Strategy for Optimizing LC-MS Data Processing in Metabolomics: A Design of Experiments Approach

A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.     

New Fluorescent Nanoparticles for Ultrasensitive Detection of Nucleic Acids by Optical Methods

For decades the detection of nucleic acids and their interactions at low abundances has been a challenging task that has thus far been solved by enzymatic target amplification. In this work we aimed at developing efficient tools for amplification‐free nucleic acid detection, which resulted in the synthesis of new fluorescent nanoparticles. Here, the fluorescent nanoparticles were made by simple and inexpensive radical emulsion polymerization of butyl acrylate in the presence of fluorescent dyes and additional functionalization reagents. This provided ultra‐bright macrofluorophores of 9–84 nm mean diameter, modified with additional alkyne and amino groups for bioconjugation. By using click and NHS chemistries, the new nanoparticles were attached to target‐specific DNA probes that were used in fluorimetry and fluorescence microscopy. Overall, these fluorescent nanoparticles and their oligonucleotide derivatives have higher photostability, brighter fluorescence and hence dramatically lower limits of target detection than the individual organic dyes. These properties make them useful in approaches directed towards ultrasensitive detection of nucleic acids, in particular for imaging and in vitro diagnostics of DNA.     

The Effect of Particle Size Distributions on the Microstructural Evolution During Sintering

Microstructural evolution and sintering behavior of powder compacts composed of spherical particles with different particle size distributions (PSDs) were simulated using a kinetic Monte Carlo model of solid‐state sintering. Compacts of monosized particles, normal PSDs with fixed mean particle radii and a range of standard deviations, and log‐normal PSDs with fixed mode and a range of skewness values were studied. Densification rate and final relative density were found to be inversely proportional to initial PSD width. Grain growth was faster during the early stages of sintering for broad PSDs, but the final grain sizes were smaller. These behaviors are explained by the smallest grains in the broader PSDs being consumed very quickly by larger neighboring grains. The elimination of the small grains reduces both the total number of necks and the neck area between particles, which in turn reduces the regions where vacancies can be annihilated, leading to slower densification rates. The loss of neck area causes grain growth by surface diffusion to become the dominant microstructural evolution mechanism, leading to poor densification. Finally, pore size was shown to increase with the width of PSDs, which also contributes to the lower densification rates.     

Catalytic Ammonia Decomposition Over Ruthenium Nanoparticles Supported on Nano-Titanates

Nanosized Na₂Ti₃O₇, K₂Ti₆O₁₃ and Cs₂Ti₆O₁₃ materials were prepared and used as supports of ruthenium nanoparticles for catalytic ammonia decomposition. It is shown that these catalysts exhibit higher catalytic activity than ruthenium supported on TiO₂ nanoparticles promoted with cesium. The difference is attributed to the use of nanostructured materials with incorporated alkali metals in the crystal lattice, which apparently gives a higher effect of the promoter. All samples were characterized by X-ray powder diffraction, transmission electron microscopy and N₂ physisorption measurements. Furthermore, the effect of ruthenium loading on the catalytic decomposition of ammonia was investigated.     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

Energy simulation for a high-rise building using IDA ICE: Investigations in different climates

In this paper a model of a high-rise building is constructed in the simulation program IDA ICE. The model is based on an IFC-model of a demonstration building constructed in Ljubljana, Slovenia, as part of an EU-project, EE-high-rise. The model’s energy performance was simulated for four cities: Umeå (Scandinavia), Ljubljana (Central Europe), Sibenik (Mediterranean) and Dubai (The Persian Gulf). Furthermore, the climate envelope of the building was modified with the aim to improve the model’s energy performance in each of the regions. The results were evaluated according to the energy requirements of passive house standard by the German Passive House Institute. The analysis suggests that the reference building model, which itself incorporates several energy efficient components, was unable to meet the German passive house standard in none of the four cities (Umeå, Ljubljana, Sibenik and Dubai) studied. By providing a combination of energy saving measures, such as modifications of thermal resistance of building envelope, the building may be able to meet the passive house standard in Ljubljana. The analysis concludes that the reduction in window area results in reduction of both heating and cooling demand. Increase in the thickness of the insulation and the thermal resistance of windows reduces the space heating demand for Umeå, Ljubljana and Sibenik (not applied for Dubai) while increasing the cooling demand for these cities. Increased airtightness has marginal effect on heating and cooling demand for all investigated cities. Reduced thermal resistance of windows will decrease cooling demand for Ljubljana, Sibenik and Dubai (not applied for Umeå). Reduced insulation thickness (not applied for Umeå) will decrease cooling demand for Ljubljana and Sibenik but not for Dubai. Reducing the insulation thickness may often result in reduced cooling demand for moderately warm countries since the average outdoor temperature could be lower than the indoor temperature during part of the cooling season. In those situations a reduced insulation thickness can cause heat flow from the relatively hot inside to the colder outside. However, for hot climates like in Dubai where outdoor temperature is higher than the indoor temperature for most of the year, reducing the insulation thickness will increase the cooling demand. This result suggests that the insulation thickness must be chosen and optimized based on heating and cooling demand, internal heat gain, and outdoor climate     

A method for cognitive 3D geological voxel modelling of AEM data

Airborne electromagnetic (AEM) data have proven successful for the purpose of near-surface geological mapping and are increasingly being collected worldwide. However, conversion of data from measured resistivity to lithology is not a straightforward task. Therefore, it is still challenging to make full use of these data. Many limitations must be considered before a successful geological interpretation can be performed and a reasonable 3D geological model constructed. In this paper, we propose a method for 3D geological modelling of AEM data in which the limitations are jointly considered together with a cognitive and knowledge-driven data interpretation. The modelling is performed iteratively by using voxel modelling techniques with tools developed for this exact purpose. Based on 3D resistivity grids, the tools allow the geologist to select voxel groups that define any desirable volumetric shape in the 3D model. Recent developments in octree modelling ensure exact modelling with a limited number of voxels.