Compression of interferometric radio-astronomical data

The volume of radio-astronomical data is a considerable burden in the processing and storing of radio observations with high time and frequency resolutions and large bandwidths. Lossy compression of interferometric radio-astronomical data is considered to reduce the volume of visibility data and to speed up processing. A new compression technique named "Dysco" is introduced that consists of two steps: a normalization step, in which grouped visibilities are normalized to have a similar distribution; and a quantization and encoding step, which rounds values to a given quantization scheme using a dithering scheme. Several non-linear quantization schemes are tested and combined with different methods for normalizing the data. Four data sets with observations from the LOFAR and MWA telescopes are processed with different processing strategies and different combinations of normalization and quantization. The effects of compression are measured in image plane. The noise added by the lossy compression technique acts like normal system noise. The accuracy of Dysco is depending on the signal-to-noise ratio of the data: noisy data can be compressed with a smaller loss of image quality. Data with typical correlator time and frequency resolutions can be compressed by a factor of 6.4 for LOFAR and 5.3 for MWA observations with less than 1% added system noise. An implementation of the compression technique is released that provides a Casacore storage manager and allows transparent encoding and decoding. Encoding and decoding is faster than the read/write speed of typical disks. The technique can be used for LOFAR and MWA to reduce the archival space requirements for storing observed data. Data from SKA-low will likely be compressible by the same amount as LOFAR. The same technique can be used to compress data from other telescopes, but a different bit-rate might be required.Comment: Accepted for publication in A&A. 13 pages, 8 figures. Abstract was abridge

A morphological algorithm for improving radio-frequency interference detection

A technique is described that is used to improve the detection of radio-frequency interference in astronomical radio observatories. It is applied on a two-dimensional interference mask after regular detection in the time-frequency domain with existing techniques. The scale-invariant rank (SIR) operator is defined, which is a one-dimensional mathematical morphology technique that can be used to find adjacent intervals in the time or frequency domain that are likely to be affected by RFI. The technique might also be applicable in other areas in which morphological scale-invariant behaviour is desired, such as source detection. A new algorithm is described, that is shown to perform quite well, has linear time complexity and is fast enough to be applied in modern high resolution observatories. It is used in the default pipeline of the LOFAR observatory.Comment: Accepted for publication in A&

Post-correlation radio frequency interference classification methods

We describe and compare several post-correlation radio frequency interference classification methods. As data sizes of observations grow with new and improved telescopes, the need for completely automated, robust methods for radio frequency interference mitigation is pressing. We investigated several classification methods and find that, for the data sets we used, the most accurate among them is the SumThreshold method. This is a new method formed from a combination of existing techniques, including a new way of thresholding. This iterative method estimates the astronomical signal by carrying out a surface fit in the time-frequency plane. With a theoretical accuracy of 95% recognition and an approximately 0.1% false probability rate in simple simulated cases, the method is in practice as good as the human eye in finding RFI. In addition it is fast, robust, does not need a data model before it can be executed and works in almost all configurations with its default parameters. The method has been compared using simulated data with several other mitigation techniques, including one based upon the singular value decomposition of the time-frequency matrix, and has shown better results than the rest.Comment: 14 pages, 12 figures (11 in colour). The software that was used in the article can be downloaded from http://www.astro.rug.nl/rfi-software

The importance of detailed epigenomic profiling of different cell types within organs.

The human body consists of hundreds of kinds of cells specified from a single genome overlaid with cell type-specific epigenetic information. Comprehensively profiling the body's distinct epigenetic landscapes will allow researchers to verify cell types used in regenerative medicine and to determine the epigenetic effects of disease, environmental exposures and genetic variation. Key marks/factors that should be investigated include regions of nucleosome-free DNA accessible to regulatory factors, histone marks defining active enhancers and promoters, DNA methylation levels, regulatory RNAs, and factors controlling the three-dimensional conformation of the genome. Here we use the lung to illustrate the importance of investigating an organ's purified cell epigenomes, and outline the challenges and promise of realizing a comprehensive catalog of primary cell epigenomes

Small-cell lung cancer-associated autoantibodies: potential applications to cancer diagnosis, early detection, and therapy

Small-cell lung cancer (SCLC) is the most aggressive lung cancer subtype and lacks effective early detection methods and therapies. A number of rare paraneoplastic neurologic autoimmune diseases are strongly associated with SCLC. Most patients with such paraneoplastic syndromes harbor high titers of antibodies against neuronal proteins that are abnormally expressed in SCLC tumors. These autoantibodies may cross-react with the nervous system, possibly contributing to autoimmune disease development. Importantly, similar antibodies are present in many SCLC patients without autoimmune disease, albeit at lower titers. The timing of autoantibody development relative to cancer and the nature of the immune trigger remain to be elucidated. Here we review what is currently known about SCLC-associated autoantibodies, and describe a recently developed mouse model system of SCLC that appears to lend itself well to the study of the SCLC-associated immune response. We also discuss potential clinical applications for these autoantibodies, such as SCLC diagnosis, early detection, and therapy

Post-correlation filtering techniques for off-axis source and RFI removal

Techniques to improve the data quality of interferometric radio observations are considered. Fundaments of fringe frequencies in the uv-plane are discussed and filters are used to attenuate radio-frequency interference (RFI) and off-axis sources. Several new applications of filters are introduced and tested. A low-pass filter in time and frequency direction on single baseline data is successfully used to lower the noise in the area of interest and to remove sidelobes coming from unmodelled off-axis sources and RFI. Related side effects of data integration, averaging and gridding are analysed, and shown to be able to cause ghosts and an increase in noise, especially when using long baselines or interferometric elements that have a large field of view. A novel projected fringe low-pass filter is shown to be potentially useful for first order source separation. Initial tests show that the filters can be several factors faster compared to common source separation techniques such as peeling and a variant of peeling that is currently being tested on LOFAR observations called "demixed peeling". Further testing is required to support the performance of the filters.Comment: 18 pages, 20 figures, accepted for publication in MNRA

DNA methylation-based biomarkers for early detection of non-small cell lung cancer: an update

Lung cancer is the number one cancer killer in the United States. This disease is clinically divided into two sub-types, small cell lung cancer, (10–15% of lung cancer cases), and non-small cell lung cancer (NSCLC; 85–90% of cases). Early detection of NSCLC, which is the more common and less aggressive of the two sub-types, has the highest potential for saving lives. As yet, no routine screening method that enables early detection exists, and this is a key factor in the high mortality rate of this disease. Imaging and cytology-based screening strategies have been employed for early detection, and while some are sensitive, none have been demonstrated to reduce lung cancer mortality. However, mortality might be reduced by developing specific molecular markers that can complement imaging techniques. DNA methylation has emerged as a highly promising biomarker and is being actively studied in multiple cancers. The analysis of DNA methylation-based biomarkers is rapidly advancing, and a large number of potential biomarkers have been identified. Here we present a detailed review of the literature, focusing on DNA methylation-based markers developed using primary NSCLC tissue. Viable markers for clinical diagnosis must be detectable in 'remote media' such as blood, sputum, bronchoalveolar lavage, or even exhaled breath condensate. We discuss progress on their detection in such media and the sensitivity and specificity of the molecular marker panels identified to date. Lastly, we look to future advancements that will be made possible with the interrogation of the epigenome