Yellow–colored mesoporous pure titania and its high stability in visible light photocatalysis

AbstractYellow–colored pure titania with a mesoporous structure was prepared by the aggregate of titania nanocrystals, which were stabilized by exfoliated titanate nanosheets via an electrostatic interaction. X–ray diffraction patterns and images of transmission electron microscope confirm that titanate sheets are randomly dispersed into the assembled titania nanocrystals without forming any self–restacked phase. This nanocrystals–nanosheets composite exhibits a mesoporous structure with pore size of ~6.5nm and surface area of 236.3m2g−1. Greatly different from the UV–responded properties of titania nanocrystals and titanate nanosheets, the absorption edge of nanocomposite red–shifts to visible light region. The visible light photocatalytic tests demonstrate that this nanocomposited titania shows excellent activity for the degradation of organic dyes, as well as a colorless organic pollutant of 2, 4–dichlorophenol. The possible photocatalytic mechanism that photogenerated holes as the mainly oxidant species in photocatalysis is proposed based on the trapping experiments of hydroxyl radicals or photogenerated holes. Moreover, as the nanocomposite depicts an extreme stability, no obvious deactivation occurs after five cycles

Transcriptome sequencing of Crucihimalaya himalaica (Brassicaceae) reveals how Arabidopsis close relative adapt to the Qinghai-Tibet Plateau

The extreme environment of the Qinghai-Tibet Plateau (QTP) provides an ideal natural laboratory for studies on adaptive evolution. Few genome/transcriptome based studies have been conducted on how plants adapt to the environments of QTP compared to numerous studies on vertebrates. Crucihimalaya himalaica is a close relative of Arabidopsis with typical QTP distribution, and is hoped to be a new model system to study speciation and ecological adaptation in extreme environment. In this study, we de novo generated a transcriptome sequence of C. himalaica, with a total of 49,438 unigenes. Compared to five relatives, 10,487 orthogroups were shared by all six species, and 4,286 orthogroups contain putative single copy gene. Further analysis identified 487 extremely significantly positively selected genes (PSGs) in C. himalaica transcriptome. Theses PSGs were enriched in functions related to specific adaptation traits, such as response to radiation, DNA repair, nitrogen metabolism, and stabilization of membrane. These functions are responsible for the adaptation of C. himalaica to the high radiation, soil depletion and low temperature environments on QTP. Our findings indicate that C. himalaica has evolved complex strategies for adapting to the extreme environments on QTP and provide novel insights into genetic mechanisms of highland adaptation in plants

Characterization and gene expression patterns analysis implies BSK family genes respond to salinity stress in cotton

Identification, evolution, and expression patterns of BSK (BR signaling kinase) family genes revealed that BSKs participated in the response of cotton to abiotic stress and maintained the growth of cotton in extreme environment. The steroidal hormone brassinosteroids (BR) play important roles in different plant biological processes. This study focused on BSK which were downstream regulatory element of BR, in order to help to decipher the functions of BSKs genes from cotton on growth development and responses to abiotic stresses and lean the evolutionary relationship of cotton BSKs. BSKs are a class of plant-specific receptor-like cytoplasmic kinases involved in BR signal transduction. In this study, bioinformatics methods were used to identify the cotton BSKs gene family at the cotton genome level, and the gene structure, promoter elements, protein structure and properties, gene expression patterns and candidate interacting proteins were analyzed. In the present study, a total of 152 BSKs were identified by a genome-wide search in four cotton species and other 11 plant species, and phylogenetic analysis revealed three evolutionary clades. It was identified that BSKs contain typical PKc and TPR domains, the N-terminus is composed of extended chains and helical structures. Cotton BSKs genes show different expression patterns in different tissues and organs. The gene promoter contains numerous cis-acting elements induced by hormones and abiotic stress, the hormone ABA and Cold-inducing related elements have the highest count, indicating that cotton BSK genes may be regulated by various hormones at different growth stages and involved in the response regulation of cotton to various stresses. The expression analysis of BSKs in cotton showed that the expression levels of GhBSK06, GhBSK10, GhBSK21 and GhBSK24 were significantly increased with salt-inducing. This study is helpful to analyze the function of cotton BSKs genes in growth and development and in response to stress

Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN)

L’exposition à une faible luminosité durant la nuit affecte-t-elle la sensorialité au petit-déjeuner ?

International audienc

La lumière de faible intensité de la nuit affecte-t-elle la température du corps humain ?

International audienc

Data-driven framework for high-Accuracy color restoration of RGBN multispectral filter array sensors under extremely low-light conditions

RGBN multispectral filter array provides a cost-effective and one-shot acquisition solution to capture well-Aligned RGB and near-infrared (NIR) images which are useful for various optical applications. However, signal responses of the R, G, B channels are inevitably distorted by the undesirable spectral crosstalk of the NIR bands, thus the captured RGB images are adversely desaturated. In this paper, we present a data-driven framework for effective spectral crosstalk compensation of RGBN multispectral filter array sensors. We set up a multispectral image acquisition system to capture RGB and NIR image pairs under various illuminations which are subsequently utilized to train a multi-Task convolutional neural network (CNN) architecture to perform simultaneous noise reduction and color restoration. Moreover, we present a technique for generating high-quality reference images and a task-specific joint loss function to facilitate the training of the proposed CNN model. Experimental results demonstrate the effectiveness of the proposed method, outperforming the state-of-The-Art color restoration solutions and achieving more accurate color restoration results for desaturated and noisy RGB images captured under extremely low-light conditions

La lumière artificielle de faible intensité pendant la nuit a-t-elle un effet sur le glucose ?

International audienc