Simultaneous determination of six penicillins in cows'' raw milk by a multiresidue high-performance liquid chromatographic method

A high-performance liquid chromatographic method based on C₁₈ solid-phase extraction and ultraviolet detection at 323 nm of analytes derivatized with benzoic anhydride and 1,2,4-triazole mercuric chloride solution was developed for the simultaneous determination of amoxicillin, penicillin G (benzylpenicillin), ampicillin, oxacillin, cloxacillin and dicloxacillin residues in raw milk. The detection limit of the method was 1.3 μg/l for penicillin G; 1.4 μg/l for amoxicillin, oxacillin and cloxacillin, 1.5 μg/l for ampicillin and 2.7 μg/l for dicloxacillin. The mean recovery was 95–102% for amoxicillin, penicillin G and ampicillin, 92–98% for oxacillin and cloxacillin and 87–94% for dicloxacillin, measured by using an internal standard. The relative repeatability standard deviation was 4–9% on level 4–15 μg/l, respectively, 2–7% on level 30–40 μg/l.     

High yield expression of non-phosphorylated protein tyrosine kinases in insect cells

The key role of kinases in signal transduction and cell growth regulation has been a long standing interest among academics and the pharmaceutical industry. Recombinant enzymes have been used to understand the mechanism of action as well as to screen for chemical inhibitors. The baculo-insect system has been the primary method used to obtain soluble and active kinases, usually producing a mixture of the kinase in various phosphorylation states in different conformations. To obtain a homogenous preparation of non-phosphorylated kinases is critical for biochemical, biophysical and kinetic studies aimed at understanding the mechanism of kinase activation. Taking advantage of the eukaryotic expression property of insect cells, we were able to obtain high yield expression of non-phosphorylated protein tyrosine kinases BTK, JAK3 and Eph2A through coexpression with the tyrosine phosphatase YopH, which suggests that this method can be applied to protein tyrosine kinases in general. We have demonstrated that the fully non-phosphorylated BTK obtained with this method is suitable for various biochemical and kinetic studies.     

Dynein is required for polarized dendritic transport and uniform microtubule orientation in axons

Axons and dendrites differ in both microtubule organization and in the organelles and proteins they contain. Here we show that the microtubule motor dynein has a crucial role in polarized transport and in controlling the orientation of axonal microtubules in Drosophila melanogaster dendritic arborization (da) neurons. Changes in organelle distribution within the dendritic arbors of dynein mutant neurons correlate with a proximal shift in dendritic branch position. Dynein is also necessary for the dendrite-specific localization of Golgi outposts and the ion channel Pickpocket. Axonal microtubules are normally oriented uniformly plus-end-distal; however, without dynein, axons contain both plus- and minus-end distal microtubules. These data suggest that dynein is required for the distinguishing properties of the axon and dendrites: without dynein, dendritic organelles and proteins enter the axon and the axonal microtubules are no longer uniform in polarity.     

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Background  

Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain.     

Tandem immunoprecipitation of phosphotyrosine-mass spectrometry (TIPY-MS) indicates C19ORF19 becomes tyrosine-phosphorylated and associated with activated epidermal growth factor receptor

To identify phosphotyrosine (pY) sites in the epidermal growth factor receptor (EGFR) network, a tandem immunoprecipitation-mass spectrometry method (TIPY-MS) was applied wherein protease-digested EGFR immune complexes were extracted with anti-pY after Rush et al. ( Nat. Biotech. 2005, 23, 94 ) and analyzed by LC-MS/MS. New pY sites in the pathway were found, including SOS1 Y1065, SOS2 Y1275, CBL-B Y889, and in the EGFR regulatory protein Mig-6 Y458. The novel human C19orf19 gene product was found EGFR-associated and phosphorylated at 5 tyrosines in response to EGFR activation and, therefore, represents a new component of the EGFR signaling network.     

An integrated approach for the analysis of biological pathways using mixed models

Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that: a) provides the ability to model and borrow strength across genes that are both up and down in a pathway, b) operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, c) exhibits improved power over widely used methods under normal location-based alternative hypotheses, and d) handles complex experimental designs for which permutation resampling is difficult. We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.