Computer Simulation Elucidates Yeast Flocculation and Sedimentation for Efficient Industrial Fermentation

Online monitoring lignocellulosic particles by focus beam reflectance measurement for efficient bioprocessing

Lignocellulose presents a promising alternative to fossil fuels. Monitoring the mass and size changes of lignocellulosic particles without disrupting the process can assist in adjusting pretreatment and enzymatic hydrolysis, where conventional sieving methods fall short. A method utilizing focused beam reflectance measurement (FBRM) was developed to establish mathematical correlations between FBRM chord information (chord length and count) and particle characteristics (weight and size) quantified through sieving. Results indicate particle size exhibits a linear correlation with the square weighted median chord length (Lsqr) with R2 at 0.93. Further, real-time bulk particle mass can be predicted using Lsqr and chord count (R2 0.98). These correlations are applicable in range 53 μm to 358.5 μm. Real-time monitoring of enzymatic hydrolysis of corn stalks has demonstrated the practical applicability of FBRM. This study introduces a novel approach for online characterization of lignocellulosic particles, thereby enhancing lignocellulosic biorefineries.

Permissive aggregative group formation favors coexistence between cooperators and defectors in yeast

In Saccharomyces cerevisiae, the FLO1 gene encodes flocculins that lead to formation of multicellular flocs, that offer protection to the constituent cells. Flo1p was found to preferentially bind to fellow cooperators compared to defectors lacking FLO1 expression, enriching cooperators within the flocs. Given this dual function in cooperation and kin recognition, FLO1 has been termed a "green beard gene". Because of the heterophilic nature of the Flo1p bond however, we hypothesize that kin recognition is permissive and depends on the relative stability of the FLO1+/flo1- versus FLO1+/FLO1+ detachment force F. We combine single-cell measurements of adhesion, individual cell-based simulations of cluster formation, and in vitro flocculation to study the impact of relative bond stability on the evolutionary stability of cooperation. We identify a trade-off between both aspects of the green beard mechanism, with reduced relative bond stability leading to increased kin recognition at the expense of cooperative benefits. We show that the fitness of FLO1 cooperators decreases as their frequency in the population increases, arising from the observed permissive character (F+- = 0.5 F++) of the Flo1p bond. Considering the costs associated with FLO1 expression, this asymmetric selection often results in a stable coexistence between cooperators and defectors.

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm

Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector machine method. Since in most of the previous studies, the coefficients of the support vector machine method had been calculated based on trial and error, in the present study, the combination of the support vector machine and the genetic algorithm is used. In the first step, the unknown parameters of the support vector machine are calculated and then, the sediment load simulation is performed. Two case studies in the present work involve two earth dams in Semnan Province called Veynakeh and Royan. Furthermore, multivariate adaptive regression spline (MARS) and MT tree model (M5T) methods are used for comparison. The results indicated that the input combination of discharge data at the current time and one, two, and three previous days has the best performance for all models. Also, the support vector machine-genetic algorithm (SVM-GA) model has a lower root mean square error (RMSE) and mean absolute error (MAE) compared to the MARS and M5T models for both stations. In addition, comparing observational data with simulation data based on the R² coefficient suggested that the SVM-GA model offers more accurate results than the other two methods. Accordingly, the SVM-GA method used in this study has a high potential for simulating sediment volume.