Flyer for UNT's 2022 Constitution Day webinar event that featured a wide-ranging conversation on data technology, surveillance, cybersecurity, social media, privacy, and information freedom in honor of Constitution Day. The event was a moderated panel presentation with speakers Amy K. Sanders, Associate Professor, School of Journalism and Media and School of Law, UT Austin; Scott H. Belshaw, founder and director, Cyber Forensics Lab, University of North Texas; Daniella Smith, Director of the Information Science Ph.D. program, University of North Texas; and moderated by Dr. Rafael Major and Dr. Catherine Borck, University of North Texas. A watch party for this webinar was held in 250H of Willis Library.

Article aims to identify other proteases that may activate FVII using zebrafish as a model.

Article describes how monitoring immunological response to physical stressors in a field setting is challenging because existing methods require a laboratory visit and traditional blood collection via venipuncture. The purpose of this study was to determine if our optimized dry blood spot (DBS) methodology yields sufficient total RNA to quantify the effect of Baker's Yeast Beta Glucan supplementation on post-exercise mRNA expression.

Video recording of UNT's 2022 Constitution Day webinar event that featured a wide-ranging conversation on data technology, surveillance, cybersecurity, social media, privacy, and information freedom in honor of Constitution Day. The event was a moderated panel presentation with speakers Amy K. Sanders, Associate Professor, School of Journalism and Media and School of Law, UT Austin; Scott H. Belshaw, founder and director, Cyber Forensics Lab, University of North Texas; Daniella Smith, Director of the Information Science Ph.D. program, University of North Texas; and moderated by Dr. Rafael Major and Dr. Catherine Borck, University of North Texas. A watch party for this webinar was held in 250H of Willis Library.

Article describes how the authors present an analysis of eight measures used for quantifying morphological complexity of natural languages. The measures they study are corpus-based measures of morphological complexity with varying requirements for corpus annotation.