Protein kinase Cι is required for Ras transformation and colon carcinogenesis in vivo

Protein kinase C ι (PKCι) has been implicated in Ras signaling, however, a role for PKCι in oncogenic Ras-mediated transformation has not been established. Here, we show that PKCι is a critical downstream effector of oncogenic Ras in the colonic epithelium. Transgenic mice expressing constitutively active PKCι in the colon are highly susceptible to carcinogen-induced colon carcinogenesis, whereas mice expressing kinase-deficient PKCι (kdPKCι) are resistant to both carcinogen- and oncogenic Ras-mediated carcinogenesis. Expression of kdPKCι in Ras-transformed rat intestinal epithelial cells blocks oncogenic Ras-mediated activation of Rac1, cellular invasion, and anchorage-independent growth. Constitutively active Rac1 (RacV12) restores invasiveness and anchorage-independent growth in Ras-transformed rat intestinal epithelial cells expressing kdPKCι. Our data demonstrate that PKCι is required for oncogenic Ras- and carcinogen-mediated colon carcinogenesis in vivo and define a procarcinogenic signaling axis consisting of Ras, PKCι, and Rac1

Quantitative investigation of two metallohydrolases by X-ray absorption spectroscopy near-edge spectroscopy

The last several years have witnessed a tremendous increase in biological applications using X-ray absorption spectroscopy (BioXAS), thanks to continuous advancements in synchrotron radiation (SR) sources and detector technology. However, XAS applications in many biological systems have been limited by the intrinsic limitations of the Extended X-ray Absorption Fine Structure (EXAFS) technique e.g., the lack of sensitivity to bond angles. As a consequence, the application of the X-ray absorption near-edge structure (XANES) spectroscopy changed this scenario that is now continuously changing with the introduction of the first quantitative XANES packages such as Minut XANES (MXAN). Here we present and discuss the XANES code MXAN, a novel XANES-fitting package that allows a quantitative analysis of experimental data applied to Zn K-edge spectra of two metalloproteins: Leptospira interrogans Peptide deformylase (LiPDF) and acutolysin-C, a representative of snake venom metalloproteinases (SVMPs) from Agkistrodon acutus venom. The analysis on these two metallohydrolases reveals that proteolytic activities are correlated to subtle conformation changes around the zinc ion. In particular, this quantitative study clarifies the occurrence of the LiPDF catalytic mechanism via a two-water-molecules model, whereas in the acutolysin-C we have observed a different proteolytic activity correlated to structural changes around the zinc ion induced by pH variations

Correlation between local vibrations and metal mass in AlB2-type transition-metal diborides

Lattice vibrations have been investigated in TiB2, ZrB2 and HfB2 by temperature-dependent extended X-ray absorption fine structure (EXAFS) experiments. Data clearly show that the EXAFS oscillations are characterized by an anomalous behavior of the Debye-Waller factor of the transition-metal-boron pair, which is suggested to be associated with a superposition of an optical mode corresponding to phonon vibrations induced by the B sublattice and an acoustic mode corresponding to the transition-metal (TM) sublattice. Data can be interpreted as a decoupling of the metal and boron vibrations observed in these transition-metal diborides (TMB2), a mechanism that may be responsible for the significant reduction of the superconducting transition temperature observed in these systems with respect to the parent MgB2 compound. The vibrational behavior of TM-TM bonds has also been investigated to study the occurrence of anisotropy and anomalies in the lattice vibrational behavior of TM-TM bonds

Visual tracking based on semantic and similarity learning

We present a method by combining the similarity and semantic features of a target to improve tracking performance in video sequences. Trackers based on Siamese networks have achieved success in recent competitions and databases through learning similarity according to binary labels. Unfortunately, such weak labels result in limiting the discriminative ability of the learned feature, thus it is difficult to identify the target itself from the distractors that have the same class. The authors observe that the inter‐class semantic features benefit to increase the separation between the target and the background, even distractors. Therefore, they proposed a network architecture which uses both similarity and semantic branches to obtain more discriminative features for locating the target accuracy in new frames. The large‐scale ImageNet VID dataset is employed to train the network. Even in the presence of background clutter, visual distortion, and distractors, the proposed method still maintains following the target. They test their method with the open benchmarks OTB and UAV123. The results show that their combined approach significantly improves the tracking ability relative to trackers using similarity or semantic features alone

Robust visual tracking based on watershed regions

Robust visual tracking is a very challenging problem especially when the target undergoes large appearance variation. In this study, the authors propose an efficient and effective tracker based on watershed regions. As middle‐level visual cues, watershed regions contain more semantics information than low‐level features, and reflect more structure information than high‐level model. First, the authors manually select the target template in initial frame, and predict the target candidate in the next frame using motion prediction. Then, the authors utilise marker‐based watershed algorithm to obtain the watershed regions of target template and candidate template, and describe each region with multiple features. Next, the authors calculate the nearest neighbour in feature space to match the watershed regions and construct an affine relation from target template to candidate template. Finally, the authors resolve the affine relation to calculate the final tracking result, and update the template for the following tracking. The authors test their tracker on some challenging sequences with appearance variation range from illumination change, partial occlusion, pose change to background clutters and compare it with some state‐of‐the‐art works. Experiment results indicate that the proposed tracker is robust to the large appearance variation and exceeds the state‐of‐the‐art trackers in most situations

Target tracking approach via quantum genetic algorithm

Aiming at an efficient feature match and similarity search in visual tracking, this study proposes a tracking algorithm based on quantum genetic algorithm. Therein, the global optimisation ability of quantum genetic algorithm is utilised. In the framework of quantum genetic algorithm, the positions of pixels are taken as individuals in population, while scale‐invariant feature transform and colour features are taken as target model. Via defining the objective function, individual's fitness values can be measured. Visual tracking is realised when the pixel point with the biggest fitness value is searched and its corresponding position is returned. The experiment results show that the tracking algorithm the authors proposed performs more efficiently when it is compared with the state‐of‐the‐art tracking algorithms

Multi‐scale mean shift tracking

In this study, a three‐dimensional mean shift tracking algorithm, which combines the multi‐scale model and background weighted spatial histogram, is proposed to address the problem of scale estimation under the framework of mean shift tracking. The target template is modelled with multi‐scale model and described with three‐dimensional spatial histogram. The tracking algorithm is implemented by three‐dimensional mean shift iteration, which translates the problem of scale estimation in two‐dimensional image plane into the localisation in three‐dimensional image space. To enhance the robustness, the background weighted histogram is employed to suppress the background information in the target candidate model. Firstly, the multi‐scale model and three‐dimensional spatial histogram are introduced to represent the target template. Then, the three‐dimensional mean shift iteration formulation is derived based on the similarity measure between the target model and the target candidate model. Finally, a multi‐scale mean shift tracking algorithm combining multi‐scale model and background weighted spatial histogram is proposed. The proposed algorithm is evaluated on some challenging sequences which contain scale changed targets and other complex appearance variations in comparison with three representative mean shift based tracking algorithms. Both the qualitative results and quantitative analysis indicate that the proposed algorithm outperforms the referenced algorithms in both tracking precision and scale estimation

Research on Energy Management Strategies of Extended-Range Electric Vehicles Based on Driving Characteristics

The extended-range electric vehicle (E-REV) can solve the problems of short driving range and long charging time of pure electric vehicles, but it is necessary to control the engine working points and allocate the power of the energy sources reasonably. In order to improve the fuel economy of the vehicle, an energy management strategy (EMS) that can adapt to the daily driving characteristics of the driver and adjust the control parameters online is proposed in this paper. Firstly, through principal component analysis (PCA) and iterative self-organizing data analysis techniques algorithm (ISODATA) of historical driving data, a typical driving cycle which can describe driving characteristics of the driver is constructed. Then offline optimization of control parameters by adaptive simulated annealing under each typical driving cycle and online recognition of driving cycles by extreme learning machine (ELM) are applied to the adaptive multi-workpoints energy management strategy (A-MEMS) of E-REV. In the end, compared with traditional rule-based control strategies, A-MEMS achieves good fuel-saving and emission-reduction result by simulation verification, and it explores a new and feasible solution for the continuous upgrade of the EMS

Distance Measures of Polarimetric SAR Image Data: A Survey

Distance measure plays a critical role in various applications of polarimetric synthetic aperture radar (PolSAR) image data. In recent decades, plenty of distance measures have been developed for PolSAR image data from different perspectives, which, however, have not been well analyzed and summarized. In order to make better use of these distance measures in algorithm design, this paper provides a systematic survey of them and analyzes their relations in detail. We divide these distance measures into five main categories (i.e., the norm distances, geodesic distances, maximum likelihood (ML) distances, generalized likelihood ratio test (GLRT) distances, stochastics distances) and two other categories (i.e., the inter-patch distances and those based on metric learning). Furthermore, we analyze the relations between different distance measures and visualize them with graphs to make them clearer. Moreover, some properties of the main distance measures are discussed, and some advice for choosing distances in algorithm design is also provided. This survey can serve as a reference for researchers in PolSAR image processing, analysis, and related fields