Determination of the rate of rapid lipid transfer induced by poly(ethylene glycol) using the SLM Fourier transform phase and modulation spectrofluorometer

Rate constants were determined for the transfer of the fluorescent lipid probe 1-palmitoyl-2-[[2-[4-(6-phenyl-trans-1,3,5-hexatrienyl)phenyl]ethyl] oxy]carbonyl]-3-sn-phosphatidylcholine (DPHpPC) between large, unilamellar extrusion vesicles composed either of dipalmitoyl phosphatidylcholine (DPPC) or of DPPC mixed with a small amount (0.5 mol%) of lyso phosphatidylcholine (Lyso PC). Transfer of the lipid probe in the presence of varying concentrations of poly(ethylene glycol) (PEG) was monitored using the SLM 48000-MHF Multi-Harmonic Fourier Transform phase and modulation spectrofluorometer to collect multifrequency phase and modulation fluorescence data sets on a subsecond time scale. The unique ability of this instrument to yield accurate fluorescence lifetime data on this time scale allowed transfer to be detected in terms of a time-dependent change in the fluorescent lifetime distribution associated with the lipid-like DPHpPC probe. This probe demonstrates two short fluoresence decay times (ca. 1.1–1.4 and 4.3–4.8 ns) in a probe-rich environment but a single long lifetime (ca. 7 ns) in a probe-poor environment. A simple two-state model for initial lipid transfer was used to analyze the multifrequency data sets collected over a 4-s time frame to obtain the time rate of change of the concentrations of donor and acceptor probe populations following rapid mixing of vesicles with PEG. The ability to measure fluorescence lifetimes on this time scale has allowed us to show that the of rate of lipid transfer increased dramatically at 35% PEG in both fusing and nonfusing vesicle systems. These results are interpreted in terms of a distinct interbilayer structure associated with intimate bilayer contact induced by high and potentially fusogenic concentrations of PEG.     

Research on front-end electronics gain compensation of MCP imaging detector北大核心CSCD

基于微通道板电子倍增电荷分割型阳极的成像探测器常用于行星大气、气辉等微弱信号探测。针对探测器读出电路增益不相等导致探测器成像产生畸变的问题,利用理论分析结合MATLAB仿真揭示了不同畸变图像的形成机制,在该基础上提出了一种探测器读出电路增益补偿方法减小探测器成像畸变。通过MATLAB仿真和实验测试结果表明该文提出的方法能够有效减小由于读出电路增益不相等导致的探测器成像畸变。     

Meaningful Secret Image Sharing with Saliency Detection

Secret image sharing (SIS), as one of the applications of information theory in information security protection, has been widely used in many areas, such as blockchain, identity authentication and distributed cloud storage. In traditional secret image sharing schemes, noise-like shadows introduce difficulties into shadow management and increase the risk of attacks. Meaningful secret image sharing is thus proposed to solve these problems. Previous meaningful SIS schemes have employed steganography to hide shares into cover images, and their covers are always binary images. These schemes usually include pixel expansion and low visual quality shadows. To improve the shadow quality, we design a meaningful secret image sharing scheme with saliency detection. Saliency detection is used to determine the salient regions of cover images. In our proposed scheme, we improve the quality of salient regions that are sensitive to the human vision system. In this way, we obtain meaningful shadows with better visual quality. Experiment results and comparisons demonstrate the effectiveness of our proposed scheme.     

Automatic Search Dense Connection Module for Super-Resolution

The development of display technology has continuously increased the requirements for image resolution. However, the imaging systems of many cameras are limited by their physical conditions, and the image resolution is often restrictive. Recently, several models based on deep convolutional neural network (CNN) have gained significant performance for image super-resolution (SR), while extensive memory consumption and computation overhead hinder practical applications. For this purpose, we present a lightweight network that automatically searches dense connection (ASDCN) for image super-resolution (SR), which effectively reduces redundancy in dense connection and focuses on more valuable features. We employ neural architecture search (NAS) to model the searching of dense connections. Qualitative and quantitative experiments on five public datasets show that our derived model achieves superior performance over the state-of-the-art models.     

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.     

弯曲圆盘换能器与镜像虚源的互辐射作用

建立了弯曲圆盘换能器镜像虚源等效模型,利用脉动球源互作用原理对弯曲圆盘与其镜像虚源的互辐射作用进行理论分析,给出了互辐射阻抗及系统谐振频率的数学表达。提出了虚源互作用的低频换能器设计思想,将刚性反射板引入弯曲圆盘换能器临近辐射面的声场中,通过理论分析、有限元模拟和样机实验研究了低频换能器谐振频率与主要结构参数之间的关系。结果表明,弯曲圆盘与镜像虚源间的互辐射作用可以有效降低换能器的谐振频率,当反射板直径与弯曲圆盘辐射面直径相当时,谐振频率可降低至其自身谐振频率的50%以下;当反射板直径为弯曲圆盘辐射面直径2倍时,谐振频率可降低至37%。     

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.     

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.     

A Novel Conflict Management Method Based on Uncertainty of Evidence and Reinforcement Learning for Multi-Sensor Information Fusion

Dempster–Shafer theory (DST), which is widely used in information fusion, can process uncertain information without prior information; however, when the evidence to combine is highly conflicting, it may lead to counter-intuitive results. Moreover, the existing methods are not strong enough to process real-time and online conflicting evidence. In order to solve the above problems, a novel information fusion method is proposed in this paper. The proposed method combines the uncertainty of evidence and reinforcement learning (RL). Specifically, we consider two uncertainty degrees: the uncertainty of the original basic probability assignment (BPA) and the uncertainty of its negation. Then, Deng entropy is used to measure the uncertainty of BPAs. Two uncertainty degrees are considered as the condition of measuring information quality. Then, the adaptive conflict processing is performed by RL and the combination two uncertainty degrees. The next step is to compute Dempster’s combination rule (DCR) to achieve multi-sensor information fusion. Finally, a decision scheme based on correlation coefficient is used to make the decision. The proposed method not only realizes adaptive conflict evidence management, but also improves the accuracy of multi-sensor information fusion and reduces information loss. Numerical examples verify the effectiveness of the proposed method.     

Significance Support Vector Regression for Image Denoising

As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual.