Novel control of cardiac myofilament response to calcium by S-glutathionylation at specific sites of myosin binding protein C

Our previous studies demonstrated a relation between glutathionylation of cardiac myosin binding protein C (cMyBP-C) and diastolic dysfunction in a hypertensive mouse model stressed by treatment with salt, deoxycorticosterone acetate, and unilateral nephrectomy. Although these results strongly indicated an important role for S-glutathionylation of myosin binding protein C as a modifier of myofilament function, indirect effects of other post-translational modifications may have occurred. Moreover, we did not determine the sites of thiol modification by glutathionylation. To address these issues, we developed an in vitro method to mimic the in situ S-glutathionylation of myofilament proteins and determined direct functional effects and sites of oxidative modification employing Western blotting and mass spectrometry. We induced glutathionylation in vitro by treatment of isolated myofibrils and detergent extracted fiber bundles (skinned fibers) with oxidized glutathione (GSSG). Immuno-blotting results revealed increased glutathionylation with GSSG treatment of a protein band around 140 kDa. Using tandem mass spectrometry, we identified the 140 kDa band as cMyBP-C and determined the sites of glutathionylation to be at cysteines 655, 479, and 627. Determination of the relation between Ca(2+)-activation of myofibrillar acto-myosin ATPase rate demonstrated an increased Ca(2+)-sensitivity induced by the S-glutathionylation. Force generating skinned fiber bundles also showed an increase in Ca-sensitivity when treated with oxidized glutathione, which was reversed with the reducing agent, dithiothreitol (DTT). Our data demonstrate that a specific and direct effect of S-glutathionylation of myosin binding protein C is a significant increase in myofilament Ca(2+)-sensitivity. Our data also provide new insights into the functional significance of oxidative modification of myosin binding protein C and the potential role of domains not previously considered to be functionally significant as controllers of myofilament Ca(2+)-responsiveness and dynamics

A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns

An extensive investigation via simulation is carried out with the aim of comparing three nonparametric, single imputation methods in the presence of multiple data patterns. The ultimate goal is to provide useful hints for users needing to quickly pick the most effective impu- tation method among the following: Forward Imputation (ForImp), considered in the two variants of ForImp with the principal compo- nent analysis (PCA), which alternates the use of PCA and the Nearest- Neighbour Imputation (NNI) method in a forward, sequential pro- cedure, and ForImp with the Mahalanobis distance, which involves the use of the Mahalanobis distance when performing NNI; the itera- tive PCA technique, which imputes missing values simultaneously via PCA; the missForest method, which is based on random forests and is developed for mixed-type data. The performance of these methods is compared under several data patterns characterized by different levels of kurtosis or skewness and correlation structures

Modeling of ALOS and COSMO-SkyMed satellite data at Mt Etna: implications on relation between seismic activation of the Pernicana fault system and volcanic unrest

We investigate the displacement induced by the 2–3 April 2010 seismic swarm (the largest event being of Ml 4.3 magnitude) by means of DInSAR data acquired over the volcano by the Cosmo-SkyMed and ALOS radar systems. Satellite observations, combined with leveling data, allowed us to perform a high-resolution modeling inversion capable of fully capturing the deformation pattern and identifying the mechanism responsible for the PFS seismic activation. The inversion results well explain high gradients in the radar line of sight displacements observed along the fault rupture. The slip distribution model indicates that the fault was characterized by a prevailing left-lateral and normal dip–slip motion with no fault dilation and, hence, excludes that the April 2010 seismic swarm is a response to accommodate the stress change induced by magma intrusions, but it is due to the tectonic loading possibly associated with sliding of the eastern flank of the volcano edifice. These results provide a completely different scenario from that derived for the 22 September 2002 M3.7 earthquake along the PFS, where the co-seismic shear-rupture was accompanied by a tensile mechanism associated with a first attempt of magma intrusion that preceded the lateral eruption occurred here a month later. These two opposite cases provide hints into the behavior of the PFS between quiescence and unrest periods at Etna and pose different implications for eruptive activity prediction and volcano hazard assessment. The dense pattern of ground deformation provided by integration of data from short revisiting time satellite missions, together with refined modeling for fault slip distribution, can be exploited at different volcanic sites, where the activity is controlled by volcano-tectonic interaction processes, for a timely evaluation of the impending hazards

Surface deformation of active volcanic areas retrieved with the SBAS-DInSAR technique: an overview

This paper presents a comprehensive overview of the surface deformation retrieval capability of the Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm, referred to as Small BAseline Subset (SBAS) technique, in the context of active volcanic areas. In particular, after a brief description of the algorithm some experiments relevant to three selected case-study areas are presented. First, we concentrate on the application of the SBAS algorithm to a single-orbit scenario, thus considering a set of SAR data composed by images acquired on descending orbits by the European Remote Sensing (ERS) radar sensors and relevant to the Long Valley caldera (eastern California) area. Subsequently, we address the capability of the SBAS technique in a multipleorbit context by referring to Mt. Etna volcano (southern Italy) test site, with respect to which two different ERS data set, composed by images acquired both on ascending and descending orbits, are available. Finally, we take advantage of the capability of the algorithm to work in a multi-platform scenario by jointly exploiting two different sets of SAR images collected by the ERS and the Environment Satellite (ENVISAT) radar sensors in the Campi Flegrei caldera (southern Italy) area. The presented results demonstrate the effectiveness of the algorithm to investigate the deformation field in active volcanic areas and the potential of the DInSAR methodologies within routine surveillance scenario

Algorithmic-type imputation techniques with different data structures : alternative approaches in comparison

In recent years, with the spread availability of large datasets from multiple sources, increasing attention has been devoted to the treatment of missing information. Recent approaches have paved the way to the development of new powerful algorithmic techniques, in which imputation is performed through computer-intensive procedures. Although most of these approaches are attractive for many reasons, less attention has been paid to the problem of which method should be preferred according to the data structure at hand. This work addresses the problem by comparing the two methods missForest and IPCA with a new method we developed within the forward imputation approach. We carried out comparisons by considering different data patterns with varying skewness and correlation of variables, in order to ascertain in which situations a given method produces more satisfying result

Algorithmic imputation techniques for missing data : performance comparisons and development perspectives

In recent years, much research has been devoted to solve the problem of missing data imputation. Although most of the novel proposals look attractive for some reason, less attention has been paid to the problem of when and why a particular method should be chosen while discarding the others. This matter is far crucial in applications, given that unsuitable solutions could heavily affect the reliability of statistical analyses. Starting from this, this work is addressed to study how well several algorithmic-type imputation methods perform in the case of quantitative data. We focus on three different logics of imputing, based respectively on the use of random forests, iterative PCA, and the forward procedure. In particular, the latter, having initially been introduced for ordinal data, has required us to develop an original adaptation so that it handles missing quantitative value

A Comprehensive Simulation Study on the Forward Imputation

The Nearest Neighbour Imputation (NNI) method has a long history in missing data imputation. Likewise, multivariate dimensional reduction techniques allow for preserving the maximum information from the data. Recently, the combined use of these methodologies has been proposed to solve data imputation problems and exploit as much as information from the complete part of the data. In this paper we perform an extensive simulation study to test the performance of this new imputation approach (called \u201cForward Imputation\u201d - ForImp). We compare the two ForImp methods developed for missing quantitative data (the first one called ForImpPCA involving the NNI method and the Principal Component Analysis (PCA) as a multivariate data analysis technique, and the second one called ForImpMahalanobis, which involves the Mahalanobis distance for NNI) with other two imputation techniques regarded as benchmark, namely Stekhoven and B\ufchlmann\u2019s missForest method, which is a nonparametric imputation technique for continuous and/or categorical data based on a random forest, and the Iterative PCA, which is an algorithmic-type technique that imputes missing values simultaneously by an iterative use of PCA. The simulation study is based on constructing simulated data with different levels of kurtosis or skewness and strength of linear relationship of variables, so that the performance of the four methods can be compared on various data patterns. Distributions used for these simulated data belong to the families of Multivariate Exponential Power and Multivariate Skew-Normal distributions, respectively. Results tend to favour ForImpMahalanobis especially in the presence of skew data with small or negative correlations of a same magnitude, or a mix of negative and positive correlations of low level, whereas ForImpPCA works better than it when a slightly higher level of correlations is present in the data

Volcanic spreading of Vesuvius, a new paradigm for interpreting its volcanic activity

We integrate geologic, structural, leveling and Differential SAR Interferometry data to show that Vesuvius began to spread onto its sedimentary substratum about 3,600 years ago. Moreover, we model the detected deformation with a solution of the lubrication approximation of the Navier-Stokes equations to show that spreading may continue for about 7,200 years more. Correlation of volcanic spreading with phases of the eruptive activity suggests that Plinian eruptions, which are thought to pose the major hazard, are less likely to occur in the near future.Published1-4partially_ope