Sub-communities within this community

Recent Submissions

  • Polarization-State Modulation in Fano Resonant Graphene Metasurface Reflector

    Amin, Muhammad; Siddiqui, Omar; Farhat, Mohamed (Journal of Lightwave Technology, IEEE, 2021-04-06) [Article]
    Although Polarization Modulation offers a high spectral efficiency, it has been not exploited much in modern communication systems because of the involvement of high switching speeds and complexity of associated hardware. We propose a dynamically controllable graphene metasurface capable to switch the polarization state of incident THz waves in real-time. The metasurface is designed by patterning two-dimensional graphene sheets with Fano-resonant chiral unit cells. Since the reflectance spectrum is characterized with co and cross polarized fields that bear asymmetric Fano line-shapes, several combinations of linear and elliptical polarization states are observed in a narrowband spectrum. At a fixed frequency, the electrostatic tunability property of graphene allows to switch between different polarization states by varying the chemical potential between 500 and 700 meV. The polarization state modulation with the proposed chiral graphene based metasurface is applied to implement a quaternary modulated digital communication system. Full-wave simulation results show the strong possibility of polarization modulation in THz communication systems. Graphene metasurfaces on account of the high electron mobility and discrete Fermi levels offer high switching speeds and seamless integration with digital systems.
  • Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and co-infections of multiple respiratory viruses by Rapid field-deployable sequencing.

    Bi, Chongwei; Ramos Mandujano, Gerardo; Tian, Yeteng; Hala, Sharif; Xu, Jinna; Mfarrej, Sara; Esteban, Concepcion Rodriguez; Delicado, Estrella Nuñez; Alofi, Fadwa S; Khogeer, Asim; Hashem, Anwar M; Almontashiri, Naif A M; Pain, Arnab; Izpisua Belmonte, Juan Carlos; Li, Mo (Med (New York, N.Y.), Elsevier BV, 2021-04-06) [Article]
    BackgroundStrategies for monitoring the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for combating the pandemic. Detection and mutation surveillance of SARS-CoV-2 and other respiratory viruses require separate and complex workflows that rely on highly-specialized facilities, personnel, and reagents. To date, no method can rapidly diagnose multiple viral infections and determine variants in a high-throughput manner.MethodsWe describe a method for multiplex isothermal amplification-based sequencing and real-time analysis of multiple viral genomes, termed NIRVANA. It can simultaneously detect SARS-CoV-2, influenza A, human adenovirus, and human coronavirus, and monitor mutations for up to 96 samples in real-time.FindingsNIRVANA showed high sensitivity and specificity for SARS-CoV-2 in 70 clinical samples with a detection limit of 20 viral RNA copies per μl of extracted nucleic acid. It also detected the influenza A co-infection in two samples. The variant analysis results of SARS-CoV-2 positive samples mirror the epidemiology of COVID-19. Additionally, NIRVANA could simultaneously detect SARS-CoV-2 and PMMoV (an omnipresent virus and water quality indicator) in municipal wastewater samples.ConclusionsNIRVANA provides high-confidence detection of both SARS-CoV-2 and other respiratory viruses and mutation surveillance of SARS-CoV-2 on the fly. We expect it to offer a promising solution for rapid field-deployable detection and mutational surveillance of pandemic viruses.FundingM.L. is supported by KAUST Office of Sponsored Research (BAS/1/1080-01). This work is supported by KAUST Competitive Research Grant (URF/1/3412-01-01, M.L. and J.C.I.B.) and Universidad Catolica San Antonio de Murcia (J.C.I.B.). A.M.H. is supported by Saudi Ministry of Education (project 436).
  • High Performance Multivariate Geospatial Statistics on Manycore Systems

    Salvaña, Mary Lai O.; Abdulah, Sameh; Huang, Huang; Ltaief, Hatem; Sun, Ying; Genton, Marc G.; Keyes, David E. (IEEE Transactions on Parallel and Distributed Systems, IEEE, 2021-04-06) [Article]
    Modeling and inferring spatial relationships and predicting missing values of environmental data are some of the main tasks of geospatial statisticians. These routine tasks are accomplished using multivariate geospatial models and the cokriging technique, which requires the evaluation of the expensive Gaussian log-likelihood function. This large-scale cokriging challenge provides a fertile ground for supercomputing implementations for the geospatial statistics community as it is paramount to scale computational capability to match the growth in environmental data. In this paper, we develop large-scale multivariate spatial modeling and inference on parallel hardware architectures. To tackle the increasing complexity in matrix operations and the massive concurrency in parallel systems, we leverage low-rank matrix approximation techniques with task-based programming models and schedule the asynchronous computational tasks using a dynamic runtime system. The proposed framework provides both the dense and approximated computations of the Gaussian log-likelihood function. It demonstrates accuracy robustness and performance scalability on a variety of computer systems. Using both synthetic and real datasets, the low-rank matrix approximation shows better performance compared to exact computation, while preserving the application requirements in both parameter estimation and prediction accuracy. We also propose a novel algorithm to assess the prediction accuracy after the online parameter estimation.
  • Simultaneous Detection and Mutation Surveillance of SARS-CoV-2 and co-infections of multiple respiratory viruses by Rapid field-deployable sequencing.

    Bi, Chongwei; Ramos Mandujano, Gerardo; Tian, Yeteng; Hala, Sharif; Xu, Jinna; Mfarrej, Sara; Esteban, Concepcion Rodriguez; Delicado, Estrella Nuñez; Alofi, Fadwa S; Khogeer, Asim; Hashem, Anwar M; Almontashiri, Naif A M; Pain, Arnab; Izpisua Belmonte, Juan Carlos; Li, Mo (Med (New York, N.Y.), Elsevier BV, 2021-04-06) [Article]
    BackgroundStrategies for monitoring the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for combating the pandemic. Detection and mutation surveillance of SARS-CoV-2 and other respiratory viruses require separate and complex workflows that rely on highly-specialized facilities, personnel, and reagents. To date, no method can rapidly diagnose multiple viral infections and determine variants in a high-throughput manner.MethodsWe describe a method for multiplex isothermal amplification-based sequencing and real-time analysis of multiple viral genomes, termed NIRVANA. It can simultaneously detect SARS-CoV-2, influenza A, human adenovirus, and human coronavirus, and monitor mutations for up to 96 samples in real-time.FindingsNIRVANA showed high sensitivity and specificity for SARS-CoV-2 in 70 clinical samples with a detection limit of 20 viral RNA copies per μl of extracted nucleic acid. It also detected the influenza A co-infection in two samples. The variant analysis results of SARS-CoV-2 positive samples mirror the epidemiology of COVID-19. Additionally, NIRVANA could simultaneously detect SARS-CoV-2 and PMMoV (an omnipresent virus and water quality indicator) in municipal wastewater samples.ConclusionsNIRVANA provides high-confidence detection of both SARS-CoV-2 and other respiratory viruses and mutation surveillance of SARS-CoV-2 on the fly. We expect it to offer a promising solution for rapid field-deployable detection and mutational surveillance of pandemic viruses.FundingM.L. is supported by KAUST Office of Sponsored Research (BAS/1/1080-01). This work is supported by KAUST Competitive Research Grant (URF/1/3412-01-01, M.L. and J.C.I.B.) and Universidad Catolica San Antonio de Murcia (J.C.I.B.). A.M.H. is supported by Saudi Ministry of Education (project 436).
  • Synthesis of gold(I)-trifluoromethyl complexes and their role in generating spectroscopic evidence for a gold(I)-difluorocarbene species.

    Nolan, Steven Patrick; Vanden Broeck, Sofie M P; Nelson, David J; Collado, Alba; Falivene, Laura; Cavallo, Luigi; Cordes, David B; Slawin, Alexandra M Z; Van Hecke, Kristof; Nahra, Fady; Cazin, Catherine S J (Chemistry (Weinheim an der Bergstrasse, Germany), Wiley, 2021-04-06) [Article]
    Readily-prepared and bench-stable [Au(CF 3 )(NHC)] compounds were synthesized using new methodologies, starting from [Au(OH)(NHC)], [Au(Cl)(NHC)] or [Au(L)(NHC)]HF 2 precursors (NHC = N-heterocyclic carbene). The mechanism of formation of these species was investigated. Consequently, a new and straightforward strategy for the mild and selective cleavage of a single carbon-fluorine bond from [Au(CF 3 )(NHC)] complexes was attempted and found to be reversible in the presence of an additional nucleophilic fluoride source. This straightforward technique has led to the unprecedented spectroscopic observation of a gold(I)-NHC difluorocarbene species.
  • Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling

    Chen, Tong; Yin, Hongzhi; Zhang, Xiangliang; Huang, Zi; Wang, Yang; Wang, Meng (arXiv, 2021-04-05) [Preprint]
    As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering. With the prominent development of deep neural networks (DNNs), there is a recent and ongoing trend of enhancing the expressiveness of FM-based models with DNNs. However, though better results are obtained with DNN-based FM variants, such performance gain is paid off by an enormous amount (usually millions) of excessive model parameters on top of the plain FM. Consequently, the heavy parameterization impedes the real-life practicality of those deep models, especially efficient deployment on resource-constrained IoT and edge devices. In this paper, we move beyond the traditional real space where most deep FM-based models are defined, and seek solutions from quaternion representations within the hypercomplex space. Specifically, we propose the quaternion factorization machine (QFM) and quaternion neural factorization machine (QNFM), which are two novel lightweight and memory-efficient quaternion-valued models for sparse predictive analytics. By introducing a brand new take on FM-based models with the notion of quaternion algebra, our models not only enable expressive inter-component feature interactions, but also significantly reduce the parameter size due to lower degrees of freedom in the hypercomplex Hamilton product compared with real-valued matrix multiplication. Extensive experimental results on three large-scale datasets demonstrate that QFM achieves 4.36% performance improvement over the plain FM without introducing any extra parameters, while QNFM outperforms all baselines with up to two magnitudes' parameter size reduction in comparison to state-of-the-art peer methods.
  • Seasonal dynamics of natural Ostreococcus viral infection at the single cell level using VirusFISH.

    M Castillo, Yaiza; Forn, Irene; Yau, Sheree; Moran, Xose Anxelu G.; Alonso-Sáez, Laura; Arandia-Gorostidi, Néstor; Vaqué, Dolors; Sebastián, Marta (Environmental microbiology, Wiley, 2021-04-05) [Article]
    Ostreococcus is a cosmopolitan marine genus of phytoplankton found in mesotrophic and oligotrophic waters, and the smallest free-living eukaryotes known to date, with a cell diameter close to 1 μm. Ostreococcus has been extensively studied as a model system to investigate viral-host dynamics in culture, yet the impact of viruses in naturally occurring populations is largely unknown. Here, we used Virus Fluorescence in situ Hybridization (VirusFISH) to visualize and quantify viral-host dynamics in natural populations of Ostreococcus during a seasonal cycle in the central Cantabrian Sea (Southern Bay of Biscay). Ostreococcus were predominantly found during summer and autumn at surface and 50 m depth, in coastal, mid-shelf and shelf waters, representing up to 21% of the picoeukaryotic communities. Viral infection was only detected in surface waters, and its impact was variable but highest from May to July and November to December, when up to half of the population was infected. Metatranscriptomic data available from the mid-shelf station unveiled that the Ostreococcus population was dominated by the species O. lucimarinus. This work represents a proof of concept that the VirusFISH technique can be used to quantify the impact of viruses on targeted populations of key microbes from complex natural communities. This article is protected by copyright. All rights reserved.
  • Phylogenomics of Porites from the Arabian Peninsula.

    Terraneo, Tullia Isotta; Benzoni, Francesca; Arrigoni, Roberto; Baird, Andrew H; Mariappan, Kiruthiga; Forsman, Zac H; Wooster, Michael K; Bouwmeester, Jessica; Marshell, Alyssa; Berumen, Michael L. (Molecular phylogenetics and evolution, Elsevier BV, 2021-04-04) [Article]
    The advent of high throughput sequencing technologies provides an opportunity to resolve phylogenetic relationships among closely related species. By incorporating hundreds to thousands of unlinked loci and single nucleotide polymorphisms (SNPs), phylogenomic analyses have a far greater potential to resolve species boundaries than approaches that rely on only a few markers. Scleractinian taxa have proved challenging to identify using traditional morphological approaches and many groups lack an adequate set of molecular markers to investigate their phylogenies. Here, we examine the potential of Restriction-site Associated DNA sequencing (RADseq) to investigate phylogenetic relationships and species limits within the scleractinian coral genus Porites. A total of 126 colonies were collected from 16 localities in the seas surrounding the Arabian Peninsula and ascribed to 12 nominal and two unknown species based on their morphology. Reference mapping was used to retrieve and compare nearly complete mitochondrial genomes, ribosomal DNA, and histone loci. De novo assembly and reference mapping to the P. lobata coral transcriptome were compared and used to obtain thousands of genome-wide loci and SNPs. A suite of species discovery methods (phylogenetic, ordination, and clustering analyses) and species delimitation approaches (coalescent-based, species tree, and Bayesian Factor delimitation) suggested the presence of eight molecular lineages, one of which included six morphospecies. Our phylogenomic approach provided a fully supported phylogeny of Porites from the Arabian Peninsula, suggesting the power of RADseq data to solve the species delineation problem in this speciose coral genus.
  • Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography

    Nguyen, Ngan; Bohak, Ciril; Engel, Dominik; Mindek, Peter; Strnad, Ondrej; Wonka, Peter; Li, Sai; Ropinski, Timo; Viola, Ivan (arXiv, 2021-04-04) [Preprint]
    Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.
  • Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

    Chen, Tong; Yin, Hongzhi; Ren, Jie; Huang, Zi; Zhang, Xiangliang; Wang, Hao (arXiv, 2021-04-04) [Preprint]
    With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, despite the necessity of inductively producing representations for completely new nodes (e.g., in streaming scenarios), few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training. Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation. In this paper, we propose WIde and DEep message passing Network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes. To further improve the training efficiency, we innovatively present an active downsampling strategy that drops unimportant neighbor nodes to facilitate faster information propagation. Experiments on three real-world heterogeneous graphs have further validated the efficacy of WIDEN on both transductive and inductive node representation learning, as well as the superior training efficiency against state-of-the-art baselines.
  • Lithium-Ion Desolvation Induced by Nitrate Additives Reveals New Insights into High Performance Lithium Batteries

    Wahyudi, Wandi; Ladelta, Viko; Tsetseris, Leonidas; Alsabban, Merfat; Guo, Xianrong; Yengel, Emre; Faber, Hendrik; Adilbekova, Begimai; Seitkhan, Akmaral; Emwas, Abdul-Hamid; Hedhili, Mohamed N.; Li, Lain-Jong; Tung, Vincent; Hadjichristidis, Nikos; Anthopoulos, Thomas D.; Ming, Jun (Advanced Functional Materials, Wiley, 2021-04-02) [Article]
    Electrolyte additives have been widely used to address critical issues in current metal (ion) battery technologies. While their functions as solid electrolyte interface forming agents are reasonably well-understood, their interactions in the liquid electrolyte environment remain rather elusive. This lack of knowledge represents a significant bottleneck that hinders the development of improved electrolyte systems. Here, the key role of additives in promoting cation (e.g., Li+) desolvation is unraveled. In particular, nitrate anions (NO3−) are found to incorporate into the solvation shells, change the local environment of cations (e.g., Li+) as well as their coordination in the electrolytes. The combination of these effects leads to effective Li+ desolvation and enhanced battery performance. Remarkably, the inexpensive NaNO3 can successfully substitute the widely used LiNO3 offering superior long-term stability of Li+ (de-)intercalation at the graphite anode and suppressed polysulfide shuttle effect at the sulfur cathode, while enhancing the performance of lithium–sulfur full batteries (initial capacity of 1153 mAh g−1 at 0.25C) with Coulombic efficiency of ≈100% over 300 cycles. This work provides important new insights into the unexplored effects of additives and paves the way to developing improved electrolytes for electrochemical energy storage applications.
  • Digital insights: bridging the phenotype-to-genotype divide

    McCabe, Matthew; Tester, Mark A. (Journal of Experimental Botany, Oxford University Press (OUP), 2021-04-02) [Article]
    The convergence of autonomous platforms for field-based phenotyping with advances in machine learning for big data analytics and rapid sequencing for genome description herald the promise of new insights and discoveries in the plant sciences. Han et al. (2021) leverage these emerging tools to navigate the challenging path from field-based mapping of phenotypic features to identifying specific genetic loci in the laboratory: in this case, loci responsible for regulating daily flowering time in lettuce. While their contribution neatly illustrates these exciting technological developments, it also highlights the work that remains to bridge these multidisciplinary fields to more fully deliver upon the promise of digital agriculture.
  • Fast-adapting and Privacy-preserving Federated Recommender System

    Wang, Qinyong; Yin, Hongzhi; Chen, Tong; Yu, Junliang; Zhou, Alexander; Zhang, Xiangliang (arXiv, 2021-04-02) [Preprint]
    In the mobile Internet era, recommender systems have become an irreplaceable tool to help users discover useful items, thus alleviating the information overload problem. Recent research on deep neural network (DNN)-based recommender systems have made significant progress in improving prediction accuracy, largely attributed to the widely accessible large-scale user data. Such data is commonly collected from users' personal devices, and then centrally stored in the cloud server to facilitate model training. However, with the rising public concerns on user privacy leakage in online platforms, online users are becoming increasingly anxious over abuses of user privacy. Therefore, it is urgent and beneficial to develop a recommender system that can achieve both high prediction accuracy and strong privacy protection. To this end, we propose a DNN-based recommendation model called PrivRec running on the decentralized federated learning (FL) environment, which ensures that a user's data is fully retained on her/his personal device while contributing to training an accurate model. On the other hand, to better embrace the data heterogeneity (e.g., users' data vary in scale and quality significantly) in FL, we innovatively introduce a first-order meta-learning method that enables fast on-device personalization with only a few data points. Furthermore, to defend against potential malicious participants that pose serious security threat to other users, we further develop a user-level differentially private model, namely DP-PrivRec, so attackers are unable to identify any arbitrary user from the trained model. Finally, we conduct extensive experiments on two large-scale datasets in a simulated FL environment, and the results validate the superiority of both PrivRec and DP-PrivRec.
  • Theory-Guided Synthesis of Highly Luminescent Colloidal Cesium Tin Halide Perovskite Nanocrystals

    Liu, Qi; Yin, Jun; Zhang, Bin-Bin; Chen, Jia-Kai; Zhou, Yang; Zhang, Lu-Min; Wang, Lu-Ming; Zhao, Qing; Hou, Jingshan; Shu, Jie; Song, Bo; Shirahata, Naoto; Bakr, Osman; Mohammed, Omar F.; Sun, Hong-Tao (Journal of the American Chemical Society, American Chemical Society (ACS), 2021-04-01) [Article]
    The synthesis of highly luminescent colloidal CsSnX<sub>3</sub> (X = halogen) perovskite nanocrystals (NCs) remains a long-standing challenge due to the lack of a fundamental understanding of how to rationally suppress the formation of structural defects that significantly influence the radiative carrier recombination processes. Here, we develop a theory-guided, general synthetic concept for highly luminescent CsSnX<sub>3</sub> NCs. Guided by density functional theory calculations and molecular dynamics simulations, we predict that, although there is an opposing trend in the chemical potential-dependent formation energies of various defects, highly luminescent CsSnI<sub>3</sub> NCs with narrow emission could be obtained through decreasing the density of tin vacancies. We then develop a colloidal synthesis strategy that allows for rational fine-tuning of the reactant ratio in a wide range but still leads to the formation of CsSnI<sub>3</sub> NCs. By judiciously adopting a tin-rich reaction condition, we obtain narrow-band-emissive CsSnI<sub>3</sub> NCs with a record emission quantum yield of 18.4%, which is over 50 times larger than those previously reported. Systematic surface-state characterizations reveal that these NCs possess a Cs/I-lean surface and are capped with a low density of organic ligands, making them an excellent candidate for optoelectronic devices without any postsynthesis ligand management. We showcase the generalizability of our concept by further demonstrating the synthesis of highly luminescent CsSnI<sub>2.5</sub>Br<sub>0.5</sub> and CsSnI<sub>2.25</sub>Br<sub>0.75</sub> NCs. Our findings not only highlight the value of computation in guiding the synthesis of high-quality colloidal perovskite NCs but also could stimulate intense efforts on tin-based perovskite NCs and accelerate their potential applications in a range of high-performance optoelectronic devices.
  • Machine-driven earth exploration: Artificial intelligence in oil and gas

    Alkhalifah, Tariq Ali; Almomin, Ali; Naamani, Ali (The Leading Edge, Society of Exploration Geophysicists, 2021-04) [Article]
    Artificial intelligence (AI), specifically machine learning (ML), has emerged as a powerful tool to address many of the challenges we face as we try to illuminate the earth and make the proper prediction of its content. From fault detection, to salt boundary mapping, to image resolution enhancements, the quest to teach our computing devices how to perform these tasks accurately, as well as quantify the accuracy, has become a feasible and sought-after objective. Recent advances in ML algorithms and availability of the modules to apply such algorithms enabled geoscientists to focus on potential applications of such tools. As a result, we held the virtual workshop, Artificially Intelligent Earth Exploration Workshop: Teaching the Machine How to Characterize the Subsurface, 23–26 November 2020.
  • Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors.

    Naveed, Hammad; Reglin, Corinna; Schubert, Thomas; Gao, Xin; Arold, Stefan T.; Maitland, Michael L (Genomics, proteomics & bioinformatics, Elsevier BV, 2021-04-01) [Article]
    Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. We have developed iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive and a negative structural signature that captures the weakly conserved structural features of drug binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We were able to confirm the interaction of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity and specificity of 52% and 55%, respectively. We have validated 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, or MHC Class I molecules can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein-small molecule interactions, when sufficient drug-target 3D data are available. The code for constructing the structural signature is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.
  • Towards Similarity-based Differential Diagnostics For Common Diseases

    Slater, Luke T; Karwath, Andreas; Williams, John A.; Russell, Sophie; Makepeace, Silver; Carberry, Alexander; Hoehndorf, Robert; Gkoutos, Georgios (Computers in Biology and Medicine, Elsevier BV, 2021-04-01) [Article]
    Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.
  • The Arab world prepares the exascale workforce

    Keyes, David E. (Communications of the ACM, Association for Computing Machinery (ACM), 2021-04) [Article]
    THE ARAB WORLD is currently host to eight supercomputers in the Top500 globally, including the current #10 and a former #7. Hardware can become a honeypot for talent attraction—senior talent from abroad, and rising talent from within. Good return on investment from leading-edge hardware motivates forging collaborative ties to global supercomputing leaders, which leads to integration into the global campaigns that supercomputing excels in, such as predicting climate change and developing sustainable energy resources for its mitigation, positing properties of new materials and catalysis by design, repurposing already-certified drugs and discovering new ones, and big data analytics and machine learning applied to science and to society. While the petroleum industry has been the historical motivation for supercomputing in the Arab World with its workloads of seismic imaging and reservoir modeling, the attraction today is universal.
  • Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations

    Lopatin, Anton; Dubovik, Oleg; Fuertes, David; Stenchikov, Georgiy L.; Lapyonok, Tatyana; Veselovskii, Igor; Wienhold, Frank G.; Shevchenko, Illia; Hu, Qiaoyun; Parajuli, Sagar (Atmospheric Measurement Techniques, Copernicus GmbH, 2021-04-01) [Article]
    Abstract. The exploration of aerosol retrieval synergies from diverse combinations of ground-based passive Sun-photometric measurements with collocated active lidar ground-based and radiosonde observations using versatile Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm is presented. Several potentially fruitful aspects of observation synergy were considered. First, a set of passive and active ground-based observations collected during both day- and nighttime was inverted simultaneously under the assumption of temporal continuity of aerosol properties. Such an approach explores the complementarity of the information in different observations and results in a robust and consistent processing of all observations. For example, the interpretation of the nighttime active observations usually suffers from the lack of information about aerosol particles sizes, shapes and complex refractive index. In the realized synergy retrievals, the information propagating from the nearby Sun-photometric observations provides sufficient constraints for reliable interpretation of both day- and nighttime lidar observations. Second, the synergetic processing of such complementary observations with enhanced information content allows for optimizing the aerosol model used in the retrieval. Specifically, the external mixture of several aerosol components with predetermined sizes, shapes and composition has been identified as an efficient approach for achieving reliable retrieval of aerosol properties in several situations. This approach allows for achieving consistent and accurate aerosol retrievals from processing stand-alone advanced lidar observations with reduced information content about aerosol columnar properties. Third, the potential of synergy processing of the ground-based Sun-photometric and lidar observations, with the in situ backscatter sonde measurements was explored using the data from KAUST.15 and KAUST.16 field campaigns held at King Abdullah University of Science and Technology (KAUST) in the August of 2015 and 2016. The inclusion of radiosonde data has been demonstrated to provide significant additional constraints to validate and improve the accuracy and scope of aerosol profiling. The results of all retrieval setups used for processing both synergy and stand-alone observation data sets are discussed and intercompared.
  • On the depth of decision trees over infinite 1-homogeneous binary information systems

    Moshkov, Mikhail (Array, Elsevier BV, 2021-04) [Article]
    In this paper, we study decision trees, which solve problems defined over a specific subclass of infinite information systems, namely: 1-homogeneous binary information systems. It is proved that the minimum depth of a decision tree (defined as a function on the number of attributes in a problem’s description) grows – in the worst case – logarithmically or linearly for each information system in this class. We consider a number of examples of infinite 1-homogeneous binary information systems, including one closely related to the decision trees constructed by the CART algorithm.

View more