Molecular and Physiological Basis for Hair Loss in \u3cem\u3eNear Naked Hairless\u3c/em\u3e and \u3cem\u3eOak Ridge Rhino-like\u3c/em\u3e Mouse Models: Tracking the Role of the \u3cem\u3eHairless\u3c/em\u3e Gene

Hairless mice have been widely used in basic research and clinical trials. Two new mouse mutants with hair loss arose spontaneously in the breeding colony of Oak Ridge National Laboratory. The first homozygotes mutant, called near naked hairless (Hrn), never develops a normal coat, while heterozygotes display a sparse coat and become completely nude as they age. The Hrn/Hrn mutant mice are significantly smaller in body size and have very short, curly, and few vibrissae. Histological analysis revealed premature keratinization in the precortical region of hair follicles, formation of mineralized dermal cysts, and loss of hair follicles. Adult heterozygotes display pili multigemini (i.e. more than one hair from one piliary canal) after the first hair cycle, suggesting abnormal regulation of hair shaft formation by the mutation. A mutation was not identified in the coding region of Hr nor in candidate genes around Hr, suggesting a possible regulatory mutation of Hr. Microarray analysis was used to survey the gene expression profile and to identify the molecular mechanisms altered by the Hrn mutation. Several pathways including Wnt/β-catenin, TGF-β, and apoptosis are significantly altered in Hrn mutants, indicating the involvement of Hrn in these pathways. Hrn mutant mice are also suggested to be a research model for human MUHH (Marie Unna Hereditary Hypotrichosis). The second mouse mutant, called rhino-like (HrrhR), displays progressive and random hair loss and wrinkling skin, leading to a rhinocerotic appearance. Histological analysis revealed the formation of utricles at as early as 10 days of age, the formation of dermal cysts, and the destruction of hair follicles. Since the phenotype in the homozygous mutants is very close to that in Hrrh mutant mice, the genomic DNA of Hr gene was directly sequenced. A nonsense mutation was identified in the exon 12, leading to significantly reduced Hr expression, probably due to nonsense-mediated decay. The allele was named as rhino in Oak Ridge with the symbol HrrhR (R for Oak Ridge). Microarray analysis of skin from mice at 7, 10, and 35 days was applied to identify the downstream events of the HrrhR mutation. Several genes including Krt1-10, Krt2-1, IL-17, and Itgb4, were identified as the potential targets of HrrhR. Wnt/β-catenin, apoptosis, and ERK/MAPK signaling pathways were altered in HrrhR/HrrhR mutant mice, suggesting a possible role of Hr to regulate these pathways. Microarray analysis also shows many immune-related genes with differential expression, indicating the possible involvement of Hr in immune response. Identification of this new Hr allele and its related research allows further understanding about the function of Hr and the mechanisms of alopecia, i.e. hair loss

Computing Equivariant Homology with a Splitting Method

We develop a new method in the computation of equivariant homology, which is based on the splitting of cofiber sequences associated to universal spaces in the category of equivariant spectra. In particular, we will compute the equivariant homology of a point when $G=D_{2p}$ and $A_5$, with coefficients in $\underline{\mathbb{Z}}$ and $A_G$.Comment: 54 pages, with generalizations and more applications compared to the previous version. The content of arXiv:2110.07695 is also include

AquaSAM: Underwater Image Foreground Segmentation

The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted. This work presents AquaSAM, the first attempt to extend the success of SAM on underwater images with the purpose of creating a versatile method for the segmentation of various underwater targets. To achieve this, we begin by classifying and extracting various labels automatically in SUIM dataset. Subsequently, we develop a straightforward fine-tuning method to adapt SAM to general foreground underwater image segmentation. Through extensive experiments involving eight segmentation tasks like human divers, we demonstrate that AquaSAM outperforms the default SAM model especially at hard tasks like coral reefs. AquaSAM achieves an average Dice Similarity Coefficient (DSC) of 7.13 (%) improvement and an average of 8.27 (%) on mIoU improvement in underwater segmentation tasks

Video Captioning with Aggregated Features Based on Dual Graphs and Gated Fusion

The application of video captioning models aims at translating the content of videos by using accurate natural language. Due to the complex nature inbetween object interaction in the video, the comprehensive understanding of spatio-temporal relations of objects remains a challenging task. Existing methods often fail in generating sufficient feature representations of video content. In this paper, we propose a video captioning model based on dual graphs and gated fusion: we adapt two types of graphs to generate feature representations of video content and utilize gated fusion to further understand these different levels of information. Using a dual-graphs model to generate appearance features and motion features respectively can utilize the content correlation in frames to generate various features from multiple perspectives. Among them, dual-graphs reasoning can enhance the content correlation in frame sequences to generate advanced semantic features; The gated fusion, on the other hand, aggregates the information in multiple feature representations for comprehensive video content understanding. The experiments conducted on worldly used datasets MSVD and MSR-VTT demonstrate state-of-the-art performance of our proposed approach

Nanomedicine: Multifunctional nanoparticles of biodegradable polymers for cancer treatment

Ph.DDOCTOR OF PHILOSOPH

Topics in Bayesian Design and Analysis for Sampling

Survey sampling is an old field, but it is changing due to recent advancement in statistics and data science. More specifically, modern statistical techniques have provided us with new tools to solve old problems in potentially better ways, and new problems arise as data with complex and rich information become more available nowadays. This dissertation is consisted of three parts, with the first part being an example of solving an old problem with new tools, the second part solving a new problem in a data-rich setting, and the third part from a design perspective. All three parts deal with modeling survey data and auxiliary information using flexible Bayesian models. In the first part, we consider Bayesian model-based inference for skewed survey data. Skewed data are common in sample surveys. Using probability proportional to size sampling as an example, where the values of a size variable are known for the population units, we propose two Bayesian model-based predictive methods for estimating finite population quantiles with skewed sample survey data. We assume the survey outcome to follow a skew-normal distribution given the probability of selection, and model the location and scale parameters of the skew-normal distribution as functions of the probability of selection. To allow a flexible association between the survey outcome and the probability of selection, the first method models the location parameter with a penalized spline and the scale parameter with a polynomial function, while the second method models both the location and scale parameters with penalized splines. Using a fully Bayesian approach, we obtain the posterior predictive distributions of the non-sampled units in the population, and thus the posterior distributions of the finite population quantiles. We show through simulations that our proposed methods are more efficient and yield shorter credible intervals with better coverage rates than the conventional weighted method in estimating finite population quantiles. We demonstrate the application of our proposed methods using data from the 2013 National Drug Abuse Treatment System Survey. In the second part, we consider inference from non-random samples in data-rich settings where high-dimensional auxiliary information is available both in the sample and the target population, with survey inference being a special case. We propose a regularized prediction approach that predicts the outcomes in the population using a large number of auxiliary variables such that the ignorability assumption is reasonable while the Bayesian framework is straightforward for quantification of uncertainty. Besides the auxiliary variables, inspired by Little and An (2004), we also extend the approach by estimating the propensity score for a unit to be included in the sample and also including it as a predictor in the machine learning models. We show through simulation studies that the regularized predictions using soft Bayesian additive regression trees (SBART) yield valid inference for the population means and coverage rates close to the nominal levels. We demonstrate the application of the proposed methods using two different real data applications, one in a survey and one in an epidemiology study. In the third part, we consider survey design for multilevel regression and post-stratification (MRP), a survey adjustment technique that corrects the known discrepancy between sample and population using shared auxiliary variables. MRP has been widely applied in survey analysis, for both probability and non-probability samples. However, literature on survey design for MRP is scarce. We propose a closed form formula to calculate theoretical margin of errors (MOEs) for various estimands based on the variance parameters in the multilevel regression model and sample sizes in the post-strata. We validate the theoretical MOEs via comparisons with the empirical MOEs in simulations studies covering various sample allocation plans. The validation procedure indicates that the theoretical MOEs based on the formula aligns with the empirical results for various estimands. We demonstrate the application of the sample size calculation formula in two different survey design scenarios, online panels that utilize quota sampling and telephone surveys with fixed total sample sizes