Characterization of Optical, Thermal, and Viscoelastic Properties of Pollenkitt in Angiosperm Pollen Using In-Line Digital Holographic Microscopy.

Pollen grains are remarkable material composites, with various organelles in their fragile interior protected by a strong shell made of sporopollenin. The outermost layer of angiosperm pollen grains contains a lipid-rich substance called pollenkitt, which is a natural bioadhesive that helps preserve structural integrity when the pollen grain is exposed to external environmental stresses. In addition, its viscous nature enables it to adhere to various floral and insect surfaces, facilitating the pollination process. To examine the physicochemical properties of aqueous pollenkitt droplets, we used in-line digital holographic microscopy to capture light scattering from individual pollenkitt particles. Comparison of pollenkitt holograms to those modeled using the Lorenz-Mie theory enables investigations into the minute variations in the refractive index and size resulting from changes in local temperature and pollen aging.

Effectiveness of Miniscrew-Supported Molar Intrusion: A Clinical Study.

Mini-screws, also known as temporary anchorage devices (TADs), offer enhanced control and versatility in orthodontic treatment by providing stable anchorage points. This clinical study aims to evaluate the effectiveness of mini-screw-supported molar intrusion in orthodontic practice. For this clinical study, a cohort of 40 orthodontic patients with various malocclusions requiring molar intrusion as part of their treatment plan was recruited. The age range of the participants spanned from 14 to 35 years, representing a diverse patient population. The intervention involved the implementation of mini-screw-supported molar intrusion on one side of the maxillary arch in each patient. To achieve this, temporary mini-screws were strategically placed, and a combination of orthodontic forces and mini-screw anchorage was employed to intrude the molars. The primary outcome measure for this study was the amount of molar intrusion achieved, which was quantified in millimeters from the initial evaluation to the final visit. Additionally, the duration of treatment required to achieve the desired molar intrusion was recorded in months. The results of this clinical study demonstrated that mini-screw-supported molar intrusion was an effective and safe orthodontic technique. On average, a significant mean molar intrusion amount of 4.8 mm (standard deviation [SD] ± 0.6) was achieved with the mini-screw-supported approach. Furthermore, the treatment duration required to attain the desired molar intrusion was relatively short, with a mean of 6.2 months (SD ± 1.1). In conclusion, this clinical study provides evidence that mini-screw-supported molar intrusion is an effective and safe approach in orthodontic practice. It offers orthodontists the advantage of enhanced control and predictability in molar intrusion procedures.

A spatio-temporal statistical model to analyze COVID-19 spread in the USA.

Coronavirus pandemic has affected the whole world extensively and it is of immense importance to understand how the disease is spreading. In this work, we provide evidence of spatial dependence in the pandemic data and accordingly develop a new statistical technique that captures the spatio-temporal dependence pattern of the COVID-19 spread appropriately. The proposed model uses a separable Gaussian spatio-temporal process, in conjunction with an additive mean structure and a random error process. The model is implemented through a Bayesian framework, thereby providing a computational advantage over the classical way. We use state-level data from the United States of America in this study. We show that a quadratic trend pattern is most appropriate in this context. Interestingly, the population is found not to affect the numbers significantly, whereas the number of deaths in the previous week positively affects the spread of the disease. Residual diagnostics establish that the model is adequate enough to understand the spatio-temporal dependence pattern in the data. It is also shown to have superior predictive power than other spatial and temporal models. In fact, we show that the proposed approach can predict well for both short term (1 week) and long term (up to three months).

Using Holographic Microscopy To Measure the Effect of Confinement on Crowding Agents in Lipid Vesicles.

The hydrodynamic effects of macromolecular crowding inside cells are often studied in vitro by using polymers as crowding agents. Confinement of polymers inside cell-sized droplets has been shown to affect the diffusion of small molecules. Here we develop a method, based on digital holographic microscopy, to measure the diffusion of polystyrene microspheres that are confined within lipid vesicles containing a high concentration of solute. We apply the method to three solutes of varying complexity: sucrose, dextran, and PEG, prepared at ∼7 % (w/w). We find that diffusion inside and outside the vesicles is the same when the solute is sucrose or dextran that is prepared below the critical overlap concentration. For poly(ethylene glycol), which is present at a concentration higher than the critical overlap concentration, the diffusion of microspheres inside vesicles is slower, hinting at the potential effects of confinement on crowding agents.

cGAN-assisted imaging through stationary scattering media.

Analyzing images taken through scattering media is challenging, owing to speckle decorrelations from perturbations in the media. For in-line imaging modalities, which are appealing because they are compact, require no moving parts, and are robust, negating the effects of such scattering becomes particularly challenging. Here we explore the use of conditional generative adversarial networks (cGANs) to mitigate the effects of the additional scatterers in in-line geometries, including digital holographic microscopy. Using light scattering simulations and experiments on objects of interest with and without additional scatterers, we find that cGANs can be quickly trained with minuscule datasets and can also efficiently learn the one-to-one statistical mapping between the cross-domain input-output image pairs. Importantly, the output images are faithful enough to enable quantitative feature extraction. We also show that with rapid training using only 20 image pairs, it is possible to negate this undesired scattering to accurately localize diffraction-limited impulses with high spatial accuracy, therefore transforming a shift variant system to a linear shift invariant (LSI) system.

Accurate and practical feature extraction from noisy holograms.

Quantitative phase imaging using holographic microscopy is a powerful and non-invasive imaging method, ideal for studying cells and quantifying their features such as size, thickness, and dry mass. However, biological materials scatter little light, and the resulting low signal-to-noise ratio in holograms complicates any downstream feature extraction and hence applications. More specifically, unwrapping phase maps from noisy holograms often fails or requires extensive computational resources. We present a strategy for overcoming the noise limitation: rather than a traditional phase-unwrapping method, we extract the continuous phase values from holograms by using a phase-generation technique based on conditional generative adversarial networks employing a Pix2Pix architecture. We demonstrate that a network trained on random surfaces can accurately generate phase maps for test objects such as dumbbells, spheres, and biconcave discoids. Furthermore, we show that even a rapidly trained network can generate faithful phase maps when trained on related objects. We are able to accurately extract both morphological and quantitative features from the noisy phase maps of human leukemia (HL-60) cells, where traditional phase unwrapping algorithms fail. We conclude that deep learning can decouple noise from signal, expanding potential applications to real-world systems that may be noisy.

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy.

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

Automatic multicell identification using a compact lensless single and double random phase encoding system.

We investigate the use of compact, lensless, single random phase encoding (SRPE) and double random phase encoding (DRPE) systems for automatic cell identification when multiple cells, either of the same or mixed classes, are in the field of view. A microscope glass slide containing the sample is inputted into the single or double random phase encoding system, which is then illuminated by a coherent or partially coherent light source generating a unique opto-biological signature (OBS) that is captured by an image sensor. Statistical features such as mean, standard deviation, skewness, kurtosis, entropy, and Pearson's correlation coefficient are extracted from the OBSs and used for cell identification with the random forest classifier. With the exception of the correlation coefficient, all features were extracted in both the spatial and frequency domains. Experiments are performed with single random phase encoding and double random phase encoding, and system analysis is presented to show the robustness and classification accuracy of the random phase encoding cell identification systems. The proposed systems are compact, as they are lensless and do not have spatial frequency bandwidth limitations due to the numerical aperture of a microscope objective lens. We demonstrate that cell identification is possible using both the SRPE and DRPE systems. While DRPE systems have been extensively used for image encryption, to the best of our knowledge, this is the first report on using DRPE for automated cell identification.

Automatic cell identification and visualization using digital holographic microscopy with head mounted augmented reality devices.

We propose a compact imaging system that integrates an augmented reality head mounted device with digital holographic microscopy for automated cell identification and visualization. A shearing interferometer is used to produce holograms of biological cells, which are recorded using customized smart glasses containing an external camera. After image acquisition, segmentation is performed to isolate regions of interest containing biological cells in the field-of-view, followed by digital reconstruction of the cells, which is used to generate a three-dimensional (3D) pseudocolor optical path length profile. Morphological features are extracted from the cell's optical path length map, including mean optical path length, coefficient of variation, optical volume, projected area, projected area to optical volume ratio, cell skewness, and cell kurtosis. Classification is performed using the random forest classifier, support vector machines, and K-nearest neighbor, and the results are compared. Finally, the augmented reality device displays the cell's pseudocolor 3D rendering of its optical path length profile, extracted features, and the identified cell's type or class. The proposed system could allow a healthcare worker to quickly visualize cells using augmented reality smart glasses and extract the relevant information for rapid diagnosis. To the best of our knowledge, this is the first report on the integration of digital holographic microscopy with augmented reality devices for automated cell identification and visualization.

Compact and field-portable 3D printed shearing digital holographic microscope for automated cell identification.

We propose a low-cost, compact, and field-portable 3D printed holographic microscope for automated cell identification based on a common path shearing interferometer setup. Once a hologram is captured from the portable setup, a 3D reconstructed height profile of the cell is created. We extract several morphological cell features from the reconstructed 3D height profiles, including mean physical cell thickness, coefficient of variation, optical volume (OV) of the cell, projected area of the cell (PA), ratio of PA to OV, cell thickness kurtosis, cell thickness skewness, and the dry mass of the cell for identification using the random forest (RF) classifier. The 3D printed prototype can serve as a low-cost alternative for the developing world, where access to laboratory facilities for disease diagnosis are limited. Additionally, a cell phone sensor is used to capture the digital holograms. This enables the user to send the acquired holograms over the internet to a computational device located remotely for cellular identification and classification (analysis). The 3D printed system presented in this paper can be used as a low-cost, stable, and field-portable digital holographic microscope as well as an automated cell identification system. To the best of our knowledge, this is the first research paper presenting automatic cell identification using a low-cost 3D printed digital holographic microscopy setup based on common path shearing interferometry.

Cell identification using single beam lensless imaging with pseudo-random phase encoding.

In this Letter, we propose a novel compact optical system for automated cell identification. Our system employs pseudo-random encoding of the light modulated by the cells under inspection to capture the unique opto-biological signature of the micro-organisms by an image sensor and without using a microscope objective lens to magnify the object beam. The proposed instrument can be fabricated using a compact light source, a thin diffuser, and an image sensor connected to computational hardware; thus, it can be compact and cost effective. Experiments are presented using the proposed system to identify and classify various micro-objects and demonstrate proof of concept. The captured opto-biological signature pattern can be attributed to the micro-object's morphology, size, sub-cellular complex structure, index of refraction, internal material composition, etc. Using the captured signature of the micro-object, we extract statistical features such as mean, variance, skewness, kurtosis, entropy, and correlation coefficients for cell identification using the random forest classifier. For comparison, similar identification experiments were repeated with a digital shearing interferometer. To the best of our knowledge, this is the first report on automated cell identification using the proposed approach.