Life Sciences Commons^™

Regulation Of Regeneration In Arabidopsis Thaliana, Md Khairul Islam, Sai Teja Mummadi, Sanzhen Liu, Hairong Wei

Michigan Tech Publications, Part 2

We employed several algorithms with high efficacy to analyze the public transcriptomic data, aiming to identify key transcription factors (TFs) that regulate regeneration in Arabidopsis thaliana. Initially, we utilized CollaborativeNet, also known as TF-Cluster, to construct a collaborative network of all TFs, which was subsequently decomposed into many subnetworks using the Triple-Link and Compound Spring Embedder (CoSE) algorithms. Functional analysis of these subnetworks led to the identification of nine subnetworks closely associated with regeneration. We further applied principal component analysis and gene ontology (GO) enrichment analysis to reduce the subnetworks from nine to three, namely subnetworks 1, 12, and 17. …

Reconstructing 42 Years (1979–2020) Of Great Lakes Surface Temperature Through A Deep Learning Approach, Miraj Kayastha, Tao Liu, Daniel Titze, Timothy C. Havens, Chenfu Huang, Pengfei Xue

Michigan Tech Publications, Part 2

Accurate estimates for the lake surface temperature (LST) of the Great Lakes are critical to understanding the regional climate. Dedicated lake models of various complexity have been used to simulate LST but they suffer from noticeable biases and can be computationally expensive. Additionally, the available historical LST datasets are limited by either short temporal coverage (<30 >years) or lower spatial resolution (0.25° × 0.25°). Therefore, in this study, we employed a deep learning model based on Long Short-Term Memory (LSTM) neural networks to produce a daily LST dataset for the Great Lakes that spans an unparalleled 42 years (1979–2020) at …

Invasive Buckthorn Mapping: A Uav-Based Approach Utilizing Machine Learning, Gis, And Remote Sensing Techniques In The Upper Peninsula Of Michigan, Vikranth Madeppa

Dissertations, Master's Theses and Master's Reports

An Invasive species is a species that is alien or non-native to the ecosystem which causes harm to economic, environmental, or human health (E.O. 13112 of Feb 3, 1999). Invasive species have posed a serious threat to ecosystems across the globe. These invasive species have impacts on the biodiversity and productivity of invaded forests. Remotely sensed data is a valuable resource for understanding and addressing issues related to invasive species. This study presents a novel approach for mapping the distribution of two invasive plant species, Common and Glossy Buckthorn, using unmanned aerial vehicles (UAVs), machine learning algorithms, geographic information systems …

Prediction Of Sumoylation Sites In Proteins From Language Model Representations, Evgenii Sidorov

Dissertations, Master's Theses and Master's Reports

Sumoylation is an essential post-translational modification intimately involved in a diverse range of eukaryotic cellular mechanisms and plays a significant role in DNA repair. Some researchers hypothesize that a high level of SUMOylation events in cancer cells improves cells' chances for survival under stress conditions by regulating tumor-related proteins.

This study belongs to a booming field of harnessing computational power to the domain of life. Prediction of protein structure, its molecular function, and the design of new drugs are just a few examples of the applications within this exciting area of research. By leveraging computational power, researchers can analyze vast …

Integrating Deep Learning And Hydrodynamic Modeling To Improve The Great Lakes Forecast, Pengfei Xue, Aditya Wagh, Gangfeng Ma, Yilin Wang, Yongchao Yang, Tao Liu, Chenfu Huang

Michigan Tech Publications

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, …

“Musical Exercise” For People With Visual Impairments: A Preliminary Study With The Blindfolded, Ridwan Ahmed Khan, Myounghoon Jeon, Tejin Yoon

Michigan Tech Publications

Performing independent physical exercise is critical to maintain one's good health, but it is specifically hard for people with visual impairments. To address this problem, we have developed a Musical Exercise platform for people with visual impairments so that they can perform exercise in a good form consistently. We designed six different conditions, including blindfolded or visual without audio conditions, and blindfolded or visual with two different types of audio feedback (continuous vs. discrete) conditions. Eighteen sighted participants participated in the experiment, by doing two exercises - squat and wall sit with all six conditions. The results show that Musical …

Tgmi: An Efficient Algorithm For Identifying Pathway Regulators Through Evaluation Of Triple-Gene Mutual Interaction, Chathura J. Gunasekara, Kui Zhang, Wenping Deng, Laura E. Brown, Hairong Wei

Michigan Tech Publications

Despite their important roles, the regulators for most metabolic pathways and biological processes remain elusive. Presently, the methods for identifying metabolic pathway and biological process regulators are intensively sought after. We developed a novel algorithm called triple-gene mutual interaction (TGMI) for identifying these regulators using high-throughput gene expression data. It first calculated the regulatory interactions among triple gene blocks (two pathway genes and one transcription factor (TF)), using conditional mutual information, and then identifies significantly interacted triple genes using a newly identified novel mutual interaction measure (MIM), which was substantiated to reflect strengths of regulatory interactions within each triple gene …

Growth Performance, Organ-Level Ionic Relations And Organic Osmoregulation Of Elaeagnus Angustifolia In Response To Salt Stress, Zhengxiang Liu, Jiangfeng Zhu, Xiuyan Yang, Haiwen Wu, Qi Wei, Hairong Wei, Huaxin Zhang

Michigan Tech Publications

Elaeagnus angustifolia is one of the most extensively afforested tree species in environment-harsh regions of northern China. Despite its exceptional tolerance to saline soil, the intrinsic adaptive physiology has not been revealed. In this study, we investigated the growth, organ-level ionic relations and organic osmoregulation of the seedlings hydroponically treated with 0, 100 and 200 mM NaCl for 30 days. We found that the growth characteristics and the whole-plant dry weight were not obviously stunted, but instead, were even slightly stimulated by the treatment of 100 mM NaCl. In contrast, these traits were significantly inhibited by 200 mM NaCl treatment. …

The Effect Of Poplar Psngs1.2 Overexpression On Growth, Secondary Cell Wall, And Fiber Characteristics In Tobacco, Tingting Lu, Lulu Liu, Minjing Wei, Yingying Liu, Zianshang Qu, Chuanping Yang, Hairong Wei, Zhigang Wei

Michigan Tech Publications

The glutamine synthetase (GS1) is a key enzyme that catalyzes the reaction of glutamate and ammonia to produce glutamine in the nitrogen (N) metabolism. Previous studies on GS1s in several plant species suggest that overexpression of GS1s can enhance N utilization, accelerate plant vegetative growth, and change wood formation. In this study, we isolated a GS1 gene, termed PsnGS1.2, from Populus simonii × Populus nigra. This gene was expressed at a higher level in roots, and relatively lower but detectable levels in xylem, leaves and phloem of P. simonii × P. nigra. The protein encoded by PsnGS1.2 is …

Algorithms For Reconstruction Of Gene Regulatory Networks From High -Throughput Gene Expression Data, Wenping Deng

Dissertations, Master's Theses and Master's Reports

Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with increased accuracy from microarray and RNA-Seq high-throughput gene expression data sets.

The first algorithm (Chapter 2) …

Bottom-Up Ggm Algorithm For Constructing Multilayered Hierarchical Gene Regulatory Networks That Govern Biological Pathways Or Processes, Sapna Kupari, Wenping Deng, Chathura J. Gunasekara, Vincent Chiang, Huann-Sheng Chen, Hairong Wei, Et. Al.

Michigan Tech Publications

Background: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways.

Results: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct an ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory …

Reliability And Validity Of Neurobehavioral Function On The Psychology Experimental Building Language Test Battery In Young Adults, Brian J. Piper, Shane Mueller, Alexander R. Geerken, Kyle L. Dixon, Gregory Kroliczak, Reid H. Olsen, Jeremy K. Miller

Michigan Tech Publications

Background. The Psychology Experiment Building Language (PEBL) software consists of over one-hundred computerized tests based on classic and novel cognitive neuropsychology and behavioral neurology measures. Although the PEBL tests are becoming more widely utilized, there is currently very limited information about the psychometric properties of these measures.

Methods. Study I examined inter-relationships among nine PEBL tests including indices of motor-function (Pursuit Rotor and Dexterity), attention (Test of Attentional Vigilance and Time-Wall), working memory (Digit Span Forward), and executive-function (PEBL Trail Making Test, Berg/Wisconsin Card Sorting Test, Iowa Gambling Test, and Mental Rotation) in a normative sample (N = 189, …

Subject Assessment Of In-Vehicle Auditory Warnings For Rail Grade Crossings, Steven Landry, Jayde Croschere, Myounghoon Jeon

Michigan Tech Publications

Human factors research has played an important role in reducing the incidents of vehicle-train collisions at rail grade crossings over the past 30 years. With the growing popularity of in-vehicle infotainment systems and GPS devices, new opportunities arise to cost-efficiently and effectively alert drivers of railroad crossings and to promote safer driving habits. To best utilize this in-vehicle technology, 32 auditory warnings (16 verbal, 7 train-related auditory icons, and 9 generic earcons) were generated and presented to 31 participants after a brief low-fidelity driving simulation. Participants rated each sound on eight dimensions deemed important in previous auditory warning literature. Preliminary …