Modeling the Effects of Protracted Cosmic Radiation in a Human Organ-on-Chip Platform.

Galactic cosmic radiation (GCR) is one of the most serious risks posed to astronauts during missions to the Moon and Mars. Experimental models capable of recapitulating human physiology are critical to understanding the effects of radiation on human organs and developing radioprotective measures against space travel exposures. The effects of systemic radiation are studied using a multi-organ-on-a-chip (multi-OoC) platform containing engineered tissue models of human bone marrow (site of hematopoiesis and acute radiation damage), cardiac muscle (site of chronic radiation damage) and liver (site of metabolism), linked by vascular circulation with an endothelial barrier separating individual tissue chambers from the vascular perfusate. Following protracted neutron radiation, the most damaging radiation component in deep space, a greater deviation of tissue function is observed as compared to the same cumulative dose delivered acutely. Further, by characterizing engineered bone marrow (eBM)-derived immune cells in circulation, 58 unique genes specific to the effects of protracted neutron dosing are identified, as compared to acutely irradiated and healthy tissues. It propose that this bioengineered platform allows studies of human responses to extended radiation exposure in an "astronaut-on-a-chip" model that can inform measures for mitigating cosmic radiation injury.

Macrophages enhance contractile force in iPSC-derived human engineered cardiac tissue.

Resident cardiac macrophages are critical mediators of cardiac function. Despite their known importance to cardiac electrophysiology and tissue maintenance, there are currently no stem-cell-derived models of human engineered cardiac tissues (hECTs) that include resident macrophages. In this study, we made an induced pluripotent stem cell (iPSC)-derived hECT model with a resident population of macrophages (iM0) to better recapitulate the native myocardium and characterized their impact on tissue function. Macrophage retention within the hECTs was confirmed via immunofluorescence after 28 days of cultivation. The inclusion of iM0s significantly impacted hECT function, increasing contractile force production. A potential mechanism underlying these changes was revealed by the interrogation of calcium signaling, which demonstrated the modulation of ß-adrenergic signaling in +iM0 hECTs. Collectively, these findings demonstrate that macrophages significantly enhance cardiac function in iPSC-derived hECT models, emphasizing the need to further explore their contributions not only in healthy hECT models but also in the contexts of disease and injury.

Microwave-transparent metallic metamaterials for autonomous driving safety.

Maintaining the surface transparency of protective covers using transparent heaters in extreme weather is imperative for enhancing safety in autonomous driving. However, achieving both high transmittance and low sheet resistance, two key performance indicators for transparent heaters, is inherently challenging. Here, inspired by metamaterial design, we report microwave-transparent, low-sheet-resistance heaters for automotive radars. Ultrathin (approximately one ten-thousandth of the wavelength), electrically connected metamaterials on a millimetre-thick dielectric cover provide near-unity transmission at specific frequencies within the W band (75-110 GHz), despite their metal filling ratio exceeding 70 %. These metamaterials yield the desired phase delay to adjust Fabry-Perot resonance at each target frequency. Fabricated microwave-transparent heaters exhibit exceptionally low sheet resistance (0.41 ohm/sq), thereby heating the dielectric cover above 180 °C at a nominal bias of 3 V. Defrosting tests demonstrate their thermal capability to swiftly remove thin ice layers in sub-zero temperatures.

BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs.

Goal: Contractile response and calcium handling are central to understanding cardiac function and physiology, yet existing methods of analysis to quantify these metrics are often time-consuming, prone to mistakes, or require specialized equipment/license. We developed BeatProfiler, a suite of cardiac analysis tools designed to quantify contractile function, calcium handling, and force generation for multiple in vitro cardiac models and apply downstream machine learning methods for deep phenotyping and classification. Methods: We first validate BeatProfiler's accuracy, robustness, and speed by benchmarking against existing tools with a fixed dataset. We further confirm its ability to robustly characterize disease and dose-dependent drug response. We then demonstrate that the data acquired by our automatic acquisition pipeline can be further harnessed for machine learning (ML) analysis to phenotype a disease model of restrictive cardiomyopathy and profile cardioactive drug functional response. To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). Results: Benchmarking against existing tools revealed that BeatProfiler detected and analyzed contraction and calcium signals better than existing tools through improved sensitivity in low signal data, reduction in false positives, and analysis speed increase by 7 to 50-fold. Of signals accurately detected by published methods (PMs), BeatProfiler's extracted features showed high correlations to PMs, confirming that it is reliable and consistent with PMs. The features extracted by BeatProfiler classified restrictive cardiomyopathy cardiomyocytes from isogenic healthy controls with 98% accuracy and identified relax90 as a top distinguishing feature in congruence with previous findings. We also show that our TCN-BiLSTM model was able to classify drug-free control and 4 cardiac drugs with different mechanisms of action at 96% accuracy. We further apply Grad-CAM on our convolution-based models to identify signature regions of perturbations by these drugs in calcium signals. Conclusions: We anticipate that the capabilities of BeatProfiler will help advance in vitro studies in cardiac biology through rapid phenotyping, revealing mechanisms underlying cardiac health and disease, and enabling objective classification of cardiac disease and responses to drugs.

An engineered human cardiac tissue model reveals contributions of systemic lupus erythematosus autoantibodies to myocardial injury.

Systemic lupus erythematosus (SLE) is a highly heterogenous autoimmune disease that affects multiple organs, including the heart. The mechanisms by which myocardial injury develops in SLE, however, remain poorly understood. Here we engineered human cardiac tissues and cultured them with IgG fractions containing autoantibodies from SLE patients with and without myocardial involvement. We observed unique binding patterns of IgG from two patient subgroups: (i) patients with severe myocardial inflammation exhibited enhanced binding to apoptotic cells within cardiac tissues subjected to stress, and (ii) patients with systolic dysfunction exhibited enhanced binding to the surfaces of viable cardiomyocytes. Functional assays and RNA sequencing (RNA-seq) revealed that IgGs from patients with systolic dysfunction exerted direct effects on engineered tissues in the absence of immune cells, altering tissue cellular composition, respiration and calcium handling. Autoantibody target characterization by phage immunoprecipitation sequencing (PhIP-seq) confirmed distinctive IgG profiles between patient subgroups. By coupling IgG profiling with cell surface protein analyses, we identified four pathogenic autoantibody candidates that may directly alter the function of cells within the myocardium. Taken together, these observations provide insights into the cellular processes of myocardial injury in SLE that have the potential to improve patient risk stratification and inform the development of novel therapeutic strategies.

Soccer's AI transformation: deep learning's analysis of soccer's pandemic research evolution.

Introduction: This paper aims to identify and compare changes in trends and research interests in soccer articles from before and during the COVID-19 pandemic. Methods: We compared research interests and trends in soccer-related journal articles published before COVID-19 (2018-2020) and during the COVID-19 pandemic (2021-2022) using Bidirectional Encoder Representations from Transformers (BERT) topic modeling. Results: In both periods, we categorized the social sciences into psychology, sociology, business, and technology, with some interdisciplinary research topics identified, and we identified changes during the COVID-19 pandemic period, including a new approach to home advantage. Furthermore, Sports science and sports medicine had a vast array of subject areas and topics, but some similar themes emerged in both periods and found changes before and during COVID-19. These changes can be broadly categorized into (a) Social Sciences and Technology; (b) Performance training approaches; (c) injury part of body. With training topics being more prominent than match performance during the pandemic; and changes within injuries, with the lower limbs becoming more prominent than the head during the pandemic. Conclusion: Now that the pandemic has ended, soccer environments and routines have returned to pre-pandemic levels, but the environment that have changed during the pandemic provide an opportunity for researchers and practitioners in the field of soccer to detect post-pandemic changes and identify trends and future directions for research.

Modeling and countering the effects of cosmic radiation using bioengineered human tissues.

Cosmic radiation is the most serious risk that will be encountered during the planned missions to the Moon and Mars. There is a compelling need to understand the effects, safety thresholds, and mechanisms of radiation damage in human tissues, in order to develop measures for radiation protection during extended space travel. As animal models fail to recapitulate the molecular changes in astronauts, engineered human tissues and "organs-on-chips" are valuable tools for studying effects of radiation in vitro. We have developed a bioengineered tissue platform for studying radiation damage in individualized settings. To demonstrate its utility, we determined the effects of radiation using engineered models of two human tissues known to be radiosensitive: engineered cardiac tissues (eCT, a target of chronic radiation damage) and engineered bone marrow (eBM, a target of acute radiation damage). We report the effects of high-dose neutrons, a proxy for simulated galactic cosmic rays, on the expression of key genes implicated in tissue responses to ionizing radiation, phenotypic and functional changes in both tissues, and proof-of-principle application of radioprotective agents. We further determined the extent of inflammatory, oxidative stress, and matrix remodeling gene expression changes, and found that these changes were associated with an early hypertrophic phenotype in eCT and myeloid skewing in eBM. We propose that individualized models of human tissues have potential to provide insights into the effects and mechanisms of radiation during deep-space missions and allow testing of radioprotective measures.

Ultrathin Metal Film on Graphene for Percolation-Threshold-Limited Thermal Emissivity Control.

Translucent Au/graphene hybrid films are shown to be effective in reducing thermal emission from the underlying surfaces when the deposition thickness of Au is close to the percolation threshold. The critical Au deposition thickness for an abrupt change in emissivity is reduced from 15 nm (Si substrate) to a percolation-threshold-limited thickness of 8.5 nm (graphene/Si substrate) because of the chemical inertness of graphene leading to the deposited Au atoms forming a thin, crystalline layer. The effect of the graphene layer on the optical properties of the hybrid film is highlighted by a drastic increase in infrared absorptivity, whereas the visible absorptivity is marginally affected by the presence of a graphene layer. The level of thermal emission from the Au/graphene hybrid films with the percolation-threshold-limited Au thickness is stable even with high background temperatures of up to 300 °C and mechanical strains of ≈4%. As an example of a thermal management application, an anti-counterfeiting device is demonstrated; thermal-camouflage-masked text fabricated with an Au/graphene hybrid film is discernible only using a thermographic camera. Ultrathin metal film assisted by a graphene layer will provide a facile platform for thermal management with semi-transparency, flexibility, and transferability to arbitrary surfaces.

Engineered cardiac tissue model of restrictive cardiomyopathy for drug discovery.

Restrictive cardiomyopathy (RCM) is defined as increased myocardial stiffness and impaired diastolic relaxation leading to elevated ventricular filling pressures. Human variants in filamin C (FLNC) are linked to a variety of cardiomyopathies, and in this study, we investigate an in-frame deletion (c.7416_7418delGAA, p.Glu2472_Asn2473delinAsp) in a patient with RCM. Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with this variant display impaired relaxation and reduced calcium kinetics in 2D culture when compared with a CRISPR-Cas9-corrected isogenic control line. Similarly, mutant engineered cardiac tissues (ECTs) demonstrate increased passive tension and impaired relaxation velocity compared with isogenic controls. High-throughput small-molecule screening identifies phosphodiesterase 3 (PDE3) inhibition by trequinsin as a potential therapy to improve cardiomyocyte relaxation in this genotype. Together, these data demonstrate an engineered cardiac tissue model of RCM and establish the translational potential of this precision medicine approach to identify therapeutics targeting myocardial relaxation.

LDA-based topic modeling for COVID-19-related sports research trends.

Introduction: The COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature. Methods: Sports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term 'COVID-19 sports'. Results: As a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. 'Sports participation', 'elite players', and 'sports industry' were macroscopically classified, and detailed research topics could be identified from each division. Conclusion: This study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research.

High-index-contrast photonic structures: a versatile platform for photon manipulation.

In optics, the refractive index of a material and its spatial distribution determine the characteristics of light propagation. Therefore, exploring both low- and high-index materials/structures is an important consideration in this regard. Hollow cavities, which are defined as low-index bases, exhibit a variety of unusual or even unexplored optical characteristics and are used in numerous functionalities including diffraction gratings, localised optical antennas and low-loss resonators. In this report, we discuss the fabrication of hollow cavities of various sizes (0.2-5 µm in diameter) that are supported by conformal dielectric/metal shells, as well as their specific applications in the ultraviolet (photodetectors), visible (light-emitting diodes, solar cells and metalenses), near-infrared (thermophotovoltaics) and mid-infrared (radiative coolers) regions. Our findings demonstrate that hollow cavities tailored to specific spectra and applications can serve as versatile optical platforms to address the limitations of current optoelectronic devices. Furthermore, hollow cavity embedded structures are highly elastic and can minimise the thermal stress caused by high temperatures. As such, future applications will likely include high-temperature devices such as thermophotovoltaics and concentrator photovoltaics.

Efficient learning representation of noise-reduced foam effects with convolutional denoising networks.

This study proposes a neural network framework for modeling the foam effects found in liquid simulation without noise. The position and advection of the foam particles are calculated using the existing screen projection method, and the noise problem that occurs in this process is prevented by using the neural network. A significant problem in the screen projection approach is the noise generated in the projection map during the projecting of momentum onto the discretized screen space. We efficiently solve this problem by utilizing a denoising neural network. Following the selection of the foam generation area using a projection map, the foam particles are generated through the inverse transformation of the 2D space into 3D space. This solves the problem of small-sized foam dissipation that occurs in conventional denoising networks. Furthermore, by integrating the proposed algorithm with the screen-space projection framework, it is able to maintain all the advantages of this approach. In conclusion, the denoising process and clean foam effects enable the proposed network to model the foam effects stably.

Saliency guided data augmentation strategy for maximally utilizing an object's visual information.

Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing augmented images, and additionally, patches that do not contain objects might be included, which degrades performance. In this paper, we propose a novel augmentation method that mixes patches in a non-overlapping manner after they are extracted from the salient regions in an image. The suggested method can make effective use of object characteristics, because the constructed image consists only of visually important regions and is robust to noise. Since the patches do not occlude each other, the semantically meaningful information in the salient regions can be fully utilized. Additionally, our method is more robust to adversarial attack than the conventional augmentation method. In the experimental results, when Wide ResNet was trained on the public datasets, CIFAR-10, CIFAR-100 and STL-10, the top-1 accuracy was 97.26%, 83.99% and 82.40% respectively, which surpasses other augmentation methods.

milliPillar: A Platform for the Generation and Real-Time Assessment of Human Engineered Cardiac Tissues.

Engineered cardiac tissues derived from human induced pluripotent stem cells (iPSCs) are increasingly used for drug discovery, pharmacology and in models of development and disease. While there are numerous platforms to engineer cardiac tissues, they often require expensive and nonconventional equipment and utilize complex video-processing algorithms. As a result, only specialized academic laboratories have been able to harness this technology. In addition, methodologies and tissue features have been challenging to reproduce between different groups and models. Here, we describe a facile technology (milliPillar) that covers the entire pipeline required for studies of engineered cardiac tissues. We include methodologies for (i) platform fabrication, (ii) cardiac tissue generation, (iii) electrical stimulation, (iv) automated real-time data acquisition, and (v) advanced video analyses. We validate these methodologies and demonstrate the versatility of the platform by showcasing the fabrication of tissues in different hydrogel materials and using cardiomyocytes derived from different iPSC lines in combination with different types of stromal cells. We also validate the long-term culture of tissues within the platform and provide protocols for automated analysis of force generation and calcium flux using both brightfield and fluorescence imaging. Lastly, we demonstrate the compatibility of the milliPillar platform with electromechanical stimulation to enhance cardiac tissue function. We expect that this resource will provide a valuable and user-friendly tool for the generation and real-time assessment of engineered human cardiac tissues for basic and translational studies.

Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile.

Cardiomyocytes derived from human induced pluripotent stem (iPS) cells enable the study of cardiac physiology and the developmental testing of new therapeutic drugs in a human setting. In parallel, machine learning methods are being applied to biomedical science in unprecedented ways. Machine learning has been used to distinguish healthy from diseased cardiomyocytes using calcium (Ca2+) transient signals. Most Ca2+ transient signals are obtained via terminal assays that do not permit longitudinal studies, although some recently developed options can circumvent these concerns. Here, we describe the use of machine learning to identify healthy and diseased cardiomyocytes according to their contractility profiles, which are derived from brightfield videos. This noncontact, label-free approach allows for the continued cultivation of cells after they have been evaluated for use in other assays and can be readily extended to organs-on-chip. To demonstrate utility, we assessed contractility profiles of cardiomyocytes obtained from patients with Timothy Syndrome (TS), a long QT disease which can lead to fatal arrhythmias, and from healthy individuals. The videos were processed and classified using machine learning methods and their performance was evaluated according to several parameters. The trained algorithms were able to distinguish the TS cardiomyocytes from healthy controls and classify two different healthy controls. The proposed computational machine learning evaluation of human iPS cell-derived cardiomyocytes' contractility profiles has the potential to identify other genetic proarrhythmic events, screen therapeutic agents for inducing or suppressing long QT events, and predict drug-target interactions. The same approach could be readily extended to the evaluation of engineered cardiac tissues within single-tissue and multi-tissue organs-on-chip.

Cooling Metals via Gap Plasmon Resonance.

We report highly emissive and radiatively cooled metallic surfaces that sustain multiple and high-amplitude gap plasmon cavity modes within the principal thermal radiation spectrum at room temperature (i.e., 8-13 µm). A square-lattice array of Cu/ZnS/Cu gap plasmon cavities with five different widths was designed to avoid the near-field coupling between adjacent cavities and the anticrossing of a cavity mode and the first diffraction mode. The gap plasmon cavities fabricated on a Si substrate exhibited an effective emissivity of >0.62, up to an incidence of 60°. Outdoor solar heating experiments showed that the Cu/ZnS/Cu multicavity array lowered the Si substrate temperature by 4 °C at a maximum solar irradiance of 800 W/m2, which is equivalent to a near-one-sun intensity, relative to a planar Cu/ZnS/Cu multilayer. Such mid-infrared spectrum management of metals enables heat dissipation via radiation, which will be further utilized for designing electrodes that cool optoelectronic devices with the same metal/dielectric/metal configuration.

Identification of Cathepsin D as a Plasma Biomarker for Alzheimer's Disease.

Although Alzheimer's disease (AD) is the most common neurodegenerative disease, there are still no drugs available to treat or prevent AD effectively. Here, we examined changes in levels of selected proteins implicated in the pathogenesis of AD using plasma samples of control subjects and patients with cognition impairment. To precisely categorize the disease, fifty-six participants were examined with clinical cognitive tests, amyloid positron emission tomography (PET) scan, and white matter hyperintensities scored by magnetic resonance imaging. Plasma cathepsin D levels of the subjects were examined by immunoblotting and enzyme-linked immunosorbent assay (ELISA). Correlation of plasma cathepsin D levels with AD-related factors and clinical characteristics were examined by statistical analysis. By analyzing quantitative immunoblot and ELISA, we found that the plasma level of cathepsin D, a major lysosomal protease, was decreased in the group with amyloid plaque deposition at the brain compared to the control group. The level of plasma cathepsin D was negatively correlated with clinical dementia rating scale sum of boxes (CDR-SB) scores. In addition, our integrated multivariable logistic regression model suggests the high performance of plasma cathepsin D level for discriminating AD from non-AD. These results suggest that the plasma cathepsin D level could be developed as a diagnostic biomarker candidate for AD.

Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records.

Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the results for complex and big EHR data. We used a system entailing sequence mining, interpretable deep learning models, and visualization on data extracted from the MIMIC-IIIdatabase for a group of patients diagnosed with heart disease. The results of sequence mining corresponded to specific pathways of interest to medical staff and were used to select patient groups that underwent these pathways. An interactive Sankey diagram representing these pathways and a heat map visually representing the weight of each variable were developed for temporal and quantitative illustration. We applied the proposed system to predict unplanned cardiac surgery using clinical pathways determined by sequence pattern mining to select cardiac surgery from complex EHRs to label subject groups and deep learning models. The proposed system aids in the selection of pathway-based patient groups, simplification of labeling, and exploratory the interpretation of the modeling results. The proposed system can help medical staff explore various pathways that patients have undergone and further facilitate the testing of various clinical hypotheses using big data in the medical domain.

Tuning of polarized room-temperature thermal radiation based on nanogap plasmon resonance.

When a one-dimensional (1D) metal array is coupled to a planar metal mirror with a dielectric gap, localized plasmon resonance is excited inside the gap at a specific polarization of light in free space. Herein, we report on the completely polarized, mid-infrared thermal radiation that is released from gap plasmon resonators with a nanometer-thick dielectric. We fabricated nanogap plasmon resonators with 1D Au or Ni array of various widths (w) using laser interference lithography. An atomic layer deposition process was used to introduce a 10 nm-thick alumina gap between a 1D metal array and a planar metal mirror. It was observed that only for the Au nanogap plasmon resonators, high-amplitude absorption peaks that were attributed to gap plasmon modes with different orders appeared at discrete wavelengths in a polarization-resolved spectrum. In addition, all the pronounced peaks were gradually redshifted with increasing w. At w = 1.2-1.6 µm, the fundamental gap plasmon mode was tuned to the main wavelengths (8-9 µm) of thermal radiation at room temperature (e.g., ∼300 K), which led to polarization-selective camouflage against standard infrared thermal imaging. The results of electromagnetic simulations quantitatively agreed with the measured absorbance spectra in both peak wavelength and amplitude. We believe that these experimental efforts towards achieving radiation/absorption spectra tailored at mid-infrared wavelengths will be further exploited in thermal-radiation harnessed energy devices, spectroscopic sensors, and radiative coolers.