In vitro expanded human CD4+CD25+ regulatory T cells suppress effector T cell proliferation.

Regulatory T cells (Tregs) have been shown to be critical in the balance between autoimmunity and tolerance and have been implicated in several human autoimmune diseases. However, the small number of Tregs in peripheral blood limits their therapeutic potential. Therefore, we developed a protocol that would allow for the expansion of Tregs while retaining their suppressive activity. We isolated CD4+CD25 hi cells from human peripheral blood and expanded them in vitro in the presence of anti-CD3 and anti-CD28 magnetic Xcyte Dynabeads and high concentrations of exogenous Interleukin (IL)-2. Tregs were effectively expanded up to 200-fold while maintaining surface expression of CD25 and other markers of Tregs: CD62L, HLA-DR, CCR6, and FOXP3. The expanded Tregs suppressed proliferation and cytokine secretion of responder PBMCs in co-cultures stimulated with anti-CD3 or alloantigen. Treg expansion is a critical first step before consideration of Tregs as a therapeutic intervention in patients with autoimmune or graft-versus-host disease

Confidence-interval construction for rate ratio in matched-pair studies with incomplete data

Matched-pair design is often used in clinical trials to increase the efficiency of establishing equivalence between two treatments with binary outcomes. In this article, we consider such a design based on rate ratio in the presence of incomplete data. The rate ratio is one of the most frequently used indices in comparing efficiency of two treatments in clinical trials. In this article, we propose 10 confidence-interval estimators for the rate ratio in incomplete matched-pair designs. A hybrid method that recovers variance estimates required for the rate ratio from the confidence limits for single proportions is proposed. It is noteworthy that confidence intervals based on this hybrid method have closed-form solution. The performance of the proposed confidence intervals is evaluated with respect to their exact coverage probability, expected confidence interval width, and distal and mesial noncoverage probability. The results show that the hybrid Agresti–Coull confidence interval based on Fieller’s theorem performs satisfactorily for small to moderate sample sizes. Two real examples from clinical trials are used to illustrate the proposed confidence intervals.postprin

Estimation of nonparametric regression models with a mixture of Berkson and classical errors

postprin

Crack propagation in brittle solid containing 3D surface fracture under uniaxial compression

2003-2004 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

The nested dirichlet distribution and incomplete categorical data analysis

The nested Dirichlet distribution (NDD) is an important distribution defined on the closed n-dimensional simplex. It includes the classical Dirichlet distribution and is useful in incomplete categorical data (ICD) analysis. In this article, we develop the distributional properties of NDD. New large-sample likelihood and small-sample Bayesian approaches for analyzing ICD are proposed and compared with existing likelihood/Bayesian strategies. We show that the new approaches have at least three advantages over existing approaches based on the traditional Dirichlet distribution in both frequentist and conjugate Bayesian inference for ICD. The new methods possess closed-form expressions for both the maximum likelihood and Bayes estimates when the likelihood function is in NDD form; produce computationally efficient EM and data augmentation algorithms when the likelihood is not in NDD form; and provide exact sampling procedures for some special cases. The methodologies are illustrated with simulated and real data.published_or_final_versio

Further properties and new applications of the nested Dirichlet distribution

Recently, Ng et al. (2009) studied a new family of distributions, namely the nested Dirichlet distributions. This family includes the traditional Dirichlet distribution as a special member and can be adopted to analyze incomplete categorical data. However, other important aspects of the family, such as marginal and conditional distributions and related properties are not yet available in the literature. Moreover, diverse applications of the family to the real world need to be further explored. In this paper, we first obtain the marginal and conditional distributions and other related properties of the nested Dirichlet distribution. We then present new applications of the family in fitting competing-risks model, analyzing incomplete categorical data and evaluating cancer diagnosis tests. Three real data involving failure times of radio transmitter receivers, attitude toward the death penalty and ultrasound ratings for breast cancer metastasis are provided. © 2009 Elsevier B.V. All rights reserved.postprin

A robust computational algorithm for inverse photomask synthesis in optical projection lithography

Inverse lithography technology formulates the photomask synthesis as an inverse mathematical problem. To solve this, we propose a variational functional and develop a robust computational algorithm, where the proposed functional takes into account the process variations and incorporates several regularization terms that can control the mask complexity. We establish the existence of the minimizer of the functional, and in order to optimize it effectively, we adopt an alternating minimization procedure with Chambolle's fast duality projection algorithm. Experimental results show that our proposed algorithm is effective in synthesizing high quality photomasks as compared with existing methods.published_or_final_versio

De-pollution efficacy of photocatalytic roofing granules

Photocatalytic building surfaces can harness sunlight to reduce urban air pollution. The NOx abatement capacity of TiO2-coated granules used in roofing products was evaluated for commercial product development. A laboratory test chamber and ancillary setup were built following conditions prescribed by ISO Standard 22197-1. It was validated by exposing reference P25-coated aluminum plates to a 3 L min−1 air flow enriched in 1 ppm NO under UVA irradiation (360 nm, 11.5 W m−2). We characterized prototype granule-surfaced asphalt shingles and loose granules prepared with different TiO2 loadings and post-treatment formulations. Tests performed at surface temperatures of 25 and 60 °C showed that NOx abatement was more effective at the higher temperature. Preliminary tests explored the use of 1 ppm NO2 and of 1 ppm and 0.3 ppm NO/NO2 mixtures. Specimens were aged in a laboratory accelerated weathering apparatus, and by exposure to the outdoor environment over periods that included dry and rainy seasons. Laboratory aging led to higher NO removal and NO2 formation rates, and the same catalyst activation was observed after field exposure with frequent precipitation. However, exposure during the dry season reduced the performance. This inactivation was mitigated by cleaning the surface of field-exposed specimens. Doubling the TiO2 loading led to a 50–150% increase in NO removal and NOx deposition rates. Application of different post-treatment coatings decreased NO removal rates (21–35%) and NOx deposition rates (26–74%) with respect to untreated granules. The mass balance of nitrogenated species was assessed by extracting granules after UV exposure in a 1 ppm NO-enriched atmosphere

Dirichlet composition distribution for compositional data with zero components: An application to fluorescence in situ hybridization (FISH) detection of chromosome

Copyright © 2021 The Authors. Zeros in compositional data are very common and can be classified into rounded and essential zeros. The rounded zero refers to a small proportion or below detection limit value, while the essential zero refers to the complete absence of the component in the composition. In this article, we propose a new framework for analyzing compositional data with zero entries by introducing a stochastic representation. In particular, a new distribution, namely the Dirichlet composition distribution, is developed to accommodate the possible essential-zero feature in compositional data. We derive its distributional properties (e.g., its moments). The calculation of maximum likelihood estimates via the Expectation-Maximization (EM) algorithm will be proposed. The regression model based on the new Dirichlet composition distribution will be considered. Simulation studies are conducted to evaluate the performance of the proposed methodologies. Finally, our method is employed to analyze a dataset of fluorescence in situ hybridization (FISH) for chromosome detection.National Natural Science Foundation of China. Grant Numbers: 12171167, 11801184; Research Grant Council of the Hong Kong Special Administrative Region. Grant Numbers: UGC/FDS14/P06/17, UGC/FDS14/P02/18

Sample Size Determination for Interval Estimation of the Prevalence of a Sensitive Attribute Under Randomized Response Models

National Natural Science Foundation of China (Grant No. 11871124); Research Grant Council of the Hong Kong Special Administrative Region (projects UGC/FDS14/P06/17 and UGC/FDS14/P02/18)