HSPA12B: A Novel Facilitator of Lung Tumor Growth

Lung tumor progression is regulated by proangiogenic factors. Heat shock protein A12B (HSPA12B) is a recently identified regulator of expression of proangiogenic factors. However, whether HSPA12B plays a role in lung tumor growth is unknown. To address this question, transgenic mice overexpressing HSPA12B (Tg) and wildtype littermates (WT) were implanted with Lewis lung cancer cells to induce lung tumorigenesis. Tg mice showed significantly higher number and bigger size of tumors than WT mice. Tg tumors exhibited increased angiogenesis and proliferation while reduced apoptosis compared with WT tumors. Interestingly, a significantly enhanced upregulation of Cox-2 was detected in Tg tumors than in WT tumors. Also, Tg tumors demonstrated upregulation of VEGF and angiopoietin-1, downregulation of AKAP12, and increased eNOS phosphorylation compared with WT tumors. Celecoxib, a selective Cox-2 inhibitor, suppressed the HSPA12B-induced increase in lung tumor burden. Moreover, celecoxib decreased angiogenesis and proliferation whereas increased apoptosis in Tg tumors. Additionally, celecoxib reduced angiopoietin-1 expression and eNOS phosphorylation but increased AKAP12 levels in Tg tumors. Our results indicate that HSPA12B stimulates lung tumor growth via a Cox-2-dependent mechanism. The present study identified HSPA12B as a novel facilitator of lung tumor growth and a potential therapeutic target for the treatment of lung cancer

A regression method for EEG-based cross-dataset fatigue detection

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices

Pathogenic Connexin-31 Forms Constitutively Active Hemichannels to Promote Necrotic Cell Death

Mutations in Connexin-31 (Cx31) are associated with multiple human diseases including erythrokeratodermia variabilis (EKV). The molecular action of Cx31 pathogenic mutants remains largely elusive. We report here that expression of EKV pathogenic mutant Cx31R42P induces cell death with necrotic characteristics. Inhibition of hemichannel activity by a connexin hemichannel inhibitor or high extracellular calcium suppresses Cx31R42P-induced cell death. Expression of Cx31R42P induces ER stress resulting in reactive oxygen species (ROS) production, in turn, to regulate gating of Cx31R42P hemichannels and Cx31R42P induced cell death. Moreover, Cx31R42P hemichannels play an important role in mediating ATP release from the cell. In contrast, no hemichannel activity was detected with cells expressing wildtype Cx31. Together, the results suggest that Cx31R42P forms constitutively active hemichannels to promote necrotic cell death. The Cx31R42P active hemichannels are likely resulted by an ER stress mediated ROS overproduction. The study identifies a mechanism of EKV pathogenesis induced by a Cx31 mutant and provides a new avenue for potential treatment strategy of the disease

A Penalized Approach to Mixed Model Selection Via Cross Validation

A linear mixed model is a useful technique to explain observations by regarding them as realizations of random variables, especially when repeated measurements are made to statistical units, such as longitudinal data. However, in practice, there are often too many potential factors considered affecting the observations, while actually, they are not. Therefore, statisticians have been trying to select significant factors out of all the potential factors, where we call the process model selection. Among those approaches for linear mixed model selection, penalized methods have been developed profoundly over the last several decades. In this dissertation, to solve the overfitting problem in most penalized methods and improve the selection accuracy, we mainly focus on a penalized approach via cross-validation. Unlike the existing methods using the whole data to fit and select models, we split the fitting process and selection into two stages. More specifically, an adaptive lasso penalized function is customized in the first stage and marginal BIC criterion is used in the second stage. We consider that the main advantage of our approach is to reduce the dependency between models construction and evaluation. Because of the complex structure of mixed models, we adopt a modified Cholesky decomposition to reparameterize the model, which in turn significantly reduces the dimension of the penalized function. Additionally, since random effects are missing, there is no closed form for the maximizer of the penalized function, thus we implement EM algorithm to obtain a full inference of parameters. Furthermore, due to the computation limit and moderately small samples in practice, some noisy factors may still remain in the model, which is particularly obvious for fixed effects. To eliminate the noisy factors, a likelihood ratio test is employed to screen the fixed effects. Regarding the overall process, we call it adaptive lasso via cross-validation. Additionally, we demonstrate that the proposed approach possesses selection and estimation consistency simultaneously. Moreover, simulation studies and real data examples are both provided to justify the method validity. At the very end, a brief conclusion is drawn and some possible further improvements are discussed

GENETIC AND FUNCTIONAL CHARACTERIZATION OF HEREDITARY INTRAOSSEOUS VASCULAR MALFORMATION

Ph.DDOCTOR OF PHILOSOPH

Machine learning methods for the detection of complex patterns in biological data

The biomedical field is being revolutionized by deep learning. The accumulation of more, and more complex data opens the opportunity for the development of new advanced deep learning models that can address fundamentally difficult question in biology. Nevertheless, the integration of deep learning into the life sciences is complicated and necessitates a careful strategy in order to tackle specific domain challenges while optimizing the vast quantities of very heterogeneous data. This thesis addresses very different biological problems using deep learning methods. A common theme to these problems is the complexity and richness of the large data sets collected.In chapter 2, I introduce \textit{Scratch-AID}, an exceptionally precise deep learning-based system designed to automate the identification of mouse scratching in a controlled environment. By employing a convolution recurrent neural network trained on video data, this system attains a recall percentage of 97.6\% and a precision percentage of 96.9\%. Therefore, \textit{Scratch-AID} can be considered a feasible substitute for manual quantification techniques in the context of pharmacological screenings and behavioral studies.Following this, in Chapter 3, I address the problem of survival in cancer patients. I present a deep learning framework called \textit{Genomic-Guided Hierarchical Attention Multi-Scale Pathology Transformer} (GG-HAMPT). I introduce an multi-level early data fusion strategy that uses the co-attention paradigm to combine genomic data and 3 level representation of whole slide Images (WSI) of cancer tissues. Employing the co-attention module, our model adeptly discerns the significance of pathological patches in correlation with grouped genomic features. In the context of predicting survival outcomes using multimodal data, including gigapixel WSIs, our GG-HAMPT model outperformed existing other weakly supervised approaches.Last, we examine the domain adaptation of pre-trained models for the purpose of classifying digital pathology images through the implementation of the LoRA Vision Transformer (LoRA-ViT). The model exhibits remarkable efficiency and accuracy. Furthermore, in a 6-class cell classification task, LoRA-ViT outperformed the native ResNet 50 model. In experiments conducted with constrained datasets, the LoRA-ViT model significantly outperformed the ResNet 50, showing its superior generalizability compared to the baseline model

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness

Investigate the impacts of PEV charcing facilities on integrated electric distribution system and electrified transportation system

Nowadays, plug-in electric vehicles (PEVs) are becoming one of the most promising solutions for consumers due to its economical and environmentally friendly characteristics. An ever-increasing number of PEVs have been radically changing the traditional view of the power industry, transportation industry, and business world. Research on grid integration of PEVs typically addresses topics at the vehicle-grid boundary, such as peak load impacts and charging control. While researchers around the world are making significant advances in these areas, there is little work addressing the coupled effects of PEV charging with the mobility-focused, transportation ecosystem to meet the dynamic needs of a changing society. In this paper, we propose a systematic co-modeling and simulation framework to investigate the impacts of PEV charging facilities on the electric distribution system and transportation system. Moreover, we explore the possibilities to improve the system\u27s stability and the efficiency of the integrated electrified transportation systems by taking advantage of a variety of charging control strategies. The case studies focus on the PEV charging facilities in a relatively small portion of electric power system and transportation system, especially in metropolitan areas. However, the ideas contained here will apply to more generally coupled, and large-scale electrified transportation systems