1,721,046 research outputs found
Convergent transcriptomic and neuroimaging signature of Autism Spectrum Disorder
Full text linkAutism Spectrum Disorder (ASD) is a multi-factorial neurodevelopmental disorder, whose causes
are still poorly understood. Effective therapies to reduce all the heterogeneous symptoms of the
disorder do not exists yet, but behavioural programs started at a very young age may improve the
quality of life of the patients. For this reason, many efforts have been dedicated to the research
of a reliable biomarker for early diagnosis. Machine learning approaches to distinguish ASDs
from healthy controls based on their brain Magnetic Resonance Images (MRIs) have been plagued
by the problem of confounders, showing poor classification performance and inconsistency in the
biomarker definition. Brain transcriptomics studies, instead, showed some converging results, but
being based on data that can be acquired only post-mortem they are not useful for diagnosis.
In this work, using an imaging transcriptomics approach, the following results have been obtained.
• A deep learning based classifier resilient to confounders and able to exploit the temporal
dimension of resting state functional MRIs has been developed, reaching an AUC of 0.89
on an independent test set.
• Five gene network modules involved in ASD have been identified, by analyzing brain transcriptomics
data of subjects with ASD and healthy controls.
• By comparing the brain regions relevant for the classifier obtained in the first step and the
brain-wide gene expression profiles of the modules of interest obtained in the second step, it
has been proved that the regions that characterize ASD brain at the neuroimaging level are
those in which four out of the five gene modules take a significantly high absolute value of
expression.
These results prove that, despite the heterogeneity of the disorder, it is possible to identify a
neuroimaging-based biomarker of ASD, confirmed by transcriptomics
Hybrid Monte Carlo methods for the diffusion of impurities in a gas
No full textThe goal of this work is to construct suitable Monte Carlo methods for kinetic equations modelling the dynamics of fine polluting powders (for example, particulate matter) interacting inelastically with a background gas. The evolution of the particles and the background motion can be described by a system of Boltzmann equations for hard spheres. The complexity of the model can be strongly reduced by assuming that the background is in thermodynamic equilibrium and that the polluting particles are sufficiently few (in comparison to the background molecules) to admit that there are no collisions among couples of them. As a consequence, for each species of pollutant, we obtain a single dissipative linear Boltzmann equation with a collision dynamic related to the gas-impurity mass ratio. The development of hybrid Monte Carlo schemes coupling a deterministic fluid solver for the background moments and a Time Relaxed Monte Carlo (TRMC) method for the diffusion of the impurities is developed and some numerical results are presented
On the Cauchy problem for a quantum kinetic equation linked to the Compton effect
No full textThe Cauchy problem is studied for an homogeneous quantum kinetic equation describing the Compton effect. Since the collision kernel commonly used in physics is highly singular, numerical simulations are performed for related collision kernels to get a preliminary insight into the behavior of the solutions. Some of the numerical results are then given a theoretical explanation. Global existence of a solution to the Cauchy problem is proven when the L1 initial data are a.e. smaller than the Planck distribution function, and non-existence of solutions to the Cauchy problem is proven when the L1 initial data are a.e. bigger than the Planck distribution function. © 2005 Elsevier Ltd. All rights reserved
Novel Machine Learning Approaches in Multi-site Analysis for Autism Spectrum Disorders
No full textApplying Machine Learning (ML) techniques on neuroanatomical Magnetic Resonance (MR) data, is becoming widespread for studying psychiatric disorders. However, such instruments require some precautions that, if not applied, may lead to inconsistent results that depend on the procedural choices made in the analysis, especially when the data under examination are extremely heterogeneous and many sources of bias are present. This is the case of studies on Autism Spectrum Disorder, in which the scarcity of data and the variability of this disease impose to examine data of subjects that differ both in the medical conditions and in the phenotypical characteristics. In this project two techniques that may be able to deal with these difficulties are proposed
Measuring the effects of confounders in medical supervised classification problems: the Confounding Index (CI)
No full textOver the years, there has been growing interest in using Machine Learning techniques for biomedical data processing. When tackling these tasks, one needs to bear in mind that biomedical data depends on a variety of characteristics, such as demographic aspects (age, gender, etc) or the acquisition technology, which might be unrelated with the target of the analysis. In supervised tasks, failing to match the ground truth targets with respect to such characteristics, called confounders, may lead to very misleading estimates of the predictive performance. Many strategies have been proposed to handle confounders, ranging from data selection, to normalization techniques, up to the use of training algorithm for learning with imbalanced data. However, all these solutions require the confounders to be known a priori. To this aim, we introduce a novel index that is able to measure the confounding effect of a data attribute in a bias-agnostic way. This index can be used to quantitatively compare the confounding effects of different variables and to inform correction methods such as normalization procedures or ad-hoc-prepared learning algorithms. The effectiveness of this index is validated on both simulated data and real-world neuroimaging data
Modelling and numerical methods for the dynamics of impurities in a gas
No full textThe dynamics of a mixture of impurities in a gas can be represented by a system of linear Boltzmann equations for hard spheres. We assume that the background is in thermodynamic equilibrium and that the polluting particles are sufficiently few (in comparison with the background molecules) to admit that there are no collisions among couples of them. In order to derive non-trivial hydrodynamic models, we close the Euler system around local Maxwellian's which are not equilibrium states.
The kinetic model is solved by using a Monte Carlo method, the hydrodynamic one by implicit-explicit Runge-Kutta schemes with weighted essentially non-oscillatory reconstruction (J. Sci. Comput. 2005; 25(1-2):129-155). Several numerical tests are then computed in order to compare the results obtained with the kinetic and the hydrodynamic models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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