A New Gridding Technique for High Density Microarray Images Using Intensity Projection Profile of Best Sub Image

As the technologies for the fabrication of high quality microarray advances rapidly, quantification of microarray data becomes a major task. Gridding is the first step in the analysis of microarray images for locating the subarrays and individual spots within each subarray. For accurate gridding of high-density microarray images, in the presence of contamination and background noise, precise calculation of parameters is essential. This paper presents an accurate fully automatic gridding method for locating suarrays and individual spots using the intensity projection profile of the most suitable subimage. The method is capable of processing the image without any user intervention and does not demand any input parameters as many other commercial and academic packages. According to results obtained, the accuracy of our algorithm is between 95-100% for microarray images with coefficient of variation less than two. Experimental results show that the method is capable of gridding microarray images with irregular spots, varying surface intensity distribution and with more than 50% contamination

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

User guide for shock and blast simulation with the OctVCE code (version 3.5+), Department of Mechanical Engineering Report 2007/13

OctVCE is a cartesian cell CFD code produced especially for numerical simulations of shock and blast wave interactions with complex geometries. Virtual Cell Embedding (VCE) was chosen as its cartesian cell kernel as it is simple to code and sufficient for practical engineering design problems. This also makes the code much more ‘user-friendly’ than structured grid approaches as the gridding process is done automatically. The CFD methodology relies on a finite-volume formulation of the unsteady Euler equations and is solved using a standard explicit Godonov (MUSCL) scheme. Both octree-based adaptive mesh refinement and shared-memory parallel processing capability have also been incorporated. For further details on the theory behind the code, see the companion report 2007/12.

The HI Parkes All Sky Survey: southern observations, calibration and robust imaging

The acquisition of HI Parkes All Shy Survey (HIPASS) southern sky data commenced at the Australia Telescope National Facility's Parkes 64-m telescope in 1997 February, and was completed in 2000 March. HIPASS is the deepest HI survey yet of the sky south of declination +2 degrees, and is sensitive to emission out to 170 h(75)(-1) Mpc. The characteristic root mean square noise in the survey images is 13.3 mJy. This paper describes the survey observations, which comprise 23 020 eight-degree scans of 9-min duration, and details the techniques used to calibrate and image the data. The processing algorithms are successfully designed to be statistically robust to the presence of interference signals, and are particular to imaging point (or nearly point) sources. Specifically, a major improvement in image quality is obtained by designing a median-gridding algorithm which uses the median estimator in place of the mean estimator.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

An improved algorithm for generating global window brightness temperatures from multiple satellite infrared imagery

An improved algorithm for the generation of gridded window brightness temperatures is presented. The primary data source is the International Satellite Cloud Climatology Project, level B3 data, covering the period from July 1983 to the present. The algorithm rakes window brightness, temperatures from multiple satellites, both geostationary and polar orbiting, which have already been navigated and normalized radiometrically to the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer, and generates 3-hourly global images on a 0.5 degrees by 0.5 degrees latitude-longitude grid. The gridding uses a hierarchical scheme based on spherical kernel estimators. As part of the gridding procedure, the geostationary data are corrected for limb effects using a simple empirical correction to the radiances, from which the corrected temperatures are computed. This is in addition to the application of satellite zenith angle weighting to downweight limb pixels in preference to nearer-nadir pixels. The polar orbiter data are windowed on the target time with temporal weighting to account for the noncontemporaneous nature of the data. Large regions of missing data are interpolated from adjacent processed images using a form of motion compensated interpolation based on the estimation of motion vectors using an hierarchical block matching scheme. Examples are shown of the various stages in the process. Also shown are examples of the usefulness of this type of data in GCM validation.

Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography-derived values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5m grids (grid-processed). LiDAR profiles were then compared to leaf biomass field profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests.

Two-way regionalized classification of multivariate datasets and its application to the assessment of hydrodynamic dispersion

Zones of mixing between shallow groundwaters of different composition were unravelled by two-way regionalized classification, a technique based on correspondence analysis (CA), cluster analysis (ClA) and discriminant analysis (DA), aided by gridding, map-overlay and contouring tools. The shallow groundwaters are from a granitoid plutonite in the Funda o region (central Portugal). Correspondence analysis detected three natural clusters in the working dataset: 1, weathering; 2, domestic effluents; 3, fertilizers. Cluster analysis set an alternative distribution of the samples by the three clusters. Group memberships obtained by correspondence analysis and by cluster analysis were optimized by discriminant analysis, gridded memberships as follows: codes 1, 2 or 3 were used when classification by correspondence analysis and cluster analysis produced the same results; code 0 when the grid node was first assigned to cluster 1 and then to cluster 2 or vice versa (mixing between weathering and effluents); code 4 in the other cases (mixing between agriculture and the other influences). Code-3 areas were systematically surrounded by code-4 areas, an observation attributed to hydrodynamic dispersion. Accordingly, the extent of code-4 areas in two orthogonal directions was assumed proportional to the longitudinal and transverse dispersivities of local soils. The results (0.7-16.8 and 0.4-4.3 m, respectively) are acceptable at the macroscopic scale. The ratios between longitudinal and transverse dispersivities (1.2-11.1) are also in agreement with results obtained by other studies.

Optical characterization of one dental composite resin using bovine enamel as reinforcing filler

The use of composite resins for restorative procedure in anterior and posterior cavities is highly common in Dentistry due to its mechanical and aesthetic properties that are compatible with the remaining dental structure. Thus, the aim of this study was to evaluate the optical characterization of one dental composite resin using bovine enamel as reinforcing filler. The same organic matrix of the commercially available resins was used for this experimental resin. The reinforcing filler was obtained after the gridding of bovine enamel fragments and a superficial treatment was performed to allow the adhesion of the filler particles with the organic matrix. Different optical images as fluorescence and reflectance were performed to compare the experimental composite with the human teeth. The present experimental resin shows similar optical properties compared with human teeth. © 2012 SPIE.

Interpolação de dados de campo potencial através da camada equivalente

O uso da técnica da camada equivalente na interpolação de dados de campo potencial permite levar em consideração que a anomalia, gravimétrica ou magnética, a ser interpolada é uma função harmônica. Entretanto, esta técnica tem aplicação computacional restrita aos levantamentos com pequeno número de dados, uma vez que ela exige a solução de um problema de mínimos quadrados com ordem igual a este número. Para viabilizar a aplicação da técnica da camada equivalente aos levantamentos com grande número de dados, nós desenvolvemos o conceito de observações equivalentes e o método EGTG, que, respectivamente, diminui a demanda em memória do computador e otimiza as avaliações dos produtos internos inerentes à solução dos problemas de mínimos quadrados. Basicamente, o conceito de observações equivalentes consiste em selecionar algumas observações, entre todas as observações originais, tais que o ajuste por mínimos quadrados, que ajusta as observações selecionadas, ajusta automaticamente (dentro de um critério de tolerância pré-estabelecido) todas as demais que não foram escolhidas. As observações selecionadas são denominadas observações equivalentes e as restantes são denominadas observações redundantes. Isto corresponde a partir o sistema linear original em dois sistemas lineares com ordens menores. O primeiro com apenas as observações equivalentes e o segundo apenas com as observações redundantes, de tal forma que a solução de mínimos quadrados, obtida a partir do primeiro sistema linear, é também a solução do segundo sistema. Este procedimento possibilita ajustar todos os dados amostrados usando apenas as observações equivalentes (e não todas as observações originais) o que reduz a quantidade de operações e a utilização de memória pelo computador. O método EGTG consiste, primeiramente, em identificar o produto interno como sendo uma integração discreta de uma integral analítica conhecida e, em seguida, em substituir a integração discreta pela avaliação do resultado da integral analítica. Este método deve ser aplicado quando a avaliação da integral analítica exigir menor quantidade de cálculos do que a exigida para computar a avaliação da integral discreta. Para determinar as observações equivalentes, nós desenvolvemos dois algoritmos iterativos denominados DOE e DOEg. O primeiro algoritmo identifica as observações equivalentes do sistema linear como um todo, enquanto que o segundo as identifica em subsistemas disjuntos do sistema linear original. Cada iteração do algoritmo DOEg consiste de uma aplicação do algoritmo DOE em uma partição do sistema linear original. Na interpolação, o algoritmo DOE fornece uma superfície interpoladora que ajusta todos os dados permitindo a interpolação na forma global. O algoritmo DOEg, por outro lado, otimiza a interpolação na forma local uma vez que ele emprega somente as observações equivalentes, em contraste com os algoritmos existentes para a interpolação local que empregam todas as observações. Os métodos de interpolação utilizando a técnica da camada equivalente e o método da mínima curvatura foram comparados quanto às suas capacidades de recuperar os valores verdadeiros da anomalia durante o processo de interpolação. Os testes utilizaram dados sintéticos (produzidos por modelos de fontes prismáticas) a partir dos quais os valores interpolados sobre a malha regular foram obtidos. Estes valores interpolados foram comparados com os valores teóricos, calculados a partir do modelo de fontes sobre a mesma malha, permitindo avaliar a eficiência do método de interpolação em recuperar os verdadeiros valores da anomalia. Em todos os testes realizados o método da camada equivalente recuperou mais fielmente o valor verdadeiro da anomalia do que o método da mínima curvatura. Particularmente em situações de sub-amostragem, o método da mínima curvatura se mostrou incapaz de recuperar o valor verdadeiro da anomalia nos lugares em que ela apresentou curvaturas mais pronunciadas. Para dados adquiridos em níveis diferentes o método da mínima curvatura apresentou o seu pior desempenho, ao contrário do método da camada equivalente que realizou, simultaneamente, a interpolação e o nivelamento. Utilizando o algoritmo DOE foi possível aplicar a técnica da camada equivalente na interpolação (na forma global) dos 3137 dados de anomalia ar-livre de parte do levantamento marinho Equant-2 e 4941 dados de anomalia magnética de campo total de parte do levantamento aeromagnético Carauari-Norte. Os números de observações equivalentes identificados em cada caso foram, respectivamente, iguais a 294 e 299. Utilizando o algoritmo DOEg nós otimizamos a interpolação (na forma local) da totalidade dos dados de ambos os levantamentos citados. Todas as interpolações realizadas não seriam possíveis sem a aplicação do conceito de observações equivalentes. A proporção entre o tempo de CPU (rodando os programas no mesmo espaço de memória) gasto pelo método da mínima curvatura e pela camada equivalente (interpolação global) foi de 1:31. Esta razão para a interpolação local foi praticamente de 1:1.

Phenology for tropoclimatological surveys and large-scale mapping

Biotic and abiotic phenological observations can be collected from continental to local spatial scale. Plant phenological observations may only be recorded wherever there is vegetation. Fog, snow and ice are available as phenological para-meters wherever they appear. The singularity of phenological observations is the possibility of spatial intensification to a microclimatic scale where the equipment of meteorological measurements is too expensive for intensive campaigning. The omnipresence of region-specific phenological parameters allows monitoring for a spatially much more detailed assessment of climate change than with weather data. We demonstrate this concept with phenological observations with the use of a special network in the Canton of Berne, Switzerland, with up to 600 observations sites (more than 1 to 10 km² of the inhabited area). Classic cartography, gridding, the integration into a Geographic Information System GIS and large-scale analysis are the steps to a detailed knowledge of topoclimatic conditions of a mountainous area. Examples of urban phenology provide other types of spatially detailed applications. Large potential in phenological mapping in future analyses lies in combining traditionally observed species-specific phenology with remotely sensed and modelled phenology that provide strong spatial information. This is a long history from cartographic intuition to algorithm-based representations of phenology.

Hermitian curve interpolation for CT-data re-sampling

Purpose: Development of an interpolation algorithm for re‐sampling spatially distributed CT‐data with the following features: global and local integral conservation, avoidance of negative interpolation values for positively defined datasets and the ability to control re‐sampling artifacts. Method and Materials: The interpolation can be separated into two steps: first, the discrete CT‐data has to be continuously distributed by an analytic function considering the boundary conditions. Generally, this function is determined by piecewise interpolation. Instead of using linear or high order polynomialinterpolations, which do not fulfill all the above mentioned features, a special form of Hermitian curve interpolation is used to solve the interpolation problem with respect to the required boundary conditions. A single parameter is determined, by which the behavior of the interpolation function is controlled. Second, the interpolated data have to be re‐distributed with respect to the requested grid. Results: The new algorithm was compared with commonly used interpolation functions based on linear and second order polynomial. It is demonstrated that these interpolation functions may over‐ or underestimate the source data by about 10%–20% while the parameter of the new algorithm can be adjusted in order to significantly reduce these interpolation errors. Finally, the performance and accuracy of the algorithm was tested by re‐gridding a series of X‐ray CT‐images. Conclusion: Inaccurate sampling values may occur due to the lack of integral conservation. Re‐sampling algorithms using high order polynomialinterpolation functions may result in significant artifacts of the re‐sampled data. Such artifacts can be avoided by using the new algorithm based on Hermitian curve interpolation

A global radiosonde and tracked balloon archive on 16 pressure levels (GRASP) back to 1905 – Part 1: Merging and interpolation to 00:00 and 12:00 GMT

Many observed time series of the global radiosonde or PILOT networks exist as fragments distributed over different archives. Identifying and merging these fragments can enhance their value for studies on the three-dimensional spatial structure of climate change. The Comprehensive Historical Upper-Air Network (CHUAN version 1.7), which was substantially extended in 2013, and the Integrated Global Radiosonde Archive (IGRA) are the most important collections of upper-air measurements taken before 1958. CHUAN (tracked) balloon data start in 1900, with higher numbers from the late 1920s onward, whereas IGRA data start in 1937. However, a substantial fraction of those measurements have not been taken at synoptic times (preferably 00:00 or 12:00 GMT) and on altitude levels instead of standard pressure levels. To make them comparable with more recent data, the records have been brought to synoptic times and standard pressure levels using state-of-the-art interpolation techniques, employing geopotential information from the National Oceanic and Atmospheric Administration (NOAA) 20th Century Reanalysis (NOAA 20CR). From 1958 onward the European Re-Analysis archives (ERA-40 and ERA-Interim) available at the European Centre for Medium-Range Weather Forecasts (ECMWF) are the main data sources. These are easier to use, but pilot data still have to be interpolated to standard pressure levels. Fractions of the same records distributed over different archives have been merged, if necessary, taking care that the data remain traceable back to their original sources. If possible, station IDs assigned by the World Meteorological Organization (WMO) have been allocated to the station records. For some records which have never been identified by a WMO ID, a local ID above 100 000 has been assigned. The merged data set contains 37 wind records longer than 70 years and 139 temperature records longer than 60 years. It can be seen as a useful basis for further data processing steps, most notably homogenization and gridding, after which it should be a valuable resource for climatological studies. Homogeneity adjustments for wind using the NOAA-20CR as a reference are described in Ramella Pralungo and Haimberger (2014). Reliable homogeneity adjustments for temperature beyond 1958 using a surface-data-only reanalysis such as NOAA-20CR as a reference have yet to be created. All the archives and metadata files are available in ASCII and netCDF format in the PANGAEA archive

Global distributions of picophytoplankton abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The smallest marine phytoplankton, collectively termed picophytoplankton, have been routinely enumerated by flow cytometry since the late 1980s, during cruises throughout most of the world ocean. We compiled a database of 40,946 data points, with separate abundance entries for Prochlorococcus, Synechococcus and picoeukaryotes. We use average conversion factors for each of the three groups to convert the abundance data to carbon biomass. After gridding with 1° spacing, the database covers 2.4% of the ocean surface area, with the best data coverage in the North Atlantic, the South Pacific and North Indian basins. The average picophytoplankton biomass is 12 ± 22 µg C L-1 or 1.9 g C m-2. We estimate a total global picophytoplankton biomass, excluding N2-fixers, of 0.53 - 0.74 Pg C (17 - 39 % Prochlorococcus, 12 - 15 % Synechococcus and 49 - 69 % picoeukaryotes). Future efforts in this area of research should focus on reporting calibrated cell size, and collecting data in undersampled regions.