An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients

Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups

Zeolite protects mice from iron-induced damage in a mouse model trial

© 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. For centuries, zeolites have been used for their utility in binding metals, and they feature in a multitude of agricultural and industrial applications in which the honeycombed zeolite structures form ideal ion exchangers, catalysts and binding agents. Zeolites are currently in a transition period, moving towards implementation in human ailments and diseases. Here, we postulated that zeolites may be able to counter the effects of excess iron and conducted a mouse model trial to gauge the utility of this notion. We used the transgenic mouse strain MexTAg299 for a thirty-week pilot trial in which iron polymaltose and/or the zeolite clinoptilolite was injected into the peritoneum twice weekly. Mice were sacrificed at the end of the trial period and examined by postmortem and histology for significant physiological differences between mouse subgroups. In this study, we demonstrated that a common zeolite, clinoptilolite, is able to maintain the general health and well-being of mice and prevent iron-induced deleterious effects following iron overload. When zeolites are given with iron biweekly as intraperitoneal injections, mice showed far less macroscopic visual organ discoloration, along with near normal histology, under iron overload conditions when compared to mice injected with iron only. The purpose of the present pilot study was to examine potential alternatives to current iron chelation treatments, and the results indicate an advantage to using zeolites in conditions of iron excess. Zeolites may have translational potential for use in cases of human iron overload

Graph Annotations in Modeling Complex Network Topologies

The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some non-trivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations. We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile. Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation of network evolution. We apply our approach to the Autonomous System (AS) topology of the Internet annotated with business relationships between ASs. This topology captures the large-scale structure of the Internet. In depth understanding of this structure and tools to model it are cornerstones of research on future Internet architectures and designs. We find that our techniques are able to accurately capture the structure of annotation correlations within this topology, thus reproducing a number of its important properties in synthetically-generated random graphs

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Rapidly variable Fe Kα\alpha line in NGC 4051

We present a detailed analysis on the variability of the Fe K emission line in NGC 4051 using ASCA data. Through simple Gaussian line fits, we find not only obvious Fe K line variability with no significant difference in the X-ray continuum flux between two ASCA observations which were separated by $\sim$ 440 days, but also rapid variability of Fe K line on time scales $\sim 10^4$ s within the second observation. During the second observation, the line is strong (EW = 733$^{+206}_{-219}$ eV) and broad ($\sigma = 0.96^{+0.49}_{-0.35}$ keV) when the source is brightest, and become weaker (EW = 165$^{+87}_{-86}$ eV) and narrower ($\sigma<0.09$ keV) whilst the source is weakest. The equivalent width of Fe K line correlates positively with the continuum flux, which shows an opposite trend with another Seyfert 1 galaxy MCG --6-30-15.Comment: 12 pages with 5 figures, to appear in ApJ Vol. 516, L6

All-optical mapping of barrel cortex circuits based on simultaneous voltage-sensitive dye imaging and channelrhodopsin-mediated photostimulation

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Neurophotonics 2 (2015): 021013, doi:10.1117/1.NPh.2.2.021013.We describe an experimental approach that uses light to both control and detect neuronal activity in mouse barrel cortex slices: blue light patterned by a digital micromirror array system allowed us to photostimulate specific layers and columns, while a red-shifted voltage-sensitive dye was used to map out large-scale circuit activity. We demonstrate that such all-optical mapping can interrogate various circuits in somatosensory cortex by sequentially activating different layers and columns. Further, mapping in slices from whisker-deprived mice demonstrated that chronic sensory deprivation did not significantly alter feedforward inhibition driven by layer 5 pyramidal neurons. Further development of voltage-sensitive optical probes should allow this all-optical mapping approach to become an important and high-throughput tool for mapping circuit interactions in the brain.This work was supported by the World Class Institute (WCI) program of the National Research Foundation of Korea (NRF) funded by Ministry of Education, Science and Technology of Korea (MEST) (NRF) Grant No. WCI 2009-003 and by the Competitive Research Programme (CRP) of NRF (Singapore) Grant No. NRF 2008 NRF-CRP 002-082

Toeplitz operators on symplectic manifolds

We study the Berezin-Toeplitz quantization on symplectic manifolds making use of the full off-diagonal asymptotic expansion of the Bergman kernel. We give also a characterization of Toeplitz operators in terms of their asymptotic expansion. The semi-classical limit properties of the Berezin-Toeplitz quantization for non-compact manifolds and orbifolds are also established.Comment: 40 page