MOTIVATION: Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual's genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. RESULTS: Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual's genetic similarity to a continental group. AVAILABILITY AND IMPLEMENTATION: AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Traditional DEM (Digital Elevation Model) generation from satellite imagery is based on stereo pair or triple images using wide baseline. These days, a number of low-cost microsatellites, such as SkySat, have been launched. The high resolution video image sequences they provided result in large number of image frames and more flexible selection of baseline, which allows us to design a 3D super resolution scene reconstruction approach based on multiple narrow baseline stereo pairs. The multiple disparity maps, generated using Phase Correlation (PC) based sub-pixel stereo matching algorithm, are co-registered pixel by pixel and up-sampled and then stacked to produce a super resolution scene depth map. The scene depth image constructed in this way has three advantages: i) “pixel locking” error typical for sub-pixel image matching is minimized; ii) super resolution is achieved; and iii) occlusion problem is minimized by multiple narrow baseline stereo pairs. A 3D scene super resolution reconstruction example is demonstrated using a SkySat video image of Usak, Western Turkey.

In this paper, we present a Phase Correlation (PC) based change detection algorithm which is able to automatically detect very small target such as the Mars rover in multi-temporal HiRISE images under illumination variation and camera motion. The robustness of PC to illumination variation was proved by our previous work while the geometric distortion caused by camera motion can be eliminated by PC-based pixel-wise image co-registration. Experiments using three HiRISE images taken under different illumination and view angle demonstrate that our algorithm can robustly detect the Mars rover as small as 6×9 pixels in conditions of varying illumination and camera 3D status.

BACKGROUND: Gene expression profiling (GEP) via microarray analysis is a widely used tool for assessing risk and other patient diagnostics in clinical settings. However, non-biological factors such as systematic changes in sample preparation, differences in scanners, and other potential batch effects are often unavoidable in long-term studies and meta-analysis. In order to reduce the impact of batch effects on microarray data, Johnson, Rabinovic, and Li developed ComBat for use when combining batches of gene expression microarray data. We propose a modification to ComBat that centers data to the location and scale of a pre-determined, 'gold-standard' batch. This modified ComBat (M-Combat) is designed specifically in the context of meta-analysis and batch effect adjustment for use with predictive models that are validated and fixed on historical data from a 'gold-standard' batch. RESULTS: We combined data from MIRT across two batches ('Old' and 'New' Kit sample preparation) as well as external data sets from the HOVON-65/GMMG-HD4 and MRC-IX trials into a combined set, first without transformation and then with both ComBat and M-ComBat transformations. Fixed and validated gene risk signatures developed at MIRT on the Old Kit standard (GEP5, GEP70, and GEP80 risk scores) were compared across these combined data sets. Both ComBat and M-ComBat eliminated all of the differences among probes caused by systematic batch effects (over 98% of all untransformed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero significant probes with ComBat and M-ComBat). The agreement in mean and distribution of risk scores, as well as the proportion of high-risk subjects identified, coincided with the 'gold-standard' batch more with M-ComBat than with ComBat. The performance of risk scores improved overall using either ComBat or M-Combat; however, using M-ComBat and the original, optimal risk cutoffs allowed for greater ability in our study to identify smaller cohorts of high-risk subjects. CONCLUSION: M-ComBat is a practical modification to an accepted method that offers greater power to control the location and scale of batch-effect adjusted data. M-ComBat allows for historical models to function as intended on future samples despite known, often unavoidable systematic changes to gene expression data.

This paper presents an efficient Phase Correlation based Image Analysis System (PCIAS) for high quality DEM generation. A multi-resolution phase correlation based disparity estimation and refinement algorithm has been implemented in PCIAS. It can easily cope with the precise disparity estimation from sub-pixel to very large disparity range with varying baseline/distance ratio in vertical or slightly oblique view stereo imaging. The PCIAS is now a fully operational, professional C++ software package equipped with a robust phase correlation engine, which is among the most advanced technology for sub-pixel image feature shift analysis, and is able to achievethpixel accuracy in dense disparity map estimation. Our experiment indicates PCIAS can generate high quality DEM from very narrow baseline satellite image pairs with view angle difference as small as just 1 degree.

To obtain depth-from-stereo imagery, it is traditionally required that the baseline separation between images (or the base-to-height ratio) be very large in order to ensure the largest image disparity range for effective measurement. Typically, aB/Hratio in the range of 0.6-1 is preferred. As a consequence, most existing stereo-matching algorithms are designed to measure disparities reliably with only integer-pixel precision. However, wide baselines may increase the possibility of occlusion occurring between highly contrasting relief, imposing a serious problem to digital elevation model (DEM) generation in urban and highly dissected mountainous areas. A narrow-baseline stereo configuration can alleviate the problem significantly but requires very precise measurements of disparity at subpixel levels. In this paper, we demonstrate a stereo-matching algorithm, based upon the robustphase correlationmethod, that is capable of directly measuring disparities up to 1/50th pixel accuracy and precision. The algorithm enables complete and dense surface shape information to be retrieved from images with unconventionally lowB/Hratios (e.g., less than 0.01), potentially allowing DEM generation from images that would otherwise not be deemed suitable for the purpose.

We demonstrate an automated stereo-matching algorithm capable of extracting disparity/depth information from stereo image pairs with an unconventionally narrow baseline separation i.e. low base-to-height ratio (B/H), thus potentially allowing digital elevation models (DEMs) to be derived from images that previously might not have been considered suitable for stereo applications. For very small B/H ratios the disparity magnitudes may be reduced to sub-pixel levels that conventional stereo-matching algorithms fail to measure. By utilising a new sub-pixel image matching algorithm, based upon the phase correlation (PC) method, we are able to measure the very subtle, sub-pixel, disparities that result from image pairs with B/H ratios as small as 0.06. Initial tests with this algorithm on SPOT 5 satellite image data have demonstrated that this routine is capable of generating very dense and detailed disparity/depth maps of a scene for DEM generation without prior knowledge of the satellite/sensor parameters, whilst also being exceptionally sensitive to small scale textural features, robust to noise, and efficient to implement.

We have developed and used a grid-based geoinformatics infrastructure and analytical methods for investigating the relationship between macro and microscale earthquake deformational processes by linking geographically distributed and computationally intensive earthquake monitoring and modelling tools. Using this infrastructure, measurement of lateral co-seismic deformation is carried out with imageodesy algorithms running on servers at the London eScience Centre. The resultant deformation field is used to initialise geomechanical simulations of the earthquake deformation running on supercomputers based at the University of Oklahoma. This paper describes the details of our work, summarizes our scientific results and details our experiences from implementing and testing the distributed infrastructure and analysis workflow.

The images of imageodesy derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) image pairs across an earthquake event exhibit severe horizontal and vertical striping noise patterns that obscure the desired information relating to earthquake-induced terrain displacements. The periodic noise patterns were analyzed to reveal that the principal cause of these errors was due to the two-way across-track scanning mechanism of the ETM+ instrument in relation to the minor shift of the satellite orbits between two acquisitions. We first focused on the design of selective filters pinpointing the noise frequencies of horizontal striping based on frequency-domain analysis, via fast Fourier transform (FFT). As a result, the horizontal striping patterns have been successfully eliminated, but meanwhile the systematic varying frequency nature of the more complicated wavy vertical striping patterns is better revealed. This has led to the successful design of an adaptive FFT frequency filtering mechanism based on the function characterizing the relationship between angular speed of a scanner imaging system and its corresponding line speed on the curved surface of the Earth. Finally, the horizontal and vertical striping patterns have been successfully eliminated by semiautomatic selective and adaptive filtering procedures without subduing the key information of coseismic displacement