Now showing items 1-20 of 180

• Integrated CO2 Capture and Conversion into Valuable Hydrocarbons

(American Chemical Society (ACS), 2019-01-10) [Preprint]
The alarming atmospheric concentration and continuous emissions of carbon dioxide (CO2) require immediate action. As a result of advances in CO2 capture and sequestration technologies (generally involving point sources such as energy generation plants), large amounts of pure CO2 will soon be available. In addition to geological storage and other applications of the captured CO2, the development of technologies able to convert this carbon feedstock into commodity chemicals may pave the way towards a more sustainable economy. Here, we present a novel multifunctional catalyst consisting of Fe2O3 encapsulated in K2CO3 that can capture and simultaneously transform CO2 into olefins. In contrast to traditional systems in Fischer-Tropsch reactions, we demonstrate that when dealing with CO2 activation (in contrast to CO), very high K loadings are key to capturing the CO2 via the well-known ‘potassium carbonate mechanism’. The proposed catalytic process is demonstrated to be as productive as existing commercial processes based on synthesis gas while relying on economically and environmentally advantageous CO2 feedstock.
• Turning a Methanation Catalyst into a Methanol Producer: In-Co Catalysts for the Direct Hydrogenation of CO2 to Methanol

(American Chemical Society (ACS), 2018-11-16) [Preprint]
The direct hydrogenation of CO2 to methanol using green hydrogen is regarded as a potential technology to reduce greenhouse gas emissions and the dependence on fossil fuels. For this technology to become feasible, highly selective and productive catalysts that can operate under a wide range of reaction conditions near thermodynamic conversion are required. Here, we demonstrate that indium in close contact with cobalt catalyses the formation of methanol from CO2 with high selectivity (>80%) and productivity (0.86 gCH3OH.gcatalyst-1.h-1) at conversion levels close to thermodynamic equilibrium, even at temperatures as high as 300 °C and at moderate pressures (50 bar). The studied In@Co system, obtained via co- precipitation, undergoes in situ transformation under the reaction conditions to form the active phase. Extensive characterization demonstrates that the active catalyst is composed of a mixed metal carbide (Co3InC0.75), indium oxide (In2O3) and metallic Co.
• Coupling elastic and acoustic wave simulations for seismic studies

(2019) [Preprint]
In this work, we provide a mechanism to couple finite difference simulations of elastic and acoustic wave equations using summation-by-parts operators and simultaneous approximation terms that weakly impose the interface conditions. Such coupling enables the application of elastic modeling to areas of special importances for seismic studies, such as near surface region and salt body, at a relatively low computational burden thanks to the background acoustic modeling. Using the proposed mechanism, we are able to perform the coupled simulation stably and accurately. In fact, the energy-conserving property in the physical systems is preserved after discretization. Numerical examples are presented to demonstrate the efficacy of the proposed mechanism as well as its promise in seismic studies.
• Localization in Adiabatic Shear Flow Via Geometric Theory of Singular Perturbations

(Journal of Nonlinear Science, Springer Nature, 2019-03-04) [Article]
We study localization occurring during high-speed shear deformations of metals leading to the formation of shear bands. The localization instability results from the competition between Hadamard instability (caused by softening response) and the stabilizing effects of strain rate hardening. We consider a hyperbolic–parabolic system that expresses the above mechanism and construct self-similar solutions of localizing type that arise as the outcome of the above competition. The existence of self-similar solutions is turned, via a series of transformations, into a problem of constructing a heteroclinic orbit for an induced dynamical system. The dynamical system is in four dimensions but has a fast–slow structure with respect to a small parameter capturing the strength of strain rate hardening. Geometric singular perturbation theory is applied to construct the heteroclinic orbit as a transversal intersection of two invariant manifolds in the phase space.
• Phylogenetic relationships among the clownfish-hosting sea anemones

(Cold Spring Harbor Laboratory, 2019-02-25) [Preprint]
The clownfish-sea anemone symbiosis has been a model system for understanding fundamental evolutionary and ecological processes. However, our evolutionary understanding of this symbiosis comes entirely from studies of clownfishes. A holistic understanding of a model mutualism requires systematic, biogeographic, and phylogenetic insight into both partners. Here, we conduct the largest phylogenetic analysis of sea anemones (Order Actiniaria) to date, with a focus on expanding the biogeographic and taxonomic sampling of the 10 nominal clownfish-hosting species. Using a combination of mtDNA and nuDNA loci we test 1) the monophyly of each clownfish-hosting family and genus, 2) the current anemone taxonomy that suggests symbioses with clownfishes evolved multiple times within Actiniaria, and 3) whether, like the clownfishes, there is evidence that host anemones have a Coral Triangle biogeographic origin. Our phylogenetic reconstruction demonstrates widespread poly- and para-phyly at the family and genus level, particularly within the family Stichodactylidae and genus Sticodactyla, and suggests that symbioses with clownfishes evolved minimally three times within sea anemones. We further recover evidence for a Tethyan biogeographic origin for some clades. Our data provide the first evidence that clownfish and some sea anemone hosts have different biogeographic origins, and that there may be cryptic species of host anemones. Finally, our findings reflect the need for a major taxonomic revision of the clownfish-hosting sea anemones.
• Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

(Cold Spring Harbor Laboratory, 2018-07-28) [Preprint]
Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but, recently, there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a large and well-characterized dataset of anatomic pathology descriptions from a major study of aging mice. We show how different design patterns based on the MPATH and MA ontologies provide orthogonal axes of analysis, and perform differently in over-representation and semantic similarity applications. We discuss how such a data-driven approach might be used generally to generate and evaluate ontology design patterns.
• Wiskott-Aldrich Syndrome Protein Regulates Nuclear Organization, Alternative Splicing and Cell Proliferation

(SSRN Preprint submitted to Cell Stem Cell, Elsevier BV, 2018-12-26) [Preprint]
Wiskott-Aldrich syndrome (WAS), caused by mutations in the WASP protein, displaysimmunological dysfunctions and predisposition to cancer. Despite studies in cell linesand mouse models the molecular mechanisms of WAS remain obscure. We generatedinduced pluripotent stem cells (iPSCs) from patients with WAS (WAS-iPSCs) andisogenic gene-corrected iPSCs by genome editing. Immune cells derived from WASiPSCs,genetically engineered B lymphoblastoid cell lines, and patient primarylymphocytes were subjected to imaging, proteomic and transcriptomic analyses. TheWAS-iPSC model not only recapitulated known disease phenotypes but also revealed,for the first time, roles of WASP in the organization of the nucleolus and nuclearspeckles and PML nuclear bodies. WASP interacts with components of the nucleolusand nuclear speckles, including chromatin modifiers and splicing factors. Innate andadaptive immune cells from WAS patients display global dysregulation of cell cycleregulation and alternative splicing. WASP mutation is sufficient to drive an acceleratedcell cycle and tumor-promoting splicing changes. Our data show that WASP acts as atumor suppressor and specific WASP mutants behave as oncogenes and cause cellintrinsicalterations that predispose patients to cancer.
• Unmatched level of molecular convergence among deeply divergent complex multicellular fungi

(Cold Spring Harbor Laboratory, 2019-02-14) [Preprint]
Convergent evolution is pervasive in nature, but it is poorly understood how various constraints and natural selection limit the diversity of evolvable phenotypes. Here, we report that, despite >650 million years of divergence, the same genes have repeatedly been co-opted for the development of complex multicellularity in the two largest clades of fungi-the Ascomycota and Basidiomycota. Co-opted genes have undergone duplications in both clades, resulting in >81% convergence across shared multicellularity-related families. This convergence is coupled with a rich repertoire of multicellularity-related genes in ancestors that predate complex multicellular fungi, suggesting that the coding capacity of early fungal genomes was well suited for the repeated evolution of complex multicellularity. Our work suggests that evolution may be predictable not only when organisms are closely related or are under similar selection pressures, but also if the genome biases the potential evolutionary trajectories organisms can take, even across large phylogenetic distances.
• Ecological specificity of the metagenome in a set of lower termite species supports contribution of the microbiome to adaptation of the host

(Cold Spring Harbor Laboratory, 2019-01-22) [Preprint]
Elucidating the interplay between hosts and their microbiomes in ecological adaptation has become a central theme in evolutionary biology. The microbiome mediated adaptation of lower termites to a wood-based diet. Lower termites have further adapted to different ecologies. Substantial ecological differences are linked to different termite life types. Termites of the wood-dwelling life type never leave their nests and feed on a uniform diet. Termites of the foraging life type forage for food outside the nest, access a more diverse diet, and grow faster. Here we reasoned that the microbiome that is directly involved in food substrate breakdown and nutrient acquisition might contribute to adaptation to these ecological differences. This should leave ecological imprints on the microbiome. To search for such imprints, we applied metagenomic shotgun sequencing in a multispecies framework covering both life types. The microbiome of foraging species was enriched with genes for starch digestion, while the metagenome of wood-dwelling species was enriched with genes for hemicellulose utilization. Furthermore, increased abundance of genes for nitrogen sequestration, a limiting factor for termite growth, aligned with faster growth. These findings are consistent with the notion that a specific subset of functions encoded by the microbiome contributes to host adaptation.
• Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

(Cold Spring Harbor Laboratory, 2019-02-02) [Preprint]
Motivation: There are now over 500 ontologies in the life sciences. Over the past years, significant resources have been invested into formalizing these biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. At the same time, ontologies have extended their amount of human-readable information such as labels and definitions as well as other meta-data. As a consequence, biomedical ontologies now form large formalized domain knowledge bases and have a potential to improve ontology-based data analysis by providing background knowledge and relations between biological entities that are not otherwise connected. Results: We evaluate the contribution of formal axioms and ontology meta-data to the ontology-based prediction of protein-protein interactions and gene-disease associations. We find that the formal axioms that have been created for the Gene Ontology and several other ontologies significantly improve ontology- based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute in varying degrees to improving data analysis. Our results have major implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings clearly motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies
• VSIM: Visualization and simulation of variants in personal genomes with an application to premarital testing

(Cold Spring Harbor Laboratory, 2019-01-25) [Preprint]
Background: Interpretation of personal genomics data, for example in genetic counseling, is challenging due to the complexity of the data and the amount of background knowledge required for its interpretation. This background knowledge is distributed across several databases. Further information about genomic features can also be predicted through machine learning methods. Making this information accessible more easily has the potential to improve interpretation of variants in personal genomes. Results: We have developed VSIM, a web application for the interpretation and visualization of variants in personal genome sequences. VSIM identifies disease variants related to Mendelian, complex, and digenic disease as well as pharmacogenomic variants in personal genomes and visualizes them using a web server. VSIM can further be used to simulate populations of children based on two parent genomes, and can be applied to support premarital genetic counseling. We make VSIM available as source code as well as through a container that can be installed easily in network environments in which genomic data is specially protected. VSIM and related documentation is freely available at https://github.com/bio-ontology-research-group/VSIM. Conclusions: VSIM is a software that provides a web-based interface to variant interpretation in genetic counseling. VSIM can also be used for premarital genetic screening by simulating a population of children and analyze the disorder they might be carrying.
• Robust Beamforming in Cache-Enabled Cloud Radio Access Networks

(arXiv, 2016-09-06) [Preprint]
Popular content caching is expected to play a major role in efficiently reducing backhaul congestion and achieving user satisfaction in next generation mobile radio systems. Consider the downlink of a cache-enabled cloud radio access network (CRAN), where each cache-enabled base station (BS) is equipped with limited-size local cache storage. The central computing unit (cloud) is connected to the BSs via a limited capacity backhaul link and serves a set of single-antenna mobile users (MUs). This paper assumes that only imperfect channel state information (CSI) is available at the cloud. It focuses on the problem of minimizing the total network power and backhaul cost so as to determine the beamforming vector of each user across the network, the quantization noise covariance matrix, and the BS clustering subject to imperfect channel state information and fixed cache placement assumptions. The paper suggests solving such a difficult, non-convex optimization problem using the semidefinite relaxation (SDR). The paper then uses the ℓ0-norm approximation to provide a feasible, sub-optimal solution using the majorization-minimization (MM) approach. Simulation results particularly show how the cache-enabled network significantly improves the backhaul cost especially at high signal-to-interference-plus-noise ratio (SINR) values as compared to conventional cache-less CRANs.
• Measurement Selection: A Random Matrix Theory Approach

(IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers (IEEE), 2018-05-15) [Article]
This paper considers the problem of selecting a set of $k$ measurements from $n$ available sensor observations. The selected measurements should minimize a certain error function assessing the error in estimating a certain $m$ dimensional parameter vector. The exhaustive search inspecting each of the $n\choose k$ possible choices would require a very high computational complexity and as such is not practical for large $n$ and $k$. Alternative methods with low complexity have recently been investigated but their main drawbacks are that 1) they require perfect knowledge of the measurement matrix and 2) they need to be applied at the pace of change of the measurement matrix. To overcome these issues, we consider the asymptotic regime in which $k$, $n$ and $m$ grow large at the same pace. Tools from random matrix theory are then used to approximate in closed-form the most important error measures that are commonly used. The asymptotic approximations are then leveraged to select properly $k$ measurements exhibiting low values for the asymptotic error measures. Two heuristic algorithms are proposed: the first one merely consists in applying the convex optimization artifice to the asymptotic error measure. The second algorithm is a low-complexity greedy algorithm that attempts to look for a sufficiently good solution for the original minimization problem. The greedy algorithm can be applied to both the exact and the asymptotic error measures and can be thus implemented in blind and channel-aware fashions. We present two potential applications where the proposed algorithms can be used, namely antenna selection for uplink transmissions in large scale multi-user systems and sensor selection for wireless sensor networks. Numerical results are also presented and sustain the efficiency of the proposed blind methods in reaching the performances of channel-aware algorithms.
• Pricing American Options by Exercise Rate Optimization

(arXiv, 2018-09-20) [Preprint]
We present a novel method for the numerical pricing of American options based on Monte Carlo simulation and optimization of exercise strategies. Previous solutions to this problem either explicitly or implicitly determine so-called optimal \emph{exercise regions}, which consist of points in time and space at which the option is exercised. In contrast, our method determines \emph{exercise rates} of randomized exercise strategies. We show that the supremum of the corresponding stochastic optimization problem provides the correct option price. By integrating analytically over the random exercise decision, we obtain an objective function that is differentiable with respect to perturbations of the exercise rate even for finitely many sample paths. Starting in a neutral strategy with constant exercise rate then allows us to globally optimize this function in a gradual manner. Numerical experiments on vanilla put options in the multivariate Black--Scholes model and preliminary theoretical analysis underline the efficiency of our method both with respect to the number of time-discretization steps and the required number of degrees of freedom in the parametrization of exercise rates. Finally, the flexibility of our method is demonstrated by numerical experiments on max call options in the Black--Scholes model and vanilla put options in Heston model and the non-Markovian rough Bergomi model.
• Hierarchical adaptive sparse grids for option pricing under the rough Bergomi model

(2018-12-21) [Preprint]
The rough Bergomi (rBergomi) model, introduced recently in [4], is a promising rough volatility model in quantitative finance. This new model exhibits consistent results with the empirical fact of implied volatility surfaces being essentially time-invariant. This model also has the ability to capture the term structure of skew observed in equity markets. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we design a novel, alternative, hierarchical approach, based on adaptive sparse grids quadrature, specifically using the same construction as multi-index stochastic collocation (MISC) [21], coupled with Brownian bridge construction and Richardson extrapolation. By uncovering the available regularity, our hierarchical method demonstrates substantial computational gains with respect to the standard MC method, when reaching a sufficiently small error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.
• IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains

(arXiv, 2018-10-26) [Preprint]
This paper proposes an extension of the Multi-Index Stochastic Collocation (MISC) method for forward uncertainty quantification (UQ) problems in computational domains of shape other than a square or cube, by exploiting isogeometric analysis (IGA) techniques. Introducing IGA solvers to the MISC algorithm is very natural since they are tensor-based PDE solvers, which are precisely what is required by the MISC machinery. Moreover, the combination-technique formulation of MISC allows the straight-forward reuse of existing implementations of IGA solvers. We present numerical results to showcase the effectiveness of the proposed approach.
• Multilevel ensemble Kalman filtering for spatio-temporal processes

(arXiv, 2018-02-02) [Preprint]
This work concerns state-space models, in which the state-space is an infinite-dimensional spatial field, and the evolution is in continuous time, hence requiring approximation in space and time. The multilevel Monte Carlo (MLMC) sampling strategy is leveraged in the Monte Carlo step of the ensemble Kalman filter (EnKF), thereby yielding a multilevel ensemble Kalman filter (MLEnKF) for spatio-temporal models, which has provably superior asymptotic error/cost ratio. A practically relevant stochastic partial differential equation (SPDE) example is presented, and numerical experiments with this example support our theoretical findings.
• Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization

(arXiv, 2018-07-02) [Preprint]
Finding the best set-up for the design of experiments is the main concern of Optimal Experimental Design (OED). We focus on the Bayesian problem of finding the set-up that maximizes the Shannon’s expected information gain. We propose using the stochastic gradient descent and its accelerated counterpart, which employs Nesterov’s method, to solve the optimization problem in OED. We couple these optimization methods with three estimators of the objective function: a double loop Monte Carlo (DLMC), a Laplace approximation of the posterior distribution and a Laplace-based importance sampling. The use of stochastic gradient methods and Laplace-based estimators allow us to afford expensive and complex models, for example, those that require solving a partial differential equation (PDE). From a theoretical viewpoint, we derive an explicit formula to compute the stochastic gradient of Monte Carlo with Laplace method. Finally, from a computational standpoint, we study four examples: three based on analytical functions and one on the finite element method solution of a PDE. The latter is an electrical impedance tomography experiment based on the complete electrode model. The accelerated stochastic gradient with Laplace approximation converges to local maxima in fewer model evaluations by up to five orders of magnitude than gradient descent with DLMC.
• Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty

(arXiv, 2018-11-28) [Preprint]
We interpret uncertainty in a model for seismic wave propagation by treating the model parameters as random variables, and apply the Multilevel Monte Carlo (MLMC) method to reduce the cost of approximating expected values of selected, physically relevant, quantities of interest (QoI) with respect to the random variables. Targeting source inversion problems, where the source of an earthquake is inferred from ground motion recordings on the Earth's surface, we consider two QoI that measure the discrepancies between computed seismic signals and given reference signals: one QoI, QoI_E, is defined in terms of the L^2-misfit, which is directly related to maximum likelihood estimates of the source parameters; the other, QoI_W, is based on the quadratic Wasserstein distance between probability distributions, and represents one possible choice in a class of such misfit functions that have become increasingly popular to solve seismic inversion in recent years. We simulate seismic wave propagation, including seismic attenuation, using a publicly available code in widespread use, based on the spectral element method. Using random coefficients and deterministic initial and boundary data, we present benchmark numerical experiments with synthetic data in a two-dimensional physical domain and a one-dimensional velocity model where the assumed parameter uncertainty is motivated by realistic Earth models. Here, the computational cost of the standard Monte Carlo method was reduced by up to 97% for QoI_E, and up to 78% for QoI_W, using a relevant range of tolerances. Shifting to three-dimensional domains is straight-forward and will further increase the relative computational work reduction.
• Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design

(arXiv, 2018-11-28) [Preprint]
An optimal experimental set-up maximizes the value of data for statistical inference and prediction, which is particularly important for experiments that are time consuming or expensive to perform. In the context of partial differential equations (PDEs), multilevel methods have been proven in many cases to dramatically reduce the computational complexity of their single-level counterparts. Here, two multilevel methods are proposed to efficiently compute the expected information gain using a Kullback-Leibler divergence measure in simulation-based Bayesian optimal experimental design. The first method is a multilevel double loop Monte Carlo (MLDLMC) with importance sampling, which greatly reduces the computational work of the inner loop. The second proposed method is a multilevel double loop stochastic collocation (MLDLSC) with importance sampling, which is high-dimensional integration by deterministic quadrature on sparse grids. In both methods, the Laplace approximation is used as an effective means of importance sampling, and the optimal values for method parameters are determined by minimizing the average computational work subject to a desired error tolerance. The computational efficiencies of the methods are demonstrated for computing the expected information gain for Bayesian inversion to infer the fiber orientation in composite laminate materials by an electrical impedance tomography experiment, given a particular set-up of the electrode configuration. MLDLSC shows a better performance than MLDLMC by exploiting the regularity of the underlying computational model with respect to the additive noise and the unknown parameters to be statistically inferred.