Drought-induced proline is mainly synthesized in leaves and transported to roots in watermelon under water deficit

Proline accumulation has been shown to occur in plants in response to various environmental stresses.Although proline metabolismrelated genes have been functionally characterized,the inter-organ transport of proline in stressed plants remains unclear.In this study,free proline was detected with significant accumulations in the roots,stems,and leaves of watermelon drought-tolerant germplasm M08 and drought-susceptible line Y34 under drought stress.Expression profiling and enzyme activity measurements revealed that ClP5CS1 gene,rather than its paralog ClP5CS2,mainly contributes to the proline synthesis in leaves via the Glu pathway.Additionally,over-expression of the ClP5CS genes significantly enhanced the drought tolerance of transgenic Arabidopsis lines.Furthermore,we confirmed that proline is mainly synthesized in leaves and transported to roots in watermelon under drought stress.Transcriptome and expression analyses revealed that the genes involved in proline metabolism exhibited different expression levels.Specifically,ClP5CS1 was upregulated in leaves and roots,while ClP5CS2 was downregulated under drought stress.Also,415 and 362 differently expressed TFs were identified in roots and leaves,respectively,with the majority upregulated in the former.Ultimately,a model for proline metabolism was proposed.The findings of this study provided new insights into the biosynthesis,transport,and regulatory mechanism of drought-induced proline in plants.

Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition

Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.

Performance Analysis for Multimedia Communication Systems with a Multilayer Queuing Network Model

Software performance evaluation in multimedia communication systems is typically formulated into a multi-layered client-server queuing network(MLCSQN) problem. However, the existing analytical methods to MLCSQN model cannot provide satisfactory solution in terms of accuracy, convergence and consideration of interlocking effects. To this end, this paper proposes a heuristic solving method for MLCSQN model to boost the performance prediction of distributed multimedia software systems. The core concept of this method is referred to as the basic model, which can be further decomposed into two sub-models: client sub-model and server sub-model. The client sub-model calculates think time for server sub-model, and the server sub-model calculates waiting time for client sub-model. Using a breadthfirst traversal from leaf nodes to the root node and vice versa, the basic model is then adapted to MLCSQN, with net sub-models iteratively resolved. Similarly, the interlocking problem is effectively addressed with the help of the basic model. This analytical solver enjoys advantages of fast convergence, independence on specific average value analysis(MVA) methods and eliminating interlocking effects.Numerical experimental results on accuracy and computation efficiency verify its superiority over anchors.

Action Recognition with Temporal Scale-Invariant Deep Learning Framework

Recognizing actions according to video features is an important problem in a wide scope of applications. In this paper, we propose a temporal scale.invariant deep learning framework for action recognition, which is robust to the change of action speed. Specifically, a video is firstly split into several sub.action clips and a keyframe is selected from each sub.action clip. The spatial and motion features of the keyframe are extracted separately by two Convolutional Neural Networks(CNN) and combined in the convolutional fusion layer for learning the relationship between the features. Then, Long Short Term Memory(LSTM) networks are applied to the fused features to formulate long.term temporal clues. Finally, the action prediction scores of the LSTM network are combined by linear weighted summation. Extensive experiments are conducted on two popular and challenging benchmarks, namely, the UCF.101 and the HMDB51 Human Actions. On both benchmarks, our framework achieves superior results over the state.of.the.art methods by 93.7% on UCF.101 and 69.5% on HMDB51, respectively.

Multi-Attention Fusion and Fine-Grained Alignment for Bidirectional Image-Sentence Retrieval in Remote Sensing

Dear editor,Cross-modal retrieval in remote sensing(RS) data has inspired increasing enthusiasm due to its merit in flexible input and efficient query. In this letter, we address to establish semantic relationship between RS images and their description sentences.

3D Perception Algorithms: Towards Perceptually Driven Compression of 3D Video

In this paper, we summarize 3D perception-oriented algorithms for perceptually driven 3D video coding. Several perceptual effects have been exploited for 2D video viewing; however, this is not yet the case for 3D video viewing. 3D video requires depth perception, which implies binocular effects such as conflicts, fusion, and rivalry. A better understanding of these effects is necessary for 3D perceptual compression, which provides users with a more comfortable visual experience for video that is delivered over a channel with limited bandwidth. We present state-of-the-art of 3D visual attention models, 3D just-noticeable difference models, and 3D texture-synthesis models that address 3D human vision issues in 3D video coding and transmission.

钼基碳掺杂二硫化钼电极的硫氧化反应性能

硫钝化是阻碍硫化物氧化反应(sulfur oxidation reaction,SOR)在硫锂电池、电化学脱硫、含硫污染物资源化等领域高效进行的重要因素.二硫化钼(MoS2)因其优异的抗硫性和可调的二维层状结构而具有良好的SOR潜能,但其本征半导体性质限制了电子传递过程.本文报道了一种抗硫性钼基碳掺杂二硫化钼电极(Mo/C-MoS2),通过碳掺杂使MoS2电极具有接近热中性的硫吸附吉布斯自由能和快速转换多硫化物的能力,有效避免了硫钝化.实验结果显示,Mo/C-MoS2能以0.37 V驱动SOR反应达到10 mA/cm2,低于Mo/MoS2电极(0.41 V)和Pt电极(0.68 V),并在恒电位电解测试中保持16.5 h的抗硫钝化稳定性.透射电子显微镜(transmission electron microscope,TEM)、X射线衍射(X-ray diffraction,XRD)、X射线光电子能谱(X-ray photoelectron spectroscopy,XPS)、拉曼散射光谱表征证实,CMoS2具有1T导电相晶体结构,提升了催化反应速率.密度泛函理论(density functional theory,DFT)计算进一步阐明,碳掺杂增强了MoS2与Na2S之间的相互作用,促使元素硫向可溶性多硫化物转换,为Mo/C-MoS2电极高效SOR和稳定抗硫性的实验结论提供了理论支撑.

向下扎根:新时代政党领导乡村治理的机制建构和效能分析

基层是国家治理的重要场域,乡村基层治理是国家治理体系的基本组成部分。“向下扎根”是新时代政党嵌入并改造乡村治理结构的整体趋势,其目标是实现党对基层工作的全面领导和乡村治理的现代化。本文聚焦新时代“党管农村”的三个关键机制——驻村工作队和第一书记制度、“一肩挑”制度、村级权力监督制度,基于实地调查和问卷数据,深度剖析党管农村的制度架构、运行状态和未来走向。新时代政党领导乡村治理正走向全面制度化和规范化,党和国家治理单元的战略性下沉(“向下扎根”)不仅将转变改革开放以来中国乡村的治理逻辑和政治生态,并且将对政党权威、政治稳定乃至国家治理产生深远影响。

Microsoft Concept Graph:Mining Semantic Concepts for Short Text Understanding

Knowlege is important for text-related applications.In this paper,we introduce Microsoft Concept Graph,a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages.Microsoft Concept Graph is built upon Probase,a universal probabilistic taxonomy consisting of instances and concepts mined from the Web.We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures,which extract 2.7 million concepts from 1.68 billion Web pages.We then use conceptualization models to represent text in the concept space to empower text-related applications,such as topic search,query recommendation,Web table understanding and Ads relevance.Since the release in 2016,Microsoft Concept Graph has received more than 100,000 pageviews,2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.

Acceleration tracking control for a spinning glide guided projectile with multiple disturbances

A novel acceleration tracking controller is proposed in this paper, for a Spinning Glide Guided Projectile(SGGP) subject to cross-coupling dynamics, external disturbances, and parametric uncertainties. The cross-coupled dynamics for the SGGP are formulated with mismatched and matched uncertainties, and then divided into acceleration and angular rate subsystems via the hierarchical principle. By exploiting the structural property of the SGGP, model-assisted Extended State Observers(ESOs) are designed to estimate online the lumped disturbances in the acceleration and angular rate dynamics. To achieve a rapid response and a strong robustness, integral sliding mode control laws and sigmoid-function-based tracking differentiators are integrated into the ESO-based Trajectory Linearization Control(TLC) framework. It is proven that the acceleration tracking controller can guarantee the ultimate boundedness of the signals in the closed-loop system and make the tracking errors arbitrarily small. The superiority and effectiveness of the proposed control scheme in its decoupling ability, accurate acceleration tracking performance and antidisturbance capability are validated through comparisons and extensive simulations.

Conceptualizing and Measuring Global Justice:Theories,Concepts,Principles and Indicators

The paper focuses on the conceptualization and measurement of global justice and discusses theories,concepts,evaluative principles,and methodologies related to the study of global justice.In this paper,we seek to clarify how to conceptualize global justice,how conceptual indicators can be selected and justified by theories,and how those indicators can be conceptually consistent with the concept of global justice.Global justice is a broad concept that is composed of multi-level and multidimensional aspects belonging to both normative and empirical realities.A coherent and integrated theoretical framework that covers the normative basis and various empirical dimensions is therefore much needed in order to address some of the basic and important questions under study.The paper seeks to synthesize the multiple theories and conceptions of global justice that exist in the academic discourse and literature into three main theoretical approaches to global justice-rights based,good based,and virtue based.These three approaches are a good sample of and reflect well the strengths of the different theoretical,intellectual and cultural traditions at play in the study of global justice.From this perspective,the synthesis of the three approaches is meant to provide us with a coherent theoretical framework that serves as the normative basis and justifies the selection of indicators for measurement.