508 research outputs found
Automatic Differentiation for Orbital-Free Density Functional Theory
Differentiable programming has facilitated numerous methodological advances
in scientific computing. Physics engines supporting automatic differentiation
have simpler code, accelerating the development process and reducing the
maintenance burden. Furthermore, fully-differentiable simulation tools enable
direct evaluation of challenging derivatives - including those directly related
to properties measurable by experiment - that are conventionally computed with
finite difference methods. Here, we investigate automatic differentiation in
the context of orbital-free density functional theory (OFDFT) simulations of
materials, introducing PROFESS-AD. Its automatic evaluation of properties
derived from first derivatives, including functional potentials, forces, and
stresses, facilitates the development and testing of new density functionals,
while its direct evaluation of properties requiring higher-order derivatives,
such as bulk moduli, elastic constants, and force constants, offers more
concise implementations compared to conventional finite difference methods. For
these reasons, PROFESS-AD serves as an excellent prototyping tool and provides
new opportunities for OFDFT
Developments and Further Applications of Ephemeral Data Derived Potentials
Machine-learned interatomic potentials are fast becoming an indispensable
tool in computational materials science. One approach is the ephemeral
data-derived potential (EDDP), which was designed to accelerate atomistic
structure prediction. The EDDP is simple and cost-efficient. It relies on
training data generated in small unit cells and is fit using a lightweight
neural network, leading to smooth interactions which exhibit the robust
transferability essential for structure prediction. Here, we present a variety
of applications of EDDPs, enabled by recent developments of the open-source
EDDP software. New features include interfaces to phonon and molecular dynamics
codes, as well as deployment of the ensemble deviation for estimating the
confidence in EDDP predictions. Through case studies ranging from elemental
carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we
demonstrate that EDDPs can be trained to cover wide ranges of pressures and
stoichiometries, and used to evaluate phonons, phase diagrams, superionicity,
and thermal expansion. These developments complement continued success in
accelerated structure prediction.Comment: 22 pages, 15 figure
An Economic Framework for Evaluating Personalized Medicine
Individual variation accounts for a wide range of medical and economic consequences, from inefficiencies in drug
discovery and development to ineffectiveness of drug treatment to drug-induced morbidity and mortality. Addressing these consequences could benefit patients, health care providers and payers, and the pharmaceutical industry. When appropriate markers are known, diagnostic tests allow precise diagnosis and dosing, prediction of disease progression, prediction of treatment response and prediction of adverse drug reactions for individual patients. There may also be substantial savings
realized by eliminating costs associated with failed treatment. We developed an analytical framework for analyzing the potential value of using a diagnostic test in clinical practice. Our framework determines the economic consequences of implementing pharmacogenomics in the clinic using a diagnostic test to predict drug response. We offer an empirical test of these ideas: we calculated the cost offset realized by predicting the likelihood of response to an alternative existing treatment using a hypothetical pharmacogenomic test in an asthma population. Because the diagnostic test is hypothetical, our framework is general and could be applied to other indications where diagnostic tests have not been developed. Our results could potentially guide future economic evaluation of new diagnostic tests. Importantly, they may also influence biomarker discovery strategies to ensure consistency between market priorities and the future stream of product introductions
Galaxy Zoo: Dust in Spirals
We investigate the effect of dust on spiral galaxies by measuring the
inclination-dependence of optical colours for 24,276 well-resolved SDSS
galaxies visually classified in Galaxy Zoo. We find clear trends of reddening
with inclination which imply a total extinction from face-on to edge-on of 0.7,
0.6, 0.5 and 0.4 magnitudes for the ugri passbands. We split the sample into
"bulgy" (early-type) and "disky" (late-type) spirals using the SDSS fracdeV (or
f_DeV) parameter and show that the average face-on colour of "bulgy" spirals is
redder than the average edge-on colour of "disky" spirals. This shows that the
observed optical colour of a spiral galaxy is determined almost equally by the
spiral type (via the bulge-disk ratio and stellar populations), and reddening
due to dust. We find that both luminosity and spiral type affect the total
amount of extinction, with "disky" spirals at M_r ~ -21.5 mags having the most
reddening. This decrease of reddening for the most luminous spirals has not
been observed before and may be related to their lower levels of recent star
formation. We compare our results with the latest dust attenuation models of
Tuffs et al. We find that the model reproduces the observed trends reasonably
well but overpredicts the amount of u-band attenuation in edge-on galaxies. We
end by discussing the effects of dust on large galaxy surveys and emphasize
that these effects will become important as we push to higher precision
measurements of galaxy properties and their clustering.Comment: MNRAS in press. 25 pages, 22 figures (including an abstract comparing
GZ classifications with common automated methods for selecting disk/early
type galaxies in SDSS data). v2 corrects typos found in proof
Spatial concordance of DNA methylation classification in diffuse glioma.
BACKGROUND: Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence.
METHODS: We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites.
RESULTS: Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account.
CONCLUSION: Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists
Economic evaluation of the specialized donor care facility for thoracic organ donor management
Background: Over the last decade two alternative models of donor care have emerged in the United States: the conventional model, whereby donors are managed at the hospital where brain death occurs, and the specialized donor care facility (SDCF), in which brain dead donors are transferred to a SDCF for medical optimization and organ procurement. Despite increasing use of the SDCF model, its cost-effectiveness in comparison to the conventional model remains unknown.
Methods: We performed an economic evaluation of the SDCF and conventional model of donor care from the perspective of U.S. transplant centers over a 2-year study period. In this analysis, we utilized nationwide data from the Scientific Registry of Transplant Recipients and controlled for donor characteristics and patterns of organ sharing across the nation\u27s organ procurement organizations (OPOs). Subgroup analysis was performed to determine the impact of the SDCF model on thoracic organ transplants.
Results: A total of 38,944 organ transplants were performed in the U.S. during the study period from 13,539 donors with an observed total organ cost of 1.26 billion (-24.6 million.
Conclusions: The U.S. SDCF model may be a less costly and more effective means of multi-organ donor management, particularly for thoracic organ donors, compared to the conventional hospital-based model
1940: Abilene Christian College Bible Lectures - Full Text
Delivered in the Auditorium of Abilene Christian College, February, 1940, Abilene, Texas.
Published April, 1940
PRICE, $1.00
FIRM FOUNDATION PUBLISHING HOUSE
Austin, Texas
ACEpotentials.jl : a Julia implementation of the atomic cluster expansion
We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor’s expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows
Opportunities to integrate new approaches in genetic toxicology: An ILSI-HESI workshop report
Genetic toxicity tests currently used to identify and characterize potential human mutagens and carcinogens rely on measurements of primary DNA damage, gene mutation, and chromosome damage in vitro and in rodents. The International Life Sciences Institute Health and Environmental Sciences Institute (ILSI-HESI) Committee on the Relevance and Follow-up of Positive Results in In Vitro Genetic Toxicity Testing held an April 2012 Workshop in Washington, DC, to consider the impact of new understanding of biology and new technologies on the identification and characterization of genotoxic substances, and to identify new approaches to inform more accurate human risk assessment for genetic and carcinogenic effects. Workshop organizers and speakers were from industry, academe, and government. The Workshop focused on biological effects and technologies that would potentially yield the most useful information for evaluating human risk of genetic damage. Also addressed was the impact that improved understanding of biology and availability of new techniques might have on genetic toxicology practices. Workshop topics included (1) alternative experimental models to improve genetic toxicity testing, (2) Biomarkers of epigenetic changes and their applicability to genetic toxicology, and (3) new technologies and approaches. The ability of these new tests and technologies to be developed into tests to identify and characterize genotoxic agents; to serve as a bridge between in vitro and in vivo rodent, or preferably human, data; or to be used to provide dose response information for quantitative risk assessment was also addressed. A summary of the workshop and links to the scientific presentations are provided.International Life Sciences Institute/Health and Environmental Sciences Institute Committe
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