Spin-Direction-Spin Coupling of Quasiguided Modes in Plasmonic Crystals.

We report an unusual spin-direction-spin coupling phenomenon of light using the leaky quasiguided modes of a waveguided plasmonic crystal. This is demonstrated as simultaneous input spin-dependent directional guiding of waves (spin-direction coupling) and wave-vector-dependent spin acquisition (direction-spin coupling) of the scattered light. These effects, manifested as the forward and the inverse spin Hall effect of light in the far field, and other accompanying spin-orbit interaction effects are observed and analyzed using a momentum (k) domain polarization Mueller matrix. Resonance-enabled enhancement of these effects is also demonstrated by utilizing the spectral Fano resonance of the hybridized modes. The fundamental origin and the unconventional manifestation of the spin-direction-spin coupling phenomenon from a relatively simple system, ability to probe and interpret the resulting spin-orbit phenomena in the far field through momentum-domain polarization analysis, and their regulated control in plasmonic-photonic crystals open up exciting avenues in spin-orbit-photonic research.

"Dial-In" Emission from a Unique Flexible Material with Polarization Tuneable Spectral Intensity.

The development of organic photoluminescent materials, which show promising roles as catalysts, sensors, organic light-emitting diodes, logic gates, etc., is a major demand and challenge for the global scientific community. In this context, a photoclick polymerization method is adopted for the growth of a unique photoluminescent three-dimensional (3D) polymer film, E, as a model system that shows emission tunability over the range 350-650 nm against the excitation range 295-425 nm. The DFT analysis of energy calculations and π-stacking supports the spectroscopic observations for the material exhibiting a broad range of emission owing to newly formed chromophoric units within the film. Full polarization spectroscopic Mueller matrix studies were employed to extract and quantify the molecular orientational order of both the ground (excitation) and excited (emission) state anisotropies through a set of newly defined parameters, namely the fluorescence diattenuation and fluorescence polarizance. The information contained in the recorded fluorescence Mueller matrix of the organic polymer material provided a useful way to control the spectral intensity of emission by using pre- and post-selection of polarization states. The observation was based on the assumption that the longer lifetime of the excited dipolar orientation is attributed to the compactness of the film.

Combined Diffusion Tensor and Magnetic Resonance Spectroscopic Imaging Methodology for Automated Regional Brain Analysis: Application in a Normal Pediatric Population.

During human brain development, anatomic regions mature at different rates. Quantitative anatomy-specific analysis of longitudinal diffusion tensor imaging (DTI) and magnetic resonance spectroscopic imaging (MRSI) data may improve our ability to quantify and categorize these maturational changes. Computational tools designed to quickly fuse and analyze imaging information from multiple, technically different datasets would facilitate research on changes during normal brain maturation and for comparison to disease states. In the current study, we developed a complete battery of computational tools to execute such data analyses that include data preprocessing, tract-based statistical analysis from DTI data, automated brain anatomy parsing from T1-weighted MR images, assignment of metabolite information from MRSI data, and co-alignment of these multimodality data streams for reporting of region-specific indices. We present statistical analyses of regional DTI and MRSI data in a cohort of normal pediatric subjects (n = 72; age range: 5-18 years; mean 12.7 ± 3.3 years) to establish normative data and evaluate maturational trends. Several regions showed significant maturational changes for several DTI parameters and MRSI ratios, but the percent change over the age range tended to be small. In the subcortical region (combined basal ganglia [BG], thalami [TH], and corpus callosum [CC]), the largest combined percent change was a 10% increase in fractional anisotropy (FA) primarily due to increases in the BG (12.7%) and TH (9%). The largest significant percent increase in N-acetylaspartate (NAA)/creatine (Cr) ratio was seen in the brain stem (BS) (18.8%) followed by the subcortical regions in the BG (11.9%), CC (8.9%), and TH (6.0%). We found consistent, significant (p < 0.01), but weakly positive correlations (r = 0.228-0.329) between NAA/Cr ratios and mean FA in the BS, BG, and CC regions. Age- and region-specific normative MR diffusion and spectroscopic metabolite ranges show brain maturation changes and are requisite for detecting abnormalities in an injured or diseased population.

Susceptibility-Weighted Imaging Identifies Iron-Oxide-Labeled Human Neural Stem Cells: Automated Computational Detection.

Neonatal hypoxic-ischemic brain injury (HII) can lead to devastating neurological outcomes such as cerebral palsy, epilepsy, and mental retardation. Human neural stem cell (hNSC) therapy provides new hope for the treatment of neonatal HII. These multipotent cells can aid in HII recovery by activating multiple reparative mechanisms including secretion of neurotrophic factors that enhance brain repair and plasticity. For clinical use of implanted hNSCs, methods are required to identify, quantify, track, and visualize migration and replication in an automated and reproducible fashion. In the current study, we used a model of unilateral HII in 10-day-old rat pups that were implanted with 250,000 Feridex-labeled hNSCs into the contralateral ventricle 3 days after HII. In addition to standard noninvasively acquired serial magnetic resonance imaging (MRI) sequences (11.7 and 4.7 T) that included diffusion-weighted imaging and T2-weighted imaging, we also acquired susceptibility-weighted imaging (SWI) 1-90 days after hNSC implantation. SWI is an advanced MRI method that enhances the visualization of iron-oxide-labeled hNSCs within affected regions of the injured neonatal brain. hNSC contrast was further enhanced by creating minimal intensity projections from the raw SWI magnitude images combined with phase information. Automated computational analysis using hierarchical region splitting (HRS) was applied for semiautomatic detection of hNSCs from SWI images. We found hNSCs in the ipsilateral HII lesion within the striatum and cortex adjacent to the lesion that corresponded to histological staining for iron. Quantitative phase values (radians) obtained from SWI revealed temporally evolving increased phase which reflects a decreased iron oxide content that is possibly related to cell division and the replicative capacity of the implanted hNSCs. SWI images also revealed hNSC migration from the contralateral injection site towards the ipsilateral HII lesion. Our results demonstrate that MRI-based SWI can monitor iron-labeled hNSCs in a clinically relevant manner and that automated computational methods such as HRS can rapidly identify iron-oxide-labeled hNSCs.

Transverse spin and transverse momentum in scattering of plane waves.

We study the near field to the far field evolution of spin angular momentum (SAM) density and the Poynting vector of the scattered waves from spherical scatterers. The results show that at the near field, the SAM density and the Poynting vector are dominated by their transverse components. While the former (transverse SAM) is independent of the helicity of the incident circular polarization state, the latter (transverse Poynting vector) depends upon the polarization state. It is further demonstrated that the interference of the transverse electric and transverse magnetic scattering modes enhances both the magnitudes and the spatial extent of the transverse SAM and the transverse momentum components.

Visualizing phase transition behavior of dilute stimuli responsive polymer solutions via Mueller matrix polarimetry.

Probing volume phase transition behavior of superdiluted polymer solutions both micro- and macroscopically still persists as an outstanding challenge. In this regard, we have explored 4 × 4 spectral Mueller matrix measurement and its inverse analysis for excavating the microarchitectural facts about stimuli responsiveness of "smart" polymers. Phase separation behavior of thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) and pH responsive poly(N,N-(dimethylamino)ethyl methacrylate) (PDMAEMA) and their copolymers were analyzed in terms of Mueller matrix derived polarization parameters, namely, depolarization (Δ), diattenuation (d), and linear retardance (δ). The Δ, d, and δ parameters provided useful information on both macro- and microstructural alterations during the phase separation. Additionally, the two step action ((i) breakage of polymer-water hydrogen bonding and (ii) polymer-polymer aggregation) at the molecular microenvironment during the cloud point generation was successfully probed via these parameters. It is demonstrated that, in comparison to the present techniques available for assessing the hydrophobic-hydrophilic switch over of simple stimuli-responsive polymers, Mueller matrix polarimetry offers an important advantage requiring a few hundred times dilute polymer solution (0.01 mg/mL, 1.1-1.4 µM) at a low-volume format.

Reparative effects of neural stem cells in neonatal rats with hypoxic-ischemic injury are not influenced by host sex.

BACKGROUND: Gender is increasingly recognized as an important influence on brain development, disease susceptibility, and response to pharmacologic/rehabilitative treatments. In regenerative medicine, it remains entirely unknown whether there is an interaction between transplanted stem cells and host gender that might bias efficacy and safety in some patients but not others. METHODS: We examined the role of recipient gender in a neonatal rat hypoxic-ischemic injury (HII) model, treated with female human neuronal stem cells (hNSCs), labeled with superparamagnetic iron oxide particles implanted into the contralateral cerebral ventricle. We monitored HII evolution (by magnetic resonance imaging, histopathology, behavioral testing) and hNSC fate (migration, replication, viability). RESULTS: Recipient gender after implantation did not influence the volume or location of ischemic injury (1, 30, or 90 d) or behavior (90 d). Superparamagnetic iron oxide labeling did not influence HII evolution. Implantation had its greatest benefit on mild/moderate injuries, which remained stable rather than increasing as in severe HII as is the natural history for such lesions. CONCLUSION: Our results suggest that hNSC treatment (including using hNSCs that are prelabeled with iron to allow tracking in real time by magnetic resonance imaging) would be equally safe and effective for male and female human newborns with mild-to-moderate HII.

Mueller matrix-based characterization of cervical tissue sections: a quantitative comparison of polar and differential decomposition methods.

Significance: Quantitative optical polarimetry has received considerable recent attention owing to its potential for being an efficient diagnosis and characterizing tool with potential applications in biomedical research and various other disciplines. In this regard, it is crucial to validate various Mueller matrix (MM) decomposition methods, which are utilized to extract and quantify the intrinsic individual polarization anisotropy properties of various complex optical media. Aim: To quantitatively compare the performance of both polar and differential MM decomposition methods for probing the structural and morphological changes in complex optical media through analyzing their intrinsic individual polarization parameters, which are extracted using the respective decomposition algorithms. We also intend to utilize the decomposition-derived anisotropy parameters to distinguish among the cervical tissues with different grades of cervical intraepithelial neoplasia (CIN) and to characterize the healing efficiency of an organic crystal. Approach: Polarization MM of the cervical tissues with different grades of CIN and the different stages of the self-healing crystal are recorded with a home-built MM imaging setup in the transmission detection geometry with a spatial resolution of ≈400 nm. The measured MMs are then processed with both the polar and differential MM decomposition methods to extract the individual polarization parameters of the respective samples. The derived polarization parameters are further analyzed to validate and compare the performance of both the MM decomposition methods for probing and characterizing the structural changes in the respective investigated optical media through their decomposition-derived intrinsic individual polarization properties. Results: Pronounced differences in the decomposed-derived polarization anisotropy parameters are observed for cervical tissue sections with different grades of CIN. While a significant increase in the depolarization parameter (Δ) is obtained with the increment of CIN stages for both the polar [Δ=0.32 for CIN grade one (CIN-I) and Δ=0.53 for CIN grade two (CIN-II))] and differential (Δ=0.35 for CIN-I and Δ=0.56 for CIN-II) decomposition methods, a trend reversal is seen for the linear diattenuation parameter (dL), indicating the structural distortion in the cervical morphology due to the CIN disease. More importantly, with the differential decomposition algorithm, the magnitude of the derived dL parameter decreases from 0.26 to 0.19 with the progression of CIN, which was not being probed by the polar decomposition method. Conclusion: Our results demonstrate that the differential decomposition of MM holds certain advantages over the polar decomposition method to characterize and probe the structural changes in the cervical tissues with different grades of CIN. Although the quantified individual polarization parameters obtained through both the MM decomposition methods can be used as useful metrics to characterize various optical media, in case of complex turbid media such as biological tissues, incorporation of the differential decomposition technique may yield more efficient information. Also, the study highlights the utilization of MM polarimetry with an appropriate decomposition technique as an efficient diagnostic and characterizing tool in the realm of biomedical clinical research, and various other disciplines.

Special Section Guest Editorial: Polarimetry in Biomedical Optics.

The editorial introduces the two-issue JBO Special Section on Polarimetry in Biomedical Optics and provides resources for further exploration.

Protocol to optimize the Rice-Vannucci rat pup model of perinatal asphyxia to ensure predictable hypoxic-ischemic cerebral lesions.

The Rice-Vannucci model in rodent pups is subject to substantial loss of animals, result inconsistency, and high lab-to-lab variability in extent and composition of induced injury. This protocol allows for highly predictable and reproducible hypoxic-ischemic cerebral injury lesions in post-natal day 10 Wistar rat pups with no mortality. We describe steps for common carotid artery ligation, brief post-operative normothermia, exposure to hypoxia, and post-hypoxic normothermia. Precise timing and temperature control in each step are crucial for a successful procedure. For complete details on the use and execution of this protocol, please refer to Hartman et al.1.

A complexity efficient penta-diagonal quantum smoothing filter for bio-medical signal denoising: a study on ECG.

Extracting information-bearing signal from a noisy environment has been a practical challenge in both classical and quantum computing formalism, especially in critical signal processing applications. To filter out the effect of noise, we propose a quantum smoothing filter built upon quantum formalism-based circuits applied for electrocardiogram signal denoising. The proposed quantum filter is a conceptually novel framework with an advantage in computational complexity as compared to the existing classical filters, such as discrete wavelet transform and empirical mode decomposition, whereas it achieves similar performance metrics for the accuracy of the filter. Further, we exploit the penta-diagonal Toeplitz structure of the smoothing filter, which gives approximately 48 % gate cost reduction for 10 qubit circuit compared to the standard Hamiltonian simulation without structure. The run-time complexity using the quantum matrix inversion technique for the structured matrix is given by O ~ κ 2 poly ( log N ) ε P for condition number κ of the N × N filter matrix within precision ε P . Embedding fixed sparsity of the banded matrix, the quantum filter shows potentially better run-time complexity than classical filtering techniques. For the quantifiable research results of our work, we have shown several performance metrics, such as mean-square error and peak signal-to-noise ratio analysis, with a bound of error due to observation noise, simulation error and quantum measurement uncertainty.

Quantitative fluorescence and elastic scattering tissue polarimetry using an Eigenvalue calibrated spectroscopic Mueller matrix system.

A novel spectroscopic Mueller matrix system has been developed and explored for both fluorescence and elastic scattering polarimetric measurements from biological tissues. The 4 × 4 Mueller matrix measurement strategy is based on sixteen spectrally resolved (λ = 400 - 800 nm) measurements performed by sequentially generating and analyzing four elliptical polarization states. Eigenvalue calibration of the system ensured high accuracy of Mueller matrix measurement over a broad wavelength range, either for forward or backscattering geometry. The system was explored for quantitative fluorescence and elastic scattering spectroscopic polarimetric studies on normal and precancerous tissue sections from human uterine cervix. The fluorescence spectroscopic Mueller matrices yielded an interesting diattenuation parameter, exhibiting differences between normal and precancerous tissues.

Probing multifractality in tissue refractive index: prospects for precancer detection.

Multiresolution analysis on the spatial refractive index inhomogeneities in the epithelium and connective tissue regions of a human cervix reveals a clear signature of multifractality. Importantly, the derived multifractal parameters, namely, the generalized Hurst exponent and the width of the singularity spectrum, derived via multifractal detrended fluctuation analysis, shows interesting differences between tissues having different grades of precancers. The refractive-index fluctuations are found to be more anticorrelated, and the strength of multifractality is observed to be considerably stronger in the higher grades of precancers. These observations on the multifractal nature of tissue refractive-index variations may prove to be valuable for developing light-scattering approaches for noninvasive diagnosis of precancer and early-stage cancer.

Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers.

Ischemic heart disease (IHD), a critical and dreadful cardiovascular disease, is a leading cause of death globally. The steady progress of IHD leads to an irreversible condition called myocardial infarction (MI). The detection of MI can be done by observing the altered electrocardiogram (ECG) characteristics. Often, automated ECG analysis is preferred in place of visual inspection to reduce time and ensure reliable detection even when the recording quality is not very good. This paper presents an automated approach to classify recent MI, past MI, and normal sinus rhythm (NSR) classes based on the morphological features of the ECG. In clinical practice, a standard 12-lead ECG setup is typically employed to identify MI. However, acquiring a 12-lead ECG is not always convenient. Hence, in this study, we have explored the possibility of using a minimal number of ECG leads by deriving the augmented limb leads using leads I and II. A well-known and widely used ensemble machine learning tool, the random forest (RF) classifier is trained using features extracted from the derived augmented limb leads and their combinations. An RF classifier built using features extracted from all limb leads has outperformed classifiers built on combinations of them with five-fold cross-validation training accuracy of 97.9 (±0.008) % and testing accuracy of 98 %.Clinical relevance- As high sensitivity is reported in identifying recent MI and past MI classes, the proposed approach is suitable for preventative healthcare applications since it is less likely that subjects with recent or past MI will be misclassified. Due to its low computational complexity, better interpretability, and comparable performance to the state-of-the-art results, the proposed approach can be employed in clinical and cardiac health screening applications. It also has the potential to be employed in remote monitoring with mobile and wearable devices because it is built on features extracted from only lead I and II ECG recordings.

Detection of Twin Pregnancies using Fetal Phonocardiogram.

Fetal phonocardiogram (fPCG), or the electronic recording of fetal heart sounds, is a safe and easily available signal that can be used to monitor fetal wellbeing. In the proposed work an attempt is made to identify twin pregnancies using fPCG data recorded from the fetus with 1/3rd power in octave band filtered output as features to train K-Nearest Neighbor (KNN) and support vector machine (SVM) classifiers. The SVM classifier with the quadratic kernel is able to identify singletons and twins with a positive predictive value of 100% and 79.1% respectively. The KNN classifier with k=10 neighbors is able to identify singletons and twins with a positive predictive value of 100% and 81.8% respectively.Clinical Relevance: Identifying twin pregnancies from singleton is an essential clinical protocol followed during late pregnancy as there may be complications like twin-twin transfusion syndrome, selective fetal growth restriction, and preterm labor in twin pregnancy [1], [2]. Ultrasound imaging is the most commonly used technique for twin pregnancy detection, though it is often not affordable or available in rural or low-income populations. Utilization of fPCG in such circumstances has immense clinical potential.

Non-invasive Coronary Artery Disease Screening Based on Electrocardiogram Characteristics and Clinical Risk Factors.

Coronary artery disease (CAD), an acute and life-threatening cardiovascular disease, is a leading cause of mortality and morbidity worldwide. Coronary angiography, the principal diagnostic tool for CAD, is invasive, expensive, and requires a lot of skilled effort. The current study aims to develop an automated and non-invasive CAD detection model and improve its performance as closely as possible to clinically acceptable diagnostic sensitivity. Electrocardiogram (ECG) characteristics are observed to be altered due to CAD and can be studied to develop a screening tool for its detection. The subject's clinical information can help broadly identify the high-cardiac-risk population and serve as a primary step in diagnosing CAD. This paper presents an approach to automatically detect CAD based on clinical data, morphological ECG features, and heart rate variability (HRV) features extracted from short-duration Lead-II ECG recordings. A few popular machine-learning classifiers, including support vector machine (SVM), random forest (RF), K-nearest neighbours (KNN), Gaussian Naïve Bayes (GNB), and multi-layer perceptron (MLP), are trained on the extracted feature space, and their performance is evaluated. Classifiers built by integrating clinical data and features extracted from ECG recordings demonstrated better performance than those built on each feature set separately, and the RF classifier outperforms other considered machine learners and reports an average testing accuracy of 94% and a G-mean score of 92% with a 5-fold cross-validation training accuracy of 95(± 0.04)%.Clinical relevance- The proposed method uses a brief, single-lead ECG recording and performs similarly to current clinical practices in an explainable manner. This makes it suitable for deployment via wearable technology (like smart watch gadgets) and telemonitoring, which may facilitate an earlier and more widespread CAD diagnosis.

A computer-aided tool for automatic volume estimation of hematoma using non-contrast brain CT scans.

The computation of hematoma volume is the key parameter for treatment planning of Intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) imaging is routinely used for the diagnosis of ICH. Hence, the development of computer-aided tools for three-dimensional (3D) computed tomography (CT) image analysis is essential to estimate the gross volume of hematoma. We propose a methodology for automatic estimation of the hematoma volume from 3D CT volumes. Our approach integrates two different methods, multiple abstract splitting (MAS) and seeded region growing (SRG) to develop a unified hematoma detection pipeline from pre-processed CT volumes. The proposed methodology was tested on 80 cases. The volume was estimated from the delineated hematoma region, validated against the ground-truth volumes, and compared with those obtained from the conventional ABC/2 approach. We also compared our results with the U-Net model (supervised technique) to show the applicability of the proposed method. The volume calculated from manually segmented hematoma was considered the ground truth. TheR2correlation coefficient between the volume obtained from the proposed algorithm and the ground truth is 0.86, which is equivalent to theR2value resulting from the comparison between the volume calculated by ABC/2 and the ground truth. The experimental results of the proposed unsupervised approach are comparable to the deep neural architecture (U-Net models). The average computation time was 132.76 ± 14 seconds. The proposed methodology provides a fast and automatic estimation of hematoma volume, which is similar to the baseline user-guided ABC/2 approach. Implementation of our method does not demand a high-end computational setup. Thus, recommended in clinical practice for computer-assistive volume estimation of hematoma from 3D CT volumes and can be implemented in a simple computer system.

Elastin overexpression by cell-based gene therapy preserves matrix and prevents cardiac dilation.

After a myocardial infarction, thinning and expansion of the fibrotic scar contribute to progressive heart failure. The loss of elastin is a major contributor to adverse extracellular matrix remodelling of the infarcted heart, and restoration of the elastic properties of the infarct region can prevent ventricular dysfunction. We implanted cells genetically modified to overexpress elastin to re-establish the elastic properties of the infarcted myocardium and prevent cardiac failure. A full-length human elastin cDNA was cloned, subcloned into an adenoviral vector and then transduced into rat bone marrow stromal cells (BMSCs). In vitro studies showed that BMSCs expressed the elastin protein, which was deposited into the extracellular matrix. Transduced BMSCs were injected into the infarcted myocardium of adult rats. Control groups received either BMSCs transduced with the green fluorescent protein gene or medium alone. Elastin deposition in the infarcted myocardium was associated with preservation of myocardial tissue structural integrity (by birefringence of polarized light; P < 0.05 versus controls). As a result, infarct scar thickness and diastolic compliance were maintained and infarct expansion was prevented (P < 0.05 versus controls). Over a 9-week period, rats implanted with BMSCs demonstrated better cardiac function than medium controls; however, rats receiving BMSCs overexpressing elastin showed the greatest functional improvement (P < 0.01). Overexpression of elastin in the infarcted heart preserved the elastic structure of the extracellular matrix, which, in turn, preserved diastolic function, prevented ventricular dilation and preserved cardiac function. This cell-based gene therapy provides a new approach to cardiac regeneration.

Probing the dynamics of an optically trapped particle by phase sensitive back focal plane interferometry.

The dynamics of an optically trapped particle are often determined by measuring intensity shifts of the back-scattered light from the particle using position sensitive detectors. We present a technique which measures the phase of the back-scattered light using balanced detection in an external Mach-Zehnder interferometer scheme where we separate out and beat the scattered light from the particle and that from the top surface of our trapping chamber. The technique has improved axial motion resolution over intensity-based detection, and can also be used to measure lateral motion of the trapped particle. In addition, we are able to track the Brownian motion of trapped 1.1 and 3 µm diameter particles from the phase jitter and show that, similar to intensity-based measurements, phase measurements can also be used to simultaneously determine displacements of the trapped particle as well as the spring constant of the trap. For lateral displacements, we have matched our experimental results with a simulation of the overall phase contour of the back-scattered light by using plane wave decomposition in conjunction with Mie scattering theory. The position resolution is limited by path drifts of the interferometer which we have presently reduced to demonstrate the capability of sub-nm displacement resolution in the axial direction for 1.1 µm diameter particles by locking the interferometer to a frequency stabilized diode laser.

Morphological Event Based Signal Quality Assessment of Electrocardiogram.

ECG signals acquired from mobile devices by unskilled users are corrupted with several noises. Poor signal quality may result in an increased number of false alarms, degrading diagnostic performance, and increasing the burden on the doctors in decoding the information for further clinical intervention. So, it is necessary to assess the quality of the signals before doing any further processing. This paper presents a method for accessing the reliability of ECG signals obtained from wearable sensors. A morphological event-based quality assessment method is proposed where a signal will be classified as GOOD/BAD. Results show that our method can achieve an accuracy = 92 % with sensitivity = 0.98 and specificity = 0.59.