SEARCH · Engineering Papers
Results for “fitting”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Alternative renormalizable SO(10) GUTs and data fitting
Not Available
Multi-modal mass-asymmetric fission of 178Pt from simultaneous mass-kinetic energy fitting
Not Available
Sas-temper: Software for fitting small-angle scattering data that provides automated reproducibility characterization
Not Available
One-size-fits-all? Top-down U.S. approach to equitable decarbonization does not fully address state and community-scale perspectives
Not Available
Comparative modeling reveals the molecular determinants of aneuploidy fitness cost in a wild yeast model
Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%–94% of the variance in aneuploid strains’ growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.
Preclinical tumor organoid models in personalized cancer therapy: Not everyone fits the mold
Highlights: • In personalized cancer medicine, each patient receives a specific treatment. • The tumor microenvironment and interpersonal differences can affect treatment. • Organoid culture systems fully recapitulate the tumor microenvironments. In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.
tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking
A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. Here, we present the development of a high-throughput and flexible ligand pose refinement workflow, called “tinyIFD”. The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (M pro ) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.
Parameter-Fitting-Free Continuum Modeling of Electric Double Layer in Aqueous Electrolyte
Not Available
No One Size Fits All: Semiconductor Nanocrystal Sizing Curves
Although colloidal quantum dots or nanocrystals (NCs) are now long past their infancy, there remain basic issues that must be addressed to fully establish their fundamental, size-dependent properties. This is especially true for model systems such as CdSe where confinement effects are responsible for changes in their optical response and where significant effort has been invested in developing theories of their underlying electronic structure. Surprisingly, there still appears to be a lack of consensus on one of the most fundamental metrics for NCs, their sizing curves. As shown here, these are empirical functions that link the size of isotropically shaped semiconductor NCs (such as the diameter, d, for spherical NCs and edge length, l, for cube-shaped ones) to corresponding changes in optical response.
Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?
The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO 2 emissions during the 1959–2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.
Major antigenic site B of human influenza H3N2 viruses has an evolving local fitness landscape
Explore the source record for details and available documents.
Epistasis shapes the fitness landscape of an allosteric specificity switch
Explore the source record for details and available documents.
The interplay of additivity, dominance, and epistasis on fitness in a diploid yeast cross
In diploid species, genetic loci can show additive, dominance, and epistatic effects. To characterize the contributions of these different types of genetic effects to heritable traits, we use a double barcoding system to generate and phenotype a panel of ~200,000 diploid yeast strains that can be partitioned into hundreds of interrelated families. This experiment enables the detection of thousands of epistatic loci, many whose effects vary across families. Here, we show traits are largely specified by a small number of hub loci with major additive and dominance effects, and pervasive epistasis. Genetic background commonly influences both the additive and dominance effects of loci, with multiple modifiers typically involved. The most prominent dominance modifier in our data is the mating locus, which has no effect on its own. Our findings show that the interplay between additivity, dominance, and epistasis underlies a complex genotype-to-phenotype map in diploids.
Self-driving laboratories to autonomously navigate the protein fitness landscape
Abstract Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.