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Warmth acclimation improves the cold-induced vasodilation reaction.
Numerical examples on color images, multispectral images, and videos illustrate that the proposed method outperforms many state-of-the-art methods in qualitative and quantitative aspects.Improving ultrasound B-mode image quality remains an important area of research. Recently, there has been increased interest in using deep neural networks to perform beamforming to improve image quality more efficiently. Several approaches have been proposed that use different representations of channel data for network processing, including a frequency domain approach that we previously developed. We previously assumed that the frequency domain would be more robust to varying pulse shapes. However, frequency and time domain implementations have not been directly compared. Additionally, because our approach operates on aperture domain data as an intermediate beamforming step, a discrepancy often exists between network performance and image quality on fully reconstructed images, making model selection challenging. Here, we perform a systematic comparison of frequency and time domain implementations. Additionally, we propose a contrast-to- noise ratio (CNR)-based regularization to address previous challenges with model selection. Training channel data were generated from simulated anechoic cysts. Test channel data were generated from simulated anechoic cysts with and without varied pulse shapes, in addition to physical phantom and in vivo data. We demonstrate that simplified time domain implementations are more robust than we previously assumed, especially when using phase preserving data representations. Specifically, 0.39dB and 0.36dB median improvements in in vivo CNR compared to DAS were achieved with frequency and time domain implementations, respectively. We also demonstrate that CNR regularization improves the correlation between training validation loss and simulated CNR by 0.83 and between simulated and in vivo CNR by 0.35 compared to DNNs trained without CNR regularization.Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.We target the problem named unsupervised domain adaptive semantic segmentation. A key in this campaign consists in reducing the domain shift, so that a classifier based on labeled data from one domain can generalize well to other domains. With the advancement of adversarial learning framework, recent works prefer the strategy of aligning the marginal distribution in the feature spaces for minimizing the domain discrepancy. However, based on the observance in experiments, only focusing on aligning global marginal distribution but ignoring the local joint distribution alignment fails to be the optimal choice. Other than that, the noisy factors existing in the feature spaces, which are not relevant to the target task, entangle with the domain invariant factors improperly and make the domain distribution alignment more difficult. To address those problems, we introduce two new modules, Significance-aware Information Bottleneck (SIB) and Category-level alignment (CLA), to construct a purified embedding based category-level adversarial network. In three domain adaptation tasks, i.e., GTA5 -> Cityscapes, SYNTHIA -> Cityscapes and Cross Season, we validate that our proposed method matches the state of the art in segmentation accuracy.Methods for measuring gut microbiota biochemical activities in vivo are needed to characterize its functional states in health and disease. To illustrate one approach, an arabinan-containing polysaccharide was isolated from pea fiber, its structure defined, and forward genetic and proteomic analyses used to compare its effects, versus unfractionated pea fiber and sugar beet arabinan, on a human gut bacterial strain consortium in gnotobiotic mice. Tasocitinib We produced 'Microbiota Functional Activity Biosensors' (MFABs) consisting of glycans covalently linked to the surface of fluorescent paramagnetic microscopic glass beads. Three MFABs, each containing a unique glycan/fluorophore combination, were simultaneously orally gavaged into gnotobiotic mice, recovered from their intestines, and analyzed to directly quantify bacterial metabolism of structurally distinct arabinans in different human diet contexts. Colocalizing pea-fiber arabinan and another polysaccharide (glucomannan) on the bead surface enhanced in vivo degradation of glucomannan. MFABs represent a potentially versatile platform for developing new prebiotics and more nutritious foods.
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