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  1. Heparin is an essential anticoagulant used for treating and preventing thrombosis. However, the complexity of heparin has hindered the development of a recombinant source, making its supply dependent on a vulnerable animal population. In nature, heparin is produced exclusively in mast cells, which are not suitable for commercial production, but mastocytoma cells are readily grown in culture and make heparan sulfate, a closely related glycosaminoglycan that lacks anticoagulant activity. Using gene expression profiling of mast cells as a guide, a multiplex genome engineering strategy was devised to produce heparan sulfate with high anticoagulant potency and to eliminate contaminating chondroitin sulfatemore »from mastocytoma cells. The heparan sulfate purified from engineered cells grown in chemically defined medium has anticoagulant potency that exceeds porcinederived heparin and confers anticoagulant activity to the blood of healthy mice. This work demonstrates the feasibility of producing recombinant heparin from mammalian cell culture as an alternative to animal sources.« less
    Free, publicly-accessible full text available January 1, 2023
  2. In engineering, students’ completion of prerequisites indicates an understanding of fundamental knowledge. Recent studies have shown a significant relationship between student performance and prior knowledge. Weak knowledge retention from prerequisite coursework can present challenges in progressive learning. This study investigates the relationship between prior knowledge and students’ performance over a few courses of Statics. Statistics has been considered as the subject of interest since it is the introductory engineering course upon which many subsequent engineering courses rely, including many engineering analysis and design courses. The prior knowledge was determined based on the quantitative and qualitative preparedness. A quiz set wasmore »designed to assess quantitative preparedness. The qualitative preparedness was assessed using a survey asking students’ subjective opinions about their preparedness at the beginning of the semester. Student performance was later quantified through final course grades. Each set of data were assigned three categories for grouping purposes to reflect preparedness: 1) high preparedness: 85% or higher score, 2) medium preparedness: between 60% and 85%, and 3) weak preparedness: 60% or lower. Pearson correlation coefficient and T-test was conducted on 129 students for linear regression and differences in means. The analysis revealed a non-significant correlation between the qualitative preparedness and final scores (p-value = 0.29). The data revealed that students underestimated their understanding of the prerequisites for the class, since the quantitative preparedness scores were relatively higher than the qualitative preparedness scores. This can be partially understood by the time gap between when prerequisites were taken and when the course under investigation was taken. Students may have felt less confident at first but were able to pick up the required knowledge quickly. A moderately significant correlation between students’ quantitative preparedness and course performance was observed (p -value < 0.05). Students with high preparedness showed > 80% final scores, with a few exceptions; students with weak preparedness also showed relatively high final scores. However, most of the less prepared students made significant efforts to overcome their weaknesses through continuous communication and follow-up with the instructor. Despite these efforts, these students could not obtain higher than 90% as final scores, which indicates that level of preparedness reflects academic excellence. Overall, this study highlights the role of prior knowledge in achieving academic excellence for engineering. The study is useful to Civil Engineering instructors to understand the role of students’ previous knowledge in their understanding of difficult engineering concepts.« less
    Free, publicly-accessible full text available July 26, 2022
  3. Free, publicly-accessible full text available November 1, 2022
  4. Free, publicly-accessible full text available December 1, 2022
  5. Monitoring water quality by detecting chemical and biological contaminants is critical to ensuring the provision and discharge of clean water, hence protecting human health and the ecosystem. Among the available analytical techniques, infrared (IR) spectroscopy provides sensitive and selective detection of multiple water contaminants. In this work, we present an application of IR spectroscopy for qualitative and quantitative assessment of chemical and biological water contaminants. We focus on in-line detection of nitrogen pollutants in the form of nitrate and ammonium for wastewater treatment process control and automation. We discuss the effects of water quality parameters such as salinity, pH, andmore »temperature on the IR spectra of nitrogen pollutants. We then focus on application of the sensor for detection of contaminants of emerging concern, such as arsenic and Per- and polyfluoroalkyl substances (PFAS) in drinking water. We demonstrate the use of multivariate statistical analysis for automated data processing in complex fluids. Finally, we discuss application of IR spectroscopy for detecting biological water contaminants. We use the metabolomic signature of E. coli bacteria to determine its presence in water as well as distinguish between different strains of bacteria. Overall, this work shows that IR spectroscopy is a promising technique for monitoring both chemical and biological contaminants in water and has the potential for real-time, inline water quality monitoring.« less
  6. Free, publicly-accessible full text available August 1, 2022
  7. Abstract The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model parameterizations, grid resolution, and the lack of appropriate observations with which to evaluate such simulations. The overarching goal of the Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C 3 LOUD-Ex) was to enhance our understanding of deep convective storm processes and their representation within numerical models. To address this goal, a field campaign was conductedmore »during July 2016 and May–June 2017 over northeastern Colorado, southeastern Wyoming, and southwestern Nebraska. Pivotal to the experiment was a novel “Flying Curtain” strategy designed around simultaneously employing a fleet of uncrewed aerial systems (UAS; or drones), high-frequency radiosonde launches, and surface observations to obtain detailed measurements of the spatial and temporal heterogeneities of cold pools. Updraft velocities were observed using targeted radiosondes and radars. Extensive datasets were successfully collected for 16 cold pool–focused and seven updraft-focused case studies. The updraft characteristics for all seven supercell updraft cases are compared and provide a useful database for model evaluation. An overview of the 16 cold pools’ characteristics is presented, and an in-depth analysis of one of the cold pool cases suggests that spatial variations in cold pool properties occur on spatial scales from O (100) m through to O (1) km. Processes responsible for the cold pool observations are explored and support recent high-resolution modeling results.« less