Selected Publications

Inhibition of the DDX3 prevents HIV-1 Tat and cocaine-induced neurotoxicity by targeting microglia activation

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I gave this talk both internally to my lab group, as well as at the Clemson SIAM Graduate Student Seminar.

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I’m here in Seattle, WA attending the IEEE International Conference on Big Data. I’ll be presenting two recent works. The first, presents a new method to validate hypothesis generation systems. The second, uses that method to determine the quality of input papers needed to make good conclusions. With two papers in the same conference, I will be giving a double-length talk! If you’re around, I’ll be at the end of the L12 session Wednesday morning.

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I have the chance to present my work at the Google Ph.D. Intern Research Conference (PIRC). This poster represents all of the work we have added to the Moliere project since our original paper last year.

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HIV-1 Associated Neurocognitive Disorder (HAND) is commonly seen in HIV-infected patients. Viral proteins including Tat cause neuronal toxicity and is worsened by drugs of abuse. To uncover potential targets for anti-HAND therapy, we employed a literature mining system, MOLIERE. Here, we validated Dead Box RNA Helicase 3 (DDX3) as a target to treat HAND via a selective DDX3 inhibitor, RK-33. The combined neurotoxicity of Tat protein and cocaine was blocked by RK-33 in rat and mouse cortical cultures. Transcriptome analysis showed that Tat-activated transcripts include makers and regulators of microglial activation, and RK-33 blocked Tat-induced activation of these mRNAs. Elevated production of proinflammatory cytokines was also inhibited by RK-33. These findings show that DDX3 contributes to microglial activation triggered by Tat and cocaine, and DDX3 inhibition shows promise as a therapy for HAND. Moreover, DDX3 may contribute to the pathology of other neurodegenerative diseases with pathological activation of microglia.
biorxiv.org, 2019.

Drive-by Health Monitoring utilizes accelerometers mounted on commercial and civilian vehicles to gather dynamic response data that can be used to continuously evaluate the health of bridges faster and with less equipment than traditional structural health monitoring practices. Because vehicles and bridges create a coupled system, vehicle acceleration data contains information about bridge frequencies that can be used as health indicators.
engrxiv.org, 2018.

The potential for automatic hypothesis generation (HG) systems to improve research productivity keeps pace with the growing set of publicly available scientific information. But as data becomes easier to acquire, we must understand the effect different textual data sources have on our resulting hypotheses. Are abstracts enough for HG, or does it need full-text papers? How many papers does an HG system need to make valuable predictions? How sensitive is a general-purpose HG system to hyperparameter values or input quality? What effect does corpus size and document length have on HG results?
BigData’18, 2018.

Projects

MOLIERE: Automatic Biomedical Hypothesis Generation

We discover potential connections within existing scientific literature. Currently, we are preparing MOLIERE for large-scale public usage.

Bridge Health Classification With Automotive Sensing

We classify bridge health using Support Vector Regression and other Machine Learning Techniques. In partnership with Clemson Civil Engineers.

Learn to Program Python

An introductory video series for people absolutly new to programming. Learn the basics of programming!

Rapid Replication of Multi-Petabyte File Systems

Distsync is a parallel storage system syncronization utility which leverages cluster computing capabilities to unify large out-of-sync distributed file systems.

Contact

  • justin@sybrandt.com
  • McAdams Hall Office 224. McMillan Rd, Clemson, SC 29631
  • Email for appointment