Search Results (161)

This paper addresses the problem of subsurface target localization from single-snapshot multimonostatic and multifrequency radar measurements. In this context, the use of subspace projection methods—known for their super-resolution capabilities—is hindered by the rank deficiency of the data correlation matrix and the lack of a Vandermonde structure, especially in near-field configurations and layered media. To overcome this issue, we propose a novel pre-processing strategy that transforms the measured data into the $\omega -k$ domain, thereby restoring the structural conditions required for subspace-based detection. The resulting algorithm, referred to as $\omega -k$ MUSIC, enables the application of subspace projection techniques in scenarios where traditional smoothing procedures are not viable. Numerical experiments in a 2-D scalar configuration demonstrate the effectiveness of the proposed method in terms of resolution and robustness under various noise conditions. A Monte Carlo simulation study is also included to provide a quantitative assessment of localization accuracy. Comparisons with conventional migration imaging highlight the superior performance of the proposed approach. Full article

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 Pro, aiming to improve measurement accuracy and field usability. A single snapshot of a tree, capturing both depth and RGB images, is used to reconstruct a 3D point cloud. The trunk orientation is estimated based on the point cloud to locate the breast height, enabling robust DBH estimation independent of the capture angle. The DBH is initially estimated by the geometrical relationship between trunk size on the image and the depth of the trunk. Finally, a pre-computed lookup table (LUT) is employed to improve the initial DBH estimates into accurate values. Experimental evaluation on 294 trees within a capture range of 0.25 m to 5 m demonstrates a mean absolute error of 0.53 cm and a root mean square error of 0.63 cm. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

Skeletal muscle regeneration requires a reliable source of myogenic progenitor cells capable of forming new fibers and creating a self-renewing satellite cell pool. Human induced pluripotent stem cell (hiPSC)-derived teratomas have emerged as a novel in vivo platform for generating skeletal myogenic progenitors, although in vivo studies to date have provided only an early single-time-point snapshot. In this study, we isolated a specific population of CD82⁺ ERBB3⁺ NGFR⁺ cells from human iPSC-derived teratomas and verified their long-term in vivo regenerative capacity following transplantation into NSG-mdx^4Cv mice. Transplanted cells engrafted, expanded, and generated human Dystrophin⁺ muscle fibers that increased in size over time and persisted stably long-term. A dynamic population of PAX7⁺ human satellite cells was established, initially expanding post-transplantation and declining moderately between 4 and 8 months as fibers matured. MyHC isoform analysis revealed a time-based shift from embryonic to neonatal and slow fiber types, indicating a slow progressive maturation of the graft. We further show that these progenitors can be cryopreserved and maintain their engraftment potential. Together, these findings give insight into the evolution of teratoma-derived human myogenic stem cell grafts, and highlight the long-term regenerative potential of teratoma-derived human skeletal myogenic progenitors. Full article

The transport of intensity equation enables quantitative phase imaging from only two axially displaced intensity images, facilitating the characterization of low-contrast samples like cells and microorganisms. However, the rapid selection of the correct defocused planes, crucial for real-time phase imaging of dynamic events, remains challenging. Additionally, the different images are normally acquired sequentially, further limiting phase-reconstruction speed. Here, we report on a system that addresses these issues and enables user-tuned defocusing with snapshot phase retrieval. Our approach is based on combining multi-color pulsed illumination with acousto-optic defocusing for microsecond-scale chromatic aberration control. By illuminating each plane with a different color and using a color camera, the information to reconstruct a phase map can be gathered in a single acquisition. We detail the fundamentals of our method, characterize its performance, and demonstrate live phase imaging of a freely moving microorganism at speeds of 150 phase reconstructions per second, limited only by the camera’s frame rate. Full article

Objective: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characteristics, potentially delaying diagnosis. Methods: A novel spectrum-aided vision enhancer (SAVE) that transforms standard RGB images into simulated narrowband imaging representations in a single step was proposed. The performances of five cutting-edge object detectors, based on You Look Only Once (YOLOv11, YOLOv10, YOLOv9, YOLOv8, and YOLOv5) models, were assessed across three lesion categories using white-light imaging (WLI) and SAVE modalities. Each YOLO model was trained separately on SAVE and WLI images, and performance was measured using precision, recall, and F1 score. Results: Among all tested configurations, YOLOv10 attained the highest overall performance, particularly under the SAVE modality, demonstrating superior precision and recall across the majority of lesion types. YOLOv9 exhibited robust performance, especially for dermatofibroma detection under SAVE, albeit slightly lagging behind YOLOv10. Conversely, YOLOv11 underperformed on acrochordon detection (cumulative F1  =  65.73%), and YOLOv8 and YOLOv5 displayed lower accuracy and higher false-positive rates, especially in WLI mode. Although SAVE improved the performance of YOLOv8 and YOLOv5, their results remained below those of YOLOv10 and YOLOv9. Conclusions: Combining the SAVE modality with advanced YOLO-based object detectors, specifically YOLOv10 and YOLOv9, markedly enhances the accuracy of lesion detection compared to conventional WLI, facilitating expedited real-time dermatological screening. These findings indicate that integrating snapshot-based narrowband imaging with deep learning object detection models can improve early diagnosis and has potential applications in broader clinical contexts. Full article

In this study, an optimization framework for turbomachinery blades using a hybrid surrogate model assisted by proper orthogonal decomposition (POD) is introduced and then applied to the aero-structural multidisciplinary design optimization of a transonic fan rotor, NASA Rotor 67. The rotor blade is optimized through blade sweeping controlled by Gaussian radial basis functions. Calculations of aerodynamic and structural performance are achieved through computational fluid dynamics and computational structural mechanics. With a number of performance snapshots, singular value decomposition is employed to extract the basis modes, which are then used as the kernel functions in training the POD-based hybrid model. The inverse multi-quadratic radial basis function is adopted to construct the response surfaces for the coefficients of kernel functions. Aerodynamic design optimization is first investigated to preliminarily explore the impact of blade sweeping. In the aero-structural optimization, the aerodynamic performance, and von Mises stress are considered equally important and incorporated into one single objective function with different weight coefficients. The results are given and compared in detail, demonstrating that the average stress is dependent on the aerodynamic loading, and the configuration with forward sweeping on inner spans and backward sweeping on outer spans is the most effective for increasing the adiabatic efficiency while decreasing the average stress when the total pressure ratio is constrained. Through this study, the optimization framework is validated and a practical configuration for reducing the stress in a transonic fan rotor is provided. Full article

Next-generation sequencing (NGS) has been well applied to assess genetic abnormalities in various biological samples to investigate disease mechanisms. With the advent of high-throughput and automatic testing platforms, NGS can identify radiation-sensitive and dose-responsive biomarkers, contributing to triage patients and determining risk groups for treatment in a nuclear emergency. While bulk NGS provides a snapshot of the average gene expression or genomic changes within a group of cells after the radiation, it cannot provide information on individual cells within the population. On the other hand, single-cell sequencing involves isolating individual cells and sequencing the genetic material from each cell separately. This approach allows for the identification of gene expression and genomic changes in individual cells, providing a high-resolution view of cellular diversity and heterogeneity within a sample. Single-cell sequencing is particularly useful to identify cell-specific features of dose-response and organ-response genes. While single-cell RNA sequencing (scRNA-seq) technology is still emerging in radiation research, it holds significant promise for identifying biomarkers related to radiation exposure and tailoring post-radiation medical care. This review aims to focus on current methods of radiation dosimetry and recently identified biomarkers associated with radiation exposure. Additionally, it addresses the development of NGS techniques in the context of radiation situations, such as cancer treatment and emergency events, with a particular emphasis on single-cell sequencing technology. Full article

As one of the key processes of photosynthesis, carbon fixation and reduction is one of the most important biochemical reactions on planet Earth. Yet, reducing oxidized carbon elements through directly harnessing solar energy by using water-soluble, simple enzymes continues to be challenging. Here, CO₂ and bicarbonate were found to be transformed into methanol by fluorescent protein mRuby by using light as the single energy input. The binding of substrates to mRuby chromophore was supported by crystallography and light spectrometry. Gas chromatography showed the generation of methanol in mRuby-bicarbonate aqueous solution upon sunlight illumination. Atomic-resolution serial structures of mRuby showed snapshots of the step-by-step reduction of bicarbonate and CO₂. The amino, imino, or carboxylate group of residues near the chromophore was within hydrogen bonding distances of the substrates, respectively. A decrease in fluorescence was observed upon binding of bicarbonate, and the energy liberated from fluorescence was presumably utilized for methanol production. This research represents an exciting example of sunlight-driven photobiocatalysis by water-soluble small proteins. The new, green, and sustainable mechanisms uncovered here indicated great promises to harness solar energy straightforwardly, for, i.e., fuel production and green chemistry. Full article

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective. Full article

Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms. Full article

Recent single-cell experiments that measure copy numbers of over 40 proteins in thousands of individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the statistical time-evolution of protein abundances in single cells, information that could yield insights into the mechanisms influencing the biochemical signaling kinetics of a cell. However, when multiple candidate models (i.e., mechanistic models applied to initial protein abundances) can potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. For example, popular approaches like Kullback–Leibler divergence and Akaike’s Information Criterion are often difficult to implement largely because mathematical expressions for the likelihoods of candidate models are typically not available. To perform model selection, we introduce an entropy-based approach that uses split-sample techniques to exploit the availability of large data sets and uses (1) existing generalized method of moments (GMM) software to estimate model parameters, and (2) standard kernel density estimators and a Gaussian copula to estimate candidate models. Using simulated data, we show that our approach can select the ”ground truth” from a set of competing mechanistic models. Then, to assess the relative support for a candidate model, we compute model selection probabilities using a bootstrap procedure. Full article

Background/Objectives: The increasing pressure from pathogens and parasites on Apis mellifera populations is resulting in significant colony losses. It is desirable to identify resistance-associated single-nucleotide polymorphisms (SNPs) and their variability for the purpose of breeding resilient honeybee lines. This study examined the genetic diversity of 13 SNPs previously studied for associations with various resistance-providing traits, including six linked to Varroa-specific hygiene, five linked to suppressed mite reproduction, one linked to immune response, and one linked to chalkbrood resistance. Methods: Genotyping was performed using a novel SNaPshot genotyping panel designed for this study. The sample pool consisted of 308 honeybee samples in total, covering all 77 administrative districts of the Czech Republic. Results: All examined loci were polymorphic. The frequency of positive alleles in our population is medium to low, depending on the specific SNP. An analysis of genotype frequencies revealed that most loci exhibited the Hardy–Weinberg equilibrium. A comparison of the allele and genotype frequencies of the same locus between samples from hives and samples from flowers revealed no significant differences. The genetic diversity, as indicated by the heterozygosity values, ranged from 0.05 to 0.50. The fixation index (F) was, on average, close to zero, indicating minimal influence of inbreeding or non-random mating on the genetic structure of the analyzed samples. Conclusions: The obtained results provide further insights into the genetic variation of SNPs associated with the immune response and resistance to pathogens in honeybee populations in the Czech Republic. This research provides a valuable foundation for future studies of honeybee diversity and breeding. Full article

Spectral imaging has been widely applied in plant phenotyping to assess corn leaf nitrogen status. Recent studies indicate that spatial variations within a single leaf’s multispectral image provide stronger signals for corn nitrogen estimation. However, current technologies for corn multispectral imaging cannot capture a large corn leaf segment with high-resolution and simple operation, limiting their efficiency and accuracy in nitrogen estimation. To address this gap, this study developed a proximal multispectral imaging device that can capture high-resolution snapshot multispectral images of a large segment of a single corn leaf. This device uses airflow to autonomously position and flatten the leaf to minimize the noise in images due to leaf curvature and simplify operation. Moreover, this device adopts a transmittance imaging regime by clamping the corn leaf between the camera and the lighting source to block the environmental lights and supply uniform lighting to capture high-resolution and high-precision leaf images within six seconds. A field assay was conducted to validate the effectiveness of the multispectral images captured by this device in assessing nitrogen status by classifying the nitrogen treatments applied to corn. Six nitrogen treatments were applied to 12 plots of corn fields, and 10 images were collected at each plot. By using the average vegetative index of the whole image, only one treatment was significantly different from the other five treatments, and no significant difference was observed among any other groups. However, by extracting the spatial and spectral features from the images and combining these features, the accuracy of nitrogen treatment classification improved compared to using the average index. In another analysis, by applying spatial–spectral analysis methods to the images, the nitrogen treatment classification accuracy has improved compared to using the average index. These results demonstrated the advantages of this high-resolution and high-throughput imaging device for distinguishing nitrogen treatments by facilitating spatial–spectral combined analysis for more precise classification. Full article

CYP2C9 metabolizes approximately 20% of clinically administered drugs. Several single-nucleotide polymorphisms (SNPs) of CYP2C9 (e.g., *2, *3, *8, and rs12777823) are used as biomarkers to predict CYP2C9 activity. However, a large proportion of variability in CYP2C9 expression remains unexplained. Background/Objectives: We previously identified a variable number tandem repeat (pVNTR) polymorphism in the CYP2C9 promoter. The short repeat (pVNTR-S) showed reduced transcriptional activity in reporter gene assays and was associated with decreased CYP2C9 mRNA expression. However, because pVNTR-S is in high linkage disequilibrium (LD) with CYP2C9*3 in the European population, whether pVNTR-S directly impacts CYP2C9 expression remains unclear. The objective of this study was to clarify the association between the pVNTR-S and CYP2C9 mRNA expression in human liver samples and to assess its impact on CYP2C9 expression independently of known CYP2C9 biomarkers. Methods: Gene expression was measured by real-time qPCR. SNPs and pVNTRs were genotyped using SNapShot assays and fragment analysis, respectively. Associations between CYP2C9 and the pVNTR-S or SNPs were analyzed using multiple linear regression. Results: Our results showed that pVNTR-S was associated with lower CYP2C9 expression (34% reduction, p-value = 0.032) in human liver samples (n = 247), while the known CYP2C9 biomarkers (CYP2C9*2, *3, *8, or rs12777823) were not. These results suggest that pVNTR-S reduces CYP2C9 expression independently of known biomarkers. Therefore, pVNTR-S may explain additional variability in CYP2C9 expression when present alone or in conjunction with other CYP2C9 alleles. Full article