Rapid one-step separation and purification of recombinant phenylalanine dehydrogenase in aqueous two-phase systems

Background: Phenylalanine dehydrogenase (PheDH; EC 1.4.1.20) is a NAD +-dependent enzyme that performs the reversible oxidative deamination of L-phenylalanine to phenylpyruvate. It plays an important role in detection and screening of phenylketonuria (PKU) diseases and production of chiral intermediates as well. The main goal of this study was to find a simple and rapid alternative method for purifying PheDH. Methods: The purification of recombinant Bacillus sphaericus PheDH was investigated in polyethylene glycol (PEG) and ammonium sulfate aqueous two-phase systems (ATPS). The influences of system parameters including PEG molecular weight and concentration, pH and (NH4)2SO4 concentration on enzyme partitioning were also studied. The purity of enzyme was analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis. Results: A single extraction process was developed for separation and purification of recombinant PheDH from E. coli BL21 (DE3). The optimized conditions for partitioning and purification of PheDH were 9% (w/w) PEG-6,000 and 16% (w/w) (NH4)2SO4 at pH 8.0. The partition coefficient, recovery, yield, purification factor and specific activity values were achieved 58.7, 135%, 94.42%, 491.93 and 9828.88 U/mg, respectively. Also, the Km values for L-phenylalanine and NAD+ in oxidative deamination were 0.21 and 0.13 mM, respectively. Conclusion: The data presented in this paper demonstrated the potential of ATPS as a versatile and scaleable process for downstream processing of recombinant PheDH

Diagonal actions in positive characteristic

We prove positive characteristic analogues of certain measure rigidity theorems in characteristic zero. More specifically we give a classification result for positive entropy measures on quotients of $\operatorname{SL}_d$ and a classification of joinings for higher rank actions on simply connected absolutely almost simple groups.Comment: 44 page

Nano-encapsulation of olive leaf phenolic compounds through WPC-pectin complexes and evaluating their release rate

In this study, W/O micro-emulsions as primary emulsions and a complex of whey protein concentrate (WPC) and pectin in the external aqueous phase were used to produce W/O/W emulsions. Average droplet size of primary W/O emulsion and multiple emulsions stabilized by WPC or WPC-pectin after one day of production was 6.16, 675.7 and 1443 nm, respectively, which achieved to 22.97, 347.7 and, 1992.4 nm after 20 days storage without any sedimentation. The encapsulation efficiency of phenolic compounds for stabilized W/O/W emulsions with WPC and WPC-pectin were 93.34 and 96.64, respectively, which was decreased to 72.73 and 88.81 at 20th storage day. The lowest release of phenolics observed in multiple emulsions of WPC-pectin. These results suggest that nano-encapsulation of olive leaf extract within inner aqueous phase of W/O/W emulsions was successful, and there could be a high potential for the application of olive leaf extract in fortification of food products. © 2015 Elsevier B.V

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data