Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

Many analyses of neuroimaging data involve studying one or more regions of interest (ROIs) in a brain image. In order to do so, each ROI must first be identified. Since every brain is unique, the location, size, and shape of each ROI varies across subjects. Thus, each ROI in a brain image must either be manually identified or (semi-) automatically delineated, a task referred to as segmentation. Automatic segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each ROI is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms either employ voting procedures or impose prior structure and subsequently find the maximum a posteriori estimator (i.e., the posterior mode) through optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. We discuss the implementation of our model via Markov chain Monte Carlo and illustrate the procedure through both simulation and application to segmentation of the hippocampus, an anatomical structure known to be associated with Alzheimer's disease.Comment: 24 pages, 10 figure

A Comparison of Bond Strength Between Direct- and Indirect-bonding Methods

The purpose of this study was to evaluate and compare the shear bond strength and the sites of bond failure for brackets bonded to teeth, using two indirect-bonding material protocols and a direct-bonding technique. Sixty extracted human premolars were collected and randomly divided into three groups. The direct-bonded group (group 1) used a light-cured adhesive and primer (Transbond XT). One indirect-bonded group (group 2) consisted of a chemical-cured primer (Sondhi Rapid Set) and light-cured adhesive (Transbond XT), whereas the other group (group 3) used a light-cured primer (Orthosolo) and adhesive (Enlight LV). Forty hours after bonding, the samples were debonded. Mean shear bond strengths were 16.27, 13.83, and 14.76 MPa for groups 1, 2, and 3, respectively. A one-way analysis of variance showed no significant difference in mean bond strength between groups (P = .21). Furthermore, a Weibull analysis showed all three groups tested provided over a 90% survival rate at normal masticatory and orthodontic force levels. For each tooth, an Adhesive Remnant Index (ARI) score was determined. Group 2 was found to have a significantly lower ARI score (P \u3c .05) compared with groups 1 and 3. In addition, Pearson correlation coefficients indicated no strong correlation between bond strength and ARI score within or across all groups

Bond Strength of Direct and Indirect Bonded Brackets After Thermocycling

Thermocycling simulates the temperature dynamics in the oral environment. With direct bonding, thermocycling reduces the bond strength of orthodontic adhesives to tooth structure. The purpose of this study was to evaluate the shear bond strengths (SBS) of one direct and two indirect bonding methods/adhesives after thermocycling. Sixty human premolars were divided into three groups. Teeth in group 1 were bonded directly with Transbond XT. Teeth in group 2 were indirect bonded with Transbond XT/Sondhi Rapid Set, which is chemically cured. Teeth in group 3 were indirect bonded with Enlight LV/Orthosolo and light cured. Each sample was thermocycled between 5°C and 55°C for 500 cycles. Mean SBS in groups 1, 2, and 3 were not statistically significantly different (13.6 ± 2.9, 12.3 ± 3.0, and 11.6 ± 3.2 MPa, respectively; P \u3e .05). However, when these values were compared with the results of a previous study using the same protocol, but without thermocycling, the SBS was reduced significantly (P = .001). Weibull analysis further showed that group 3 had the lowest bonding survival rate at the minimum clinically acceptable bond-strength range. The Adhesive Remnant Index was also determined, and group 2 had a significantly (P \u3c .05) higher percentage of bond failures at the resin/enamel interface

Small-amplitude normal modes of a vortex in a trapped Bose-Einstein condensate

We consider a cylindrically symmetric trap containing a small Bose-Einstein condensate with a singly quantized vortex on the axis of symmetry. A time-dependent variational Lagrangian analysis yields the small-amplitude dynamics of the vortex and the condensate, directly determining the equations of motion of the coupled normal modes. As found previously from the Bogoliubov equations, there are two rigid dipole modes and one anomalous mode with a negative frequency when seen in the laboratory frame.Comment: 4 pages, no figures, Revte

Control-Group Feature Normalization for Multivariate Pattern Analysis Using the Support Vector Machine

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We also show that control-based normalization provides better interpretation with respect to the estimated multivariate disease pattern and improves the classifier performance in many cases

Temporal and Spatial Distribution of the Oriental Beetle (Coleoptera: Scarabaeidae) in a Golf Course Environment

The mating season of the oriental beetle, Exomala orientalis (Waterhouse), in 1994 and 1995 at Bethpage State Park, Farmingdale, NY (40° 45′ N, 73° 28′ W) began in the middle of June, peaked in the 1st wk of July, and ended in the middle of August. There were differences in the emergence schedule among fairways as well as local differences between roughs and fairway. Both sexes were most active around sunset on shorter-cut turf (i.e., fairways, greens, and tees, versus roughs), and the few individuals seen during the daylight hours were mostly males. These males were generally found perched on vegetation at the border of the fairway. Feeding was not observed, except on flowers by females devoid of mature eggs. This study confirms our observations on the pattern of activity in an earlier study conducted with the use of synthetic pheromone traps. It also explains the difficulty encountered by earlier workers in finding adults of this insect in the field. Implications of the above findings on the management of the oriental beetle are discusse

DNA repair deficiency biomarkers and the 70-gene ultra-high risk signature as predictors of veliparib/carboplatin response in the I-SPY 2 breast cancer trial.

Veliparib combined with carboplatin (VC) was an experimental regimen evaluated in the biomarker-rich neoadjuvant I-SPY 2 trial for breast cancer. VC showed improved efficacy in the triple negative signature. However, not all triple negative patients achieved pathologic complete response and some HR+HER2- patients responded. Pre-specified analysis of five DNA repair deficiency biomarkers (BRCA1/2 germline mutation; PARPi-7, BRCA1ness, and CIN70 expression signatures; and PARP1 protein) was performed on 116 HER2- patients (VC: 72 and concurrent controls: 44). We also evaluated the 70-gene ultra-high risk signature (MP1/2), one of the biomarkers used to define subtype in the trial. We used logistic modeling to assess biomarker performance. Successful biomarkers were combined using a simple voting scheme to refine the 'predicted sensitive' group and Bayesian modeling used to estimate the pathologic complete response rates. BRCA1/2 germline mutation status associated with VC response, but its low prevalence precluded further evaluation. PARPi-7, BRCA1ness, and MP1/2 specifically associated with response in the VC arm but not the control arm. Neither CIN70 nor PARP1 protein specifically predicted VC response. When we combined the PARPi-7 and MP1/2 classifications, the 42% of triple negative patients who were PARPi7-high and MP2 had an estimated pCR rate of 75% in the VC arm. Only 11% of HR+/HER2- patients were PARPi7-high and MP2; but these patients were also more responsive to VC with estimated pathologic complete response rates of 41%. PARPi-7, BRCA1ness and MP1/2 signatures may help refine predictions of VC response, thereby improving patient care

Addressing Confounding in Predictive Models with an Application to Neuroimaging

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease effects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples