A R-Script for Generating Multiple Sclerosis Lesion Pattern Discrimination Plots

Càlcul estadístic R; Geoestadística; Esclerosi múltipleCálculo estadístico R; Geoestadística; Esclerosis múltipleR statistical computing; Geostatistics; Multiple sclerosisOne significant characteristic of Multiple Sclerosis (MS), a chronic inflammatory demyelinating disease of the central nervous system, is the evolution of highly variable patterns of white matter lesions. Based on geostatistical metrics, the MS-Lesion Pattern Discrimination Plot reduces complex three- and four-dimensional configurations of MS-White Matter Lesions to a well-arranged and standardized two-dimensional plot that facilitates follow-up, cross-sectional and medication impact analysis. Here, we present a script that generates the MS-Lesion Pattern Discrimination Plot, using the widespread statistical computing environment R. Input data to the script are Nifti-1 or Analyze-7.5 files with individual MS-White Matter Lesion masks in Montreal Normal Brain geometry. The MS-Lesion Pattern Discrimination Plot, variogram plots and associated fitting statistics are output to the R console and exported to standard graphics and text files. Besides reviewing relevant geostatistical basics and commenting on implementation details for smooth customization and extension, the paper guides through generating MS-Lesion Pattern Discrimination Plots using publicly available synthetic MS-Lesion patterns. The paper is accompanied by the R script LDPgenerator.r, a small sample data set and associated graphics for comparison

Rising groundwater levels is challenging future groundwater management in alpine valleys

In einigen alpinen Gemeinden in Österreich sind die lokalen Grundwasserspiegel in den letzten Jahrzehnten auf kritische Niveaus angestiegen. Einerseits werden die Flächenversiegelung, der Wegfall von Retentionsräumen und die lokale Versickerung von Niederschlagswasser als Gründe für diese Entwicklung gesehen. Andererseits unterliegen Grundwasserressourcen dem Klimawandel, der sich örtlich mit variabler Grundwasserneubildung durch extreme Niederschlagsereignisse oder starke Schneeschmelze bemerkbar macht. In diesem Beitrag wird anhand einer Modellierungsstudie die Sensitivität eines lokalen, oberflächennahen Grundwasserleiters in Bezug auf naturräumliche, klimatische und anthropogene Entwicklungen analysiert. Es zeigt sich, dass eine unkontrollierte Interaktion von Oberflächengewässern maßgeblich und langfristig in den Grundwasserhaushalt eingreifen kann. Dies gilt insbesondere, wenn die Transferrate von Oberflächengewässern durch hydraulische Maßnahmen (Drainagen, Dichtwände, Sohlabdichtungen) oder natürliche Phänomene wie Hochwasser verändert wird. Die Studie verfolgt das Ziel, multiple Einflussfaktoren auf alpines Grundwassermanagement zu untersuchen und im Hinblick auf mögliche zukünftige Entwicklungen zu bewerten.Universität Greifswald (1032

Steigende Grundwasserspiegel als Herausforderung für zukünftiges Grundwassermanagement in alpinen Tälern

In some Alpine communities in Austria, local groundwater levels have risen to critical levels. Increasing land sealing, loss of retention areas and local infiltration of precipitation are seen as reasons for this development. Furthermore, groundwater resources are subject to climate change, which is locally perceptible by variable groundwater recharge through extreme precipitation events or snow melt. In this paper, a modelling study is employed to analyse the sensitivity of a local, near-surface groundwater aquifer with regard to physiographical, climatic and anthropogenic developments. It is shown that an uncontrolled interaction with surface waters can significantly affect the groundwater balance, for example if the groundwater/surface water exchange rate is altered by hydraulic measures (e.g. drainage systems, barrier walls) or by natural phenomena such as flooding events. This study aims to assess multiple influences on alpine groundwater management and to evaluate their role for potential future developments

Geostatistical Analysis of White Matter Lesions in Multiple Sclerosis Identifies Gender Differences in Lesion Evolution

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system with presumed autoimmune origin. The development of lesions within the gray matter and white matter, which are highly variable with respect to number, total volume, morphology and spatial evolution and which only show a limited correlation with clinical disability, is a hallmark of the disease. Population-based studies indicate a distinct outcome depending on gender. Here, we studied gender-related differences in the evolution of white matter MS-lesions (MS-WML) in early MS by using geostatistical methods. Within a 3 years observation period, a female and a male MS patient group received disease modifying drugs and underwent standardized annual brain magnetic resonance imaging, accompanied by neurological examination. MS-WML were automatically extracted and the derived binary lesion masks were subject to geostatistical analysis, yielding quantitative spatial-statistics metrics on MS-WML pattern morphology and total lesion volume (TLV). Through the MS-lesion pattern discrimination plot, the following differences were disclosed: corresponding to gender and MS-WML pattern morphology at baseline, two female subgroups (F1, F2) and two male subgroups (M1, M2) are discerned that follow a distinct MS-WML pattern evolution in space and time. F1 and M1 start with medium-level MS-WML pattern smoothness and TLV, both behave longitudinally quasi-static. By contrast, F2 and M2 start with high-level MS-WML pattern smoothness and medium-level TLV. F2 and M2 longitudinal development is characterized by strongly diminishing MS-WML pattern smoothness and TLV, i.e., continued shrinking and break-up of MS-WML. As compared to the male subgroup M2, the female subgroup F2 shows continued, increased MS-WML pattern smoothness and TLV. Data from neurological examination suggest a correlation of MS-WML pattern morphology metrics and EDSS. Our results justify detailed studies on gender-related differences.(VLID)338736

Heuristics and Pattern Recognition in Complex Geo-Referenced Systems

Non

SoilGrids250m: Global gridded soil information based on machine learning

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total)

Examples of covariates used to generate SoilGrids: TWI is the Topographic Wetness Index (values multiplied by 100), EVI is the MODIS Enhanced Vegetation Index (values multiplied by 10,000), s.d. LST is the long-term standard deviation of MODIS Land Surface Temperatures (values in Celsius degrees).

<p>Location: San Francisco bay area, California. Size of the bounding box is 300 by 300 km.</p

Correlation (density) plots produced as a result of 10–fold cross-validation.

<p>See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169748#pone.0169748.t001" target="_blank">Table 1</a> for more details.</p