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In this study, a novel strategy based on acetone stable-isotope derivatization coupled with HPLC-MS for profiling and accurate quantification of aminophospholipids (phosphatidylethanolamine and phosphatidylserine) in biological samples was developed. Acetone derivatization leads to alkylation of the primary amino groups of aminophospholipids with an isopropyl moiety; the use of deuterium-labeled acetone (d6-acetone) introduced a 6 Da mass shift that was ideally suited for profiling and quantification analysis with high selectivity and accuracy. After derivatization, significantly increased column efficiency for chromatographic separation and detection sensitivity for MS analysis of aminophospholipids was observed. Furthermore, an accuracy quantification method was developed. Aminophospholipids in biological samples were derivatized with d0-acetone; while more than two aminophospholipid standards were selected for each class of aminophospholipid and derivatized with d6-acetone, which were then used as the internal standards to typically construct a calibration curve for each class to normalize the nonuniformity response caused by the differential fragmentation kinetics resulting from the distinct chemical constitution of individual aminophospholipid species in the biological samples. The excellent applicability of the developed method was validated by profiling and quantification of aminophospholipids presented in liver samples from rats fed with different diets.

A cDNA encoding a bifunctional acetylxylan esterase/xylanase, XynS20E, was cloned from the ruminal fungus Neocallimastix patriciarum. A putative conserved domain of carbohydrate esterase family 1 was observed at the N-terminus and a putative conserved domain of glycosyl hydrolase family 11 was detected at the C-terminus of XynS20E. To examine the enzyme activities, XynS20E was expressed in Escherichia coli as a recombinant His6 fusion protein and purified by immobilized metal ion-affinity chromatography. Response surface modeling combined with central composite design and regression analysis was then applied to determine the optimal temperature and pH conditions of the recombinant XynS20E. The optimal conditions for the highest xylanase activity of the recombinant XynS20E were observed at a temperature of 49°C and a pH of 5.8, while those for the highest carbohydrate esterase activity were observed at a temperature of 58°C and a pH of 8.2. Under the optimal conditions for the enzyme activity, the xylanase and acetylxylan esterase specific activities of the recombinant XynS20E toward birchwood xylan were 128.7 and 873.1 U mg−1, respectively. To our knowledge, this is the first report of a bifunctional xylanolytic enzyme with acetylxylan esterase and xylanase activities from rumen fungus. [ABSTRACT FROM AUTHOR]

The complexity of semiconductor manufacturing is increasing due to the smaller feature sizes, greater number of layers, and existing process reentry characteristics. As a result, it is difficult to manage and clarify responsibility for low yields in specific products. This paper presents a comprehensive data mining method for predicting and classifying the product yields in semiconductor manufacturing processes. A genetic programming (GP) approach, capable of constructing a yield prediction system and performing automatic discovery of the significant factors that might cause low yield, is presented. Comparison with the results then is performed using a decision tree induction algorithm. Moreover, this research illustrates the robustness and effectiveness of this method using a well-known DRAM fab’s real data set, with discussion of the results. [ABSTRACT FROM AUTHOR]