Composition of cometary particles collected during two periods of the Rosetta mission: multivariate evaluation of mass spectral data.

The instrument COSIMA (COmetary Secondary Ion Mass Analyzer) onboard of the European Space Agency mission Rosetta collected and analyzed dust particles in the neighborhood of comet 67P/Churyumov-Gerasimenko. The chemical composition of the particle surfaces was characterized by time-of-flight secondary ion mass spectrometry. A set of 2213 spectra has been selected, and relative abundances for CH-containing positive ions as well as positive elemental ions define a set of multivariate data with nine variables. Evaluation by complementary chemometric techniques shows different compositions of sample groups collected during two periods of the mission. The first period was August to November 2014 (far from the Sun); the second period was January 2015 to February 2016 (nearer to the Sun). The applied data evaluation methods consider the compositional nature of the mass spectral data and comprise robust principal component analysis as well as classification with discriminant partial least squares regression, k-nearest neighbor search, and random forest decision trees. The results indicate a high importance of the relative abundances of the secondary ions C+ and Fe+ for the group separation and demonstrate an enhanced content of carbon-containing substances in samples collected in the period with smaller distances to the Sun.

A laser desorption ionization/matrix-assisted laser desorption ionization target system applicable for three distinct types of instruments (LinTOF/curved field RTOF, LinTOF/RTOF and QqRTOF) with different performance characteristics from three vendors.

RATIONALE: We have developed a target system which enables the use of only one target (i.e. target preparation set) for three different laser desorption ionization (LDI)/matrix-assisted laser desorption ionization (MALDI) mass spectrometric instruments. The focus was on analysing small biomolecules with LDI for future use of the system for the study of meteorite samples (carbonaceous chondrites) using devices with different mass spectrometric performance characteristics. METHODS: Three compounds were selected due to their potential presence in meteoritic chondrites: tryptophan, 2-deoxy-d-ribose and triphenylene. They were prepared (with and without MALDI matrix, i.e. MALDI and LDI) and analysed with three different mass spectrometers (LinTOF/curved field RTOF, LinTOF/RTOF and QqRTOF). The ion sources of two of the instruments were run at high vacuum, and one at intermediate pressure. Two devices used a laser wavelength of 355 nm and one a wavelength of 337 nm. RESULTS: The developed target system operated smoothly with all devices. Tryptophan, 2-deoxy-d-ribose and triphenylene showed similar desorption/ionization behaviour for all instruments using the LDI mode. Interestingly, protonated tryptophan could be observed only with the LinTOF/curved field RTOF device in LDI and MALDI mode, while sodiated molecules were observed with all three instruments (in both ion modes). Deprotonated tryptophan was almost completely obscured by matrix ions in the MALDI mode whereas LDI yielded abundant deprotonated molecules. CONCLUSIONS: The presented target system allowed successful analyses of the three compounds using instruments from different vendors with only one preparation showing different analyser performance characteristics. The elemental composition with the QqRTOF analyser and the high-energy 20 keV collision-induced dissociation fragmentation will be important in identifying unknown compounds in chondrites.

Mechanical and electrostatic experiments with dust particles collected in the inner coma of comet 67P by COSIMA onboard Rosetta.

The in situ cometary dust particle instrument COSIMA (COmetary Secondary Ion Mass Analyser) onboard ESA's Rosetta mission has collected about 31 000 dust particles in the inner coma of comet 67P/Churyumov-Gerasimenko since August 2014. The particles are identified by optical microscope imaging and analysed by time-of-flight secondary ion mass spectrometry. After dust particle collection by low speed impact on metal targets, the collected particle morphology points towards four families of cometary dust particles. COSIMA is an in situ laboratory that operates remotely controlled next to the comet nucleus. The particles can be further manipulated within the instrument by mechanical and electrostatic means after their collection by impact. The particles are stored above 0°C in the instrument and the experiments are carried out on the refractory, ice-free matter of the captured cometary dust particles. An interesting particle morphology class, the compact particles, is not fragmented on impact. One of these particles was mechanically pressed and thereby crushed into large fragments. The particles are good electrical insulators and transform into rubble pile agglomerates by the application of an energetic indium ion beam during the secondary ion mass spectrometry analysis.This article is part of the themed issue 'Cometary science after Rosetta'.

High-molecular-weight organic matter in the particles of comet 67P/Churyumov-Gerasimenko.

The presence of solid carbonaceous matter in cometary dust was established by the detection of elements such as carbon, hydrogen, oxygen and nitrogen in particles from comet 1P/Halley. Such matter is generally thought to have originated in the interstellar medium, but it might have formed in the solar nebula-the cloud of gas and dust that was left over after the Sun formed. This solid carbonaceous material cannot be observed from Earth, so it has eluded unambiguous characterization. Many gaseous organic molecules, however, have been observed; they come mostly from the sublimation of ices at the surface or in the subsurface of cometary nuclei. These ices could have been formed from material inherited from the interstellar medium that suffered little processing in the solar nebula. Here we report the in situ detection of solid organic matter in the dust particles emitted by comet 67P/Churyumov-Gerasimenko; the carbon in this organic material is bound in very large macromolecular compounds, analogous to the insoluble organic matter found in the carbonaceous chondrite meteorites. The organic matter in meteorites might have formed in the interstellar medium and/or the solar nebula, but was almost certainly modified in the meteorites' parent bodies. We conclude that the observed cometary carbonaceous solid matter could have the same origin as the meteoritic insoluble organic matter, but suffered less modification before and/or after being incorporated into the comet.

Severe drought stress is affecting selected primary metabolites, polyphenols, and volatile metabolites in grapevine leaves (Vitis vinifera cv. Pinot noir).

Extreme weather conditions with prolonged dry periods and high temperatures as well as heavy rain events can severely influence grapevine physiology and grape quality. The present study evaluates the effects of severe drought stress on selected primary metabolites, polyphenols and volatile metabolites in grapevine leaves. Among the 11 primary metabolites, 13 polyphenols and 95 volatiles which were analyzed, a significant discrimination between control and stressed plants of 7 primary metabolites, 11 polyphenols and 46 volatile metabolites was observed. As single parameters are usually not specific enough for the discrimination of control and stressed plants, an unsupervised (PCA) and a supervised (PLS-DA) multivariate approach were applied to combine results from different metabolic groups. In a first step a selection of five metabolites, namely citric acid, glyceric acid, ribose, phenylacetaldehyde and 2-methylbutanal were used to establish a calibration model using PLS regression to predict the leaf water potential. The model was strong enough to assign a high number of plants correctly with a correlation of 0.83. The PLS-DA provides an interesting approach to combine data sets and to provide tools for the specific evaluation of physiological plant stresses.

Comet 67P/Churyumov-Gerasimenko sheds dust coat accumulated over the past four years.

Comets are composed of dust and frozen gases. The ices are mixed with the refractory material either as an icy conglomerate, or as an aggregate of pre-solar grains (grains that existed prior to the formation of the Solar System), mantled by an ice layer. The presence of water-ice grains in periodic comets is now well established. Modelling of infrared spectra obtained about ten kilometres from the nucleus of comet Hartley 2 suggests that larger dust particles are being physically decoupled from fine-grained water-ice particles that may be aggregates, which supports the icy-conglomerate model. It is known that comets build up crusts of dust that are subsequently shed as they approach perihelion. Micrometre-sized interplanetary dust particles collected in the Earth's stratosphere and certain micrometeorites are assumed to be of cometary origin. Here we report that grains collected from the Jupiter-family comet 67P/Churyumov-Gerasimenko come from a dusty crust that quenches the material outflow activity at the comet surface. The larger grains (exceeding 50 micrometres across) are fluffy (with porosity over 50 per cent), and many shattered when collected on the target plate, suggesting that they are agglomerates of entities in the size range of interplanetary dust particles. Their surfaces are generally rich in sodium, which explains the high sodium abundance in cometary meteoroids. The particles collected to date therefore probably represent parent material of interplanetary dust particles. This argues against comet dust being composed of a silicate core mantled by organic refractory material and then by a mixture of water-dominated ices. At its previous recurrence (orbital period 6.5 years), the comet's dust production doubled when it was between 2.7 and 2.5 astronomical units from the Sun, indicating that this was when the nucleus shed its mantle. Once the mantle is shed, unprocessed material starts to supply the developing coma, radically changing its dust component, which then also contains icy grains, as detected during encounters with other comets closer to the Sun.

Multivariate linear QSPR/QSAR models: Rigorous evaluation of variable selection for PLS.

Basic chemometric methods for making empirical regression models for QSPR/QSAR are briefly described from a user's point of view. Emphasis is given to PLS regression, simple variable selection and a careful and cautious evaluation of the performance of PLS models by repeated double cross validation (rdCV). A demonstration example is worked out for QSPR models that predict gas chromatographic retention indices (values between 197 and 504 units) of 209 polycyclic aromatic compounds (PAC) from molecular descriptors generated by Dragon software. Most favorable models were obtained from data sets containing also descriptors from 3D structures with all H-atoms (computed by Corina software), using stepwise variable selection (reducing 2688 descriptors to a subset of 22). The final QSPR model has typical prediction errors for the retention index of ±12 units (95% tolerance interval, for test set objects). Programs and data are provided as supplementary material for the open source R software environment.

RMol: a toolset for transforming SD/Molfile structure information into R objects.

BACKGROUND: The graph-theoretical analysis of molecular networks has a long tradition in chemoinformatics. As demonstrated frequently, a well designed format to encode chemical structures and structure-related information of organic compounds is the Molfile format. But when it comes to use modern programming languages for statistical data analysis in Bio- and Chemoinformatics, R as one of the most powerful free languages lacks tools to process Molfile data collections and import molecular network data into R. RESULTS: We design an R object which allows a lossless information mapping of structural information from Molfiles into R objects. This provides the basis to use the RMol object as an anchor for connecting Molfile data collections with R libraries for analyzing graphs. Associated with the RMol objects, a set of R functions completes the toolset to organize, describe and manipulate the converted data sets. Further, we bypass R-typical limits for manipulating large data sets by storing R objects in bz-compressed serialized files instead of employing RData files. CONCLUSIONS: By design, RMol is a R toolset without dependencies to other libraries or programming languages. It is useful to integrate into pipelines for serialized batch analysis by using network data and, therefore, helps to process sdf-data sets in R efficiently. It is freely available under the BSD licence. The script source can be downloaded from http://sourceforge.net/p/rmol-toolset.

Information indices with high discriminative power for graphs.

In this paper, we evaluate the uniqueness of several information-theoretic measures for graphs based on so-called information functionals and compare the results with other information indices and non-information-theoretic measures such as the well-known Balaban J index. We show that, by employing an information functional based on degree-degree associations, the resulting information index outperforms the Balaban J index tremendously. These results have been obtained by using nearly 12 million exhaustively generated, non-isomorphic and unweighted graphs. Also, we obtain deeper insights on these and other topological descriptors when exploring their uniqueness by using exhaustively generated sets of alkane trees representing connected and acyclic graphs in which the degree of a vertex is at most four.

Random projection for dimensionality reduction--applied to time-of-flight secondary ion mass spectrometry data.

Random projection (RP) is a simple and fast linear method for dimensionality reduction of high-dimensional multivariate data, independent from the data. The method is briefly described and a new memory-saving algorithm is presented for the generation of random projection vectors. Application of RP to data from scanning experiments with a time-of-flight secondary ion mass spectrometer (TOF-SIMS) showed that data reduced by RP have a satisfying discriminant property for separating target material and minerals without using any knowledge about the composition of the sample. A selection method--based on low dimensional RP data--is described and successfully tested for automatic recognition of characteristic, diverse locations of a sample surface. RP is demonstrated as an unbiased, powerful method, especially for large data sets, severe hardware restrictions (such as in space experiments) or the need for fast data evaluation of hyperspectral data.

Novel topological descriptors for analyzing biological networks.

BACKGROUND: Topological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information. RESULTS: In this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem. CONCLUSIONS: Our study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.

A large scale analysis of information-theoretic network complexity measures using chemical structures.

This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design. Numerous such measures have been developed so far but many of them lack a meaningful interpretation, e.g., we want to examine which kind of structural information they detect. Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks. Starting from several sets containing real and synthetic chemical structures represented by graphs, we study the relatedness between a classical (partition-based) complexity measure called the topological information content of a graph and some others inferred by a different paradigm leading to partition-independent measures. Moreover, we evaluate the uniqueness of network complexity measures numerically. Generally, a high uniqueness is an important and desirable property when designing novel topological descriptors having the potential to be applied to large chemical databases.

On entropy-based molecular descriptors: statistical analysis of real and synthetic chemical structures.

This paper presents an analysis of entropy-based molecular descriptors. Specifically, we use real chemical structures, as well as synthetic isomeric structures, and investigate properties of and among descriptors with respect to the used data set by a statistical analysis. Our numerical results provide evidence that synthetic chemical structures are notably different to real chemical structures and, hence, should not be used to investigate molecular descriptors. Instead, an analysis based on real chemical structures is favorable. Further, we find strong hints that molecular descriptors can be partitioned into distinct classes capturing complementary information.

Chemometric evaluation of time-of-flight secondary ion mass spectrometry data of minerals in the frame of future in situ analyses of cometary material by COSIMA onboard ROSETTA.

Chemometric data evaluation methods for time-of-flight secondary ion mass spectrometry (TOF-SIMS) have been tested for the characterization and classification of minerals. Potential applications of these methods include the expected data from cometary material to be measured by the COSIMA instrument onboard the ESA mission ROSETTA in the year 2014. Samples of the minerals serpentine, enstatite, olivine, and talc have been used as proxies for minerals existing in extraterrestrial matter. High mass resolution TOF-SIMS data allow the selection of peaks from inorganic ions relevant for minerals. Multivariate cluster analysis of peak intensity data by principal components analysis and the new method CORICO showed a good separation of the mineral classes. Classification by k nearest-neighbor classification (KNN) or binary decision trees (CART method) results in more than 90% correct class assignments in a leave-one-out cross validation.

Exploration of anthropological specimens by GC-MS and chemometrics.

Anthropological specimens combine a variety of unfavorable characteristics, rendering their evaluation an analytical challenge. Their remarkable status is primarily based on two characteristics: (i) these very rare samples of human origin are testimonies of human history and are, therefore, available only in minute amounts for analytical purposes, and (ii) the analysis of these samples is extremely limited by the decomposition of molecules, which are easily detected in living organisms, such as nucleic acids and proteins, but are subject to rapid post-mortem decay. In this article, we review the methods and results of archaeometry, emphasizing the role of MS combined with chemometrics. Focusing on experimental results for fatty acid profiles, specimens from mummies from different civilizations were compared. Considering in particular the Tyrolean Iceman, the application of chemometric methods to GC-MS data recovers essential information about the preservation and the storage conditions of mummies.

Fatty acid composition and preservation of the Tyrolean Iceman and other mummies.

In anthropology, objective parameters to adequately describe storage conditions and the preservation of mummies have yet to be identified. Considering that fatty acids degrade to stable products, we analysed their profile in human mummies and in control samples by gas chromatography coupled to mass spectrometry (GC/MS). Originating from different epochs and civilizations, samples of the Tyrolean Iceman, other glacier corpses, a freeze dried mummy, corpses from a permafrost region, a corpse mummified immersed in water, and a desert mummy were evaluated. Chemometric analysis based on the concentrations of 16 fatty acids revealed the degree of similarity between anthropologic and fresh corpse samples, which was mainly influenced by the content of palmitic acid, oleic acid, and 10-hydroxystearic acid. The presence of 10-hydroxystearic acid was associated with immersion in water, whereas dry mummification was accompanied by high contents of oleic acid. Samples of the Tyrolean Iceman clustered between fresh tissue and those of other glacier corpses indicating the good preservation of this mummy. Thus, environmental post-mortem conditions were associated with characteristic fatty acid patterns suggesting that chemometric analysis of fatty acid contents may add to our knowledge about post-mortem storage conditions and the preservation of human corpses.