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The MATLAB software toolbox for MEG and EEG analysis

I watched an episode of The Big Bang Theory last night where Dr. Sheldon Cooper was airing an episode of "Fun with Flags," which gave me the idea for today's blog post title. A while ago, I detailed how to create ROIs in AFNI using a variety of different methods. And I even included a

The Functional Imaging Analysis Contest (FIAC) datasets were analyzed with the AFNI software package. Two types of linear regression analyses were carried out: "fixed shape" hemodynamic response, where a preselected incomplete gamma function is used to model each brief activation episode, and "varia …

Hi, I am currently writing some packages for AFNI/FSL/SPM for my favourite distro (Gentoo). I plan to use these toolkits via nipype. On the example of AFNI: I am currently placing everything into /...

In honor of Data Privacy Day--an international effort to raise awareness and promote privacy and data protection best practices--we offer this fifth

Background— Restenosis is a serious complication of coronary angioplasty that involves the proliferation and migration of vascular smooth muscle cells (VSMCs) from the media to the intima, synthesis

Analysis of exhaled breath samples reveals the presence of many volatile organic compounds (VOCs). The VOC composition of the breath, the so-called breath profile, contains a variety of information including the health status and condition of the organism that...

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ABSTRACT Bifidobacteria are a major microbial component of infant gut microbiota, which is believed to promote health benefits for the host and stimulate maturation of the immune system. Despite their perceived importance, very little is known about the ...

Surface-based brain imaging analysis offers the advantages of preserving the topology of cortical activation, increasing statistical power of group-level statistics, estimating cortical thickness, and visualizing with ease the pattern of activation across ...

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows:  boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.

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