MidDRIFTS-PLSR-based quantification of physico-chemical soil properties across two agroecological zones in Southwest Germany: generic independent validation surpasses region specific cross-validation

Development of spectroscopic prediction models via partial least squares regression (PLSR) suggests that model performance is highly affected by means of calibration and nature of the dataset. This study compares the predictive performance of PLSR models obtained by cross-validation and independent validation to quantify physico-chemical soil properties from their mid-infrared diffuse reflectance Fourier transform spectra (midDRIFTS) across two contrasting regions, Kraichgau (K) and Swabian Alb (SA), in Southwest Germany. We evaluated the capability of midDRIFTS-PLSR models for predicting total carbon (TC), organic carbon (TOC), inorganic carbon (TIC), nitrogen (TN), mineral N (N_min), C:N ratio, hot water extractable C and N (C_HWE, N_HWE), microbial biomass C and N (C_mic, N_mic), pH, bulk density, and clay, silt and sand contents of 126 soil samples. Based on calibrated models, most soil properties were predicted successfully using either calibration approach with residual prediction deviations ≥3 and R² > 0.9, except for N_min, C/N ratio, pH, bulk density and sand. However, predictive performance of generic independent validation derived models (GIC) of test set was considerably higher than generic cross-validation models. Validation using GIC models gave relatively the same predictive performance with those obtained in calibration except for N_min. Validation of region specific cross-validated models, however, resulted in successful predictions only for TC, TIC, TOC and TN in SA and TC and TIC and TOC in K. Our results show the superiority of independent validation over both generic and region specific cross-validation as a robust tool for predicting soil properties without further laboratory measurements.     

Über die Autoxydation von Bicyclo[2,2,2]octen

On the Autoxidation of Bicyclo[2,2,2]octene During the autoxidation of bicyclo[2,2,2]octene at 90°C about 50% of epoxide are obtained. The most important byproduct obviously is a polymeric peroxide. From the acidic products formed in low amounts, cis-1, 4-Cyclohexane dicarboxylic acid could be isolated as the corresponding dimethyl ester.     

Single‐image Tomography: 3D Volumes from 2D Cranial X‐Rays

As many different 3D volumes could produce the same 2D x‐ray image, inverting this process is challenging. We show that recent deep learning‐based convolutional neural networks can solve this task. As the main challenge in learning is the sheer amount of data created when extending the 2D image into a 3D volume, we suggest firstly to learn a coarse, fixed‐resolution volume which is then fused in a second step with the input x‐ray into a high‐resolution volume. To train and validate our approach we introduce a new dataset that comprises of close to half a million computer‐simulated 2D x‐ray images of 3D volumes scanned from 175 mammalian species. Future applications of our approach include stereoscopic rendering of legacy x‐ray images, re‐rendering of x‐rays including changes of illumination, view pose or geometry. Our evaluation includes comparison to previous tomography work, previous learning methods using our data, a user study and application to a set of real x‐rays.     

An Improved Silicon Neuron

We describe an improved spiking silicon neuron (SN) [6] that approximates the dynamics of ionic currents of a real nerve cell. The improved version has less circuitry and fewer parameters than previous circuits thereby improving the spiking characteristics. We describe the differential equations governing the revised circuits and use them to explain the spiking properties of the SN. We also describe how to tune the parameters efficiently to bring the neuron quickly into its correct operating regime. The new neurons are sufficiently robust for operation in large networks. We demonstrate their robustness by comparing the neuron's frequency-current curve between different chips for the same set of parameter values.     

An Approach to the Parameterization of Structure for Fast Categorization

A decomposition is described, which parameterizes the geometry and appearance of contours and regions of gray-scale images with the goal of fast categorization. To express the contour geometry, a contour is transformed into a local/global space, from which parameters are derived classifying its global geometry (arc, inflexion or alternating) and describing its local aspects (degree of curvature, edginess, symmetry). Regions are parameterized based on their symmetric axes, which are evolved with a wave-propagation process enabling to generate the distance map for fragmented contour images. The methodology is evaluated on three image sets, the Caltech 101 set and two sets drawn from the Corel collection. The performance nearly reaches the one of other categorization systems for unsupervised learning.     

Identifying the unknowns by aligning fragmentation trees

Mass spectrometry allows sensitive, automated, and high-throughput analysis of small molecules. In principle, tandem mass spectrometry allows us to identify "unknown" small molecules not in any database, but the automated interpretation of such data is in its infancy. Fragmentation trees have recently been introduced for the automated analysis of the fragmentation patterns of small molecules. We present a method for the automated comparison of such fragmentation patterns, based on aligning the compounds' fragmentation trees. We cluster compounds based solely on their fragmentation patterns and show a good agreement with known compound classes. Fragmentation pattern similarities are strongly correlated with the chemical similarity of molecules. We present a tool for searching a database for compounds with fragmentation pattern similar to an unknown sample compound. We apply this tool to metabolites from Icelandic poppy. Our method allows fully automated computational identification of small molecules that cannot be found in any database.     

An X-ray-based method for the determination of the contrast agent propagation in 3-D vessel structures

A method for the determination of the contrast-agent propagation in vessel trees is presented. A standard three-dimensional (3-D) rotational angiography procedure is performed to reconstruct the morphology of the contrast-filled vessel tree in a 3-D volume. An additional fluoroscopy projection series acquired with a fixed projection angle delivers the temporal information of the bolus propagating. The mapping of the propagation information from the two-dimensional projections to the 3-D image data set is the topic of this paper. A symbolic tree structure is built up that represents the vessel tree including bifurcations. Neighborhood relations between vessel pieces are given in three dimensions. This facilitates filtering procedures and plausibility controls of the resulting time dependent 3-D data set. The presented method proved to be accurate with phantom data and gives novel insight in the feeding structure of arterio-venous malformations and aneurysms.     

Discrete element model development of ZTA ceramic granular powder using micro computed tomography

Particle based simulations in 3D space using discrete element method provide valuable insight into the static and dynamic granular phenomena. Such phenomenon have to be considered during the production of ceramic components made up of dry pressed granulated ceramic powders. In the present work, 3D geometric parameters of zirconia toughened alumina (ZTA) granules in terms of their shape, size and spatial distribution are obtained using state of the art micro computed tomography ($\mu$CT). Computed tomography data provided an ample amount of information to obtain a 3D arrangement of particles in a defined space for discrete element method simulations. However, during simulations, the residual forces initiate spatial disturbance of the particles which lead to their rearrangement, despite the fact that the particle assembly while scanning was in a static equilibrium. Further appropriate $\mu$CT data processing steps along with statistical analyses of the polydisperse particle system are therefore proposed to dissolve the paradox. Possible pitfalls of model development are identified and corrected by geometrical and physical constraints. It is observed that the frequency of particle contacts can be used to characterize the stability of a particle system. Thereby, a coherent and consistent model in the form of a stable particle assembly for discrete element simulations is successfully developed.