Search

Modern applications in chemometrics and bioinformatics result in compositional data sets with a high proportion of zeros. An example are microbiome data, where zeros refer to measurements below the detection limit of one count. When building statistical models, it is important that zeros are replaced by sensible values. Different replacement techniques from compositional data analysis are considered and compared by a simulation study and examples. The comparison also includes a recently proposed method [1] based on deep learning. Detailed insights into the appropriateness of the methods for a problem at hand are provided, and differences in the outcomes of statistical results are discussed.

Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mik\v{s}ov\'a, Rieser, and Filzmoser (2019) have introduced a method for the identification of spatial patterns with increased element concentrations in samples along a linear sampling transect. This procedure is based on fitting Generalized Additive Models (GAMs) to the concentration data, and computing a curvature measure from the pairwise log-ratios of these fits. The higher the curvature, the more likely one or both elements of the pair indicate local mineralization. This method is applied on two geochemical data sets which have been collected specifically for the purpose of mineral exploration. The aim is to test the technique for its ability to identify pathfinder elements to detect mineralized zones, and to verify whether the method can indicate which sampling material is best suited for this purpose. Reference: Mik\v{s}ov\'a D., Rieser C., Filzmoser P. (2019). "Identification of mineralization in geochemistry along a transect based on the spatial curvature of log-ratios." arXiv, (1912.02867)., Comment: 12 pages, 8 figures

The European Union (EU) Sustainable Development Goals (SDG) indicator set replaced the EU Sustainable Development Strategy (SDS) in 2017. The selected indicators of this set were chosen for the analysis to classify the sample of the 28 EU countries along with Norway according to their performance in sustainability. In the selection of indicators, priority was given to the indicators reflecting the social dimension of SD, along with important representatives of the economic, ecological and institutional dimensions of SD generally. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA) were applied to the data of 12 indicators in the period 2012- 2016. By means of the HCA, four clusters were created in each year of the period 2012-2016 using the indicator values of particular years and then using all the indicator values in all the monitored years for the general assignment of countries to particular clusters. According to changes in the assignment to particular clusters over the years, the sustainability of development and the path of SD in the examined countries are assessed. As regards the core countries of each cluster, cluster 1 includes the most developed EU countries and is thus evaluated as the best performing cluster. Cluster 2 including the least developed EU countries is evaluated as the worst performing cluster. Cluster 3 predominantly includes the transitive economies and it is evaluated as the second best performing cluster according to the indicators applied. Cluster 4 containing the Southern countries is assessed as the second worst performing cluster. From the shifts of countries that occurred between the years, the shift of Ireland from cluster 3 to cluster 1 in 2013 must be emphasised as the move towards higher sustainability. The shift of Slovakia and Hungary from cluster 2 to cluster 3 in 2013 is also evaluated as progress towards higher sustainability. W Unii Europejskiej w 2017 r. Cele zrównoważonego rozwoju zastąpiły dotychczasową Strategię zrównoważo-nego rozwoju. W tej pracy wybrane wskaźniki odnoszące się do nowych Celów zrównoważonego rozwoju stano-wią podstawę klasyfikacji 28 krajów Wspólnoty oraz Norwegii. Wśród tych wskaźników priorytetowo potrakto-wano te odnoszące się do wymiaru społecznego zrównoważonego rozwoju, uzupełniając dyskusję o podstawowe wskaźniki ekonomiczne, ekologiczne i instytucjonalne. Przeanalizowano okres obejmujący lata 2012-2016. Wo-bec wybranych 12 wskaźników zastosowano hierarchiczną analizę skupień i analizę głównych składników. Utworzono cztery klastry w ramach każdego roku z analizowanego okresu, określając wartości wskaźników dla po-szczególnych lat, a następnie określenie wszystkich wartości wskaźników dla wszystkich monitorowanych lat umożliwiło przypisanie krajów do poszczególnych klastrów. Określenie zmian w przypisaniu do poszczególnych klastrów na przestrzeni lat umożliwiło ocenę zrównoważoności rozwoju i określenie ścieżki zrównoważonego rozwoju badanych krajów. Jeśli chodzi o główne kraje każdego klastra, to klaster 1 obejmuje najbardziej rozwi-nięte kraje UE i dlatego jest oceniany jako klaster, który osiąga najlepsze wyniki. Klaster 2 uwzględnia najsłabiej rozwinięte kraje i oceniony jest jako ten, który osiąga najgorsze wyniki. Klaster 3 obejmuje głównie gospodarki znajdujące się w okresie przejściowym i jest oceniany jako drugi osiągający najlepsze wyniki. Klaster 4 obejmuje kraje Południa i jest oceniany jako drugi osiągający najgorsze wyniki. Uwzględniając zmiany jakie zaszły w okre-sie kolejnych lat, należy podkreślić przesunięcie Irlandii z klastra 3 do klastra 1 w 2013 r., co oznacza ruch w kierunku większej zrównoważoności. Tak samo należy ocenić przejście w tym samym roku Słowacji i Węgier z klastra 2 do klastra 3. Web of Science 14 2 24 7

The UpDeep project aims at developing geochemical expertise on deep buried exploration methodology and anchoring the knowledge accrued as a local surface geochemical business into Europe. Soil partial leaches and biogeochemistry have solid status in grass roots exploration in other continents, but have as yet gained only minor ground in Europe. Anthropogenic impacts and lack of continuous research are identified as the main factors contributing to their low acceptance. The driving-forces for these extremely low environmental impact sampling techniques are increased public environmental awareness and prolonged processes to gain land access permits. UpDeep focuses on scientificevidencing of soil partial leaches and biogeochemistry in mineral exploration as a collaboration of the research institutions GTK, BRGM, GEUS, VUT and VTT, and a geo-consulting company GeoPool to promote the use of surface geochemistry across Europe. UpDeep has utilized existing soil partial leach and biogeochemical data from Finland, and will acquire new datasets over known mineralizations in France and Greenland. The business feasibility and marketstudy will be conducted by Spinverse. We tackle several technical aspects of surface geochemistry including efficient sampling protocols and sample media selection, production of a surface geochemical standard reference material bank, and compositional data analysis. A web based data analysis and delivery platform will be constructed to facilitate quick delivery of results and continuous interaction in data interpretation between the geochemical consultant and an exploration company. An educational component on producing learning materials and proving classes for the exploration industry should increase awareness and understanding of the suitability of surface geochemical sampling media in different environments. The general business model will be modular to suit the needs of customers with backgrounds in surface geochemical exploration.UpDeep is a three year project (2017-2020, http://projects.gtk.fi/updeep/) funded by the European Information and Technology Raw Materials.

International audience; The instrument Cometary Secondary Ion Mass Analyzer (COSIMA) on board of the European Space Agency mission Rosetta to the comet 67P/Churyumov-Gerasimenko is a secondary ion mass spectrometer with a time-of-flight mass analyzer. It collected near the comet several thousand particles, imaged them, and analyzed the elemental and chemical compositions of their surfaces. In this study, variables have been generated from the spectral data covering the mass ranges of potential C-, H-, N-, and O-containing ions. The variable importance in binary discriminations between spectra measured on cometary particles and those measured on the target background has been estimated by the univariate t test and the multivariate methods discriminant partial least squares, random forest, and a robust method based on the log ratios of all variable pairs. The results confirm the presence of organic substances in cometary matter—probably a complex macromolecular mixture.

The GEMAS (geochemical mapping of agricultural soil) project collected 2108 Ap horizon soil samples from regularly ploughed fields in 33 European countries, covering 5.6 million km2. The