Examples of the effects of different averaging methods on carbon dioxide fluxes calculated using the eddy correlation method

International audienceThree hours of high frequency vertical windspeed and carbon dioxide concentration data recorded over tropical forest in Brazil are presented and discussed in relation to various detrending techniques used in eddy correlation analysis. Running means with time constants 100, 1000 and 1875s and a 30 minute linear detrend, as commonly used to determine fluxes, have been calculated for each case study and are presented. It is shown that, for different trends in the background concentration of carbon dioxide, the different methods can lead to the calculation of radically different fluxes over an hourly period. The examples emphasise the need for caution when interpreting eddy correlation derived fluxes especially for short term process studies. Keywords: Eddy covariance; detrending; running mean; carbon dioxide; tropical fores

Selective complexation of divalent cations by a cyclic α,β-peptoid hexamer: a spectroscopic and computational study

We describe the qualitative and quantitative analysis of the complexation properties towards cations of a cyclic peptoid hexamer composed of alternating α- and β-peptoid monomers, which bear exclusively chiral (S)-phenylethyl side chains (spe) that have no noticeable chelating properties. The binding of a series of monovalent and divalent cations was assessed by 1H NMR, circular dichroism, fluorescence and molecular modelling. In contrast to previous studies on cations binding by 18-membered α-cyclopeptoid hexamers, the 21-membered cyclopeptoid cP1 did not complex monovalent cations (Na+, K+, Ag+) but showed selectivity for divalent cations (Ca2+, Ba2+, Sr2+ and Mg2+). Hexacoordinated C-3 symmetrical complexes were demonstrated for divalent cations with ionic radii around 1 Å (Ca2+ and Ba2+), while 5-coordination is preferred for divalent cations with larger (Ba2+) or smaller ionic radii (Mg2+)

Influence of washing and quenching in profiling the metabolome of adherent mammalian cells: A case study with the metastatic breast cancer cell line MDA-MB-231

Metabolome characterisation is a powerful tool in oncology. To obtain a valid description of the intracellular metabolome, two of the preparatory steps are crucial, namely washing and quenching. Washing must effectively remove the extracellular media components and quenching should stop the metabolic activities within the cell, without altering the membrane integrity of the cell. Therefore, it is important to evaluate the efficiency of the washing and quenching solvents. In this study, we employed two previously optimised protocols for simultaneous quenching and extraction, and investigated the effects of a number of washing steps/solvents and quenching solvent additives, on metabolite leakage from the adherent metastatic breast cancer cell line MDA-MB-231. We explored five washing protocols and five quenching protocols (including a control for each), and assessed for effectiveness by detecting ATP in the medium and cell morphology changes through scanning electron microscopy (SEM) analyses. Furthermore, we studied the overall recovery of eleven different metabolite classes using the GC-MS technique and compared the results with those obtained from the ATP assay and SEM analysis. Our data demonstrate that a single washing step with PBS and quenching with 60% methanol supplemented with 70 mM HEPES (−50 °C) results in minimum leakage of intracellular metabolites. Little or no interference of PBS (used in washing) and methanol/HEPES (used in quenching) on the subsequent GC-MS analysis step was noted. Together, these findings provide for the first time a systematic study into the washing and quenching steps of the metabolomics workflow for studying adherent mammalian cells, which we believe will improve reliability in the application of metabolomics technology to study adherent mammalian cell metabolism

Orthodontics in the era of big data analytics

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/1/ocr12279_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149344/2/ocr12279.pd

Effect of capping groups at the N- and C-termini on the conformational preference of α,β-peptoids

invitation, special theme issue on Foldamer ChemistryInternational audienc

Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ¹H NMR spectral data to reduce interference and enhance robust biomarkers selection.

We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data