A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html

How Story Works in Mobile App Stores? Exploring the Same-Side Effect from the Storytelling Perspective

The growing number of mobile apps has contributed to an innovation diffusion paradox whereby the accelerated pace with which mobile apps are being developed and updated has stymied their own diffusion. Due to consumers’ limited personal involvement with mobile apps, storytelling, as an emerging and novel product recommendation format, is gaining traction as a promotional mechanism for diffusing mobile apps within the ecosystem. Storytelling is particularly amenable to the context of mobile app stores by giving affective meaning to the focal app being promoted and strengthening its association with other apps available from these stores. To this end, we construct a research model to illustrate how consumers’ demand for related mobile apps is shaped by similarity in functional and visual attributes between these apps and the focal app being promoted via storytelling. Our model also sheds light on how the preceding effects could be mitigated by within-developer influence

MOLECULAR ANALYSIS OF YEAST SPLICEOSOME COMPONENTS FOR THEIR ROLES IN AGROBACTERIUM-YEAST DNA TRANSFER

Ph.DDOCTOR OF PHILOSOPH

Optical designs and image processing algorithms for optical coherence tomography detection of glaucoma

textOptical Coherence Tomography (OCT) is an optical tomography technique which provides high resolution non-invasive three-dimensional (3D) structural images of the sample based on coherent properties of light. The dissertation focuses on the use of OCT systems for detecting glaucoma, which is the second leading cause of blindness worldwide. First, as a prerequisite of analyzing ophthalmologic OCT images, a retinal sublayer segmentation algorithm is presented and implemented with GPU assisted computation. Then, a polarization-sensitive optical coherence tomography (PS-OCT) system was constructed for the study of glaucoma. Three closely related clinical and animal studies on early-stage glaucoma detection using either OCT or PS-OCT were performed. Statistical analysis of the study results indicates that the scattering property of retinal nerve fiber layer (RNFL) is the earliest indicator for glaucoma. Finally, to investigate the scattering properties of RNFL, a pathlength-multiplexed scattering-angle-diverse optical coherence tomography (PM-SAD-OCT) system was designed and built. PM-SAD-OCT images were collected from human and rodent retina as well as earthworm nerve cord. PM-SAD-OCT system shows promising potentials to detect neurodegenerative diseases including glaucoma.Biomedical Engineerin

Telling an Attractive Digital Story: Unraveling the Effects of Digital Product Placement Strategy on Product Exposure

The accelerated pace with which mobile apps are being launched has translated into an innovation diffusion paradox for mobile app stores. To cope with the avalanche of newly launched apps, conventional product promotion has given way to digital storytelling as a means of bolstering individuals’ exposure to these apps. Digital storytelling, as an emerging and novel format of product placement, has been credited for boosting consumers’ receptivity to featured products through compelling narrative, direct links, and rich media. In this study, we construct and empirically validate a research model that illustrates how digital storytelling can be strategized for product promotion in mobile app stores. In so doing, we endeavor to not only offer an in-depth appreciation of how digital storytelling can aid in promoting mobile apps through the presentation of engaging content but to also shed light on how these promotional effects could be moderated through rich delivery

Research progress on the relationship between fibroblast growth factor 23 and chronic kidney disease

Chronic kidney disease(CKD)is now a global public health problem. In chronic kidney disease(CKD)patients，almost all have complications such as calcium and phosphorus metabolism disorders，hyperparathyroidism，cardiovascular disease，anemia，and inflammation，which seriously affect the progress and prognosis of CKD. Fibroblast growth factor 23(FGF23) is a bone-derived hormone that regulates the metabolism of phosphate and vitamin D. In the past，FGF23 was generally considered to play only an important role in the regulation of calcium and phosphorus metabolism. In recent years FGF23has been found to be associated with the occurrence or progression of various CKD complications. This opens up new horizons for studying the role of FGF23 in the course of chronic kidney disease. FGF23 is expected to become a new therapeutic target in the future，improving the prognosis of patients with CKD. This article will review the biological characteristics of FGF23 and its role in the progression of CKD.  And briefly discuss its potential future role in chronic kidney disease

Bridge Designing Based on the New Combined Stretch-Shear Deformation Formula

This paper discovered a phenomenon in which the mass point in unit cell cannot keep balance in current elastic theory. Under different stress states, the absolute values of all equilibrium stress on the mass point are greater than the absolute values of principal stress. Thus, based on new concept of point stress balance, this paper introduces the new formula of stretch-shear combined deformation. The new formula explains the issue that, in the state of stretch-shear, constructions destroy more easily than in the state of compress-shear. Besides, based on new concepts of point stress balance, this paper also establishes a new theory of strength that is much more accurate than the third and fourth strength theory, validated in the Damage Mechanics National Key Laboratory of Tsinghua University. Comparisons of experiment data show the errors calculated from the new theory are only 1%, while errors based on the third and fourth strength theory are 14.2% and 18.2%. Therefore, the author suggests using the new stretch-shear formula to solve problems in bridge engineering in the future

Controlling the Amount of Verbatim Copying in Abstractive Summarization

An abstract must not change the meaning of the original text. A single most effective way to achieve that is to increase the amount of copying while still allowing for text abstraction. Human editors can usually exercise control over copying, resulting in summaries that are more extractive than abstractive, or vice versa. However, it remains poorly understood whether modern neural abstractive summarizers can provide the same flexibility, i.e., learning from single reference summaries to generate multiple summary hypotheses with varying degrees of copying. In this paper, we present a neural summarization model that, by learning from single human abstracts, can produce a broad spectrum of summaries ranging from purely extractive to highly generative ones. We frame the task of summarization as language modeling and exploit alternative mechanisms to generate summary hypotheses. Our method allows for control over copying during both training and decoding stages of a neural summarization model. Through extensive experiments we illustrate the significance of our proposed method on controlling the amount of verbatim copying and achieve competitive results over strong baselines. Our analysis further reveals interesting and unobvious facts.Comment: AAAI 2020 (Main Technical Track