Truncating FLNC Mutations Are Associated With High-Risk Dilated and Arrhythmogenic Cardiomyopathies

BACKGROUND: Filamin C (encoded by the FLNC gene) is essential for sarcomere attachment to the plasmatic membrane. FLNC mutations have been associated with myofibrillar myopathies, and cardiac involvement has been reported in some carriers. Accordingly, since 2012, the authors have included FLNC in the genetic screening of patients with inherited cardiomyopathies and sudden death. OBJECTIVES: The aim of this study was to demonstrate the association between truncating mutations in FLNC and the development of high-risk dilated and arrhythmogenic cardiomyopathies. METHODS: FLNC was studied using next-generation sequencing in 2,877 patients with inherited cardiovascular diseases. A characteristic phenotype was identified in probands with truncating mutations in FLNC. Clinical and genetic evaluation of 28 affected families was performed. Localization of filamin C in cardiac tissue was analyzed in patients with truncating FLNC mutations using immunohistochemistry. RESULTS: Twenty-three truncating mutations were identified in 28 probands previously diagnosed with dilated, arrhythmogenic, or restrictive cardiomyopathies. Truncating FLNC mutations were absent in patients with other phenotypes, including 1,078 patients with hypertrophic cardiomyopathy. Fifty-four mutation carriers were identified among 121 screened relatives. The phenotype consisted of left ventricular dilation (68%), systolic dysfunction (46%), and myocardial fibrosis (67%); inferolateral negative T waves and low QRS voltages on electrocardiography (33%); ventricular arrhythmias (82%); and frequent sudden cardiac death (40 cases in 21 of 28 families). Clinical skeletal myopathy was not observed. Penetrance was >97% in carriers older than 40 years. Truncating mutations in FLNC cosegregated with this phenotype with a dominant inheritance pattern (combined logarithm of the odds score: 9.5). Immunohistochemical staining of myocardial tissue showed no abnormal filamin C aggregates in patients with truncating FLNC mutations. CONCLUSIONS: Truncating mutations in FLNC caused an overlapping phenotype of dilated and left-dominant arrhythmogenic cardiomyopathies complicated by frequent premature sudden death. Prompt implantation of a cardiac defibrillator should be considered in affected patients harboring truncating mutations in FLNC.Instituto de Salud Carlos III [PI11/0699, PI14/0967, PI14/01477, RD012/0042/0029, RD012/0042/0049, RD012/0042/0066, RD12/0042/0069]; Spanish Ministry of Economy and Competitiveness [SAF2015-71863-REDT]; Plan Nacional de I+D+I; Plan Estatalde I+D+I, European Regional Development Fund; Health in Code SLS

An overview of the Italian forest biodiversity and its conservation level, based on the first outcomes of the 4th Habitat Report ex-Art. 17

In 2019 the 4th Report ex-Art. 17 on the conservation status (CS) of Annex I Habitats of the 92/43/EEC Directive was expected by every EU/28 country, with reference to the period 2013-18. In Italy, the process was in charge to the Italian Institute for Environmental Protection and Research (ISPRA), on behalf of the Ministry for Environment, Land and Sea Protection (MATTM), with the scientific support of the Italian Botanical Society (SBI). A large group of thematic and territorial experts elaborated the available data concerning the 124 types of terrestrial and inland water Habitats present in Italy, 39 of which are represented by Forest Habitats (Group 9),. The main aim of the work was the evaluation of the overall CS of each Habitat by Biogeographic Region (Mediterranean, Continental and Alpine), for a total amount of 294 assessments. A high proportion of these (92, corresponding to 31% of the total) referred to Forest Habitats, including 20 marginal types for which the CS was not requested. The analysis was carried out at different scales: a) administrative territory, through the data contained in the ISPRA database, whose compilation was in charge to the Regions and Autonomous Provinces; b) Natura 2000 site, with the latest updates available (Standard Data Forms updated to 2018); c) national scale, implementing the distribution maps for each Habitat based on the European grid ETRS89-LAEA5210 (10x10 km2 mesh); d) Biogeographic Region, scale of the final assessment. Cartographic outcomes, associated databases and additional data used for the assessments will be available online on the ISPRA Portal as soon as the validation process by the European Commission will be completed. A dedicated archive named "HAB_IT" has been created in the national database "VegItaly" (1), managed by the Italian Society of Vegetation Science, where the phytosociological relevés representative of the various Annex I Habitats in Italy will be archived and freely accessible. An overview of the results regarding the Forest habitats is here provided, including a comparison with the outcomes of the former reporting cycle, the 3rd Report ex-Art. 17 (2). In several cases (e.g. 9120, 91L0), the distribution maps have been remarkably improved due to better knowledge and more fitful interpretation. The conservation status resulted as Favourable (FV) for 6,7%, Inadequate (U1) for 58,7% and Bad (U1) for 32,0% of the 72 assessed forest Habitat types. In no case there was an improvement of the conservation status, while in 6 cases a worsening of the conditions resulted from the data analysis, pointing out the Habitats types with a higher need of action. Similarly to other projects carried out as a team by the network of Annex I Habitat experts of the Italian Botanical Society and the Italian Society for Vegetation Science (e.g. 3, 4), this is another step in the direction of supporting the implementation of the 92/43/EEC "Habitat" Directive in Italy and Europe. On this ground, the high biodiversity of the Italian forest Habitats could be emphasized, however results pointed out that some rare or endemic types (e.g. Alnus cordata or Betula aetnensis-dominated forests) are still scarcely acknowledged by the most prominent EU conservation tools such as the Annex I to the "Habitat" Directive. 1) F. Landucci et al. (2012) Plant Biosyst., 146(4), 756-763 2) P. Genovesi et al. (2014) ISPRA, Serie Rapporti, 194/2014 3) E. Biondi et al. (2009) Società Botanica Italiana, MATTM, D.P.N., http://vnr.unipg.it/habitat/ 4) D. Gigante et al. (2016) Plant Sociology, 53(2), 77-8

Depth estimation using a single spherical image

A estimativa de profundidade é um componente essencial de diversas aplicações de visão computacional, e um dos assuntos mais extensivamente estudados na área. Recentemente, houveram avanços na utilização de métodos de aprendizagem de máquina para realizar a estimativa a partir de uma única imagem, diferentemente do método tradicional de casamento estéreo, que utiliza duas ou mais imagens. Imagens esféricas, ou omnidirecionais, possuem um campo de visão de 360 e oferecem informação contextual muito maior da cena em relação a imagens planares. A estimativa de profundidade de cenas esféricas pode ser de grande utilidade para diversas aplicações, como navegação, compreensão de cena e realidade virtual. Tradicionalmente, no entanto, são necessárias múltiplas câmeras esféricas ou câmeras especializadas para a estimativa de profundidade na esfera, e com a popularização de câmeras 360 e o fácil acesso a métodos de geração de panoramas 360 , é de interesse poder aplicar as técnicas de estimativa de profundidade utilizando um única imagem ao domínio das imagens esféricas. Este trabalho propõe um método para estimar profundidades utilizando apenas uma única imagem esférica a partir da divisão da esfera e projeção em planos. São estimadas as profundidades no domínio planar utilizando métodos já existentes, e então projeta-se as estimativas de volta para a esfera, combinando as estimativas de cada divisão da esfera em um único mapa de profundidades para toda a esfera.Depth estimation is an essential component in many computer vision applications, and one of the most extensively studied subjects in the field. Recently, advancements were made in applying machine learning methods to estimate depths from a single image, differently from traditional stereo matching methods, which need two or more images. Spherical, or omnidirectional, images, have a 360 field of view and provide higher contextual information about a scene in comparison to planar images. Depth estimation of spherical scenes can be an asset for many applications, such as navigation, scene understanding and virtual reality. Normally, however, multiple spherical cameras, or specialized camera configurations are needed to perform depth estimation on spherical images, and with the rise in ease of access to 360 cameras and panorama generation tools, it is of interest to be able to apply single image depth estimation method to spherical images. This work proposes a method for estimating depth from a single spherical image by dividing the sphere and projecting each section onto a plane. Depths are estimated on the planar domain using existing methods, and these estimates are projected back to the sphere, combining each sections’ estimates into a single depth map for the whole sphere

Epitome sacramentorum a sacris canonibus, et oecumenicis consiliis, atque a S. doctoribus excerpta

per R. Praesb. D. Laurentium Petium de ColoniaBogensignaturen: A-M¹²Titeleinfassung, Randleiste

Depth estimation using a single spherical image

A estimativa de profundidade é um componente essencial de diversas aplicações de visão computacional, e um dos assuntos mais extensivamente estudados na área. Recentemente, houveram avanços na utilização de métodos de aprendizagem de máquina para realizar a estimativa a partir de uma única imagem, diferentemente do método tradicional de casamento estéreo, que utiliza duas ou mais imagens. Imagens esféricas, ou omnidirecionais, possuem um campo de visão de 360 e oferecem informação contextual muito maior da cena em relação a imagens planares. A estimativa de profundidade de cenas esféricas pode ser de grande utilidade para diversas aplicações, como navegação, compreensão de cena e realidade virtual. Tradicionalmente, no entanto, são necessárias múltiplas câmeras esféricas ou câmeras especializadas para a estimativa de profundidade na esfera, e com a popularização de câmeras 360 e o fácil acesso a métodos de geração de panoramas 360 , é de interesse poder aplicar as técnicas de estimativa de profundidade utilizando um única imagem ao domínio das imagens esféricas. Este trabalho propõe um método para estimar profundidades utilizando apenas uma única imagem esférica a partir da divisão da esfera e projeção em planos. São estimadas as profundidades no domínio planar utilizando métodos já existentes, e então projeta-se as estimativas de volta para a esfera, combinando as estimativas de cada divisão da esfera em um único mapa de profundidades para toda a esfera.Depth estimation is an essential component in many computer vision applications, and one of the most extensively studied subjects in the field. Recently, advancements were made in applying machine learning methods to estimate depths from a single image, differently from traditional stereo matching methods, which need two or more images. Spherical, or omnidirectional, images, have a 360 field of view and provide higher contextual information about a scene in comparison to planar images. Depth estimation of spherical scenes can be an asset for many applications, such as navigation, scene understanding and virtual reality. Normally, however, multiple spherical cameras, or specialized camera configurations are needed to perform depth estimation on spherical images, and with the rise in ease of access to 360 cameras and panorama generation tools, it is of interest to be able to apply single image depth estimation method to spherical images. This work proposes a method for estimating depth from a single spherical image by dividing the sphere and projecting each section onto a plane. Depths are estimated on the planar domain using existing methods, and these estimates are projected back to the sphere, combining each sections’ estimates into a single depth map for the whole sphere