Lasiodiplodan, an exocellular (1 -> 6)-beta-d-glucan from Lasiodiplodia theobromae MMPI: production on glucose, fermentation kinetics, rheology and anti-proliferative activity

Lasiodiplodan, an exopolysaccharide of the (1 -> 6)-beta-d-glucan type, is produced by Lasiodiplodia theobromae MMPI when grown under submerged culture on glucose. The objective of this study was to evaluate lasiodiplodan production by examining the effects of carbon (glucose, fructose, maltose, sucrose) and nitrogen sources (KNO3, (NH4)(2)SO4, urea, yeast extract, peptone), its production in shake flasks compared to a stirred-tank bioreactor, and to study the rheology of lasiodiplodan, and lasiodiplodan's anti-proliferative effect on breast cancer MCF-7 cells. Although glucose (2.05 +/- A 0.05 g L-1), maltose (2.08 +/- A 0.04 g L-1) and yeast extract (2.46 +/- A 0.06 g L-1) produced the highest amounts of lasiodiplodan, urea as N source resulted in more lasiodiplodan per unit biomass than yeast extract (0.74 +/- A 0.006 vs. 0.22 +/- A 0.008 g g(-1)). A comparison of the fermentative parameters of L. theobromae MMPI in shake flasks and a stirred-tank bioreactor at 120 h on glucose as carbon source showed maximum lasiodiplodan production in agitated flasks (7.01 +/- A 0.07 g L-1) with a specific yield of 0.25 +/- A 0.57 g g(-1) and a volumetric productivity of 0.06 +/- A 0.001 g L-1 h(-1). A factorial 2(2) statistical design developed to evaluate the effect of glucose concentration (20-60 g L-1) and impeller speed (100-200 rpm) on lasiodiplodan production in the bioreactor showed the highest production (6.32 g L-1) at 72 h. Lasiodiplodan presented pseudoplastic behaviour, and the apparent viscosity increased at 60A degrees C in the presence of CaCl2. Anti-proliferative activity of lasiodiplodan was demonstrated in MCF-7 cells, which was time- and dose-dependent with an IC50 of 100 mu g lasiodiplodan mL(-1).

Bilayer segmentation of live video in uncontrolled environments for background substitution: an overview and main challenges

Bilayer segmentation of live video in uncontrolled environments is an essential task for home applications in which the original background of the scene must be replaced, as in videochats or traditional videoconference. The main challenge in such conditions is overcome all difficulties in problem-situations (e. g., illumination change, distract events such as element moving in the background and camera shake) that may occur while the video is being captured. This paper presents a survey of segmentation methods for background substitution applications, describes the main concepts and identifies events that may cause errors. Our analysis shows that although robust methods rely on specific devices (multiple cameras or sensors to generate depth maps) which aid the process. In order to achieve the same results using conventional devices (monocular video cameras), most current research relies on energy minimization frameworks, in which temporal and spacial information are probabilistically combined with those of color and contrast.