Multivariate modality inference using Gaussian kernel

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets

Some recent advances in multivariate statistics: modality inference and statistical monitoring of clinical trials with multiple co-primary endpoints

This dissertation focuses on two topics in multivariate statistics. The first part develops an inference procedure and fast computation tool for the modal clustering method proposed by Li et al. (2007). The modal clustering, based on the kernel density estimate, clusters data using their associations within a single mode, with the final number of clusters equaling the number of modes, otherwise known as the modality of the distribution of the data. This method provides a flexible tool for clustering data of low to moderate dimensions with arbitrary distributional shapes. In contrast to Li and colleagues, we expand their method by proposing a procedure that determines the number of clusters in the data. A test statistic and its asymptotic distribution are derived to assess the significance of each mode within the data. The inference procedure is tested on both simulated and real data sets. In addition, an R computing package is developed (Modalclust) that implements the modal clustering procedure using parallel processing which dramatically increases computing speed over the previously available method. This package is available on the Comprehensive R Archive Network (CRAN). The second part of this dissertation develops methods of statistical monitoring of clinical trials with multiple co-primary endpoints, where success is defined as meeting both endpoints simultaneously. In practice, a group sequential design method is used to stop trials early for promising efficacy, and conditional power is used for futility stopping rules. In this dissertation we show that stopping boundaries for the group sequential design with multiple co-primary endpoints should be the same as those for studies with single endpoints. Lan and Wittes (1988) proposed the B-value tool to calculate the conditional power of single endpoint trials and we extend this tool to calculate the conditional power for studies with multiple co-primary endpoints. We consider the cases of two-arm studies with co-primary normal and binary endpoints and provide several examples of implementation with simulated trials. A fixed-weight sample size re-estimation approach based on conditional power is introduced. Finally, we discuss the possibility of blinded interim analyses for multiple endpoints using the modality inference method introduced in the first part

Failure Mode Prediction of Resistance Spot Welded Quenching and Partitioning Steel

In recent years, a novel Advanced High-Strength Steels called quenching and partitioning (Q&P) steel has been applied in the automotive industry because its good combination of strength and ductility. In this study, an experimental setup by adopting digital image correlation (DIC) method was firstly developed to establish the constitutive relationship of fusion zone in the spot welds produced by Q&P980. Stress-strain relationship extracted from the tensile bar within the fusion zone and compared the results to that of base metal. The fusion zone of Q&P980 found to have a higher tensile strength and similar elongation compared with base metal. A numerical model established to predict the failure mode of joints generated by Q&P980 after obtaining the constitutive relationship of fusion zone. The predicted failure mode was in good coherence with the experimental results under the lap-shear test conditions. The developed FE model was proven efficient in tensile strength and failure mode characterization of spot welded specimen. This study could provide solutions to maintain or even improve vehicle crashworthiness of lightweight vehicle structures

Transfer Learning in General Lensless Imaging through Scattering Media

Recently deep neural networks (DNNs) have been successfully introduced to the field of lensless imaging through scattering media. By solving an inverse problem in computational imaging, DNNs can overcome several shortcomings in the conventional lensless imaging through scattering media methods, namely, high cost, poor quality, complex control, and poor anti-interference. However, for training, a large number of training samples on various datasets have to be collected, with a DNN trained on one dataset generally performing poorly for recovering images from another dataset. The underlying reason is that lensless imaging through scattering media is a high dimensional regression problem and it is difficult to obtain an analytical solution. In this work, transfer learning is proposed to address this issue. Our main idea is to train a DNN on a relatively complex dataset using a large number of training samples and fine-tune the last few layers using very few samples from other datasets. Instead of the thousands of samples required to train from scratch, transfer learning alleviates the problem of costly data acquisition. Specifically, considering the difference in sample sizes and similarity among datasets, we propose two DNN architectures, namely LISMU-FCN and LISMU-OCN, and a balance loss function designed for balancing smoothness and sharpness. LISMU-FCN, with much fewer parameters, can achieve imaging across similar datasets while LISMU-OCN can achieve imaging across significantly different datasets. What's more, we establish a set of simulation algorithms which are close to the real experiment, and it is of great significance and practical value in the research on lensless scattering imaging. In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs

Comparison of statistical approaches to rare variant analysis for quantitative traits

With recent advances in technology, deep sequencing data will be widely used to further the understanding of genetic influence on traits of interest. Therefore not only common variants but also rare variants need to be better used to exploit the new information provided by deep sequencing data. Recently, statistical approaches for analyzing rare variants in genetic association studies have been proposed, but many of them were designed only for dichotomous outcomes. We compare the type I error and power of several statistical approaches applicable to quantitative traits for collapsing and analyzing rare variant data within a defined gene region. In addition to comparing methods that consider only rare variants, such as indicator, count, and data-adaptive collapsing methods, we also compare methods that incorporate the analysis of common variants along with rare variants, such as CMC and LASSO regression. We find that the three methods used to collapse rare variants perform similarly in this simulation setting where all risk variants were simulated to have effects in the same direction. Further, we find that incorporating common variants is beneficial and using a LASSO regression to choose which common variants to include is most useful when there is are few common risk variants compared to the total number of risk variants

Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution

Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance from pre-trained diffusion models. An apparent drawback is that they need to optimize from scratch for each prompt, which is computationally expensive and often yields poor visual fidelity. In this paper, we propose DreamPortrait, which aims to generate text-guided 3D-aware portraits in a single-forward pass for efficiency. To achieve this, we extend Score Distillation Sampling from datapoint to distribution formulation, which injects semantic prior into a 3D distribution. However, the direct extension will lead to the mode collapse problem since the objective only pursues semantic alignment. Hence, we propose to optimize a distribution with hierarchical condition adapters and GAN loss regularization. For better 3D modeling, we further design a 3D-aware gated cross-attention mechanism to explicitly let the model perceive the correspondence between the text and the 3D-aware space. These elaborated designs enable our model to generate portraits with robust multi-view semantic consistency, eliminating the need for optimization-based methods. Extensive experiments demonstrate our model's highly competitive performance and significant speed boost against existing methods

Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer

Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms

SirT1 modulates the estrogen–insulin-like growth factor-1 signaling for postnatal development of mammary gland in mice

INTRODUCTION: Estrogen and insulin-like growth factor-1 (IGF-1) play important roles in mammary gland development and breast cancer. SirT1 is a highly conserved protein deacetylase that can regulate the insulin/IGF-1 signaling in lower organisms, as well as a growing number of transcription factors, including NF-κB, in mammalian cells. Whether SirT1 regulates the IGF-1 signaling for mammary gland development and function, however, is not clear. In the present study, this role of SirT1 was examined by studying SirT1-deficient mice. METHODS: SirT1-deficient (SirT1(ko/ko)) mice were generated by crossing a new strain of mice harboring a conditional targeted mutation in the SirT1 gene (SirT1(co/co)) with CMV-Cre transgenic mice. Whole mount and histology analyses, immunofluorescence staining, immunohistochemistry, and western blotting were used to characterize mammary gland development in virgin and pregnant mice. The effect of exogenous estrogen was also examined by subcutaneous implantation of a slow-releasing pellet in the subscapular region. RESULTS: Both male and female SirT1(ko/ko )mice can be fertile despite the growth retardation phenotype. Virgin SirT1(ko/ko )mice displayed impeded ductal morphogenesis, whereas pregnant SirT1(ko/ko )mice manifested lactation failure due to an underdeveloped lobuloalveolar network. Estrogen implantation was sufficient to rescue ductal morphogenesis. Exogenous estrogen reversed the increased basal level of IGF-1 binding protein-1 expression in SirT1(ko/ko )mammary tissues, but not that of IκBα expression, suggesting that increased levels of estrogen enhanced the production of local IGF-1 and rescued ductal morphogenesis. Additionally, TNFα treatment enhanced the level of the newly synthesized IκBα in SirT1(ko/ko )cells. SirT1 deficiency therefore affects the cellular response to multiple extrinsic signals. CONCLUSION: SirT1 modulates the IGF-1 signaling critical for both growth regulation and mammary gland development in mice. SirT1 deficiency deregulates the expression of IGF-1 binding protein-1 and attenuates the effect of IGF-1 signals, including estrogen-stimulated local IGF-1 signaling for the onset of ductal morphogenesis. These findings suggest that the enzymatic activity of SirT1 may influence both normal growth and malignant growth of mammary epithelial cells