Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

Cytoskeletal changes during poliovirus infection in an intestinal cell line

Background and Objectives: Although polioviral replication has been extensively studied, cytoskeletal changes in the host cell during poliovirus replication have not been extensively investigated. We studied the ultrastructural and cytoskeletal changes in host cells during poliovirus infection. Methods: Fluorescence staining of filamentous actin with a fluorescein-isothiocynate labelled mycotoxin, in the absence and presence of microfilament inhibitors cytochalasins B and D, and electron microscopy were used to investigate the role and fate of actin microfilaments during poliovirus infection, morphogenesis and release in an intestinal cell line, HRT-18. Results: At 10 h post-infection, fluorescence staining of actin showed focal areas of fluorescence in the cytoplasm. By 16 h, these became more prominent and increased in number, and by 18-22 h they coalesced to enclose areas of the cytoplasm. These changes in the actin profile were confirmed by electron microscopy, where small actin bundles appeared in association with vesicles, increased in size, number and thickness, enclosed areas of cytoplasm with numerous vesicles and were finally seen in association with crystalline arrays of virus near the periphery of the cells. The addition of microfilament inhibitors cytochalasins B and D, after the initial period of adsorption resulted in complete inhibition of changes in the actin profile and of viral release, indicating that microfilament inhibitors prevented both polymerization of actin and movement of the virus within the cell. Interpretation and Conclusion: In poliovirus infection, both intracellular movement and release of virus appear to be related to cytoskeletal changes, particularly involving actin microfilaments

NETWORK FLOW WITH FUZZY ARC LENGTHS USING HAAR RANKING

ABSTRACT Shortest path problem is a classical and the most widely studied phenomenon in combinatorial optimization. In a classical shortest path problem, the distance of the arcs between different nodes of a network are assumed to be certain. In some uncertain situations, the distance will be calculated as a fuzzy number depending on the number of parameters considered. This article proposes a new approach based on Haar ranking of fuzzy numbers to find the shortest path between nodes of a given network. The combination of Haar ranking and the well-known Dijkstra's algorithm for finding the shortest path have been used to identify the shortest path between given nodes of a network. The numerical examples ensure the feasibility and validity of the proposed method

Cosmological models with bulk viscosity in presence of adiabatic matter creation and with G, c and Lambda variables

Some properties of cosmological models with a time variable bulk viscous coefficient in presence of adiabatic mater creation and G, c, Lambda variables are investigated in the framework of flat FRW line element. We trivially find a set of solutions through Dimensional Analysis. In all the studied cases it is found that the behaviour of these constants is inversely prportional to the cosmic time.Comment: 12 pages. We have been rewriting and completing the bibliography of this paper. Submitted to General Relativity and Gravitatio

Nonlinear spinor field in Bianchi type-I Universe filled with viscous fluid: numerical solutions

We consider a system of nonlinear spinor and a Bianchi type I gravitational fields in presence of viscous fluid. The nonlinear term in the spinor field Lagrangian is chosen to be $\lambda F$, with $\lambda$ being a self-coupling constant and $F$ being a function of the invariants $I$ an $J$ constructed from bilinear spinor forms $S$ and $P$. Self-consistent solutions to the spinor and BI gravitational field equations are obtained in terms of $\tau$, where $\tau$ is the volume scale of BI universe. System of equations for $\tau$ and \ve, where \ve is the energy of the viscous fluid, is deduced. This system is solved numerically for some special cases.Comment: 15 pages, 4 figure

Random walk centrality for temporal networks

Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included

Squeeze-and-Breathe Evolutionary Monte Carlo Optimisation with Local Search Acceleration and its application to parameter fitting

Motivation: Estimating parameters from data is a key stage of the modelling process, particularly in biological systems where many parameters need to be estimated from sparse and noisy data sets. Over the years, a variety of heuristics have been proposed to solve this complex optimisation problem, with good results in some cases yet with limitations in the biological setting. Results: In this work, we develop an algorithm for model parameter fitting that combines ideas from evolutionary algorithms, sequential Monte Carlo and direct search optimisation. Our method performs well even when the order of magnitude and/or the range of the parameters is unknown. The method refines iteratively a sequence of parameter distributions through local optimisation combined with partial resampling from a historical prior defined over the support of all previous iterations. We exemplify our method with biological models using both simulated and real experimental data and estimate the parameters efficiently even in the absence of a priori knowledge about the parameters.Comment: 15 Pages, 3 Figures, 6 Tables; Availability: Matlab code available from the authors upon reques

Nitric oxide sensing in plants is mediated by proteolytic control of group VII ERF transcription factors

Nitric oxide (NO) is an important signaling compound in prokaryotes and eukaryotes. In plants, NO regulates critical developmental transitions and stress responses. Here, we identify a mechanism for NO sensing that coordinates responses throughout development based on targeted degradation of plant-specific transcriptional regulators, the group VII ethylene response factors (ERFs). We show that the N-end rule pathway of targeted proteolysis targets these proteins for destruction in the presence of NO, and we establish them as critical regulators of diverse NO-regulated processes, including seed germination, stomatal closure, and hypocotyl elongation. Furthermore, we define the molecular mechanism for NO control of germination and crosstalk with abscisic acid (ABA) signaling through ERF-regulated expression of ABSCISIC ACID INSENSITIVE5 (ABI5). Our work demonstrates how NO sensing is integrated across multiple physiological processes by direct modulation of transcription factor stability and identifies group VII ERFs as central hubs for the perception of gaseous signals in plants

Genetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer's and Parkinson's diseases

BACKGROUND: Clinical, pathological and genetic overlap between sporadic frontotemporal dementia (FTD), Alzheimer's disease (AD) and Parkinson's disease (PD) has been suggested; however, the relationship between these disorders is still not well understood. Here we evaluated genetic overlap between FTD, AD and PD to assess shared pathobiology and identify novel genetic variants associated with increased risk for FTD. METHODS: Summary statistics were obtained from the International FTD Genomics Consortium, International PD Genetics Consortium and International Genomics of AD Project (n>75 000 cases and controls). We used conjunction false discovery rate (FDR) to evaluate genetic pleiotropy and conditional FDR to identify novel FTD-associated SNPs. Relevant variants were further evaluated for expression quantitative loci. RESULTS: We observed SNPs within the HLA, MAPT and APOE regions jointly contributing to increased risk for FTD and AD or PD. By conditioning on polymorphisms associated with PD and AD, we found 11 loci associated with increased risk for FTD. Meta-analysis across two independent FTD cohorts revealed a genome-wide signal within the APOE region (rs6857, 3′-UTR=PVRL2, p=2.21×10–12), and a suggestive signal for rs1358071 within the MAPT region (intronic=CRHR1, p=4.91×10−7) with the effect allele tagging the H1 haplotype. Pleiotropic SNPs at the HLA and MAPT loci associated with expression changes in cis-genes supporting involvement of intracellular vesicular trafficking, immune response and endo/lysosomal processes. CONCLUSIONS: Our findings demonstrate genetic pleiotropy in these neurodegenerative diseases and indicate that sporadic FTD is a polygenic disorder where multiple pleiotropic loci with small effects contribute to increased disease risk