Compressed sensing: Reconstruction of non-uniformly sampled multidimensional NMR data

© 2018 Wiley Periodicals, Inc. Nuclear magnetic resonance (NMR) spectroscopy is widely used across the physical, chemical, and biological sciences. A core component of NMR studies is multidimensional experiments, which enable correlation of properties from one or more NMR-active nuclei. In high-resolution biomolecular NMR, common nuclei are1H,15N, and13C, and triple resonance experiments using these three nuclei form the backbone of NMR structural studies. In other fields, a range of other nuclei may be used. Multidimensional NMR experiments provide unparalleled information content, but this comes at the price of long experiment times required to achieve the necessary resolution and sensitivity. Non-uniform sampling (NUS) techniques to reduce the required data sampling have existed for many decades. Recently, such techniques have received heightened interest due to the development of compressed sensing (CS) methods for reconstructing spectra from such NUS datasets. When applied jointly, these methods provide a powerful approach to dramatically improve the resolution of spectra per time unit and under suitable conditions can also lead to signal-to-noise ratio improvements. In this review, we explore the basis of NUS approaches, the fundamental features of NUS reconstruction using CS and applications based on CS approaches including the benefits of expanding the repertoire of biomolecular NMR experiments into higher dimensions. We discuss some of the recent algorithms and software packages and provide practical tips for recording and processing NUS data by CS

Spin asymmetries for electron-thallium scattering calculated with the relativistic convergentclose-coupling method

Spin asymmetries for elastic and inelastic scattering of electrons from thallium are presented. Thalliumis a heavy target (Z 81) and the spin asymmetries can be caused by relativistic effects (spin-orbit interactions) in addition to exchange effects

Short-interval intracortical inhibition: Comparison between conventional and threshold-tracking techniques

BACKGROUND: Short-interval intracortical inhibition (SICI) is conventionally measured as the relative amplitude reduction of motor evoked potentials (MEPs) by subthreshold conditioning stimuli. In threshold-tracking SICI (T-SICI), stimulus intensity is instead adjusted repeatedly to maintain a constant MEP and inhibition is measured as the relative threshold increase. T-SICI is emerging as a useful diagnostic test, but its relationship to conventional amplitude SICI (A-SICI) is unclear. OBJECTIVE: To compare T-SICI and its reliability with conventional A-SICI measurements. METHODS: In twelve healthy volunteers (6 men, median age 30 years), conventional and T-SICI were recorded at conditioning stimuli (CS) of 50-80% resting motor threshold (RMT) and interstimulus interval of 2.5 ms. Measurements were repeated on the same day and at least a week later by a single operator. RESULTS: Across the CS range, mean group T-SICI showed a strong linear relationship to the mean group values measured by conventional technique (y = 29.7-0.3x, R2 = 0.99), but there was considerable interindividual variability. At CS 60-80% RMT, T-SICI had excellent intraday (intraclass correlation coefficient, ICC, 0.81-0.92) and adequate-to-excellent interday (ICC 0.61-0.88) reproducibility. Conventional SICI took longer to complete (median of 5.8 vs 3.8 min, p < 0.001) and tended to have poorer reproducibility (ICC 0.17-0.42 intraday, 0.37-0.51 interday). With T-SICI, smaller sample sizes were calculated for equally powered interventional studies. CONCLUSION: The close relationship between conventional and T-SICI suggests that both techniques reflect similar cortical inhibitory mechanisms. Threshold-tracking measurements of SICI may be able to improve reproducibility, to shorten acquisition time and to reduce sample sizes for interventional studies compared with the conventional technique

Encouraging good writing practice in first-year psychology students writing: an intervention using Turnitin

There is growing concern among many regarding plagiarism within student writing . This has promoted investigation in to both the factors that predict plagiarism and potential methods of reducing plagiarism. Consequently, we developed and evaluated an intervention to enhance good practice within academic writing through the use of the plagiarism detection software Turnitin. One hundred and sixteen first-year Psychology students submitted work to Turnitin and 71 of these students evaluated their learning experiences. For the next assignment the students completed, there was a reduction in academic misconduct cases compared to the previous year and students evaluated the session positively. The findings have implications for teaching good practice in academic writing

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015

Application of genomic and quantitative genetic tools to identify candidate resistance genes for brown rot resistance in peach.

The availability of a complete peach genome assembly and three different peach genome sequences created by our group provide new opportunities for application of genomic data and can improve the power of the classical Quantitative Trait Loci (QTL) approaches to identify candidate genes for peach disease resistance. Brown rot caused by Monilinia spp., is the most important fungal disease of stone fruits worldwide. Improved levels of peach fruit rot resistance have been identified in some cultivars and advanced selections developed in the UC Davis and USDA breeding programs. Whole genome sequencing of the Pop-DF parents lead to discovery of high-quality SNP markers for QTL genome scanning in this experimental population. Pop-DF created by crossing a brown rot moderately resistant cultivar 'Dr. Davis' and a brown rot resistant introgression line, 'F8,1-42', derived from an initial almond × peach interspecific hybrid, was evaluated for brown rot resistance in fruit of harvest maturity over three seasons. Using the SNP linkage map of Pop-DF and phenotypic data collected with inoculated fruit, a genome scan for QTL identified several SNP markers associated with brown rot resistance. Two of these QTLs were placed on linkage group 1, covering a large (physical) region on chromosome 1. The genome scan for QTL and SNP effects predicted several candidate genes associated with disease resistance responses in other host-pathogen systems. Two potential candidate genes, ppa011763m and ppa026453m, may be the genes primarily responsible for M. fructicola recognition in peach, activating both PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI) responses. Our results provide a foundation for further genetic dissection, marker assisted breeding for brown rot resistance, and development of peach cultivars resistant to brown rot

Application of random coherence order selection in gradient-enhanced multidimensional NMR

Development of multidimensional NMR is essential to many applications, for example in high resolution structural studies of biomolecules. Multidimensional techniques enable separation of NMR signals over several dimensions, improving signal resolution, whilst also allowing identification of new connectivities. However, these advantages come at a significant cost. The Fourier transform theorem requires acquisition of a grid of regularly spaced points to satisfy the Nyquist criterion, while frequency discrimination and acquisition of a pure phase spectrum require acquisition of both quadrature components for each time point in every indirect (non-acquisition) dimension, adding a factor of 2$^{N−1}$ to the number of free-induction decays which must be acquired, where $N$ is the number of dimensions. Compressed sensing (CS) ℓ$_{1}$-norm minimisation in combination with non-uniform sampling (NUS) has been shown to be extremely successful in overcoming the Nyquist criterion. Previously, maximum entropy reconstruction has also been used to overcome the limitation of frequency discrimination, processing data acquired with only one quadrature component at a given time interval, known as random phase detection (RPD), allowing a factor of two reduction in the number of points for each indirect dimension (Maciejewski et al. 2011 $\small \textit{PNAS}$ 108 16640). However, whilst this approach can be easily applied in situations where the quadrature components are acquired as amplitude modulated data, the same principle is not easily extended to phase modulated (P-/N-type) experiments where data is acquired in the form exp ($\textit{iωt}$) or exp (−$\textit{iωt}$), and which make up many of the multidimensional experiments used in modern NMR. Here we demonstrate a modification of the CS ℓ$_1$-norm approach to allow random coherence order selection (RCS) for phase modulated experiments; we generalise the nomenclature for RCS and RPD as random quadrature detection (RQD). With this method, the power of RQD can be extended to the full suite of experiments available to modern NMR spectroscopy, allowing resolution enhancements for all indirect dimensions; alone or in combination with NUS, RQD can be used to improve experimental resolution, or shorten experiment times, of considerable benefit to the challenging applications undertaken by modern NMR.This is the final version of the article. It first appeared from IOP Publishing via http://dx.doi.org/10.1088/1742-6596/699/1/01200