Three-dimensional magnetohydrodynamical simulations of the morphology of head-tail radio galaxies based on magnetic tower jet model

The distinctive morphology of head-tail radio galaxies reveals strong interactions between the radio jets and their intra-cluster environment, the general consensus on the morphology origin of head-tail sources is that radio jets are bent by violent intra-cluster weather. We demonstrate in this paper that such strong interactions provide a great opportunity to study the jet properties and also the dynamics of intra-cluster medium (ICM). By three-dimensional magnetohydrodynamical simulations, we analyse the detailed bending process of a magnetically dominated jet, based on the magnetic tower jet model. We use stratified atmospheres modulated by wind/shock to mimic the violent intra-cluster weather. Core sloshing is found to be inevitable during the wind-cluster core interaction, which induces significant shear motion and could finally drive ICM turbulence around the jet, making it difficult for jet to survive. We perform detailed comparison between the behaviour of pure hydrodynamical jets and magnetic tower jet, and find that the jet-lobe morphology could not survive against the violent disruption in all of our pure hydrodynamical jet models. On the other hand, the head-tail morphology is well reproduced by using a magnetic tower jet model bent by wind, in which hydrodynamical instabilities are naturally suppressed and the jet could always keep its integrity under the protection of its internal magnetic fields. Finally, we also check the possibility for jet bending by shock only. We find that shock could not bend jet significantly, so could not be expected to explain the observed long tails in head-tail radio galaxies.Comment: submitted to ApJ on December 9, 2016, and accepted on March 1st, 201

Large-Scale Multi-Label Learning with Incomplete Label Assignments

Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches mainly focus on exploiting label correlations. It is usually assumed, explicitly or implicitly, that the label sets for training instances are fully labeled without any missing labels. However, in many real-world multi-label datasets, the label assignments for training instances can be incomplete. Some ground-truth labels can be missed by the labeler from the label set. This problem is especially typical when the number instances is very large, and the labeling cost is very high, which makes it almost impossible to get a fully labeled training set. In this paper, we study the problem of large-scale multi-label learning with incomplete label assignments. We propose an approach, called MPU, based upon positive and unlabeled stochastic gradient descent and stacked models. Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data. Extensive experiments on two real-world multi-label datasets show that our MPU model consistently outperform other commonly-used baselines

The effect of repeated freeze-thaw cycles on the meat quality of rabbit

[EN] We investigated the effect of repeated freeze-thaw cycles on the quality of rabbit meat. Twenty-five Hyla rabbits were slaughtered using standard commercial procedures. A freeze-thaw procedure—i.e., seven days frozen at –18°C followed by thawing at 4°C for 12h— was repeated 5 times, and 9 Longissimus thoracis et lumborum muscles were randomly selected at pre-set cycles (0, 1, 2, 3, and 5). The Longissimus lumborum muscles were used to determine meat quality parameters, while the Longissimus thoracis muscles were used for chemical analysis. During the repeated freeze-thaw process, muscle pH, redness, hardness, and water holding capacity gradually decreased, whereas meat lightness and yellowness gradually increased. The amount of total volatile basic nitrogen significantly increased (P<0.05) and exceeded the threshold value for frozen meat after 5 repeated freeze-thaw cycles. The metmyoglobin proportion, thiobarbituric acid-reactive substances (TBARS) and protein carbonyl content in rabbit meat samples increased with a higher number of freeze-thaw cycles (P<0.05), and the proportions of these compounds were positively correlated. During the repeated freeze-thaw process, extractable haeme iron levels significantly decreased (P<0.05), and non-haeme iron levels markedly increased (P<0.05). An sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis indicated that the degradation of both water- and salt-soluble proteins was more prevalent in samples subjected to higher numbers of freeze-thaw cycles. Additionally, a principal component analysis identified good correlations between physicochemical properties (TBARS, protein carbonyl levels and metmyoglobin content) and quality parameters (thawing loss, redness, lightness and hardness). Taken together, we conclude that the repeated freeze-thaw process can strongly affect rabbit meat quality as well as its physicochemical properties.The authors gratefully acknowledge financial support from the General Program of National Natural Science Foundation of China (31671787), the National Rabbit Industry Technology System Programme (Grant No. CARS-43-E-1), and the Chongqing Herbivorous Livestock Industry Technology System (Y201706).Wang, Z.; He, Z.; Gan, X.; Li, H. (2018). The effect of repeated freeze-thaw cycles on the meat quality of rabbit. World Rabbit Science. 26(2):165-177. https://doi.org/10.4995/wrs.2018.8616SWORD165177262Ali S., Rajput N., Li C.B., Zhang W., Zhou G.H. 2016. Effect of freeze-thaw cycles on lipid oxidation and myowater in broiler chickens. Revista Brasileira de Ciência Avícola, 18: 35-40.https://doi.org/10.1590/1516-635x1801035-040Ali S., Zhang W., Rajput N., Khan M.A., Li C.B., Zhou G.H. 2015. 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A Random Multi-Trajectory Generation Method for Online Emergency Threat Management (Analysis and Application in Path Planning Algorithm)

This paper presents a novel randomized path planning algorithm, which is a goal and homology biased sampling based algorithm called Multiple Guiding Attraction based Random Tree, and robots can use it to tackle pop-up and moving threats under kinodynamic constraints. Our proposed method considers the kinematics and dynamics constraints, using obstacle information to perform informed sampling and redistribution around collision region toward valid routing. We pioneeringly propose a multiple path planning method using ‘Extending Forbidden’ algorithm, rather than using variant cost principles for online threat management. The threat management method performs online path switching between the planned multiple paths, which is proved with better time performance than conventional approaches. The proposed method has advantage in exploration in obstacle crowded environment, where narrow corridor fails using the general sampling based exploration methods. We perform detailed comparative experiments with peer approaches in cluttered environment, and point out the advantages in time and mission performance

A Novel Fifth-Degree Cubature Kalman Filter for Real-Time Orbit Determination by Radar

A novel fifth-degree cubature Kalman filter is proposed to improve the accuracy of real-time orbit determination by ground-based radar. A fully symmetric cubature rule, approaching the Gaussian weighted integral of a nonlinear function in general form, is introduced, and the specific points and weights are calculated by matching the monomials of degree not greater than five with the exact values. On the basis of the above rule, a novel fifth-degree cubature Kalman filter, which can achieve a higher accuracy than UKF and CKF, is derived under the Bayesian filtering framework. Then, to describe the nonlinear system more accurately, the orbital dynamics equation with J2 perturbation is used as the state equation, and the nonlinear relationship between the radar measurement elements and orbital states is built as the measurement equation. The simulation results show that, compared with the traditional third-degree algorithm, the proposed fifth-degree algorithm has a higher accuracy of orbit determination

On the Interpretation of the Scalings of Density Fluctuations from In-situ Solar Wind Observations: Insights from 3D Turbulence Simulations

Solar wind turbulence is often perceived as weakly compressible and the density fluctuations remain poorly understood both theoretically and observationally. Compressible magnetohydrodynamic simulations provide useful insights into the nature of density fluctuations. We discuss a few important effects related to 3D simulations of turbulence and in-situ observations. The observed quantities such as the power spectrum and variance depend on the angle between the sampling trajectory and the mean magnetic field due to anisotropy of the turbulence. The anisotropy effect is stronger at smaller scales and lower plasma beta. Additionally, in-situ measurements tend to exhibit a broad range of variations, even though they could be drawn from the same population with the defined averages, so a careful averaging may be needed to reveal the scaling relations between density variations and other turbulence quantities such as turbulent Mach number from observations.Comment: 11 pages, 5 figures, accepted by The Astrophysical Journa

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM- MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22

Observational and Simulative Study of a Local Severe Precipitation Event Caused by a Cold Vortex over Northeast China

A severe precipitation event around Changchun-Yongji in Jilin Province, China, during 27–29 July 2010 was investigated, with a focus on the comparative analysis of 2 heavy precipitation episodes. This was done using NCEP gridded analysis data, intensive surface observations, and radar and satellite measurements. The Weather Research and Forecast (WRF) model was used to simulate the precipitation process and explore mechanisms for the development and dissipation of the severe precipitation event. Precipitation in the first stage was induced by the convergence of northwesterly winds at the rear of the cold vortex and southwesterly winds that reached the rainfall region. However, in the second stage, because of the blockage caused by Changbai Mountain, winds at the bottom of the cold vortex turned from the northwest to the northeast. These winds strongly converged with the southwesterly winds and continuously triggered new convective clouds, which were associated with cold centers at the surface. The intensity of the cold center modulated the strength of the convective cells and resulting precipitation quantity. Furthermore, the local terrain features and direction of the motion of the airflows were critical in triggering convection

Frequent Mutations in Natural Killer/T Cell Lymphoma

Extranodal natural killer (NK)/T cell lymphoma (ENKTL-NT or NKTCL), with its aggressive nature and poor prognosis, has been widely studied to discover more effective treatment options. Various somatic gene alterations have been identified by traditional Sanger sequencing. However, recently, novel gene mutations in NKTCL have been revealed by next-generation sequencing (NGS) technology, suggesting the potential for novel targeted therapies. This review discusses recurrent aberrations in NKTCL detected by NGS, which can be categorized into three main groups, specifically, tumor suppressors (TP53, DDX3X, and MGA), the JAK/STAT cascade, and epigenetic modifiers (KMT2D, BCOR, ARID1A, and EP300). Some epigenetic dysregulation and DDX3X mutation, which have been rarely identified by traditional sequencing technology, were recently uncovered with high frequencies by NGS. In this review, we summarize the mutational frequencies of various genes in NKTCL. In general, based on our analysis, BCOR is the most frequently mutated gene (16.9%), followed by TP53 (14.7%), and DDX3X (13.6%). The characterization of such genes provides new insight into the pathogenesis of this disease and indicates new biomarkers or therapeutic targets

EYA4 Promotes Cell Proliferation Through Downregulation of p27Kip1 in Glioma

Background/Aims: Accumulating evidence suggests that Eyes Absent Homologue 4 (EYA4) plays an important role in tumorigenesis and progression of various cancers. However, the role of EYA4 in glioma development is still unclear. Methods: The expression of EYA4 was examined in glioma tissues by immunohistochemistry. Cell viability and apoptosis were analyzed by CCK-8, BrdU assay, and flow cytometry. Results: We found that EYA4 was upregulated in glioma, and its expression was positively correlated with advanced tumor stage. Moreover, higher expression of EYA4 predicted a worse overall survival in patients with glioma. Forced overexpression of EYA4 enhanced glioma cell proliferation, and EYA4 suppressed the expression of p27Kip1 directly in these cells. Furthermore, Six1 was required for EYA4 to suppress the expression of p27Kip1 in glioma. Conclusion: Together, we demonstrate that EYA4 promotes cell proliferation by directly suppressing the expression of p27Kip1 in glioma