The bacterium Pantoea sp. BCCS 001 GH produces an exopolysaccharide (EPS) named Pantoan through using sugar beet molasses (SBM) as an inexpensive and widely available carbon source. This study aims to investigate the kinetics and optimization of the Pantoan biosynthesis using Pantoea sp. BCCS 001 GH in submerged culture. During kinetics studies, the logistic model and Luedeking–Piret equation are precisely fit with the obtained experimental data. The response surface methodology (RSM)-central composite design (CCD) method is applied to evaluate the effects of four factors (SBM, peptone, Na2HPO4, and Triton X-100) on the concentration of Pantoan in batch culture of Pantoea sp. BCCS 001 GH. The experimental and predicted maximum Pantoan production yields are found 9.9 ± 0.5 and 10.30 g/L, respectively, and the best prediction factor concentrations are achieved at 31.5 g/L SBM, 2.73 g/L peptone, 3 g/L Na2HPO4, and 0.32 g/L Triton X-100 after 48 h of submerged culture fermentation, at 30 °C. The functional groups and major monosaccharides (glucose and galactose) of a purified Pantoan are described and confirmed by 1HNMR and FTIR. The produced Pantoan is also characterized by thermogravimetric analysis and the rheological properties of the biopolymer are investigated. The present work guides the design and optimization of the Pantoea sp. BCCS 001 GH culture media, to be fine-tuned and applied to invaluable EPS, which can be applicable in food and biotechnology applications.

No abstract

Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users’ movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic-decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters by using an efficient EM algorithm, which distributes over the users, and has a linear time complexity. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives.

No abstract

Microbial exopolysaccharides have recently served as an efficient substrate for the production of biocompatible metal nanoparticles given their favorable stabilizing and reducing properties given their favorable stabilizing and reducing properties.

No abstract

No abstract

No abstract