Diffusion Tensor Imaging in Alzheimer's disease

Attentional control and Information processing speed are central concepts in cognitive psychology and neuropsychology. Functional neuroimaging and neuropsychological assessment have depicted theoretical models considering attention as a complex and non-unitary process. One of its component processes, Attentional set-shifting ability, is commonly assessed using the Trail Making Test (TMT). Performance in the TMT decreases with increasing age in adults, Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Besides, speed of information processing (SIP) seems to modulate attentional performance. While neural correlates of attentional control have been widely studied, there are few evidences about the neural substrates of SIP in these groups of patients. Different authors have suggested that it could be a property of cerebral white matter, thus, deterioration of the white matter tracts that connect brain regions related to set-shifting may underlie the age-related, MCI and AD decrease in performance. The aim of this study was to study the anatomical dissociation of attentional and speed mechanisms. Diffusion tensor imaging (DTI) provides a unique insight into the cellular integrity of the brain, offering an in vivo view into the microarchitecture of cerebral white matter. At the same time, the study of ageing, characterized by white matter decline, provides the opportunity to study the anatomical substrates speeded or slowed information processing. We hypothesized that FA values would be inversely correlated with time to completion on Parts A and B of the TMT, but not the derived scores B/A and B-A.

Preprocessing and analyzing genetic data with complex networks: An application to Obstructive Nephropathy

Many diseases have a genetic origin, and a great effort is being made to detect the genes that are responsible for their insurgence. One of the most promising techniques is the analysis of genetic information through the use of complex networks theory. Yet, a practical problem of this approach is its computational cost, which scales as the square of the number of features included in the initial dataset. In this paper, we propose the use of an iterative feature selection strategy to identify reduced subsets of relevant features, and show an application to the analysis of congenital Obstructive Nephropathy. Results demonstrate that, besides achieving a drastic reduction of the computational cost, the topologies of the obtained networks still hold all the relevant information, and are thus able to fully characterize the severity of the disease.

Knowledge Discovery in Spectral Data by Means of Complex Networks

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease. The structural analysis of the resulting network can then be used to feed standard data-mining algorithms, for instance for the classification of new (unlabeled) subjects. Furthermore, we show how the structure of the network is resilient to the presence of external additive noise, and how it can be used to extract relevant knowledge about the development of the disease.

Network-based biomarkers in Alzheimer's disease: review and future directions

By 2050 it is estimated that the number of worldwide Alzheimer?s disease (AD) patients will quadruple from the current number of 36 million people. To date, no single test, prior to postmortem examination, can confirm that a person suffers from AD. Therefore, there is a strong need for accurate and sensitive tools for the early diagnoses of AD. The complex etiology and multiple pathogenesis of AD call for a system-level understanding of the currently available biomarkers and the study of new biomarkers via network-based modeling of heterogeneous data types. In this review, we summarize recent research on the study of AD as a connectivity syndrome. We argue that a network-based approach in biomarker discovery will provide key insights to fully understand the network degeneration hypothesis (disease starts in specific network areas and progressively spreads to connected areas of the initial loci-networks) with a potential impact for early diagnosis and disease-modifying treatments. We introduce a new framework for the quantitative study of biomarkers that can help shorten the transition between academic research and clinical diagnosis in AD.

Translocation and Functional Analysis of Pseudomonas savastanoi pv. savastanoi NCPPB 3335 Type III Secretion System Effectors Reveals Two Novel Effector Families of the Pseudomonas syringae Complex

Pseudomonas savastanoi pv. savastanoi NCPPB 3335 causes olive knot disease and is a model pathogen for exploring bacterial infection of woody hosts. The type III secretion system (T3SS) effector repertoire of this strain includes 31 effector candidates plus two novel candidates identified in this study which have not been reported to translocate into plant cells. In this work, we demonstrate the delivery of seven NCPPB 3335 effectors into Nicotiana tabacum leaves, including three proteins from two novel families of the P. syringae complex effector super-repertoire (HopBK and HopBL), one of which comprises two proteins (HopBL1 and HopBL2) that harbor a SUMO protease domain. When delivered by P. fluorescens heterologously expressing a P. syringae T3SS, all seven effectors were found to suppress the production of defense-associated reactive oxygen species. Moreover, six of these effectors, including the truncated versions of HopAA1 and HopAZ1 encoded by NCPPB 3335, suppressed callose deposition. The expression of HopAZ1 and HopBL1 by functionally effectorless P. syringae pv. tomato DC3000D28E inhibited the hypersensitive response in tobacco and, additionally, expression of HopBL2 by this strain significantly increased its competitiveness in N. benthamiana. DNA sequences encoding HopBL1 and HopBL2 were uniquely detected in a collection of 31 P. savastanoi pv. savastanoi strains and other P. syringae strains isolated from woody hosts, suggesting a relevant role of these two effectors in bacterial interactions with olive and other woody plants.