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The Impact Of Location Tax Incentives On The Growth Of Rural Economy: Evidence From Ghana, Nana Okyir Baidoo, Eric Amankwaah, Nicholas Mensah

All Rsif Scholars' Publications

Over the last two decades, location tax incentives programs serving as a motivating force have been progressively well known as activities to draw in and/or hold foreign direct investments (FDIs) in economically blighted areas. The study examined the impact of location tax incentives on the growth of the rural economy of Ghana from the period of 1994 to 2018. The data were sourced from Ghana Investment Promotion, UNCTAD, and the World Tax Database. Using ARDL Cointegration and Error Correction Models were estimated to examine the static and dynamic long-run effects as well as the short-run dynamics of the system and …

Drivers Of Post-Harvest Aflatoxin Contamination: Evidence Gathered From Knowledge Disparities And Field Surveys Of Maize Farmers In The Rift Valley Region Of Kenya, Grace Wanjiku Gachara, Rashid Suleiman, Sara El Kadili, Essaid Ait Barka, Beatrice Kilima, Rachid Lahlali Rachid Lahlali

All Rsif Scholars' Publications

Maize-dependent populations in sub-Saharan Africa are continually exposed to aflatoxinpoisoning owing to their regular consumption of this dietetic cereal. Being a staple in Kenyanhouseholds, consumption of maize-based meals is done almost daily, thereby exposing consumers toaflatoxicoses. This study assessed awareness levels, knowledge disparities, and perceptions regardingaflatoxin contamination at the post-harvest phase among farmers in the Rift Valley Region of Kenya.Households were randomly selected using a geographical positioning system (GPS) overlay of theagro-ecological zones within Uasin Gishu and Elgeyo Marakwet counties. Face-to-face interviewswere conducted in 212 smallholder and large-scale farms. The study documented the demographicprofiles of farmers and knowledge, awareness, and …

Robust Control Strategies For Optimal Operation Of Distributed Generation In Smart Microgrids, Chi-Thang Phan-Tan

Theses

The high penetration of PV systems and fast communications networks increase the potential for PV inverters to support the stability and performance of smart grids and microgrids. PV inverters in the distribution network can work cooperatively and follow centralized and decentralized control commands to optimize energy production while meeting grid code requirements. However, there are older autonomous inverters that have already been installed and will operate in the same network as smart controllable ones. This research proposes a decentralized optimal control (DOC) that performs multi-objective optimization for a group of PV inverters in a network of existing residential loads and …

Glioma And The Gut-Brain Axis: Opportunities And Future Perspectives, Antonio Dono, Jack Nickles, Ana G Rodriguez-Armendariz, Braden C Mcfarland, Nadim J Ajami, Leomar Y Ballester, Jennifer A Wargo, Yoshua Esquenazi

Faculty, Staff and Student Publications

The gut-brain axis has presented a valuable new dynamic in the treatment of cancer and central nervous system (CNS) diseases. However, little is known about the potential role of this axis in neuro-oncology. The goal of this review is to highlight potential implications of the gut-brain axis in neuro-oncology, in particular gliomas, and future areas of research. The gut-brain axis is a well-established biochemical signaling axis that has been associated with various CNS diseases. In neuro-oncology, recent studies have described gut microbiome differences in tumor-bearing mice and glioma patients compared to controls. These differences in the composition of the microbiome …

A Novel Group Of Genes That Cause Endocrine Resistance In Breast Cancer Identified By Dynamic Gene Expression Analysis, Arvand Asghari, Katherine Wall, Michael Gill, Natascha Del Vecchio, Farnaz Allahbakhsh, Jacky Wu, Nan Deng, W Jim Zheng, Hulin Wu, Michihisa Umetani, Vahed Maroufy

Faculty, Staff and Student Publications

Breast cancer (BC) is the most common type of cancer diagnosed in women. Among female cancer deaths, BC is the second leading cause of death worldwide. For estrogen receptor-positive (ER-positive) breast cancers, endocrine therapy is an effective therapeutic approach. However, in many cases, an ER-positive tumor becomes unresponsive to endocrine therapy, and tumor regrowth occurs after treatment. While some genetic mutations contribute to resistance in some patients, the underlying causes of resistance to endocrine therapy are mostly undetermined. In this study, we utilized a recently developed statistical approach to investigate the dynamic behavior of gene expression during the development of …

Effects Of Tamoxifen Inducible Mercremer On Gene Expression In Cardiac Myocytes In Mice, Leila Rouhi, Siyang Fan, Sirisha M Cheedipudi, Melis Olcum, Hyun-Hwan Jeong, Zhongming Zhao, Priyatansh Gurha, Ali J Marian

Faculty, Staff and Student Publications

The Cre-LoxP technology, including the tamoxifen (TAM) inducible MerCreMer (MCM), is increasingly used to delineate gene function, understand the disease mechanisms, and test therapeutic interventions. We set to determine the effects of TAM-MCM on cardiac myocyte transcriptome. Expression of the MCM was induced specifically in cardiac myocytes upon injection of TAM to myosin heavy chain 6-MCM (Myh6-Mcm) mice for 5 consecutive days. Cardiac function, myocardial histology, and gene expression (RNA-sequencing) were analyzed 2 weeks after TAM injection. A total of 346 protein coding genes (168 up- and 178 down-regulated) were differentially expressed. Transcript levels of 85 genes, analyzed …

An Ensemble-Based Deep Convolutional Neural Network For Computer-Aided Polyps Identification From Colonoscopy, Pallabi Sharma, Bunil Kumar Balabantaray, Kangkana Bora, Saurav Mallik, Kunio Kasugai, Zhongming Zhao

Faculty, Staff and Student Publications

Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for …

A Deep Learning-Based Framework For Supporting Clinical Diagnosis Of Glioblastoma Subtypes, Sana Munquad, Tapas Si, Saurav Mallik, Asim Bikas Das, Zhongming Zhao

Faculty, Staff and Student Publications

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms …

Crucial Roles Of Microrna-16-5p And Microrna-27b-3p In Ameloblast Differentiation Through Regulation Of Genes Associated With Amelogenesis Imperfecta, Akiko Suzuki, Hiroki Yoshioka, Teng Liu, Aania Gull, Naina Singh, Thanh Le, Zhongming Zhao, Junichi Iwata

Faculty, Staff and Student Publications

Amelogenesis imperfecta is a congenital disorder within a heterogeneous group of conditions characterized by enamel hypoplasia. Patients suffer from early tooth loss, social embarrassment, eating difficulties, and pain due to an abnormally thin, soft, fragile, and discolored enamel with poor aesthetics and functionality. The etiology of amelogenesis imperfecta is complicated by genetic interactions. To identify mouse amelogenesis imperfecta-related genes (mAIGenes) and their respective phenotypes, we conducted a systematic literature review and database search and found and curated 70 mAIGenes across all of the databases. Our pathway enrichment analysis indicated that these genes were enriched in tooth development-associated pathways, forming four …

Sulfhemoglobinemia And Methemoglobinemia Following Acetaminophen Overdose, Justin A Seltzer, Irvan Bubic, Garret A Winkler, Nathan A Friedman, Jessica Bagby, Christian A Tomaszewski, Richard F Clark, Allyson Kreshak, Daniel R Lasoff

Faculty, Staff and Student Publications

INTRODUCTION: Though acetaminophen overdoses are common, acetaminophen induced methemoglobinemia is rare and it is thought to be due to oxidative stress from reactive metabolites. However, few prior cases of sulfhemoglobinemia in the setting of acetaminophen overdose have been reported. We report a case of mixed methemoglobinemia and sulfhemoglobinemia in the setting of a large, isolated acetaminophen ingestion.

CASE REPORT: A 30-year-old African American male presented after intentionally ingesting 50 tablets of 500 mg acetaminophen two days prior. He was cyanotic and tachypneic. Peripheral oxygen saturation was 78 % on room air and minimally improved with high-flow oxygen. He was noted …

Dimensionality Reduction And Louvain Agglomerative Hierarchical Clustering For Cluster-Specified Frequent Biomarker Discovery In Single-Cell Sequencing Data, Soumita Seth, Saurav Mallik, Tapas Bhadra, Zhongming Zhao

Faculty, Staff and Student Publications

The major interest domains of single-cell RNA sequential analysis are identification of existing and novel types of cells, depiction of cells, cell fate prediction, classification of several types of tumor, and investigation of heterogeneity in different cells. Single-cell clustering plays an important role to solve the aforementioned questions of interest. Cluster identification in high dimensional single-cell sequencing data faces some challenges due to its nature. Dimensionality reduction models can solve the problem. Here, we introduce a potential cluster specified frequent biomarkers discovery framework using dimensionality reduction and hierarchical agglomerative clustering Louvain for single-cell RNA sequencing data analysis. First, we pre-filtered …

Explanation-Driven Deep Learning Model For Prediction Of Brain Tumour Status Using Mri Image Data, Loveleen Gaur, Mohan Bhandari, Tanvi Razdan, Saurav Mallik, Zhongming Zhao

Faculty, Staff and Student Publications

Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality …

Breast Cancer Detection: Shallow Convolutional Neural Network Against Deep Convolutional Neural Networks Based Approach, Himanish Shekhar Das, Akalpita Das, Anupal Neog, Saurav Mallik, Kangkana Bora, Zhongming Zhao

Faculty, Staff and Student Publications

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).

Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help …

The Wnt/Β-Catenin Pathway Regulates Expression Of The Genes Involved In Cell Cycle Progression And Mitochondrial Oxidative Phosphorylation In The Postmitotic Cardiac Myocytes, Melis Olcum, Sirisha M Cheedipudi, Leila Rouhi, Siyang Fan, Hyun-Hwan Jeong, Zhongming Zhao, Priyatansh Gurha, Ali J Marian

Faculty, Staff and Student Publications

INTRODUCTION: Aging is associated with cardiac myocyte loss, sarcopenia, and cardiac dysfunction. Adult cardiac myocytes are postmitotic cells with an insufficient proliferative capacity to compensate for myocyte loss. The canonical WNT (cWNT) pathway is involved in the regulation of cell cycle reentry in various cell types. The effects of the cWNT pathway on the expression of genes involved in cell cycle reentry in the postmitotic cardiac myocytes are unknown.

AIM: The aim of the study was to identify genes whose expression is regulated by the β-catenin, the indispensable component to the cWNT signaling, in the postmitotic myocytes.

METHODS AND RESULTS: …

Prediction Of Intensive Care Unit Admission (>24h) After Surgery In Elective Noncardiac Surgical Patients Using Machine Learning Algorithms, Lan Lan, Fangwei Chen, Jiawei Luo, Mengjiao Li, Xuechao Hao, Yao Hu, Jin Yin, Tao Zhu, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

METHOD: Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, …

Prediction Of Fluid Intelligence From T1-W Mri Images: A Precise Two-Step Deep Learning Framework, Mingliang Li, Mingfeng Jiang, Guangming Zhang, Yujun Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

The Adolescent Brain Cognitive Development (ABCD) Neurocognitive Prediction Challenge (ABCD-NP-Challenge) is a community-driven competition that challenges competitors to develop algorithms to predict fluid intelligence scores from T1-w MRI images. In this work, a two-step deep learning pipeline is proposed to improve the prediction accuracy of fluid intelligence scores. In terms of the first step, the main contributions of this study include the following: (1) the concepts of the residual network (ResNet) and the squeeze-and-excitation network (SENet) are utilized to improve the original 3D U-Net; (2) in the segmentation process, the pixels in symmetrical brain regions are assigned the same label; …

Distinguish Bipolar And Major Depressive Disorder In Adolescents Based On Multimodal Neuroimaging: Results From The Adolescent Brain Cognitive Development Stud, Yujun Liu, Kai Chen, Yangyang Luo, Jiqiu Wu, Qu Xiang, Li Peng, Jian Zhang, Weiling Zhao, Mingliang Li, Xiaobo Zhou

Faculty, Staff and Student Publications

BACKGROUND: Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health.

METHODS: We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of …

Multiplexed Engineering And Precision Gene Editing In Cellular Immunotherapy, Alexander Biederstädt, Gohar Shahwar Manzar, May Daher

Faculty, Staff and Student Publications

The advent of cellular immunotherapy in the clinic has entirely redrawn the treatment landscape for a growing number of human cancers. Genetically reprogrammed immune cells, including chimeric antigen receptor (CAR)-modified immune effector cells as well as T cell receptor (TCR) therapy, have demonstrated remarkable responses across different hard-to-treat patient populations. While these novel treatment options have had tremendous success in providing long-term remissions for a considerable fraction of treated patients, a number of challenges remain. Limited

Re-Thinking Therapeutic Development For Cns Metastatic Disease, Chantal Saberian, Michael A Davies

Faculty, Staff and Student Publications

There has been unprecedented progress in the development of systemic therapies for patients with metastatic melanoma over the last decade. There is now tremendous potential and momentum to further and markedly reduce the impact of this disease. However, developing more effective treatments for metastases to the CNS remains a critical challenge for patients with melanoma. Melanoma patients with active CNS metastases have largely been excluded from both early-phase and registration trials for all currently approved targeted and immune therapies for this disease. While this exclusion has generally been justified in clinical research due to concerns about poor prognosis, lack of …

Deep Learning Auto-Segmentation Of Cervical Skeletal Muscle For Sarcopenia Analysis In Patients With Head And Neck Cancer, Mohamed A Naser, Kareem A Wahid, Aaron J Grossberg, Brennan Olson, Rishab Jain, Dina El-Habashy, Cem Dede, Vivian Salama, Moamen Abobakr, Abdallah S R Mohamed, Renjie He, Joel Jaskari, Jaakko Sahlsten, Kimmo Kaski, Clifton D Fuller

Faculty, Staff and Student Publications

Background/purpose: Sarcopenia is a prognostic factor in patients with head and neck cancer (HNC). Sarcopenia can be determined using the skeletal muscle index (SMI) calculated from cervical neck skeletal muscle (SM) segmentations. However, SM segmentation requires manual input, which is time-consuming and variable. Therefore, we developed a fully-automated approach to segment cervical vertebra SM.

Materials/methods: 390 HNC patients with contrast-enhanced CT scans were utilized (300-training, 90-testing). Ground-truth single-slice SM segmentations at the C3 vertebra were manually generated. A multi-stage deep learning pipeline was developed, where a 3D ResUNet auto-segmented the C3 section (33 mm window), the middle slice of the …

Evaluation Of Deep Learning-Based Multiparametric Mri Oropharyngeal Primary Tumor Auto-Segmentation And Investigation Of Input Channel Effects: Results From A Prospective Imaging Registry, Kareem A Wahid, Sara Ahmed, Renjie He, Lisanne V Van Dijk, Jonas Teuwen, Brigid A Mcdonald, Vivian Salama, Abdallah S R Mohamed, Travis Salzillo, Cem Dede, Nicolette Taku, Stephen Y Lai, Clifton D Fuller, Mohamed A Naser

Faculty, Staff and Student Publications

Background/purpose: Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance.

Materials/methods: GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, …

Epithelial Immunomodulation By Aerosolized Toll-Like Receptor Agonists Prevents Allergic Inflammation In Airway Mucosa In Mice, David L Goldblatt, Gabriella Valverde Ha, Shradha Wali, Vikram V Kulkarni, Michael K Longmire, Ana M Jaramillo, Rosha P Chittuluru, Adrienne Fouts, Margarita Martinez-Moczygemba, Jonathan T Lei, David P Huston, Michael J Tuvim, Burton F Dickey, Scott E Evans

Faculty, Staff and Student Publications

Allergic asthma is a chronic inflammatory respiratory disease associated with eosinophilic infiltration, increased mucus production, airway hyperresponsiveness, and airway remodeling. Epidemiologic data reveal that the prevalence of allergic sensitization and associated diseases has increased in the twentieth century. This has been hypothesized to be partly due to reduced contact with microbial organisms (the hygiene hypothesis) in industrialized society. Airway epithelial cells, once considered a static physical barrier between the body and the external world, are now widely recognized as immunologically active cells that can initiate, maintain, and restrain inflammatory responses, such as those that mediate allergic disease. Airway epithelial cells …

Comparison Of Three Exercise Interventions With And Without Gemcitabine Treatment On Pancreatic Tumor Growth In Mice: No Impact On Tumor Infiltrating Lymphocytes, Priti Gupta, Charles F Hodgman, Claudia Alvarez-Florez, Keri L Schadler, Melissa M Markofski, Daniel P O'Connor, Emily C Lavoy

Faculty, Staff and Student Publications

Exercise has been shown to slow pancreatic tumor growth, but whether exercise interventions of differing volume or intensity yield differential effects on tumor outcomes is unknown. In this study, we compared three exercise training interventions implemented with and without chemotherapy on pancreatic tumor growth in mice. Methods: Male C57BL/6 mice (6-8 weeks old) were subcutaneously inoculated with pancreatic ductal adenocarcinoma tumor cells (PDAC 4662). Upon tumor detection, mice received gemcitabine 15 mg/kg intraperitoneally 3 days/week and were assigned to exercise: high volume continuous exercise (HVCE), low volume continuous exercise (LVCE), high intensity interval training (HIIT), or sedentary (SED). HVCE ran …

B Cells Are Required To Generate Optimal Anti-Melanoma Immunity In Response To Checkpoint Blockade, Shubhra Singh, Jason Roszik, Neeraj Saini, Vipul Kumar Singh, Karishma Bavisi, Zhiqiang Wang, Long T Vien, Zixi Yang, Suprateek Kundu, Richard E Davis, Laura Bover, Adi Diab, Sattva S Neelapu, Willem W Overwijk, Kunal Rai, Manisha Singh

Faculty, Staff and Student Publications

Immunotherapies such as checkpoint blockade therapies are known to enhance anti-melanoma CD8+ T cell immunity, but only a fraction of patients treated with these therapies achieve durable immune response and disease control. It may be that CD8+ T cells need help from other immune cells to generate effective and long-lasting anti-tumor immunity or that CD8+ T cells alone are insufficient for complete tumor regression and cure. Melanoma contains significant numbers of B cells; however, the role of B cells in anti-melanoma immunity is controversial. In this study, B16 melanoma mouse models were used to determine the role of B cells …

Automated Segmentation Of Colorectal Liver Metastasis And Liver Ablation On Contrast-Enhanced Ct Images, Brian M Anderson, Bastien Rigaud, Yuan-Mao Lin, A Kyle Jones, Hynseon Christine Kang, Bruno C Odisio, Kristy K Brock

Faculty, Staff and Student Publications

Objectives: Colorectal cancer (CRC), the third most common cancer in the USA, is a leading cause of cancer-related death worldwide. Up to 60% of patients develop liver metastasis (CRLM). Treatments like radiation and ablation therapies require disease segmentation for planning and therapy delivery. For ablation, ablation-zone segmentation is required to evaluate disease coverage. We hypothesize that fully convolutional (FC) neural networks, trained using novel methods, will provide rapid and accurate identification and segmentation of CRLM and ablation zones.

Methods: Four FC model styles were investigated: Standard 3D-UNet, Residual 3D-UNet, Dense 3D-UNet, and Hybrid-WNet. Models were trained on 92 patients from …

The Other Legacy Of Qasim Amin: The View From 1908, Hoda Yousef

Faculty Publications

No abstract provided.

The Religious Communication Approach And Political Behavior, Paul Djupe, Jacob R. Neiheisel

Faculty Publications

No abstract provided.

Book Review: Transforming Print: Collection Development And Management For Our Connected Future, Debra Andreadis

Faculty Publications

No abstract provided.

Divine Attribution? The Interaction Of Religious And Secular Beliefs On Climate Change Attitudes, Paul Djupe, Ryan P. Burge

Faculty Publications

No abstract provided.

Effects Of Co-Incubation Of Lps-Stimulated Raw 264.7 Macrophages On Leptin Production By 3t3-L1 Adipocytes: A Method For Co-Incubating Distinct Adipose Tissue Cell Lines, Cristina Caldari, Jordan Beck

Faculty Publications

No abstract provided.