Characterization of four vaccine-related polioviruses including two intertypic type 3/type 2 recombinants associated with aseptic encephalitis

Temperature sensitivity of 4 poliovirus type 3 isolates. (DOC 31 kb

Cerebral alveolar echinococcosis: A report of two cases

status: publishe

Feature-Enhanced Deep Learning Network for Digital Elevation Model Super-Resolution

The high-resolution digital elevation model (HR DEM) plays an important role in hydrological analysis, cartographic generalization, and national security. As the main high-precision DEM data supplementary method, DEM super-resolution (DEM SR) based on deep learning has been widely studied. However, its accuracy has fallen into a bottleneck at present, which is more prominent in complex regions. The reason for this issue is that the existing methods are difficult to capture enough local features from the low-resolution input data, and a part of the global information (contour information of long-distance features, such as rivers and ridges) will also be lost in the network transmission process. To resolve this issue, a novel feature-enhanced deep learning network (FEN) is designed in this article. The proposed FEN includes a global feature SR (GFSR) module and a local feature SR (LFSR) module. The former provides global information by using an interpolation method (Kriging), including geographical laws (spatial autocorrelation). The latter fully captures the features in the input data by integrating powerful feature extraction modules and then provides sufficient local features for DEM SR tasks. Thus, DEM SR tasks for complex regions can be realized by integrating the results of GFSR and LFSR modules. Extensive experiments show that FEN achieves state-of-the-art performance in DEM SR tasks facing complex regions. Specifically, compared with the existing DEM SR method (TfaSR, SRResNet, Bicubic, SRCNN, and Kriging), the result by FEN is closer to HR DEM and can retain more local DEM features. Meanwhile, the FEN is more than 20% ahead of other DEM SR methods based on deep learning in elevation accuracy

A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution

High-resolution DEMs can provide accurate geographic information and can be widely used in hydrological analysis, path planning, and urban design. As the main complementary means of producing high-resolution DEMs, the DEM super-resolution (SR) method based on deep learning has reached a bottleneck. The reason for this phenomenon is that the DEM super-resolution method based on deep learning lacks a part of the global information it requires. Specifically, the multilevel aggregation process of deep learning has difficulty sufficiently capturing the low-level features with dependencies, which leads to a lack of global relationships with high-level information. To address this problem, we propose a global-information-constrained deep learning network for DEM SR (GISR). Specifically, our proposed GISR method consists of a global information supplement module and a local feature generation module. The former uses the Kriging method to supplement global information, considering the spatial autocorrelation rule. The latter includes a residual module and the PixelShuffle module, which is used to restore the detailed features of the terrain. Compared with the bicubic, Kriging, SRCNN, SRResNet, and TfaSR methods, the experimental results of our method show a better ability to retain terrain features, and the generation effect is more consistent with the ground truth DEM. Meanwhile, compared with the deep learning method, the RMSE of our results is improved by 20.5% to 68.8%

An Intuitionistic Fuzzy Similarity Approach for Clustering Analysis of Polygons

Accurate and reasonable clustering of spatial data results facilitates the exploration of patterns and spatial association rules. Although a broad range of research has focused on the clustering of spatial data, only a few studies have conducted a deeper exploration into the similarity approach mechanism for clustering polygons, thereby limiting the development of spatial clustering. In this study, we propose a novel fuzzy similarity approach for spatial clustering, called Extend Intuitionistic Fuzzy Set-Interpolation Boolean Algebra (EIFS-IBA). When discovering polygon clustering patterns by spatial clustering, this method expresses the similarities between polygons and adjacent graph models. Shape-, orientation-, and size-related properties of a single polygon are first extracted, and are used as indices for measuring similarities between polygons. We then transform the extracted properties into a fuzzy format through normalization and fuzzification. Finally, the similarity graph containing the neighborhood relationship between polygons is acquired, allowing for clustering using the proposed adjacency graph model. In this paper, we clustered polygons in Staten Island, United States. The visual result and two evaluation criteria demonstrated that the EIFS-IBA similarity approach is more expressive compared to the conventional similarity (ConS) approach, generating a clustering result more consistent with human cognition

Variable solitary fibrous tumor locations: CT and MR imaging features

The aim of the study is to describe the radiological imaging features of different solitary fibrous tumors (SFTs) locations and present histopathological correlations. From 2007 to 2013, 20 cases of histologically confirmed that SFTs were retrospectively analyzed with computed tomography (CT; 9/20), magnetic resonance imaging (MRI; 5/20), or both CT and MRI (6/20). All 20 SFTs were well defined, lobular, soft-tissue masses, and 60% were located outside of the pleura. One pleural case invaded to the 10th thoracic vertebra and had lung metastases. Images revealed 11 heterogeneous lesions that exceeded 3.0 ± 0.203 cm along the greatest axis with patchy necrotic foci, and 9 homogeneous lesions 3.0 ± 0.203 cm along the greatest axis appeared to be mixed patterns, whereas SFTs < 3.0 ± 0.203 cm had isodense appearances. SFTs cells were CD34 immunopositive and surgery was a first-line treatment choice.status: publishe

A Power-Frequency Electric Field Sensor for Portable Measurement

In this paper, a new type of electric field sensor is proposed for the health and safety protection of inspection staff in high-voltage environments. Compared with the traditional power frequency electric field measurement instruments, the portable instrument has some special performance requirements and, thus, a new kind of double spherical shell sensor is presented. First, the mathematical relationships between the induced voltage of the sensor, the output voltage of the measurement circuit, and the original electric field in free space are deduced theoretically. These equations show the principle of the proposed sensor to measure the electric field and the effect factors of the measurement. Next, the characteristics of the sensor are analyzed through simulation. The simulation results are in good agreement with the theoretical analysis. The influencing rules of the size and material of the sensor on the measurement results are summarized. Then, the proposed sensor and the matching measurement system are used in a physical experiment. After calibration, the error of the measurement system is discussed. Lastly, the directional characteristic of the proposed sensor is experimentally tested