Engineering Commons^™

An Elastic-Net Logistic Regression Approach To Generate Classifiers And Gene Signatures For Types Of Immune Cells And T Helper Cell Subsets, Arezo Torang, Paraag Gupta, David J. Klinke Ii

Faculty & Staff Scholarship

Background: Host immune response is coordinated by a variety of different specialized cell types that vary in time and location. While host immune response can be studied using conventional low-dimensional approaches, advances in transcriptomics analysis may provide a less biased view. Yet, leveraging transcriptomics data to identify immune cell subtypes presents challenges for extracting informative gene signatures hidden within a high dimensional transcriptomics space characterized by low sample numbers with noisy and missing values. To address these challenges, we explore using machine learning methods to select gene subsets and estimate gene coefficients simultaneously. Results: Elastic-net logistic regression, a type of …

Neutrophil Extracellular Traps In Breast Cancer And Beyond: Current Perspectives On Net Stimuli, Thrombosis And Metastasis, And Clinical Utility For Diagnosis And Treatment, Hunter T. Snoderly, Brian A. Boone, Margaret F. Bennewitz

Faculty & Staff Scholarship

Abstract

The formation of neutrophil extracellular traps (NETs), known as NETosis, was first observed as a novel immune response to bacterial infection, but has since been found to occur abnormally in a variety of other inflammatory disease states including cancer. Breast cancer is the most commonly diagnosed malignancy in women. In breast cancer, NETosis has been linked to increased disease progression, metastasis, and complications such as venous thromboembolism. NET-targeted therapies have shown success in preclinical cancer models and may prove valuable clinical targets in slowing or halting tumor progression in breast cancer patients. We will briefly outline the mechanisms by …

Drilling Performance Monitoring And Optimization: A Data-Driven Approach, Shan E Zehra Lashari, Ali Takbiri-Borujeni, Ebrahim Fathi, Ting Sun, Reza Rahmani, Mehdi Khazaeli

Faculty & Staff Scholarship

Abstract Drilling performance monitoring and optimization are crucial in increasing the overall NPV of an oil and gas project. Even after rigorous planning, drilling phase of any project can be hindered by unanticipated problems, such as bit balling. The objective of this paper is to implement artifcial intelligence technique to develop a smart model for more accurate and robust real-time drilling performance monitoring and optimization. For this purpose, the back propagation, feed forward neural network model was developed to predict rate of penetration (ROP) using diferent input parameters such as weight on bit, rotations per minute, mud fow (GPM) and …

Cancer Nanotechnology: Enhancing Tumor Cell Response To Chemotherapy For Hepatocellular Carcinoma Therapy, Sun Yongbing, Ma Wen, Yang Yuanyuan, He Mengxue, Li Aimin, Bai Lei, Yu Bin, Yu Zhiqiang

Faculty & Staff Scholarship

Abstract

Hepatocellular carcinoma (HCC) is one of the deadliest cancers due to its complexities, reoccurrence after surgical resection, metastasis and heterogeneity. In addition to sorafenib and lenvatinib for the treatment of HCC approved by FDA, various strategies including transarterial chemoembolization, radiotherapy, locoregional therapy and chemotherapy have been investigated in clinics. Recently, cancer nanotechnology has got great attention for the treatment of various cancers including HCC. Both passive and active targetings are progressing at a steady rate. Herein, we describe the lessons learned from pathogenesis of HCC and the understanding of targeted and non-targeted nanoparticles used for the delivery of small …

Dataset On Hydrophobicity Indices And Differential Scanning Calorimetry Thermograms For Poly(Hema)-Based Hydrogels, Ankita Bhat, Blake Smith, Cerasela-Zoica Dinu, Anthony Guiseppi-Elie

Faculty & Staff Scholarship

Hydrophobicity indices for poly(HEMA)-based hydrogels:HEMA, AEMA, and DMAEMA calculated from two differentmethods: 1) Partition coefficients, and 2) Kyte-Doolittle scaleare depicted.Thermograms from differential scanning calorimetry ofpoly(HEMA)-based hydrogels containing AEMA, DMAEMA, and amixture of AEMA and DMAEMA are included to represent the glasstransition temperature (Tg) values of the hydrogels.More information on the methodology to calculate the hydro-phobicity indices using the aforementioned methods and theprocedure for using a differential scanning calorimeter and anal-ysis of a thermogram is described.Details of how the changes in the feed composition ofpoly(HEMA)-based hydrogels was made is provided in theresearch article‘MOLECULAR ENGINEERING OF POLY(HEMA-co-PEGMA)-BASED HYDROGELS: ROLE OF MINOR AEMA …