Intrinsic protein disorder in histone lysine methylation

Histone lysine methyltransferases (HKMTs), catalyze mono-, di- and trimethylation of lysine residues, resulting in a regulatory pattern that controls gene expression. Their involvement in many different cellular processes and diseases makes HKMTs an intensively studied protein group, but scientific interest so far has been concentrated mostly on their catalytic domains. In this work we set out to analyze the structural heterogeneity of human HKMTs and found that many contain long intrinsically disordered regions (IDRs) that are conserved through vertebrate species. Our predictions show that these IDRs contain several linear motifs and conserved putative binding sites that harbor cancer-related SNPs. Although there are only limited data available in the literature, some of the predicted binding regions overlap with interacting segments identified experimentally. The importance of a disordered binding site is illustrated through the example of the ternary complex between MLL1, menin and LEDGF/p75. Our suggestion is that intrinsic protein disorder plays an as yet unrecognized role in epigenetic regulation, which needs to be further elucidated through structural and functional studies aimed specifically at the disordered regions of HKMTs. Reviewers: This article was reviewed by Arne Elofsson and Piotr Zielenkiewicz. © 2016 The Author(s)

Validation of the self regulation questionnaire as a measure of health in quality of life research

<p>Abstract</p> <p>Objectives</p> <p>Several epidemiological studies address psychosomatic 'self regulation' as a measure of quality of life aspects. However, although widely used in studies with a focus on complementary cancer treatment, and recognized to be associated with better survival of cancer patients, it is unclear what the 'self regulation' questionnaire exactly measures.</p> <p>Design and setting</p> <p>In a sample of 444 individuals (27% healthy, 33% cancer, 40% other internal diseases), we performed reliability and exploratory factor analyses, and correlated the 16-item instrument with external measures such as the Hospital Anxiety and Depression Scale, the Herdecke Quality of Life questionnaire, and autonomic regulation questionnaire.</p> <p>Results</p> <p>The 16-item pool had a very good internal consistency (Cronbach's alpha = 0.948) and satisfying/good (r_rt= 0.796) test-retest reliability after 3 months. Exploratory factor analysis indicated 2 sub-constructs: (1) Ability to change behaviour in order to reach goals, and (2) Achieve satisfaction and well-being. Both sub-scales correlated well with quality of life aspects, particularly with Initiative Power/Interest, Social Interactions, Mental Balance, and negatively with anxiety and depression.</p> <p>Conclusions</p> <p>The Self Regulation Questionnaire (SRQ) was found to be a valid and reliable tool which measures unique psychosomatic abilities. Self regulation deals with competence and autonomy and can be regarded as a problem solving capacity in terms of an active adaptation to stressful situations to restore wellbeing. The tool is an interesting option to be used particularly in complementary medicine research with a focus on behavioural modification.</p

Removal of Spectro-Polarimetric Fringes by 2D Pattern Recognition

We present a pattern-recognition based approach to the problem of removal of polarized fringes from spectro-polarimetric data. We demonstrate that 2D Principal Component Analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us in principle to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.Comment: ApJ, in pres

Coronagraphic observations of Si X 1430 nm acquired by DKIST/Cryo-NIRSP with methods for telluric absorption correction

We report commissioning observations of the Si X 1430 nm solar coronal line observed coronagraphically with the Cryogenic Near-Infrared Spectropolarimeter (Cryo-NIRSP) at the National Science Foundation's Daniel K. Inouye Solar Telescope (DKIST). These are the first known spatially resolved observations of this spectral line, which has strong potential as a coronal magnetic field diagnostic. The observations target a complex active region located on the solar northeast limb on 4 March 2022. We present a first analysis of this data, which extracts the spectral line properties through a careful treatment of the variable atmospheric transmission that is known to impact this spectral window. Rastered images are created and compared with EUV observations from the SDO/AIA instrument. A method for estimating the electron density from the Si X observations is then demonstrated that makes use of the forbidden line's density-sensitive emissivity and an emission-measure analysis of the SDO/AIA bandpass observations. In addition, we derive an effective temperature and non-thermal line width across the region. This study informs the calibration approaches required for more routine observations of this promising diagnostic line.Comment: 12 pages, 9 figures, Accepted for publication in Ap

Exploiting Parallel R in the Cloud with SPRINT

BACKGROUND: Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. OBJECTIVES: Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon’s Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. METHODS: The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. RESULTS: It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of algorithm. Resource underutilization can further improve the time to result. End-user’s location impacts on costs due to factors such as local taxation. Conclusions: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds