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The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (R eff ) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median R eff below 1 when combined with either stay at home orders (median R eff 0.97, 95% confidence interval (CI) 0.58-1.39) or face masks (median R eff 0.97, 95% CI 0.58-1.39). While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

Aqueous solutions of tetrabutylphosphonium hydroxide have been evaluated as pretreatment media for rice husks, prior to sulphuric acid hydrolysis or cellulase enzymatic hydrolysis. Varying the water:tetrabutylphosphonium hydroxide ratio varied the rate of delignification, as well as silica, lignin and cellulose solubility. Pre-treatment with 60wt% hydroxide dissolved the rice husk and the regenerated material was thus heavily disrupted. Sulphuric acid hydrolysis of 60wt%-treated samples yielded the highest amount of glucose per gram of rice husk. Solutions with good lignin and silica solubility but only moderate to negligible cellulose solubility (10-40wt% hydroxide) were equally effective as pre-treatment media for both acid and enzymatic hydrolysis. However, pre-treatment with 60wt% hydroxide solutions was incompatible with downstream enzymatic hydrolysis. This was due to significant incorporation of phosphonium species in the regenerated biomass, which significantly inhibited the activity of the cellulase enzymes., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Several transition metals react with H2O2 and produce reactive oxygen species (ROS) responsible for oxidative damage linked to many diseases and disorders, and species that form coordination complexes with these metal ions show promise as antioxidants. The present study demonstrates that metal-mediated radical and non-radical oxidative DNA damage decreases when selenium dioxide (SeO2) and sodium selenite (Na2SeO3) are present. Radical-induced damage is associated with production of 8-hydroxy-2'-deoxyguanosine (8-OH-dG), which arises from ROS generated at or near the guanine base, and the selenium compounds reduce Fe(II)-, Cr(III)- and Cu(II)-mediated radical damage to differing degrees based on the identity of the metal ion and the order in which the metals, selenium compounds and DNA are combined. Radical damage arising from Fe(II) and Cr(III) decreases substantially when they are pre-incubated with the selenium compounds prior to adding DNA. Non-radical damage is associated with oxidation of the adenine base in the presence of high H2O2 concentrations through an ionic mechanism, and this type of damage also decreases significantly when the selenium compounds are allowed to interact with the metal ions before adding DNA. Fluorescence studies using dihydrodichlorofluorescein diacetate (DCF-DA) to probe ROS formation indicate that the majority of the SeO2- and SeO3(2-)-metal systems in combination with H2O2 (no DNA present) produce ROS to the same degree as the metal/H2O2 systems in the absence of the selenium compounds, suggesting that selenium-metal complexes react with H2O2 in a sacrificial manner that protects DNA from oxidative damage., (Copyright © 2013 Elsevier Inc. All rights reserved.)

We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.

Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to refine points within each iteration.

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.

We present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R(n): evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. We show that EPSAs can be cast as stochastic pattern search methods, and we use this observation to prove that EPSAs have a probabilistic, weak stationary point convergence theory. This convergence theory is distinguished by the fact that the analysis does not approximate the stochastic process of EPSAs, and hence it exactly characterizes their convergence properties.

This paper gives proof of convergence for a learning algorithm that describes how anoles (lizards found in the Caribbean) learn foraging threshold distance. An anole will pursue a prey if and only if it is within this threshold of the anole's perch. The learning algorithm was proposed by Roughgarden and his colleagues. They experimentally determined that this algorithm quickly converges to the foraging threshold that is predicted by optimal foraging theory. We provide analytic confirmation that the optimal foraging behavior as predicted by Roughgarden's model can be attained by a lizard that follows this simple and zoologically plausible rule of thumb. Copyright 1999 Academic Press.

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity. Finally, we discuss how the accuracy of these measures can be influence by the method used to extract the blood vessel segments.

This paper considers the protein energy minimization problem for lattice and off-lattice protein folding models that explicitly represent side chains. Lattice models of proteins have proven useful tools for reasoning about protein folding in unrestricted continuous space through analogy. This paper provides the first illustration of how rigorous algorithmic analyses of lattice models can lead to rigorous algorithmic analyses of off-lattice models. We consider two side chain models: a lattice model that generalizes the HP model (Dill, 1985) to explicitly represent side chains on the cubic lattice and a new off-lattice model, the HP Tangent Spheres Side Chain model (HP-TSSC), that generalizes this model further by representing the backbone and side chains of proteins with tangent spheres. We describe algorithms with mathematically guaranteed error bounds for both of these models. In particular, we describe a linear time performance guaranteed approximation algorithm for the HP side chain model that constructs conformations whose energy is better than 86% of optimal in a face-centered cubic lattice, and we demonstrate how this provides a better than 70% performance guarantee for the HP-TSSC model. Our analysis provides a mathematical methodology for transferring performance guarantees on lattices to off-lattice models. These results partially answer the open question of Ngo et al. (1994) concerning the complexity of protein folding models that include side chains.

This paper addresses the robustness of intractability arguments for simplified models of protein folding that use lattices to discretize the space of conformations that a protein can assume. We present two generalized NP-hardness results. The first concerns the intractability of protein folding independent of the lattice used to define the discrete protein-folding model. We consider a previously studied model and prove that for any reasonable lattice the protein-structure prediction problem is NP-hard. The second hardness result concerns the intractability of protein folding for a class of energy formulas that contains a broad range of mean force potentials whose form is similar to commonly used pair potentials (e.g., the Lennard-Jones potential). We prove that protein-structure prediction is NP-hard for any energy formula in this class. These are the first robust intractability results that identify sources of computational complexity of protein-structure prediction that transcend particular problem formulations.

Automatic measurement of blood vessel tortuosity is a useful capability for automatic ophthalmological diagnostic tools. We describe a suite of automated tortuosity measures for blood vessel segments extracted from RGB retinal images. The tortuosity measures were evaluated in two classification tasks: (1) classifying the tortuosity of blood vessel segments and (2) classifying the tortuosity of blood vessel networks. These tortuosity measures were able to achieve a classification rate of 91% for the first problem and 95% on the second problem, which confirms that they capture much of the ophthalmologists' notion of tortuosity.

We present performance-guaranteed approximation algorithms for the protein folding problem in the hydrophobic-hydrophilic model (Dill, 1985). Our algorithms are the first approximation algorithms in the literature with guaranteed performance for this model (Dill, 1994). The hydrophobic-hydrophilic model abstracts the dominant force of protein folding: the hydrophobic interaction. The protein is modeled as a chain of amino acids of length n that are of two types; H (hydrophobic, i.e., nonpolar) and P (hydrophilic, i.e., polar). Although this model is a simplification of more complex protein folding models, the protein folding structure prediction problem is notoriously difficult for this model. Our algorithms have linear (3n) or quadratic time and achieve a three-dimensional protein conformation that has a guaranteed free energy no worse than three-eighths of optimal. This result answers the open problem of Ngo et al. (1994) about the possible existence of an efficient approximation algorithm with guaranteed performance for protein structure prediction in any well-studied model of protein folding. By achieving speed and near-optimality simultaneously, our algorithms rigorously capture salient features of the recently proposed framework of protein folding by Sali et al. (1994). Equally important, the final conformations of our algorithms have significant secondary structure (antiparallel sheets, beta-sheets, compact hydrophobic core). Furthermore, hypothetical folding pathways can be described for our algorithms that fit within the framework of diffusion-collision protein folding proposed by Karplus and Weaver (1979). Computational limitations of algorithms that compute the optimal conformation have restricted their applicability to short sequences (length < or = 90). Because our algorithms trade computational accuracy for speed, they can construct near-optimal conformations in linear time for sequences of any size.

Two infant siblings (male and female) manifested extreme hypotonia and flaccidity at birth and had a rapidly fatal course. In each, rod-like structures were demonstrated within a variety of skeletal muscles, and accumulations of thin filaments were seen in numerous muscle fibers. The possibility exists that this represents a severe and rapidly fatal form of nemaline myopathy that should be included in the differential diagnosis of infantile hypotonias.