Learning Diatoms Classification from a Dry Test Slide by Holographic Microscopy

Imaging and Quantitative Analysis on the Etching of Diatom Frustules via Digital Holographic Microscopy

Frustules, whose length spans from a few micrometers to more than a hundred micrometers, have been the subject of various modifications to improve their physical properties because of their complex porous silica structure. However, three-dimensional measurements of these changes can be challenging because of the complex 3D architecture and limitations of known methods. In this study, we present a new method that applies digital holographic microscopy (DHM) to analyze controlled etched frustules and observe real-time degradation of frustules at the single-cell level. Frustules obtained from Craspedostauros sp. diatoms were etched in 1 N NaOH for 5 min at 25 and 60 °C, respectively, and the frustule's valve was analyzed using DHM. DHM uses a combination of holography and tomography to reconstruct a 3D refractive index image of the frustule. Measurements of the width, volume, and surface area are achieved. Results showed that at 60 °C of etching, a significant difference with the unetched frustule was observed for all measurements but with high fluctuation values. Finally, real-time observation of the degradation of the frustule is observed when immersed in a high concentration of NaOH. This is the first time the real-time etching of the frustule is observed at the single-cell level. This research provides an easy estimation of the 3D measurements of frustules that may provide new fundamental information and applications.

A comparative assessment of time-consuming and laborious diatom analysis：Brief experimentation with suggestion of automatic identification

Diatom testing is considered a useful method for providing supportive evidence for the diagnosis of drowning in forensic pathology. However, various factors remain controversial for recognizing diatoms, such as being time-consuming and laborious and influencing the consistency of the results. Given the absence of precise and well-defined studies on this subject, this study aimed to determine the relationship between the ability to identify diatoms and researchers with different technical backgrounds. A total of 55 samples from 18 cases, including water, lungs, liver, and kidneys, were treated using the microwave digestion-vacuum filtration-automated scanning electron microscopy (MD-VF-Auto SEM), which was used to compare diatom analyses among three groups of well-trained forensic pathologists (FPs), trained junior employees (JEs), and new trainees (TEs). In addition to achieving similar accuracy of positive findings from drowning cases, counting efficiency was evaluated based on taxonomy records and counting time after viewing more than 5500 diatom images. In contrast to the higher counting efficiency of the JE group than that of the TE group, we observed a statistically significant difference (p < 0.05) in the diatom classification between these two groups. Based on our experiments, an efficient analysis for automatically identifying and classifying diatoms is urgently required.

Complete holography-based system for the identification of microparticles in water samples

Here, we present a comprehensive holography-based system designed for detecting microparticles through microscopic holographic projections of water samples. This system is designed for researchers who may be unfamiliar with holographic technology but are engaged in microparticle research, particularly in the field of water analysis. Additionally, our innovative system can be deployed for environmental monitoring as a component of an autonomous sailboat robot. Our system's primary application is for large-scale classification of diverse microplastics that are prevalent in water bodies worldwide. This paper provides a step-by-step guide for constructing our system and outlines its entire processing pipeline, including hologram acquisition for image reconstruction.

On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Digital holographic approaches to the detection and characterization of microplastics in water environments

Microplastic (MP) pollution is seriously threatening the environmental health of the world, which has accelerated the development of new identification and characterization methods. Digital holography (DH) is one of the emerging tools to detect MPs in a high-throughput flow. Here, we review advances in MP screening by DH. We examine the problem from both the hardware and software viewpoints. Automatic analysis based on smart DH processing is reported by highlighting the role played by artificial intelligence for classification and regression tasks. In this framework, the continuous development and availability in recent years of field-portable holographic flow cytometers for water monitoring also is discussed.

An Efficient Method of Observing Diatom Frustules via Digital Holographic Microscopy

Herein, we propose a convenient method to enable pretreatment of target objects using digital holographic microscopy (DHM). As a test sample, we used diatom frustules (Nitzschia sp.) as the target objects. In the generally used sample preparation method, the frustule suspension is added dropwise onto a glass substrate or into a glass chamber. While our work confirms good observation of purified frustules using the typical sample preparation method, we also demonstrate a new procedure to observe unseparated structures of frustules prepared by baking them on a mica surface. The baked frustules on the mica surface were transferred to a glass chamber with 1% sodium dodecyl sulfate solution. In this manner, the unseparated structures of the diatom frustules were clearly observed. Furthermore, metal-coated frustules prepared by sputtering onto them on a mica surface were also clearly observed using the same procedure. Our method can be applied for the observation of any target object that is pretreated on a solid surface. We expect our proposed method to be a basis for establishing DHM techniques for microscopic observations of biomaterials.

Real-time 3D imaging of ocean algae with crosstalk suppressed single-shot digital holographic microscopy

Digital holographic microscopy (DHM) has the potential to reconstruct the 3D shape of volumetric samples from a single-shot hologram in a label-free and noninvasive manner. However, the holographic reconstruction is significantly compromised by the out-of-focus image resulting from the crosstalk between refocused planes, leading to the low fidelity of the results. In this paper, we propose a crosstalk suppression algorithm-assisted 3D imaging method combined with a home built DHM system to achieve accurate 3D imaging of ocean algae using only a single hologram. As a key step in the algorithm, a hybrid edge detection strategy using gradient-based and deep learning-based methods is proposed to offer accurate boundary information for the downstream processing. With this information, the crosstalk of each refocused plane can be estimated with adjacent refocused planes. Empowered by this method, we demonstrated successful 3D imaging of six kinds of ocean algae that agree well with the ground truth; we further demonstrated that this method could achieve real-time 3D imaging of the quick swimming ocean algae in the water environment. To our knowledge, this is the first time single-shot DHM is reported in 3D imaging of ocean algae, paving the way for on-site monitoring of the ocean algae.

Novel Approach to Freshwater Diatom Profiling and Identification Using Raman Spectroscopy and Chemometric Analysis

(1) An approach with great potential for fast and cost-effective profiling and identification of diatoms in lake ecosystems is presented herein. This approach takes advantage of Raman spectroscopy. (2) The study was based on the analysis of 790 Raman spectra from 29 species, belonging to 15 genera, 12 families, 9 orders and 4 subclasses, which were analysed using chemometric methods. The Raman data were first analysed by a partial least squares regression discriminant analysis (PLS-DA) to characterise the diatom species. Furthermore, a method was developed to streamline the integrated interpretation of PLS-DA when a high number of significant components is extracted. Subsequently, an artificial neural network (ANN) was used for taxa identification from Raman data. (3) The PLS interpretation produced a Raman profile for each species reflecting its biochemical composition. The ANN models were useful to identify various taxa with high accuracy. (4) Compared to studies in the literature, involving huge datasets one to four orders of magnitude larger than ours, high sensitivity was found for the identification of Achnanthidium exiguum (67%), Fragilaria pararumpens (67%), Amphora pediculus (71%), Achnanthidium minutissimum (80%) and Melosira varians (82%).

Kinematic analysis and visualization of Tetraselmis microalgae 3D motility by digital holography

A study on locomotion in a 3D environment of Tetraselmis microalgae by digital holographic microscopy is reported. In particular, a fast and semiautomatic criterion is revealed for tracking and analyzing the swimming path of a microalga (i.e., Tetraselmis species) in a 3D volume. Digital holography (DH) in a microscope off-axis configuration is exploited as a useful method to enable fast autofocusing and recognition of objects in the field of view, thus coupling DH with appropriate numerical algorithms. Through the proposed method we measure, simultaneously, the tri-dimensional paths followed by the flagellate microorganism and the full set of the kinematic parameters that describe the swimming behavior of the analyzed microorganisms by means of a polynomial fitting and segmentation. Furthermore, the method is capable to furnish the accurate morphology of the microorganisms at any instant of time along its 3D trajectory. This work launches a promising trend having as the main objective the combined use of DH and motility microorganism analysis as a label-free and non-invasive environmental monitoring tool, employable also for in situ measurements. Finally, we show that the locomotion can be visualized intriguingly by different modalities to furnish marine biologists with a clear 3D representation of all the parameters of the kinematic set in order to better understand the behavior of the microorganism under investigation.