Recent Submissions

  • Localization in Adiabatic Shear Flow Via Geometric Theory of Singular Perturbations

    Lee, Min-Gi; Katsaounis, Theodoros; Tzavaras, Athanasios (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

    Titus, Benjamin M; Benedict, Charlotte; Laroche, Robert; Gusmao, Luciana; Van Deusen, Vanessa; Chiodo, Tommaso; Meyer, Christopher P; Berumen, Michael L.; Bartholomew, Aaron; Yanagi, Kensuke; Reimer, James D; Fujii, Takuma; Daly, Marymegan; Rodriguez, Estefania (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.
  • Unmatched level of molecular convergence among deeply divergent complex multicellular fungi

    Merenyi, Zsolt; Prasanna, Arun N; Zheng, Wang; Kovacs, Karoly; Hegedus, Botond; Balint, Balazs; Papp, Balazs; Townsend, Jeffrey P; Nagy, Laszlo G (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.
  • Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

    Smaili, Fatima Z.; Gao,Xin; Hoehndorf, Robert (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

    Althagafi, Azza Th.; Hoehndorf, Robert (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.
  • Ecological specificity of the metagenome in a set of lower termite species supports contribution of the microbiome to adaptation of the host

    Waidele, Lena; Korb, Judith; Voolstra, Christian R.; Dedeine, Franck; Staubach, Fabian (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.
  • Integrated CO2 Capture and Conversion into Valuable Hydrocarbons

    Ramirez, Adrian; Ould-Chikh, Samy; Gevers, Lieven; Chowdhury, Abhishek Dutta; Abou-Hamad, Edy; Aguilar-Tapia, Antonio; Hazemann, Jean-Louis; Wehbe, Nimer; Al Abdulghani, Abdullah J.; Kozlov, Sergey M.; Cavallo, Luigi; Gascon, Jorge (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.
  • Role of MPK4 in pathogen-associated molecular pattern-triggered alternative splicing in Arabidopsis

    Jeremie, Bazin; Mariappan, Kiruthiga Gayathri; Blein, Thomas; Volz, Ronny; Crespi, Martin; Hirt, Heribert (Cold Spring Harbor Laboratory, 2019-01-04) [Preprint]
    Alternative splicing (AS) of pre-mRNAs in plants is an important mechanism of gene regulation in environmental stress tolerance but plant signals involved are essentially unknown. Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) is mediated by mitogen-activated protein kinases and the majority of PTI defense genes are regulated by MPK3, MPK4 and MPK6. These responses have been mainly analyzed at the transcriptional level, however many splicing factors are direct targets of MAPKs. Here, we studied alternative splicing induced by the PAMP flagellin in Arabidopsis. We identified 506 PAMP-induced differentially alternatively spliced (DAS) genes. Although many DAS genes are targets of nonsense-mediated degradation (NMD), only 19% are potential NMD targets. Importantly, of the 506 PAMP-induced DAS genes, only 89 overlap with the set of 1849 PAMP-induced differentially expressed genes (DEG), indicating that transcriptome analysis does not identify most DAS events. Global DAS analysis of mpk3, mpk4, and mpk6 mutants revealed that MPK4 is a key regulator of PAMP-induced differential splicing, regulating AS of a number of splicing factors and immunity-related protein kinases, such as the calcium-dependent protein kinase CPK28, the cysteine-rich receptor like kinases CRK13 and CRK29 or the FLS2 co-receptor SERK4/BKK1.These data suggest that MAP kinase regulation of splicing factors is a key mechanism in PAMP-induced AS regulation of PTI.
  • Coupling elastic and acoustic wave simulations for seismic studies

    Gao, Longfei; Keyes, David E. (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.
  • Phenotypic, functional and taxonomic features predict host-pathogen interactions: Table S1; Figure S1

    Liu-Wei, Wang; Kafkas, Senay; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-12-31) [Preprint]
    Identification of host-pathogen interactions (HPIs) can reveal mechanistic insights of infectious diseases for potential treatments and drug discoveries. Current computational methods for the prediction of HPIs often rely on our knowledge on the sequences and functions of pathogen proteins, which is limited for many species, especially for species of emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. We developed an ontology-based method that prioritizes potential interaction protein partners for pathogens using machine learning models. Our method exploits the underlying disease mechanisms by associating phenotypic and functional features of pathogens and human proteins, corroborated by multiple ontologies as background knowledge. Additionally, by embedding the phenotypic information of the pathogens within a formally represented taxonomy, we demonstrate that our model can also accurately predict interaction partners for pathogens without known phenotypes, using a combination of their taxonomic relationships with other pathogens and information from ontologies as background knowledge. Our results show that the integration of phenotypic, functional and taxonomic knowledge not only improves the prediction of HPIs, but also enables us to investigate novel pathogens in emerging infectious diseases.
  • Wiskott-Aldrich Syndrome Protein Regulates Nuclear Organization, Alternative Splicing and Cell Proliferation

    Li, Mo; Suzuki, Keiichiro; Zhou, Xuan; Yamauchi, Takayoshi; Moresco, James J.; Dunn, Sarah; Benitez, Reyna Hernandez-; Hishida, Tomoaki; Kim, Na Young; Andijani, Manal M.; Ku, Manching; Takahashi, Yuta; Xu, Jinna; Qiu, Jinsong; Huang, Ling; Benner, Christopher; Aizawa, Emi; Qu, Jing; Liu, Guang-Hui; Li, Zhongwei; Yi, Fei; Bi, Chongwei; Ghosheh, Yanal; Shao, Changwei; Shokhirev, Maxim; Comoli, Patrizia; Frassoni, Francesco; III, John R. Yates; Fu, Xiang-Dong; Esteban, Concepcion Rodriguez; Belmonte, Juan Carlos Izpisua (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.
  • Precision phenotyping reveals novel loci for quantitative resistance to septoria tritici blotch in European winter wheat

    Yates, Steven; Mikaberidze, Alexey; Krattinger, Simon G.; Abrouk, Michael; Hund, Andreas; Yu, Kang; Studer, Bruno; Fouche, Simone; Meile, Lukas; Pereira, Danilo; Karisto, Petteri; McDonald, Bruce (Cold Spring Harbor Laboratory, 2018-12-21) [Preprint]
    Accurate, high-throughput phenotyping for quantitative traits is the limiting factor for progress in plant breeding. We developed automated image analysis to measure quantitative resistance to septoria tritici blotch (STB), a globally important wheat disease, enabling identification of small chromosome intervals containing plausible candidate genes for STB resistance. 335 winter wheat cultivars were included in a replicated field experiment that experienced natural epidemic development by a highly diverse but fungicide-resistant pathogen population. More than 5.4 million automatically generated phenotypes were associated with 13,648 SNP markers to perform a GWAS. We identified 26 chromosome intervals explaining 1.9-10.6% of the variance associated with four resistance traits. Seventeen of the intervals were less than 5 Mbp in size and encoded only 173 genes, including many genes associated with disease resistance. Five intervals contained four or fewer genes, providing high priority targets for functional validation. Ten chromosome intervals were not previously associated with STB resistance. Our experiment illustrates how high-throughput automated phenotyping can accelerate breeding for quantitative disease resistance. The SNP markers associated with these chromosome intervals can be used to recombine different forms of quantitative STB resistance that are likely to be more durable than pyramids of major resistance genes.
  • Hierarchical adaptive sparse grids for option pricing under the rough Bergomi model

    Ben Hammouda, Chiheb; Bayer, Christian; Tempone, Raul (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.
  • Searching and mapping genomic subsequences in nanopore raw signals through novel dynamic time warping algorithms

    Han, Renmin; Wang, Sheng; Gao, Xin (Cold Spring Harbor Laboratory, 2018-12-10) [Preprint]
    Nanopore sequencing is a promising technology to generate ultra-long reads based on the direct measurement of electrical current signals when a DNA molecule passes through a nanopore. These ultra-long reads are critical for detecting large structural variations in the genome. However, it is challenging to use nanopore sequencing to identify single nucleotide polymorphisms (SNPs) or other modifications such as methylations, especially at a low sequencing coverage, due to the high error rate in the base-called reads. It is possible to correct the base-calling error through the subsequence search by mapping a SNP-containing genomic region to the long nanopore raw signal sequences that contain this region and taking consensus of these signals. Nevertheless, the ultra-long raw signals and an order of magnitude difference in the sampling speed between the two sequences make the traditional algorithms infeasible to solve the problem. Here we propose two novel algorithms, the direct subsequence dynamic time warping for nanopore raw signal search (DSDTWnano) and the continuous wavelet subsequence dynamic time warping for nanopore raw signal search (cwSDTWnano), to enable the direct subsequence searching and exact mapping in nanopore raw signals. The proposed algorithms are based on the idea of subsequence-extended dynamic time warping and directly operate on the raw signals, without any loss of information. DSDTWnano could ensure an output of highly accurate query results and cwSDTWnano is the accelerated version of DSDTWnano, with the help of seeding and multi-scale coarsening of signals that are based on continuous wavelet transform. Furthermore, a novel error function is proposed to specify the mapping accuracy between a genomic sequence and an electrical current signal sequence, which may serve as the standard criterion for further genome-to-signal mapping studies. Comprehensive experiments on three real-world nanopore datasets (human and lambda phage) demonstrate the efficiency and effectiveness of the proposed algorithms. Finally, we show the power of our algorithms in SNP detection under a low coverage (20x) on E. coli, with >95% detection rate. Our program is available at https://github.com/icthrm/cwSDTWnano.git.
  • PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research

    Kafkas, Senay; Abdelhakim, Marwa; Hashish, Yasmeen; Kulmanov, Maxat; Abdellatif, Marwa; Schofield, Paul N; Hoehndorf, Robert (Cold Spring Harbor Laboratory, 2018-12-10) [Preprint]
    Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate phenotypes with infectious disease. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/, and the data is freely available through a public SPARQL endpoint.
  • Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty

    Ballesio, Marco; Beck, Joakim; Pandey, Anamika; Parisi, Laura; von Schwerin, Erik; Tempone, Raul (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

    Beck, Joakim; Dia, Ben Mansour; Espath, Luis; Tempone, Raul (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.
  • Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation

    Mueller, Matthias; Casser, Vincent; Smith, Neil; Michels, Dominik L.; Ghanem, Bernard (arXiv, 2018-11-22) [Conference Paper]
    Automating the navigation of unmanned aerial vehicles (UAVs) in diverse scenarios has gained much attention in recent years. However, teaching UAVs to fly in challenging environments remains an unsolved problem, mainly due to the lack of training data. In this paper, we train a deep neural network to predict UAV controls from raw image data for the task of autonomous UAV racing in a photo-realistic simulation. Training is done through imitation learning with data augmentation to allow for the correction of navigation mistakes. Extensive experiments demonstrate that our trained network (when sufficient data augmentation is used) outperforms state-of-the-art methods and flies more consistently than many human pilots. Additionally, we show that our optimized network architecture can run in real-time on embedded hardware, allowing for efficient onboard processing critical for real-world deployment.
  • Turning a Methanation Catalyst into a Methanol Producer: In-Co Catalysts for the Direct Hydrogenation of CO2 to Methanol

    Bavykina, Anastasiya; Yarulina, Irina; Gevers, Lieven; Hedhili, Mohamed \nNejib; Miao, Xiaohe; Ramirez, Adrian; Pustovarenko, Oleksii; Dikhtiarenko, Alla; Cadiau, Amandine; Ould-Chikh, Samy; Gascon, Jorge (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.
  • TGF-b2, catalase activity, H2O2 output and metastatic potential of diverse types of tumour

    Haidar, Malak; Metheni, Mehdi; Batteux, Frederic; Langsley, Gordon (Cold Spring Harbor Laboratory, 2018-11-14) [Preprint]
    Theileria annulata is a protozoan parasite that infects and transforms bovine macrophages causing a myeloid-leukaemia-like disease called tropical theileriosis. TGF-b2 is highly expressed in many cancer cells and is significantly increased in Theileria-transformed macrophages, as are levels of Reactive Oxygen Species (ROS), notably H2O2. Here, we describe the interplay between TGF-b2 and ROS in cellular transformation. We show that TGF-b2 drives expression of catalase to reduce the amount of H2O2 produced by T. annulata-transformed bovine macrophages, as well as by human lung (A549) and colon cancer (HT-29) cell lines. Theileria-transformed macrophages attenuated for dissemination express less catalase and produce more H2O2, but regain both virulent migratory and matrigel traversal phenotypes when stimulated with TGF-b2, or catalase that reduce H2O2 output. Increased H2O2 output therefore, underpins the aggressive dissemination phenotype of diverse tumour cell types, but in contrast, too much H2O2 can dampen dissemination.

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