Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease

Model-based investigations of transneuronal spreading mechanisms in neurodegenerative diseases relate the pattern of pathology severity to the brain’s connectivity matrix, which reveals information about how pathology propagates through the connectivity network. Such network models typically use networks based on functional or structural connectivity in young and healthy individuals, and only end-stage patterns of pathology, thereby ignoring/excluding the effects of normal aging and disease progression. Here, we examine the sequence of changes in the elderly brain’s anatomical connectivity over the course of a neurodegenerative disease. We do this in a data-driven manner that is not dependent upon clinical disease stage, by using event-based disease progression modeling. Using data from the Alzheimer’s Disease Neuroimaging Initiative dataset, we sequence the progressive decline of anatomical connectivity, as quantified by graph-theory metrics, in the Alzheimer’s disease brain. Ours is the first single model to contribute to understanding all three of the nature, the location, and the sequence of changes to anatomical connectivity in the human brain due to Alzheimer’s disease. Our experimental results reveal new insights into Alzheimer’s disease: that degeneration of anatomical connectivity in the brain may be a viable, even early, biomarker and should be considered when studying such neurodegenerative diseases

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10×7 matrix of white circles. During each trial (N=24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetacking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers

Stability of Single Particle Tracers for Differentiating Between Heavy- and Light-Duty Vehicle Emissions

To determine the size and chemical composition of particles derived from on-road vehicle emissions, individual particles were sampledcontinuously with an aerosol time-of-flight mass spectrometer (ATOFMS) at the Caldecott Tunnel in Northern California. In this tunnel, traffic is segregated, such that in theory only light duty vehicle emissions or a mix of heavy- (HDV) and light-duty vehicle (LDV) emissions can be sampled separately. Two studies were carried out, one in November 1997 anda secondin July 2000, time periods with average ambient temperatures of 10–15 and 26–32 1C, respectively, with the instrument operating at ambient outdoor temperatures. Analysis of the chemical composition of the particles sampled in these studies shows that sampling conditions can strongly impact the determination of suitable markers for identifying particles emitted from different vehicle types during ambient studies

SOCS-1 regulates IL-15–driven homeostatic proliferation of antigen-naive CD8 T cells, limiting their autoimmune potential

Mice that are deficient in suppressor of cytokine signaling–1 (SOCS-1) succumb to neonatal mortality that is associated with extensive cellular infiltration of many tissues. T cells seem to be necessary for disease, which can be alleviated largely by neutralizing interferon-γ. Examining T cell receptor (TCR) specificity shows that even monospecific T cells can mediate disease in SOCS-1–deficient mice, although disease onset is substantially faster with a polyclonal T cell repertoire. A major phenotype of SOCS-1−/− mice is the accumulation of CD44highCD8+ peripheral T cells. We show that SOCS-1–deficient CD8, but not CD4, T cells proliferate when transferred into normal (T cell–sufficient) mice, and that this is dependent on two signals: interleukin (IL)-15 and self-ligands that are usually only capable of stimulating homeostatic expansion in T cell–deficient mice. Our findings reveal that SOCS-1 normally down-regulates the capacity of IL-15 to drive activation and proliferation of naive CD8 T cells receiving TCR survival signals from self-ligands. We show that such dysregulated proliferation impairs the deletion of a highly autoreactive subset of CD8 T cells, and increases their potential for autoimmunity. Therefore, impaired deletion of highly autoreactive CD8 T cells, together with uncontrolled activation of naive CD8 T cells by homeostatic survival ligands, may provide a basis for the T cell–mediated disease of SOCS-1−/− mice

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Bostonia: The Boston University Alumni Magazine. Volume 12

Founded in 1900, Bostonia magazine is Boston University’s main alumni publication

Bostonia: The Boston University Alumni Magazine. Volume 10

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Melting, Solidification, and Crystallization of a Thermoplastic Polyurethane as a Function of Hard Segment Content

Thermoplastic polyurethanes (TPU) with varying hard segment contents (HSC) are monitored during melting and solidifying (20 K/min , Tmax = 220 ° C) by small-angle and wide-angle X-ray scattering (WAXS and SAXS). Hard segments: MDI/BD. Soft segments: PTHF1000. The neat materials are injection-molded, having small amorphous hard domains (chord length d⎯⎯h ∼ 35% show sharp Bragg peaks and larger hard domains ( d⎯⎯h > 7 nm ). When heated, small domains melt, but crystallization in the remaining large domains is not detected. Upon cooling, large agglomerates segregate first, which crystallize immediately. Segregation starts for HSC = 42% at 160 °C and for HSC = 75% at 210 °C. When HSC ≤ 30%, the morphologies before and after are similar, but afterward, many hard blocks are dissolved in the soft phase at the expense of the hard domain fraction. In heating and cooling the melts, multiple homogenization and segregation processes are observed, which are explained by the agglomeration of hard blocks of different lengths in the colloidal fluid