Intensity-based likelihood functions in crystallographic applications have the potential to enhance the quality of structures derived from marginal diffraction data. Their usage, however, is complicated by the ability to efficiently compute these target functions. Here, a numerical quadrature is developed that allows the rapid evaluation of intensity-based likelihood functions in crystallographic applications. By using a sequence of change-of-variable transformations, including a nonlinear domain-compression operation, an accurate, robust and efficient quadrature is constructed. The approach is flexible and can incorporate different noise models with relative ease.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397490 | PMC |
| http://dx.doi.org/10.1107/S2059798320008372 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Practical identifiability is a critical concern in data-driven modeling of mathematical systems. In this paper, we propose a novel framework for practical identifiability analysis to evaluate parameter identifiability in mathematical models of biological systems. Starting with a rigorous mathematical definition of practical identifiability, we demonstrate its equivalence to the invertibility of the Fisher Information Matrix.
View Article and Find Full Text PDFPhotodynamic bactericidal nanomaterials in food packaging: From principle to application.
J Food Sci
January 2025
Shandong Peanut Research Institute, Key Laboratory of Peanut Biology and Breeding, Ministry of Agriculture and Rural Affairs, Qingdao, PR China.
Compared to traditional preservatives, photodynamic inactivation (PDI) offers a promising bactericidal approach due to its nontoxic nature and low propensity for microbial resistance. In this paper, we initially investigate the principles and antibacterial mechanisms underlying PDI. We then review factors influencing PDI's germicidal efficacy in food preservation.
View Article and Find Full Text PDFDistributed model building and recursive integration for big spatial data modeling.
Biometrics
January 2025
Department of Biostatistics, Brown University, Providence, RI 02912, United States.
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain.
View Article and Find Full Text PDFTesting measurement invariance in a conditional likelihood framework by considering multiple covariates simultaneously.
Behav Res Methods
January 2025
Paris Lodron University Salzburg, Kapitelgasse 4-6, 5020, Salzburg, Austria.
This article addresses the problem of measurement invariance in psychometrics. In particular, its focus is on the invariance assumption of item parameters in a class of models known as Rasch models. It suggests a mixed-effects or random intercept model for binary data together with a conditional likelihood approach of both estimating and testing the effects of multiple covariates simultaneously.
View Article and Find Full Text PDFAntimicrobial Peptides Against Arboviruses: Mechanisms, Challenges, and Future Directions.
Probiotics Antimicrob Proteins
January 2025
Department of Virology, Pasteur Institute of Iran, Tehran, 13169-43551, Iran.
This review delves into the potential of antimicrobial peptides (AMPs) as promising candidates for combating arboviruses, focusing on their mechanisms of antiviral activity, challenges, and future directions. AMPs have shown promise in preventing arbovirus attachment to host cells, inducing interferon production, and targeting multiple viral stages, illustrating their multifaceted impact on arbovirus infections. Structural elucidation of AMP-viral complexes is explored to deepen the understanding of molecular determinants governing viral neutralization, paving the way for structure-guided design.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!