Impact of tissue microstructure on a model of cardiac electromechanics based on MRI data

Cardiac motion is a vital process as it sustains the pumping of blood in the body. For this reason motion abnormalities are often associated with severe cardiac pathologies. Clinical imaging techniques, such as MRI, are powerful in assessing motion abnormalities but their connection with pathology often remains unknown.

Computational models of cardiac motion, integrating imaging data, would thus be of great help in linking tissue structure (i.e. cells organisation into fibres and sheets) to motion abnormalities and to pathology. Current models, though, are not able yet to correctly predict realistic cardiac motion in the healthy or diseased heart.

Our hypothesis is that a more realistic description of tissue structure within an electromechanical model of the heart, with structural information extracted from data rather than mathematically defined, and a more careful definition of tissue material properties, would better represent the high heterogeneity of cardiac tissue, thus improving the predictive power of the model

Precision Spectroscopy of Polarized Molecules in an Ion Trap

Polar molecules are desirable systems for quantum simulations and cold chemistry. Molecular ions are easily trapped, but a bias electric field applied to polarize them tends to accelerate them out of the trap. We present a general solution to this issue by rotating the bias field slowly enough for the molecular polarization axis to follow but rapidly enough for the ions to stay trapped. We demonstrate Ramsey spectroscopy between Stark-Zeeman sublevels in 180Hf19F+ with a coherence time of 100 ms. Frequency shifts arising from well-controlled topological (Berry) phases are used to determine magnetic g-factors. The rotating-bias-field technique may enable using trapped polar molecules for precision measurement and quantum information science, including the search for an electron electric dipole moment.Comment: Accepted to Scienc

The bilingual system MUSCLEF at QA@ CLEF 2006

International audienceThis paper presents our bilingual question answering system MUSCLEF. We underline the difficulties encountered when shifting from a mono to a cross-lingual system, then we focus on the evaluation of three modules of MUSCLEF: question analysis, answer extraction and fusion. We finally present how we re-used different modules of MUSCLEF to participate in AVE (Answer Validation Exercise)

Estrategia competitiva y satisfacción del cliente en la tienda Marathon Chimbote 2018

La presente investigación tuvo como objetivo general determinar la relación entre la estrategia competitiva y la satisfacción del cliente en la tienda Marathon Chimbote 2018. La población para este estudio estuvo conformada por 200 clientes, a quienes se les realizó una encuesta para recolectar los datos que fueron procesados en el paquete SPSS, en donde se encontró que el coeficiente de correlación Rho de Spearman fue de 0.735, resultado que permitió concluir que la estrategia competitiva se relaciona de forma positiva y moderada con la satisfacción del cliente, asimismo, el nivel de significancia fue de 0.000 por lo que se rechaza Ho y se acepta Hi: La estrategia competitiva se relaciona con la satisfacción del cliente en la tienda Marathon Chimbote 2018; además que el 55% de los clientes opinó que se encuentra algo en desacuerdo con las estrategias competitivas implementadas en Marathon; que, respecto a la satisfacción del cliente, el 51% de los clientes opinó estar insatisfecho; y que las dimensiones de especialización se relaciona positiva y moderadamente con la satisfacción del cliente, pues el coeficiente de correlación Rho de Spearman fue de 0.558; también que la dimensión identificación de la marca se relaciona positiva y moderadamente con la satisfacción del cliente, pues el coeficiente de correlación Rho de Spearman fue de 0.656; también que la dimensión servicio se relaciona positiva y moderadamente con la satisfacción del cliente, pues el coeficiente de correlación Rho de Spearman fue de 0.551; y que la dimensión política de precios se relaciona positiva y moderadamente con la satisfacción del cliente, pues se obtuvo un coeficiente de correlación Rho de Spearman de 0.548

Inference of ventricular activation properties from non-invasive electrocardiography

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-based torso-biventricular modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical cardiac magnetic resonance (CMR) imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces, and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties from non-invasive data, with higher accuracy for earliest activation sites, endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than the fibre or sheet-normal speeds.Comment: Submitted to Medical Image Analysi

Grandmaster-Level Chess Without Search

The recent breakthrough successes in machine learning are mainly attributed to scale: namely large-scale attention-based architectures and datasets of unprecedented scale. This paper investigates the impact of training at scale for chess. Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. We annotate each board in the dataset with action-values provided by the powerful Stockfish 16 engine, leading to roughly 15 billion data points. Our largest model reaches a Lichess blitz Elo of 2895 against humans, and successfully solves a series of challenging chess puzzles, without any domain-specific tweaks or explicit search algorithms. We also show that our model outperforms AlphaZero's policy and value networks (without MCTS) and GPT-3.5-turbo-instruct. A systematic investigation of model and dataset size shows that strong chess performance only arises at sufficient scale. To validate our results, we perform an extensive series of ablations of design choices and hyperparameters

Learning Universal Predictors

Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile representations enabling general problem solving. But, what are the limits of meta-learning? In this work, we explore the potential of amortizing the most powerful universal predictor, namely Solomonoff Induction (SI), into neural networks via leveraging meta-learning to its limits. We use Universal Turing Machines (UTMs) to generate training data used to expose networks to a broad range of patterns. We provide theoretical analysis of the UTM data generation processes and meta-training protocols. We conduct comprehensive experiments with neural architectures (e.g. LSTMs, Transformers) and algorithmic data generators of varying complexity and universality. Our results suggest that UTM data is a valuable resource for meta-learning, and that it can be used to train neural networks capable of learning universal prediction strategies.Comment: 32 pages, 11 figure

Putting music to trial : Consensus on key methodological challenges investigating music-based rehabilitation

Publisher Copyright: © 2022 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of New York Academy of Sciences.Major advances in music neuroscience have fueled a growing interest in music-based neurological rehabilitation among researchers and clinicians. Musical activities are excellently suited to be adapted for clinical practice because of their multisensory nature, their demands on cognitive, language, and motor functions, and music's ability to induce emotions and regulate mood. However, the overall quality of music-based rehabilitation research remains low to moderate for most populations and outcomes. In this consensus article, expert panelists who participated in the Neuroscience and Music VII conference in June 2021 address methodological challenges relevant to music-based rehabilitation research. The article aims to provide guidance on challenges related to treatment, outcomes, research designs, and implementation in music-based rehabilitation research. The article addresses how to define music-based rehabilitation, select appropriate control interventions and outcomes, incorporate technology, and consider individual differences, among other challenges. The article highlights the value of the framework for the development and evaluation of complex interventions for music-based rehabilitation research and the need for stronger methodological rigor to allow the widespread implementation of music-based rehabilitation into regular clinical practice.Peer reviewe

Parasite-Derived Plasma Microparticles Contribute Significantly to Malaria Infection-Induced Inflammation through Potent Macrophage Stimulation

There is considerable debate as to the nature of the primary parasite-derived moieties that activate innate pro-inflammatory responses during malaria infection. Microparticles (MPs), which are produced by numerous cell types following vesiculation of the cellular membrane as a consequence of cell death or immune-activation, exert strong pro-inflammatory activity in other disease states. Here we demonstrate that MPs, derived from the plasma of malaria infected mice, but not naive mice, induce potent activation of macrophages in vitro as measured by CD40 up-regulation and TNF production. In vitro, these MPs induced significantly higher levels of macrophage activation than intact infected red blood cells. Immunofluorescence staining revealed that MPs contained significant amounts of parasite material indicating that they are derived primarily from infected red blood cells rather than platelets or endothelial cells. MP driven macrophage activation was completely abolished in the absence of MyD88 and TLR-4 signalling. Similar levels of immunogenic MPs were produced in WT and in TNF−/−, IFN-γ−/−, IL-12−/− and RAG-1−/− malaria-infected mice, but were not produced in mice injected with LPS, showing that inflammation is not required for the production of MPs during malaria infection. This study therefore establishes parasitized red blood cell-derived MPs as a major inducer of systemic inflammation during malaria infection, raising important questions about their role in severe disease and in the generation of adaptive immune responses