Automatic quality control in clinical (1) H MRSI of brain cancer.

MRSI grids frequently show spectra with poor quality, mainly because of the high sensitivity of MRS to field inhomogeneities. These poor quality spectra are prone to quantification and/or interpretation errors that can have a significant impact on the clinical use of spectroscopic data. Therefore, quality control of the spectra should always precede their clinical use. When performed manually, quality assessment of MRSI spectra is not only a tedious and time-consuming task, but is also affected by human subjectivity. Consequently, automatic, fast and reliable methods for spectral quality assessment are of utmost interest. In this article, we present a new random forest-based method for automatic quality assessment of (1) H MRSI brain spectra, which uses a new set of MRS signal features. The random forest classifier was trained on spectra from 40 MRSI grids that were classified as acceptable or non-acceptable by two expert spectroscopists. To account for the effects of intra-rater reliability, each spectrum was rated for quality three times by each rater. The automatic method classified these spectra with an area under the curve (AUC) of 0.976. Furthermore, in the subset of spectra containing only the cases that were classified every time in the same way by the spectroscopists, an AUC of 0.998 was obtained. Feature importance for the classification was also evaluated. Frequency domain skewness and kurtosis, as well as time domain signal-to-noise ratios (SNRs) in the ranges 50-75 ms and 75-100 ms, were the most important features. Given that the method is able to assess a whole MRSI grid faster than a spectroscopist (approximately 3 s versus approximately 3 min), and without loss of accuracy (agreement between classifier trained with just one session and any of the other labelling sessions, 89.88%; agreement between any two labelling sessions, 89.03%), the authors suggest its implementation in the clinical routine. The method presented in this article was implemented in jMRUI's SpectrIm plugin. Copyright © 2016 John Wiley & Sons, Ltd.

The nitrogen cycle of tropical montane forest in Ecuador turns inorganic under environmental change

Water-bound nitrogen (N) cycling in temperate terrestrial ecosystems of the Northern Hemisphere is today mainly inorganic because of anthropogenic release of reactive N to the environment. In little-industrialized and remote areas, in contrast, a larger part of N cycling occurs as dissolved organic N (DON). In a north Andean tropical montane forest in Ecuador, the N cycle changed markedly during 1998–2010 along with increasing N deposition and reduced soil moisture. The DON concentrations and the fractional contribution of DON to total N significantly decreased in rainfall, throughfall, and soil solutions. This inorganic turn of the N cycle was most pronounced in rainfall and became weaker along the flow path of water through the system until it disappeared in stream water. Decreasing organic contributions to N cycling were caused not only by increasing inorganic N input but also by reduced DON production and/or enhanced DON decomposition. Accelerated DON decomposition might be attributable to less waterlogging and higher nutrient availability. Significantly increasing NO3-N concentrations and NO3-N/NH4-N concentration ratios in throughfall and litter leachate below the thick organic layers indicated increasing nitrification. In mineral soil solutions, in contrast, NH4-N concentrations increased and NO3-N/NH4-N concentration ratios decreased significantly, suggesting increasing net ammonification. Our results demonstrate that the remote tropical montane forests on the rim of the Amazon basin experienced a pronounced change of the N cycle in only one decade. This change likely parallels a similar change which followed industrialization in the temperate zone of the Northern Hemisphere more than a century ago.

Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Untangling a Holocene pollen record with forest model simulations and independent climate data

Adaptation potential of forests to rapid climatic changes can be assessed from vegetation dynamics during past climatic changes as preserved in fossil pollen data. However, pollen data reflect the integrated effects of climate and biotic processes, such as establishment, survival, competition, and migration. To disentangle these processes, we compared an annually laminated late Würm and Holocene pollen record from the Central Swiss Plateau with simulations of a dynamic forest patch model. All input data used in the simulations were largely independent from pollen data; i.e. the presented analysis is non-circular. Temperature and precipitation scenarios were based on reconstructions from pollen-independent sources. The earliest arrival times of the species at the study site after the last glacial were inferred from pollen maps. We ran a series of simulations under different combinations of climate and immigration scenarios. In addition, the sensitivity of the simulated presence/absence of four major species to changes in the climate scenario was examined. The pattern of the pollen record could partly be explained by the used climate scenario, mostly by temperature. However, some features, in particular the absence of most species during the late Würm could only be simulated if the winter temperature anomalies of the used scenario were decreased considerably. Consequently, we had to assume in the simulations, that most species immigrated during or after the Younger Dryas (12 000 years BP), Abies and Fagus even later. Given the timing of tree species immigration, the vegetation was in equilibrium with climate during long periods, but responded with lags at the time-scale of centuries to millennia caused by a secondary succession after rapid climatic changes such as at the end of Younger Dryas, or immigration of dominant taxa. Climate influenced the tree taxa both directly and indirectly by changing inter-specific competition. We concluded, that also during the present fast climatic change, species migration might be an important process, particularly if geographic barriers, such as the Alps are in the migrational path.