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Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitrodiagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

Motivation:We tackle the problem of finding regularities in microarray data. Various data mining tools, such as clustering, classification, Bayesian networks and association rules, have been applied so far to gain insight into gene-expression data. Association rule mining techniques used so far work on discretizations of the data and cannot account for cumulative effects. In this paper, we investigate the use of quantitative association rules that can operate directly on numeric data and represent cumulative effects of variables. Technically speaking, this type of quantitative association rules based on half-spaces can find non-axis-parallel regularities.Results:We performed a variety of experiments testing the utility of quantitative association rules for microarray data. First of all, the results should be statistically significant and robust against fluctuations in the data. Next, the approach should be scalable in the number of variables, which is important for such high-dimensional data. Finally, the rules should make sense biologically and be sufficiently different from rules found in regular association rule mining working with discretizations. In all of these dimensions, the proposed approach performed satisfactorily. Therefore, quantitative association rules based on half-spaces should be considered as a tool for the analysis of microarray gene-expression data.Availability:The code is available from the authors on request.Contact:kramer@in.tum.de

Abstract: A green phototrophic bacterium was enriched with ferrous iron as sole electron donor and was isolated in defined coculture with a spirilloid chemoheterotrophic bacterium. The coculture oxidized ferrous iron to ferric iron with stoichiometric formation of cell mass from carbon dioxide. Sulfide, thiosulfate, or elemental sulfur was not used as electron donor in the light. Hydrogen or acetate in the presence of ferrous iron increased the cell yield of the phototrophic partner, and hydrogen could also be used as sole electron source. Complexed ferric iron was slowly reduced to ferrous iron in the dark, with hydrogen as electron source. Similar to Chlorobium limicola, the phototrophic bacterium contained bacteriochlorophyll c and chlorobactene as photosynthetic pigments, and also resembled representatives of this species morphologically. On the basis of 16S rRNA sequence comparisons, this organism clusters with Chlorobium, Prosthecochloris, and Pelodictyon species within the green sulfur bacteria phylum. Since the phototrophic partner in the coculture KoFox is only moderately related to the other members of the cluster, it is proposed as a new species, Chlorobium ferrooxidans. The chemoheterotrophic partner bacterium, strain KoFum, was isolated in pure culture with fumarate as sole substrate. The strain was identified as a member of the ɛ-subclass of the Proteobacteria closely related to “Geospirillum arsenophilum” on the basis of physiological properties and 16S rRNA sequence comparison. The “Geospirillum” strain was present in the coculture only in low numbers. It fermented fumarate, aspartate, malate, or pyruvate to acetate, succinate, and carbon dioxide, and could reduce nitrate to dinitrogen gas. It was not involved in ferrous iron oxidation but possibly provided a thus far unidentified growth factor to the phototrophic partner.

Die Blattoberflachen von Sinapis arvensis L., Beta vulgaris L., Hordeum vulgare L. und Galium aparine L. weisen einen unterschiedlichen Lipophiliegrad auf, der durch Randwinkelmessungen von Wassertropfen sichtbar wird. Durch Einsatz von Tensiden mit einem Ethylenoxydanteil von 5 bis 18 Gruppen wird eine unterschiedlwiche Verbesserung der Benetzung erreicht, wobei die hydrophile Oberflache von S. arvensis durch Tenside mit einem hohen Hydrophilie-Lipophilie-Balance-Wert (HLB-Wert) von 16 und H. vulgare mit einem geringeren HLB-Wert von 9,8 am besten benetzt wurden. Bei B. vulgaris lag die Benetzung aller eingesetzten Tenside gleich. Die Randwinkelmessung ergab zwar eine Reduzierung des Randwinkels in Abhangigkeit von der Zunahme des Lipophilie-grades des Tensides, bei Berucksichtigung der Oberflachenspannung der tensidischen Losungen wurde diese Differenzierung jedoch vollstandig kompensiert.

Some of the strains used for the biological acidification in breweries belong to L. delbrueckiisubsp. delbrueckii, L. delbrueckiisubsp. lactisor L. fermentum. However, more recent studies showed that most strains isolated are physiologically different from the above mentioned species and were tentatively allocated to Lactobacillus amylovorus. Genotypic studies of 25 strains exclusively isolated from beer malts and beer worts, showed, that there were differences to the type strain of L. amylovorusconcerning DNA-DNA similarities and the sequences of their 16S and 23S rRNA genes. Therefore, we propose to combine these strains in a new species of the genus Lactobacillus, namely L. amylolyticus. Strain DSM 11664 is proposed as the type strain. An rRNA targeted oligonucleotide probe was designed that allows a fast and reliable identification of Lactobacillus amylolyticus.

Aqueous solutions of certain trisiloxane surfactants wet low energy surfaces (i. e., polypropylene, wax covered leaves). Investigations on the macroscopic spreading velocity, the phase behavior as well as first attempts to visualize slowly spreading drops of pure surfactants did not solve the questions about the structure of rapidly spreading surfactant solution drops and the evolution of the corresponding dynamic energy balance. Therefore, a strategy for the hydrophilic modification of fractal Ir electrodes surfaces for investigating these effects was developed.