Identification and characterisation of hypomethylated DNA loci controlling quantitative resistance in Arabidopsis

Variation in DNA methylation enables plants to inherit traits independently of changes to DNA sequence. Here, we have screened an Arabidopsis population of epigenetic recombinant inbred lines (epiRILs) for resistance against Hyaloperonospora arabidopsidis (Hpa). These lines share the same genetic background, but show variation in heritable patterns of DNA methylation. We identified 4 epigenetic quantitative trait loci (epiQTLs) that provide quantitative resistance without reducing plant growth or resistance to other (a)biotic stresses. Phenotypic characterisation and RNA-sequencing analysis revealed that Hpa-resistant epiRILs are primed to activate defence responses at the relatively early stages of infection. Collectively, our results show that hypomethylation at selected pericentromeric regions is sufficient to provide quantitative disease resistance, which is associated with genome-wide priming of defence-related genes. Based on comparisons of global gene expression and DNA methylation between the wild-type and resistant epiRILs, we discuss mechanisms by which the pericentromeric epiQTLs could regulate the defence-related transcriptome

Caratterizzazione geofisica e monitoraggio microsismico di un ammasso roccioso instabile

Nella presente nota vengono illustrati i risultati preliminari della caratterizzazione geofisica e del monitoraggio microsismico dell'ammasso roccioso instabile di Madonna del Sasso (Verbania). I dati raccolti hanno permesso di meglio comprendere le ragioni dell'instabilità in atto, distinguendo chiaramente le frequenze fondamentali di vibrazione dell'ammasso instabile e le sue direzioni di oscillazione. Sono state inoltre stabilite utili correlazioni tra gli stessi parametri ed i fattori ambientali che influenzano l'ammasso ed un utile confronto in back analysis dei dati di monitoraggio geotecnico raccolti in passato sul medesimo sito

Progress in noncommutative function theory

In this expository paper we describe the study of certain non-self-adjoint operator algebras, the Hardy algebras, and their representation theory. We view these algebras as algebras of (operator valued) functions on their spaces of representations. We will show that these spaces of representations can be parameterized as unit balls of certain $W^{*}$-correspondences and the functions can be viewed as Schur class operator functions on these balls. We will provide evidence to show that the elements in these (non commutative) Hardy algebras behave very much like bounded analytic functions and the study of these algebras should be viewed as noncommutative function theory

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

Combining low cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. We present a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully mobile system for real-time 3D EEG imaging. We discuss the benefits and challenges of a fully portable system, including technical limitations as well as real-time reconstruction of 3D images of brain activity. We present examples of the brain activity captured in a simple experiment involving imagined finger tapping, showing that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using a off-the-shelf consumer neuroheadset is lower compared to that obtained using high density standard EEG equipment, we propose that mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings

DEXi-Dairy: an ex post multicriteria tool to assess the sustainability of dairy production systems in various European regions

Growing awareness of global challenges and increasing pressures on the farming sector, including the urgent requirement to rapidly cut greenhouse gases (GHG) emissions, emphasize the need for sustainable production, which is particularly relevant for dairy production systems. Comparing dairy production systems across the three sustainability dimensions is a considerable challenge, notably due to the heterogeneity of production conditions in Europe. To overcome this, we developed an ex post multicriteria assessment tool that adopts a holistic approach across the three sustainability dimensions. This tool is based on the DEXi framework, which associates a hierarchical decision model with an expert perspective and follows a tree shaped structure; thus, we called it the DEXi-Dairy tool. For each dimension of sustainability, qualitative attributes were defined and organized in themes, sub-themes, and indicators. Their choice was guided by three objectives: (i) better describe main challenges faced by European dairy production systems, (ii) point out synergies and trade-offs across sustainability dimensions, and (iii) contribute to the identification of GHG mitigation strategies at the farm level. Qualitative scales for each theme, sub-theme, and indicator were defined together with weighting factors used to aggregate each level of the tree. Based on selected indicators, a list of farm data requirements was developed to populate the sustainability tree. The model was then tested on seven case study farms distributed across Europe. DEXi-Dairy presents a qualitative method that allows for the comparison of different inputs and the evaluation of the three sustainability dimensions in an integrated manner. By assessing synergies and trade-offs across sustainability dimensions, DEXi-Dairy is able to reflect the heterogeneity of dairy production systems. Results indicate that, while trade-offs occasionally exist among respective selected sub-themes, certain farming systems tend to achieve a higher sustainability score than others and hence could serve as benchmarks for further analyses

Dynamic causal modelling for EEG and MEG

Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnetic resonance imaging (fMRI) to quantify effective connectivity between brain areas. Recently, this framework has been extended and established in the magneto/encephalography (M/EEG) domain. DCM for M/EEG entails the inversion a full spatiotemporal model of evoked responses, over multiple conditions. This model rests on a biophysical and neurobiological generative model for electrophysiological data. A generative model is a prescription of how data are generated. The inversion of a DCM provides conditional densities on the model parameters and, indeed on the model itself. These densities enable one to answer key questions about the underlying system. A DCM comprises two parts; one part describes the dynamics within and among neuronal sources, and the second describes how source dynamics generate data in the sensors, using the lead-field. The parameters of this spatiotemporal model are estimated using a single (iterative) Bayesian procedure. In this paper, we will motivate and describe the current DCM framework. Two examples show how the approach can be applied to M/EEG experiments

Moving magnetoencephalography towards real-world applications with a wearable system

Imaging human brain function with techniques such as magnetoencephalography1 (MEG) typically requires a subject to perform tasks whilst their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or in adult studies that require unconstrained head movement (e.g. spatial navigation). Here, we develop a new type of MEG system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible due to the integration of new quantum sensors2,3 that do not rely on superconducting technology, with a novel system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution whilst subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Results compare well to the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterisation of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment, and understanding the pathophysiology of movement disorders