Controlling ERK Activation Dynamics in Mammary Epithelial Cells with Alternating Electric Fields through Microelectrodes.

Amplitude, duration, and frequency of activation of the extracellular-signal-regulated kinase (ERK) pathway code distinct information to instruct cells to migrate, proliferate, or differentiate. Synchronized frequency control of ERK activation would provide a powerful approach to regulate cell behaviors. Here we demonstrated modulation of ERK activities using alternative current (AC) electric fields (EFs) applied through high-k dielectric passivated microelectrodes. Both the amplitude and frequency of ERK activation can be precisely synchronized and modulated. ERK activation in our system is independent of Faradaic currents and electroporation, thus excluding mechanisms of changes in pH, reactive oxygen species, and other electrochemical reaction. Further experiments pinpointed a mechanism of phosphorylation site of epidermal growth factor (EGF) receptor to activate the EGFR-ERK pathway, and independent of EGF. AC EFs thus provide a powerful platform for practical and precise control of EGFR-ERK pathway

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

Comparative analysis of airborne laser bathymetric waveforms denoising algorithms

Denoising fitting of airborne laser bathymetry data is a key step in extracting the bottom terrain. The algorithm effects of wavelet adaptive threshold denoising, empirical model denoising (EMD) and joint denoising are compared in this paper, and then multivariate Gaussian fitting is used to test the denoising effect. The optimal denoising algorithm and parameter selection are obtained by comparison, and it is realized that the high-precision extraction of seabed features. This study has shown that: when the sounding data is denoised by wavelet threshold, the fixed threshold wavelet denoising effect is superior to other denoising effects, and the denoising decomposition level is more than 6 layers, which tends to be stable. The average accuracy of the algorithm reaches 8.218 2 after the fifth-order Gaussian fitting of the denoising data. The algorithm has strong robustness, it can meet the technical requirements of blue-green laser practical application, and provides a reference for accurately extracting seafloor feature information

Research Progress in Mathematical Analysis of Map Projection by Computer Algebra

Map projection is an important component of modern cartography, and involves many fussy mathematical analysis processes, such as the power series expansions of elliptical functions, differential of complex and implicit functions, elliptical integral and the operation of complex numbers. The derivation of these problems by hand not only consumes much time and energy but also makes mistake easily, and sometimes can not be realized at all because of the impossible complexity. The research achievements in mathematical analysis of map projection by computer algebra are systematically reviewed in five aspects, i.e., the symbolic expressions of forward and inverse solution of ellipsoidal latitudes, the direct transformations between map projections with different distortion properties, expressions of Gauss projection by complex function, mathematical analysis of oblique Mercator projection, polar chart projection with its transformation. Main problems that need to be further solved in this research field are analyzed. It will be helpful to promote the development of map projection

RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks

The digital elevation model (DEM) is known as one kind of the most significant fundamental geographical data models. The theory, method and application of DEM are hot research issues in geography, especially in geomorphology, hydrology, soil and other related fields. In this paper, we improve the efficient sub-pixel convolutional neural networks (ESPCN) and propose recursive sub-pixel convolutional neural networks (RSPCN) to generate higher-resolution DEMs (HRDEMs) from low-resolution DEMs (LRDEMs). Firstly, the structure of RSPCN is described in detail based on recursion theory. This paper explores the effects of different training datasets, with the self-adaptive learning rate Adam algorithm optimizing the model. Furthermore, the adding-“zero” boundary method is introduced into the RSPCN algorithm as a data preprocessing method, which improves the RSPCN method’s accuracy and convergence. Extensive experiments are conducted to train the method till optimality. Finally, comparisons are made with other traditional interpolation methods, such as bicubic, nearest-neighbor and bilinear methods. The results show that our method has obvious improvements in both accuracy and robustness and further illustrate the feasibility of deep learning methods in the DEM data processing area

Simplified Expansions of Common Latitudes with Geodetic Latitude and Geocentric Latitude as Variables

Using the symbolic calculation program Mathematica and based on the power series expansions of the common latitude with geodetic latitude as a variable, power series expansions of the common latitude with geocentric latitude as the variable are derived. The coefficients of the two groups of formulas are based on the ellipsoid eccentricity e and the ellipsoid third flattening n, which make the expansions more uniform. Taking the CGCS2000 as an example, numerical analysis is applied to verify the accuracy and reliability of the derived power series expansions. By analyzing and calculating the truncation error of the common latitude based on ellipsoidal eccentricity e and the third flattening n expansion to different orders, we obtain simplified, practical formulas for the common latitude that satisfy the requirement of geodesic accuracy. Moreover, we show that the practical formula derived has higher calculation efficiency and easier dissemination, enriches the theory of map projection, and provides a basis for better display of remote sensing images

The Design and Development of a Ship Trajectory Data Management and Analysis System Based on AIS

To address the data storage, management, analysis, and mining of ship targets, the object-oriented method was employed to design the overall structure and functional modules of a ship trajectory data management and analysis system (STDMAS). This paper elaborates the detailed design and technical information of the system’s logical structure, module composition, physical deployment, and main functional modules such as database management, trajectory analysis, trajectory mining, and situation analysis. A ship identification method based on the motion features was put forward. With the method, ship trajectory was first partitioned into sub-trajectories in various behavioral patterns, and effective motion features were then extracted. Machine learning algorithms were utilized for training and testing to identify many types of ships. STDMAS implements such functions as database management, trajectory analysis, historical situation review, and ship identification and outlier detection based on trajectory classification. STDMAS can satisfy the practical needs for the data management, analysis, and mining of maritime targets because it is easy to apply, maintain, and expand

An Alteration of Gauss Projection Based on Oblique Deformed Ellipsoid

For east-west spanning line, to reduce abscissa value of Gauss projection, the oblique reference ellipsoid was constructed by means of least square method. Via theory of coordinate system transformation, spatial rectangular coordinates of target region in each coordinate system were carried out, and then geodetic coordinates of target region on oblique reference ellipsoid were relatively given. Through ellipsoid transformation, oblique deformed ellipsoid was established to lessen distortion of projection caused by height. Taking one railway for example, it were shown that "An alteration of Gauss projection based on oblique deformed ellipsoid" could greatly deplete abscissa components, avoid zoning of Gauss projection and reduce height effectively, as well as the relevant distortion it caused. Strict mathematical model and clear operation process of the Gauss projection are convenient for programming of relative software, which can be applied in engineering

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%