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Recently, when handling high-dimensional data, it has become extremely difficult to search this optimal subset of selected features due to the restriction of reducing the exponential increase of the search procedure, and most of those feature selection models neglect the interactions of features or feature and decision class. This paper develops a novel feature selection approach using symmetric uncertainty and hybrid optimization for high-dimensional data (FSUHO) for high-dimensional data. First, to fully reflect the interaction relationship of features or feature and decision class, the F-relevance between features and the C-correlation between feature and decision class based on the symmetric uncertainty are constructed to remove those redundant features. Then, a strong correlation threshold is improved based on the C-correlation and random coefficient to prevent the removal of the effective features in this first stage. Second, to decrease this expensive computational consumption, one criterion for judging a weakly correlated feature is designed to sort all features, and another criterion is developed to select the class center. The similarity between features and class centers is calculated, and similar features are clustered into one class. Then, the symmetric uncertainty correlation-based feature clustering model can be constructed in this second stage. In the third stage, a hybrid optimization approach of particle swarm optimizer (PSO) and wild horse optimizer (WHO) for feature selection is proposed, where the association-guided group initialization probability with a multiobjective optimized particle selection scheme is defined as a criterion for the PSO in selecting stallion particles for the WHO, and the improved WHO is developed by integrating the nonlinear inertial weight factor and the Brownian motion operator to obtain the optimal subset of selected features. Finally, a novel three-stage feature selection algorithm is developed. Experimental results apply to 16 datasets prove the efficiency of FSUHO in tackling high-dimensional feature selection problems in metrics of classification accuracy and running time. [ABSTRACT FROM AUTHOR]

The most basic and important attributes of virtual reality (VR) are immersion, interactivity and creativity. For this young media, the continuous development and change and the exploration of content are the current situation of VR art. The arrival of 5G communication technology provides an important foundation for the popularization of VR technology. The rapid development of technology and hardware rewrites and expands the possibility of VR art design expression at any time. As the latest technology of natural human–computer interaction, somatosensory interaction technology enables users to interact with computers directly through body movements, gestures, etc., and control the environment at will. Its core value lies in that it not only enables the computer to have more accurate and effective "eyes" to observe the world, not only to understand human body language, but also to complete various instructions according to the body language, to achieve real-time interaction with people. Gesture, as a common communication mode with the ability of information expression, has the advantages of intuitionistic, natural and easy to understand. Therefore, the somatosensory interaction technology based on gesture recognition has become an effective means of natural human–computer interaction. With the rapid development of key technologies of VR, VR has been widely used in games, fitness, real estate, education, film and television and other industries. In recent years, VR systems are everywhere, including virtual home decoration system, virtual panoramic VR live broadcasting system, virtual fitness system, etc. The design and implementation of this VR system mainly includes four parts: scene modeling, rendering and rendering, performance and interaction, and network application. Based on this, this paper uses the comprehensive data analysis technology to build the human–computer interaction design model in the virtual environment. The AI and speech information are combined to perform the optimal analysis. [ABSTRACT FROM AUTHOR]

Key message: The OsPLS2 locus was isolated and cloned by map-based cloning that encodes a Upf1-like helicase. Disruption of OsPLS2 accelerated light-dependent leaf senescence in the rice mutant of ospls2. Leaf senescence is a very complex physiological process controlled by both genetic and environmental factors, however its underlying molecular mechanisms remain elusive. In this study, we report a novel Oryza sativa premature leaf senescence mutant (ospls2). Through map-based cloning, a G-to-A substitution was determined at the 1st nucleotide of the 13th intron in the OsPLS2 gene that encodes a Upf1-like helicase. This mutation prompts aberrant splicing of OsPLS2 messenger and consequent disruption of its full-length protein translation, suggesting a negative role of OsPLS2 in regulating leaf senescence. Wild-type rice accordingly displayed a progressive drop of OsPSL2 protein levels with age-dependent leaf senescence. Shading and light filtration studies showed that the ospls2 phenotype, which was characteristic of photo-oxidative stress and reactive oxygen species (ROS) accumulation, was an effect of irritation by light. When continuously exposed to far-red light, exogenous H2O2 and/or abscisic acid (ABA), the ospls2 mutant sustained hypersensitive leaf senescence. In consistence, light and ROS signal pathways in ospls2 were activated by down-regulation of phytochrome genes, and up-regulation of PHYTOCHROME-INTERACTING FACTORS (PIFs) and WRKY genes, all promoting leaf senescence. Together, these data indicated that OsPLS2 played an essential role in leaf senescence and its disruption triggered light-dependent leaf senescence in rice. [ABSTRACT FROM AUTHOR]

Abstract: Key messageTheOsPLS3locus was isolated by map-based cloning that encodes a DUF266-containing protein. OsPLS3regulates the onset of leaf senescence in rice.Abstract: Glycosyltransferases (GTs) are one of the most important enzyme groups required for the modification of plant secondary metabolites and play a crucial role in plant growth and development, however the biological functions of most GTs remain elusive. We reported here the identification and characterization of a novel Oryza sativa premature leaf senescence mutant (ospls3). Through map-based cloning strategy, we determined that 22-bp deletion in the OsPLS3 gene encoding a domain of unknown function 266 (DUF266)-containing protein, a member of GT14-like, underlies the premature leaf senescence phenotype in the ospls3 mutant. The OsPLS3 mRNA levels progressively declined with the age-dependent leaf senescence in wild-type rice, implying a negative role of OsPLS3 in regulating leaf senescence. Physiological analysis, and histochemical staining and transmission electron microscopy assays indicated that the ospls3 mutant accumulated higher levels of ethylene and reactive oxygen species than its wild type. Furthermore, the ospls3 mutant showed hypersensitivity to exogenous 1-aminocyclopropane-1-carboxylic acid, H2O2 and high level of cytokinins. Our results indicated that the DUF266-containing gene OsPLS3 plays an important role in the onset of leaf senescence, in part through cytokinin and ethylene signaling in rice. [ABSTRACT FROM AUTHOR]

The α-FeO/TiO bi-functional composites with hierarchical and hollow structures are fabricated through a hydrothermal route. The adsorption performance and photocatalytic activity of the composites towards Pb are investigated in this work. Different adsorption kinetics models and equilibrium models are used to explore the adsorption behavior of hierarchical α-FeO/TiO hollow spheres. Experimental data show that adsorption kinetics of the hierarchical α-FeO/TiO hollow spheres can be fitted well by the pseudo-second-order model, while the isothermal data can be perfectly described by the Langmuir adsorption model. The maximum adsorption capacity of the hierarchical α-FeO/TiO hollow spheres is 32.36 mg g. Moreover, the hierarchical α-FeO/TiO hollow spheres possess photocatalytic oxidation character under simulated solar light irradiation. The results demonstrate that the hierarchical α-FeO/TiO hollow spheres, as effective and cheap materials, can be applied to the removal of heavy metal ions from wastewater. [ABSTRACT FROM AUTHOR]

The economic importance of oat stem rust is becoming more recognized with the increases of oat cultivated acreage and the disease damage in China. However, there is still no information on races of the pathogen and resistance of oat cultivars. In 2012 and 2013, 26 Puccinia graminis f. sp. avenae ( Pga) isolates were obtained from 11 oat stem rust samples collected from oat fields in Hebei province, one of China's major oat growing provinces. The isolates were tested on 16 Pg single-gene lines that have been used to differentiate Pga races in North America. From the isolates, three races, TKR, TJM and TKM, were identified at 61.6, 30.8 and 7.6 % frequencies, respectively. Virulences to resistance genes Pg1, Pg2, Pg3, Pg4, Pg8, Pg9, Pg12, Pg16 and the gene(s) in the universally susceptible variety 'Marvellous' were detected at 100 % frequency, to Pg10 at 69.2, to Pg13 at 61.6, to Pg-a at 61.6 %, and to the gene(s) in Rodney 0 at 84.6 %. No virulence to Pg6 and Pg15 was observed. The above races were used to evaluate the resistance in oat cultivars as seedlings grown in a greenhouse. Of 35 oat cultivars tested, only 13 (37.1 %) were resistant to all three races. This is the first study to identify Pga races and oat stem rust resistance in Chinese oat cultivars. [ABSTRACT FROM AUTHOR]