical microscope imaging and analyzed with standard image processing; this provided complementary data to MLR. Average porosity vs. dose varied quadratically from ~0 to ~15% in the 0-16 kGy range and from ~0 to ~18% over the 16-114 kGy range. These results provide evidence for utilizing "green"/renewable (under $0.01) PLA resin beads for rapid and accurate (+/-5-10%) gamma dosimetry over a wide 0-120 kGy range, using simple to deploy mass and void measuring techniques using common laboratory equipment.

Integration of Geological, Petrographic and Abrasion Resistance Data of the Gneisses from Santa Catarina Granulithic Complex: An Application in the Road Paving Sector

Among the factors that contribute to the adequate performance of the aggregates, the abrasion resistance of these materials stands out, a parameter strongly related to the mineral composition of the rock. Therefore, this work aims to analyze and integrate data from Los Angeles abrasion tests to the petrographic analysis of gneisses of the Santa Catarina Granulithic Complex in order to understand the petrographic characteristics that can interfere or control the resistance results, such as: composition, texture, mineralogical alterations, granulometry and microfractures. The work was developed in four stages: (1) field activities, where descriptions of the outcrops of the four quarries studied were performed, focusing on the characterization of the compositional, textural and structural aspects of the rocks, in addition to sample collection and photographic survey; (2) petrography via conventional optical microscope and BSE images and semi quantitative analysis by electron microscope; (3) analysis of Los Angeles abrasion test data samples from the studied quarries and (4) their integration with petrographic data. Two main lithologies, enderbitic and tonalitic gneiss were identified. The results of the Los Angeles abrasion tests showed that the crushed aggregates (gneiss) of Gaspar Quarry presented the lowest abrasion values, followed by the Vale Selke, Rio Branco and 1001 quarries. Regarding composition, the enderbitic gneiss presented an average loss of 17.2%, while in the tonalitic gneiss the loss was 17.7%. The composition (high content of mafic minerals), texture and the presence of localized microfractures were the main factors that contributed to the higher values found in quarry 1001. However, both lithologies present acceptable abrasion results, in view of the specifications of the regulatory services, DNIT (031/2006) and DNIT (141/2010). The present study shows a good correlation between the result of the Los Angeles abrasion and the petrographic data, showing that composition is the main determining factor of Los Angeles abrasion results.

Simulation-driven learning: a deep learning approach for image scanning microscopy via physical imaging models.

Image reconstruction based on deep learning has become an effective tool in fluorescence microscopy. Most deep learning reconstruction methods ignore the mechanism of the imaging process where a large number of datasets are required. In addition, a lot of time is spent solving the aliasing problem from multi-scaled image pairs for data pre-processing. Here we demonstrate an improved generative adversarial network for image scanning microscopy (ISM) that can be trained by simulation data and has good generalization. Based on physical imaging models, this method can generate matching image pairs from simulation images and uses them as datasets for network training, without capturing a large number of real ISM images and avoiding image alignment preprocessing. Simulation and experimental results show that this simulation data-driven method improves the imaging quality of conventional microscopic images and reduces the cost of experiments. This method provides inspiration for optimizing network generalizability of the deep learning network.

Enhanced Measurement Accuracy for Nanostructures Using Hybrid Metrology

Light-matter interplay is widely used for analyzing the topology of surfaces on small scales for use in areas such as nanotechnology, nanoelectronics, photonics, and advanced materials. Conventional optical microscope imaging methods are limited in resolution to a value comparable to the wavelength, the so-called Abbe limit, and cannot be used to measure nano-sized structures. Scatterometry and Mueller ellipsometry are spectroscopic optical methods that can measure structures smaller than the wavelength. However, the relative uncertainties of the structure dimensions measured with scatterometry increase with decreasing structure size, and the industry is therefore replacing simple intensity based scatterometry with Mueller ellipsometry for the most demanding measurements. The accuracy of Mueller ellipsometry and scatterometry are closely related to the ability of the employed regression and regularization algorithms to extract the structural dimension. In this work, we demonstrate how the measurement accuracy on three-dimensional periodic structures may be increased by measuring the same periodic structure with multiple techniques and applying a χ2-regression method that finds the best solution based on the input from all the instruments. We furthermore report on a new and improved calibration method for Mueller ellipsometry and demonstrate how the Mueller matrix may be used to find the geometrical anisotropy of the structure.

A virtual microscope for simulation of Nanostructures

Light-matter interplay is widely used for analyzing the topology of surfaces on small scales for use in areas such as nanotechnology, nanoelectronics, photonics, and advanced materials. Conventional optical microscope imaging methods are limited in resolution to a value comparable to the wavelength, the so-called Abbe limit, and cannot be used to measure nano-sized structures. Scatterometry is an optical method that can measure structures smaller than the wavelength. However, the relative uncertainties of the structure dimensions measured with scatterometry increase with decreasing structure size, and the industry is therefore looking for replacing simple intensity based scatterometry with a phase-sensitive measurement method such as coherent Mueller ellipsometry. In this work, we present a virtual microscope capable of simulating the coherent Mueller ellipsometry and scatterometry response from one-dimensional and two-dimensional periodic structures. Furthermore, we argue that coherent nonnormalized Mueller ellipsometry gives results with less uncertainties than standard normalized Mueller ellipsometry.

Microorganism image classification with circle-based Multi-Region Binarization and mutual-information-based feature selection

BackgroundAn important part of water quality control is waterborne pathogen detection. Conventional microorganism detections are done by costly and complicated biochemical approaches, which leave chemical pollutions and require professional laboratories, technicians, and much time. Recently, machine-learning-based image classification algorithms are developed for faster, cheaper, simpler, and more environmentally friendly microorganism detection. Most of them are designed for conventional microscopic images. On the other hand, the optofluidic system is a smaller and cheaper alternative to conventional microscopes, so we aim to propose a microorganism classification algorithm for optofluidic images. MethodIn our previous study, the Binarized-Greyscale-Hybrid Algorithm with Multi-Region Binarization (BiGHAM) algorithm was developed for our optofluidic image dataset. In this paper, a new circle-based region partition method for the Multi-Region Binarization and a new mutual-information-based feature selection method were proposed to improve the BiGHAM method. Additionally, an implementation example of how to adapt and apply our method on an open dataset was presented. Results and conclusionWith the circle-based region partition method and the new feature combination selected based on mutual information, the improved-BiGHAM algorithm achieved higher classification accuracies, and the algorithm is simpler than the previous BiGHAM method during training. For our optofluidic image dataset, the accuracy improvement for the 1000-image-per-class case was from 94.8 to 96.3%, and for the 30-image-per-class case was from 77.8 to 85.6% with the support vector machine as the classifier. The adapted algorithm also works well on the open dataset, showing the potential to be extended to applications on different microorganism images.

Advances in Endoscopic Photoacoustic Imaging.

Photoacoustic (PA) imaging is able to provide extremely high molecular contrast while maintaining the superior imaging depth of ultrasound (US) imaging. Conventional microscopic PA imaging has limited access to deeper tissue due to strong light scattering and attenuation. Endoscopic PA technology enables direct delivery of excitation light into the interior of a hollow organ or cavity of the body for functional and molecular PA imaging of target tissue. Various endoscopic PA probes have been developed for different applications, including the intravascular imaging of lipids in atherosclerotic plaque and endoscopic imaging of colon cancer. In this paper, the authors review representative probe configurations and corresponding preclinical applications. In addition, the potential challenges and future directions of endoscopic PA imaging are discussed.

Photon Scattering Signal Amplification in Gold-Viral Particle Ligation Towards Fast Infection Screening

The polarization states of scattered photons can be used to map or image the anisotropic features of a nanostructure. However, the scattering strength depends heavily on the refractivity contrast in the near field under measurement, which limits the imaging sensitivity for viral particles which have little refractivity contrast with their nano-ambientes. In this paper, we show the photon scattering signal strength can be magnified by introducing a more abrupt change of refractivity at the virus particle using antibody-conjugated gold nanoparticles (AuNPs), allowing the presence of such viruses to be detected. Using two different deep learning methods to minimize scattering noise, the photon states scattering signal of a AuNPs ligated virus is enhanced significantly compared to that of a bare virus particle. This is confirmed by Finite Difference Time Domain (FDTD) numerical simulations. The sensitivity of the polarization state scattering spectra from a virus-gold particle doublet is 5.4 times higher than that of a conventional microscope image.

Novel (Phenothiazinyl)Vinyl-Pyridinium Dyes and Their Potential Applications as Cellular Staining Agents.

We report here the synthesis and structural characterization of novel cationic (phenothiazinyl)vinyl-pyridinium (PVP) dyes, together with optical (absorption/emission) properties and their potential applicability as fluorescent labels. Convective heating, ultrasound irradiation and mechanochemical synthesis were considered as alternative synthetic methodologies proficient for overcoming drawbacks such as long reaction time, nonsatisfactory yields or solvent requirements in the synthesis of novel dye (E)-1-(3-chloropropyl)-4-(2-(10-methyl-10H-phenothiazin-3-yl)vinyl)pyridin-1-ium bromide 3d and its N-alkyl-2-methylpyridinium precursor 1c. The trans geometry of the newly synthesized (E)-4-(2-(7-bromo-10-ethyl-10H-phenothiazin-3-yl)vinyl)-1-methylpyridin-1-ium iodide 3b and (E)-1-methyl-4-(2-(10-methyl-10H-phenothiazin-3-yl)vinyl)pyridin-1-ium tetrafluoroborate 3a′ was confirmed by single crystal X-ray diffraction. A negative solvatochromism of the dyes in polar solvents was highlighted by UV-Vis spectroscopy and explanatory insights were supported by molecular modeling which suggested a better stabilization of the lowest unoccupied molecular orbitals (LUMO). The photostability of the dye 3b was investigated by irradiation at 365 nm in different solvents, while the steady-state and time-resolved fluorescence properties of dye 3b and 3a′ in solid state were evaluated under one-photon excitation at 485 nm. The in vitro cytotoxicity of the new PVP dyes on B16-F10 melanoma cells was evaluated by WST-1 assay, while their intracellular localization was assessed by epi-fluorescence conventional microscopy imaging as well as one- and two-photon excited confocal fluorescence lifetime imaging microscopy (FLIM). PVP dyes displayed low cytotoxicity, good internalization inside melanoma cells and intense fluorescence emission inside the B16-F10 murine melanoma cells, making them suitable staining agents for imaging applications.

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images.

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

Analysis methods for interrogating spatial organisation of single molecule localisation microscopy data.

Single-molecule localisation microscopy (SMLM) gives access to biological information below the diffraction limit, allowing nanoscale cellular structures to be probed. The data output is unlike that of conventional microscopy images, instead consisting of an array of molecular coordinates. These represent a spatial point pattern that attempts to approximate, as closely as possible, the underlying positions of the molecules of interest. Here, we review the analysis methods that can be used to extract biological insight from SMLM data, in particular for the application of quantifying nanoscale molecular clustering. We review how some of the common artefacts inherent in SMLM can corrupt the acquired data, and therefore, how the output of SMLM cluster analysis should be interpreted.

Tunable-illumination for laser Fourier ptychographic microscopy based on a background noise-reducing system

It is inherently difficult for a conventional microscopic imaging system to produce images with both wide field of view (FOV) and high-resolution because of its limited space bandwidth product. Fourier ptychographic microscopy (FPM) has been developed to overcome this limitation by using angle-varied illumination to enhance the performance of the system. In this paper, we propose a FPM imaging approach that employs a laser source with a newly developed illumination control optical system. The proposed system consists of a digital micromirror device and a zoom system with a condenser lens to provide sample illumination with angle-varied pattern directions and controllable pattern sizes. In addition, a total internal reflection prism is employed in the system to remove unexpected background noise and extend the FOV for reconstruction. The proposed system is validated against a USAF resolution target and a biological sample, and as a result reconstructs images with higher resolution and more details once the positional misalignment correction algorithm is employed. We envision that the proposed FPM system will be suitable for high-speed imaging applications.

Characterization of Komagataeibacter xylinus by a polarization modulation imaging method

Komagataeibacter xylinus (K. xylinus) has been used for a long time as one of the main cellulose producers among bacterium. In order to gain a better understanding of the physiological and biochemical mechanisms of cellulose production, an efficient and non-invasive visualization method is highly demanded to monitor the morphological changes of K. xylinus during each stage of its development. In this study, a polarization parametric indirect microscopic imaging (PIMI) technique was applied to image the morphology variations of K. xylinus. The optical field with precisely controlled polarization status was incident on the sample and far-field images of the sample were taken under different illumination polarization. In the calculated parametric images, sub-diffraction features related to the morphology details of the K. xylinus were visualized with high contrast and spatial resolution, which is typically irresolvable in conventional microscopic images. Finite-difference time-domain simulations verified the visualized scattering distributions in PIMI imaging results. We believe these pieces of evidence proved the possibility of developing a platform for characterizing the structures and morphological changes of cells in the cultivating process.