With facilities such as Shaheen (one of the world’s fastest super computers) and the CORNEA Visualization Center, the CEMSE Division is one of the best-equipped places in the world to carry out cutting-edge, interdisciplinary research. CEMSE is associated with KAUST's Computational Bioscience Research Center and the Geometric Modeling and Scientific Visualization Research Center.

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Recent Submissions

  • Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    Azad, Mohammad (2018-06-06)
    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.
  • Numerical Computation of Detonation Stability

    Kabanov, Dmitry (2018-06-03)
    Detonation is a supersonic mode of combustion that is modeled by a system of conservation laws of compressible fluid mechanics coupled with the equations describing thermodynamic and chemical properties of the fluid. Mathematically, these governing equations admit steady-state travelling-wave solutions consisting of a leading shock wave followed by a reaction zone. However, such solutions are often unstable to perturbations and rarely observed in laboratory experiments. The goal of this work is to study the stability of travelling-wave solutions of detonation models by the following novel approach. We linearize the governing equations about a base travelling-wave solution and solve the resultant linearized problem using high-order numerical methods. The results of these computations are postprocessed using dynamic mode decomposition to extract growth rates and frequencies of the perturbations and predict stability of travelling-wave solutions to infinitesimal perturbations. We apply this approach to two models based on the reactive Euler equations for perfect gases. For the first model with a one-step reaction mechanism, we find agreement of our results with the results of normal-mode analysis. For the second model with a two-step mechanism, we find that both types of admissible travelling-wave solutions exhibit the same stability spectra. Then we investigate the Fickett’s detonation analogue coupled with a particular reaction-rate expression. In addition to the linear stability analysis of this model, we demonstrate that it exhibits rich nonlinear dynamics with multiple bifurcations and chaotic behavior.
  • Underwater Wireless Optical Communications Systems: from System-Level Demonstrations to Channel Modeling

    Oubei, Hassan M. (2018-06)
    Approximately, two-thirds of earth's surface is covered by water. There is a growing interest from the military and commercial communities in having, an efficient, secure and high bandwidth underwater wireless communication (UWC) system for tactical underwater applications such as oceanography studies and offshore oil exploration. The existing acoustic and radio frequency (RF) technologies are severely limited in bandwidth because of the strong frequency dependent attenuation of sound in seawater and the high conductivity of seawater at radio frequencies, respectively. Recently, underwater wireless optical communication (UWOC) has been proposed as the best alternative or complementary solution to meet this challenge. Taking advantage of the low absorption window of seawater in blue-green (400-550 nm) regime of the electromagnetic spectrum, UWOC is expected to establish secure, efficient and high data rate communication links over short and moderate distances (< 100 m) for versatile applications such as underwater oil pipe inspection, remotely operated vehicle (ROV) and sensor networks. UWOC uses the latest gallium nitrite (GaN) visible light-emitting diode (LED) and laser diode (LD) transmitters. Although some research on LED lased UWOC is being conducted, both the military and academic 5 research communities are favoring the use of laser beams, which potentially could enhance the available bandwidth by up to three orders of magnitude. However, the underwater wireless channel is optically very challenging and difficult to predict. The propagation of laser beams in seawater is significantly affected by the harsh marine environments and suffers from severe attenuation which is a combined effect of absorption and scattering, optical turbulence, and multipath effects at high transmission rates. These limitations distort the intensity and phase structure of the optical beam leading to a decrease in signal-to-noise ratio (SNR) which ultimately degrades the performance of UWOC links by increasing the probability of error. In this dissertation, we seek to experimentally demonstrate the feasibility of short range (≤ 20 m) UWOC systems over various underwater channel water types using different modulation schemes as well as to model and describe the statistical properties of turbulence-induced fading in underwater wireless optical channels using laser beam intensity fluctuations measurements.
  • Hydrothermal synthesis of p-type nanocrystalline NiO nanoplates for high response and low concentration hydrogen gas sensor application

    Nakate, Umesh T.; Lee, Gun Hee; Ahmad, Rafiq; Patil, Pramila; Bhopate, Dhanaji P.; Hahn, Y.B.; Yu, Y.T.; Suh, Eun-kyung (Elsevier BV, 2018-05-30)
    High quality nanocrystalline NiO nanoplates were synthesized using surfactant and template free hydrothermal route. The gas sensing properties of NiO nanoplates were investigated. The nanoplates morphology of NiO with average thickness ~20 nm and diameter ~100 nm has been confirmed by FE-SEM and TEM. Crystalline quality of NiO has been studied using HRTEM and SAED techniques. Structural properties and elemental compositions have been analysed by XRD and energy dispersive spectrometer (EDS) respectively. The detailed investigation of structural parameters has been carried out. The optical properties of NiO were analyzed from UV-Visible and photoluminescence spectra. NiO nanoplates have good selectivity towards hydrogen (H2) gas. The lowest H2 response of 3% was observed at 2 ppm, whereas 90% response was noted for 100 ppm at optimized temperature of 200 °C with response time 180 s. The H2 responses as functions of different operating temperature as well as gas concentrations have been studied along with sensor stability. The hydrogen sensing mechanism was also elucidated.
  • Flexible InGaN nanowire membranes for enhanced solar water splitting

    Elafandy, Rami T.; Elafandy, Rami T.; Min, Jung-Wook; Zhao, Chao; Ng, Tien Khee; Ooi, Boon S. (The Optical Society, 2018-05-30)
    III-Nitride nanowires (NWs) have recently emerged as potential photoelectrodes for efficient solar hydrogen generation. While InGaN NWs epitaxy over silicon is required for high crystalline quality and economic production, it leads to the formation of the notorious silicon nitride insulating interface as well as low electrical conductivity which both impede excess charge carrier dynamics and overall device performance. We tackle this issue by developing, for the first time, a substrate-free InGaN NWs membrane photoanodes, through liftoff and transfer techniques, where excess charge carriers are efficiently extracted from the InGaN NWs through a proper ohmic contact formed with a high electrical conductivity metal stack membrane. As a result, compared to conventional InGaN NWs on silicon, the fabricated free-standing flexible membranes showed a 10-fold increase in the generated photocurrent as well as a 0.8 V cathodic shift in the onset potential. Through electrochemical impedance spectroscopy, accompanied with TEM-based analysis, we further demonstrated the detailed enhancement within excess charge carrier dynamics of the photoanode membranes. This novel configuration in photoelectrodes demonstrates a novel pathway for enhancing the performance of III-nitrides photoelectrodes to accelerate their commercialization for solar water splitting.
  • Quantified Hole Concentration in AlGaN Nanowires for High-Performance Ultraviolet Emitters

    Zhao, Chao; Ebaid, Mohamed; Zhang, Huafan; Priante, Davide; Janjua, Bilal; Zhang, Daliang; Wei, Nini; Alhamoud, Abdullah; Shakfa, M. Khaled; Ng, Tien Khee; Ooi, Boon S. (Royal Society of Chemistry (RSC), 2018-05-29)
    P-type doping in wide bandgap and new classes of ultra-wide bandgap materials has long been a scientific and engineering problem. The challenges arise from the large activation energy of dopants and high densities of dislocations in materials. We report here, a significantly enhanced p-type conduction using high-quality AlGaN nanowires. For the first time, the hole concentration in Mg-doped AlGaN nanowires is quantified. The incorporation of Mg into AlGaN was verified by correlation with photoluminescence and Raman measurements. The open-circuit potential measurements further confirmed the p-type conductivity; while Mott-Schottky experiments measured a hole concentration of 1.3×1019 cm-3. These results from photoelectrochemical measurements allow us to design prototype ultraviolet (UV) light-emitting diodes (LEDs) incorporating the AlGaN quantum-disks-in-nanowire and optimized p-type AlGaN contact layer for UV-transparency. The ~335-nm LEDs exhibited a low turn-on voltage of 5 V with a series resistance of 32 Ω, due to the efficient p-type doping of the AlGaN nanowires. The bias-dependent Raman measurements further revealed the negligible self-heating of devices. This study provides an attractive solution to evaluate electrical properties of AlGaN, which is applicable to other wide bandgap nanostructures. Our results are expected to open doors to new applications for wide and ultra-wide bandgap materials.
  • Positivity-preserving CE/SE schemes for solving the compressible Euler and Navier–Stokes equations on hybrid unstructured meshes

    Shen, Hua; Parsani, Matteo (Elsevier BV, 2018-05-28)
    We construct positivity-preserving space–time conservation element and solution element (CE/SE) schemes for solving the compressible Euler and Navier–Stokes equations on hybrid unstructured meshes consisting of triangular and rectangular elements. The schemes use an a posteriori limiter to prevent negative densities and pressures based on the premise of preserving optimal accuracy. The limiter enforces a constraint for spatial derivatives and does not change the conservative property of CE/SE schemes. Several numerical examples suggest that the proposed schemes preserve accuracy for smooth flows and strictly preserve positivity of densities and pressures for the problems involving near vacuum and very strong discontinuities.
  • INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles

    Opitz, Thomas; Huser, Raphaël; Bakka, Haakon; Rue, Haavard (Springer Nature, 2018-05-25)
    This work is motivated by the challenge organized for the 10th International Conference on Extreme-Value Analysis (EVA2017) to predict daily precipitation quantiles at the 99.8% level for each month at observed and unobserved locations. Our approach is based on a Bayesian generalized additive modeling framework that is designed to estimate complex trends in marginal extremes over space and time. First, we estimate a high non-stationary threshold using a gamma distribution for precipitation intensities that incorporates spatial and temporal random effects. Then, we use the Bernoulli and generalized Pareto (GP) distributions to model the rate and size of threshold exceedances, respectively, which we also assume to vary in space and time. The latent random effects are modeled additively using Gaussian process priors, which provide high flexibility and interpretability. We develop a penalized complexity (PC) prior specification for the tail index that shrinks the GP model towards the exponential distribution, thus preventing unrealistically heavy tails. Fast and accurate estimation of the posterior distributions is performed thanks to the integrated nested Laplace approximation (INLA). We illustrate this methodology by modeling the daily precipitation data provided by the EVA2017 challenge, which consist of observations from 40 stations in the Netherlands recorded during the period 1972–2016. Capitalizing on INLA’s fast computational capacity and powerful distributed computing resources, we conduct an extensive cross-validation study to select the model parameters that govern the smoothness of trends. Our results clearly outperform simple benchmarks and are comparable to the best-scoring approaches of the other teams.
  • A volume integral equation solver for quantum-corrected transient analysis of scattering from plasmonic nanostructures

    Sayed, Sadeed Bin; Uysal, Ismail Enes; Bagci, Hakan; Ulku, H. Arda (IEEE, 2018-05-24)
    Quantum tunneling is observed between two nanostructures that are separated by a sub-nanometer gap. Electrons “jumping” from one structure to another create an additional current path. An auxiliary tunnel is introduced between the two structures as a support for this so that a classical electromagnetic solver can account for the effects of quantum tunneling. The dispersive permittivity of the tunnel is represented by a Drude model, whose parameters are obtained from the electron tunneling probability. The transient scattering from the connected nanostructures (i.e., nanostructures plus auxiliary tunnel) is analyzed using a time domain volume integral equation solver. Numerical results demonstrating the effect of quantum tunneling on the scattered fields are provided.
  • PTK2B/Pyk2 overexpression improves a mouse model of Alzheimer's disease

    Giralt, Albert; de Pins, Benoît; Cifuentes-Díaz, Carmen; López-Molina, Laura; Farah, Amel Thamila; Tible, Marion; Deramecourt, Vincent; Arold, Stefan T.; Ginés, Silvia; Hugon, Jacques; Girault, Jean-Antoine (Elsevier BV, 2018-05-24)
    Pyk2 is a Ca2+-activated non-receptor tyrosine kinase enriched in forebrain neurons and involved in synaptic regulation. Human genetic studies associated PTK2B, the gene coding Pyk2, with risk for Alzheimer's disease (AD). We previously showed that Pyk2 is important for hippocampal function, plasticity, and spine structure. However, its potential role in AD is unknown. To address this question we used human brain samples and 5XFAD mice, an amyloid mouse model of AD expressing mutated human amyloid precursor protein and presenilin1. In the hippocampus of 5XFAD mice and in human AD patients' cortex and hippocampus, Pyk2 total levels were normal. However, Pyk2 Tyr-402 phosphorylation levels, reflecting its autophosphorylation-dependent activity, were reduced in 5XFAD mice at 8 months of age but at 3 months. We crossed these mice with Pyk2−/− mice to generate 5XFAD animals devoid of Pyk2. At 8 months the phenotype of 5XFAD x Pyk2−/− double mutant mice was not different from that of 5XFAD. In contrast, overexpression of Pyk2 in the hippocampus of 5XFAD mice, using adeno-associated virus, rescued autophosphorylated Pyk2 levels and improved synaptic markers and performance in several behavioral tasks. Both Pyk2−/− and 5XFAD mice showed an increase of potentially neurotoxic Src cleavage product, which was rescued by Pyk2 overexpression. Manipulating Pyk2 levels had only minor effects on Aβ plaques, which were slightly decreased in hippocampus CA3 region of double mutant mice and increased following overexpression. Our results show that Pyk2 is not essential for the pathogenic effect of human amyloidogenic mutations in the 5XFAD mouse model. However, the slight decrease in plaque number observed in these mice in the absence of Pyk2 and their increase following Pyk2 overexpression suggest a contribution of this kinase in plaque formation. Importantly, a decreased function of Pyk2 was observed in 5XFAD mice, indicated by its decreased autophosphorylation and associated Src alterations. Overcoming this deficit by Pyk2 overexpression improved the behavioral and molecular phenotype of 5XFAD mice. Thus, our results in a mouse model of AD suggest that Pyk2 impairment may play a role in the symptoms of the disease.
  • Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems

    Charara, Ali (2018-05-24)
    Covariance matrices are ubiquitous in computational sciences, typically describing the correlation of elements of large multivariate spatial data sets. For example, covari- ance matrices are employed in climate/weather modeling for the maximum likelihood estimation to improve prediction, as well as in computational ground-based astronomy to enhance the observed image quality by filtering out noise produced by the adap- tive optics instruments and atmospheric turbulence. The structure of these covariance matrices is dense, symmetric, positive-definite, and often data-sparse, therefore, hier- archically of low-rank. This thesis investigates the performance limit of dense matrix computations (e.g., Cholesky factorization) on covariance matrix problems as the number of unknowns grows, and in the context of the aforementioned applications. We employ recursive formulations of some of the basic linear algebra subroutines (BLAS) to accelerate the covariance matrix computation further, while reducing data traffic across the memory subsystems layers. However, dealing with large data sets (i.e., covariance matrices of billions in size) can rapidly become prohibitive in memory footprint and algorithmic complexity. Most importantly, this thesis investigates the tile low-rank data format (TLR), a new compressed data structure and layout, which is valuable in exploiting data sparsity by approximating the operator. The TLR com- pressed data structure allows approximating the original problem up to user-defined numerical accuracy. This comes at the expense of dealing with tasks with much lower arithmetic intensities than traditional dense computations. In fact, this thesis con- solidates the two trends of dense and data-sparse linear algebra for HPC. Not only does the thesis leverage recursive formulations for dense Cholesky-based matrix al- gorithms, but it also implements a novel TLR-Cholesky factorization using batched linear algebra operations to increase hardware occupancy and reduce the overhead of the API. Performance reported of the dense and TLR-Cholesky shows many-fold speedups against state-of-the-art implementations on various systems equipped with GPUs. Additionally, the TLR implementation gives the user flexibility to select the desired accuracy. This trade-off between performance and accuracy is, currently, a well-established leading trend in the convergence of the third and fourth paradigm, i.e., HPC and Big Data, when moving forward with exascale software roadmap.
  • A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    Werfelmann, Robert (2018-05-24)
    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.
  • A note on intrinsic conditional autoregressive models for disconnected graphs

    Freni-Sterrantino, Anna; Ventrucci, Massimo; Rue, Haavard (Elsevier BV, 2018-05-23)
    In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
  • Biosensor for the detection of Listeria monocytogenes: emerging trends

    Soni, Dharmendra Kumar; Ahmad, Rafiq; Dubey, Suresh Kumar (Informa UK Limited, 2018-05-23)
    The early detection of Listeria monocytogenes (L. monocytogenes) and understanding the disease burden is of paramount interest. The failure to detect pathogenic bacteria in the food industry may have terrible consequences, and poses deleterious effects on human health. Therefore, integration of methods to detect and trace the route of pathogens along the entire food supply network might facilitate elucidation of the main contamination sources. Recent research interest has been oriented towards the development of rapid and affordable pathogen detection tools/techniques. An innovative and new approach like biosensors has been quite promising in revealing the foodborne pathogens. In spite of the existing knowledge, advanced research is still needed to substantiate the expeditious nature and sensitivity of biosensors for rapid and in situ analysis of foodborne pathogens. This review summarizes recent developments in optical, piezoelectric, cell-based, and electrochemical biosensors for Listeria sp. detection in clinical diagnostics, food analysis, and environmental monitoring, and also lists their drawbacks and advantages.
  • Over-Sampling Codebook-Based Hybrid Minimum Sum-Mean-Square-Error Precoding for Millimeter-Wave 3D-MIMO

    Mao, Jiening; Gao, Zhen; Wu, Yongpeng; Alouini, Mohamed-Slim (Institute of Electrical and Electronics Engineers (IEEE), 2018-05-23)
    Abstract: Hybrid precoding design is challenging for millimeter-wave (mmWave) massive MIMO. Most prior hybrid precoding schemes are designed to maximize the sum spectral efficiency (SSE), while seldom investigate the bit-error-rate (BER). Therefore, this letter designs an over-sampling codebook (OSC)-based hybrid minimum sum-mean-square-error (min-SMSE) precoding to optimize the BER. Specifically, given the effective baseband channel consisting of the real channel and analog precoding, we first design the digital precoder/combiner based on min-SMSE criterion to optimize the BER. To further reduce the SMSE between the transmit and receive signals, we propose an OSC-based joint analog precoder/combiner (JAPC) design. Simulation results show that the proposed scheme can achieve the better performance than its conventional counterparts.
  • In silico exploration of Red Sea Bacillus genomes for natural product biosynthetic gene clusters

    Othoum, Ghofran K; Bougouffa, Salim; Razali, Rozaimi; Bokhari, Ameerah; Alamoudi, Soha; Antunes, André; Gao, Xin; Hoehndorf, Robert; Arold, Stefan T.; Gojobori, Takashi; Hirt, Heribert; Mijakovic, Ivan; Bajic, Vladimir B.; Lafi, Feras Fawzi; Essack, Magbubah (Springer Nature, 2018-05-22)
    BackgroundThe increasing spectrum of multidrug-resistant bacteria is a major global public health concern, necessitating discovery of novel antimicrobial agents. Here, members of the genus Bacillus are investigated as a potentially attractive source of novel antibiotics due to their broad spectrum of antimicrobial activities. We specifically focus on a computational analysis of the distinctive biosynthetic potential of Bacillus paralicheniformis strains isolated from the Red Sea, an ecosystem exposed to adverse, highly saline and hot conditions.ResultsWe report the complete circular and annotated genomes of two Red Sea strains, B. paralicheniformis Bac48 isolated from mangrove mud and B. paralicheniformis Bac84 isolated from microbial mat collected from Rabigh Harbor Lagoon in Saudi Arabia. Comparing the genomes of B. paralicheniformis Bac48 and B. paralicheniformis Bac84 with nine publicly available complete genomes of B. licheniformis and three genomes of B. paralicheniformis, revealed that all of the B. paralicheniformis strains in this study are more enriched in nonribosomal peptides (NRPs). We further report the first computationally identified trans-acyltransferase (trans-AT) nonribosomal peptide synthetase/polyketide synthase (PKS/ NRPS) cluster in strains of this species.ConclusionsB. paralicheniformis species have more genes associated with biosynthesis of antimicrobial bioactive compounds than other previously characterized species of B. licheniformis, which suggests that these species are better potential sources for novel antibiotics. Moreover, the genome of the Red Sea strain B. paralicheniformis Bac48 is more enriched in modular PKS genes compared to B. licheniformis strains and other B. paralicheniformis strains. This may be linked to adaptations that strains surviving in the Red Sea underwent to survive in the relatively hot and saline ecosystems.
  • Tropospheric biennial oscillation and south Asian summer monsoon rainfall in a coupled model

    Konda, Gopinadh; Chowdary, Jasti S.; Srinivas, G; Gnanaseelan, C; Parekh, Anant; Attada, Raju; Rama Krishna, S S V S (Springer Nature, 2018-05-22)
    In this study Tropospheric Biennial Oscillation (TBO) and south Asian summer monsoon rainfall are examined in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. High correlation between the observations and model TBO index suggests that the model is able to capture most of the TBO years. Spatial patterns of rainfall anomalies associated with positive TBO over the south Asian region are better represented in the model as in the observations. However, the model predicted rainfall anomaly patterns associated with negative TBO years are improper and magnitudes are underestimated compared to the observations. It is noted that positive (negative) TBO is associated with La Niña (El Niño) like Sea surface temperature (SST) anomalies in the model. This leads to the fact that model TBO is El Niño-Southern Oscillation (ENSO) driven, while in the observations Indian Ocean Dipole (IOD) also plays a role in the negative TBO phase. Detailed analysis suggests that the negative TBO rainfall anomaly pattern in the model is highly influenced by improper teleconnections allied to IOD. Unlike in the observations, rainfall anomalies over the south Asian region are anti-correlated with IOD index in CFSv2. Further, summer monsoon rainfall over south Asian region is highly correlated with IOD western pole than eastern pole in CFSv2 in contrast to the observations. Altogether, the present study highlights the importance of improving Indian Ocean SST teleconnections to south Asian summer rainfall in the model by enhancing the predictability of TBO. This in turn would improve monsoon rainfall prediction skill of the model.
  • GLAM: Glycogen-derived Lactate Absorption Map for visual analysis of dense and sparse surface reconstructions of rodent brain structures on desktop systems and virtual environments

    Agus, Marco; Boges, Daniya; Gagnon, Nicolas; Magistretti, Pierre J.; Hadwiger, Markus; Cali, Corrado (Elsevier BV, 2018-05-21)
    Human brain accounts for about one hundred billion neurons, but they cannot work properly without ultrastructural and metabolic support. For this reason, mammalian brains host another type of cells called “glial cells”, whose role is to maintain proper conditions for efficient neuronal function. One type of glial cell, astrocytes, are involved in particular in the metabolic support of neurons, by feeding them with lactate, one byproduct of glucose metabolism that they can take up from blood vessels, and store it under another form, glycogen granules. These energy-storage molecules, whose morphology resembles to spheres with a diameter ranging 10–80 nanometers roughly, can be easily recognized using electron microscopy, the only technique whose resolution is high enough to resolve them. Understanding and quantifying their distribution is of particular relevance for neuroscientists, in order to understand where and when neurons use energy under this form. To answer this question, we developed a visualization technique, dubbed GLAM (Glycogen-derived Lactate Absorption Map), and customized for the analysis of the interaction of astrocytic glycogen on surrounding neurites in order to formulate hypotheses on the energy absorption mechanisms. The method integrates high-resolution surface reconstruction of neurites, astrocytes, and the energy sources in form of glycogen granules from different automated serial electron microscopy methods, like focused ion beam scanning electron microscopy (FIB-SEM) or serial block face electron microscopy (SBEM), together with an absorption map computed as a radiance transfer mechanism. The resulting visual representation provides an immediate and comprehensible illustration of the areas in which the probability of lactate shuttling is higher. The computed dataset can be then explored and quantified in a 3D space, either using 3D modeling software or virtual reality environments. Domain scientists have evaluated the technique by either using the computed maps for formulating functional hypotheses or for planning sparse reconstructions to avoid excessive occlusion. Furthermore, we conducted a pioneering user study showing that immersive VR setups can ease the investigation of the areas of interest and the analysis of the absorption patterns in the cellular structures.
  • Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    Hou, Siqing (2018-05-21)
    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.
  • ComplexContact: a web server for inter-protein contact prediction using deep learning

    Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo (Oxford University Press (OUP), 2018-05-20)
    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

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