Chemistry
https://hdl.handle.net/1808/97
2024-03-28T12:45:03Z
2024-03-28T12:45:03Z
Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools
Desaire, Heather
Chua, Aleesa E.
Isom, Madeline
Jarosova, Romana
Hua, David
https://hdl.handle.net/1808/34745
2023-08-17T06:07:12Z
2023-06-21T00:00:00Z
Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools
Desaire, Heather; Chua, Aleesa E.; Isom, Madeline; Jarosova, Romana; Hua, David
ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like “but,” “however,” and “although.” With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.
2023-06-21T00:00:00Z
Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools
Desaire, Heather
Chua, Aleesa E.
Isom, Madeline
Jarosova, Romana
Hua, David
https://hdl.handle.net/1808/34727
2023-08-15T06:07:52Z
2023-06-07T00:00:00Z
Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools
Desaire, Heather; Chua, Aleesa E.; Isom, Madeline; Jarosova, Romana; Hua, David
ChatGPT has enabled access to artificial intelligence (AI)-generated writing for the masses, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent. Addressing this need, we report a method for discriminating text generated by ChatGPT from (human) academic scientists, relying on prevalent and accessible supervised classification methods. The approach uses new features for discriminating (these) humans from AI; as examples, scientists write long paragraphs and have a penchant for equivocal language, frequently using words like “but,” “however,” and “although.” With a set of 20 features, we built a model that assigns the author, as human or AI, at over 99% accuracy. This strategy could be further adapted and developed by others with basic skills in supervised classification, enabling access to many highly accurate and targeted models for detecting AI usage in academic writing and beyond.
2023-06-07T00:00:00Z
Breaking Barriers: Diversity and Equity in Chemistry
Sun, Shuai
Kaiser, John
Meier, Alex
https://hdl.handle.net/1808/34661
2023-07-28T06:05:53Z
2023-07-01T00:00:00Z
Breaking Barriers: Diversity and Equity in Chemistry
Sun, Shuai; Kaiser, John; Meier, Alex
The field of chemistry has long been associated with the pursuit of objective facts and the uncovering of the building blocks of our universe. However, this view can often exclude the important role that diversity, equity, and inclusion (DEI) play in the advancement of scientific knowledge. By highlighting the contributions of minority chemists and integrating DEI principles into chemistry education, we can promote a more inclusive environment and foster greater understanding of the complex connections between chemistry and society.
In the first section, we provide a biography of each chemist, discussing their personal and professional lives and how their minority identity has interacted with their careers. The second section summarizes their research and accomplishments in the field of chemistry, emphasizing the importance of their work and the implications it has had on the broader scientific community. Finally, the third section explores how their research is related to the topics and contents taught in general chemistry, creating a connection between the material students learn in the classroom and the real-world applications of chemistry.
In recent years, there has been a growing recognition of the need to incorporate DEI into STEM education, and chemistry is no exception. Despite this, there remains a scarcity of learning materials that directly introduce diversity and equality in chemistry education. As a result, students may view chemistry as an isolated discipline that is removed from the broader community.
This book aims to challenge that perception by introducing readers to minority chemists, their research, and the ways in which their work is related to topics taught in general chemistry courses. By exploring the lives and research of chemists who come from diverse backgrounds, we hope to showcase the importance of diverse perspectives in the advancement of the field and inspire a new generation of scientists who embrace and promote DEI in their own work. Each chapter of this book is divided into three main sections, highlighting the personal and professional lives of these extraordinary individuals and demonstrating the impact their work has had on the field.
This book is being made available in both PDF and ePub formats for the convenience of the reader.
2023-07-01T00:00:00Z
Assessing Breast Cancer Molecular Subtypes Using Extracellular Vesicles’ mRNA
Hu, Mengjia
Brown, Virginia
Jackson, Joshua M.
Wijerathne, Harshani
Pathak, Harsh
Koestler, Devin C.
Nissen, Emily
Hupert, Mateusz L.
Muller, Rolf
Godwin, Andrew K.
Witek, Malgorzata A.
Soper, Steven A.
https://hdl.handle.net/1808/34588
2023-07-12T06:05:55Z
2023-01-01T00:00:00Z
Assessing Breast Cancer Molecular Subtypes Using Extracellular Vesicles’ mRNA
Hu, Mengjia; Brown, Virginia; Jackson, Joshua M.; Wijerathne, Harshani; Pathak, Harsh; Koestler, Devin C.; Nissen, Emily; Hupert, Mateusz L.; Muller, Rolf; Godwin, Andrew K.; Witek, Malgorzata A.; Soper, Steven A.
Extracellular vesicles (EVs) carry RNA cargo that is believed to be associated with the cell-of-origin and thus have the potential to serve as a minimally invasive liquid biopsy marker for supplying molecular information to guide treatment decisions (i.e., precision medicine). We report the affinity isolation of EV subpopulations with monoclonal antibodies attached to the surface of a microfluidic chip that is made from a plastic to allow for high-scale production. The EV microfluidic affinity purification (EV-MAP) chip was used for the isolation of EVs sourced from two-orthogonal cell types and was demonstrated for its utility in a proof-of-concept application to provide molecular subtyping information for breast cancer patients. The orthogonal selection process better recapitulated the epithelial tumor microenvironment by isolating two subpopulations of EVs: EVEpCAM (epithelial cell adhesion molecule, epithelial origin) and EVFAPα (fibroblast activation protein α, mesenchymal origin). The EV-MAP provided recovery >80% with a specificity of 99 ± 1% based on exosomal mRNA (exo-mRNA) and real time–droplet digital polymerase chain reaction results. When selected from the plasma of healthy donors and breast cancer patients, EVs did not differ in size or total RNA mass for both markers. On average, 0.5 mL of plasma from breast cancer patients yielded ∼2.25 ng of total RNA for both EVEpCAM and EVFAPα, while in the case of cancer-free individuals, it yielded 0.8 and 1.25 ng of total RNA from EVEpCAM and EVFAPα, respectively. To assess the potential of these two EV subpopulations to provide molecular information for prognostication, we performed the PAM50 test (Prosigna) on exo-mRNA harvested from each EV subpopulation. Results suggested that EVEpCAM and EVFAPα exo-mRNA profiling using subsets of the PAM50 genes and a novel algorithm (i.e., exo-PAM50) generated 100% concordance with the tumor tissue.
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry, copyright © 2023 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.analchem.3c00624.
2023-01-01T00:00:00Z