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Complete spatiotemporal quantification of cardiac motion in mice through multi-view magnetic resonance imaging and super-resolution reconstruction
Abstract Background: Structural indices of cardiac diseases estimated via cardiac magnetic resonance imaging (CMR) have shown promise as early-stage markers. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive, especially in mice. The high metabolic rates and rapid cardiac motion affect high-resolution imaging, thus compromising strain accuracy. We hypothesize that a super-resolution reconstruction (SRR) framework that combines low-resolution scans at multiple orientations will enhance the reliability of complete spatiotemporal strains in mice. Methods: Multi-view cine CMR comprising short- and long-axis (SA and LA) fast low angle shot scans were obtained in a cohort of wild-type-mice (n = 5) and a diabetic mouse (n = 1). The “SRR in CMR” approach, consisting of tissue-class -specific scattered data interpolation, was used to generate full four-dimensional (4D) images of high spatial resolution. Image registration using the diffeomorphic demons algorithm was applied to quantify complete spatiotemporal motion in terms of 4D myocardial strains. The effects of SRR on CMR quality were verified in all mice through image metrics, namely, root mean squared error (MSE) and structural similarity index. Strain calculations were validated against an in silico heart model phantom through MSE analysis, followed by investigations of strain accuracy and reproducibility for all mice using MSE and coefficient of variation analyses. Results: SRR-derived strains were validated against a kinematic benchmark established through the in-silico heart model phantom. Improvements in global strain accuracy were confirmed in both in-plane (radial and circumferential) and through-plane (longitudinal) strains. Mouse-specific SRR provided near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain reproducibility and intra-cohort homogeneity in wild-type mice, with global longitudinal strain lying of ≈-14%. Conclusions: A comprehensive methodology was presented to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain analysis in small animals.
Experimental and predictive analysis of deep eutectic solvent gel membranes for efficient CO2 separation
Abstract The capture of Carbon Dioxide (CO2) is very relevant nowadays as global warming hits its peak. The separation of CO2 using membranes has received wide recognition by researchers because of its energy efficiency. Various Ionic Liquid supported membranes have been proven effective in this regard; however, their higher cost and toxicity are limitations, which opens possibilities for Deep Eutectic Solvents (DES). This work explains how DES gel membrane fabrication separates CO2 from CO2/CH4 mixtures. DES, composed of choline chloride and glycerol, is mixed with Pebax1657 polymer, and Polyvinylidene fluoride sheets are used as supports for casting. Fourier transform infrared spectroscopy has been used to confirm the synthesis of DES. X-ray Diffraction and Scanning Electron Microscopy analysis were used to analyse the membrane structure and cross-section. The physicochemical properties of DES are measured at a temperature range from 293.15 to 343.15 K. Pure and mixed gas permeabilities of CO2 and CH4 with increased pressure have been calculated. The highest permeability values obtained for pure and mixed gas CO2 were 138.98 Barrer and 93.17 Barrer, respectively. Density Functional Theory (DFT) is also applied to predict the interaction energy between DES and gas molecules. The efficacy of the DES-gel membrane was evaluated against other DES-supported liquid Membranes, revealing that DES may serve as a viable substitute for hazardous and costly ionic liquids.
Experimental study of tunnel effects on deformation mitigation in soft clay excavation using centrifuge and PIV
Association and binding nature of sodium dodecyl sulfate with ofloxacin antibiotic drug in potassium-based electrolyte solutions: a conductometric and UV–Visible spectroscopic investigation
miR-221 activates Sox11 to reduce brain injury after intracerebral hemorrhage via inhibiting neuroinflammation
RQdeltaCT: an open-source R package for relative quantification of gene expression using delta Ct methods
Distributed optical fiber acoustic wave sensor detection technology for gangue slurry pipeline conveying blockage
XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults
Spatial and seasonal patterns in fish assemblages of the Bakırçay river are associated with physicochemical and habitat parameters
Surface-tailored graphene nanosheets targeting PI3K/Akt signaling of breast cancer cells
Author Correction: Far-infrared radiation alleviates steatohepatitis and fibrosis in metabolic dysfunction-associated fatty liver disease
Suitability of soil and landscape for rapeseed (Brassica napus subsp. napus L.) growing
Investigation of the mechanical properties of pineapple leaf fibre-reinforced biocomposites
Abstract This study explores the production process and mechanical properties of biocomposites reinforced with pineapple leaf fibres. The biocomposite fabrication involves the use of various techniques, including compression moulding, to integrate the natural fibres into a polyester matrix. The mechanical performance of the resulting composites is evaluated through a comprehensive set of tests, including tensile strength measurements and Energy Dispersive X-Ray Spectroscopy (EDX). Key factors influencing mechanical behaviour, such as fibre content, orientation, and production variables, are systematically examined. Additionally, the chemical composition of the composites is assessed to understand its role in their performance. The findings provide valuable insights into the potential applications of pineapple leaf fibre-based biocomposites in industries requiring lightweight, sustainable materials with enhanced mechanical properties. This paper aims to establish a clear link between production techniques and mechanical performance, highlighting the practical potential of these biocomposites for environmentally-conscious material solutions.
A noncarcinoma mouse cell line is nonsusceptible to Newcastle disease virus established by spontaneous immortalization
Conditional POD for predicting extreme events in turbulent flow time signals
Cost-effectiveness of polatuzumab vedotin plus chemoimmunotherapy for untreated diffuse large B-cell lymphoma in China
Comparative evaluation of CAM methods for enhancing explainability in veterinary radiography
Abstract Explainable Artificial Intelligence (XAI) encompasses a broad spectrum of methods that aim to enhance the transparency of deep learning models, with Class Activation Mapping (CAM) methods widely used for visual interpretability. However, systematic evaluations of these methods in veterinary radiography remain scarce. This study presents a comparative analysis of eleven CAM methods, including GradCAM, XGradCAM, ScoreCAM, and EigenCAM, on a dataset of 7362 canine and feline X-ray images. A ResNet18 model was chosen based on the specificity of the dataset and preliminary results where it outperformed other models. Quantitative and qualitative evaluations were performed to determine how well each CAM method produced interpretable heatmaps relevant to clinical decision-making. Among the techniques evaluated, EigenGradCAM achieved the highest mean score and standard deviation (SD) of 2.571 (SD = 1.256), closely followed by EigenCAM at 2.519 (SD = 1.228) and GradCAM++ at 2.512 (SD = 1.277), with methods such as FullGrad and XGradCAM achieving worst scores of 2.000 (SD = 1.300) and 1.858 (SD = 1.198) respectively. Despite variations in saliency visualization, no single method universally improved veterinarians’ diagnostic confidence. While certain CAM methods provide better visual cues for some pathologies, they generally offered limited explainability and didn’t substantially improve veterinarians’ diagnostic confidence.