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Objectives This updated meta-analysis aimed to assess the diagnostic accuracy of circulating cell-free DNA (cfDNA) in breast cancer (BC). Content An extensive systematic search was performed in PubMed, Scopus, Embase, and Science Direct databases to retrieve all related literature. Various diagnostic estimates, including sensitivity (SE), specificity (SP), likelihood ratios (LRs), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (sROC) curve, were also calculated using bivariate linear mixed models. Summary In this meta-analysis, 57 unique articles (130 assays) on 4246 BC patients and 2,952 controls, were enrolled. For quantitative approaches, pooled SE, SP, PLR, NLR, DOR, and AUC were obtained as 0.80, 0.88, 6.7, 0.23, 29, and 0.91, respectively. Moreover, for qualitative approaches, pooled SE and SP for diagnostic performance were obtained as 0.36 and 0.98, respectively. In addition, PLR was 14.9 and NLR was 0.66. As well, the combined DOR was 23, and the AUC was 0.79. Outlook Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.

Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the training samples into consideration, which usually makes the learned CNNs sub-optimal especially for remote sensing scenes with large intra-class variance and low inter-class variance. To address this problem, deep metric learning, which incorporates the metric learning into the deep model, is used to maximize the inter-class variance and minimize the intra-class variance for better representation. This work introduces structured metric learning for remote sensing scene representation, a special deep metric learning which can take full advantage of the training batch. However, the deep metrics only consider the pairwise correlation between the training samples, and ignores the classwise correlation from the class view. To take the classwise penalization into consideration, this work defines the center points of the learned features of each class in the training process to represent the class. Through increasing the variance between different center points and decreasing the variance between the learned features from each class and the corresponding center point, the representational ability can be further improved. Therefore, this work develops a novel center-based structured metric learning to take advantage of both the deep metrics and the center points. Finally, joint supervision of the cross-entropy loss and the center-based structured metric learning is developed for the land-use classification in remote sensing. It can joint learn the center points and the deep metrics to take advantage of the point-wise, the pairwise, and the classwise correlation. Experiments are conducted over three real-world remote sensing scene datasets, namely UC Merced Land-Use dataset, Brazilian Coffee Scene dataset, and Google dataset. The classification performance can achieve 97.30%, 91.24%, and 92.04% with the proposed method over the three datasets which are better than other state-of-the-art methods under the same experimental setups. The results demonstrate that the proposed method can improve the representational ability for the remote sensing scenes.

Inverse synthetic aperture radar (ISAR) imaging plays an important role in aerial target identification. However, traditional ISAR methods are based on rigid body dynamics, which cannot depict micro-moving parts, such as the rotating propellers of an aircraft. In this study, a novel wideband radar imaging algorithm is proposed to identify rapidly rotating objects. The proposed algorithm contains two key steps: the first involves the establishment of the algebraic equations based on the high-resolution range profile series. The second step consists of a solution for these equations, for which the authors provide the steepest descent iterative method as well as several improvements to enhance imaging quality. A few necessary preprocesses and potential challenging scenarios are also considered. The results are used from simulations and field data to exhibit the effectiveness and advantages of the proposed algorithm.

Environmental matrix S in the guidance instrument systematic error model is usually seriously ill-conditioned, which has a strong impact on the accuracy of the result of error separation. In this paper, new algorithm for guidance instrument error separation is presented. Based on some numerical iterative method, the new algorithm firstly converts the ill-conditioned algebraic equations to the corresponding stiff dynamic system, and stiff stable numerical method is applied to solve the stiff dynamic system. The convergence of the new method is proved. The method which used to solve the stiff dynamic system is given in the paper. Compared with the PB method (primary Bayesian), the numerical experiments illustrate that the new method is more effective for the Guidance Instrument Error separation.

As the environment of battlefield is increasingly complex, single UAV(Unmanned Aerial Vehicle) has problem in reconnaissance mission, which needs cooperative reconnaissance of multi-UAV. However, the multi-UAV cooperative reconnaissance mission planning has trouble in searching the excellent solution, because of its large searching space but sparse solution space. Heuristic algorithm, which is widely used in multi-UAV cooperative reconnaissance mission planning at present, is difficult to find a reliable initial solution, limiting the rate of convergence. Aiming at this problem, this paper proposes the statistical physics method: the small world network and probability field fractal method to present the initial planning scheme, and the sequential kernel function method to adjust the planning scheme. Finally, according to the simulation results, statistical physics method can solve the problem effectively, and is a highly efficient way.

Inverse synthetic aperture radar (ISAR) imaging is a useful method of target identification in radar detection. However, rapidly rotating parts, such as propellers and blades on aircrafts, cannot be focused by conventional imaging algorithm. In this paper, we introduce a novel method for imaging rapidly rotating targets by wideband radar. This method is an algebraic method which is based on building and solving equations. Simulations are presented to demonstrate the effectiveness of these methods.

Deep Belief Network is an algorithm among deep learning. It is an effective method of solving the problems from neural network with deep layers, such as low velocity and the overfitting phenomenon in learning. In this paper, we will introduce how to process a Deep Belief Network by using Restricted Boltzmann Machines. What is more, we will combine the Deep Belief Network together with softmax classifier, and use it in the recognition of handwritten numbers.

Micro motion is a common phenomenon in radar detection. Although In ISAR imaging, micro-motion parts attached to target always have an impact on the quantity of the image. In this passage we put forward a method to remove the effect of micro-Doppler phenomenon in the range profile sequences. This method is based on Inverse-Radon transformations and can achieve clear image of rigid body of target. In the end, we test this method by simulation in Matlab.

To verify the feasibility of regional intra-arterial infusion chemotherapy with gemcitabine for the treatment of locally advanced pancreatic cancer.10 Beagle dogs were divided into two groups: the experimental group and the control group. In the experimental group, gemcitabine (45 mg/kg) were infused via a transfemorally placed catheter into the celiac axis and superior mesenteric artery. IAC was given over 30 min with the help of DSA. In the control group, chemotherapy was performed via peripheral veins with the same dosage as the experimental group. Blood samples, heart, lung, liver, kidney, pancreas and parapancreatic tissues were obtained for drug concentration determination and pathological examination.The serum concentration of experimental group were significantly higher than those of the control group 2 hr, 4 hr and 8 hr after treatment (P0.05). Area under curve after dosage-based calibration in experimental group was much higher than that in control group (P0.05). The mean retention time of the drug in the animal body was significantly longer in experimental group than in control group. Pancreatic gemcitabine titer in experimental group was significantly higher than that in control group 4 hr and 8 hr after drug administration. There were a large number of red blood cells in the renal glomerulus and tubules, congestion and hemorrahge in the pulmonary capillaries and myocardium in the control group according to pathological examinations. However, infiltration of neutrophilic white blood cells, hemorrahge and fibrinous exudation were present in parapancreatic tissues which were absent in the group.Regional intra-arterial infusion chemotherapy with gemcitabine could increase serum and pancreatic drug concentration, prolongs retention time of the drug in the animal body, and is feasible in the treatment of pancreatic cancer.