Continuous 3D Myocardial Motion Tracking via Echocardiography.

Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally. However, current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions, hindering the early identification of myocardial dysfunction. To address these challenges, this paper introduces the Neural Cardiac Motion Field (NeuralCMF). NeuralCMF leverages implicit neural representation (INR) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart. This method surpasses pixel-wise limitations by offering the capability to continuously query the precise shape and motion of the myocardium at any specific point throughout the cardiac cycle, enhancing the detailed analysis of cardiac dynamics beyond traditional speckle tracking. Notably, NeuralCMF operates without the need for paired datasets, and its optimization is self-supervised through the physics knowledge priors in both space and time dimensions, ensuring compatibility with both 2D and 3D echocardiogram video inputs. Experimental validations across three representative datasets support the robustness and innovative nature of the NeuralCMF, marking significant advantages over existing state-of-the-art methods in cardiac imaging and motion tracking. Code is available at: https://njuvision.github.io/NeuralCMF.

Integrated photonic encoder for low power and high-speed image processing.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

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.

Roadmap for Optical Metasurfaces.

Metasurfaces have recently risen to prominence in optical research, providing unique functionalities that can be used for imaging, beam forming, holography, polarimetry, and many more, while keeping device dimensions small. Despite the fact that a vast range of basic metasurface designs has already been thoroughly studied in the literature, the number of metasurface-related papers is still growing at a rapid pace, as metasurface research is now spreading to adjacent fields, including computational imaging, augmented and virtual reality, automotive, display, biosensing, nonlinear, quantum and topological optics, optical computing, and more. At the same time, the ability of metasurfaces to perform optical functions in much more compact optical systems has triggered strong and constantly growing interest from various industries that greatly benefit from the availability of miniaturized, highly functional, and efficient optical components that can be integrated in optoelectronic systems at low cost. This creates a truly unique opportunity for the field of metasurfaces to make both a scientific and an industrial impact. The goal of this Roadmap is to mark this "golden age" of metasurface research and define future directions to encourage scientists and engineers to drive research and development in the field of metasurfaces toward both scientific excellence and broad industrial adoption.

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.

Poverty, not the poor.

This review explains how and why the United States has systemically high poverty. Descriptive evidence shows that U.S. poverty is (i) a huge share of the population, (ii) a perennial outlier among rich democracies, (iii) staggeringly high for certain groups, (iv) unexpectedly high for those who "play by the rules," and (v) pervasive across various groups and places. This review then discusses and critiques three prevailing approaches focused on the individual poor rather than the systemically high poverty: (i) behavioral explanations "fixing the poor," (ii) emotive compassion "dramatizing the poor," and (iii) cultural explanations both dramatizing and fixing the poor. The essay then reviews political explanations that emphasize the essential role of social policy generosity, political choices to penalize risks, power resources of collective political actors, and institutions. This review demonstrates a long emerging but ascending and warranted shift away from individualistic explanations of the poor toward political explanations of poverty.

Pseudoscientific and Unhealthy Approaches to Gastrointestinal Health and Detoxification in Natural Medicine.

This paper discusses concerns with specific approaches in identifying and eliminating gastrointestinal (GI) pathogens, as well as detoxifying toxic metals, that may be misleading and harmful to a patient's health. These are non-scientific methods that claim to improve GI microbial balance and mineral nutritional status that persist in the nutritional and natural medicine market, and unfortunately many are actively promoted through specific products and protocols marketed by nutritional supplement companies that should know better. The potential toxicity and mucosal damage of the long-term use of aggressive laxative herbs such as Cascara sagrada, rhubarb and/or Senna, as well as potential adverse events from ingredients containing fulvic acids and/or humic acids are discussed.

Novel Estimates of Mortality Associated With Poverty in the US.

This cohort study estimated association between poverty and mortality and quantified the proportion and number of deaths associated with poverty.

The Long Arm of Prospective Childhood Income for Mature Adult Health in the United States.

Pioneering scholarship links retrospective childhood conditions to mature adult health. We distinctively provide critical evidence with prospective state-of-the-art measures of parent income observed multiple times during childhood in the 1970s to 1990s. Using the Panel Study of Income Dynamics, we analyze six health outcomes (self-rated health, heart attack, stroke, life-threatening chronic conditions, non-life-threatening chronic conditions, and psychological distress) among 40- to 65-year-olds. Parent relative income rank has statistically and substantively significant relationships with five of six outcomes. The relationships with heart attack, stroke, and life-threatening chronic conditions are particularly strong. Parent income rank performs slightly better than alternative prospective and retrospective measures. At the same time, we provide novel validation on which retrospective measures (i.e., father's education) perform almost as well as prospective measures. Furthermore, we inform several perennial debates about how relative versus absolute income and other measures of socioeconomic status and social class influence health.

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.

A modular hierarchical array camera.

Array cameras removed the optical limitations of a single camera and paved the way for high-performance imaging via the combination of micro-cameras and computation to fuse multiple aperture images. However, existing solutions use dense arrays of cameras that require laborious calibration and lack flexibility and practicality. Inspired by the cognition function principle of the human brain, we develop an unstructured array camera system that adopts a hierarchical modular design with multiscale hybrid cameras composing different modules. Intelligent computations are designed to collaboratively operate along both intra- and intermodule pathways. This system can adaptively allocate imagery resources to dramatically reduce the hardware cost and possesses unprecedented flexibility, robustness, and versatility. Large scenes of real-world data were acquired to perform human-centric studies for the assessment of human behaviours at the individual level and crowd behaviours at the population level requiring high-resolution long-term monitoring of dynamic wide-area scenes.

Review of bio-optical imaging systems with a high space-bandwidth product.

Optical imaging has served as a primary method to collect information about biosystems across scales-from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics-the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

CrossNet++: Cross-Scale Large-Parallax Warping for Reference-Based Super-Resolution.

The ability of camera arrays to efficiently capture higher space-bandwidth product than single cameras has led to various multiscale and hybrid systems. These systems play vital roles in computational photography, including light field imaging, 360 VR camera, gigapixel videography, etc. One of the critical tasks in multiscale hybrid imaging is matching and fusing cross-resolution images from different cameras under perspective parallax. In this paper, we investigate the reference-based super-resolution (RefSR) problem associated with dual-camera or multi-camera systems. RefSR consists of super-resolving a low-resolution (LR) image given an external high-resolution (HR) reference image, where they suffer both a significant resolution gap ( 8×) and large parallax (  âˆ¼ 10% pixel displacement). We present CrossNet++, an end-to-end network containing novel two-stage cross-scale warping modules, image encoder and fusion decoder. The stage I learns to narrow down the parallax distinctively with the strong guidance of landmarks and intensity distribution consensus. Then the stage II operates more fine-grained alignment and aggregation in feature domain to synthesize the final super-resolved image. To further address the large parallax, new hybrid loss functions comprising warping loss, landmark loss and super-resolution loss are proposed to regularize training and enable better convergence. CrossNet++ significantly outperforms the state-of-art on light field datasets as well as real dual-camera data. We further demonstrate the generalization of our framework by transferring it to video super-resolution and video denoising.

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.

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.

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.

Rank Minimization for Snapshot Compressive Imaging.

Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple frames are mapped into a single measurement, with video compressive imaging and hyperspectral compressive imaging as two representative applications. Though exciting results of high-speed videos and hyperspectral images have been demonstrated, the poor reconstruction quality precludes SCI from wide applications. This paper aims to boost the reconstruction quality of SCI via exploiting the high-dimensional structure in the desired signal. We build a joint model to integrate the nonlocal self-similarity of video/hyperspectral frames and the rank minimization approach with the SCI sensing process. Following this, an alternating minimization algorithm is developed to solve this non-convex problem. We further investigate the special structure of the sampling process in SCI to tackle the computational workload and memory issues in SCI reconstruction. Both simulation and real data (captured by four different SCI cameras) results demonstrate that our proposed algorithm leads to significant improvements compared with current state-of-the-art algorithms. We hope our results will encourage the researchers and engineers to pursue further in compressive imaging for real applications.