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Cavitation during the protein misfolding cyclic amplification (PMCA) method – The trigger for de novo prion generation?

The protein misfolding cyclic amplification (PMCA) technique has become a widely-adopted method for amplifying minute amounts of the infectious conformer of the prion protein (PrP). PMCA involves repeated cycles of 20 kHz sonication and incubation, during which the infectious conformer seeds the conversion of normally folded protein by a templating interaction. Recently, it has proved possible to create an infectious PrP conformer without the need for an infectious seed, by including RNA and the phospholipid POPG as essential cofactors during PMCA. The mechanism underpinning this de novo prion formation remains unknown. In this study, we first establish by spin trapping methods that cavitation bubbles formed during PMCA provide a radical-rich environment. Using a substrate preparation comparable to that employed in studies of de novo prion formation, we demonstrate by immuno-spin trapping that PrP- and RNA-centered radicals are generated during sonication, in addition to PrP-RNA cross-links. We further show that serial PMCA produces protease-resistant PrP that is oxidatively modified. We suggest a unique confluence of structural (membrane-mimetic hydrophobic/hydrophilic bubble interface) and chemical (ROS) effects underlie the phenomenon of de novo prion formation by PMCA, and that these effects have meaningful biological counterparts of possible relevance to spontaneous prion formation in vivo. - Highlights: • Sonication during PMCA generates free radicals at the surface of cavitation bubbles. • PrP-centered and RNA-centered radicals are formed in addition to PrP-RNA adducts. • De novo prions may result from ROS and structural constraints during cavitation.

60 APPLIED LIFE SCIENCES↗

E-selectin ligand-1 (ESL-1) is a novel adiponectin binding protein on cell adhesion

Background: Adiponectin (APN) is an adipocyte-derived bioactive molecule with anti-diabetic and anti-atherogenic properties. Although anti-diabetic effects are mostly mediated by the adiponectin receptors AdipoR1 and AdipoR2, the anti-atherogenic mechanisms have not been fully elucidated. Methods and Results: In this study, we identified E-selectin ligand (ESL)-1 as a novel APN-binding protein by mass spectrometry analysis of HepG2 cell-derived immunoprecipitant with an anti-APN antibody. Cell adhesion assays using fluorescence-labelled monocyte cell line THP-1 cells and human umbilical vein endothelial cells (HUVECs) revealed that APN-pre-treated THP-1 cells had reduced binding ability to HUVECs. This APN-mediated suppressive effect on monocyte binding to endothelial cells was partially abrogated by targeting ESL-1 with shRNA in THP-1 cells. In addition, serial mutagenesis analysis disclosed that five extracellular amino acids close to the N-terminus of ESL-1 were essential for binding with APN. Conclusion: Our results highlight the fact that interaction between APN and ESL-1 could provide a fundamental mechanism underlying the anti-atherogenic properties of APN. - Highlights: • E-selectin ligand (ESL)-1 was identified as an adiponectin (APN)-binding protein. • ESL-1 bound to APN at its N-terminal 6th-10th amino acids. • shESL-1 reduced the suppressive effect of APN on adhesion of THP-1 cells to HUVECs. • Interaction with ESL may be involved in the anti-atherogenic effects of APN.

60 APPLIED LIFE SCIENCES↗

Cultured human amniocytes express hTERT, which is distributed between nucleus and cytoplasm and is secreted in extracellular vesicles

Background: An increasing number of studies on stem cells suggests that the therapeutic effect they exert is primarily mediated by a paracrine regulation through extracellular vesicles (EVs) giving solid grounds for stem cell EVs to be exploited as agents for treating diseases or for restoring damaged tissues and organs. Due to their capacity to differentiate in all embryonic germ layers, amniotic fluid stem cells (AFCs), represent a highly promising cell type for tissue regeneration, which however is still poorly studied and in turn underutilized. In view of this, we conducted a first investigation on the expression of human hTERT gene − known to be among the key triggers of organ regeneration − in AFCs and in the EVs they secrete. Methods: Isolated AFCs were evaluated by RT-qPCR for hTERT expression. The clones expressing the highest levels of transcript, were analyzed by Immunofluorescence imaging and Nuclear/cytoplasmic fractionation in order to evaluate hTERT subcellular localization. We then separated EVs from FBS depleted culture medium by serial (ultra) centrifugations steps and characterized them using Western blotting, Atomic force Microscopy and Nanoplasmonic assay. Results: We first demonstrated that primary cultures of AFCs express the gene hTERT at different levels. Then we evidenced that in AFCs with the higher transcript levels, the hTERT protein is present in the nuclear and cytoplasmic compartment. Finally, we found that cytosolic hTERT is embodied in the EVs that AFCs secrete in the extracellular milieu. Conclusions: Our study demonstrates for the first time the expression of the full protein hTERT by AFCs and its release outside the cell mediated by EVs, indicating a new extra telomeric role for this protein. This finding represents an initial but crucial evidence for considering AFCs derived EVs as new potential sources for tissue regeneration. - Highlights: • Primary cultures of AFCs express the gene hTERT at different levels. • hTERT protein is present in the nuclear and cytoplasmic compartment of AFCs. • Cytosolic hTERT is embodied in AFCs secreted extracellular vesicles. • Those findings indicate a new potential extra telomeric role for hTERT. • AFCs derived EVs might represent a useful tool for regenerative medicine.

60 APPLIED LIFE SCIENCES↗

Amide-linked local anesthetics preferentially target leukemia stem cell through inhibition of Wnt/β-catenin

Highlights: • Local anaesthetics target malignant (LSC) and normal stem cells (HSC). • HSC are less sensitive than LSC to local anaesthetics. • Lidocaine and bupivacaine is more potent than ropivacaine. • Local anaesthetics acts on LSC and HSC via Wnt/β-catenin-dependent manner. The anti-cancer activities of amide-linked local anesthetics have been demonstrated in various types of bulky/differentiated cancer cells. However, whether these anesthetics also affect biological functions of cancer stem cells is largely unknown. In this study, we systematically investigated the effects of three commonly used amide-linked local anesthetics (ropivacaine, lidocaine and bupivacaine) on leukemia stem cell (LSC) derived from two different leukemia diseases (acute myeloid leukemia, n = 8 and chronic myeloid leukemia, n = 8) as well as normal hematopoietic stem cell (HSC) derived from cord blood donors (n = 8) as comparison. We show that all three local anesthetics at clinically achievable concentrations significantly inhibit colony formation and serial replating of LSC in a dose-dependent manner, suggesting their inhibitory effects on LSC differentiation, proliferation and self-renewal. In addition, lidocaine and bupivacaine are more potent than ropivacaine. However, local anesthetics at the same concentrations do not affect LSC and HSC survival, demonstrating the differentiation and self-renewal as the primary effects of local anesthetics on LSC and HSC. Interestingly, local anesthetics display certain selectivity between LSC and HSC by having higher efficacy on LSC than HSC. Mechanism studies using both pharmacological and genetic approaches demonstrate that these local anesthetics target LSC via inhibiting Wnt/β-catenin but not Hedgehog or NF-ĸB signaling. Our work is the first to demonstrate the possible influence of amide-linked local anesthetics on cancer as well as normal stem cells via inhibiting Wnt/β-catenin signaling. Our findings contribute to the comprehensive understanding of potential implication of amide-linked local aesthesis in tumor biology.

60 APPLIED LIFE SCIENCES↗

Antibiotic anisomycin selectively targets leukemia cell lines and patient samples through suppressing Wnt/β-catenin signaling

Highlights: • Anisomycin effectively targets CML cell lines and CD34 stem/progenitor cells. • Anisomycin has little or no effect on normal bone marrow (NBM) CD34 cells. • Anisomycin acts on CML cells through inhibiting Wnt/β-catenin signaling pathway. • Anisomycin augments the effects of BCR-ABL TKI in CML. Chronic myeloid leukemia (CML) responds well to BCR-ABL tyrosine kinase inhibitors (TKI), such as imatinib and dasatinib. However, these inhibitors have been less effective as single agents in the blast phase-CML. In this work, we show that anisomycin, a clinically available drug, targets CML cells at all stages of development and enhances BCR-ABL TKIs’ efficacy. Anisomycin at nanomolar concentration inhibits proliferation and induces apoptosis in a panel of CML cell lines in a dose-dependent manner. It induces apoptosis CD34 stem/progenitor cells isolated from patients with blast phase CML. Using colony formation and serial replating assays, we further show that anisomycin inhibits CML CD34 cell differentiation, proliferation and self-renewal. Additionally, anisomycin is less effective in normal bone marrow (NBM) CD34 cells, suggesting the selective anti-leukemia activity of anisomycin. Combination of anisomycin with imatinib or dasatinib achieves significantly better efficacy than TKI alone in leukemia cell lines and patient samples while sparing normal counterparts. Mechanistically, we demonstrate that p38 MAPK/JNK activation is not required for anti-leukemia activities of anisomycin. Instead, anisomycin displays preferential inhibitory effects to Wnt/β-catenin-mediated signaling in CML. Our work provides the preclinical evidence on the potent efficacy of anisomycin in leukemia and its mechanisms of action. Our work suggests that anisomycin is a potential drug to overcome resistance to BCR-ABL TKI treatment in blast phase CML.

60 APPLIED LIFE SCIENCES↗

Programming with BIG data in R: Scaling analytics from one to thousands of nodes

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.

97 MATHEMATICS AND COMPUTING↗

Mutation in galP improved fermentation of mixed sugars to succinate using engineered Escherichia coli AS1600a and AM1 mineral salts medium

Escherichia coli KJ122 was engineered to produce succinate from glucose using the wild type GalP for glucose uptake instead of the native phosphotransferase system (ptsI mutation). This strain now ferments 10% xylose poorly. Mutants were selected by serial transfers in AM1 mineral salts medium with 10% xylose. Clones from this population all exhibited a similar improvement, co-fermentation of an equal mixture of xylose and glucose. One of these, AS1600a, produced 84.26 ± 1.37 g/L succinate, equivalent to that produced by the parent (KJ122) from 10% glucose (85.46 ± 1.78 g/L). AS1600a was sequenced and found to contain a mutation in galactose permease (GalP, G236D). This mutation was shown to be responsible for the improvement in fermentation using KJΔgalP as the host and expression vectors with native galP and with mutant galP*. Strain AS1600a and KJΔgalP(pLOI5746; galP*) also co-fermented a mixture of glucose, xylose, arabinose, and galactose in sugarcane bagasse hydrolysate using mineral salts medium.

59 BASIC BIOLOGICAL SCIENCES↗

Preface to the Special Issue on TOUGH Symposium 2015

The TOUGH Symposium 2015 was held in Berkeley, California, September 28-30, 2015. The TOUGH family of codes, developed at the Energy Geosciences Division of Lawrence Berkeley National Laboratory (LBNL), is a suite of computer programs for the simulation of multiphase and multicomponent fluid and heat flows in porous and fractured media with applications in many geosciences fields, such as geothermal reservoir engineering, nuclear waste disposal, geological carbon sequestration, oil and gas reservoirs, gas hydrate research, vadose zone hydrology and environmental remediation. Since the first release in the 1980s, many modifications and enhancements have been continuously made to TOUGH and its various descendants (iTOUGH2, TOUGH+, TOUGH-MP, TOUGHREACT, TOUGH+HYDRATE, TMVOC...), at LBNL and elsewhere. Today, these codes are used worldwide in academia, government organizations and private companies in problems involving coupled hydrological, thermal, biogeochemical and geomechanical processes. The Symposia, organized every 2-3 years, bring together developers and users for an open exchange on recent code enhancements and applications. In 2015, the Symposium was attended by one hundred participants, representing thirty-four nationalities. This Special Issue in Computers & Geosciences gathers extended versions of selected Symposium proceedings related to (i) recent enhancements to the TOUGH family of codes and (ii) coupled flow and geomechanics processes modeling. In this special issue, a paper by Jung et al. provides a new base version of TOUGH, TOUGH3. This latest version addresses the computational robustness and efficiency needed for the increasing complexity of the simulated processes and the growing size of model domains. Moreover, it consolidates the serial and parallel versions into a single code, hence, inconsistencies between different versions are less likely to occur, and code maintenance and development become more efficient. Finsterle et al. present a review of the capabilities of iTOUGH2 and illustrate the workflow of an integrated simulation-optimization framework. Liu et al. present an approach to reduce the computational cost of performing Bayesian inverse modeling and demonstrate their approach using a synthetic experiment simulated with TOUGH2. Regarding the mesh generation process, the paper by Doughty presents a generalization of the grid-generation algorithm embedded in TOUGH2 (MESHMAKER) to create one-column grids with fractal dimension, useful for representing flow through fractures or highly heterogeneous media with fractal geometry, and also for inverse methods. Sentís and Gable present a module to interface the open source code Lagrit with TOUGH2, that assists in the processes of mesh generation and model setup, and that can be particularly useful for complex geometries and large models. Bonduà et al. present a pre-processing tool (VORO2MESH) that features a full 3D Voronoi approach for structured and unstructured grids, and an enhanced post-processor (TOUGH2Viewer) for the visualization of 3D Voronoi grids. The literature review shows that coupled fluid flow and geomechanics has gained increasing attention in the last years in many geosciences fields. In this context, several codes of the TOUGH family have been successfully coupled with geomechanics software. The paper by Rutqvist presents a review of some of these TOUGH-based geomechanics models, of which the TOUGH-FLAC simulator – presented for the first time in the TOUGH Symposium 2003 – is well-known worldwide and has been used in a large variety of applications, encompassing the application domains of the TOUGH codes. Blanco-Martín et al. present an extension of TOUGH-FLAC to the finite strain framework, motivated by the existence of large strains associated with rock salt deformation in the context of heat-generating nuclear waste disposal. Following a paper published in the Special Issue of the TOUGH Symposium 2012 in this journal, Kim et al. present the current status of the TOUGH-RSBN simulator for coupled hydro-mechanical modeling of hydraulic fracturing using a discrete fracture network approach, and present some validation and demonstration cases. Rinaldi and Nespoli present TOUGH-SEED, an injection-induced seismicity modeling approach using TOUGH2 and a geomechanics stochastic model, and apply it to the Basel EGS project. Finally, Rinaldi et al. use the PEST protocol implemented in iTOUGH2, combined with TOUGH-FLAC as the forward model, to perform inverse modeling for the In Salah CO2 demonstration site in Algeria. Using pore pressures and InSAR displacements as observations, this approach allows for a multi-physics joint inversion and takes advantage of the inverse capabilities of iTOUGH2 (parameter estimation, sensitivity analysis, uncertainty estimation …), applied to coupled fluid flow and geomechanics. On behalf of the members of the TOUGH Symposium 2015 Organizing Committee, I hope that this collection of papers will provide the readers of Computers & Geosciences with a recent view of the capabilities of the TOUGH family of codes, as well as with current modeling research domains in the geosciences. Last but not least, I would like to express my gratitude to all the reviewers who kindly contributed to this Special Issue, as well as the Journal staff who have made this publication possible.

99 GENERAL AND MISCELLANEOUS↗

A Post-Transcriptional Feedback Mechanism for Noise Suppression and Fate Stabilization

Diverse biological systems utilize fluctuations (“noise”) in gene expression to drive lineage-commitment decisions. However, once a commitment is made, noise becomes detrimental to reliable function, and the mechanisms enabling post-commitment noise suppression are unclear. Here, we find that architectural constraints on noise suppression are overcome to stabilize fate commitment. Using single-molecule and time-lapse imaging, we find that—after a noise-driven event—human immunodeficiency virus (HIV) strongly attenuates expression noise through a non-transcriptional negative-feedback circuit. Feedback is established through a serial cascade of post-transcriptional splicing, whereby proteins generated from spliced mRNAs auto-deplete their own precursor unspliced mRNAs. Strikingly, this auto-depletion circuitry minimizes noise to stabilize HIV’s commitment decision, and a noise-suppression molecule promotes stabilization. Lastly, this feedback mechanism for noise suppression suggests a functional role for delayed splicing in other systems and may represent a generalizable architecture of diverse homeostatic signaling circuits.

59 BASIC BIOLOGICAL SCIENCES↗

A Zika virus mutation enhances transmission potential and confers escape from protective dengue virus immunity

Zika virus (ZIKV) and dengue virus (DENV) are arthropod-borne pathogenic flaviviruses that co-circulate in many countries. To understand some of the pressures that influence ZIKV evolution, we mimic the natural transmission cycle by repeating serial passaging of ZIKV through cultured mosquito cells and either DENV-naive or DENV-immune mice. Compared with wild-type ZIKV, the strains passaged under both conditions exhibit increased pathogenesis in DENV-immune mice. Application of reverse genetics identifies an isoleucine-to-valine mutation (I39V) in the NS2B proteins of both passaged strains that confers enhanced fitness and escape from pre-existing DENV immunity. Introduction of I39V or I39T, a naturally occurring homologous mutation detected in recent ZIKV isolates, increases the replication of wild-type ZIKV in human neuronal precursor cells and laboratory-raised mosquitoes. Our data indicate that ZIKV strains with enhanced transmissibility and pathogenicity can emerge in DENV-naive or -immune settings, and that NS2B-I39 mutants may represent ZIKV variants of interest.

59 BASIC BIOLOGICAL SCIENCES↗

Application of organic- and nanoparticle-modified foams in foamed concrete: Reinforcement and stabilization mechanisms

Highlights: • An ultra-stable foam was fabricated by modifying the gas–liquid interface. • A homogeneous and finer pore structure of foamed concrete was generated. • Stabilization and enhancement mechanisms were investigated. - Abstract: In this study, an ultra-stable foam used for foamed concrete was fabricated by modifying the gas–liquid interface by coupling the effects of an organic surfactant and nanoparticles. A serial of techniques was employed to study the products and microstructure development of foam and foamed concrete, respectively. The results show that nano-silica and hydroxypropyl methylcellulose can slow the coalescence and disproportionation of the bubbles by adsorbing at the air bubble surface and increasing the viscosity of cell wall paste, thus preventing gas transfer and hindering physical drainage between the gaseous and liquid phases. Also, a more homogeneous and finer pore structure is generated with the inclusion of the organic surfactant and nanoparticles. The pozzolanic reaction between the nano-silica and Ca(OH){sub 2} results in an increased C-S-H content and the densification of the cell wall structure. In addition, the stability of foamed concrete with modification and relevant stabilization mechanisms were also investigated.

36 MATERIALS SCIENCE↗

An overlapping Domain Decomposition preconditioning method for monolithic solution of shear bands

Metals subjected to high strain rate impact often exhibit a sudden and profound drop in the material’s load bearing capability, a ductile failure phenomenon known as shear banding. Shear bands, characterized as material instabilities, are driven by shear heating and described as narrow regions that have sustained intense plastic deformation and high temperature rise. This coupled thermo-mechanical localization problem can be formulated as a nonlinear system with two balance equations, momentum and energy, and two constitutive laws for elasticity and plasticity. Here in our formulation, mixed finite elements are used to discretize the equations in space and an implicit finite difference scheme is used to advance the system in time, where at every time step, a Newton type method is used to solve the system monolithically. To that end, a block Jacobian matrix is formed analytically using Gâteaux derivatives. The resulting Jacobian is sparse, nonsymmetric and its sparsity pattern and eigenvalue content vary with the different stages of shear bands formation, consequently posing a significant challenge to iterative solvers. To address this issue, an overlapping Domain Decomposition preconditioner that takes into account the physics of shear bands and the domain in which it forms, is proposed. The key idea is to resolve the shear band domain accurately while only solving the remaining domain approximately, with selective updates when necessary. The preconditioner is formed on the basis of an additive Schwartz method that is applied to a Schur complement partition of the system. The proposed preconditioner is implemented in parallel and tested on three different benchmark problems as compared against off-the-shelf solvers. We study the effect of h- and k-refinement that are obtained from Isogeometric discretizations of the system, the overlapping strategy and its parallel performance. Excellent performance is demonstrated in serial and parallel on all benchmark examples.

42 ENGINEERING↗

PPINN: Parareal physics-informed neural network for time-dependent PDEs

Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supervised learning to a great degree. While effective for relatively short-term time integration, when long time integration of the time-dependent PDEs is sought, the time–space domain may become arbitrarily large and hence training of the neural network may become prohibitively expensive. To this end, we develop a parareal physics-informed neural network (PPINN), hence decomposing a long-time problem into many independent short-time problems supervised by an inexpensive/fast coarse-grained (CG) solver. In particular, the serial CG solver is designed to provide approximate predictions of the solution at discrete times, while initiate many fine PINNs simultaneously to correct the solution iteratively. There is a two-fold benefit from training PINNs with small-data sets rather than working on a large-data set directly, i.e., training of individual PINNs with small-data is much faster, while training the fine PINNs can be readily parallelized. Consequently, compared to the original PINN approach, the proposed PPINN approach may achieve a significant speed-up for long-time integration of PDEs, assuming that the CG solver is fast and can provide reasonable predictions of the solution, hence aiding the PPINN solution to converge in just a few iterations. To investigate the PPINN performance on solving time-dependent PDEs, we first apply the PPINN to solve the Burgers equation, and subsequently we apply the PPINN to solve a two-dimensional nonlinear diffusion–reaction equation. Furthermore, our results demonstrate that PPINNs converge in a few iterations with significant speed-ups proportional to the number of time-subdomains employed.

42 ENGINEERING↗

Mesoscale modeling and semi-analytical approach for the microstructure-aware effective thermal conductivity of porous polygranular materials

Here we established a comprehensive modeling approach for investigating the microstructure-aware effective thermal conductivity ($κ_{eff}$) for porous microstructures containing solid particles and gaseous pores. Our approach combines the mesoscale computational modeling framework and the semi-analytical method, allowing for efficient prediction of $κ_{eff}$ for realistic porous microstructures, while considering complicated microstructural thermal conduction pathways effectively in the prediction. We used the diffuse-interface mesoscale computational model to generate extensive simulated $κ_{eff}$ data for realistic digital representations of microstructures with wide ranges of porosity ($f_p$), thermal conductivity of the gas phase ($κ_g$), and thermal conductivity of the solid phase ($κ_s$). From the simulated data, we identified two property variation regimes for $κ_{eff}$: (1) a slow $κ_{eff}$ increase for $κ_s ~ κ_g$; and (2) a faster $κ_{eff}$ increase for $κ_s \gg κ_g$. To capture the key features of the relationship between the microstructure and $κ_{eff}$, we derived a semi-analytical model by introducing structure and intensification factors. The two new factors incorporate the calibrated effective contribution of the solid volume with $κ_s$ and additional interfacial effects into the prediction of $κ_{eff}$, respectively, allowing for consideration of parallel, serial, and interfacial conduction mechanisms effectively. Using the selected simulation data, we quantified key model parameters within the semi-analytical model and verified that the parameterized model exhibits excellent agreement with simulated $κ_{eff}$ for the entire range of the parameter space.

36 MATERIALS SCIENCE↗

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

36 MATERIALS SCIENCE↗

Factorization in large-scale many-body calculations

One approach for solving interacting many-fermion systems is the configuration-interaction method, also sometimes called the interacting shell model, where one finds eigenvalues of the Hamiltonian in a many-body basis of Slater determinants (antisymmetrized products of single-particle wavefunctions). The resulting Hamiltonian matrix is typically very sparse, but for large systems the nonzero matrix elements can nonetheless require terabytes or more of storage. An alternate algorithm, applicable to a broad class of systems with symmetry, in our case rotational invariance, is to exactly factorize both the basis and the interaction using additive/multiplicative quantum numbers; such an algorithm recreates the many-body matrix elements on the fly and can reduce the storage requirements by an order of magnitude or more. Here, we discuss factorization in general and introduce a novel, generalized factorization method, essentially a ‘double-factorization’ which speeds up basis generation and set-up of required arrays. Although we emphasize techniques, we also place factorization in the context of a specific (unpublished) configuration-interaction code, BIGSTICK, which runs both on serial and parallel machines, and discuss the savings in memory due to factorization.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Scalable Metropolis Monte Carlo for simulation of hard shapes

We design and implement a scalable hard particle Monte Carlo simulation toolkit (HPMC), and release it open source as part of HOOMD-blue. HPMC runs in parallel on many CPUs and many GPUs using domain decomposition. We employ BVH trees instead of cell lists on the CPU for fast performance, especially with large particle size disparity, and optimize inner loops with SIMD vector intrinsics on the CPU. Our GPU kernel proposes many trial moves in parallel on a checkerboard and uses a block-level queue to redistribute work among threads and avoid divergence. HPMC supports a wide variety of shape classes, including spheres/disks, unions of spheres, convex polygons, convex spheropolygons, concave polygons, ellipsoids/ellipses, convex polyhedra, convex spheropolyhedra, spheres cut by planes, and concave polyhedra. NVT and NPT ensembles can be run in 2D or 3D triclinic boxes. Additional integration schemes permit Frenkel–Ladd free energy computations and implicit depletant simulations. In a benchmark system of a fluid of 4096 pentagons, HPMC performs 10 million sweeps in 10 min on 96 CPU cores on XSEDE Comet. The same simulation would take 7.6 h in serial. HPMC also scales to large system sizes, and the same benchmark with 16.8 million particles runs in 1.4 h on 2048 GPUs on OLCF Titan.

Computer Science↗