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Theoretical Assessment of Plant Alkaloids for Potential Toxicity and Environmental Impacts
Get PDFAlkaloids are secondary plant metabolites that serve as a defense mechanism against herbivores and other predators. There are over 12,000 alkaloids known. Plant alkaloids exhibit a great cause for concern as they are often found in various food sources such as wheats, cereals, spices, herbs, and honey. The toxicity of plant alkaloids poses as a significant danger to both wildlife and humans. Genotoxicity, carcinogenicity, pneumotoxicity and, more notably, hepatotoxicity are all associated with the chronic consumption of plant alkaloids. It is believed that the physical and chemical properties of plant alkaloids attribute to their toxicity. The objective of this study was to provide a theoretical analysis of the physical, chemical, and toxicological characteristics of 64 plant alkaloids through in silico toxicology or computational methods. Chemical databases such as PubChem, Chemspider, ChemIDplus, and the NIH provide the initial chemical and physical properties of each alkaloid. Spartan ’18 Parallel Suite and T.E.S.T. by the EPA were used to further provide additional chemical, physical, and toxicological characteristics. It was displayed how positive log P values can indicate the bioaccumulation of plant alkaloids in the body thus producing toxicity. Whereas negative log P values will likely cause the plant alkaloid to excrete from the body following consumption. The LD50 value proved to be another indicator of toxicity for plant alkaloids. Quantitative structure-activity relationship (QSAR) and quantum mechanical parameters such as density functional model, highest-occupied molecular orbital, lowest-unoccupied molecular orbital, and electrostatic potential provided additional information as to why computational methods are useful when predicting toxicity. Furthermore, this study examined the beneficial and harmful characteristics of plant alkaloids as well as its environmental impact. The final aim of this study was to describe why theoretical tests and computational methods are valuable in predicting the toxicity of chemicals
Freeman Honors Newsletter, Spring/Summer 2021 Issue
Get PDFSpring/Summer 2021 Newsletterhttps://digitalscholarship.tsu.edu/freeman_honors/1004/thumbnail.jp
JohnWBlandScrapbook-Page 10
No full texthttps://digitalscholarship.tsu.edu/bland_scrapbook/1010/thumbnail.jp
JohnWBlandScrapbook-Page 30
No full texthttps://digitalscholarship.tsu.edu/bland_scrapbook/1031/thumbnail.jp
Writing 101, Publishing, Marketing
No full textDaphine Priscilla Jack: Author, Criminal Justice, Reform Advocate
Carlos Wallace: Best-Selling Author, Filmmaker, Co-creator of VR Eval
Liz Fablus-Wallace: Best-Selling Author, Publisher Publicist
Avery Washington: Best-Selling Author Poet Family, Advocate, Publisher, Podcaster, Speaker
Lynn C. Page: Author and Illustrato
Teaching, Learning and Academic Integrity During the Pandemic
Get PDFAbstract
The COVID 19 pandemic is responsible for instructional delivery changes in the higher education environment. The forced closing of many traditional classrooms, and teaching virtually, is taking the transfer of knowledge to students into a different realm. Adjustments must be made by students and professors alike to ensure that students continue to learn. However, as institutions transition away from the face to face classroom setting to the virtual platform, opportunities for cheating increases and changes to teaching and assessment should be implemented. Included among the innovations and adjustments must be testing and assessment methods that will hopefully guarantee learning but minimize the opportunity to cheat and ensure academic integrity among students.
Keywords: virtual, assessment, pandemic, integrit
The Good, Bad and Ugly of Innovations in Human Services Administration: Evidence from New York Counties
Get PDFCounties are often seen as “forgotten” and understudied governments in the family of local jurisdictions. The recent growing demand for public assistance led to a renewed interest in county governance, specifically in relation to the administration of social services. In order to cope with a post-recession growing workload, many counties began using technology and other innovative methods to serve clients effectively. This research seeks to learn and make sense of innovation practices in providing social safety services in several counties in New York State. In particular, we attempt to respond to the following questions. First, what types of innovations are taking place and in which social safety net programs? Second, how differently are social safety programs being managed as a result of these reforms and what are the consequences? The findings of this study have important implications for studies on counties, innovations, and the delivery of social safety programs
Interdistrict and Charter School Mobility in Arizona: Understanding the Dynamics of Public School Choice
Get PDFWe investigate the mobility patterns of elementary students enrolled in Arizona’s traditional public school districts and charter schools. We address interdistrict and charter school mobility simultaneously. Most student movement is interdistrict or between school districts. In Arizona, interdistrict mobility has played a greater role in creating and sustaining the “educational market” than charter schools. There is also a substantial amount of student movement from charter schools to school districts. Regression analyses suggested that the relationship between demographic and achievement variables and the different types of student mobility differed across the two sectors. We also document regional differences in mobility patterns, which indicate that education markets vary considerably across and even within local contexts
Patient-Provider Communication Training Models for Interactive Speech Devices
No full textPatient-provider communication plays a major role in healthcare with its main goal being to improve the patient’s health and build a trustworthy relationship between the patient and the doctor. Provider’s efficiency and effectiveness in communication can be improved through training in order to meet the essential elements of communication that are relevant during medical encounters. We surmised that speech-enabled conversational agents could be used as a training tool. In this study, we propose designing an ontology-based interaction model that can direct software agents to train dental and medical students. We transformed sample scenario scripts into a formalized ontology training model that links utterances of the user and the machine that expresses patient-provider communication. We created two instance-based models from the ontology to test the operational execution of the model using a prototype software engine. The assessment revealed that the dialogue engine was able to handle about 62% of the dialogue links. Future direction of this work will focus on further enhancing and capturing the features of patient-provider communication, and eventual deployment for pilot testing
Effects of nonlinearity and network architecture on the performance of supervised neural networks
Get PDFThe nonlinearity of activation functions used in deep learning models is crucial for the success of predictive models. Several simple nonlinear functions, including Rectified Linear Unit (ReLU) and Leaky-ReLU (L-ReLU) are commonly used in neural networks to impose the nonlinearity. In practice, these functions remarkably enhance the model accuracy. However, there is limited insight into the effects of nonlinearity in neural networks on their performance. Here, we investigate the performance of neural network models as a function of nonlinearity using ReLU and L-ReLU activation functions in the context of different model architectures and data domains. We use entropy as a measurement of the randomness, to quantify the effects of nonlinearity in different architecture shapes on the performance of neural networks. We show that the ReLU nonliearity is a better choice for activation function mostly when the network has sufficient number of parameters. However, we found that the image classification models with transfer learning seem to perform well with LReLU in fully connected layers. We show that the entropy of hidden layer outputs in neural networks can fairly represent the fluctuations in information loss as a function of nonlinearity. Furthermore, we investigate the entropy profile of shallow neural networks as a way of representing their hidden layer dynamics