Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.Comment: 11 pages, 9 figure

Infinite Dimensional Control Problems with Positivity State Constraints: a Banach Lattice Approach

This paper is devoted to studying a family of deterministic optimal control problems in an infinite dimension. The difficult feature of such problems is the presence of positivity state constraints, which arise very often in economic applications (our main motivation). To deal with such constraints we set up the problem in a Banach space with a Riesz space structure (i.e., a Banach lattice) and not necessarily reflexive, like $\mathcal{C}^0$. In this setting, which seems to be new in this context, we are able, using a type of infinite-dimensional Perron-Frobenius Theorem, to find explicit solutions of the HJB equation associated to a suitable auxiliary problem and to use such results to get information about the optimal paths of the starting problem. This was not possible to perform in the previously used infinite-dimensional setting where the state space was an $\mathrm{L}^2$ space

Solid solutions of rare earth cations in mesoporous anatase beads and their performances in dye-sensitized solar cells

Solid solutions of the rare earth (RE) cations Pr3+, Nd3+, Sm3+, Gd3+, Er3+ and Yb3+ in anatase TiO2 have been synthesized as mesoporous beads in the concentration range 0.1-0.3% of metal atoms. The solid solutions were have been characterized by XRD, SEM, diffuse reflectance UV-Vis spectroscopy, BET and BJH surface analysis. All the solid solutions possess high specific surface areas, up to more than 100 m2/g. The amount of adsorbed dye in each photoanode has been determined spectrophotometrically. All the samples were tested as photoanodes in dye-sensitized solar cells (DSSCs) using N719 as dye and a nonvolatile, benzonitrile based electrolyte. All the cells were have been tested by conversion efficiency (J-V), quantum efficiency (IPCE), electrochemical impedance spectroscopy (EIS) and dark current measurements. While lighter RE cations (Pr3+, Nd3+) limit the performance of DSSCs compared to pure anatase mesoporous beads, cations from Sm3+ onwards enhance the performance of the devices. A maximum conversion efficiency of 8.7% for Er3+ at a concentration of 0.2% has been achieved. This is a remarkable efficiency value for a DSSC employing N719 dye without co-adsorbents and a nonvolatile electrolyte. For each RE cation the maximum performances are obtained for a concentration of 0.2% metal atoms. © 2015, Nature Publishing Group. All rights reserved

MultiLink Analysis: Brain Network Comparison via Sparse Connectivity Analysis

Abstract The analysis of the brain from a connectivity perspective is revealing novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. Additionally, exploratory data analysis is made complex by the high dimensionality of data. Indeed, to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousands of connections. In this paper, we propose an approach to identify the multivariate relationships in brain connections that characterize two distinct groups, hence permitting the investigators to immediately discover the subnetworks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections do not necessarily maximize the accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and murine brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches and by using high-dimensional datasets

Use of multivariate image analysis for the evaluation of total mixed rations in dairy cow feeding

Multivariate image analysis was applied for the evaluation of total mixed rations (TMR) used in dairy cow feeding. The estimation of the correlations between images and chemical-physical traits of TMR was performe

An application of Z-Box method in dairy cow feedingto estimate the relationships among peNDF, otherfeed variables and productive data

Physically effective NDF (peNDF) is defined as the fraction of fibrethat stimulates chewing and contributes to the floating mat oflarge particles in the rumen, and consequently to its regular activity.PeNDF is calculated from a physical effectiveness factor (pef),varying from 0 (NDF stimulates no chewing) to 1 (max chewing),which may be obtained by laboratory-based particle sizing techniques,such as Penn State Particle Separator, Mertens Separator,Z-Box, Cut Accuracy Test, based on the proportion of DM retainedon sieves (by horizontal or vertical shaking). We chose Z-Boxmethod, thanks to its easy use and applicability to as-is feed andtotal mixed rations (TMR), and we are trying to obtain an estimatingequation which may predict milk fat content and/or other productivedata from peNDF and other variables measured on TMR.To this aim, samples of TMR collected from several farms aresieved (3 sub samples each), and undergo proximate analysis,NDF, ADF, ADL and starch. Milk yield, milk fat, water addiction toTMR are collected on farm; qualitative data such as type of forage,breed, season, geographical origin and altitude (plain/hill/mountain)are also taken into account, to estimate their possible effect.As a first step, in order to investigate the complex relationshipsexisting among this wide set of variables, Principal ComponentAnalysis (PCA) is used as a data exploration tool. Two PCA models(presence of silage or not in TMR) are calculated separately. Foreach PCA model, the overall correlations among all the consideredvariables and their relative importance are investigated by meansof the loadings plots, posing particular attention to the correlationswith peNDF and with milk fat. Moreover, it is also possible to identifyhow the different groups of samples depend on specific variables

Functional connectivity modules in recurrent neural networks: function, origin and dynamics

Understanding the ubiquitous phenomenon of neural synchronization across species and organizational levels is crucial for decoding brain function. Despite its prevalence, the specific functional role, origin, and dynamical implication of modular structures in correlation-based networks remains ambiguous. Using recurrent neural networks trained on systems neuroscience tasks, this study investigates these important characteristics of modularity in correlation networks. We demonstrate that modules are functionally coherent units that contribute to specialized information processing. We show that modules form spontaneously from asymmetries in the sign and weight of projections from the input layer to the recurrent layer. Moreover, we show that modules define connections with similar roles in governing system behavior and dynamics. Collectively, our findings clarify the function, formation, and operational significance of functional connectivity modules, offering insights into cortical function and laying the groundwork for further studies on brain function, development, and dynamics