Taxonomy of hybridly polarized Stokes vortex beams.

Structured beams carrying topological defects, namely phase and Stokes singularities, have gained extensive interest in numerous areas of optics. The non-separable spin and orbital angular momentum states of hybridly polarized Stokes singular beams provide additional freedom for manipulating optical fields. However, the characterization of hybridly polarized Stokes vortex beams remains challenging owing to the degeneracy associated with the complex polarization structures of these beams. In addition, experimental noise factors such as relative phase, amplitude, and polarization difference together with beam fluctuations add to the perplexity in the identification process. Here, we present a generalized diffraction-based Stokes polarimetry approach assisted with deep learning for efficient identification of Stokes singular beams. A total of 15 classes of beams are considered based on the type of Stokes singularity and their associated mode indices. The resultant total and polarization component intensities of Stokes singular beams after diffraction through a triangular aperture are exploited by the deep neural network to recognize these beams. Our approach presents a classification accuracy of 98.67% for 15 types of Stokes singular beams that comprise several degenerate cases. The present study illustrates the potential of diffraction of the Stokes singular beam with polarization transformation, modeling of experimental noise factors, and a deep learning framework for characterizing hybridly polarized beams.

Multimodal on-chip nanoscopy and quantitative phase imaging reveals the nanoscale morphology of liver sinusoidal endothelial cells.

Visualization of three-dimensional (3D) morphological changes in the subcellular structures of a biological specimen is a major challenge in life science. Here, we present an integrated chip-based optical nanoscopy combined with quantitative phase microscopy (QPM) to obtain 3D morphology of liver sinusoidal endothelial cells (LSEC). LSEC have unique morphology with small nanopores (50-300 nm in diameter) in the plasma membrane, called fenestrations. The fenestrations are grouped in discrete clusters, which are around 100 to 200 nm thick. Thus, imaging and quantification of fenestrations and sieve plate thickness require resolution and sensitivity of sub-100 nm along both the lateral and the axial directions, respectively. In chip-based nanoscopy, the optical waveguides are used both for hosting and illuminating the sample. The fluorescence signal is captured by an upright microscope, which is converted into a Linnik-type interferometer to sequentially acquire both superresolved images and phase information of the sample. The multimodal microscope provided an estimate of the fenestration diameter of 119 ± 53 nm and average thickness of the sieve plates of 136.6 ± 42.4 nm, assuming the constant refractive index of cell membrane to be 1.38. Further, LSEC were treated with cytochalasin B to demonstrate the possibility of precise detection in the cell height. The mean phase value of the fenestrated area in normal and treated cells was found to be 161 ± 50 mrad and 109 ± 49 mrad, respectively. The proposed multimodal technique offers nanoscale visualization of both the lateral size and the thickness map, which would be of broader interest in the fields of cell biology and bioimaging.

3D full-wave multi-scattering forward solver for coherent microscopes.

A rigorous forward model solver for conventional coherent microscope is presented. The forward model is derived from Maxwell's equations and models the wave behaviour of light matter interaction. Vectorial waves and multiple-scattering effect are considered in this model. Scattered field can be calculated with given distribution of the refractive index of the biological sample. Bright field images can be obtained by combining the scattered field and reflected illumination, and experimental validation is included. Insights into the utility of the full-wave multi-scattering (FWMS) solver and comparison with the conventional Born approximation based solver are presented. The model is also generalizable to the other forms of label-free coherent microscopes, such as quantitative phase microscope and dark-field microscope.

Single-shot multispectral quantitative phase imaging of biological samples using deep learning.

Multispectral quantitative phase imaging (MS-QPI) is a high-contrast label-free technique for morphological imaging of the specimens. The aim of the present study is to extract spectral dependent quantitative information in single-shot using a highly spatially sensitive digital holographic microscope assisted by a deep neural network. There are three different wavelengths used in our method: λ=532, 633, and 808 nm. The first step is to get the interferometric data for each wavelength. The acquired datasets are used to train a generative adversarial network to generate multispectral (MS) quantitative phase maps from a single input interferogram. The network was trained and validated on two different samples: the optical waveguide and MG63 osteosarcoma cells. Validation of the present approach is performed by comparing the predicted MS phase maps with numerically reconstructed (F T+T I E) phase maps and quantifying with different image quality assessment metrices.

Scalable-resolution structured illumination microscopy.

Structured illumination microscopy suffers from the need of sophisticated instrumentation and precise calibration. This makes structured illumination microscopes costly and skill-dependent. We present a novel approach to realize super-resolution structured illumination microscopy using an alignment non-critical illumination system and a reconstruction algorithm that does not need illumination information. The optical system is designed to encode higher order frequency components of the specimen by projecting PSF-modulated binary patterns for illuminating the sample plane, which do not have clean Fourier peaks conventionally used in structured illumination microscopy. These patterns fold high frequency content of sample into the measurements in an obfuscated manner, which are de-obfuscated using multiple signal classification algorithm. This algorithm eliminates the need of clean peaks in illumination and the knowledge of illumination patterns, which makes instrumentation simple and flexible for use with a variety of microscope objective lenses. We present a variety of experimental results on beads and cell samples to demonstrate resolution enhancement by a factor of 2.6 to 3.4 times, which is better than the enhancement supported by the conventional linear structure illumination microscopy where the same objective lens is used for structured illumination as well as collection of light. We show that the same system can be used in SIM configuration with different collection objective lenses without any careful re-calibration or realignment, thereby supporting a range of resolutions with the same system.

Fluorescence fluctuation-based super-resolution microscopy using multimodal waveguided illumination.

Photonic chip-based total internal reflection fluorescence microscopy (c-TIRFM) is an emerging technology enabling a large TIRF excitation area decoupled from the detection objective. Additionally, due to the inherent multimodal nature of wide waveguides, it is a convenient platform for introducing temporal fluctuations in the illumination pattern. The fluorescence fluctuation-based nanoscopy technique multiple signal classification algorithm (MUSICAL) does not assume stochastic independence of the emitter emission and can therefore exploit fluctuations arising from other sources, as such multimodal illumination patterns. In this work, we demonstrate and verify the utilization of fluctuations in the illumination for super-resolution imaging using MUSICAL on actin in salmon keratocytes. The resolution improvement was measured to be 2.2-3.6-fold compared to the corresponding conventional images.

A Systematic Review of Sodium Disorders in HHV-6 Encephalitis.

Human herpesvirus 6 (HHV-6) encephalitis has a high mortality rate. Among those who survive, ~80% develop some type of permanent neurologic disorder. Early diagnosis and treatment may help prevent long-term sequelae. There have been several case reports as well as retrospective and prospective studies associating HHV-6 encephalitis with some form of sodium imbalance, either hyponatremia or hypernatremia; however, the exact frequency post-HCT is unknown, with reports ranging from 30% to 100%. We performed a systematic review of the literature and found 34 cases of HHV-6 encephalitis reported in conjunction with sodium imbalance that documented the timing of that imbalance relative to the onset of encephalitis. Sodium imbalance occurred before or at the onset of HHV-6 encephalitis in all but 2 cases (94%). This finding supports previous suggestions that sodium imbalance can be considered an early indicator of the potential development or presence of HHV-6 encephalitis in at-risk patient populations.

Silicon substrate significantly alters dipole-dipole resolution in coherent microscope.

Considering a coherent microscopy setup, influences of the substrate below the sample in the imaging performances are studied, with a focus on high refractive index substrate such as silicon. Analytical expression of 3D full-wave vectorial point spread function, i.e. the dyadic Green's function is derived for the optical setup together with the substrate. Numerical analysis are performed in order to understand and compare magnification, depth of field, and resolution when using silicon substrate versus the conventional glass substrate or usually modelled condition of no substrate. Novel insights are generated about the scope of resolution improvement due to near field effect of the silicon substrate. Importantly, we show that the expected resolution varies greatly with the position of the sources and the substrate interface relative to the focal plane. Both better and worse resolution as compared to glass substrate may be expected with small changes in their positions. Therefore, our studies show that deriving a single indicative number of expected resolution is neither possible nor judicious for the case of silicon substrate.

Object detection neural network improves Fourier ptychography reconstruction.

High resolution microscopy is heavily dependent on superb optical elements and superresolution microscopy even more so. Correcting unavoidable optical aberrations during post-processing is an elegant method to reduce the optical system's complexity. A prime method that promises superresolution, aberration correction, and quantitative phase imaging is Fourier ptychography. This microscopy technique combines many images of the sample, recorded at differing illumination angles akin to computed tomography and uses error minimisation between the recorded images with those generated by a forward model. The more precise knowledge of those illumination angles is available for the image formation forward model, the better the result. Therefore, illumination estimation from the raw data is an important step and supports correct phase recovery and aberration correction. Here, we derive how illumination estimation can be cast as an object detection problem that permits the use of a fast convolutional neural network (CNN) for this task. We find that faster-RCNN delivers highly robust results and outperforms classical approaches by far with an up to 3-fold reduction in estimation errors. Intriguingly, we find that conventionally beneficial smoothing and filtering of raw data is counterproductive in this type of application. We present a detailed analysis of the network's performance and provide all our developed software openly.

On-chip TIRF nanoscopy by applying Haar wavelet kernel analysis on intensity fluctuations induced by chip illumination.

Photonic-chip based TIRF illumination has been used to demonstrate several on-chip optical nanoscopy methods. The sample is illuminated by the evanescent field generated by the electromagnetic wave modes guided inside the optical waveguide. In addition to the photokinetics of the fluorophores, the waveguide modes can be further exploited for introducing controlled intensity fluctuations for exploitation by techniques such as super-resolution optical fluctuation imaging (SOFI). However, the problem of non-uniform illumination pattern generated by the modes contribute to artifacts in the reconstructed image. To alleviate this problem, we propose to perform Haar wavelet kernel (HAWK) analysis on the original image stack prior to the application of (SOFI). HAWK produces a computational image stack with higher spatio-temporal sparsity than the original stack. In the case of multimoded non-uniform illumination patterns, HAWK processing breaks the mode pattern while introducing spatio-temporal sparsity, thereby differentially affecting the non-uniformity of the illumination. Consequently, this assists nanoscopy methods such as SOFI to better support super-resolution, which is otherwise compromised due to spatial correlation of the mode patterns in the raw image. Furthermore, applying HAWK prior to SOFI alleviates the problem of artifacts due to non-uniform illumination without degrading temporal resolution. Our experimental results demonstrate resolution enhancement as well as reduction in artifacts through the combination of HAWK and SOFI.

Soft thresholding schemes for multiple signal classification algorithm.

Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. The resulting image is strongly sensitive to this parameter and the subjectivity arising from multiple practical factors makes it difficult to determine the right rule of selection. We address this issue by proposing soft thresholding schemes derived from a new generalized framework for indicator function design. We show that the new schemes significantly alleviate the subjectivity and sensitivity of hard thresholding while retaining the super-resolution ability. We also evaluate the trade-off between resolution and contrast and the out-of-focus light rejection using the various indicator functions. Through this, we create significant new insights into the use and further optimization of MUSICAL for a wide range of practical scenarios.

Study of electric fields of diffraction from spatial light modulator: discussion.

A complete study of electric field vectors and efficiencies of diffraction orders for a phase pattern addressed to a pixelated spatial light modulator (SLM) is discussed here. General mathematical expressions of electric field vectors from SLM are explored here analytically for an arbitrary pattern on SLM with a given input electric field. Using the general expression, we calculate orientations of the electric fields of diffraction for sinusoidal and binary patterns of varying duty cycles. The patterns result in diffracted beams of various orders with different efficiencies calculated analytically and matching well with the experimental results. The binary pattern with 50% duty cycle delivers significant distribution of energies where all the even orders are absent, and the diffraction efficiency of the first-order beam can be close to 40% using an appropriately chosen modulation depth. Using the general expression, it is possible to obtain fields and efficiencies of diffraction for any arbitrary pattern on SLM. The discussion might help researchers using SLM in standard optics experiments.

Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals.

Ultrasound based structural health monitoring of piezoelectric material is challenging if a damage changes at a microscale over time. Classifying geometrically similar damages with a difference in diameter as small as 100 µ m is difficult using conventional sensing and signal analysis approaches. Here, we use an unconventional ultrasound sensing approach that collects information of the entire bulk of the material and investigate the applicability of machine learning approaches for classifying such similar defects. Our results show that appropriate feature design combined with simple k-nearest neighbor classifier can provide up to 98% classification accuracy even though conventional features for time-series data and a variety of classifiers cannot achieve close to 70% accuracy. The newly proposed hybrid feature, which combines frequency domain information in the form of power spectral density and time domain information in the form of sign of slope change, is a suitable feature for achieving the best classification accuracy on this challenging problem.

Biomolecular inflammatory response to surgical energy usage in laparoscopic surgery: results of a randomized study.

OBJECTIVE: Use of surgical energy is integral to laparoscopic surgery (LS). Energized dissection (ED) has a potential to impact the biomolecular expression of inflammation due to ED-induced collateral inflammation. We did this triple-blind randomized controlled (RCT) study to assess this biomolecular footprint in an index LS, i.e., laparoscopic cholecystectomy (LC). METHODS AND PROCEDURES: This RCT was conducted in collaboration with tertiary-level institutions, from January 2014 to December 2014 with institutional review board clearance. Consecutive, unselected, consenting candidates for LC were randomized (after anesthesia induction) into group I (ED) and group II (non-ED). They were managed with compliance to universal protocols for ethics, informed consent, anesthesia, drug usage and clinical pathway with blinded observers. Biomolecular inflammatory markers, i.e., interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α) and highly sensitive CRP (HS-CRP), were measured with blood drawn juxta-preoperatively (H0), at 4 h (H4) and at 24 h (H24). The quantitative changes induced by ED on IL-6, TNF-α and HS-CRP at H0, H4 and H24 with their kinetic behavior were the study endpoint. Prospective data were analyzed statistically with a p value of <0.05 being significant. RESULTS: Two cases from the ED group had biliary injury and hence were withdrawn from analysis. The ED (n = 49) and non-ED (n = 51) groups had similar demographic, clinical and H0 biomolecular variables. There was a significant increase in IL-6, TNF-α and HS-CRP from H0 to H4 in both the groups (p values <0.001). From H4 to H24, all three cytokines showed significant increase in ED group (p < 0.05), whereas in the non-ED group, IL-6 showed significant fall (p = 0.004) and TNF-α showed no significant change (p = 0.063). Both the groups showed H4-H24 elevation of HS-CRP (p = 0.000). CONCLUSION: Energized dissection adds to the cytokine-mediated postoperative inflammation. The additional ED-induced inflammation can be measured objectively by IL-6 and TNF-α levels. CLINICAL TRIALS REGISTRY: Clinical Trials Registry, India (REF/2014/06/007153).

Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes.

We propose a method for classifying radiometric oceanic color data measured by hyperspectral satellite sensors into known spectral classes, irrespective of the downwelling irradiance of the particular day, i.e., the illumination conditions. The focus is not on retrieving the inherent optical properties but to classify the pixels according to the known spectral classes of the reflectances from the ocean. The method compensates for the unknown downwelling irradiance by white balancing the radiometric data at the ocean pixels using the radiometric data of bright pixels (typically from clouds). The white-balanced data is compared with the entries in a pre-calibrated lookup table in which each entry represents the spectral properties of one class. The proposed approach is tested on two datasets of in situ measurements and 26 different daylight illumination spectra for medium resolution imaging spectrometer (MERIS), moderate-resolution imaging spectroradiometer (MODIS), sea-viewing wide field-of-view sensor (SeaWiFS), coastal zone color scanner (CZCS), ocean and land colour instrument (OLCI), and visible infrared imaging radiometer suite (VIIRS) sensors. Results are also shown for CIMEL's SeaPRISM sun photometer sensor used on-board field trips. Accuracy of more than 92% is observed on the validation dataset and more than 86% is observed on the other dataset for all satellite sensors. The potential of applying the algorithms to non-satellite and non-multi-spectral sensors mountable on airborne systems is demonstrated by showing classification results for two consumer cameras. Classification on actual MERIS data is also shown. Additional results comparing the spectra of remote sensing reflectance with level 2 MERIS data and chlorophyll concentration estimates of the data are included.

Multi-resolution subspace-based optimization method for solving three-dimensional inverse scattering problems.

An innovative methodology is proposed to solve quantitative three-dimensional microwave imaging problems formulated within the contrast source framework. The introduced technique is based on the combination of an efficient iterative multiscaling strategy aimed at mitigating local minimum issue arising in inverse scattering problems, and a local search algorithm based on the subspace-based optimization method (SOM) devoted to effectively retrieving both the "deterministic" and the "ambiguous" parts of the unknown contrast currents. To achieve this goal, a nested iteration process is adopted in which the outer loop iteratively refines the region of interest (ROI) where the scatterers are detected, while the inner loop retrieves the dielectric properties of the scatterers within the ROIs. Selected numerical examples are also given to show the validity and robustness of the proposed algorithm in comparison with state-of-the-art techniques.

Outcomes of laparoscopic cholecystectomy done with surgical energy versus done without surgical energy: a prospective-randomized control study.

OBJECTIVE: Laparoscopic cholecystectomy (LC), a gold standard procedure can be done without energized dissection (ED). We did a randomized study for the outcomes of LC done with ED or without ED, i.e., with cold dissection (CD). METHODS AND PROCEDURES: At a tertiary level institution, open-ended prospective-randomized control study was conducted between September 2008 and June 2013. Consecutive, unselected, consenting candidates for LC were enrolled following standard ethics, informed consent, anesthesia, and clinical pathway protocol. They were allocated to control group (LC with ED) or study group (LC with CD, as per our published technique with the option for rescue ED). The study points were based upon Clavien-Dindo grading of postoperative complications. They were either, peri-operative events potentially affecting, hospital stay (Grade I) or Grade II-V, e.g., peri-operative hemodynamic instability, needing intervention/blood transfusion, injury to biliary ducts/hollow viscous, postoperative biliary leak, postoperative re-intervention, re-hospitalization, mortality, and any adverse event during a 90-day follow-up period. The data were prospectively collected in an integrated "hospital information system" that could be retrieved only by independent external coordinators. RESULTS: Demographics, co-morbidities, and gallbladder inflammation profile of the control group (n = 361) and study group (n = 384) were comparable. There was no rescue ED usage in the study group. Hospital stay (Grade I adverse outcome dependent) was longer, i.e., 1.6 ± 1.03 in the control versus 1.35 ± 1.2 days in the study group (p < 0.001). Grade II-IV complications were significantly more (p < 0.009) in control group. There was one common bile duct (CBD) injury in each group. The index bilio-enteric anastomosis for CBD injury in control group failed and needed a revision with multiple interventions. There was one grade V adverse outcome, i.e., mortality in the control group. CONCLUSION: Avoiding the use of ED in LC is associated with better outcomes.

Feature-based filter design for resolution enhancement of known features in microscopy.

We present a new feature-based design concept for filter design in which a filter is designed specifically to image a known feature with dimensions lower than the optical resolution of the system. Unlike the conventional filter design based on focal spot engineering, we use the complete response of the microscope to form a resolution factor and consider minimizing the resolution factor as the design goal. We consider three design goals (i.e., resolution factors) based on the system response and show that a feature matching approach is more suitable in practice. It is shown that a three-bar pattern and grid of square dots of critical dimension, 0.08λ, can be resolved using a simple two-layer feature-based filter designed for radially polarized illumination in aplanatic solid immersion lens microscopy. An example that requires a more complex filter is also shown. Applicability of the concept of feature-based filter design in diverse microscopy scenarios is discussed as well.

Fluorescence microscopy and correlative brightfield videos of mitochondria and vesicles in H9c2 cardiomyoblasts.

This paper presents data acquired to study the dynamics and interactions of mitochondria and subcellular vesicles in living cardiomyoblasts. The study was motivated by the importance of mitochondrial quality control and turnover in cardiovascular health. Although fluorescence microscopy is an invaluable tool, it presents several limitations. Correlative fluorescence and brightfield images (label-free) were therefore acquired with the purpose of achieving virtual labelling via machine learning. In comparison with the fluorescence images of mitochondria, the brightfield images show vesicles and subcellular components, providing additional insights about sub-cellular components. A large part of the data contains correlative fluorescence images of lysosomes and/or endosomes over a duration of up to 400 timepoints (>30 min). The data can be reused for biological inferences about mitochondrial and vesicular morphology, dynamics, and interactions. Furthermore, virtual labelling of mitochondria or subcellular vesicles can be achieved using these datasets. Finally, the data can inspire new imaging experiments for cellular investigations or computational developments. The data is available through two large, open datasets on DataverseNO.