Comparison of Gaussian process modeling software

Gaussian process fitting, or kriging, is often used to create a model from a set of data. Many available software packages do this, but we show that very different results can be obtained from different packages even when using the same data and model. We describe the parameterization, features, and optimization used by eight different fitting packages that run on four different platforms. We then compare these eight packages using various data functions and data sets, revealing that there are stark differences between the packages. In addition to comparing the prediction accuracy, the predictive variance--which is important for evaluating precision of predictions and is often used in stopping criteria--is also evaluated

Biochar Supplementation in Growing and Finishing Diets

Two metabolism studies were conducted to evaluate the effects of biochar (0, 0.8, or 3% of diet dry matter) on digestibility and methane production in growing and finishing diets. Intake was not affected by biochar inclusion in the growing diet and increased with 0.8% biochar inclusion in the finishing study. Digestibility tended to increase quadratically with biochar inclusion in the growing study while digestibility tended to linearly decrease with biochar inclusion in the finishing study. Methane production (g/d) decreased 10.7% in the growing study and 9.9% in the finishing study with 0.8% biochar compared to no biochar. Methane production was reduced 10.6% and 18.4% in the growing and finishing studies, respectively, when measured as g/lb of intake. Although biochar is not FDA approved for animal feeding, the initial research shows potential as a methane mitigation strategy in both growing and finishing diets

Fibronectin Unfolding Revisited: Modeling Cell Traction-Mediated Unfolding of the Tenth Type-III Repeat

Fibronectin polymerization is essential for the development and repair of the extracellular matrix. Consequently, deciphering the mechanism of fibronectin fibril formation is of immense interest. Fibronectin fibrillogenesis is driven by cell-traction forces that mechanically unfold particular modules within fibronectin. Previously, mechanical unfolding of fibronectin has been modeled by applying tensile forces at the N- and C-termini of fibronectin domains; however, physiological loading is likely focused on the solvent-exposed RGD loop in the 10th type-III repeat of fibronectin (10FNIII), which mediates binding to cell-surface integrin receptors. In this work we used steered molecular dynamics to study the mechanical unfolding of 10FNIII under tensile force applied at this RGD site. We demonstrate that mechanically unfolding 10FNIII by pulling at the RGD site requires less work than unfolding by pulling at the N- and C- termini. Moreover, pulling at the N- and C-termini leads to 10FNIII unfolding along several pathways while pulling on the RGD site leads to a single exclusive unfolding pathway that includes a partially unfolded intermediate with exposed hydrophobic N-terminal β-strands – residues that may facilitate fibronectin self-association. Additional mechanical unfolding triggers an essential arginine residue, which is required for high affinity binding to integrins, to move to a position far from the integrin binding site. This cell traction-induced conformational change may promote cell detachment after important partially unfolded kinetic intermediates are formed. These data suggest a novel mechanism that explains how cell-mediated forces promote fibronectin fibrillogenesis and how cell surface integrins detach from newly forming fibrils. This process enables cells to bind and unfold additional fibronectin modules – a method that propagates matrix assembly

Design and construction of the MicroBooNE detector

This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported

Parallels between Pathogens and Gluten Peptides in Celiac Sprue

Pathogens are exogenous agents capable of causing disease in susceptible organisms. In celiac sprue, a disease triggered by partially hydrolyzed gluten peptides in the small intestine, the offending immunotoxins cannot replicate, but otherwise have many hallmarks of classical pathogens. First, dietary gluten and its peptide metabolites are ubiquitous components of the modern diet, yet only a small, genetically susceptible fraction of the human population contracts celiac sprue. Second, immunotoxic gluten peptides have certain unusual structural features that allow them to survive the harsh proteolytic conditions of the gastrointestinal tract and thereby interact extensively with the mucosal lining of the small intestine. Third, they invade across epithelial barriers intact to access the underlying gut-associated lymphoid tissue. Fourth, they possess recognition sequences for selective modification by an endogenous enzyme, transglutaminase 2, allowing for in situ activation to a more immunotoxic form via host subversion. Fifth, they precipitate a T cell–mediated immune reaction comprising both innate and adaptive responses that causes chronic inflammation of the small intestine. Sixth, complete elimination of immunotoxic gluten peptides from the celiac diet results in remission, whereas reintroduction of gluten in the diet causes relapse. Therefore, in analogy with antibiotics, orally administered proteases that reduce the host's exposure to the immunotoxin by accelerating gluten peptide destruction have considerable therapeutic potential. Last but not least, notwithstanding the power of in vitro methods to reconstitute the essence of the immune response to gluten in a celiac patient, animal models for the disease, while elusive, are likely to yield fundamentally new systems-level insights

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

First measurement of θ<inf>13</inf> from delayed neutron capture on hydrogen in the Double Chooz experiment

The Double Chooz experiment has determined the value of the neutrino oscillation parameter θ13 from an analysis of inverse beta decay interactions with neutron capture on hydrogen. This analysis uses a three times larger fiducial volume than the standard Double Chooz assessment, which is restricted to a region doped with gadolinium (Gd), yielding an exposure of 113.1 GW-ton-years. The data sample used in this analysis is distinct from that of the Gd analysis, and the systematic uncertainties are also largely independent, with some exceptions, such as the reactor neutrino flux prediction. A combined rate- and energy-dependent fit finds sin22θ13=0.097±0.034 (stat.)±0.034 (syst.), excluding the no-oscillation hypothesis at 2.0. This result is consistent with previous measurements of sin22θ13

Data from fitting Gaussian process models to various data sets using eight Gaussian process software packages

The article of record as published may be found at http://dx.doi.org/10.1016/j.dib.2017.12.012This data article provides the summary data from tests comparing various Gaussian process software packages. Each spreadsheet represents a single function or type of function using a particular input sample size. In each spreadsheet, a row gives the results for a particular replication using a single package. Within each spreadsheet there are the results from eight Gaussian process model-fitting packages on five replicates of the surface. There is also one spreadsheet comparing the results from two packages performing stochastic kriging. These data enable comparisons between the packages to determine which package will give users the best results.Office of Naval Research via NPS's CRUSERNaval Supply Systems Command Fleet LogisticsGrant number N00244-15-2-000

Gradient Based Criteria for Sequential Design

Computer simulation experiments are commonly used as an inexpensive alternative to real-world experiments to form a metamodel that approximates the input-output relationship of the real-world experiment. While a user may want to understand the entire response surface, they may also want to focus on interesting regions of the design space, such as where the gradient is large. In this paper we present an algorithm that adaptively runs a simulation experiment that focuses on finding areas of the response surface with a large gradient while also gathering an understanding of the entire surface. We consider the scenario where small batches of points can be run simultaneously, such as with multi-core processors

Data from fitting Gaussian process models to various data sets using eight Gaussian process software packages

This data article provides the summary data from tests comparing various Gaussian process software packages. Each spreadsheet represents a single function or type of function using a particular input sample size. In each spreadsheet, a row gives the results for a particular replication using a single package. Within each spreadsheet there are the results from eight Gaussian process model-fitting packages on five replicates of the surface. There is also one spreadsheet comparing the results from two packages performing stochastic kriging. These data enable comparisons between the packages to determine which package will give users the best results