Measurement of the Longitudinal Spin Transfer to Lambda and Anti-Lambda Hyperons in Polarised Muon DIS

The longitudinal polarisation transfer from muons to lambda and anti-lambda hyperons, D_LL, has been studied in deep inelastic scattering off an unpolarised isoscalar target at the COMPASS experiment at CERN. The spin transfers to lambda and anti-lambda produced in the current fragmentation region exhibit different behaviours as a function of x and xF . The measured x and xF dependences of D^lambda_LL are compatible with zero, while D^anti-lambda_LL tends to increase with xF, reaching values of 0.4 - 0.5. The resulting average values are D^lambda_LL = -0.012 +- 0.047 +- 0.024 and D^anti-lambda_LL = 0.249 +- 0.056 +- 0.049. These results are discussed in the frame of recent model calculations.Comment: 13 pages, 7 figure

Autoregressive models for biomedical signal processing

Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially, this data is subject to measurement errors as well as uncertainties in the underlying system model. As a result, standard signal processing using autoregressive model estimators may be biased. We present a framework for autoregressive modelling that incorporates these uncertainties explicitly via an overparameterised loss function. To optimise this loss, we derive an algorithm that alternates between state and parameter estimation. Our work shows that the procedure is able to successfully denoise time series and successfully reconstruct system parameters. This new paradigm can be used in a multitude of applications in neuroscience such as brain-computer interface data analysis and better understanding of brain dynamics in diseases such as epilepsy

Path Signatures for Seizure Forecasting

Forecasting the state of a system from an observed time series is the subject of research in many domains, such as computational neuroscience. Here, the prediction of epileptic seizures from brain measurements is an unresolved problem. There are neither complete models describing underlying brain dynamics, nor do individual patients exhibit a single seizure onset pattern, which complicates the development of a `one-size-fits-all' solution. Based on a longitudinal patient data set, we address the automated discovery and quantification of statistical features (biomarkers) that can be used to forecast seizures in a patient-specific way. We use existing and novel feature extraction algorithms, in particular the path signature, a recent development in time series analysis. Of particular interest is how this set of complex, nonlinear features performs compared to simpler, linear features on this task. Our inference is based on statistical classification algorithms with in-built subset selection to discern time series with and without an impending seizure while selecting only a small number of relevant features. This study may be seen as a step towards a generalisable pattern recognition pipeline for time series in a broader context

The Yeast PNC1 Longevity Gene Is Up-Regulated by mRNA Mistranslation

Translation fidelity is critical for protein synthesis and to ensure correct cell functioning. Mutations in the protein synthesis machinery or environmental factors that increase synthesis of mistranslated proteins result in cell death and degeneration and are associated with neurodegenerative diseases, cancer and with an increasing number of mitochondrial disorders. Remarkably, mRNA mistranslation plays critical roles in the evolution of the genetic code, can be beneficial under stress conditions in yeast and in Escherichia coli and is an important source of peptides for MHC class I complex in dendritic cells. Despite this, its biology has been overlooked over the years due to technical difficulties in its detection and quantification. In order to shed new light on the biological relevance of mistranslation we have generated codon misreading in Saccharomyces cerevisiae using drugs and tRNA engineering methodologies. Surprisingly, such mistranslation up-regulated the longevity gene PNC1. Similar results were also obtained in cells grown in the presence of amino acid analogues that promote protein misfolding. The overall data showed that PNC1 is a biomarker of mRNA mistranslation and protein misfolding and that PNC1-GFP fusions can be used to monitor these two important biological phenomena in vivo in an easy manner, thus opening new avenues to understand their biological relevance

Evaluating the effect of heart rate on T2 balanced steady-state free precession cardiac MRI mapping

PURPOSE: To evaluate heart rate as a patient-related confounder in a commonly applied T2 balanced steady-state free precession (bSSFP) mapping sequence used for myocardial tissue characterization. MATERIALS AND METHODS: This retrospective analysis included prospectively (from December 2013 to November 2021) acquired cardiac MRI (1.5 T) datasets with T2 bSSFP mapping from 69 healthy volunteers. Phantom studies and Bloch simulations were performed with heart rates of 60–130 beats per minute and different resting periods (three, six, or nine R-R intervals). Sequence parameters (repetition time, echo time, flip angle, echo train length) were matched across volunteer, phantom, and simulation measurements. Reference values covered clinically relevant T1 and T2 properties found in native myocardium (short, 1041 and 44 msec; medium, 1293 and 43 msec; long, 1534 and 40 msec). A mixed linear model assessed the effect of heart rate on T2 values in volunteer measurements. RESULTS: The study included 69 healthy volunteers (median age, 34 years; 44 female and 25 male). Heart rate influenced T2 values acquired with three R-R resting periods (r = −0.38, P = .002; linear regression slope, −0.7 msec/10 beats per minute [95% CI: −1.2, −0.1]). In simulation and phantom measurements, T2 values acquired with three R-R resting periods strongly correlated with heart rate, irrespective of myocardial T1 and T2 properties (r ≤ −0.88; P < .01 for all measurements). Heart rate dependency was reduced with increased resting periods in simulations and phantom measurements. Short myocardial T1 and T2 values derived from T2 bSSFP with nine R-R resting periods were not dependent on heart rate (r = −0.41; P = .33). CONCLUSION: T2 bSSFP with three R-R resting periods underestimates T2 values with increasing heart rates. Use of longer resting periods with T2 bSSFP mapping sequences reduced heart rate dependency

Autonomic Management of Large Clusters and Their Integration into the Grid

We present a framework for the co-ordinated, autonomic management of multiple clusters in a compute center and their integration into a Grid environment. Site autonomy and the automation of administrative tasks are prime aspects in this framework. The system behavior is continuously monitored in a steering cycle and appropriate actions are taken to resolve any problems. All presented components have been implemented in the course of the EU project DataGrid: The Lemon monitoring components, the FT fault-tolerance mechanism, the quattor system for software installation and configuration, the RMS job and resource management system, and the Gridification scheme that integrates clusters into the Grid

A flexible framework for sparse simultaneous component based data integration

<p>Abstract</p> <p>1 Background</p> <p>High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account.</p> <p>2 Results</p> <p>We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of <it>Escherichia coli </it>samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks.</p> <p>3 Conclusion</p> <p>Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach).</p> <p>4 Availability</p> <p>The additional file contains a MATLAB implementation of the sparse simultaneous component method.</p

VIH2 Regulates the Synthesis of Inositol Pyrophosphate InsP₈ and Jasmonate-Dependent Defenses in Arabidopsis

Diphosphorylated inositol polyphosphates, also referred to as inositol pyrophosphates, are important signaling molecules that regulate critical cellular activities in many eukaryotic organisms, such as membrane trafficking, telomere maintenance, ribosome biogenesis, and apoptosis. In mammals and fungi, two distinct classes of inositol phosphate kinases mediate biosynthesis of inositol pyrophosphates: Kcs1/IP6K- and Vip1/PPIP5K-like proteins. Here, we report that PPIP5K homologs are widely distributed in plants and that Arabidopsis thaliana VIH1 and VIH2 are functional PPIP5K enzymes. We show a specific induction of inositol pyrophosphate InsP8 by jasmonate and demonstrate that steady state and jasmonate-induced pools of InsP8 in Arabidopsis seedlings depend on VIH2. We identify a role of VIH2 in regulating jasmonate perception and plant defenses against herbivorous insects and necrotrophic fungi. In silico docking experiments and radioligand binding-based reconstitution assays show high-affinity binding of inositol pyrophosphates to the F-box protein COI1-JAZ jasmonate coreceptor complex and suggest that coincidence detection of jasmonate and InsP8 by COI1-JAZ is a critical component in jasmonate-regulated defenses