Clinical and molecular characterization of a cardiac ryanodine receptor founder mutation causing catecholaminergic polymorphic ventricular tachycardia

Background Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a difficult-to-diagnose cause of sudden cardiac death (SCD). We identified a family of 1400 individuals with multiple cases of CPVT, including 36 SCDs during youth. Objectives We sought to identify the genetic cause of CPVT in this family, to preventively treat and clinically characterize the mutation-positive individuals, and to functionally characterize the pathogenic mechanisms of the mutation. Methods Genetic testing was performed for 1404 relatives. Mutation-positive individuals were preventively treated with β-blockers and clinically characterized with a serial exercise treadmill test (ETT) and Holter monitoring. In vitro functional studies included caffeine sensitivity and store overload–induced calcium release activity of the mutant channel in HEK293 cells. Results We identified the p.G357S_RyR2 mutation, in the cardiac ryanodine receptor, in 179 family members and in 6 SCD cases. No SCD was observed among treated mutation-positive individuals over a median follow-up of 37 months; however, 3 relatives who had refused genetic testing (confirmed mutation-positive individuals) experienced SCD. Holter monitoring did not provide relevant information for CPVT diagnosis. One single ETT was unable to detect complex cardiac arrhythmias in 72% of mutation-positive individuals, though the serial ETT improved the accuracy. Functional studies showed that the G357S mutation increased caffeine sensitivity and store overload–induced calcium release activity under conditions that mimic catecholaminergic stress. Conclusion Our study supports the use of genetic testing to identify individuals at risk of SCD to undertake prophylactic interventions. We also show that the pathogenic mechanisms of p.G357S_RyR2 appear to depend on β-adrenergic stimulation

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Phenology of phytoplankton blooms and its response to environmental changes in the Baltic Sea

Changes in the occurrence of cyanobacteria blooms affect the entire Baltic Sea ecosystem. The difficulty of conducting phenological studies is due to the natural variability of cyanobacteria blooms and the temporal and spatial constraints this imposes on bloom detection. However, using in situ observations, a coupled physical-biological model and sediment trap data, it was possible to determine how the phenology of cyanobacteria blooms is changing in the eastern Baltic Sea and how these changes are related to environmental conditions.Veränderungen im Auftreten von Cyanobakterienblüten wirken sich auf das gesamte Ökosystem aus. Die Schwierigkeit bei der Durchführung phänologischer Studien liegt in der natürlichen Variabilität von Cyanobakterienblüten und den damit verbundenen zeitlichen und räumlichen Einschränkungen bei der Erkennung der Blüte. Mit Hilfe von In-situ-Daten, einem gekoppelten physikalisch-biologischen Modell und Daten aus Sinkstofffallen konnte jedoch ermittelt werden, wie sich die Phänologie der Cyanobakterienblüte in der Ostsee verändert und wie diese Veränderungen mit Umweltparametern zusammenhängen

Prueba de toxicidad para la tierra Fuller contaminada con aceite dieléctrico usando lombrices eisenia foetida y suelos con diferentes contenidos de carbono

Fuller’s earth is an adsorbent material used in the electric industry for dielectric oil regeneration. The high amount of polyaromatic hydrocarbons removed from oil and adsorbed in the Fuller’s earth makes this material a hazardous waste. It is necessary to implement a toxicity test and apply a suitable treatment to safely dispose of this waste in a landfill or repurpose it for reuse. In this paper, the toxicity of Fuller’s earth contaminated with dielectric oil is assessed before and after treatment. The toxic potential of the Fuller’s earth and the dielectric oil extracted through decontamination processes was evaluated in two types of soil with different carbon contents, analyzing the effects on the test organisms, Eisenia Foetida earthworms. These tests showed that decontaminated Fuller’s earth is non-toxic, and that  the toxicity of the contaminated Fuller’s earth, or its extracts after treatment, represented by the median lethal concentration (LC50) depends significantly on the type of soil used.La tierra de Fuller es un material adsorbente utilizado en la industria eléctrica para la regeneración de aceite dieléctrico. La gran cantidad de hidrocarburos poliaromáticos eliminados del petróleo y adsorbidos en la tierra de Fuller hace que este material sea un desecho peligroso. Es necesario implementar una prueba de toxicidad y aplicar un tratamiento adecuado para desechar de manera segura estos desechos en un vertedero o reciclar para su reutilización. En este documento, la toxicidad de la tierra de Fuller contaminada con aceite dieléctrico se evalúa antes y después del tratamiento. El potencial tóxico de la tierra de Fuller y el aceite dieléctrico extraído mediante procesos de descontaminación se evaluó en dos tipos de suelo con diferentes contenidos de carbono, analizando los efectos en los organismos de prueba, las lombrices de tierra Eisenia Foetida. Estas pruebas mostraron que la tierra de Fuller descontaminada no es tóxica, y que la toxicidad de la tierra de Fuller contaminada, o sus extractos después del tratamiento, representada por la concentración letal media (CL50) depende significativamente del tipo de suelo utilizado

Online Estimation Techniques for Natural and Excitation Frequencies on MDOF Vibrating Mechanical Systems

An online algebraic estimation technique for natural and forcing frequencies for a class of uncertain and lumped-parameter vibrating mechanical systems with n degrees of freedom is described. In general, realistic vibrating systems can be affected by unknown exogenous excitation forces with multiple and independent frequency harmonic components. Hence, natural frequencies as well as excitation force frequencies can be simultaneously computed from an algebraic approach into a small interval of time during online operation of the mechanical system. Measurements of an available output signal, associated with some specific degree of freedom, are only required for frequency estimation in time-domain. Information on mass, stiffness and damping matrices are not necessary for multifrequency estimation algorithms. Some analytical, numerical and experimental results on a cantilever Euler–Bernoulli beam are described to show and validate the acceptable estimation of multiple frequencies in forced multiple degrees of freedom vibrating systems

Evaluación de ganancia de peso, conversión y consumo en pollos de engorde alimentados con raciones alternativas

En la granja experimental Santo Domingo ubicada a 13 km del Municipio de Florenc ia, Departamento del Caquetá, vía Morelia con una humedad relativa superior al 80%, altitud media de 242 msnm, con precipitación media anual de 3840 mm y temperatura promedio de 27Cº, se realizó se evaluó la ganancia de peso y costo de producción, utilizando materias primas alternativas como el maíz en pollos de la línea avícola Cobb 45, usando 50 machos. Se tuvieron en cuenta 5 lotes, cada uno con I O animales en diferentes rac iones alimentic ias, lote 1: 100% concentrado correspondiente al lote control, lote 2: 25% maíz - 75% concentrado, lote 3: 50% maíz - 50% concentrado, lote 4: 75% maíz- 25% concentrado y lote 5: 100% maíz dando como resultado una diferencia en el peso final con respecto al lote control de 130 gramos pa ra el lote 2, 475gr en el lote 3, 1295 gr con respecto al lote 4 y 2600gr con respecto al lote 5, evidenciándose una mayor ganancia de peso y conversión a limenticia en los lotes 2 y 3

ACIDO 13-METILICOS ANOICO: UN NUEVO ACIDO GRASO DE LA ESPONJA MARINA Agelas sventres

El ácido graso 13-metilicosanoico ligado a fosfolípidos fue identificado por primera vez en la esponja marina Agelas Sventres (Porifera, Demospongiae). La composición de ácidos grasos se caracterizó por una gran presencia de ácidos con ramificaciones iso y anteiso que representan el 30% de la mezcla, en tanto que, los ácidos: Hexadecanoico, 15-metilhexadecanoico, 5,9-hexacosadienoico. Para la caracterización de los compuestos se utilizó cromatografia de gases de alta resolución (CGAR) y cromatografía de gases de alta resolucion acoplado a espectrometría de masas (CGAR-EM) usuando derivados éster metílico, pirrolidida y éster beta-picolínico

New insights into radioresistance in breast cancer identify a dual function of miR‐122 as a tumor suppressor and oncomiR

Radioresistance of tumor cells gives rise to local recurrence and disease progression in many patients. MicroRNAs (miRNAs) are master regulators of gene expression that control oncogenic pathways to modulate the radiotherapy response of cells. In the present study, differential expression profiling assays identified 16 deregulated miRNAs in acquired radioresistant breast cancer cells, of which miR‐122 was observed to be up‐regulated. Functional analysis revealed that miR‐122 has a role as a tumor suppressor in parental cells by decreasing survival and promoting radiosensitivity. However, in radioresistant cells, miR‐122 functions as an oncomiR by promoting survival. The transcriptomic landscape resulting from knockdown of miR‐122 in radioresistant cells showed modulation of the ZNF611, ZNF304, RIPK1, HRAS, DUSP8 and TNFRSF21 genes. Moreover, miR‐122 and the set of affected genes were prognostic factors in breast cancer patients treated with radiotherapy. Our data indicate that up‐regulation of miR‐122 promotes cell survival in acquired radioresistant breast cancer and also suggest that miR‐122 differentially controls the response to radiotherapy by a dual function as a tumor suppressor an and oncomiR dependent on cell phenotype

Machine learning in marine ecology: an overview of techniques and applications

International audienceMachine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets