Mutations in LONP1, a mitochondrial matrix protease, cause CODAS syndrome.

Cerebral, ocular, dental, auricular, skeletal anomalies (CODAS) syndrome (MIM 600373) was first described and named by Shehib et al, in 1991 in a single patient. The anomalies referred to in the acronym are as follows: cerebral-developmental delay, ocular-cataracts, dental-aberrant cusp morphology and delayed eruption, auricular-malformations of the external ear, and skeletal-spondyloepiphyseal dysplasia. This distinctive constellation of anatomical findings should allow easy recognition but despite this only four apparently sporadic patients have been reported in the last 20 years indicating that the full phenotype is indeed very rare with perhaps milder or a typical presentations that are allelic but without sufficient phenotypic resemblance to permit clinical diagnosis. We performed exome sequencing in three patients (an isolated case and a brother and sister sib pair) with classical features of CODAS. Sanger sequencing was used to confirm results as well as for mutation discovery in a further four unrelated patients ascertained via their skeletal features. Compound heterozygous or homozygous mutations in LONP1 were found in all (8 separate mutations; 6 missense, 1 nonsense, 1 small in-frame deletion) thus establishing the genetic basis of CODAS and the pattern of inheritance (autosomal recessive). LONP1 encodes an enzyme of bacterial ancestry that participates in protein turnover within the mitochondrial matrix. The mutations cluster at the ATP-binding and proteolytic domains of the enzyme. Biallelic inheritance and clustering of mutations confirm dysfunction of LONP1 activity as the molecular basis of CODAS but the pathogenesis remains to be explored.

1D.03: Inactive matrix GLA protein is associated with renal resistive index in a population-based study

OBJECTIVE: Renal resistive index (RRI) varies directly with renal vascular stiffness and pulse pressure. RRI correlates positively with arteriolosclerosis in damaged kidneys and predicts progressive renal dysfunction. Matrix Gla-protein (MGP) is a vascular calcification inhibitor that needs vitamin K to be activated. Inactive MGP, known as desphospho-uncarboxylated MGP (dp-ucMGP), can be measured in plasma and has been associated with various cardiovascular (CV) markers, CV outcomes and mortality. In this study we hypothesize that increased RRI is associated with high levels of dp-ucMGP. DESIGN AND METHOD: We recruited participants via a multi-center family-based cross-sectional study in Switzerland exploring the role of genes and kidney hemodynamics in blood pressure regulation. Dp-ucMGP was quantified in plasma samples by sandwich ELISA. Renal doppler sonography was performed using a standardized protocol to measure RRIs on 3 segmental arteries in each kidney. The mean of the 6 measures was reported. Multiple regression analysis was performed to estimate associations between RRI and dp-ucMGP adjusting for sex, age, pulse pressure, mean pressure, renal function and other CV risk factors. RESULTS: We included 1035 participants in our analyses. Mean values were 0.64 ± 0.06 for RRI and 0.44 ± 0.21 (nmol/L) for dp-ucMGP. RRI was positively associated with dp-ucMGP both before and after adjustment for sex, age, body mass index, pulse pressure, mean pressure, heart rate, renal function, low and high density lipoprotein, smoking status, diabetes, blood pressure and cholesterol lowering drugs, and history of CV disease (P < 0.001). CONCLUSIONS: RRI is independently and positively associated with high levels of dp-ucMGP after adjustment for pulse pressure and common CV risk factors. Further studies are needed to determine if vitamin K supplementation can have a positive effect on renal vascular stiffness and kidney function.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Matrix Analytic Analysis of Delays in Communication Systems

Tässä päättötyössä annetaan kuvaus kehitetystä sovelluksesta Quasi Birth Death processien ratkaisuun. Tämä ohjelma on tähän mennessä ainutlaatuinen ja sen avulla voi ratkaista sarjan tehtäviä ja sitä tarvitaan kommunikaatio systeemien analyysiin. Mainittuun sovellukseen on annettu kuvaus ja määritelmä. Lyhyt kuvaus toisesta sovelluksesta Quasi Birth Death prosessien tehtävien ratkaisuun on myös annettu

Structural factors impacting carrier transport and electroluminescence from Si nanocluster-sensitized Er ions.

We present an analysis of factors influencing carrier transport and electroluminescence (EL) at 1.5 µm from erbium-doped silicon-rich silica (SiOx) layers. The effects of both the active layer thickness and the Si excess content on the electrical excitation of erbium are studied. We demonstrate that when the thickness is decreased from a few hundred to tens of nanometers the conductivity is greatly enhanced. Carrier transport is well described in all cases by a Poole-Frenkel mechanism, while the thickness-dependent current density suggests an evolution of both density and distribution of trapping states induced by Si nanoinclusions. We ascribe this observation to stress-induced effects prevailing in thin films, which inhibit the agglomeration of Si atoms, resulting in a high density of sub-nm Si inclusions that induce traps much shallower than those generated by Si nanoclusters (Si-ncs) formed in thicker films. There is no direct correlation between high conductivity and optimized EL intensity at 1.5 µm. Our results suggest that the main excitation mechanism governing the EL signal is impact excitation, which gradually becomes more efficient as film thickness increases, thanks to the increased segregation of Si-ncs, which in turn allows more efficient injection of hot electrons into the oxide matrix. Optimization of the EL signal is thus found to be a compromise between conductivity and both number and degree of segregation of Si-ncs, all of which are governed by a combination of excess Si content and sample thickness. This material study has strong implications for many electrically driven devices using Si-ncs or Si-excess mediated EL.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Inactive Matrix Gla-Protein is associated with arterial stiffness in an adult population-based study

Increased pulse wave velocity (PWV) is a marker of aortic stiffness and an independent predictor of mortality. Matrix Gla-protein (MGP) is a vascular calcification inhibitor that needs vitamin K to be activated. Inactive MGP, known as desphospho-uncarboxylated MGP (dp-ucMGP), can be measured in plasma and has been associated with various cardiovascular markers, cardiovascular outcomes, and mortality. In this study, we hypothesized that high levels of dp-ucMGP are associated with increased PWV. We recruited participants via a multicenter family-based cross-sectional study in Switzerland. Dp-ucMGP was quantified in plasma by sandwich ELISA. Aortic PWV was determined by applanation tonometry using carotid and femoral pulse waveforms. Multiple regression analysis was performed to estimate associations between PWV and dp-ucMGP adjusting for age, renal function, and other cardiovascular risk factors. We included 1001 participants in our analyses (475 men and 526 women). Mean values were 7.87±2.10 m/s for PWV and 0.43±0.20 nmol/L for dp-ucMGP. PWV was positively associated with dp-ucMGP both before and after adjustment for sex, age, body mass index, height, systolic and diastolic blood pressure (BP), heart rate, renal function, low- and high-density lipoprotein, glucose, smoking status, diabetes mellitus, BP and cholesterol lowering drugs, and history of cardiovascular disease (P≤0.01). In conclusion, high levels of dp-ucMGP are independently and positively associated with arterial stiffness after adjustment for common cardiovascular risk factors, renal function, and age. Experimental studies are needed to determine whether vitamin K supplementation slows arterial stiffening by increasing MGP carboxylation.

Mueller matrix microscope with a dual continuous rotating compensator setup and digital demodulation

In this paper we describe a new Mueller matrix (MM) microscope that generalizes and makes quantitative the polarized light microscopy technique. In this instrument all the elements of the MU are simultaneously determined from the analysis in the frequency domain of the time-dependent intensity of the light beam at every pixel of the camera. The variations in intensity are created by the two compensators continuously rotating at different angular frequencies. A typical measurement is completed in a little over one minute and it can be applied to any visible wavelength. Some examples are presented to demonstrate the capabilities of the instrument.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

Blind Inversion of Wiener System using a minimization-projection (MP) approach

In this paper, a new algorithm for blind inversion of Wiener systems is presented. The algorithm is based on minimization of mutual information of the output samples. This minimization is done through a Minimization-Projection (MP) approach, using a nonparametric “gradient” of mutual information.

A Gradient based algorithm for blind inversion of Wiener System using multi-variate score functions

A Wiener system is a linear time-invariant filter, followed by an invertible nonlinear distortion. Assuming that the input signal is an independent and identically distributed (iid) sequence, we propose an algorithm for estimating the input signal only by observing the output of the Wiener system. The algorithm is based on minimizing the mutual information of the output samples, by means of a steepest descent gradient approach.

Improving algorithm speed in PNL mixture separation and Wiener system inversion

This paper proposes a very simple method for increasing the algorithm speed for separating sources from PNL mixtures or invertingWiener systems. The method is based on a pertinent initialization of the inverse system, whose computational cost is very low. The nonlinear part is roughly approximated by pushing the observations to be Gaussian; this method provides a surprisingly good approximation even when the basic assumption is not fully satisfied. The linear part is initialized so that outputs are decorrelated. Experiments shows the impressive speed improvement.