Procrustes analysis for diffusion tensor image processing

There is an increasing need to develop processing tools for diffusion tensor image data with the consideration of the non-Euclidean nature of the tensor space. In this paper Procrustes analysis, a non-Euclidean shape analysis tool under similarity transformations (rotation, scaling and translation), is proposed to redefine sample statistics of diffusion tensors. A new anisotropy measure Procrustes Anisotropy (PA) is defined with the full ordinary Procrustes analysis. Comparisons are made with other anisotropy measures including Fractional Anisotropy and Geodesic Anisotropy. The partial generalized Procrustes analysis is extended to a weighted generalized Procrustes framework for averaging sample tensors with different fractions of contributions to the mean tensor. Applications of Procrustes methods to diffusion tensor interpolation and smoothing are compared with Euclidean, Log-Euclidean and Riemannian methods

Bayesian multi-tensor diffusion MRI and tractography

Bayesian multi-tensor diffusion MRI and tractograph

Weighted generalised Procrustes analysis of diffusion tensors

Weighted generalised Procrustes analysis of diffusion tensor

Data_Sheet_1_Dietary behaviors of rural residents in northeastern China: implications for designing intervention information and targeting high-risk population.docx

BackgroundDietary behavior is a pivotal modifiable determinant in reducing the occurrence of obesity/overweight and chronic non-communicable diseases. Improving the dietary behavior of rural residents in China is imminent due to the poor performance of their dietary behavior. Nutrition knowledge and health literacy are considered as elements that are linked intimately to healthy dietary behaviors but lack research in the Chinese setting.PurposeThe study is designed to explore the relationship between nutritional knowledge, health literacy and dietary behaviors and to analyze the performance under different demographic characteristics.MethodsA face-to-face survey of 400 rural residents on their nutrition knowledge, functional health literacy and dietary intake of five food categories consisting of 32 items was conducted based on a validated questionnaire. Descriptive analysis, difference test including ANOVA, t-test and non-parametric test, and multivariate linear regression were used for data analysis.ResultsThe results indicate that declarative nutrition knowledge, individuals’ information application capacity, and dietary behaviors, especially the intake of fruits, dairy and beans, and vegetable are not ideal and requires improvement. Male, elder, low-income, unmarried, and low-education populations performed significantly worse and were the high-risk group. Procedural nutrition knowledge, information access capacity, information understanding capacity, and information application capacity have remarkable effects on better dietary behavior.ConclusionThis study provides evidence-based guidance for prioritizing information and populations for healthy dietary interventions.</p

Procrustes analysis of diffusion tensor data

Diffusion tensor imaging (DTI) is becoming increasingly important in clinical studies of diseases such as multiple sclerosis and schizophrenia, and also in investigating brain connectivity. Hence, there is a growing need to process diffusion tensor (DT) images within a statistical framework based on appropriate mathematical metrics. However, the usual Euclidean operations are often unsatisfactory for diffusion tensors due to the symmetric, positive-definiteness property. A DT is a type of covariance matrix and non-Euclidean metrics have been adapted naturally for DTI processing [1]. In this paper, Procrustes analysis has been used to define a weighted mean of diffusion tensors that provides a suitable average of a sample of tensors. For comparison, six geodesic paths between a pair of diffusion tensors are plotted using the Euclidean as well as various non-Euclidean distances. We also propose a new measure of anisotropy -Procrustes anisotropy (PA). Fractional anisotropy (FA) and PA maps from an interpolated and smoothed diffusion tensor field from a healthy human brain are shown as an application of the Procrustes method

Visible-light photochromism of phosphomolybdic acid and polyvinyl alcohol by inorganic-organic nanocomposite multilayer films

<p>A novel visible-light photochromic inorganic-organic multilayer was constructed based on phosphomolybdic acid (PMoA) and polyvinyl alcohol (PVA) which was prepared using the layer-by-layer (LbL) assembly technique to form the multilayer film. The structures of the multilayer films were characterized <i>via</i> Fourier transform infrared spectra (FT-IR) and atomic force microscopy (AFM). The grown process, internal interaction, the surface topography and photochromic properties could be obviously studied through ultraviolet–visible (UV–vis) spectra and X-ray photoelectron spectra (XPS). The advantage of the structure and performance of the multilayer films prepared by layer-by-layer method could be found. It was suggested that the nearly linear growth process in peak-top absorbance in multilayer assembly. The PVA polymer substrate could disperse PMoA particles and changed the surface morphology. The polymer skeleton and PMoA particles were with strong interfacial interactions. The PMoA/PVA LbL film had wonderful visible light response. The oxygen acted a significant part during the bleaching process. According to XPS resoults, 51% of Mo⁶⁺ in the PMoA turned into Mo⁵⁺, obvious photoinduce oxidation and reduction reactions happened from PVA and PMoA through the proton transfer mechanism.</p> <p>A novel visible-light photochromic hybrid film composed of PMoA and PVA was prepared by the layer-by-layer (LbL) assembly technique. The photo-reduction process occurred according to the proton charge transfer mechanism.</p

Comparison of cell counting and developmental stage classification (Objective 20X).

<p>Comparison of cell counting and developmental stage classification (Objective 20X).</p

Object localization using non-Euclidean metrics

In this paper, we proposed to use non-Euclidean statistical metrics to localize multiple 3D anatomical structures by estimating the object’s position, orientation, and size in medical images. Precise orientation estimation is extremely important especially for model-based image segmentation algorithms as even a very small change in shape model orientation can lead to inaccurate localization and segmentation. We statistically evaluated accuracy of orientation estimation using various metrics: Euclidean, Mean Hermitian, Log-Euclidean, Root-Euclidean, Cholesky decomposition, and Procrustes Size-and-Shape. Experimental results showed that non-Euclidean metrics, particularly Mean Hermitian and Cholesky decomposition, provided more accurate estimates than Euclidean metrics. We presented the effectiveness of the proposed method using abdominal and hand computed tomography (CT) images and magnetic resonance (MR) images of the foot.</p

Dendrogram of 16 <i>S</i>. <i>enterica</i> serovar Indiana isolates constructed based on <i>Xba</i>I PFGE patterns.

<p>Stains information, pulsotype, mutations in QRDRs, resistance determinants/genes and resistance profiles are shown on the right. * PMQR, Plasmid Mediated Quinolone Resistance. **QRDRs, Quinolone Resistance Determining Regions. ***AMP, ampicillin; CAZ, ceftazidime; CHL, chloramphenicol; CIP, ciprofloxacin; CTX, cefotaxime; GEN, gentamicin; SXT, trimethoprim/sulfamethoxazole; TET, tetracycline.</p

Object localization using non-Euclidean metrics

In this paper, we proposed to use non-Euclidean statistical metrics to localize multiple 3D anatomical structures by estimating the object’s position, orientation, and size in medical images. Precise orientation estimation is extremely important especially for model-based image segmentation algorithms as even a very small change in shape model orientation can lead to inaccurate localization and segmentation. We statistically evaluated accuracy of orientation estimation using various metrics: Euclidean, Mean Hermitian, Log-Euclidean, Root-Euclidean, Cholesky decomposition, and Procrustes Size-and-Shape. Experimental results showed that non-Euclidean metrics, particularly Mean Hermitian and Cholesky decomposition, provided more accurate estimates than Euclidean metrics. We presented the effectiveness of the proposed method using abdominal and hand computed tomography (CT) images and magnetic resonance (MR) images of the foot.</p