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Results for “biasing”
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Disclosure of Double Exchange Bias Effect in Chromium (III) Oxide Nanoparticles
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Cross Modality Bias in Visual Question Answering: A Causal View With Possible Worlds VQA
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The bias of a two‐dimensional view: comparing two‐dimensional and three‐dimensional mesophyll surface area estimates using noninvasive imaging
Summary The mesophyll surface area exposed to intercellular air space per leaf area ( S m ) is closely associated with CO 2 diffusion and photosynthetic rates. S m is typically estimated from two‐dimensional (2D) leaf sections and corrected for the three‐dimensional (3D) geometry of mesophyll cells, leading to potential differences between the estimated and actual cell surface area. Here, we examined how 2D methods used for estimating S m compare with 3D values obtained from high‐resolution X‐ray microcomputed tomography (microCT) for 23 plant species, with broad phylogenetic and anatomical coverage. Relative to 3D, uncorrected 2D S m estimates were, on average, 15–30% lower. Two of the four 2D S m methods typically fell within 10% of 3D values. For most species, only a few 2D slices were needed to accurately estimate S m within 10% of the whole leaf sample median. However, leaves with reticulate vein networks required more sections because of a more heterogeneous vein coverage across slices. These results provide the first comparison of the accuracy of 2D methods in estimating the complex 3D geometry of internal leaf surfaces. Because microCT is not readily available, we provide guidance for using standard light microscopy techniques, as well as recommending standardization of reporting S m values.
Maturation experiments reveal bias in the chemistry of fossil melanosomes
Fossil melanosomes are a major focus of paleobiological research because they can inform on the original coloration, phylogenetic affinities, and internal anatomy of ancient animals. Recent studies of vertebrate melanosomes revealed tissue-specific trends in melanosome-metal associations that can persist in fossils. In some fossil vertebrates, however, melanosomes from all body regions are enriched only in Cu, suggesting diagenetic overprinting of original chemistry. We tested this hypothesis using laboratory experiments on melanosomes from skin and liver of the African clawed frog Xenopus laevis. After maturation in Cu-rich media, the metal chemistry of melanosomes from these tissues converged toward a common composition, and original differences in Cu oxidation state were lost. Elevated Cu concentrations and a pervasive Cu(II) signal are likely indicators of diagenetically altered melanosomes. These results provide a robust experimental basis for interpretating the chemistry of fossil melanosomes.
Evaluation of the Component of Bias of X-Ray Microanalysis Related to Surface Relief of the Specimen
A semiempirical approach to the a priori evaluation of the effect of the surface relief on the results of X-ray spectral analysis of binary compounds is proposed. Dimensionless parameter k characterizing the effect of the relief on the intensity of intrinsic X-ray radiation is determined using the experimental data obtained for reference monocrystal specimens GaAs with known geometrical parameters of the relief.
Tunneling current noise spectra of biased impurity with a phonon mode
We report the results of theoretical investigations of the tunneling current noise spectra through a single-level impurity both in the presence and in the absence of electron–phonon interaction based on the nonequilibrium Green’s functions formalism. We show that due to the quantum nature of tunneling, the Fano factor is dramatically different from the Poisson limit both in the presence and in the absence of inelastic processes. The results are demonstrated to be sensitive to the tunneling contact parameters.
Measurement of charged particle spectra in minimum-bias events from proton–proton collisions at $\sqrt{s}=13\,\text {TeV} $
Pseudorapidity, transverse momentum, and multiplicity distributions are measured in the pseudorapidity range $|\eta | < 2.4$ for charged particles with transverse momenta satisfying $p_{\mathrm {T}} > 0.5\,\text {GeV} $ in proton–proton collisions at a center-of-mass energy of $\sqrt{s} = 13\,\text {TeV} $ . Measurements are presented in three different event categories. The most inclusive of the categories corresponds to an inelastic $\mathrm {p}$ $\mathrm {p}$ data set, while the other two categories are exclusive subsets of the inelastic sample that are either enhanced or depleted in single diffractive dissociation events. As a result, the measurements are compared to predictions from Monte Carlo event generators used to describe high-energy hadronic interactions in collider and cosmic-ray physics.
Recovering Evapotranspiration Trends from Biased CMIP5 Simulations and Sensitivity to Changing Climate over North America
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Plastome evolution in annual Brachypodium species reveals widespread heteroplasmy and chloroplast capture, lineage-specific codon usage bias, and low positive selection
Comparative genomics and plastome phylogenomics have advanced significantly in recent years, highlighting the diversity, possible admixture, and non-neutral evolution of the predominantly considered non-recombinant chloroplast genomes in angiosperms. The grass genus Brachypodium serves as a powerful model for studying evolutionary processes in monocots. We analyzed 287 plastomes across the native circum-Mediterranean range of the three annual Brachypodium species ( B. distachyon, B. stacei, B. hybridum ), focusing on their structural variation, selection patterns and phylogenomic relationships. Our analyses confirmed the differentiation of the S and D plastomes, inherited respectively from the diploid progenitor species B. stacei and B. distachyon . We identified novel structural rearrangements and indels, and unique repeat motifs, along with widespread heteroplasmy, particularly in ancestral B. hybridum -D plastotypes. SNP diversity varied among plastotypes, reflecting population dynamics and evolutionary histories, with B. hybridum -D plastotypes showing the highest normalized diversity and B. hybridum -S the lowest. Positive selection was detected in 29 plastid genes by Tajima’s neutrality test, and in nine genes by site and branch-site evolutionary models, including matK, ndhF, rbcL, and rpoC2. Phylogenomic analyses revealed well-supported clades corresponding to the S and D plastome lineages, with frequent chloroplast capture events and long-distance dispersals shaping their evolutionary trajectories.
High-burnup isotopics bias and uncertainty estimations for LANCR02
Calculated nuclide concentrations are compared against destructive radiochemical assays of modern, high-burnup BWR fuel to validate the LANCR02 lattice physics code. Good agreement is observed between different lattice physics methodologies - including the LANCR02 implementation of Method of Characteristics neutron and gamma transport solver and the TGBLA06 methodology - and against measurements. (authors)
Bias Between ENDF/B-VIII and ENDF/B-VII.1 for LEU Pin Array Systems
Extensive testing of new cross section data is performed in SCALE prior to its release. The release of ENDF/B-VIII.0 requires the generation and testing of new data libraries which are planned to be included in the release of SCALE 6.3 in 2019 or 2020. KENO benchmark results have been published previously for beta and final releases of ENDF/B-VIII.0 using the entire Verified, Archived Library of Inputs and Data (VALID). This work presents an energy-dependent change in the results for low-enriched uranium (LEU) pin array systems. The process of identifying the difference is highlighted as a case study to determine how data testing and benchmarking can be performed to provide more detailed feedback to the data community on potential data deficiencies.
Objective-trait-bias metaheuristics for design optimization of optical structures
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Loads Bias Genetic and Signaling Switches in Synthetic and Natural Systems
Biological protein interactions networks such as signal transduction or gene transcription networks are often treated as modular, allowing motifs to be analyzed in isolation from the rest of the network. Modularity is also a key assumption in synthetic biology, where it is similarly expected that when network motifs are combined together, they do not lose their essential characteristics. However, the interactions that a network module has with downstream elements change the dynamical equations describing the upstream module and thus may change the dynamic and static properties of the upstream circuit even without explicit feedback. In this work we analyze the behavior of a ubiquitous motif in gene transcription and signal transduction circuits: the switch. We show that adding an additional downstream component to the simple genetic toggle switch changes its dynamical properties by changing the underlying potential energy landscape, and skewing it in favor of the unloaded side, and in some situations adding loads to the genetic switch can also abrogate bistable behavior. We find that an additional positive feedback motif found in naturally occurring toggle switches could tune the potential energy landscape in a desirable manner. We also analyze autocatalytic signal transduction switches and show that a ubiquitous positive feedback switch can lose its switch-like properties when connected to a downstream load. Our analysis underscores the necessity of incorporating the effects of downstream components when understanding the physics of biochemical network motifs, and raises the question as to how these effects are managed in real biological systems. This analysis is particularly important when scaling synthetic networks to more complex organisms.
Variation and selection on codon usage bias across an entire subphylum
Not Available
Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region
This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interaction and lake-effect processes (e.g., near-surface temperature, humidity, wind, precipitation, and surface turbulent fluxes), enabling direct comparison with the observational datasets. Users can analyze and visualize these NetCDF files with common tools such as Python (e.g., xarray, netCDF4, numpy, pandas), NCO/CDO, Panoply, or ncview; NetCDF variables can also be converted to other formats (e.g., CSV, GeoTIFF) using these utilities.
Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models
Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors --- the context of how and where content is posted --- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.
Bias and Uncertainty Under-Prediction in Monte Carlo Depletion
Abstract not provided.