Search Results (74)

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases. Full article

Background: Reliable and non-invasive methods for assessing welfare in poultry are essential for improving evidence-based welfare monitoring and advancing management practices in commercial production systems. The iris-to-pupil (IP) ratio, previously validated by our group in primates and cattle, reflects autonomic nervous system balance and may serve as a physiological indicator of stress in laying hens. This study evaluated the utility of the IP ratio under field conditions across diverse commercial layer housing systems. Materials and Methods: In total, 296 laying hens (Lohmann Brown, n = 269; White Leghorn, n = 27) were studied across four locations in Canada housed under different systems: Guelph (indoor; pen), Spring Island (outdoor and scratch; organic), Ottawa (outdoor, indoor and scratch; free-range), and Toronto (outdoor and hobby; free-range). High-resolution photographs of the eye were taken under ambient lighting. Light intensity was measured using the light meter app. The IP ratio was calculated using NIH ImageJ software (Version 1.54p). Statistical analysis included one-way ANOVA and linear regression using GraphPad Prism (Version 5). Results: Birds housed outdoors had the highest IP ratios, followed by those in scratch systems, while indoor and pen-housed birds had the lowest IP ratios (p < 0.001). Subgroup analyses of birds in Ottawa and Spring Island farms confirmed significantly higher IP ratios in outdoor environments compared to indoor and scratch systems (p < 0.001). The IP ratio correlated weakly with ambient light intensity (r² = 0.25) and age (r² = 0.05), indicating minimal influence of these variables. Although White Leghorn hens showed lower IP ratios than Lohmann Browns, this difference was confounded by housing type; all White Leghorns were housed in pens. Thus, housing system but not breed was the primary driver of IP variation. Conclusions: The IP ratio is a robust, non-invasive physiological marker of welfare assessment in laying hens, sensitive to housing environment but minimally influenced by light or age. Its potential for integration with digital imaging technologies supports its use in scalable welfare assessment protocols. Full article

Three-dimensional (3D) cellular models, such as spheroids, serve as pivotal systems for understanding complex biological phenomena in histology, oncology, and tissue engineering. In response to the growing need for advanced imaging capabilities, we present a novel multi-modal Raman light sheet microscope designed to capture elastic (Rayleigh) and inelastic (Raman) scattering, along with fluorescence signals, in a single platform. By leveraging a shorter excitation wavelength (532 nm) to boost Raman scattering efficiency and incorporating robust fluorescence suppression, the system achieves label-free, high-resolution tomographic imaging without the drawbacks commonly associated with near-infrared modalities. An accompanying Deep Image Prior (DIP) seamlessly integrates with the microscope to provide unsupervised denoising and resolution enhancement, preserving critical molecular details and minimizing extraneous artifacts. Altogether, this synergy of optical and computational strategies underscores the potential for in-depth, 3D imaging of biomolecular and structural features in complex specimens and sets the stage for future advancements in biomedical research, diagnostics, and therapeutics. Full article

This paper presents a complete system for autonomous navigation in GPS-denied environments using a minimal sensor suite that operates onboard a robotic vehicle. Our system utilizes a single camera and, given a target destination without prior knowledge of the environment, replans in real time to generate a collision-free trajectory that avoids static and dynamic obstacles. To achieve this, we introduce, for the first time, a local Euclidean Signed Distance Field (ESDF) map with variable size and resolution, which scales as a function of the vehicle’s velocity. The map is updated at a high rate, requiring minimal computational power. Additionally, a short-term vicinity-based memory is maintained for previously observed areas to facilitate smooth trajectory generation, addressing the limited field-of-view provided by the RGB-D camera. System validation is carried out by deploying our algorithm on a differential drive vehicle in both simulation and real-world experiments involving static and dynamic obstacles. We benchmark our robotic system against state-of-the-art autonomous navigation frameworks, successfully navigating to designated target locations while avoiding obstacles in both static and dynamic scenarios, all without introducing additional computational overhead. Our approach consistently achieves the target goals even in complex settings where current state-of-the-art methods may fall short. Full article

Direction of arrival (DOA) estimation plays a critical role in remote sensing, where it aids in identifying and tracking multiple targets across complex environments, from atmospheric monitoring to resource mapping. Leveraging difference covariance array (DCA) for DOA estimation has become prevalent, particularly with sparse arrays capable of resolving more targets than the number of sensors. This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. By symmetrically placing a minimum redundancy array (MRA) and its complementary MRA on both sides of a sparse uniform linear array (ULA), this configuration maximizes degrees of freedom (DOFs) and minimizes mutual coupling effects. Expressions for calculating sensor positions and optimal element allocation methods to maximize DOFs are derived. Simulation experiments in various scenarios have shown the advantages of the proposed array in DOA estimation, including a strong ability to estimate multi-targets, high angular resolution, low estimation error, and strong robustness to mutual coupling. Full article

Real-time GIS enables multiple geographically dislocated users to collaboratively edit geospatial data. However, being based on the strong consistency model, traditional real-time GIS implementations cannot provide fully automatic conflict resolution. In highly dynamic situations with increased probability for conflicts, this will hinder user experience. Conflict-free replicated data types (CRDTs), a technology based on a more relaxed concurrency control model called strong eventual consistency, can resolve all conflicts in real time, letting the users work on their local copies of the data without any restrictions. The application of CRDTs to real-time geospatial geometry co-editing has, to the best of our knowledge, not been investigated. Within this research, we therefore developed a simple web-based real-time geospatial geometry co-editing system using an existing CRDT implementation in Javascript coupled with OpenLayers. When applied to the co-editing of geospatial geometry in its native form, standard CRDT conflict resolution mechanics exhibit some issues. As an attempt to address these issues, we developed an advanced operation generation technique named “tentative operations”. This technique allows for the operations to be generated over the most recent session-wide state of the data, which in effect highly reduces concurrency and provides “geometry aware” conflict resolution. The tests we conducted using the developed system showed that in low-latency network conditions, the negative effects of standard CRDT conflict resolution mechanics do get minimized even under increased system loads. Full article

Thin clouds in Remote Sensing (RS) imagery can negatively impact subsequent applications. Current Deep Learning (DL) approaches often prioritize information recovery in cloud-covered areas but may not adequately preserve information in cloud-free regions, leading to color distortion, detail loss, and visual artifacts. This study proposes a Sparse Transformer-based Generative Adversarial Network (SpT-GAN) to solve these problems. First, a global enhancement feature extraction module is added to the generator’s top layer to enhance the model’s ability to preserve ground feature information in cloud-free areas. Then, the processed feature map is reconstructed using the sparse transformer-based encoder and decoder with an adaptive threshold filtering mechanism to ensure sparsity. This mechanism enables that the model preserves robust long-range modeling capabilities while disregarding irrelevant details. In addition, inverted residual Fourier transformation blocks are added at each level of the structure to filter redundant information and enhance the quality of the generated cloud-free images. Finally, a composite loss function is created to minimize error in the generated images, resulting in improved resolution and color fidelity. SpT-GAN achieves outstanding results in removing clouds both quantitatively and visually, with Structural Similarity Index (SSIM) values of 98.06% and 92.19% and Peak Signal-to-Noise Ratio (PSNR) values of 36.19 dB and 30.53 dB on the RICE1 and T-Cloud datasets, respectively. On the T-Cloud dataset, especially with more complex cloud components, the superior ability of SpT-GAN to restore ground details is more evident. Full article

True time delay digital beamforming enables large squint-free bandwidths and high beamcounts, ideal for Low Earth Orbit (LEO) satellite communication links. This work proposes a true time delay architecture using Variable Fractional Delay (VFD). True time delay eliminates many analog beamforming performance constraints including inaccurate beam steering and limited beamcounts, while managing system quantization error. This article presents a method of implementing true time delay using a VFD digital filter with sufficient time resolution to minimize quantization error and enable both gigahertz bandwidths and sampling frequencies. Simulations of antenna patterns utilizing the proposed VFD digital filters demonstrate satisfactory LEO beamforming performance with only a 29-tap filter. The VFD filter was implemented using a Xilinx Virtex Ultrascale FPGA and demonstrated a 1077% reduction in dynamic power and a minimum 498% reduction in logic resources, with only a modest increase in multipliers required when compared to Farrow-based architectures previously proposed in the literature. Full article

The edge detection method based on the magnetic gradient tensor data plays an important role in magnetic exploration because it is free from geomagnetic interference and contains more abundant information. This paper proposes a new anomaly edge detection method using the magnetic gradient tensor components. The model is established to compare with other methods, such as directional total horizontal derivative (THD_z), analytical signal (AS), tilt angle, theta map, and so on, under conditions of vertical magnetization, oblique magnetization, and noise interference. Through the study of the anomaly distribution of the rectangular model, it is observed that the edge detection method proposed in this paper is nearly impervious to noise interference, exhibits strong anti-interference capabilities, delivers a high-quality boundary identification effect, and provides greater accuracy in anomaly edges with minimal error. When multiple anomalous bodies are present, the edge detection results are less susceptible to interference from each other, resulting in higher resolution. The efficiency of the algorithm is demonstrated by real magnetic data from some study areas in Jiangxi Province, China. The experimental results show that the proposed method is more precise and accurate than the total horizontal derivative, analytical signal, tilt angle, and theta map methods. Full article

In recent advancements, the traditional von Neumann architecture has been challenged by the computational needs of AI. This is due to its high power and data transfer costs. As a solution, the computing-in-memory (CIM) architecture, which combines storage and computation, has gained attention for its superior computational power and energy efficiency. Within CIM, using resistive random access memory (RRAM) arrays, the readout circuit, which converts analog outputs from multiply–accumulate operations into digital signals, faces limitations due to its area and power consumption. There are mainly two types of CIM readout circuits for analog types: the traditional ADC type and the non-traditional type. This paper presents two types of readout circuit designs. The first is a low-power, compact successive approximation register (SAR) analog-to-digital converter (ADC) readout circuit. The core circuit is an 8-bit SAR ADC operating at 70 MS/s. It incorporates a linearity-improved bootstrapped switch to minimize leakage and enhance linearity, whose spurious-free dynamic range (SFDR) has been improved by 10.1 dB from 76.78 dB to 86.88 dB, and whose signal-to-noise and distortion ratio (SNDR) has increased by 4.56 dB from 75.13 dB to 79.69 dB. The delay of a transconductance-enhanced dynamic comparator is reduced from 184 ps to 149 ps, presenting a performance improvement of approximately 20%. Concurrently, the energy consumption decreased from 178 μm to 132 μm, attaining an improvement of roughly 26%. A “sandwich” capacitor structure is used that reduces the overall area of the layout. After layout and post-simulation, this circuit occupies only 49.6 μm × 51.5 μm, consumes 553 μW power, has a SINAD of 46.22 dB, and has an SFDR of 57.21 dB. The second is a current controlled oscillator (CCO)-type readout circuit, which comprises a CCO oscillator with low process-sensitivity. The readout circuit also utilizes an op-amp and current mirrors for a negative feedback loop, ensuring a constant voltage across the RRAM arrays. The frequency generated through the CCO is controlled by the current, and quantified by a counter, supporting different weights quantification per ReRAM column without additional digital weighting. This circuit achieves 95-level resolution, 5.2 μs delay, and an average consumption of 183.1 μW. A comparative analysis highlights that traditional ADC readout circuits offer high resolution and speed but are limited by their high power and area costs, often overshadowing CIM arrays’ benefits. Thus, for applications with more lenient resolution and speed requirements, non-traditional readout circuits present considerable advantages. Full article

Background and Objectives: Ketogenic diet therapy (KDT) has been used as a non-pharmacological treatment for childhood refractory epilepsy. Its efficacy and safety have been described in numerous studies and reviews. However, there have been fewer studies evaluating the challenges experienced by patients and their family members when starting KDT. When implementing a new treatment method, challenges arise for both the healthcare professionals and patients, making it important to summarize the initial results and compare them with the experiences of other centers. To analyze and evaluate the efficacy and safety of KDT in children with epilepsy, as well as to consider the challenges faced by their parents/caregivers. Materials and Methods: A retrospective analysis of patients’ data (N = 30) and an analysis of the completed questionnaires of the parents/caregivers (N = 22) occurred. Results: In the study group, 66.7% of the patients had a >50% decrease in seizure frequency, and 2/3 of them had a >90% decrease in seizure frequency or were seizure-free, which enabled reducing the anti-seizure medications in 36.4% of the patients, as well as reducing the hospital visits. Cognitive improvement and better alertness were subjectively reported by 59.1% of the parents/caregivers. No dangerous long-term adverse effects of KDT have been observed in the study group. The patients with generalized epilepsy experienced significantly more adverse events. Most of the adverse effects of KDT were related to the digestive system, but usually they were temporary and controllable. The challenges of the parents/caregivers were mostly related to social life issues and financial difficulties; the medical-related challenges were minimal. Conclusions: KDT is an effective and safe treatment option for children with drug-resistant epilepsy, and the challenges faced by families are resolvable. In order to ensure effective KDT, a multidisciplinary team is required. This would ensure smooth and comprehensive care and the timely resolution of emerging problems. The cooperation of the families undergoing KDT is also important, enabling them to share their experiences. Full article

Cardiomyocyte hypertrophy, characterized by an increase in cell size, is associated with various cardiovascular diseases driven by factors including hypertension, myocardial infarction, and valve dysfunction. In vitro primary cardiomyocyte culture models have yielded numerous insights into the intrinsic and extrinsic mechanisms driving hypertrophic growth. However, due to limitations in current approaches, the dynamics of cardiomyocyte hypertrophic responses remain poorly characterized. In this study, we evaluate the application of the Holomonitor M4 digital holographic imaging microscope to track dynamic changes in cardiomyocyte surface area and volume in response to norepinephrine treatment, a model hypertrophic stimulus. The Holomonitor M4 permits non-invasive, label-free imaging of three-dimensional changes in cell morphology with minimal phototoxicity, thus enabling long-term imaging studies. Untreated and norepinephrine-stimulated primary neonatal rat cardiomyocytes were live-imaged on the Holomonitor M4, which was followed by image segmentation and single-cell tracking using the HOLOMONITOR App Suite software version 4.0.1.546. The 24 h treatment of cultured cardiomyocytes with norepinephrine increased cardiomyocyte spreading and optical volume as expected, validating the reliability of the approach. Single-cell tracking of both cardiomyocyte surface area and three-dimensional optical volume revealed dynamic increases in these parameters throughout the 24 h imaging period, demonstrating the potential of this technology to explore cardiomyocyte hypertrophic responses with greater temporal resolution; however, technological limitations were also observed and should be considered in the experimental design and interpretation of results. Overall, leveraging the unique advantages of the Holomonitor M4 digital holographic imaging system has the potential to empower future work towards understanding the molecular and cellular mechanisms underlying cardiomyocyte hypertrophy with enhanced temporal clarity. Full article

Three-dimensional shape sensing in soft and continuum robotics is a crucial aspect for stable actuation and control in fields such as minimally invasive surgery, engine repairs and search and rescue operations, as the estimation of complex curvatures while using continuum robotic tools is required to manipulate through fragile paths. This challenge has been addressed using a range of different sensing techniques, for example, Fibre Bragg grating (FBG) technology, inertial measurement unit (IMU) sensor networks, or stretch sensors. Previously, an optics-based method using optoelectronic sensors was explored, offering a simple and cost-effective solution for shape sensing in a flexible tendon-actuated manipulator in two orientations. This was based on proximity-modulated angle estimation and has been the basis for the shape sensing method addressed in this paper. The improved and miniaturised technique demonstrated in this paper is based on the use of a convex shaped reflector with optoelectronic sensors integrated into a tendon-actuated robotic manipulator. Upgraded sensing capability is achieved using optimisation of the convex reflector shape in terms of sensor range and resolution, and improved calibration is achieved through the integration of spherical bearings for friction-free motion. Shape estimation is achieved in two orientations upon calibration of sensors, with a maximum Root-Mean-Square Error (RMS) of 3.37°. Full article

Podostroma cornu-damae, one of the lethal toxic mushrooms, is known to contain macrocyclic trichothecene mycotoxins exhibiting potent cytotoxic effects, attracting attention as an important research subject for scientists interested in natural product chemistry and toxicity research. To investigate the mycotoxins from the toxic mushroom P. cornu-damae and evaluate their cytotoxic activities, the fungus was large-cultured on solid plates and successively extracted to acquire a crude methanol (MeOH) extract. After performing successive separation and purification processes, a total of eight macrocyclic trichothecenes were isolated from the MeOH extract of plate cultures of P. cornu-damae using the liquid chromatography/mass spectrometry (LC/MS)-guided isolation technique. Extensive interpretation of nuclear magnetic resonance (NMR) spectroscopic and high-resolution (HR)-electrospray ionization (ESI)-MS data allowed for the structural identification of all isolated macrocyclic trichothecenes, including satratoxin I (1), satratoxin H (2), roridin E (3), miophytocen D (4), roridin L-2 (5), trichoverritone (6), 12′-episatratoxin H (7), and roridin F (8). We conducted a cytotoxicity evaluation of compounds 1–8 against 4T1 breast cancer cells and fibroblast cell lines (L929 cells) using the Counting Kit-8 (CCK-8) cell viability assay to validate their cytotoxic potential. Our results indicated that compounds 1–6 lack anti-cancer effects on 4T1 cells and have minimal impact on the viability of the fibroblast cell line, L929 cells. In contrast, compounds 7 and 8 exhibited no cytotoxicity in normal cells (L929) and demonstrated specific cytotoxicity in breast cancer cell lines. Notably, the cytotoxic effects of compounds 7 and 8 in 4T1 cells were significantly stronger than those observed with free doxorubicin. These findings suggest that compounds 7 and 8 may possess targeted anti-cancer effects, specifically against breast cancer cells, emphasizing their efficient and selective toxicity towards breast cancer cells. Full article

Cavitation resulting from underwater explosions in compressible multiphase or multicomponent flows presents significant challenges due to the dynamic nature of shock–cavitation–structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock–cavitation–structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface-capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces: a free surface and a rigid surface. The simulations are conducted using the unstructured compressible Navier–Stokes (UCNS3D) finite-volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilizing the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing. Full article