A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

ICGE: an R package for detecting relevant clusters and atypical units in gene expression

Background: Gene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample ... ) belongs to one of these previously identified clusters or to a new group. Results: ICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use. Conclusions: We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Computational study of the vortex path variation with the VG height

An extensive range of conventional, vane-type, passive vortex generators (VGs) are in use for successful applications of flow separation control. In most cases, the VG height is designed with the same thickness as the local boundary layer at the VG position. However, in some applications, these conventional VGs may produce excess residual drag. The so-called low-profile VGs can reduce the parasitic drag associated to this kind of passive control devices. As suggested by many authors, low-profile VGs can provide enough momentum transfer over a region several times their own height for effective flow-separation control with much lower drag. The main objective of this work is to study the variation of the path and the development of the primary vortex generated by a rectangular VG mounted on a flat plate with five different device heights h = delta, h(1) = 0.8 delta, h(2) = 0.6 delta, h(3) = 0.4 delta and h(4) = 0.2 delta, where delta is the local boundary layer thickness. For this purpose, computational simulations have been carried out at Reynolds number Re = 1350 based on the height of the conventional VG h = 0.25m with the angle of attack of the vane to the oncoming flow beta = 18.5 degrees. The results show that the VG scaling significantly affects the vortex trajectory and the peak vorticity generated by the primary vortex.