A computational method is developed to carry out explicit solvent simulations of complex molecular systems under conditions of constant pH. In constant-pH simulations, preidentified ionizable sites ...are allowed to spontaneously protonate and deprotonate as a function of time in response to the environment and the imposed pH. The method, based on a hybrid scheme originally proposed by H. A. Stern (J. Chem. Phys. 2007, 126, 164112), consists of carrying out short nonequilibrium molecular dynamics (neMD) switching trajectories to generate physically plausible configurations with changed protonation states that are subsequently accepted or rejected according to a Metropolis Monte Carlo (MC) criterion. To ensure microscopic detailed balance arising from such nonequilibrium switches, the atomic momenta are altered according to the symmetric two-ends momentum reversal prescription. To achieve higher efficiency, the original neMD–MC scheme is separated into two steps, reducing the need for generating a large number of unproductive and costly nonequilibrium trajectories. In the first step, the protonation state of a site is randomly attributed via a Metropolis MC process on the basis of an intrinsic pK a; an attempted nonequilibrium switch is generated only if this change in protonation state is accepted. This hybrid two-step inherent pK a neMD–MC simulation method is tested with single amino acids in solution (Asp, Glu, and His) and then applied to turkey ovomucoid third domain and hen egg-white lysozyme. Because of the simple linear increase in the computational cost relative to the number of titratable sites, the present method is naturally able to treat extremely large systems.
Kernel methods, e.g., composite kernels (CKs) and spatial-spectral kernels (SSKs), have been demonstrated to be an effective way to exploit the spatial-spectral information nonlinearly for improving ...the classification performance of hyperspectral image (HSI). However, these methods are always conducted with square-shaped window or superpixel techniques. Both techniques are likely to misclassify the pixels that lie at the boundaries of class, and thus a small target is always smoothed away. To alleviate these problems, in this paper, we propose a novel patch-based low rank component induced spatial-spectral kernel method, termed LRCISSK, for HSI classification. First, the latent low-rank features of spectra in each cubic patch of HSI are reconstructed by a low rank matrix recovery (LRMR) technique, and then, to further explore more accurate spatial information, they are used to identify a homogeneous neighborhood for the target pixel (i.e., the centroid pixel) adaptively. Finally, the adaptively identified homogenous neighborhood which consists of the latent low-rank spectra is embedded into the spatial-spectral kernel framework. It can easily map the spectra into the nonlinearly complex manifolds and enable a classifier (e.g., support vector machine, SVM) to distinguish them effectively. Experimental results on three real HSI datasets validate that the proposed LRCISSK method can effectively explore the spatial-spectral information and deliver superior performance with at least 1.30% higher OA and 1.03% higher AA on average when compared to other state-of-the-art classifiers.
Through the patent database of 5074 patent data, the patent map of technical progress of RV reducer in recent 74 years at home and abroad is studied. The patent map of the technical field of RV ...reducer is drawn by using international patent classification (IPC classification), patent map method and patent information data mining technology. From the global application number, application trend, the main application countries, the main competition institutions, patent distribution pattern, the main technology hot spot changes, key technology development areas and other aspects of a detailed analysis. The current situation of the research in the field of RV reducer is analyzed, and the development trend of the future technology is predicted, and the reference for the localization strategy layout of RV reducer is provided.
Breast ultrasound segmentation remains challenging because of the blurred boundaries, irregular shapes, and the presence of shadowing and speckle noise. The majority of approaches stack convolutional ...layers to extract advanced semantic information, which makes it difficult to handle multiscale issues. To address those issues, we propose a three-path U-structure network (TPUNet) that consists of a three-path encoder and an attention-based feature fusion block (AFF Block). Specifically, instead of simply stacking convolutional layers, we design a three-path encoder to capture multiscale features through three independent encoding paths. Additionally, we design an attention-based feature fusion block to weight and fuse feature maps in spatial and channel dimensions. The AFF Block encourages different paths to compete with each other in order to synthesize more salient feature maps. We also investigate a hybrid loss function for reducing false negative regions and refining the boundary segmentation, as well as the deep supervision to guide different paths to capture the effective features under the corresponding receptive field sizes. According to experimental findings, our proposed TPUNet achieves more excellent results in terms of quantitative analysis and visual quality than other rival approaches.
This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral ...features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels. Secondly, the similarities between each target superpixel and its ASs are calculated to construct the spatial features. Finally, a weighted AS-based composite kernel (WASCK) method for HSI classification is proposed. In order to avoid seeking for the optimal superpixel scale and fuse the multiscale spatial features, the MWASCK method uses multiscale weighted superpixel neighbor information. Experiments from two real HSIs indicate that superior performance of the WASCK and MWASCK methods compared with some popular classification methods.
Over the past few decades, the research into and application of hyperspectral images has made significant progress, with interest levels progressing from very little to intense. Among the many ...aspects of this research ,hyperspectral image classification has become one of the most-studied topics,with experiments showing that the spatial spectral joint classification method can achieve better classification results than the pixel-by-pixel classification method that relies on spectral information alone. Here, we classified and analyzed a number of spatial spectral joint classification methods.First,we introduced two kinds of spatial dependence relations between adjacent pixels in hyperspectral images;using these,we were able to classify existing spatial-spectral classification methods into fixed-dependent neighborhood-based and adaptive neighborhood-based types. In addition, we were able to further divide existing methods into two types, single-dependency and double-dependency, based on whether or not we used tw
It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain ...MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.
Desmoplastic small round cell tumor (DSRCT) is a rare, aggressive, mesenchymal malignancy of a separate clinicopathological entity. It has a predilection for young men, with no evidence of any ethnic ...predilection. The current diagnostic gold standard for DSRCT includes histopathologic, immunohistochemical, and cytogenetic studies in order to confirm the variable phenotypic expression and characteristic chromosomal translocation.
A 65-year-old man presented with a sensation of an abdominal mass and a presentation of an incomplete bowel obstruction. Initial lab tests were in the normal range except for carbohydrate antigen. Contrast-enhanced CT showed that a large, mass-confounding density was occupied in the omentum majus area of the middle and lower abdominal wall. A 3D reconstruction of the images was performed to clarify the relationship between the tumor and the colon and was confirmed by a colonoscopy. After surgery, immunohistochemistry and fluorescence
hybridization (FISH) revealed EWSR1-WT1 gene rearrangement at 22q12, confirming the diagnosis of desmoplastic small round cell tumor.
Being different from the predilection of DSRCT for young men, the patient in our case is a 65-year-old man with a huge mass involving the transverse colon and the bladder.
A family of hybrid simulation methods that combines the advantages of Monte Carlo (MC) with the strengths of classical molecular dynamics (MD) consists in carrying out short non-equilibrium MD (neMD) ...trajectories to generate new configurations that are subsequently accepted or rejected via an MC process. In the simplest case where a deterministic dynamic propagator is used to generate the neMD trajectories, the familiar Metropolis acceptance criterion based on the change in the total energy ΔE, min1, exp{-βΔE}, guarantees that the hybrid algorithm will yield the equilibrium Boltzmann distribution. However, the functional form of the acceptance probability is more complex when the non-equilibrium switching process is generated via a non-deterministic stochastic dissipative propagator coupled to a heat bath. Here, we clarify the conditions under which the Metropolis criterion remains valid to rigorously yield a proper equilibrium Boltzmann distribution within hybrid neMD-MC algorithm.
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