Jason Morrison

Bio: Jason Morrison is an academic researcher from University of Manitoba. The author has contributed to research in topics: Hyperspectral imaging & Piecewise linear function. The author has an hindex of 15, co-authored 45 publications receiving 1877 citations. Previous affiliations of Jason Morrison include Carleton University.

A review and evaluation of intraurban air pollution exposure models

Abstract: The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.

Linear-Space Data Structures for Range Mode Query in Arrays

Abstract: A mode of a multiset S is an element a?S of maximum multiplicity; that is, a occurs at least as frequently as any other element in S. Given an array A[1:n] of n elements, we consider a basic problem: constructing a static data structure that efficiently answers range mode queries on A. Each query consists of an input pair of indices (i,j) for which a mode of A[i:j] must be returned. The best previous data structure with linear space, by Krizanc, Morin, and Smid (Proceedings of the International Symposium on Algorithms and Computation (ISAAC), pp. 517---526, 2003), requires $\varTheta (\sqrt{n}\log\log n)$ query time in the worst case. We improve their result and present an O(n)-space data structure that supports range mode queries in $O(\sqrt{n/\log n})$ worst-case time. In the external memory model, we give a linear-space data structure that requires $O(\sqrt{n/B})$ I/Os per query, where B denotes the block size. Furthermore, we present strong evidence that a query time significantly below $\sqrt{n}$ cannot be achieved by purely combinatorial techniques; we show that boolean matrix multiplication of two $\sqrt{n} \times \sqrt{n}$ matrices reduces to n range mode queries in an array of size O(n). Additionally, we give linear-space data structures for the dynamic problem (queries and updates in near O(n 3/4) time), for orthogonal range mode in d dimensions (queries in near O(n 1?1/2d ) time) and for half-space range mode in d dimensions (queries in $O(n^{1-1/d^{2}})$ time). Finally, we complement our dynamic data structure with a reduction from the multiphase problem, again supporting that we cannot hope for much more efficient data structures.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Using Fourier transform IR spectroscopy to analyze biological materials

Abstract: IR spectroscopy is an excellent method for biological analyses. It enables the nonperturbative, label-free extraction of biochemical information and images toward diagnosis and the assessment of cell functionality. Although not strictly microscopy in the conventional sense, it allows the construction of images of tissue or cell architecture by the passing of spectral data through a variety of computational algorithms. Because such images are constructed from fingerprint spectra, the notion is that they can be an objective reflection of the underlying health status of the analyzed sample. One of the major difficulties in the field has been determining a consensus on spectral pre-processing and data analysis. This manuscript brings together as coauthors some of the leaders in this field to allow the standardization of methods and procedures for adapting a multistage approach to a methodology that can be applied to a variety of cell biological questions or used within a clinical setting for disease screening or diagnosis. We describe a protocol for collecting IR spectra and images from biological samples (e.g., fixed cytology and tissue sections, live cells or biofluids) that assesses the instrumental options available, appropriate sample preparation, different sampling modes as well as important advances in spectral data acquisition. After acquisition, data processing consists of a sequence of steps including quality control, spectral pre-processing, feature extraction and classification of the supervised or unsupervised type. A typical experiment can be completed and analyzed within hours. Example results are presented on the use of IR spectra combined with multivariate data processing.