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FEMP developed the Energy Savings Performance Contracting Energy Sales Agreement (ESPC ESA) Toolkit to provide federal agency contracting officers and other acquisition team members with information that will facilitate the timely execution of ESPC ESA projects.
Radar software is key for producing scientific insight from radar data. There are several platforms for interacting with radar data; the open-source variants are well documented in Heistermann et al. 2014. The Python ARM Radar Toolkit (Py-ART; Helmus and Collis, 2016) is one of these. This document is a guiding roadmap for development for Py-ART. It sets priorities for U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility-funded work and it advises the community which contributions are desired and will receive the most assistance from ARM-funded software engineers.
We present preliminary results of the Roofline Toolkit for multicore, many core, and accelerated architectures. This paper focuses on the processor architecture characterization engine, a collection of portable instrumented micro benchmarks implemented with Message Passing Interface (MPI), and OpenMP used to express thread-level parallelism. These benchmarks are specialized to quantify the behavior of different architectural features. Compared to previous work on performance characterization, these microbenchmarks focus on capturing the performance of each level of the memory hierarchy, along with thread-level parallelism, instruction-level parallelism and explicit SIMD parallelism, measured in the context of the compilers and run-time environments. We also measure sustained PCIe throughput with four GPU memory managed mechanisms. By combining results from the architecture characterization with the Roofline model based solely on architectural specifications, this work offers insights for performance prediction of current and future architectures and their software systems. To that end, we instrument three applications and plot their resultant performance on the corresponding Roofline model when run on a Blue Gene/Q architecture.
Nannochloropsis oceanica CCMP1779 is an oleaginous heterokont microalga that is particularly amenable to synthetic biology approaches. The underlying complex genetic engineering often requires the expression of multiple transgenes (gene stacking). We integrated several techniques into a vector toolkit for multi-gene expression, including: multiple resistance marker genes, bidirectional promoters, and assembly of multiple expression cassettes into a single vector. We developed a series of gateway entry and destination vectors for assembly of two bidirectional promoter expression cassettes, which facilitates combinatorial high-capacity gene stacking. Several endogenous bidirectional promoters were demonstrated to be effective for driving transgene expression, with a dual-luciferase reporter system used to monitor transgene expression under different environmental conditions. The bidirectional promoter between the nitrate reductase and the nitrate transporter genes drives nitrogen source dependent transcription, enabling conditional transgene expression. Lethal concentrations of the antibiotics, Blasticidin, G418, and Nourseothricin, were determined and the corresponding marker genes used for genomic transformation selection.
The goal of this work is to validate the user-friendly Geant4-based Monte Carlo toolkit TOol for PArticle Simulation (TOPAS) for brachytherapy applications. Brachytherapy simulations performed with TOPAS were systematically compared with published TG-186 reference data. The photon emission energy spectrum, the air-kerma strength, and the dose-rate constant of the model-based dose calculation algorithm (MBDCA)-WG generic Ir-192 source were extracted. For dose calculations, a track-length estimator was implemented. Additionally, the four Joint AAPM/ESTRO/ABG MBDCA-WG test cases were evaluated through histograms of the local and global dose difference volumes. A prostate, a palliative lung, and a breast case were simulated. For each case, the dose ratio map, the histogram of the global dose difference volume, and cumulative dose-volume histograms were calculated. The air-kerma strength was (9.772 ± 0.001) × 10 -8 U Bq -1 (within 0.3% of the reference value). The dose-rate constant was 1.1107 ± 0.0005 cGy h -1 U -1 (within 0.01% of the reference value). For all cases, at least 96.9% of voxels had a local dose difference within [-1%, 1%] and at least 99.9% of voxels had a global dose difference within [-0.1%, 0.1%]. The implemented track-length estimator scorer was more efficient than the default analog dose scorer by a factor of 237. For all clinical cases, at least 97.5% of voxels had a global dose difference within [-1%, 1%]. Dose-volume histograms were consistent with the reference data. TOPAS was validated for high-dose-rate brachytherapy simulations following the TG-186 recommended approach for MBDCAs. Built on top of Geant4, TOPAS provides broad access to a state-of-the-art Monte Carlo code for brachytherapy simulations.
In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory ToolKit). DFTTK is based on the atomate package and integrates our experiences in the last decades on the development of theoretical methods and computational softwares. It includes task submissions on all major operating systems and task executions on high-performance computing environments. Furthermore, the distribution of the DFTTK package comes with examples of calculations of phonon density of states, heat capacity, entropy, enthalpy, and free energy under the quasi-harmonic phonon scheme for the stoichiometric phases of Al, Ni, Al 3 Ni, AlNi, AlNi 3 , Al 3 Ni 4 , and Al 3 Ni 5 , and the fcc solution phases treated using the special quasirandom structures at the compositions of Al 3 Ni, AlNi, and AlNi 3 .
Tools capable of imaging and perturbing mechanical signaling pathways with fine spatiotemporal resolution have been elusive, despite their importance in diverse cellular processes. The challenge in creating a mechanogenetic toolkit (i.e., selective and quantitative activation of genetically encoded mechanoreceptors) stems from the fact that many mechanically activated processes are localized in space and time yet additionally require mechanical loading to become activated. To address this challenge, we synthesized magnetoplasmonic nanoparticles that can image, localize, and mechanically load targeted proteins with high spatiotemporal resolution. We demonstrate their utility by investigating the cell-surface activation of two mechanoreceptors: Notch and E-cadherin. By measuring cellular responses to a spectrum of spatial, chemical, temporal, and mechanical inputs at the single-molecule and single-cell levels, we reveal how spatial segregation and mechanical force cooperate to direct receptor activation dynamics. This generalizable technique can be utilized to control and understand diverse mechanosensitive processes in cell signaling.
Here, we developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae , which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3 AI , KanMX AI , HphMX AI , and NatMX AI ), and an array of ~24,000 “double-barcoder” strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.
In this paper, an efficient image-based simulation toolkit for material characterization is presented, which is scalable to work from personal computers to workstations. The effective thermal conductivity, elasticity, and permeability are evaluated employing a computational homogenization framework based on the Finite Element Method (FEM). Two complementary open-source packages are presented: one developed in Python, which can convert digital images into voxel meshes (pyTomoviewer); the other developed in Julia, that can run numerical simulations to compute effective material properties (chpack). Also, a CUDA C version of chpack is provided (chfem_gpu). They were designed to deal with large multi-phase models, so strategies were devised to minimize their memory footprint, while avoiding a high toll on execution time. The voxel-based approach significantly simplifies the FEM meshes and allows efficient matrix-free implementations. In that sense, to handle large linear systems of equations, the element-by-element (EBE) technique is adopted, in conjunction with a low-memory implementation of the Preconditioned Conjugate Gradient (PCG) method. Finally, the code was thoroughly tested on an artificial geometry made of a square array of cylinders, for which analytical solutions exist, as well as on a real micro-tomographic reconstruction of FiberForm TM , a carbon preform commonly used in thermal protection systems.
Here, we introduce LAVA, a general-purpose python toolkit to provide user-friendly and high throughput calculations for material properties using both LAMMPS and VASP. It contains a set of classes as well as pre-processing and post-processing functions to prepare, execute and extract information from LAMMPS/VASP simulations. An overview of the program structure is provided. The current version contains modules to calculate elastic and mechanical properties such as lattice constant, cohesive energy, cold curve, elastic constants, bulk and shear modulus, volume conserving/non-conserving deformation path, vacancy/interstitial formation energy, surface energy, stacking fault energy, melting point, radial distribution function, and thermal expansion. These functionalities are demonstrated for different interatomic potentials and DFT calculations in Al.
CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.
The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.
Radiotherapy with ion beams like proton and carbon has been used for treatment of eye uveal melanoma for many years. In this research, we have developed a new phantom of human eye for Monte Carlo simulation of tumors treatment to use in GEANT4 toolkit. Total depth−dose profiles for the proton, alpha, and carbon incident beams with the same ranges have been calculated in the phantom. Moreover, the deposited energy of the secondary particles for each of the primary beams is calculated. The dose curves are compared for 47.8 MeV proton, 190.1 MeV alpha, and 1060 MeV carbon ions that have the same range in the target region reaching to the center of tumor. The passively scattered spread-out Bragg peak (SOBP) for each incident beam as well as the flux curves of the secondary particles including neutron, gamma, and positron has been calculated and compared for the primary beams. The high sharpness of carbon beam's Bragg peak with low lateral broadening is the benefit of this beam in hadrontherapy but it has disadvantages of dose leakage in the tail after its Bragg peak and high intensity of neutron production. However, proton beam, which has a good conformation with tumor shape owing to the beam broadening caused by scattering, can be a good choice for the large-size tumors.
In this study, we have extended the detector response function toolkit (DRiFT) to provide modeling capabilities of semiconductor sensors. DRiFT provides realistic nuclear instrumentation response by post-processing Monte-Carlo N-particle (MCNP®) radiation transport outputs. MCNP® is capable of modeling radiation transport in complex environments, but has limited detector physics and readout electronics modeling capabilities. Semiconductor detector response can be calculated with a high-fidelity for a flexible range of environments by utilizing MCNP® to simulate radiation interactions inside of detector volumes, and then using DRiFT to model charge transport and signal formation in the semiconductor, as well as the readout electronics. DRiFT models charge transport in the semiconductor, the preamplifier, shaping amplifier, pulse pile-up, and electronic noise to generate detector response. The semiconductor application in DRiFT can model a range of semiconductor materials, shapes, and sizes; and is demonstrated here for a large volume coaxial high-purity germanium (HPGe) detector. Here, we compare detector response functions of a coaxial HPGe detector with measurement of 60 Co, 133 Ba, and 137 Cs at varying count rates, and we conduct a parameter study to demonstrate the effect of changing parameters in the DRiFT simulation. The HPGe detector response function shows excellent agreement with measurements of difference sources with varying dead times and count rates.
We report that a revised atomic deexcitation framework for the Geant4 general purpose Monte Carlo toolkit capable of simulating full Auger deexcitation cascades was implemented in June 2015 release (version 10.2 Beta). An overview of this refined framework and testing of its capabilities is presented for the irradiation of gold nanoparticles (NP) with keV photon and MeV proton beams. The resultant energy spectra of secondary particles created within and that escape the NP are analyzed and discussed. It is anticipated that this new functionality will improve and increase the use of Geant4 in the medical physics, radiobiology, nanomedicine research and other low energy physics fields.
Chlorinated dibenzo-p-dioxins (CDDs) are a series of mono- to octa-chlorinated homologous chemicals commonly referred to as polychlorinated dioxins. One of the most potent, well-known, and persistent member of this family is 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). As part of translational research to make computerized models accessible to health risk assessors, we present a Berkeley Madonna recoded version of the human physiologically based pharmacokinetic (PBPK) model used by the U.S. Environmental Protection Agency (EPA) in the recent dioxin assessment. This model incorporates CYP1A2 induction, which is an important metabolic vector that drives dioxin distribution in the human body, and it uses a variable elimination half-life that is body burden dependent. To evaluate the model accuracy, the recoded model predictions were compared with those of the original published model. The simulations performed with the recoded model matched well with those of the original model. The recoded model was then applied to available data sets of real life exposure studies. The recoded model can describe acute and chronic exposures and can be useful for interpreting human biomonitoring data as part of an overall dioxin and/or dioxin-like compounds risk assessment. - Highlights: • The best available dioxin PBPK model for interpreting human biomonitoring data is presented. • The original PBPK model was recoded from acslX to the Berkeley Madonna (BM) platform. • Comparisons were made of the accuracy of the recoded model with the original model. • The model is a useful addition to the ATSDR's BM based PBPK toolkit that supports risk assessors. • The application of the model to real-life exposure data sets is illustrated.
Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.
Lignin is a bountiful biopolymer of phenylpropanoid monomers that is critical for plant structure and function. Based on the abundance of lignin in the biosphere and interest in lignin valorization, a more comprehensive understanding of lignin biosynthesis is imperative. We introduce an open-source software toolkit, Lignin-KMC, that combines kinetic Monte Carlo and first-principles calculations of radical coupling events to model lignin biosynthesis in silico. Lignification is simulated using the Gillespie algorithm with rates derived from density functional theory calculations of individual fragment couplings. Using this approach, we confirm experimental findings regarding the impact of lignification conditions on the polymer structure such as (1) the positive correlation between sinapyl alcohol fraction and depolymerization yield and (2) the primarily benzodioxane linked structure of C-lignin. Furthermore, we identify the in planta monolignol supply rate as a possible control mechanism for lignin biosynthesis based on evolutionary stresses. These examples not only highlight the robustness of our modeling framework but also motivate future studies of new lignin types, unexplored monolignol chemistries, and lignin structure predictions, all with an overarching aim of developing a more comprehensive molecular understanding of native lignin, which, in turn, can accelerate the biological and chemistry communities interested in this important biopolymer.