In Silico Synchronization of Cellular Populations Through Expression Data Deconvolution

Cellular populations are typically heterogenous collections of cells at different points in their respective cell cycles, each with a cell cycle time that varies from individual to individual. As a result, true single-cell behavior, particularly that which is cell-cycle--dependent, is often obscured in population-level (averaged) measurements. We have developed a simple deconvolution method that can be used to remove the effects of asynchronous variability from population-level time-series data. In this paper, we summarize some recent progress in the development and application of our approach, and provide technical updates that result in increased biological fidelity. We also explore several preliminary validation results and discuss several ongoing applications that highlight the method's usefulness for estimating parameters in differential equation models of single-cell gene regulation.Comment: accepted for the 48th ACM/IEEE Design Automation Conferenc

Empowering Catering Sales Managers with Pricing Authority

In the hotel business, catering sales managers often encounter potential clients who expect to negotiate for items such as room rental fees, audiovisual charges, and bartending fees. This article addresses both the advantages and disadvantages of empowering sales managers with the authority to reduce or waive these charges. Thus, hoteliers are advised to extend a structured yield management mindset into the hotel’s function-space area

BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach

Background: With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses. Results: We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under the curve, bias and MSE for parameter estimates), our simulation results indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missingness due to MAR and MNAR. Further, our approach was competitive with other methods tailored specifically to MNAR in situations where missing data were completely MNAR. Applying our approach to an analysis of metabolomics data from a mouse myocardial infarction revealed several statistically significant metabolites not previously identified that were of direct biological relevance to the study. Conclusions: Our findings demonstrate that BayesMetab has improved performance in imputing the missing values and performing statistical inference compared to other current methods when missing values are due to a mixture of MNAR and MAR. Analysis of real metabolomics data strongly suggests this mixture is likely to occur in practice, and thus, it is important to consider an imputation model that accounts for a mixture of missing data types

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.</p

An Interview on Leadership with Al Carey, CEO, PepsiCo Beverages

Paul T. Barrett, PhD, CPA, is dean and professor of business for the College of Business and Economics, Longwood University, Farmville, VA 23909. James C. Haug, DBA, is associate professor of management, Longwood University, College of Business and Economics, Farmville, VA 23909. John N. Gaskins, PhD, currently serves as associate professor of marketing and retailing, Longwood University, College of Business and Economics, Farmville, VA 23909

Anisotropies in the diffuse gamma-ray background measured by the Fermi-LAT

The small angular scale fluctuations of the (on large scale) isotropic gamma-ray background (IGRB) carry information about the presence of unresolved source classes. A guaranteed contribution to the IGRB is expected from the unresolved gamma-ray AGN while other extragalactic sources, Galactic gamma-ray source populations and dark matter Galactic and extragalactic structures (and sub-structures) are candidate contributors. The IGRB was measured with unprecedented precision by the Large Area Telescope (LAT) on-board of the Fermi gamma-ray observatory, and these data were used for measuring the IGRB angular power spectrum (APS). Detailed Monte Carlo simulations of Fermi-LAT all-sky observations were performed to provide a reference against which to compare the results obtained for the real data set. The Monte Carlo simulations are also a method for performing those detailed studies of the APS contributions of single source populations, which are required in order to identify the actual IGRB contributors. We present preliminary results of an anisotropy search in the IGRB. At angular scales <2° (e.g., above multipole 155), angular power above the photon noise level is detected, at energies between 1 and 10 GeV in each energy bin, with statistical significance between 7.2 and 4.1σ. The obtained energy dependences point to the presence of one or more unclustered source populations with the components having an average photon index Γ=2.40±0.07

Anisotropies in the diffuse gamma-ray background measured by Fermi LAT

The small angular scale fluctuations of the (on large scale) isotropic gamma-ray background (IGRB) carry information about the presence of unresolved source classes. A guaranteed contribution to the IGRB is expected from the unresolved gamma-ray AGN while other extragalactic sources, Galactic gamma-ray source populations and dark matter Galactic and extragalactic structures (and sub-structures) are candidate contributors. The IGRB was measured with unprecedented precision by the Large Area Telescope (LAT) on-board of the Fermi gamma-ray observatory, and these data were used for measuring the IGRB angular power spectrum (APS). Detailed Monte Carlo simulations of Fermi-LAT all-sky observations were performed to provide a reference against which to compare the results obtained for the real data set. The Monte Carlo simulations are also a method for performing those detailed studies of the APS contributions of single source populations, which are required in order to identify the actual IGRB contributors. We present preliminary results of an anisotropy search in the IGRB. At angular scales <2deg (e.g. above multipole 155), angular power above the photon noise level is detected, at energies between 1 and 10 GeV in each energy bin, with statistical significance between 7.2 and 4.1 sigmas. The energy not dependence of the fluctuation anisotropy is pointing to the presence of one or more unclustered source populations, while the energy dependence of the intensity anisotropy is consistent with source populations having average photon index 2.40\pm0.07.Comment: 6 pages, Proceedings of the RICAP 2011 Conference, submitted to NIM

Health literacy and health behaviors among adults with prediabetes, 2016 behavioral risk factor surveillance system

Objectives: Evidence is needed for designing interventions to address health literacy–related issues among adults with prediabetes to reduce their risk of developing type 2 diabetes. This study assessed health literacy and behaviors among US adults with prediabetes and the mediating role of health literacy on health behaviors. Methods: We used data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) (N = 54 344 adults). The BRFSS health literacy module included 3 questions on levels of difficulty in obtaining information, understanding health care providers, and comprehending written information. We defined low health literacy as a response of “somewhat difficult” or “very difficult” to at least 1 of these 3 questions. Respondents self-reported their prediabetes status. We included 3 health behavior indicators available in the BRFSS survey—current smoking, physical inactivity, and inadequate sleep, all measured as binary outcomes (yes/no). We used a path analysis to examine pathways among prediabetes, health literacy, and health behaviors. Results: About 1 in 5 (19.0%) adults with prediabetes had low health literacy. The rates of physical inactivity (31.0% vs 24.6%, P <.001) and inadequate sleep (38.8% vs 33.5%, P <.001) among adults with prediabetes were significantly higher than among adults without prediabetes. The path analysis showed a significant direct effect of prediabetes and health literacy on health behaviors. The indirect effect of prediabetes through health literacy on health behaviors was also significant. Conclusion: BRFSS data from 2016 showed that rates of low health literacy and unhealthy behaviors were higher among adults with prediabetes than among adults without prediabetes. Interventions are needed to assist adults with prediabetes in comprehending, communicating about, and managing health issues to reduce the risk of type 2 diabetes. © 2020, Association of Schools and Programs of Public Health