King Abdullah University of Science and Technology

About: King Abdullah University of Science and Technology is a education organization based out in Jeddah, Saudi Arabia. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 6221 authors who have published 22019 publications receiving 625706 citations. The organization is also known as: KAUST.

Ocean solutions to address climate change and its effects on marine ecosystems

Abstract: The Paris agreement target of limiting global surface warming to 15-2°C compared to pre-industrial levels by 2100 will heavily impact the ocean While ambitious mitigation and adaptation are both needed, the ocean provides major opportunities for action to reduce climate change globally and its impacts on vital ecosystems and ecosystem services A comprehensive and systematic assessment of 13 global- and local-scale, ocean-based measures was performed to help steer the development and implementation of technologies and actions towards a sustainable outcome We show that (1) all measures have tradeoffs and multiple criteria must be used for a comprehensive assessment of their potential, (2) greatest benefit is derived by combining global and local solutions, some of which could be implemented or scaled-up immediately, (3) some measures are too uncertain to be recommended yet, (4) political consistency must be achieved through effective cross-scale governance mechanisms, (5) scientific effort must focus on effectiveness, co-benefits, disbenefits, and costs of poorly tested as well as new and emerging measures

The genome and life-stage specific transcriptomes of Globodera pallida elucidate key aspects of plant parasitism by a cyst nematode

Abstract: Globodera pallida is a devastating pathogen of potato crops, making it one of the most economically important plant parasitic nematodes. It is also an important model for the biology of cyst nematodes. Cyst nematodes and root-knot nematodes are the two most important plant parasitic nematode groups and together represent a global threat to food security. We present the complete genome sequence of G. pallida, together with transcriptomic data from most of the nematode life cycle, particularly focusing on the life cycle stages involved in root invasion and establishment of the biotrophic feeding site. Despite the relatively close phylogenetic relationship with root-knot nematodes, we describe a very different gene family content between the two groups and in particular extensive differences in the repertoire of effectors, including an enormous expansion of the SPRY domain protein family in G. pallida, which includes the SPRYSEC family of effectors. This highlights the distinct biology of cyst nematodes compared to the root-knot nematodes that were, until now, the only sedentary plant parasitic nematodes for which genome information was available. We also present in-depth descriptions of the repertoires of other genes likely to be important in understanding the unique biology of cyst nematodes and of potential drug targets and other targets for their control. The data and analyses we present will be central in exploiting post-genomic approaches in the development of much-needed novel strategies for the control of G. pallida and related pathogens.

Self-Supervised Learning by Cross-Modal Audio-Video Clustering

Abstract: Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other with good accuracy. Their intrinsic differences make cross-modal prediction a potentially more rewarding pretext task for self-supervised learning of video and audio representations compared to within-modality learning. Based on this intuition, we propose Cross-Modal Deep Clustering (XDC), a novel self-supervised method that leverages unsupervised clustering in one modality (e.g., audio) as a supervisory signal for the other modality (e.g., video). This cross-modal supervision helps XDC utilize the semantic correlation and the differences between the two modalities. Our experiments show that XDC outperforms single-modality clustering and other multi-modal variants. XDC achieves state-of-the-art accuracy among self-supervised methods on multiple video and audio benchmarks. Most importantly, our video model pretrained on large-scale unlabeled data significantly outperforms the same model pretrained with full-supervision on ImageNet and Kinetics for action recognition on HMDB51 and UCF101. To the best of our knowledge, XDC is the first self-supervised learning method that outperforms large-scale fully-supervised pretraining for action recognition on the same architecture.

(Gold Core)@(Ceria Shell) Nanostructures for Plasmon-Enhanced Catalytic Reactions under Visible Light

Abstract: Driving catalytic reactions with sunlight is an excellent example of sustainable chemistry. A prerequisite of solar-driven catalytic reactions is the development of photocatalysts with high solar-harvesting efficiencies and catalytic activities. Herein, we describe a general approach for uniformly coating ceria on monometallic and bimetallic nanocrystals through heterogeneous nucleation and growth. The method allows for control of the shape, size, and type of the metal core as well as the thickness of the ceria shell. The plasmon shifts of the Au@CeO2 nanostructures resulting from the switching between Ce(IV) and Ce(III) are observed. The selective oxidation of benzyl alcohol to benzaldehyde, one of the fundamental reactions for organic synthesis, performed under both broad-band and monochromatic light, demonstrates the visible-light-driven catalytic activity and reveals the synergistic effect on the enhanced catalysis of the Au@CeO2 nanostructures.

Giant Photoluminescence Enhancement in CsPbCl3 Perovskite Nanocrystals by Simultaneous Dual-Surface Passivation

Abstract: The presence of localized trap states on the surface of CsPbCl3 perovskite nanocrystals (NCs) is one of the greatest challenges precluding the development of optoelectronic applications of these NCs. Passivation of these defect sites provides a promising pathway to remediating their electronic and optical properties, such as the photoluminescence quantum yield (PLQY). Herein, we demonstrate a postsynthetic dual-surface treatment using trivalent metal ion salts, i.e., YCl3, as a new passivation approach that enhances the PLQY up to 60% while preserving the NC size and crystal structure. Such remarkable enhancement of the PLQY along with prolongation of the average PL lifetimes of treated NCs samples indicates effective passivation of the surface defects and subsequent suppression of the formation of surface nonradiative recombination centers. As a segue toward optoelectronic applications, we probed the photoelectric performance of the NCs using ultraflexible devices; we found that YCl3-treated CsPbCl3 NC f...