Contact angle measurements on superhydrophobic Carbon Nanotube Forests : effect of fluid pressure

In this paper the effect of pressure on the contact angle of a water drop on superhydrophobic Carbon Nanotube (CNT) forests is studied. Superhydrophobic CNT forests are obtained from a new and simple functionalization strategy, based on the gold-thiol affinity. Using a specifically devised experimental setup, we then show that these surfaces are able to withstand high excess pressures (larger than 10 kPa) without transiting toward a roughness-invaded state, therefore preserving their low adhesion properties. Together with the relatively low technical cost of the process, this robustness versus pressure makes such surfaces very appealing for practical integration into microfluidic systems.Comment: accepted for publication in Europhysics Letter

Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches

Automatic, fast and robust characterization of noise distributions for diffusion MRI

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g. coils sensitivity maps or reconstruction coefficients), which is not usually available. We introduce a new method where a change of variable naturally gives rise to a particular form of the gamma distribution for background signals. The first moments and maximum likelihood estimators of this gamma distribution explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the method automatic and robust to artifacts. Experiments on synthetic datasets show that the proposed method can reliably estimate both the degrees of freedom and the standard deviation. The worst case errors range from below 2% (spatially uniform noise) to approximately 10% (spatially variable noise). Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variances than compared methods.Comment: v2: added publisher DOI statement, fixed text typo in appendix A

RubiX: combining spatial resolutions for Bayesian inference of crossing fibers in diffusion MRI

The trade-off between signal-to-noise ratio (SNR) and spatial specificity governs the choice of spatial resolution in magnetic resonance imaging (MRI); diffusion-weighted (DW) MRI is no exception. Images of lower resolution have higher signal to noise ratio, but also more partial volume artifacts. We present a data-fusion approach for tackling this trade-off by combining DW MRI data acquired both at high and low spatial resolution. We combine all data into a single Bayesian model to estimate the underlying fiber patterns and diffusion parameters. The proposed model, therefore, combines the benefits of each acquisition. We show that fiber crossings at the highest spatial resolution can be inferred more robustly and accurately using such a model compared to a simpler model that operates only on high-resolution data, when both approaches are matched for acquisition time

The advantages of Structural Equation Modelling to address the complexity of spatial reference learning

Background: Cognitive performance is a complex process influenced by multiple factors. Cognitive assessment in experimental animals is often based on longitudinal datasets analyzed using uni- and multi-variate analyses, that do not account for the temporal dimension of cognitive performance and also do not adequately quantify the relative contribution of individual factors onto the overall behavioral outcome. To circumvent these limitations, we applied an Autoregressive Latent Trajectory (ALT) to analyze the Morris water maze (MWM) test in a complex experimental design involving four factors: stress, age, sex, and genotype. Outcomes were compared with a traditional Mixed-Design Factorial ANOVA (MDF ANOVA). Results: In both the MDF ANOVA and ALT models, sex, and stress had a significant effect on learning throughout the 9 days. However, on the ALT approach, the effects of sex were restricted to the learning growth. Unlike the MDF ANOVA, the ALT model revealed the influence of single factors at each specific learning stage and quantified the cross interactions among them. In addition, ALT allows us to consider the influence of baseline performance, a critical and unsolved problem that frequently yields inaccurate interpretations in the classical ANOVA model. Discussion: Our findings suggest the beneficial use of ALT models in the analysis of complex longitudinal datasets offering a better biological interpretation of the interrelationship of the factors that may influence cognitive performance.European Commission (FP7) “SwitchBox” (Contract HEALTH-F2-2010-259772) project and co-financed by the Portuguese North Regional Operational Program (ON.2, O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER), and by Fundação Calouste Gulbenkian—Inovar em Saúde (“Envelhecimento cognitivo saudável—proporcionar saúde mental no processo biológico do envelhecimento,” Contract P-139977). In addition, this study was supported by Portuguese Foundation for Science and Technology (FCT) grant PTDC/SAU-NMC/113934/2009, Canon Foundation and a Grant-in-Aid for Scientific Research on Innovative Areas (Brain Environment) of Ministry of Education, Science, Sports, and Culture of Japan

An operator expansion for the elastic limit

A leading twist expansion in terms of bi-local operators is proposed for the structure functions of deeply inelastic scattering near the elastic limit $x \to 1$, which is also applicable to a range of other processes. Operators of increasing dimensions contribute to logarithmically enhanced terms which are supressed by corresponding powers of $1-x$. For the longitudinal structure function, in moment ($N$) space, all the logarithmic contributions of order $\ln^k N/N$ are shown to be resummable in terms of the anomalous dimension of the leading operator in the expansion.Comment: 9 pages, 1 figure, uses REVTEX 3.1 and axodra

High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project

Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7 T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2⁎ relaxation times, increased B1+ inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. specific absorption rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7 T image acquisitions for the HCP that allow us to efficiently obtain high quality, high resolution whole brain in-vivo dMRI data at 7 T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3 T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7 T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015