Novel ionization reagent for the measurement of gas-phase ammonia and amines using a stand-alone atmospheric pressure gas chromatography (APGC) source.

RationaleContaminants present in ambient air or in sampling lines can interfere with the target analysis through overlapping peaks or causing a high background. This study presents a positive outcome from the unexpected presence of N-methyl-2-pyrrolidone, released from a PALL HEPA filter, in the analysis of atmospherically relevant gas-phase amines using chemical ionization mass spectrometry.MethodsGas-phase measurements were performed using a triple quadrupole mass spectrometer equipped with a modified atmospheric pressure gas chromatography (APGC) source which allows sampling of the headspace above pure amine standards. Gas-phase N-methyl-2-pyrrolidone (NMP) emitted from a PALL HEPA filter located in the inlet stream served as the ionizing agent.ResultsThis study demonstrates that some alkylamines efficiently form a [NMP + amine+H]+ cluster with NMP upon chemical ionization at atmospheric pressure. The extent of cluster formation depends largely on the proton affinity of the amine compared with that of NMP. Aromatic amines (aniline, pyridine) and diamines (putrescine) were shown not to form cluster ions with NMP.ConclusionsThe use of NMP as an ionizing agent with stand-alone APGC provided high sensitivity for ammonia and the smaller amines. The main advantages, in addition to sensitivity, are direct sampling into the APGC source and avoiding uptake on sampling lines which can be a significant problem with ammonia and amines

A stability based transition prediction criterion for up to Mach 8 hypersonic flows

International audienceLaminar-turbulent boundary layer transition has an important impact on wall heat exchange and friction, especially in case of high speed vehicles. Much work has been done to implement efficient transition prediction tools directly into RANS solvers, mostly in the transonic domain. Current work aims at reaching Mach 8 in case of 2D ideal gas flow with a constant Prandtl number, and to develop a criterion based on stability computations taking into account both first and second modes of instability. Model development followed by a first implementation into a boundary layer code will be presented, followed by simple validations. Although the objective is to take into account pressure gradient effects as well as imposed wall temperatures, the first step presented here will be limited to adiabatic conditions

Ruling out pyridine dinucleotides as true TRPM2 channel activators reveals novel direct agonist ADP-ribose-2'-phosphate

Transient receptor potential melastatin 2 (TRPM2), a Ca(2+)-permeable cation channel implicated in postischemic neuronal cell death, leukocyte activation, and insulin secretion, is activated by intracellular ADP ribose (ADPR). In addition, the pyridine dinucleotides nicotinamide-adenine-dinucleotide (NAD), nicotinic acid-adenine-dinucleotide (NAAD), and NAAD-2'-phosphate (NAADP) have been shown to activate TRPM2, or to enhance its activation by ADPR, when dialyzed into cells. The precise subset of nucleotides that act directly on the TRPM2 protein, however, is unknown. Here, we use a heterologously expressed, affinity-purified-specific ADPR hydrolase to purify commercial preparations of pyridine dinucleotides from substantial contaminations by ADPR or ADPR-2'-phosphate (ADPRP). Direct application of purified NAD, NAAD, or NAADP to the cytosolic face of TRPM2 channels in inside-out patches demonstrated that none of them stimulates gating, or affects channel activation by ADPR, indicating that none of these dinucleotides directly binds to TRPM2. Instead, our experiments identify for the first time ADPRP as a true direct TRPM2 agonist of potential biological interest

Language Independent Statistical Models for on-Line Handwriting Recognition

http://www.suvisoft.comThis paper deals with a language modeling approach that is dedicated to an on-line handwriting recognition system. Three main goals are set: i) performances, ii) versatility, and iii) resources. To achieve these goals we propose a statistical word n-class approach, which uses a learning stage to cluster words in classes and defines an estimation of the probability distribution of sequences of classes. Very large corpora from three different languages (English, French and Italian) have been used to train and test the language models. The efficiency of these models are evaluated not only from a linguistic point of view, using perplexity measurements, but also combined inside the recognition system on real ink signals corresponding to written sentences. Using a tri-class model allows a word error rate reduction ranging from to 50 to 60% according to the language