Osteochondral fluid transport in an ex vivo system.

OBJECTIVE: Alterations to bone-to-cartilage fluid transport may contribute to the development of osteoarthritis (OA). Larger biological molecules in bone may transport from bone-to-cartilage (e.g., insulin, 5 kDa). However, many questions remain about fluid transport between these tissues. The objectives of this study were to (1) test for diffusion of 3 kDa molecular tracers from bone-to-cartilage and (2) assess potential differences in bone-to-cartilage fluid transport between different loading conditions. DESIGN: Osteochondral cores extracted from bovine femurs (N = 10 femurs, 10 cores/femur) were subjected to either no-load (i.e., pure diffusion), pre-load only, or cyclic compression (5 ± 2% or 10 ± 2% strain) in a two-chamber bioreactor. The bone was placed into the bone compartment followed by a 3 kDa dextran tracer, and tracer concentrations in the cartilage compartment were measured every 5 min for 120 min. Tracer concentrations were analyzed for differences in beginning, peak, and equilibrium concentrations, loading effects, and time-to-peak tracer concentration. RESULTS: Peak tracer concentration in the cartilage compartment was significantly higher compared to the beginning and equilibrium tracer concentrations. Cartilage-compartment tracer concentration and maximum fluorescent intensity were influenced by strain magnitude. No time-to-peak relationship was found between strain magnitudes and cartilage-compartment tracer concentration. CONCLUSION: This study shows that bone-to-cartilage fluid transport occurs with 3 kDa dextran molecules. These are larger molecules to move between bone and cartilage than previously reported. Further, these results demonstrate the potential impact of cyclic compression on osteochondral fluid transport. Determining the baseline osteochondral fluid transport in healthy tissues is crucial to elucidating the mechanisms OA pathology.

Compressive video via IR-pulsed illumination.

We propose and demonstrate a compressive temporal imaging system based on pulsed illumination to encode temporal dynamics into the signal received by the imaging sensor during exposure time. Our approach enables >10x increase in effective frame rate without increasing camera complexity. To mitigate the complexity of the inverse problem during reconstruction, we introduce two keyframes: one before and one after the coded frame. We also craft what we believe to be a novel deep learning architecture for improved reconstruction of the high-speed scenes, combining specialized convolutional and transformer architectures. Simulation and experimental results clearly demonstrate the reconstruction of high-quality, high-speed videos from the compressed data.

A test of the predictive validity of relative versus absolute income for self-reported health and well-being in the United States.

BACKGROUND: A classic debate concerns whether absolute or relative income is more salient. Absolute values resources as constant across time and place while relative contextualizes one's hierarchical location in the distribution of a time and place. OBJECTIVE: This study investigates specifically whether absolute income or relative income matters more for health and well-being. METHODS: We exploit within-person, within-age, and within-time variation with higher-quality income measures and multiple health and well-being outcomes in the United States. Using the Panel Study of Income Dynamics and the Cross-National Equivalent File, we estimate three-way fixed effects models of self-rated health, poor health, psychological distress, and life satisfaction. RESULTS: For all four outcomes, relative income has much larger standardized coefficients than absolute income. Robustly, the confidence intervals for relative income do not overlap with zero. By contrast, absolute income mostly has confidence intervals that overlap with zero, and its coefficient is occasionally signed in the wrong direction. A variety of robustness checks support these results. CONCLUSIONS: Relative income has far greater predictive validity than absolute income for self-reported health and well-being. CONTRIBUTION: Compared to earlier studies, this study provides a more rigorous comparison and test of the predictive validity of absolute and relative income that is uniquely conducted with data on the United States. This informs debates on income measurement, the sources of health and well-being, and inequalities generally. Plausibly, these results can guide any analysis that includes income in models.

Snapshot ptychography on array cameras.

We use convolutional neural networks to recover images optically down-sampled by 6.7 × using coherent aperture synthesis over a 16 camera array. Where conventional ptychography relies on scanning and oversampling, here we apply decompressive neural estimation to recover full resolution image from a single snapshot, although as shown in simulation multiple snapshots can be used to improve signal-to-noise ratio (SNR). In place training on experimental measurements eliminates the need to directly calibrate the measurement system. We also present simulations of diverse array camera sampling strategies to explore how snapshot compressive systems might be optimized.

Photon-limited bounds for phase retrieval.

We show that the optimal Cramér-Rao lower bound on the mean-square error for the estimation of a coherent signal from photon-limited intensity measurements is equal to the number of signal elements, or the number of signal elements minus one when we account for the unobservable reference phase. Whereas this bound is attained by phase-quadrature holography, we also show that it can be attained through a phase-retrieval system that does not require a coherent reference. We also present the bounds for classic phase-retrieval and ptychography, and show that practical coding strategies can approach optimal performance.

Noise suppression for ballistic-photons based on compressive in-line holographic imaging through an inhomogeneous medium.

Noise suppression is one of the most important tasks in imaging through inhomogeneous mediums. Here, we proposed a denoising approach based on compressive in-line holography for imaging through an inhomogeneous medium. A reference-beam-free system with a low-cost continuous-wave laser is presented. The suppression against the noise, which is brought by the scattering photons, is presented in simulations using the proposed algorithm. The noise immunity is demonstrated in lensless imaging behind a random phase mask with an optical depth of 1.42 by single exposure, as well as behind a ground glass with an optical depth of 6.38 by multiple exposures.

The Levels and Trends in Deep and Extreme Poverty in the United States, 1993-2016.

Recently, there has been tremendous interest in deep and extreme poverty in the United States. We advance beyond prior research by using higher-quality data, improving measurement, and following leading standards in international income research. We estimate deep (less than 20% of medians) and extreme (less than 10% of medians) poverty in the United States from 1993 to 2016. Using the Current Population Survey, we match the income definition of the Luxembourg Income Study and adjust for underreporting using the Urban Institute's TRIM3 model. In 2016, we estimate that 5.2 to 7.2 million Americans (1.6% to 2.2%) were deeply poor and 2.6 to 3.7 million (0.8% to 1.2%) were extremely poor. Although deep and extreme poverty fluctuated over time, including declines from 1993 to 1995 and 2007 to 2010, we find significant increases from lows in 1995 to peaks in 2016 in both deep (increases of 48% to 93%) and extreme poverty (increases of 54% to 111%). We even find significant increases with thresholds anchored at 1993 medians. With homelessness added, deep poverty would be 7% to 8% higher and extreme poverty 19% to 23% higher in 2016, which suggests that our estimates are probably lower bounds. The rise of deep/extreme poverty is concentrated among childless households. Among households with children, the expansion of SNAP benefits has led to declines in deep/extreme poverty. Ultimately, we demonstrate that estimates of deep/extreme poverty depend critically on the quality of income measurement.

Personalized Nutrition: Translating the Science of NutriGenomics Into Practice: Proceedings From the 2018 American College of Nutrition Meeting.

Adverse reactions to foods and adverse drug reactions are inherent in product defects, medication errors, and differences in individual drug exposure. Pharmacogenetics is the study of genetic causes of individual variations in drug response and pharmacogenomics more broadly involves genome-wide analysis of the genetic determinants of drug efficacy and toxicity. The similarity of nutritional genomics and pharmacogenomics stems from the innate goal to identify genetic variants associated with metabolism and disease. Thus, nutrigenomics can be thought of as encompassing gene-diet interactions involving diverse compounds that are present in even the simplest foods. The advances in the knowledge base of the complex interactions among genotype, diet, lifestyle, and environment is the cornerstone that continues to elicit changes in current medical practice to ultimately yield personalized nutrition recommendations for health and risk assessment. This information could be used to understand how foods and dietary supplements uniquely affect the health of individuals and, hence, wellness. The individual's gut microbiota is not only paramount but pivotal in embracing the multiple-functional relationships with complex metabolic mechanisms involved in maintaining cellular homeostasis. The genetic revolution has ushered in an exciting era, one in which many new opportunities are expected for nutrition professionals with expertise in nutritional genomics. The American College of Nutrition's conference focused on "Personalized Nutrition: Translating the Science of NutriGenomics Into Practice" was designed to help to provide the education needed for the professional engagement of providers in the personalized medicine era.

Twin-Image-Free Holography: A Compressive Sensing Approach.

Holographic reconstruction is troubled by the phase-conjugate wave front arising from Hermitian symmetry of the complex field. The so-called twin image obfuscates the reconstruction in solving the inverse problem. Here we quantitatively reveal how and how much the twin image affects the reconstruction and propose a compressive sensing (CS) approach to reconstruct a hologram completely free from the twin image. Using the canonical basis, the incoherence condition of CS is naturally satisfied by the Fourier transformation associated with wave propagation. With the propagation kernel function related to the distance, the object wave diffracts into a sharp pattern while the phase-conjugate wave diffracts into a diffuse pattern. An iterative algorithm using a total variation sparsity constraint could filter out the diffuse conjugated signal and overcome the inherent physical symmetry of holographic reconstruction. The feasibility is verified by simulation and experimental results, as well as a comparative study to an existing phase retrieval method.

Field of view in monocentric multiscale cameras.

Conventionally, the field of view of a camera is understood as the angular extent of a convex circular or rectangular region. Parallel camera architectures with computational image stitching, however, allow implementation of a field of view with an arbitrary shape. Monocentric multiscale lenses further allow the implementation of an arbitrary field of view in camera volumes comparable to conventional single-lens systems. In contrast with conventional wide-field-of-view systems, multiscale design can also achieve nearly uniform resolution across the entire field of view. This paper presents several design studies obtaining unconventional fields of view using this approach.

Full field-of-view digital lens-free holography for weak-scattering objects based on grating modulation.

Grating-based single-shot digital lens-free holography with spatial spectral multiplexing is proposed to realize full field-of-view (FOV) imaging for weak-scattering objects. Multiple object waves are generated by a one-dimensional grating that is placed in near contact with the object to avoid the cross talk among different diffraction orders during reconstruction. A multiplexed off-axis hologram is created by interference between the object waves and reference wave and captured by an image sensor in one shot. Multiple imaging areas corresponding to the captured object waves can be simultaneously retrieved during reconstruction. A formula which guarantees full FOV imaging without cross talk or information loss is presented. The imaging experiments of a USAF resolution target are presented to demonstrate the feasibility of this method.

Single-sensor multispeaker listening with acoustic metamaterials.

Designing a "cocktail party listener" that functionally mimics the selective perception of a human auditory system has been pursued over the past decades. By exploiting acoustic metamaterials and compressive sensing, we present here a single-sensor listening device that separates simultaneous overlapping sounds from different sources. The device with a compact array of resonant metamaterials is demonstrated to distinguish three overlapping and independent sources with 96.67% correct audio recognition. Segregation of the audio signals is achieved using physical layer encoding without relying on source characteristics. This hardware approach to multichannel source separation can be applied to robust speech recognition and hearing aids and may be extended to other acoustic imaging and sensing applications.

Galilean monocentric multiscale optical systems.

The first generation of monocentric multiscale gigapixel cameras used Keplerian designs to enable full field coverage. This paper considers alternative designs that remove the requirement that adjacent subimages overlap. Removing this constraint enables Galilean designs that reduce system volume and improve relative illumination and image quality. The entrance aperture can also be moved to more closely approximate telecentricity and gaps in the field of view can be filled using multiple co-boresighted MMS cameras. Even with multiple cameras, Galilean systems can still reduce the total volume by 10 times relative to previous Keplerian designs.

Efficient block-wise algorithm for compressive holography.

Compressive holography is a relatively time-consuming image estimation in convex optimized problem. We propose an efficient block-wise algorithm to limit the searching space and reduce the calculation time while keeping the reconstruction quality. The effective anti-aliasing boundary of the sub-hologram is located to determine the block size for compressive reconstruction in the total-variation two-step iterative shrinkage/thresholding algorithm. Padded sub-holograms could be reconstructed in parallel by using multi-core processors. Compared with the traditional compressive holography, the block-wise algorithm could take approximately 1/50 of the reconstruction time and achieve an improved reconstruction quality.

Compressive sensing and adaptive sampling applied to millimeter wave inverse synthetic aperture imaging.

In order to improve speed and efficiency over traditional scanning methods, a Bayesian compressive sensing algorithm using adaptive spatial sampling is developed for single detector millimeter wave synthetic aperture imaging. The application of this algorithm is compared to random sampling to demonstrate that the adaptive algorithm converges faster for simple targets and generates more reliable reconstructions for complex targets.

Heterogeneous camera array for multispectral light field imaging.

Multispectral light field acquisition is challenging due to the increased dimensionality of the problem. In this paper, inspired by anaglyph theory (i.e. the ability of human eyes to synthesize colored stereo perception from color-complementary (such as red and cyan) views), we propose to capture the multispectral light field using multiple cameras with different wide band filters. A convolutional neural network is used to extract the joint information of different spectral channels and to pair the cross-channel images. In our experiment, results on both synthetic data and real data captured by our prototype system validate the effectiveness and accuracy of proposed method.

Snapshot fan beam coded aperture coherent scatter tomography.

We use coherently scattered X-rays to measure the molecular composition of an object throughout its volume. We image a planar slice of the object in a single snapshot by illuminating it with a fan beam and placing a coded aperture between the object and the detectors. We characterize the system and demonstrate a resolution of 13 mm in range and 2 mm in cross-range and a fractional momentum transfer resolution of 15%. In addition, we show that this technique allows a 100x speedup compared to previously-studied pencil beam systems using the same components. Finally, by scanning an object through the beam, we image the full 4-dimensional data cube (3 spatial and 1 material dimension) for complete volumetric molecular imaging.

Efficient patch-based approach for compressive depth imaging.

We present efficient camera hardware and algorithms to capture images with extended depth of field. The camera moves its focal plane via a liquid lens and modulates the scene at different focal planes by shifting a fixed binary mask, with synchronization achieved by using the same triangular wave to control the focal plane and the pizeoelectronic translator that shifts the mask. Efficient algorithms are developed to reconstruct the all-in-focus image and the depth map from a single coded exposure, and various sparsity priors are investigated to enhance the reconstruction, including group sparsity, tree structure, and dictionary learning. The algorithms naturally admit a parallel computational structure due to the independent patch-level operations. Experimental results on both simulation and real datasets demonstrate the efficacy of the new hardware and the inversion algorithms.

Inositol's and other nutraceuticals' synergistic actions counteract insulin resistance in polycystic ovarian syndrome and metabolic syndrome: state-of-the-art and future perspectives.

The incidence of metabolic syndrome (MetS), type II diabetes (T2D) and polycystic ovarian syndrome (PCOS) has been progressively increasing. Insulin resistance (InsR) seems to play a key role in a majority of phenotypes of these conditions, altering metabolic homeostasis, within muscle, liver, adipose and other tissues. Hyperinsulinemia is often associated with InsR and causes hormonal imbalances especially within ovaries and adrenals. Inositol is a polyalcohol, naturally occurring as nine stereoisomers, including D-chiro-inositol (DCI) and myo-inositol (MI), which have prominent roles in the metabolism of glucose and free fatty acids. MI and DCI have been classified as insulin-sensitizers and seem to adequately counteract several InsR-related metabolic alterations with a safe nutraceutical profile. Based on our analysis of selected studies that investigated MI and/or DCI, we conclude that supplementation with MI and/or DCI complement each other in their metabolic actions and act in synergy with other insulin sensitizing drugs and/or nutraceuticals. Nevertheless, considering the possible severe bias due to different methodologies across published studies, we conclude that there is a need for further studies on larger cohorts and with greater statistical power. These should further clarify outcomes and suitable therapeutic dosages of MI and DCI, possibly based on each patient's clinical status.

Path Dependency and the Politics of Socialized Health Care.

Rich democracies exhibit vast cross-national and historical variation in the socialization of health care. Yet, cross-national analyses remain relatively rare in the health policy literature, and health care remains relatively neglected in the welfare state literature. We analyze pooled time series models of the public share of total health spending for eighteen rich democracies from 1960 to 2010. Building on path dependency theory, we present a strategy for modeling the relationship between the initial 1960 public share and the current public share. We also examine two contrasting accounts for how the 1960 public share interacts with conventional welfare state predictors: the self-reinforcing hypothesis expecting positive feedbacks and the counteracting hypothesis expecting negative feedbacks. We demonstrate that most of the variation from 1960 to 2010 in the public share can be explained by a country's initial value in 1960. This 1960 value has a large significant effect in models of 1961-2010, and including the 1960 value alters the coefficients of conventional welfare state predictors. To investigate the mechanism whereby prior social policy influences public opinion about current social policy, we use the 2006 International Social Survey Programme (ISSP). This analysis confirms that the 1960 values predict individual preferences for government spending on health. Returning to the pooled time series, we demonstrate that the 1960 values interact significantly with several conventional welfare state predictors. Some interactions support the self-reinforcing hypothesis, while others support the counteracting hypothesis. Ultimately, this study illustrates how historical legacies of social policy exert substantial influence on the subsequent politics of social policy.