An Integrative Analysis of microRNA and mRNA Expression—A Case Study

Background: MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression.Results: We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression.Discussion: Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure—the co-clustering frequency—to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general

On Comparing the Clustering of Regression Models Method with K-means Clustering

Gene clustering is a common question addressed with microarray data. Previous methods, such as K-means clustering and hierarchical clustering, base gene clustering directly on the observed measurements. A new model-based clustering method, the clustering of regression models (CORM) method, bases the clustering of genes on their relationship to covariates. It explicitly models different sources of variations and bases gene clustering solely on the systematic variation. Both being partitional clustering, CORM is closely related to K-means clustering. In this paper, we discuss the relationship between the two clustering methods in terms of both model formulation and implications on other important aspects of cluster analysis. We show that the two methods can both be considered as solutions to a least squares problem with missing data but they each concern a different type of least squares. We also show that CORM tends to provide stable clusters across samples and is particularly useful if the cluster averages are used as predictors for sample classification. Finally we illustrate the application of CORM to a set of time course data measured on four yeast samples, which has a complicated experimental design and is difficult for K-means to handle

Business Analysis and Future Development of an Electric Vehicle Company -- Tesla

The boom in electric vehicles in recent years has caught the attention of many companies that are investing or will be investing in the industry due to the increasing demand for electric cars. Tesla as a leader of the electric vehicles (EVs) industry, its development is of vital significance for referential value. Previous research on electric vehicle acceptance and behavioral intention of purchase is comprehensive, which could enable the EVs industry to understand consumer psychology. However, there is little analysis of the business strategy and future development of specific companies. When it comes to sustainability, almost every company has a path that is best suited to. This paper presents a comprehensive review of the historical background of Tesla, followed by in-depth states on its current strategy and future analysis. Given recommendations on its future development, Tesla could engage more in other different industries to increase the source of revenue and invest more into the development of autonomous public transportation, such as electric car-sharing services (ECS). These will help Tesla move steadily into the next stage

Preparation and Properties of Bilayer Composite Materials of Cu-coated Fe and CuSn10

Bilayer composite materials of Cu-coated Fe and CuSn10 containing 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50% Cu-coated Fe were prepared in mesh belt sintering furnace. Microscopic pore morphology of materials was observed, bending strength was tested. Results show that, There is a good bonding between Cu-coated Fe and CuSn10, with the increase of Cu-coated Fe content from 0% to 50%, bending strength of bilayer composite materials increases

Palindrome distributions and their applications

Master'sMASTER OF SCIENC

An effective thermal conductivity model for fractal porous media with rough surfaces

Funding Information: This project was supported by the National Natural Science Foundation of China (No. 41572116), and the Hubei Provincial Natural Science Foundation of China (No. 2018CFA051). Dr. Zhou would like to thank the Royal Society to support his trip to China with the International Exchange Programme and Mr. Qin would like to thanks the British Council and China Scholarship Council to support his stay at the University of Aberdeen through the UK-China PhD Placement Programme.Peer reviewe

Finding gene clusters for a replicated time course study

BACKGROUND: Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. FINDINGS: In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. CONCLUSIONS: The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism

Effects of Compound Danshen tablets on spatial cognition and expression of brain β-amyloid precursor protein in a rat model of alzheimer's disease

AbstractObjectiveTo observe the effects of Compound Danshen Tablets (CDST) on spatial cognition and expression of brain b-amyloid precursor protein (β-APP) in a rat model of Alzheimer's disease.MethodsThe rat model of Alzheimer's disease (AD) was established using D-galactose to cause subacute aging combined with Meynert nucleus damage. Rat behavior was monitored using the Morris water maze, and the expression of β-APP in rat brain tissue was detected via immunohistochemistry.ResultsCDST significantly improved spatial cognition and decreased β-APP expression in the cortex and hippocampus (P<0.05, P<0.01).ConclusionsCDST can significantly improve spatial cognition in a rat model of AD. This observation is possibly related to a reduction in β-APP expression in the rat brain

Modeling the elastic characteristics of overpressure due to thermal maturation in organic shales

Modeling the overpressure of organic shales caused by thermal maturation and its elastic responses is crucial for geophysical characterization of source rocks and unconventional shale reservoirs. Thermal maturation involves the generation of excess fluid contents (oil and gas) and can cause the overpressure if an organic shale preserves the produced fluids partly or wholly. The solid organic matter (e.g., kerogen or solid bitumen) with the potential of generating hydrocarbon presents two types of morphology in organic shales: scattered patches as pore-fillings and continuous network as load-bearings. According to the kerogen morphology, two bulk volume models are devised to simulate the elasticity of organic shales using respective rock-physics modeling schemes. The rock physics modeling combined with the density and compressibility of pore-fillings are demonstrated to effectively capture the excess pore pressure characteristics due to thermal maturation in organic shales. The basic principle of solving the overpressure is that the pore space volume equals the total volume of all components within the pores before and after the maturation. According to the modeling results, the elastic characteristics of overpressure due to thermal maturation reveal a decrease in velocity and a slight decrease in density. Besides, for an organic shale with a relatively rigid framework, it tends to yield higher overpressure than a shale with a relatively compliant framework. With proper calibration, the modeling strategy shows its potential in quantitatively interpreting the well-log data of organic shale formation within the thermal maturation window.Document Type: Original articleCited as: Qin, X., Zhao, L., Zhu, J., Han, D. Modeling the elastic characteristics of overpressure due to thermal maturation in organic shales. Advances in Geo-Energy Research, 2023, 10(3): 174-188. https://doi.org/10.46690/ager.2023.12.0