197 research outputs found
Machine Learning for functional genomics: some experiments with supervised learning on microarray data set
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Lithium complexes with a [(CP2Ti2F7)-Ti- ](-) ligand: F-19 NMR probe for lithium solvation
No full textReaction of the in situ prepared lithium fluoride with [Cp TiF3](2) afforded [{Li(Cp 2Ti2F7)}(2)(THF)(2)].2THF, 3.2THE The addition of 12-crown-4 to a THF solution of 3.2THF results in [{Li(Cp 2Ti2F7)}(2)(mu(2)-eta(1):eta(1)-12-crown-4)].(12-crown-4), 4.(12-crown-4). In the solid state structures of 3.2THF and 4.(12-crown-4), which consist of dimeric [Li(Cp 2Ti2F7)](2) units, the lithium atom is coordinated by three fluorine atoms and an oxygen atom from the ether. Dimeric and monomeric (with respect to lithium) species were observed in solution of 3.2THF or 4.(12-crown-4). The solvation of the lithium atom in monomeric species [Li(Cp 2Ti2F7)S-n] with S = CDCl3 12-crown-4, THF-d(8), was studied by variable temperature (NMR)-N-19 spectroscopy Slow exchange of coordinated deuterated trichloromethane and 12-crown-4 on lithium ion was observed in a CDCl3/12-crown-4 solution of 4.(12-crown-4) by F-19 NMR spectroscopy up to 302 K
Synthesis and the crystal structures of a monoanionic tetrafluorodentate ligand and its complex with lanthanum ion
No full textNew organotitanium fluorides [Hdmpy](+)[(C5Me4R)(2)Ti2F7](-) (R = Me 4, Et 5, dmpy = 2,6-dimethylpyridine, lutidine) have been prepared from (C5Me4R)TiF3 and 2,6-dimethyIpyridine . (HF)(2). The compounds 4 and 5 react with La(CF3SO3)(3) to give [La{(C5Me4R)(2)Ti2F7}(3)] (R = Me 6, Et 7) containing the [(C5Me4R)(2)Ti2F7](-) anion as a tetrafluorodentate ligand in the crystal structures of 4 and 7. The cation-anion pair is connected by a hydrogen bond in 4 and the all-fluorine environment of 12 fluorine atoms coordinated to a lanthanum ion is found in 7. (C) 2001 Elsevier Science Ltd. All rights reserved
Synaptic Scaling Improves the Stability of Neural Mass Models Capable of Simulating Brain Plasticity
No full textAbstract: Neural mass models offer a way of studying the development and behaviour of large-scale brain networks through computer simulations. Such simulations are currently mainly research tools but as they improve, they could soon play a role in understanding, predicting and optimising pa- tient treatments, particularly in relation to effects and outcomes of brain injury. To bring us closer to this goal we took an existing state-of-the-art neural mass model capable of simulating connection growth through simu- lated plasticity processes. We identified and addressed some of the model’s limitations by implementing biologically plausible mechanisms. The main limitation of the original model was its instability, which we addressed by incorporating a representation of the mechanism of synaptic scaling and examining the effects of optimising parameters in the model. We show that the updated model retains all the merits of the original model, while being more stable and capable of generating networks that are in several aspects similar to those found in real brains
Time-Resolved Optical Studies of Quasiparticle Dynamics in High-Temperature Superconductors: Experiments and Theory
No full textDemocratized image analytics by visual programming through integration of deep models and small-scale machine learning
No full textAnalysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we use the visual programming toolbox Orange (http://orange.biolab.si) to simplify image analysis by integrating deep-learning embedding, machine learning procedures, and data visualization. Orange supports the construction of data analysis workflows by assembling components for data preprocessing, visualization, and modeling. We equipped Orange with components that use pre-trained deep convolutional networks to profile images with vectors of features. These vectors are used in image clustering and classification in a framework that enables mining of image sets for both novel and experienced users. We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing, identification of developmental competence in mouse oocytes, subcellular protein localization in yeast, and developmental morphology of social amoebae
Explotación científica de productos PAZ en tareas de vigilancia y monitorización costera
No full textLa misión PAZ surge ante la necesidad de un satélite SAR español que pueda proporcionar productos imagen radar para usuarios de seguridad y defensa, civiles y científicos. INTA es el responsable de la dirección técnica del Segmento Terreno, así como del desarrollo y operación del Centro de Calibración y Validación y de la Explotación Científica. Dentro de este ámbito de explotación, se desarrolla un demostrador de aplicaciones SAR marítimas (DeMSAR) como herramienta robusta capaz de llevar a cabo tareas de detección sobre la superficie marina, empleando las imágenes adquiridas por radares de apertura sintética. Se desarrolla bajo un marco de colaboración entre el INTA y la Universidad de Alcalá con el fin de convertirse en un demostrador de las capacidades de los sistemas aerotransportados de INTA y, en el futuro, para procesar los datos adquiridos por el sensor PAZ. Con capacidad de operar en modo automático de detección de barcos o mediante librerías de procesado SAR, DeMSAR ofrece una gran versatilidad al usuario en tareas de procesado tales como filtrado de ruido speckle, detección de líneas de costa, estimación de máscaras de tierra y detección y caracterización de barcos
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