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2022 Regional Government Information Librarians (REGIL) Meeting
Get PDFDiscussion of the microfiche phaseout by the GPO, National Collection Service areas, and investigations into an all digital FDLP.Minutes of the April 20th, 2022 meeting of the Regional Libraries
Fluorescent Detection of Secondary Structure in Pancreatic Cancer
Get PDFPancreatic cancer has one of the highest mortality rates among all cancers largely due to the late-stage onset of symptoms and the lack of early detection methods. Extracellular vesicles (EVs) could serve the potential as the next biomarker for pancreatic cancer detection because they directly reflect the state and composition of their parent cells. This work aimed to generate a high-throughput assay by combining immunoprecipitation of EVs with ?-sheet staining by Thioflavin T (ThT) in a 96-well plate based off previous findings that ThT could be used to measure the elevated ?-sheet richness found in tumor-derived EVs. This research tested four different immunoprecipitation methods in a 96-well plate. Although this work was not able to successfully create a high-throughput assay, it offers insight to increase fluorescent sensitivity by using a fluorescent microscope and optimizing immunocapture of EVs by developing an improved mixing method in 96-well plate
Aspirin and Statin Use for Primary Prevention of Cardiovascular Disease
Get PDFCardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States and aspirin and statins are well-known medications associated with CVD prevention. There are well-known benefits of aspirin for secondary prevention of CVD, but aspirin?s role in primary prevention remains controversial. The decision to start aspirin for primary prevention is individualized to the specific patient and situation. Statins are a drug used as first-line therapy in cholesterol management, though there is a complicated relationship with adherence due to real and/or perceived safety issues associated with statin use. The decision to start statins needs to be determined on an individualized basis.
The United States Preventive Services Task Force (USPSTF) has level B recommendations for low-dose aspirin (81 mg) and level B recommendation for statins in primary prevention of CVD. However, preliminarily updated recommendations for aspirin use in late 2021 are proposing the decision to change aspirin use to a level C recommendation. In addition, the American Heart Association (AHA) and American College of Cardiology (ACC) developed a calculator in 2013 to determine a patient?s 10-year CVD risk. The guidelines coupled with the risk calculator offers providers a valuable decision-making tool. However, despite available guidelines and the calculator, aspirin and statin prescription and adherence remains suboptimal.
The purpose of the project was successful adoption of the 2016 USPSTF guideline on aspirin and statin use for primary prevention of CVD by North Dakota State University (NDSU) staff participating in a NDSU Health Screening. The screening collected participant data, recorded data into the calculator, and provided recommendations based off the USPSTF guidelines for participants to discuss with their primary care provider (PCP).
Evaluations were performed through use of post-implementation surveys. Results demonstrated proper participant use of aspirin and statins according to USPSTF guidelines, with a majority expressing awareness of the guidelines. Participants reported a positive viewpoint of the calculator and intent to provide results to their PCP. Conclusively, the project supports use of the 2016 USPSTF guidelines regarding the use of aspirin and statins for primary prevention of CVD along with the risk calculator in health screenings and primary care
Using Artificial Intelligence to Derive a Public Transit Risk Index
Get PDFRaj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).A terrorist attack on the public transportation system of a city can cripple its economy. Uninformed investments in countermeasures may result in a waste of resources if the risk is negligible. However, risks are difficult to quantify in an objective manner because of uncertainties, speculations, and subjective assumptions. This study contributes a probabilistic model, validated by ten different machine learning methods applied to the fusion of six heterogeneous datasets, to objectively quantify risks at different jurisdictional scales. The risk index is purposefully simple to quickly inform a proportional prioritization of resources to make fair investment decisions that stakeholders can easily understand, and to guide policy formulation. The main finding is that the risk indices among public transit jurisdictions in the United States distribute normally. This result enables agencies to evaluate the quality of their risk index calculations by detecting an outlier or a large deviation from the expected value.https://www.ugpti.org/about/staff/viewbio.php?id=7
Comparison of Non-Learned and Learned Molecule Representations for Catalyst Discovery
Get PDFCatalyst discovery is one very important task in storing renewable energy to address climate change and energy scarcity globally. Catalyst candidates can be represented by molecular descriptors and also can be modeled by graphs. Properties of catalyst candidates can be calculated by Density Functional Theory (DFT). Machine learning algorithms are applied to predict properties of catalyst candidates because DFT is computationally expensive. However, machine learning algorithms cannot operate over some standard molecular formats. Therefore, to represent molecules in the format that is required by machine learning algorithms is the primary task to tackle. Thus, this paper compared non-learned and learned representation methods. Accuracy of each representation method using RMSE are provided and discussed. The results show that the learned representations perform in a more stable manner than non-learned representations regardless of the linear models used for the downstream task
Stochastic Processes, and Development of the Barndorff-Nielsen and Shephard Model for Financial Markets
Get PDFIn this paper, we introduce Brownian motion, and some of its drawbacks in connection to the financial modeling. We then introduce geometric Brownian motion as the basis for European call option pricing as we navigate our way through the Black-Scholes-Merton equation. L?vy Processes round out the background information of the paper as we discuss Poisson and compound Poisson processes and the pricing of European call options using the stochastic calculus of jump processes. Ornstein-Uhlenbeck processes are then constructed. Finally we review and analyze the Barndorff-Nielsen and Shepard model. We provide its application to price European call options using the fast Fourier transform and the direct integration method
Welcome to the (Writing Program) Archives: Making a Case for Writing Program Material Curation
Get PDFWith this project, I examined archival documents from the North Dakota State University First-Year Writing Program that were created by a previous writing program administrator, Amy Rupiper Taggart, with the goal of reviewing and understanding the materials through the lens of writing program administration, organizing them using archival theories and concepts, documenting and displaying that organization through the use of a finding aid, then bringing together the fields of archival theory and writing program administration to make recommendations. Through the process of exploring the materials a number of key findings emerged, and these led directly into my considerations and recommendations about deciding what to keep and discard, how material should be organized and maintained in a usable format, and how WPAs should approach the archiving of material that requires contextual knowledge or contains private information, and a discussion of three key considerations writing program administrators can use when assessing materials
Perspectives on Using Connected Vehicles for Transportation Infrastructure Condition Monitoring
Get PDFRaj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The condition of surface transportation infrastructure directly affects the economic health of a nation. However, it is difficult to justify the large sums of money needed to extend current methods to monitor all the multimodal infrastructure. The convergence of connected vehicle and cloud computing technologies presents an opportunity to automate the collection of ride quality and imagery data to continuously assess the condition of all roadways and railways. This paper presents several perspectives to help policy and standardization initiatives promote adoption.https://www.ugpti.org/about/staff/viewbio.php?id=7
Reducing Risks by Transporting Dangerous Cargo in Drones
Get PDFRaj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The transportation of dangerous goods by truck or railway multiplies the risk of harm to people and the environment when accidents occur. Many manufacturers are developing autonomous drones that can fly heavy cargo and safely integrate into the national air space. Those developments present an opportunity to not only diminish risk but also to decrease cost and ground traffic congestion by moving certain types of dangerous cargo by air. This work identified a minimal set of metropolitan areas where initial cargo drone deployments would be the most impactful in demonstrating the safety, efficiency, and environmental benefits of this technology. The contribution is a new hybrid data mining workflow that combines unsupervised machine learning (UML) and geospatial information system (GIS) techniques to inform managerial or investment decision making. The data mining and UML techniques transformed comprehensive origin?destination records of more than 40 commodity category movements to identify a minimal set of metropolitan statistical areas (MSAs) with the greatest demand for transporting dangerous goods. The GIS part of the workflow determined the geodesic distances between and within all pairwise combinations of MSAs in the continental United States. The case study of applying the workflow to a commodity category of dangerous goods revealed that cargo drone deployments in only nine MSAs in four U.S. states can transport 38% of those commodities within 400 miles. The analysis concludes that future cargo drone technology has the potential to replace the equivalent of 4.7 million North American semitrailer trucks that currently move dangerous cargo through populated communities.The author conducted this work with support from North Dakota State University and the Mountain-Plains Consortium, a University Transportation Center funded by the U.S. Department of Transportation. Grant Number: 69A3551747108.https://www.ugpti.org/about/staff/viewbio.php?id=7