Optimization of methods and treatment conditions for studying effects of fatty acids on cell growth

Antiproliferative properties of molecular regulators of lipid metabolism have been increasingly studied during recent years. Discussion is ongoing concerning optimal treatment conditions and assays used for monitoring proliferation and cytotoxicity. The objective of the present work was to optimize methods and treatment conditions used for studying antiproliferative effects of fatty acids and analogs, represented by palmitic acid (PA) and the β-oxidation-restricted fatty acid analog tetradecylthioacetic acid (TTA), in rat (BT4Cn) and human (D54Mg and GaMg) glioma cell lines. Changes in [³H]thymidine incorporation preceded changes in cell number in TTA-treated glioma cell cultures, and the growth inhibition was more significantly expressed by [³H]thymidine incorporation than cell number. Addition of bovine serum albumin decreased cellular fatty acid uptake and reduced the effects of TTA and PA on [³H]thymidine incorporation. Determination of the antiproliferative effect of TTA in BT4Cn cells by MTT conversion and [³H]thymidine incorporation yielded concordant results. TTA-mediated reduction in cell number corresponded to reduction in cellular protein and total DNA content in BT4Cn cells. Reduced growth potential in TTA-treated multicellular D54Mg and GaMg spheroids supported the findings from monolayer cultures. In conclusion, cell density, treatment period, fatty acid administration, and methods for growth determination may profoundly influence the outcome of cell growth experiments. Thus, experimental conditions should be carefully controlled when performing cell growth experiments, and effects on cell growth should preferably be confirmed by different methods. Karl Johan Tronstad and Kjetil Berge contributed equally to this article.     

Future trends in data mining

Over recent years data mining has been establishing itself as one of the major disciplines in computer science with growing industrial impact. Undoubtedly, research in data mining will continue and even increase over coming decades. In this article, we sketch our vision of the future of data mining. Starting from the classic definition of “data mining”, we elaborate on topics that — in our opinion — will set trends in data mining.     

Diterpene structure elucidation from 13cnmr spectra with inductive logic programming

We present a novel application ofInductive Logic Programming (ILP) to the problem of diterpene structure elucidation from 13 CNMR spectra. Diterpenes are organic compounds oflow molecular weight with a skeleton of 20 carbon atoms. They are of significant chemical and commercial interest because oftheir use as lead compounds in the search for new pharmaceutical effectors. The interpretation of diterpene 13 CNMR spectra normally requires specialists with detailed spectroscopic knowledge and substantial experience in natural products chemistry, specifically knowledge on peak patterns and chemical structures. Given a database ofpeak patterns for diterpenes with known structure, we apply several ILP approaches to discover correlations between peak patterns and chemical structure. The approaches used include first - order inductive learning, relational instance based learning, induction oflogical decision trees, and inductive constraint logic. Performance close to that of domain experts is achieved, which suffices for practical use.     

Performance evaluation of ambient services by combining robotic frameworks and a smart environment platform

Mobile robots and smart environments are two areas of research that can easily profit from each other. Smart environments, which are spaces unobtrusively equipped with sensors and actuators, providing ambient services to the people living within. Mobile robots inside those smart environments can use the existing infrastructure to increase their performance while decreasing the cost of local sensor systems. On the other side, evaluation of ambient services is often a laborious task. This work presents an approach that simplifies the evaluation by making use of two frameworks from robotics to perform tests in simulated smart environments. A method based on the language as action principle is used to extract realistic behavior of people living in real-world smart environments. Using this data, many different scenarios with varying configurations (different floor layouts, numbers and types of sensors, different number of people and pets) can easily be simulated and the performance of the ambient services evaluated.     

Moisture Diffusion Coefficients for Modeling the First and Second Drying Sections of Green Bricks

A model has been developed that describes the dependence of the moisture diffusion coefficient on the water fraction. Until the end of shrinkage has been achieved, the moisture diffusion coefficient is proportional to the second power of the water fraction. Due to shrinkage, the relevant capillary spaces available for water transport become smaller. Consequently, the moisture diffusion coefficient decreases continually. After the end of shrinkage, the flow resistance to the water moving toward the surface increases sharply due to penetrating air. This leads to a steep drop of the moisture diffusion coefficient by several powers of ten. Measurements were carried out with specimens of defined geometry to determine the moisture diffusion coefficient. On the basis of a specified limiting value, the model is capable of calculating the moisture diffusion for all initially specified raw materials moistures. The moisture can also be determined if the degree of drying shrinkage is known. Using the determined moisture diffusion coefficient, the first and the second drying section can be located. Drying tests were carried out in a laboratory dryer and the experimental results obtained were compared to the simulation results. The simulation results are in good agreement with the experimental results.     

Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics

The effect of coffee consumption on human health is still discussed controversially. Here, we report results from a metabolomics study of coffee consumption, where we measured 363 metabolites in blood serum of 284 male participants of the Cooperative Health Research in the Region of Augsburg study population, aged between 55 and 79 years. A statistical analysis of the association of metabolite concentrations and the number of cups of coffee consumed per day showed that coffee intake is positively associated with two classes of sphingomyelins, one containing a hydroxy‐group (SM(OH)) and the other having an additional carboxy‐group (SM(OH,COOH)). In contrast, long‐ and medium‐chain acylcarnitines were found to decrease with increasing coffee consumption. It is noteworthy that the concentration of total cholesterol also rises with an increased coffee intake in this study group. The association observed here between these hydroxylated and carboxylated sphingolipid species and coffee intake may be induced by changes in the cholesterol levels. Alternatively, these molecules may act as scavengers of oxidative species, which decrease with higher coffee intake. In summary, we demonstrate strong positive associations between coffee consumption and two classes of sphingomyelins and a negative association between coffee consumption and long‐ and medium‐chain acylcarnitines.     

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