The emergence of automated high-frequency flow cytometry: revealing temporal and spatial phytoplankton variability

Phytoplankton observation is the product of a number of trade-offs related to sampling processes, required level of diversity and size spectrum analysis capabilities of the techniques involved. Instruments combining the morphological and high-frequency analysis for phytoplankton cells are now available. This paper presents an application of the automated high-resolution flow cytometer Cytosub as a tool for analysing phytoplanktonic cells in their natural environment. High resolution data from a temporal study in the Bay of Marseille (analysis every 30 min over 1 month) and a spatial study in the Southern Indian Ocean (analysis every 5 min at 10 knots over 5 days) are presented to illustrate the capabilities and limitations of the instrument. Automated high-frequency flow cytometry revealed the spatial and temporal variability of phytoplankton in the size range 1−∼50 μm that could not be resolved otherwise. Due to some limitations (instrumental memory, volume analysed per sample), recorded counts could be statistically too low. By combining high-frequency consecutive samples, it is possible to decrease the counting error, following Poisson’s law, and to retain the main features of phytoplankton variability. With this technique, the analysis of phytoplankton variability combines adequate sampling frequency and effective monitoring of community changes.

Relating plankton assemblages to environmental variables using instruments towed by ships-of-opportunity

Undulating Oceanographic Recorders (UORs) and Continuous Plankton Recorders (CPRs) equipped with a suite of sensors were towed by merchant vessels in the North Sea between 1988 and 1991, recording a range of environmental variables. These were used to interpret the results of analyses of the plankton taken on CPR tows off the northeast coast of the UK in 1989 and in the Skagerrak and Kattegat in July 1988 and through 1989. Correlations were found between the biota and the environmental variables. The tidal front off the northeast coast of the UK and the front between the low salinity water in the Kattegat and the higher salinity water in the Skagerrak were dominant factors correlating with the distribution of the plankton assemblages. Discontinuities, defining the positions of the fronts, in the values of physical variables (temperature and, where measured, salinity and turbidity) were closely identified with geographical divisions between plankton assemblages. Measures of irradiance were found to be important on several occasions, presumably due to diel migrations of the zooplankton.

Automated analysis of non-mass-enhancing lesions in breast MRI based on morphological, kinetic, and spatio-temporal moments and joint segmentation-motion compensation technique

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) represents an established method for the detection and diagnosis of breast lesions. While mass-like enhancing lesions can be easily categorized according to the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon, a majority of diagnostically challenging lesions, the so called non-mass-like enhancing lesions, remain both qualitatively as well as quantitatively difficult to analyze. Thus, the evaluation of kinetic and/or morphological characteristics of non-masses represents a challenging task for an automated analysis and is of crucial importance for advancing current computer-aided diagnosis (CAD) systems. Compared to the well-characterized mass-enhancing lesions, non-masses have no well-defined and blurred tumor borders and a kinetic behavior that is not easily generalizable and thus discriminative for malignant and benign non-masses. To overcome these difficulties and pave the way for novel CAD systems for non-masses, we will evaluate several kinetic and morphological descriptors separately and a novel technique, the Zernike velocity moments, to capture the joint spatio-temporal behavior of these lesions, and additionally consider the impact of non-rigid motion compensation on a correct diagnosis.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.

A method for semi-automated objective quantification of linear bedforms from multi-scale Digital Elevation Models

The increasing availability of large, detailed digital representations of the Earth’s surface demands the application of objective and quantitative analyses. Given recent advances in the understanding of the mechanisms of formation of linear bedform features from a range of environments, objective measurement of their wavelength, orientation, crest and trough positions, height and asymmetry is highly desirable. These parameters are also of use when determining observation-based parameters for use in many applications such as numerical modelling, surface classification and sediment transport pathway analysis. Here, we (i) adapt and extend extant techniques to provide a suite of semi-automatic tools which calculate crest orientation, wavelength, height, asymmetry direction and asymmetry ratios of bedforms, and then (ii) undertake sensitivity tests on synthetic data, increasingly complex seabeds and a very large-scale (39 000km2) aeolian dune system. The automated results are compared with traditional, manually derived,measurements at each stage. This new approach successfully analyses different types of topographic data (from aeolian and marine environments) from a range of sources, with tens of millions of data points being processed in a semi-automated and objective manner within minutes rather than hours or days. The results from these analyses show there is significant variability in all measurable parameters in what might otherwise be considered uniform bedform fields. For example, the dunes of the Rub’ al Khali on the Arabian peninsula are shown to exhibit deviations in dimensions from global trends. Morphological and dune asymmetry analysis of the Rub’ al Khali suggests parts of the sand sea may be adjusting to a changed wind regime from that during their formation 100 to 10 ka BP.

Data processing of remotely sensed airborne hyperspectral data using the Airborne Processing Library (APL): Geocorrection algorithm descriptions and spatial accuracy assessment

Remote sensing airborne hyperspectral data are routinely used for applications including algorithm development for satellite sensors, environmental monitoring and atmospheric studies. Single flight lines of airborne hyperspectral data are often in the region of tens of gigabytes in size. This means that a single aircraft can collect terabytes of remotely sensed hyperspectral data during a single year. Before these data can be used for scientific analyses, they need to be radiometrically calibrated, synchronised with the aircraft's position and attitude and then geocorrected. To enable efficient processing of these large datasets the UK Airborne Research and Survey Facility has recently developed a software suite, the Airborne Processing Library (APL), for processing airborne hyperspectral data acquired from the Specim AISA Eagle and Hawk instruments. The APL toolbox allows users to radiometrically calibrate, geocorrect, reproject and resample airborne data. Each stage of the toolbox outputs data in the common Band Interleaved Lines (BILs) format, which allows its integration with other standard remote sensing software packages. APL was developed to be user-friendly and suitable for use on a workstation PC as well as for the automated processing of the facility; to this end APL can be used under both Windows and Linux environments on a single desktop machine or through a Grid engine. A graphical user interface also exists. In this paper we describe the Airborne Processing Library software, its algorithms and approach. We present example results from using APL with an AISA Eagle sensor and we assess its spatial accuracy using data from multiple flight lines collected during a campaign in 2008 together with in situ surveyed ground control points.