Phosphorus sorption, supply potential and availability in soils with contrasting parent material and soil chemical properties

Soil phosphorus (P) management requires a more targeted and soil-specific approach than is currently applied for agronomic recommendations and environmental evaluation. Phosphorus buffering capacities control the supply of P in the soil solution and were measured across Irish soils with contrasting parent material and chemical properties. Langmuir sorption buffer capacities (MBCs) and binding energies (b) were strongly correlated with soil pH and extractable aluminium (Al). A broken-line regression fitted to the relationship between MBC and Al derived a change-point value for Al above which MBC increased linearly. Soils above the change point were predominantly acidic to neutral with non-calcareous parent material, with larger buffering capacities and binding energies than calcareous soils. Ratios of Mehlich3-Al and P (Al:P) were used to relate buffering capacity to supply potential in non-calcareous soils. Large ratios of Al:P were associated with poor P availability, characteristic of strongly P-fixing soils. Threshold values of iron-oxide paper strip P (FeO-P) and Morgan's P revealed Al:P ratios where soils began to supply P in available form. The change-point for Morgan's P fell within the current target index for P availability; however, the confidence interval was more compatible with previous agronomic P indices used in Ireland. Relationships between Morgan's P and measures of extractable P, M3-P and Olsen P, deviated in calcareous soils at large soil P contents, indicative of P precipitation processes dominating in these soils. Identifying differences in soil P buffering capacity at the laboratory scale would improve agronomic and environmental assessment at field and catchment scales

The use of random projections for the analysis of mass spectrometry imaging data

The ‘curse of dimensionality’ imposes fundamental limits on the analysis of the large, information rich datasets that are produced by mass spectrometry imaging. Additionally, such datasets are often too large to be analyzed as a whole and so dimensionality reduction is required before further analysis can be performed. We investigate the use of simple random projections for the dimensionality reduction of mass spectrometry imaging data and examine how they enable efficient and fast segmentation using k-means clustering. The method is computationally efficient and can be implemented such that only one spectrum is needed in memory at any time. We use this technique to reveal histologically significant regions within MALDI images of diseased human liver. Segmentation results achieved following a reduction in the dimensionality of the data by more than 99% (without peak picking) showed that histologic changes due to disease can be automatically visualized from molecular images. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-014-1024-7) contains supplementary material, which is available to authorized users

PRM28 CREATION OF A NEW PSORIASIS QUALITY OF LIFE MEASURE FROM FIVE PREEXISTENT INSTRUMENTS USING RASCH ANALYSIS: THE CALIPSO QUESTIONNAIRE

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