A Bayesian approach to modeling phytoplankton population dynamics from size distribution time series

The rates of cell growth, division, and carbon loss of microbial populations are key parameters for understanding how organisms interact with their environment and how they contribute to the carbon cycle. However, the invasive nature of current analytical methods has hindered efforts to reliably quantify these parameters. In recent years, size-structured matrix population models (MPMs) have gained popularity for estimating division rates of microbial populations by mechanistically describing changes in microbial cell size distributions over time. Motivated by the mechanistic structure of these models, we employ a Bayesian approach to extend size-structured MPMs to capture additional biological processes describing the dynamics of a marine phytoplankton population over the day-night cycle. Our Bayesian framework is able to take prior scientific knowledge into account and generate biologically interpretable results. Using data from an exponentially growing laboratory culture of the cyanobacterium Prochlorococcus, we isolate respiratory and exudative carbon losses as critical parameters for the modeling of their population dynamics. The results suggest that this modeling framework can provide deeper insights into microbial population dynamics provided by size distribution time-series data.

Does the Type of Records Affect the Estimates of the Parameters?

A general family of distributions, namely Kumaraswamy generalized family of (Kw-G) distribution, is considered for estimation of the unknown parameters and reliability function based on record data from Kw-G distribution. The maximum likelihood estimators (MLEs) are derived for unknown parameters and reliability function, along with its confidence intervals. A Bayesian study is carried out under symmetric and asymmetric loss functions in order to find the Bayes estimators for unknown parameters and reliability function. Future record values are predicted using Bayesian approach and non Bayesian approach, based on numerical examples and a monte carlo simulation.

baredSC: Bayesian approach to retrieve expression distribution of single-cell data

Background The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the distribution of the expression of a specific gene or the correlation between the expressions of two genes. Results We show that the main technical noise associated with these scRNA-seq experiments is due to the sampling, i.e., Poisson noise. We present a new tool named baredSC, for Bayesian Approach to Retrieve Expression Distribution of Single-Cell data, which infers the intrinsic expression distribution in scRNA-seq data using a Gaussian mixture model. baredSC can be used to obtain the distribution in one dimension for individual genes and in two dimensions for pairs of genes, in particular to estimate the correlation in the two genes’ expressions. We apply baredSC to simulated scRNA-seq data and show that the algorithm is able to uncover the expression distribution used to simulate the data, even in multi-modal cases with very sparse data. We also apply baredSC to two real biological data sets. First, we use it to measure the anti-correlation between Hoxd13 and Hoxa11, two genes with known genetic interaction in embryonic limb. Then, we study the expression of Pitx1 in embryonic hindlimb, for which a trimodal distribution has been identified through flow cytometry. While other methods to analyze scRNA-seq are too sensitive to sampling noise, baredSC reveals this trimodal distribution. Conclusion baredSC is a powerful tool which aims at retrieving the expression distribution of few genes of interest from scRNA-seq data.

The rise of grasslands is linked to atmospheric CO2 decline in the late Palaeogene

Grasslands are predicted to experience a major biodiversity change by the year 2100. A better understanding of how grasslands have responded to past environmental changes will help predict the outcome of current and future environmental changes. Here, we explore the relationship between past atmospheric CO2 and temperature fluctuations and the shifts in diversification rate of Poaceae (grasses) and Asteraceae (daisies), two exceptionally species-rich grassland families (~11,000 and ~23,000 species, respectively). To this end, we develop a Bayesian approach that simultaneously estimates diversification rates through time from time-calibrated phylogenies and correlations between environmental variables and diversification rates. Additionally, we present a statistical approach that incorporates the information of the distribution of missing species in the phylogeny. We find strong evidence supporting a simultaneous increase in diversification rates for grasses and daisies after the most significant reduction of atmospheric CO2 in the Cenozoic (~34 Mya). The fluctuations of paleo-temperatures, however, appear not to have had a significant relationship with the diversification of these grassland families. Overall, our results shed new light on our understanding of the origin of grasslands in the context of past environmental changes.

Bayesian method for estimating Weibull parameters for wind resource assessment in the Equatorial region: a comparison between two-parameter and three-parameter Weibull distributions

The two-parameter Weibull distribution has garnered much attention in the assessment of windenergy potential. The estimation of the shape and scale parameters of the distribution has broughtforth a successful tool for the wind energy industry. However, it may be inappropriate to use thetwo-parameter Weibull distribution to assess energy at every location, especially at sites wherelow wind speeds are frequent, such as the Equatorial region. In this work, a robust technique inwind resource assessment using a Bayesian approach for estimating Weibull parameters is firstproposed. Secondly, the wind resource assessment techniques using a two-parameter Weibulldistribution and a three-parameter Weibull distribution which is a generalized form of twoparameterWeibull distribution are compared. Simulation studies confirm that the Bayesianapproach seems a more robust technique for accurate estimation of Weibull parameters. Theresearch is conducted using data from seven sites in Equatorial region from 1o N of Equator to 19oSouth of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shiftparameter is a better fit for wind data having a higher percentage of low wind speeds (0-1 m/s) andlow skewness. However, wind data with a smaller percentage of low wind speeds and highskewness showed better results with a two-parameter distribution that is a special case of threeparameterWeibull distribution with zero shift parameter. The results also demonstrate that theproposed Bayesian approach and application of a three-parameter Weibull distribution areextremely useful in accurate estimate of wind power and annual energy production.