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.

Anapole nanolasers for mode-locking and ultrafast pulse generation

Abstract: Nanophotonics is a rapidly developing field of research with many suggestions for a design of nanoantennas, sensors and miniature metadevices Despite many proposals for passive nanophotonic devices, the efficient coupling of light to nanoscale optical structures remains a major challenge In this article, we propose a nanoscale laser based on a tightly confined anapole mode By harnessing the non-radiating nature of the anapole state, we show how to engineer nanolasers based on InGaAs nanodisks as on-chip sources with unique optical properties Leveraging on the near-field character of anapole modes, we demonstrate a spontaneously polarized nanolaser able to couple light into waveguide channels with four orders of magnitude intensity than classical nanolasers, as well as the generation of ultrafast (of 100 fs) pulses via spontaneous mode locking of several anapoles Anapole nanolasers offer an attractive platform for monolithically integrated, silicon photonics sources for advanced and efficient nanoscale circuitry

2.3 Gbit/s underwater wireless optical communications using directly modulated 520 nm laser diode.

Abstract: We experimentally demonstrate a record high-speed underwater wireless optical communication (UWOC) over 7 m distance using on-off keying non-return-to-zero (OOK-NRZ) modulation scheme. The communication link uses a commercial TO-9 packaged pigtailed 520 nm laser diode (LD) with 1.2 GHz bandwidth as the optical transmitter and an avalanche photodiode (APD) module as the receiver. At 2.3 Gbit/s transmission, the measured bit error rate of the received data is 2.23×10(-4), well below the forward error correction (FEC) threshold of 2×10(-3) required for error-free operation. The high bandwidth of the LD coupled with high sensitivity APD and optimized operating conditions is the key enabling factor in obtaining high bit rate transmission in our proposed system. To the best of our knowledge, this result presents the highest data rate ever achieved in UWOC systems thus far.

DeepGOPlus: improved protein function prediction from sequence.

Abstract: Motivation Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein-protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time. Results We developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins (if available). We evaluate the performance of DeepGOPlus using the CAFA3 evaluation measures and achieve an Fmax of 0.390, 0.557 and 0.614 for BPO, MFO and CCO evaluations, respectively. These results would have made DeepGOPlus one of the three best predictors in CCO and the second best performing method in the BPO and MFO evaluations. We also compare DeepGOPlus with state-of-the-art methods such as DeepText2GO and GOLabeler on another dataset. DeepGOPlus can annotate around 40 protein sequences per second on common hardware, thereby making fast and accurate function predictions available for a wide range of proteins. Availability and implementation http://deepgoplus.bio2vec.net/ . Supplementary information Supplementary data are available at Bioinformatics online.

Assessing Symbiodinium diversity in scleractinian corals via next‐generation sequencing‐based genotyping of the ITS2 rDNA region

Abstract: The persistence of coral reef ecosystems relies on the symbiotic relationship between scleractinian corals and intracellular, photosynthetic dinoflagellates in the genus Symbiodinium. Genetic evidence indicates that these symbionts are biologically diverse and exhibit discrete patterns of environmental and host distribution. This makes the assessment of Symbiodinium diversity critical to understanding the symbiosis ecology of corals. Here, we applied pyrosequencing to the elucidation of Symbiodinium diversity via analysis of the internal transcribed spacer 2 (ITS2) region, a multicopy genetic marker commonly used to analyse Symbiodinium diversity. Replicated data generated from isoclonal Symbiodinium cultures showed that all genomes contained numerous, yet mostly rare, ITS2 sequence variants. Pyrosequencing data were consistent with more traditional denaturing gradient gel electrophoresis (DGGE) approaches to the screening of ITS2 PCR amplifications, where the most common sequences appeared as the most intense bands. Further, we developed an operational taxonomic unit (OTU)-based pipeline for Symbiodinium ITS2 diversity typing to provisionally resolve ecologically discrete entities from intragenomic variation. A genetic distance cut-off of 0.03 collapsed intragenomic ITS2 variants of isoclonal cultures into single OTUs. When applied to the analysis of field-collected coral samples, our analyses confirm that much of the commonly observed Symbiodinium ITS2 diversity can be attributed to intragenomic variation. We conclude that by analysing Symbiodinium populations in an OTU-based framework, we can improve objectivity, comparability and simplicity when assessing ITS2 diversity in field-based studies.