Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Mathematical models (2)
- Plant growth (2)
- Agriculture performance (1)
- Algebraic graph-assisted bidirectional transformers (1)
- Algorithms (1)
-
- Animal populations (1)
- Berhu penalty (1)
- Bi-connectivity (1)
- COVID-19 (Disease) (1)
- Cancer-associated fibroblasts (1)
- Chloropicrin (1)
- College of Forest Resources and Environmental Science (1)
- Computational social science (1)
- Computer (1)
- Computer-Assisted (1)
- Department of Mathematical Sciences (1)
- Electroculture (1)
- Epidemics (1)
- Fruit yield (1)
- Glutamine/ammonium metabolism (1)
- Graph Theory and RNA Secondary Structures (1)
- Huber loss (1)
- Huber-Berhu partial least squares (1)
- Image Processing (1)
- Image Processing, Computer-Assisted (1)
- Laserculture (1)
- Machine learning (1)
- Magneticculture (1)
- Marine ecology (1)
- Mathematical modeling (1)
Articles 1 - 12 of 12
Full-Text Articles in Mathematics
On The Relationship Between Pain Variability And Relief In Randomized Clinical Trials, Siddharth Tiwari '22
On The Relationship Between Pain Variability And Relief In Randomized Clinical Trials, Siddharth Tiwari '22
Student Publications & Research
Previous research suggests greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects (natural history and regression-to-the-mean); for this reason, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, N = 134) while adjusting for confounding effects via a no-treatment group. Results agree between the two placebo groups: both placebo groups showed a negligible correlation between baseline variability and adjusted response (r sp (CI 95% ) = 0.13 (−0.09, 0.37) and 0.01 (−0.15, 0.20) for Placebo I and II, …
On The Relationship Between Pain Variability And Relief In Randomized Clinical Trials, Siddharth Tiwari '22
On The Relationship Between Pain Variability And Relief In Randomized Clinical Trials, Siddharth Tiwari '22
Student Publications & Research
Previous research suggests greater baseline variability is associated with greater pain relief in those who receive a placebo. However, studies that evidence this association do not control for confounding effects (natural history and regression-to-the-mean); for this reason, we analyzed data from two randomized clinical trials (Placebo I and Placebo II, N = 134) while adjusting for confounding effects via a no-treatment group. Results agree between the two placebo groups: both placebo groups showed a negligible correlation between baseline variability and adjusted response (r sp (CI 95% ) = 0.13 (−0.09, 0.37) and 0.01 (−0.15, 0.20) for Placebo I and II, …
Graph-Theoretic Partitioning Of Rnas And Classification Of Pseudoknots-Ii, Louis Petingi
Graph-Theoretic Partitioning Of Rnas And Classification Of Pseudoknots-Ii, Louis Petingi
Publications and Research
Dual graphs have been applied to model RNA secondary structures with pseudoknots, or intertwined base pairs. In previous works, a linear-time algorithm was introduced to partition dual graphs into maximally connected components called blocks and determine whether each block contains a pseudoknot or not. As pseudoknots can not be contained into two different blocks, this characterization allow us to efficiently isolate smaller RNA fragments and classify them as pseudoknotted or pseudoknot-free regions, while keeping these sub-structures intact. Moreover we have extended the partitioning algorithm by classifying a pseudoknot as either recursive or non-recursive in order to continue with our research …
Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei
Awegnn: Auto-Parametrized Weighted Element-Specific Graph Neural Networks For Molecules., Timothy Szocinski, Duc Duy Nguyen, Guo-Wei Wei
Mathematics Faculty Publications
While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex …
Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan
Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan
Mathematics Faculty Publications
The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as …
Mathematical Models Of Covid-19, Kate Faria
Mathematical Models Of Covid-19, Kate Faria
Honors Program Theses and Projects
For more than a year, the COVID-19 pandemic has been a major public health issue, affecting the lives of most people around the world. With both people’s health and the economy at great risks, governments rushed to control the spread of the virus. Containment measures were heavily enforced worldwide until a vaccine was developed and distributed. Although researchers today know more about the characteristics of the virus, a lot of work still needs to be done in order to completely remove the disease from the population. However, this is true for most of the infectious diseases in existence, including Influenza, …
Evaluation Of Broadcast Steam Application With Mustard Seed Meal In Fruiting Strawberry, Dong Sub Kim, Steven Kim, Steven A. Fennimore
Evaluation Of Broadcast Steam Application With Mustard Seed Meal In Fruiting Strawberry, Dong Sub Kim, Steven Kim, Steven A. Fennimore
Mathematics and Statistics Faculty Publications and Presentations
Soil disinfestation with steam has potential to partially replace fumigants such as methyl bromide, chloropicrin, and 1,3-dichloropropene because it is effective, safer to apply, and has less negative impact on the environment. Here, we compared the efficacy of steam and steam + mustard seed meal (MSM) to chloropicrin on soil disinfection, plant growth, and fruit yield in a strawberry (Fragaria ×ananassa) fruiting field. The MSM was applied at 3368 kg·ha−1 before the steam application. Steam was injected into a 3-m-wide reverse tiller that was set to till 30 to 40 cm deep. Soil temperatures at depths of …
Hb-Pls: A Statistical Method For Identifying Biological Process Or Pathway Regulators By Integrating Huber Loss And Berhu Penalty With Partial Least Squares Regression, Wenping Deng, Kui Zhang, Cheng He, Sanzhen Liu, Hairong Wei
Hb-Pls: A Statistical Method For Identifying Biological Process Or Pathway Regulators By Integrating Huber Loss And Berhu Penalty With Partial Least Squares Regression, Wenping Deng, Kui Zhang, Cheng He, Sanzhen Liu, Hairong Wei
Michigan Tech Publications
Gene expression data features high dimensionality, multicollinearity, and non-Gaussian distribution noise, posing hurdles for identification of true regulatory genes controlling a biological process or pathway. In this study, we integrated the Huber loss function and the Berhu penalty (HB) into partial least squares (PLS) framework to deal with the high dimension and multicollinearity property of gene expression data, and developed a new method called HB-PLS regression to model the relationships between regulatory genes and pathway genes. To solve the Huber-Berhu optimization problem, an accelerated proximal gradient descent algorithm with at least 10 times faster than the general convex optimization solver …
Modeling The Bidirectional Glutamine/ Ammonium Conversion Between Cancer Cells And Cancer-Associated Fibroblasts, Peter Hinow, Gabriella Pinter, Wei Yan, Shizhen Emily Wang
Modeling The Bidirectional Glutamine/ Ammonium Conversion Between Cancer Cells And Cancer-Associated Fibroblasts, Peter Hinow, Gabriella Pinter, Wei Yan, Shizhen Emily Wang
Mathematical Sciences Faculty Articles
Like in an ecosystem, cancer and other cells residing in the tumor microenvironment engage in various modes of interactions to buffer the negative effects of environmental changes. One such change is the consumption of common nutrients (such as glutamine/Gln) and the consequent accumulation of toxic metabolic byproducts (such as ammonium/NH4). Ammonium is a waste product of cellular metabolism whose accumulation causes cell stress. In tumors, it is known that it can be recycled into nutrients by cancer associated fibroblasts (CAFs). Here we present monoculture and coculture growth of cancer cells and CAFs on different substrates: glutamine and ammonium. …
Understanding Changes In Marine Communities Through A Discretized, Size-Structured Matrix Model, Courtney Swanson
Understanding Changes In Marine Communities Through A Discretized, Size-Structured Matrix Model, Courtney Swanson
Senior Seminars and Capstones
We study a discretized, size-structured matrix model which calculates population in a marine community over time. A portion of this model is a discretized version of the McKendrick-von Foerster equation, so we spend some time studying the process of discretizing that equation. We implement a mini model containing 10 size categories instead of the original 50, and we looked at how the marine community behaves over 40 years. We discuss some of the challenges when implementing this model.
A Review On Electroculture, Magneticulture And Laserculture To Boostplant Growth, Victor Christianto, Florentin Smarandache
A Review On Electroculture, Magneticulture And Laserculture To Boostplant Growth, Victor Christianto, Florentin Smarandache
Branch Mathematics and Statistics Faculty and Staff Publications
While several reviews on potential applications of electroculture are available, in this survey, we discuss these issues from history, starting from earliest experiments by Ross. And in the last section, we discuss possible application of laserculture, another form of potential improvement. It is our hope that what we present here may be found useful for improving agricultural performance in many countries, as well as reducing dependence on fertilizer.
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …