Colloids moving from the stream into the hyporheic zone may have a negative impact on aquatic ecosystems as they are potential contaminants or carriers of contaminants. Moreover, retained colloids in ...the hyporheic zone could not only reduce the exchange flux between the stream and streambed but also change the conditions of the bed, affecting the habitats for aquatic organisms. Previous studies focused on the exchange flux across the sediment–water interface, but the colloid transport processes and distribution of retained colloids in the streambed have received little attention. We conducted experiments within a laboratory flume to examine these processes in a streambed driven by bedform‐induced hyporheic flow. Retained colloids measured in the bed at the end of the experiments revealed colloid retention mainly in the shallow layer of hyporheic zone (0–5 cm below the interface). The results demonstrated significant effects of particle trapping and settling on the colloid transport and distribution in the streambed. Retention leads to the formation of a colloid‐filled shallow layer in the bed. Particle paths based on model simulations showed that colloid settling in pore water modifies the direction of colloid transport and allows the colloid particles to move more deeply in the bed.
Deep learning methods have been successfully applied to learn feature representations for high-dimensional data, where the learned features are able to reveal the nonlinear properties exhibited in ...the data. In this paper, deep learning method is exploited for feature extraction of hyperspectral data, and the extracted features can provide good discriminability for classification task. Training a deep network for feature extraction and classification includes unsupervised pretraining and supervised fine-tuning. We utilized stacked denoise autoencoder (SDAE) method to pretrain the network, which is robust to noise. In the top layer of the network, logistic regression (LR) approach is utilized to perform supervised fine-tuning and classification. Since sparsity of features might improve the separation capability, we utilized rectified linear unit (ReLU) as activation function in SDAE to extract high level and sparse features. Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR) can achieve higher accuracies than the popular support vector machine (SVM) classifier.
Abstract
X-ray detectors are broadly utilized in medical imaging and product inspection. Halide perovskites recently demonstrate excellent performance for direct X-ray detection. However, ionic ...migration causes large noise and baseline drift, limiting the detection and imaging performance. Here we largely eliminate the ionic migration in cesium silver bismuth bromide (Cs
2
AgBiBr
6
) polycrystalline wafers by introducing bismuth oxybromide (BiOBr) as heteroepitaxial passivation layers. Good lattice match between BiOBr and Cs
2
AgBiBr
6
enables complete defect passivation and suppressed ionic migration. The detector hence achieves outstanding balanced performance with a signal drifting one order of magnitude lower than all previous studies, low noise (1/
f
noise free), a high sensitivity of 250 µC Gy
air
−1
cm
–2
, and a spatial resolution of 4.9 lp mm
−1
. The wafer area could be easily scaled up by the isostatic-pressing method, together with the heteroepitaxial passivation, strengthens the competitiveness of Cs
2
AgBiBr
6
-based X-ray detectors as next-generation X-ray imaging flat panels.
Graph construction, which is at the heart of graph-based semisupervised learning (SSL), is investigated by using manifold learning (ML) approaches. Since each ML method can be demonstrated to ...correspond to a specific graph, we build the relation between ML and SSL via the graph, where ML methods are employed for graph construction. Moreover, sparsity is important for the efficiency of SSL algorithms, and therefore, local ML (LML)-method-based sparse graphs are utilized. The LML-based graphs are able to capture the local geometric properties of hyperspectral data and, thus, are beneficial for classification of data with complex geometry and multiple submanifolds. In experiments with Hyperion and AVIRIS hyperspectral data, graphs constructed by two LML methods, namely, locally linear embedding and local tangent space alignment (LTSA), performed better than several popular graph construction methods, and the highest accuracies were obtained by using graphs provided by LTSA.
The naturally occurring saponins exhibit remarkable interfacial activity and also possess many biological activities linking to human health benefits, which make them particularly attractive as ...bifunctional building blocks for formulation of colloidal multiphase food systems. This review focuses on two commonly used food-grade saponins, Quillaja saponins (QS) and glycyrrhizic acid (GA), with the aim of clarifying the relationship between the structural features of saponin molecules and their subsequent self-assembly and interfacial properties. The recent applications of these two saponins in various colloidal multiphase systems, including liquid emulsions, gel emulsions, aqueous foams and complex emulsion foams, are then discussed. A particular emphasis is on the unique use of GA and GA nanofibrils as sole stabilizers for fabricating various multiphase food systems with many advanced qualities including simplicity, ultrastability, stimulability, structural viscoelasticity and processability. These natural saponin and saponin-based colloids are expected to be used as sustainable, plant-based ingredients for designing future foods, cosmetics and pharmaceuticals.
An optical-resolution photoacoustic microscope (OR-PAM) with capability of fast axial-scanning was developed by using a tunable acoustic gradient (TAG) lens. The TAG lens was designed to continuously ...changing the focal plane of OR-PAM by modulating its refractive power with fast-changing ultrasonic standing wave. The performance was shown by imaging a carbon fiber. We achieved a DoF of about 750 μm. The head of a zebrafish was also imaged to further demonstrate the feasibility of our method.
When soy glycinin (11S) is heated for a prolonged time at pH 2 (20 h at 85 °C), a mixture is formed consisting of long semiflexible 11S fibrils and small peptides. The surface and foaming properties ...of this mixture were investigated at different pHs, and compared to the behavior of pure fibrils and pure peptides, to determine the individual contributions of these two factions to the behavior of the mixture. The adsorption of these three systems at air–water interfaces and the resulting surface rheological properties were studied by combining drop shape analysis tensiometry, ellipsometry, and surface large amplitude oscillatory dilatational (LAOD) rheology. Lissajous plots of surface pressure versus deformation were used to analyze the surface rheological response in terms of interfacial microstructure. Our results show that the adsorption kinetics, dilatational rheological properties, and the foaming behavior of the mixture were mainly dominated by the small peptides in the fibril system. Compared to pH 2, the fibril mixture at pH 5 and 7 provides much better foam stability and appears to be a very promising protein material to make stable foams, even at low protein concentration (0.1 wt %). The presence of fibril clusters and peptide aggregates at pH 5 and 7 contributed to foam stability of the mixture. In contrast, pure fibril formed an interface with a highly pH-responsive adsorption and rheological behavior, and the foamability and foam stability of the pure fibrils were very poor.
With the development of hybrid imaging scanners, micro-CT is widely used in locating abnormalities, studying drug metabolism, and providing structural priors to aid image reconstruction in functional ...imaging. Due to the low contrast of soft tissues, segmentation of soft tissue organs from mouse micro-CT images is a challenging problem. In this paper, we propose a mouse segmentation scheme based on dynamic contrast enhanced micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic contrast enhanced images were acquired before and after injection of non-ionic iodinated contrast agents (iohexol). Then the feature vector of each voxel was extracted from the signal intensities at different time points. Based on these features, the heart, liver, spleen, lung, and kidney could be classified into different categories and extracted from separate categories by morphological processing. The bone structure was segmented using a thresholding method. Our method was validated on seven BALB/c mice using two different classifiers: a support vector machine classifier with a radial basis function kernel and a random forest classifier. The results were compared to manual segmentation, and the performance was assessed using the Dice similarity coefficient, false positive ratio, and false negative ratio. The results showed high accuracy with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the spleen to 0.929 ± 0.006 for the kidney.
Understanding amazingly complex brain functions and pathologies requires a complete cerebral vascular atlas in stereotaxic coordinates. Making a precise atlas for cerebral arteries and veins has been ...a century-old objective in neuroscience and neuropathology. Using micro-optical sectioning tomography (MOST) with a modified Nissl staining method, we acquired five mouse brain data sets containing arteries, veins, and microvessels. Based on the brain-wide vascular spatial structures and brain regions indicated by cytoarchitecture in one and the same mouse brain, we reconstructed and annotated the vascular system atlas of both arteries and veins of the whole mouse brain for the first time. The distributing patterns of the vascular system within the brain regions were acquired and our results show that the patterns of individual vessels are different from each other. Reconstruction and statistical analysis of the microvascular network, including derivation of quantitative vascular densities, indicate significant differences mainly in vessels with diameters less than 8 μm and large than 20 μm across different brain regions. Our precise cerebral vascular atlas provides an important resource and approach for quantitative studies of brain functions and diseases.
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