Reconstruction of Fibroin Nanofibers (FNFs) via Electrospinning: Fabrication of Poly(vinyl alcohol)/FNFs Composite Nanofibers from Aqueous Solution

Fibroin nanofibers (FNFs) achieved from physical treated silk can keep its original crystal structure, showing excellent mechanical properties, however, processing the FNFs into fibers is still a challenge. Herein, a brand-new environmentally friendly approach is proposed to manufacture FNFs-based composite nanofibers. The water-soluble polymer, poly(vinyl alcohol) PVA, was applied to increase the viscoelasticity of the spinning dope, and the content of FNFs can reach up to 20 wt%. The established phase image of spinning suggested that the concentrations ranging from 6 wt% to 8 wt% are premium to achieving relatively homogenous FNFs/PVA nanofibers. Random fibers were deposited on a fixed collector, while the fiber orientation intensity increased with the rotational speed of drum and started decreasing after 12 m/s. The mechanical properties of the composite nanofibers showed the similar tendency of variation of fiber orientation. In addition, chemical changes, crystallinity, and thermal properties of the composite nanofibers were further clarified by means of FTIR, DSC, and TG. As a result, high FNFs contained nanofibers with excellent thermal properties were created from an aqueous solution. This study is the first original work to realize the spinnability of FNFs, which provides a new insight of the FNFs.

Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images

In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.

Radiomics Analysis of Contrast-Enhanced CT for the Preoperative Prediction of Microvascular Invasion in Mass-Forming Intrahepatic Cholangiocarcinoma

BackgroundMicrovascular invasion (MVI) has been shown to be closely associated with postoperative recurrence and metastasis in patients with intrahepatic cholangiocarcinoma (ICC). We aimed to develop a radiomics prediction model based on contrast-enhanced CT (CECT) to distinguish MVI in patients with mass-forming ICC.Methods157 patients were included and randomly divided into training (n=110) and test (n=47) datasets. Radiomic signatures were built based on the recursive feature elimination support vector machine (Rfe-SVM) algorithm. Significant clinical-radiologic factors were screened, and a clinical model was built by multivariate logistic regression. A nomogram was developed by integrating radiomics signature and the significant clinical risk factors.ResultsThe portal phase image radiomics signature with 6 features was constructed and provided an area under the receiver operating characteristic curve (AUC) of 0.804 in the training and 0.769 in the test datasets. Three significant predictors, including satellite nodules (odds ratio [OR]=13.73), arterial hypo-enhancement (OR=4.31), and tumor contour (OR=4.99), were identified by multivariate analysis. The clinical model using these predictors exhibited an AUC of 0.822 in the training and 0.756 in the test datasets. The nomogram combining significant clinical factors and radiomics signature achieved satisfactory prediction efficacy, showing an AUC of 0.886 in the training and 0.80 in the test datasets.ConclusionsBoth CECT radiomics analysis and radiologic factors have the potential for MVI prediction in mass-forming ICC patients. The nomogram can further improve the prediction efficacy.

Coronary Angiography Print: An Automated Accurate Hidden Biometric Method Based on Filtered Local Binary Pattern Using Coronary Angiography Images

Background and purpose: Biometrics is a commonly studied research issue for both biomedical engineering and forensics sciences. Besides, the purpose of hidden biometrics is to discover hidden biometrics features. This work aims to demonstrate the biometric identification ability of coronary angiography images. Material and method: A new coronary angiography images database was collected to develop an automatic identification model. The used database was collected from 51 subjects and contains 2156 images. The developed model has to preprocess; feature generation using local binary pattern; feature selection with neighborhood component analysis; and classification phases. In the preprocessing phase; image rotations; median filter; Gaussian filter; and speckle noise addition functions have been used to generate filtered images. A multileveled extractor is presented using local binary pattern and maximum pooling together. The generated features are fed to neighborhood component analysis and the selected features are classified using k nearest neighbor classifier. Results: The presented angiography image identification method attained 99.86% classification accuracy on the collected database. Conclusions: The obtained findings demonstrate that the angiography images can be utilized as biometric identification. Moreover, we discover a new hidden biometric feature using coronary angiography images and name of this hidden biometric is coronary angiography print.

Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model

In this paper, we propose an improved algorithm based on the active contour model Mumford-Shah model for CT images, which is the subject of this study. After analyzing the classical Mumford-Shah model and related improvement algorithms, we found that most of the improvement algorithms start from the initialization strategy of the model and the minimum value solution of the energy generalization function, so we will also improve the classical Mumford-Shah model from these two perspectives. For the initialization strategy of the Mumford-Shah model, we propose to first reduce the dimensionality of the image data by the PCA principal component analysis method, and for the reduced image feature vector, we use K -means, a general clustering method, as the initial position algorithm of the segmentation curve. For the image data that have completed the above two preprocessing processes, we then use the Mumford-Shah model for image segmentation. The Mumford-Shah curve evolution model solves the image segmentation by finding the minimum of the energy generalization of its model to obtain the optimal result of image segmentation, so for solving the minimum of the Mumford-Shah model, we first optimize the discrete problem of the energy generalization of the model by the convex relaxation technique and then use the Chambolle-Pock pairwise algorithm We then use the Chambolle-Pock dual algorithm to solve the optimization problem of the model after convex relaxation and finally obtain the image segmentation results. Finally, a comparison with the existing model through many numerical experiments shows that the model proposed in this paper calculates the texture image segmentation with high accuracy and good edge retention. Although the work in this paper is aimed at two-phase image segmentation, it can be easily extended to multiphase segmentation problems.

Probing atom dynamics of excited Co-Mo-S nanocrystals in 3D

Advances in electron microscopy have enabled visualizations of the three-dimensional (3D) atom arrangements in nano-scale objects. The observations are, however, prone to electron-beam-induced object alterations, so tracking of single atoms in space and time becomes key to unravel inherent structures and properties. Here, we introduce an analytical approach to quantitatively account for atom dynamics in 3D atomic-resolution imaging. The approach is showcased for a Co-Mo-S nanocrystal by analysis of time-resolved in-line holograms achieving ~1.5 Å resolution in 3D. The analysis reveals a decay of phase image contrast towards the nanocrystal edges and meta-stable edge motifs with crystallographic dependence. These findings are explained by beam-stimulated vibrations that exceed Debye-Waller factors and cause chemical transformations at catalytically relevant edges. This ability to simultaneously probe atom vibrations and displacements enables a recovery of the pristine Co-Mo-S structure and establishes, in turn, a foundation to understand heterogeneous chemical functionality of nanostructures, surfaces and molecules.

Development and application of a tender X-ray ptychographic coherent diffraction imaging system on BL27SU at SPring-8

Ptychographic coherent diffraction imaging (CDI) allows the visualization of both the structure and chemical state of materials on the nanoscale, and has been developed for use in the soft and hard X-ray regions. In this study, a ptychographic CDI system with pinhole or Fresnel zone-plate optics for use in the tender X-ray region (2–5 keV) was developed on beamline BL27SU at SPring-8, in which high-precision pinholes optimized for the tender energy range were used to obtain diffraction intensity patterns with a low background, and a temperature stabilization system was developed to reduce the drift of the sample position. A ptychography measurement of a 200 nm thick tantalum test chart was performed at an incident X-ray energy of 2.500 keV, and the phase image of the test chart was successfully reconstructed with approximately 50 nm resolution. As an application to practical materials, a sulfur polymer material was measured in the range of 2.465 to 2.500 keV including the sulfur K absorption edge, and the phase and absorption images were successfully reconstructed and the nanoscale absorption/phase spectra were derived from images at multiple energies. In 3 GeV synchrotron radiation facilities with a low-emittance storage ring, the use of the present system will allow the visualization on the nanoscale of the chemical states of various light elements that play important roles in materials science, biology and environmental science.