Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification.

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

Unlabelled: As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification.

Hitting Times of Some Critical Events in RNA Origins of Life.

Can a replicase be found in the vast sequence space by random drift? We partially answer this question through a proof-of-concept study of the times of occurrence (hitting times) of some critical events in the origins of life for low-dimensional RNA sequences using a mathematical model and stochastic simulation studies from Python software. We parameterize fitness and similarity landscapes for polymerases and study a replicating population of sequences (randomly) participating in template-directed polymerization. Under the ansatz of localization where sequence proximity correlates with spatial proximity of sequences, we find that, for a replicating population of sequences, the hitting and establishment of a high-fidelity replicator depends critically on the polymerase fitness and sequence (spatial) similarity landscapes and on sequence dimension.

Author Correction: Hysteresis control of epithelial-mesenchymal transition dynamics conveys a distinct program with enhanced metastatic ability.

The original version of this Article contained an error in the spelling of the author Daniel D. Liu, which was incorrectly given as Daniel Liu. This has now been corrected in both the PDF and HTML versions of the Article.

Hysteresis control of epithelial-mesenchymal transition dynamics conveys a distinct program with enhanced metastatic ability.

Epithelial-mesenchymal transition (EMT) have been extensively characterized in development and cancer, and its dynamics have been modeled as a non-linear process. However, less is known about how such dynamics may affect its biological impact. Here, we use mathematical modeling and experimental analysis of the TGF-β-induced EMT to reveal a non-linear hysteretic response of E-cadherin repression tightly controlled by the strength of the miR-200s/ZEBs negative feedback loop.

Experimental Design of Formulations Utilizing High Dimensional Model Representation.

Many applications involve formulations or mixtures where large numbers of components are possible to choose from, but a final composition with only a few components is sought. Finding suitable binary or ternary mixtures from all the permissible components often relies on simplex-lattice sampling in traditional design of experiments (DoE), which requires performing a large number of experiments even for just tens of permissible components. The effect rises very rapidly with increasing numbers of components and can readily become impractical.

Lasting effects on body weight and mammary gland gene expression in female mice upon early life exposure to n-3 but not n-6 high-fat diets.

Exposure to an imbalance of nutrients prior to conception and during critical developmental periods can have lasting consequences on physiological processes resulting in chronic diseases later in life. Developmental programming has been shown to involve structural and functional changes in important tissues. The aim of the present study was to investigate whether early life diet has a programming effect on the mammary gland.

Gene Selection with Sequential Classification and Regression Tree Algorithm.

Background: In the typical setting of gene-selection problems from high-dimensional data, e.g., gene expression data from microarray or next-generation sequencing-based technologies, an enormous volume of high-throughput data is generated, and there is often a need for a simple, computationally-inexpensive, non-parametric screening procedure than can quickly and accurately find a low-dimensional variable subset that preserves biological information from the original very high-dimensional data (dimension > 40,000).

Integrative database management for mouse development: systems and concepts.

Cells in the developing embryo must integrate complex signals from the genome and environment to make decisions about their behavior or fate. The ability to understand the fundamental biology of the decision-making process, and how these decisions may go awry during abnormal development, requires a systems biology paradigm. Presently, the ability to build models with predictive capability in birth defects research is constrained by an incomplete understanding of the fundamental parameters underlying embryonic susceptibility, sensitivity, and vulnerability.