Identification of the Photoreceptor Transcriptional Co-Repressor SAMD11 as Novel Cause of Autosomal Recessive Retinitis Pigmentosa

Retinitis pigmentosa (RP), the most frequent form of inherited retinal dystrophy is characterized by progressive photoreceptor degeneration. Many genes have been implicated in RP development, but several others remain to be identified. Using a combination of homozygosity mapping, whole-exome and targeted next-generation sequencing, we found a novel homozygous nonsense mutation in SAMD11 in five individuals diagnosed with adult-onset RP from two unrelated consanguineous Spanish families. SAMD11 is ortholog to the mouse major retinal SAM domain (mr-s) protein that is implicated in CRX-mediated transcriptional regulation in the retina. Accordingly, protein-protein network analysis revealed a significant interaction of SAMD11 with CRX. Immunoblotting analysis confirmed strong expression of SAMD11 in human retina. Immunolocalization studies revealed SAMD11 was detected in the three nuclear layers of the human retina and interestingly differential expression between cone and rod photoreceptors was observed. Our study strongly implicates SAMD11 as novel cause of RP playing an important role in the pathogenesis of human degeneration of photoreceptors.This work was supported by several grants from the Spanish Centre for Biomedical Network Research on Rare Diseases (CIBERER)(06/07/0036), Instituto de Salud Carlos III (ISCIII, Spanish Ministry of Health)/FEDER, including FIS (PI013/00226) and RETICS (RD09/0076/00101 and RD12/0034/0010), Ministry of Economy and Competitiveness (MINECO), including FEDER (BFU2012-36845), and BIO2011-27069, Conselleria de Educació of the Valencia Community (PROMETEOII/2014/025), Spanish National Organization of the Blind (ONCE) and the Spanish Fighting Blindness Foundation (FUNDALUCE). M.C. was sponsored by the Miguel Servet Program for Researchers in the Spanish National Health Service (CP12/03256) and RSA by Sara Borrel Postdoctoral Program (CD12/00676), both from the ISCIII/FEDER. A.A-F. was sponsored by CIBERER, RPC is supported by Fundación Conchita Rábago (FCR), L.C is sponsored by RETICS (RD12/0034/0010) from ISCIII and L.d.S. was supported by CAPES Foundation, Ministry of Education of Brazil

Two new convolutions for the fractional Fourier transform

In this paper we introduce two novel convolutions for the fractional Fourier transforms (FRFT), and prove natural algebraic properties of the corresponding multiplications such as commutativity, associativity and distributivity, which may be useful in signal processing and other types of applications. We analyze a consequent comparison with other known convolutions, and establish a necessary and sufficient conditions for the solvability of associated convolution equations of both the first and second kind in L^1(R) and L^2(R) spaces. An example satisfying the sufficient and necessary condition for the solvability of the equations is given at the end of the paper

Railway wheel-flat and rail surface defect modelling and analysis by time–frequency techniques

Damage to the surface of railway wheels and rails commonly occurs in most railways. If not detected, it can result in rapid deterioration and possible failure of rolling stock and infrastructure components causing higher maintenance costs. This paper presents an investigation into the modelling and simulation of wheel flat and rail surface defects. A simplified mathematical model was developed and a series of experiments were carried out on a roller rig. Time-frequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Because of this it is widely used for dynamic system analysis and condition monitoring and has been used in this paper for the detection of wheel flats and rail surface defects. Three commonly used time-frequency analysis techniques: Short-Time-Fourier-Transform (STFT); Wigner-Ville Transform (WVT) and Wavelet Transform (WT) were investigated in this work

Volume-based large dynamic graph analytics

\u3cp\u3eWe present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-Time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.\u3c/p\u3