A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications

Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets

Automated Identification of Cell Type Specific Genes in the Mouse Brain by Image Computing of Expression Patterns

Background: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. Results: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. Conclusions: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain

IPO underpricing in the Chinese A-share market: Winner’s curse and ex ante uncertainty

A large number of researches shows that Initial Public Offerings (IPOs) of common stocks are systematically priced at a discount to their initial trading price. The large underpricing magnitude in the Chinese IPO market has drawn much attention. In this paper, A-share initial public offerings (IPOs) were 36.6458% underpriced from 2010 to 2016. I consider two hypotheses might explain the IPO underpricing in Chinese primary market, namely winner’s curse and ex ante uncertainty. By examining the hypothesis and finding that there is a strong positive correlation between the market-adjusted returns that are required for new stock issues and the degree of ex ante uncertainty associated with the IPO. I conclude that the cross-section pattern of underpricing can be explained in terms of winner’s curse theory and ex ante uncertainty

Radial free forearm flap versus pectoralis major pedicled flap for reconstruction in patients with tongue cancer : assessment of quality of life

This study investigated the quality of life of Chinese patients with tongue cancer who had undergone immediate flap reconstruction surgery. In addition, we compared 2 groups of patients: those who had received radial forearm free flap (RFFF) surgery and others who had received pectoralis major myocutaneous flap (PMMF) surgery. Patients who received RFFF or PMMF reconstruction after primary tongue cancer treated with total and subtotal tongue resection were eligible for the current study. The patients? demographic data, medical history, and quality of life scores (14-item Oral Health Impact Profile (OHIP-14) and the University of Washington Quality of Life (UW-QOL) questionnaires) were collected. A total of 41 of 63 questionnaires were returned (65.08%). There were significant differences between the 2 groups in the gender (p< .05). Patients reconstructed with RFFF performed better in the shoulder domains, in addition to worse appearance domains. Using either RFFF or PMMF for reconstruction of defects after tongue cancer resection significantly influences a patient?s quality of life. Data from this study provide useful information for physicians and patients during their discussion of reconstruction modalities for tongue cancers

Analysis of 126 hospitalized elder maxillofacial trauma victims in central China

Background: The aim of this study was to analyzed the characteristics and treatment of maxillofacial injuries in the elder patients with maxillofacial injuries in central China. Material and Methods: We retrospectively analyzed the characteristics and treatment of maxillofacial injuries in the patients over the age of 60 to analyze the trends and clinical characteristics of maxillofacial trauma in elder patients from the First Affiliated Hospital of Zhengzhou University (from 2010 to 2013) in central China and to present recommendations on prevention and management. Results: Of the 932 patients with maxillofacial injuries, 126 aged over 60 years old accounting for 13.52% of all the patients (male:female, 1.74:1; mean age, 67.08 years old). Approximately 52% of the patients were injured by falls. The most frequently observed type of injuries was soft tissue injuries (100%), followed by facial fractures (83.05%). Of the patients with soft tissue injuries, the abrasions accounted the most, followed by lacerations. The numbers of patients of midface fracture (60 patients) were almost similar to the number of lower face fractures (66 patients). Eighty two patients (65.08%%) demonstrated associated injuries, of which craniocerebral injuries were the most prevalent. One hundred and four patients (82.54%) had other systemic medical conditions, with cardiovascular diseases the most and followed by metabolic diseases and musculoskeletal conditions. Furthermore, the study indicated a relationship between maxillofacial fractures and musculoskeletal conditions. Only 13 patients (10.32%) sustained local infections, of whom had other medical conditions. Most of the facial injuries (85.71%) in older people were operated including debridement, fixing loose teeth, reduction, intermaxillary fixation and open reduction and internal fixation (ORIF). Conclusions: Our analysis of the characteristics of maxillofacial injuries in the elder patents may help to promote clinical research to develop more effective treatment and possibly prevent such injuries

An Observational Study of Engineering Online Education During the COVID-19 Pandemic

Although online education has become a viable and major component of higher education in many fields, its employment in engineering disciplines has been limited. COVID-19 pandemic compelled the global and abrupt conversion of conventional face-to-face instruction to the online format. The negative impact of such sudden change is undeniable. Urgent and careful planning is needed to mitigate pandemic negative effects on engineering education, especially for vulnerable, disadvantaged, and underrepresented students who have to deal with additional challenges (e.g. digital equity gap). To enhance engineering online instruction during the pandemic era, we conducted an observational study at California State University, Long Beach (a minority-serving institution). 110 faculty and 627 students from six engineering departments participated in our surveys and answered quantitative and qualitative questions to highlight the challenges they experienced during the online instruction in Spring 2020. In this work, we present the results of these surveys in detail and propose solutions to address the identified issues including logistical, technical, learning/teaching challenges, assessment methods, and hands-on training. As the pandemic continues, sharing these results with other educators can help with more effective planning and choice of best practices to improve the online engineering education during COVID-19 and beyond.Comment: 10 pages, 3 figures, 2 table

Response characteristics of root to moisture change at seedling stage of Kengyilia hirsuta

Kengyilia hirsuta is an important pioneer plant distributed on the desertified grassland of the Qinghai-Tibet Plateau. It has strong adaptability to alpine desert habitats, so it can be used as a sand-fixing plant on sandy alpine land. To study the response mechanisms of root morphological and physiological characteristics of K. hirsuta to sandy soil moisture, 10%, 25% and 40% moisture levels were set up through potted weighing water control method. The biomass, root-shoot ratio, root architecture parameters, and biochemical parameters malondialdehyde, free proline, soluble protein, indole-3-acetic acid, abscisic acid, cytokinin, gibberellin, relative conductivity and antioxidant enzyme activities were measured in the trefoil stage, and the response mechanisms of roots at different moisture levels were analyzed. The results showed that with the increase of soil moisture, root morphological indexes such as root biomass, total root length, total root volume and total root surface increased, while the root topological index decreased continuously. The malondialdehyde content, relative conductivity, superoxide dismutase activity, peroxidase activity, catalase activity, free proline content, soluble protein content, abscisic acid content and cytokinin content at the 25% and 40% moisture levels were significantly decreased compared with the 10% level (P&lt; 0.05). Thus, the root growth of K. hirsuta was restricted by the 10% moisture level, but supported by the 25% and 40% moisture levels. An artificial neural network revealed that total root length, total root surface area, root link average length, relative conductivity, soluble protein, free proline and moisture level were the key factors affecting root development. These research results could contribute to future agricultural sustainability

Physical Therapy Management Of A Manual Laborer With Chronic Rotator Cuff Tendinopathy: A Case Report

Background: Tendinopathy is characterized by tendon thickening, localized pain and chronic degeneration reflective of failed healing. 38% of manual laborers who participate in daily moderate to heavy lifting will experience Rotator Cuff Tendinopathy(RCT). There is a lack of research investigating the PT management of manual laborers who have RCT, but must continue to participate in harmful activities to fulfill occupational responsibilities. Purpose: The purpose of this case report was to describe the PT management of a patient with rotator cuff tendinopathy who, due to work requirements continued to participate in activities detrimental to the health of the supraspinatus and function of the shoulder girdle.https://dune.une.edu/pt_studcrposter/1036/thumbnail.jp

A Mesh Generation and Machine Learning Framework for Drosophila Gene Expression Pattern Image Analysis

Background: Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions. Results: We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/. Conclusions: Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods