Other Engineering Research Open House Presentation Schedule

Poster ID: 94

2:00 - 3:00

Brown, Bartley



Production Pipelines for the Analysis of Bulk and Single-Cell RNA-Sequencing Data

Authors: Brown, Bartley; Elias Shaeffer; Thomas Casavant; James Howe; Terry Braun

The underlying cause of tumor formation in cancer, and what differentiates primary tumor from metastatic tumors remains to be understood. Differential gene expression between normal tissues, tumor and metastatic tumors provides clues about the genes and pathways involved.  To better understand gene expression in cancer, we have constructed processing pipelines for both bulk and single-cell RNA-sequencing to identify differentially expressed genes.  Our pipelines utilize community- and commercially-developed tools, including Salmon, Tximport and Deseq2 for bulk RNA-seq; and CellRanger and Seurat for single-cell RNA-sequencing. Additionally, various R packages are used for data visualization and gene annotation.

Three-Minute Video: https://youtu.be/ymEcBP5WnDE

Presentation: brown_cbcb_vroh_2020.pdf


Poster ID: 95

9:00 - 10:00

Demiray, Bekir Zahit



D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

Authors: Demiray, Bekir Z; Sit, Muhammed; Demir, Ibrahim

LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. A number of studies in flooding suggest the usage of high-resolution DEMs as inputs in the applications improve the overall reliability and accuracy. Despite the importance of high-resolution DEM, many areas in the United States and the world do not have access to high-resolution DEM due to technological limitations or the cost of the data collection. With recent development in Graphical Processing Units (GPU) and novel algorithms, deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets. Numerous new methods have been proposed such as Generative Adversarial Networks (GANs) to create intelligent models that correct and augment large-scale datasets. In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data.

Three-Minute Video: https://youtu.be/TC5xt3FIQ7c

Presentation: demiray_ece_vroh_2020.pdf


Poster ID: 96

11:00 - 12:00

Pietan, Lucas



Differential Gene Expression in Rat Cortical Neurons Exposed to Therapeutic Levels of Lithium

Authors: Pietan, Lucas; Reichman, Rachel; Keen, Henry; Merrill, Ronald; Recka, Nicole; Chimenti, Michael; Strack, Stefan; Braun, Terry; Casavant, Thomas; Willour, Virginia

Lithium is one of the leading treatments for patients suffering from bipolar disorder and suicidal behavior. It is unclear how lithium exerts its effects on patients. In this study, we performed a genome-wide investigation of the change in gene expression following treatment of LiCl in rat cortical neurons using RNA sequencing. Our differential expression analysis found 159 significant DEGs with pathway analysis resulting in 51 significant Gene Ontology terms. Two of the top GO terms overlap two pathways involved in the most intriguing and investigated hypotheses in suicide and bipolar disorder research and contributes to our understanding in these fields.

Three-Minute Video: https://youtu.be/9q2qd4dF5wY

Presentation: pietan_bme_vroh_2020.pdf


Poster ID: 97

10:00 - 11:00

Saktrakulkla, Panithi



PCBs in Food

Authors: Saktrakulkla, Panithi; Lan, Tuo; Hua, Jason; Marek, Rachel F; Thorne, Peter S; and Hornbuckle, Keri C

We measured the concentrations of 205 polychlorinated biphenyl (PCB) congeners in 26 food items: beef steak, butter, canned tuna, catfish, cheese, eggs, french fries, fried chicken, ground beef, ground pork, hamburger, hot dog, ice cream, liver, luncheon meat, margarine, meat-free dinner, milk, pizza, poultry, salmon, sausage, shrimp, sliced ham, tilapia, and vegetable oil. Using Diet History Questionnaire II, we calculated the PCB dietary exposure in mothers and children participating in the AESOP Study in East Chicago, Indiana, and Columbus Junction, Iowa. Salmon had the highest concentration followed by canned tuna, but fish is a minor contributor to exposure. Other animal proteins are more important sources of PCB dietary exposure in this study population. Despite the inclusion of few congeners and food types in previous studies, we found evidence of a decline in PCB concentrations over the last 20 years. We also found strong associations of PCB congener distributions with Aroclors in most foods and found manufacturing byproduct PCBs, including PCB11, in tilapia and catfish. The reduction in PCB levels in food indicates that dietary exposure is comparable to PCB inhalation exposures reported for the same study population.

Three-Minute Video: https://youtu.be/HNF5ZuaaF-w

Presentation: saktrakulkla_iihr_vroh_2020.pdf