Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing 1H-1H TOCSY NMR.

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the cellular pathways of development, and enhances our understanding of best practices for in vitro differentiation to guide cellular therapies. In this work, the dynamic evolution of adipose-tissue-derived human Mesenchymal stem cells (AT-derived hMSCs) after fourteen days of cultivation, adipocyte and osteocyte differentiation, was inspected based on 1H-1H TOCSY using machine learning. Multi-class classification in addition to the novelty detection of metabolites was established based on a control hMSC sample after four days' cultivation and we successively detected the changes of metabolites in differentiated MSCs following a set of 1H-1H TOCSY experiments. The classifiers Kernel Null Foley-Sammon Transform and Kernel Density Estimation achieved a total classification error between 0% and 3.6% and false positive and false negative rates of 0%. This approach was successfully able to automatically reveal metabolic changes that accompanied MSC cellular evolution starting from their undifferentiated status to their prolonged cultivation and differentiation into adipocytes and osteocytes using machine learning supporting the research in the field of metabolic pathways of stem cell differentiation.

Novelty detection for metabolic dynamics established on breast cancer tissue using 2D NMR TOCSY spectra.

Most metabolic profiling approaches focus only on identifying pre-known metabolites on NMR TOCSY spectrum using configured parameters. However, there is a lack of tasks dealing with automating the detection of new metabolites that might appear during the dynamic evolution of biological cells. Novelty detection is a category of machine learning that is used to identify data that emerge during the test phase and were not considered during the training phase. We propose a novelty detection system for detecting novel metabolites in the 2D NMR TOCSY spectrum of a breast cancer-tissue sample. We build one- and multi-class recognition systems using different classifiers such as, Kernel Null Foley-Sammon Transform, Kernel Density Estimation, and Support Vector Data Description. The training models were constructed based on different sizes of training data and are used in the novelty detection procedure. Multiple evaluation measures were applied to test the performance of the novelty detection methods. Depending on the training data size, all classifiers were able to achieve 0% false positive rates and total misclassification error in addition to 100% true positive rates. The median total time for the novelty detection process varies between 1.5 and 20 seconds, depending on the classifier and the amount of training data. The results of our novel metabolic profiling method demonstrate its suitability, robustness and speed in automated metabolic research.

Atmospheric Correction for High-Resolution Shape from Shading on Mars.

Digital Elevation Models (DEMs) of planet Mars are crucial for many remote sensing applications and for landing site characterization of rover missions. Shape from Shading (SfS) is known to work well as a complementary method to greatly enhance the quality of photogrammetrically obtained DEMs of planetary surfaces with respect to the effective resolution and the overall accuracy. In this work, we extend our previous lunar shape and albedo from shading framework by embedding the Hapke photometric reflectance model in an atmospheric model such that it is applicable to Mars. Compared to previous approaches, the proposed method is capable of directly estimating the atmospheric parameters from a given scene without the need for external data, and assumes a spatially varying albedo. The DEMs are generated from imagery of the Context Camera (CTX) onboard the Mars Reconnaissance Orbiter (MRO) and are validated for clear and opaque atmospheric conditions. We analyze the necessity of using atmospheric compensation depending on the atmospheric conditions. For low optical depths, the Hapke model without an atmospheric component is still applicable to the Martian surface. For higher optical depths, atmospheric compensation is required to obtain good quality DEMs.

Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR).

Metabolomics is an expanding field of medical diagnostics since many diseases cause metabolic reprogramming alteration. Additionally, the metabolic point of view offers an insight into the molecular mechanisms of diseases. Due to the complexity of metabolic assignment dependent on the 1D NMR spectral analysis, 2D NMR techniques are preferred because of spectral resolution issues. Thus, in this work, we introduce an automated metabolite identification and assignment from 1H-1H TOCSY (total correlation spectroscopy) using real breast cancer tissue. The new approach is based on customized and extended semi-supervised classifiers: KNFST, SVM, third (PC3) and fourth (PC4) degree polynomial. In our approach, metabolic assignment is based only on the vertical and horizontal frequencies of the metabolites in the 1H-1H TOCSY. KNFST and SVM show high performance (high accuracy and low mislabeling rate) in relatively low size of initially labeled training data. PC3 and PC4 classifiers showed lower accuracy and high mislabeling rates, and both classifiers fail to provide an acceptable accuracy at extremely low size (≤9% of the entire dataset) of initial training data. Additionally, semi-supervised classifiers were implemented to obtain a fully automatic procedure for signal assignment and deconvolution of TOCSY, which is a big step forward in NMR metabolic profiling. A set of 27 metabolites were deduced from the TOCSY, and their assignments agreed with the metabolites deduced from a 1D NMR spectrum of the same sample analyzed by conventional human-based methodology.

First In Situ Identification of Ultradian and Infradian Rhythms, and Nocturnal Locomotion Activities of Four Colonies of Red Wood Ants ( Formica rufa-Group).

In situ activity patterns of 2 Formica rufa-group species ( F. pratensis; F. polyctena) were continuously studied at 4 different red wood-ant nests for 6 months in each of the years 2010, 2011, 2012, and 2016 and related to weather factors and variations in the Earth's magnetic field. The in situ activity patterns of both species were similarly periodic and exhibited ultradian, and short- and long infradian rhythms under natural LD conditions. Crepuscular and nocturnal activities shorter than or equal to 4 h were observed in both species, especially at the new moon and first quarter after the astronomical twilight in a period of darkness in fall. We hypothesize that local variability in the Earth's magnetic field affects these long-term activity patterns, whereas humidity and temperature were more strongly associated with ultradian rhythms (less than 20 h).

Degassing Rhythms and Fluctuations of Geogenic Gases in A Red Wood-Ant Nest and in Soil in The Neuwied Basin (East Eifel Volcanic Field, Germany).

Geochemical tracers of crustal fluids (CO2, He, Rn) provide a useful tool for the identification of buried fault structures. We acquired geochemical data during 7-months of continual sampling to identify causal processes underlying correlations between ambient air and degassing patterns of three gases (CO2, He, Rn) in a nest of red wood ants (Formica polyctena; "RWA") and the soil at Goloring in the Neuwied Basin, a part of the East Eifel Volcanic Field (EEVF). We explored whether temporal relations and degassing rhythms in soil and nest gas concentrations could be indicators of hidden faults through which the gases migrate to the surface from depth. In nest gas, the coupled system of CO2-He and He concentrations exceeding atmospheric standards 2-3 fold suggested that RWA nests may be biological indicators of hidden degassing faults and fractures at small scales. Equivalently periodic degassing infradian rhythms in the RWA nest, soil, and three nearby minerals springs suggested NW-SE and NE-SW tectonic linkages. Because volcanic activity in the EEVF is dormant, more detailed information on the EEVF's tectonic, magmatic, and degassing systems and its active tectonic fault zones are needed. Such data could provide additional insights into earthquake processes that are related to magmatic processes at the lower crust.

Can a Red Wood-Ant Nest Be Associated with Fault-Related CH4 Micro-Seepage? A Case Study from Continuous Short-Term In-Situ Sampling.

We measured methane (CH4) and stable carbon isotope of methane (δ13C-CH4) concentrations in ambient air and within a red wood-ant (RWA; Formica polyctena) nest in the Neuwied Basin (Germany) using high-resolution in-situ sampling to detect microbial, thermogenic, and abiotic fault-related micro-seepage of CH4. Methane degassing from RWA nests was not synchronized with earth tides, nor was it influenced by micro-earthquake degassing or concomitantly measured RWA activity. Two δ13C-CH4 signatures were identified in nest gas: -69‰ and -37‰. The lower peak was attributed to microbial decomposition of organic matter within the RWA nest, in line with previous observations that RWA nests are hot-spots of microbial CH4. The higher peak has not been reported in previous studies. We attribute this peak to fault-related CH4 emissions moving via fault networks into the RWA nest, which could originate either from thermogenic or abiotic CH4 formation. Sources of these micro-seepages could be Devonian schists, iron-bearing "Klerf Schichten", or overlapping micro-seepage of magmatic CH4 from the Eifel plume. Given the abundance of RWA nests on the landscape, their role as sources of microbial CH4 and biological indicators for abiotically-derived CH4 should be included in estimation of methane emissions that are contributing to climatic change.

Time-of-day-dependent global distribution of lunar surficial water/hydroxyl.

A new set of time-of-day-dependent global maps of the lunar near-infrared water/hydroxyl (H2O/OH) absorption band strength near 2.8 to 3.0 µm constructed on the basis of Moon Mineralogy Mapper (M3) data is presented. The analyzed absorption band near 2.8 to 3.0 µm indicates the presence of surficial H2O/OH. To remove the thermal emission component from the M3 reflectance spectra, a reliable and physically realistic mapping method has been developed. Our maps show that lunar highlands at high latitudes show a stronger H2O/OH absorption band in the lunar morning and evening than at midday. The amplitude of these time-of-day-dependent variations decreases with decreasing latitude of the highland regions, where below about 30°, absorption strength becomes nearly constant during the lunar day at a similar level as in the high-latitude highlands at midday. The lunar maria exhibit weaker H2O/OH absorption than the highlands at all, but showing a smaller difference from highlands absorption levels in the morning and evening than at midday. The level around midday is generally higher for low-Ti than for high-Ti mare surfaces, where it reaches near-zero values. Our observations contrast with previous studies that indicate a significant concentration of surficial H2O/OH at high latitudes only. Furthermore, although our results generally support the commonly accepted mechanism of H2O/OH formation by adsorption of solar wind protons, they suggest the presence of a more strongly bounded surficial H2O/OH component in the lunar highlands and parts of the mare regions, which is not removed by processes such as diffusion/thermal evaporation and photolysis in the course of the lunar day.

Direct plantlet inoculation with soil or insect-associated fungi may control cabbage root fly maggots.

A potential Delia radicum biological control strategy involving cauliflower plantlet inoculation with various fungi was investigated in a series of laboratory and glasshouse experiments. In addition to entomopathogenic fungi, fungi with a high rhizosphere competence and fungi with the ability to survive as saprotrophs in soil were tested. The following fungal species were evaluated in the experiments: Trichoderma atroviride, T. koningiopsis, T. gamsii, Beauveria bassiana, Metharhizium anisopliae, M. brunneum and Clonostachys solani. A commercial carbosulfan-based insecticide was used as a positive control. Additionally, two commercial products, one based on B. bassiana (Naturalis) and one on Bacillus thuringiensis (Delfin) were used as reference biocontrol agents. The aims were (i) to assess the pathogenicity of the selected fungal isolates to Delia radicum, (ii) to evaluate the fungal isolates' rhizosphere competence, with the emphasis on the persistence of the original inoculum on the growing roots, (iii) to assess possible endophytic plant tissue colonization, and (iv) to evaluate potential plant growth stimulating effects of the added inoculi. Significant pathogenicity of tested fungi against Delia radicum was confirmed in in vitro and glasshouse experiments. All tested fungi persisted on cauliflower rhizoplane. More importantly, the added fungi were found on thoroughly washed roots outside the original point of inoculation. This provided us with evidence that our tested fungi could be transferred via or grow with the elongating roots. In addition to colonizing the rhizoplane, some fungi were found inside the plant root or stem tissue, thus exhibiting endophytic characteristics. The importance of fungal ecology as a criterion in appropriate biological control agent selection is discussed.

Early Results of Three-Year Monitoring of Red Wood Ants' Behavioral Changes and Their Possible Correlation with Earthquake Events.

Short-term earthquake predictions with an advance warning of several hours or days are currently not possible due to both incomplete understanding of the complex tectonic processes and inadequate observations. Abnormal animal behaviors before earthquakes have been reported previously, but create problems in monitoring and reliability. The situation is different with red wood ants (RWA; Formica rufa-group (Hymenoptera: Formicidae)). They have stationary mounds on tectonically active, gas-bearing fault systems. These faults may be potential earthquake areas. For three years (2009-2012), two red wood ant mounds (Formica rufa-group), located at the seismically active Neuwied Basin (Eifel, Germany), have been monitored 24/7 by high-resolution cameras with both a color and an infrared sensor. Early results show that ants have a well-identifiable standard daily routine. Correlation with local seismic events suggests changes in the ants' behavior hours before the earthquake: the nocturnal rest phase and daily activity are suppressed, and standard daily routine does not resume until the next day. At present, an automated image evaluation routine is being applied to the more than 45,000 hours of video streams. Based on this automated approach, a statistical analysis of the ants' behavior will be carried out. In addition, other parameters (climate, geotectonic and biological), which may influence behavior, will be included in the analysis.