Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.     

A new approach to the decontamination of asbestos-polluted waters by treatment with oxalic acid under power ultrasound

A suspension of chrysotile asbestos fibres in aqueous 0.5M oxalic acid was subjected to power ultrasound with the aim to disrupt and detoxify the mineral by the leaching action of oxalic acid on its structural cations acting simultaneously with a vigorous acoustic cavitation. Sonication was performed in a "cavitating tube", a vertical hollow vibrating cylinder made of titanium, operating at 19.2 kHz and 150 W. Treatment lasted from 2.5 to 21 h. Scanning electron microscopy (SEM) showed that the joint action of the chelating agent and ultrasound (though not of either when applied independently) mostly converted asbestos fibres into micrometric aggregates and nano-sized debris, whose morphology totally differed from asbestos fibres. When treated suspensions were filtered through CA membranes (pore size 0.20 microm), more than half of the asbestos went through the filter because it had either been brought in solution or dispersed in the form of extremely small particles. Most of the structural metal ions were brought into solution (ICP-AES). After the treatment the BET surface area of the recovered solid was tenfold greater than the original. The crystalline fraction of residual solids, though resembling the original sample in XRD, was shown by micro-Raman spectra to be made of antigorite, a polymorph form of serpentine. Furthermore, as the length of these antigorite fibrils lay outside the fibre range rated as a health hazard under worldwide regulations, our procedure can be employed for the decontamination of chrysotile-polluted waters and sediments.     

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals.     

湍流的相干结构:推演方法和结果

本文讨论壁面湍流发展的相干结构的观点.在简要的历史文献综述后,我们回顾一些基本观点, 并且介绍相干结构的思想.基于大量主要是由实验所得的结果,本文通过广泛运用的事件检 测技术,探讨湍流边界层内部和外部区域发生的现象.我们从边界层内部区域发生的现象、 边界层外部大尺度运动的发展和涡结构动力学的角度来描述流动的现象.在文章的 第2部分,介绍从背景流动中推演出湍流相干结构的各种方法以及在各种方法框架下所得到的结果, 讨论速度梯度张量不变量、压力的Hessian矩阵分析和本征正交分解等方法.每一个过程 都有``相干结构'的特定的定义,满足恰当的数学构架,并可以对湍流数据做相干结构动力 学分析.这一工作可能会对当前流体动力学家在湍流研究中用到的最新理论和技术的传播有 所贡献.     

Phenylhydrazine–borane adduct—characterized in the solid state and in solution

The 1:1 phenylhydrazine–borane adduct, obtained from phenylhydrazine and sodium borohydride in acidic solution, was investigated by IR, NMR spectroscopy, and MS spectrometry. Ab initio MO calculations indicated the isomer in which the boron center is attached to the primary amino group as the more stable. This forecast was confirmed by solution‐state ¹H, ¹¹B, ¹³C, and ¹⁵N NMR spectroscopy, in agreement with the molecular structure in the solid state determined by X‐ray analysis. © 2002 Wiley Periodicals, Inc. Heteroatom Chem 13:366–372, 2002; Published online in Wiley Interscience (www.interscience.wiley.com). DOI 10.1002/hc.10049     

CFD modelling of a spray deposition process of paint

The paint deposition process by spraying has been studied by means of Computational Fluid Dynamics in order to predict the final thickness of the coating and to determine theoretically the overspraying phenomenon. The VOF model has been used to describe the impact phenomena onto the wall and the Euler-Lagrange approach to simulate droplet trajectories on their way to the surface. Particular attention has been devoted to the prediction of the maximum diameter reached by an impinging droplet at the end of the spreading phase. This diameter is very important in the study of the paint processes because the high viscosity and the small surface tension of paints reduce the impingement practically only to the spreading phase. Two different configurations of atomizers have been considered. The air flux provides a finer atomisation of the liquid, gives to the droplets the necessary velocity to reach the wall, but is also the main cause of overspray since the small droplets tend to follow its deviation near the wall.