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How data & AI are transforming cancer detection, prevention, and treatment.

James Ragsdale, doctoral student and instructor, Social Psychology Interdisciplinary Graduate Program, Graduate School, Â鶹ӳ»­

We will explore the ethical dilemmas that shape scientific research, from historical controversies to contemporary challenges in data science. Biases, social pressures, and small but consequential decisions all influence the integrity of scientific findings. Through case studies, ranging from Semmelweis to climate change, and real-world examples like vaccine misinformation and the replication crisis, we will examine how cognitive biases contribute to ethical lapses and public distrust in science. Finally, we will discuss strategies to enhance transparency and reduce bias in data-driven fields, ensuring a more trustworthy foundation for scientific inquiry.

Moderator

Albert Gonzales, Ph.D., assistant professor, Department of Physiology and Cell Biology, Â鶹ӳ»­ School of Medicine

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Panelists

Karla Hernandez, director, Biotechnology Program and Teaching Assistant Professor, Biochemistry and Molecular Biology, University of Â鶹ӳ»­ Reno

AI: Use responsibly

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AI is here... Now what?

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James Ragsdale, Doctoral Student and Instructor, Social Psychology Interdisciplinary Graduate Program, Graduate School, Â鶹ӳ»­

Trust the data? Bias and the fragility of scientific integrity

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Ankita Shukla, assistant professor, Computer Science & Engineering, Â鶹ӳ»­

AI: Social good

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Nicholas Seltzer, Ph.D., Associate Professor, Department of Political Science at Â鶹ӳ»­

Are we cyborgs yet?

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AI for applications in Eco-conservation and Science.

Details pending.

M. Griswold, Ph.D., program director, Health Workforce Research, School of Medicine, Â鶹ӳ»­

Â鶹ӳ»­ Instant Atlas website

A presentation of county level data presented on the website with an emphasis on social determinants of health and population health data. The project management plan which involves training undergraduate and graduate students in data base collection and maintenance with the resulting publication of the biennial Â鶹ӳ»­ Rural and Frontier Health Data Book for the legislature and health policy makers.

Lukasz Sznajder, Ph.D., assistant professor, Department of Chemistry and Biochemistry, University of Â鶹ӳ»­, Las Vegas

RNA toxicity in neuropsychiatric disorders

Tong Zhou, Ph.D., assistant professor, Department of Physiology and Cell Biology, School of Medicine, Â鶹ӳ»­

The expanding territory of small noncoding RNAs

Emerging evidence indicates that the territory of small noncoding RNAs keeps expanding. In comparison with the well-known microRNAs, noncanonical small noncoding RNAs, such as, tRNA derived small RNAs (tsRNAs) and rRNA derived small RNAs (rsRNAs), have be discovered in both prokaryotes and eukaryotes and found to play various roles in biological processes (e.g., epigenetic regulation, translational inhibition, immune response, and cell differentiation). Digging into small RNA sequencing data, with a focus on these noncanonical small RNAs will bring us novel insights into RNA biology research. The superior information capacity within these small RNAs potentially contributes to future molecular medicine.

Anna Panorska, Ph.D., professor, Department of Mathematics & Statistics, College of Science, Â鶹ӳ»­

Machine learning for the classification of bacterial virulence via their nanomotion

The World Health Organization highlights the urgent need to address the global threat posed by antibiotic-resistant bacteria. Efficient and rapid detection of bacterial response to antibiotics and their virulence state is crucial for the effective treatment of bacterial infections. However, current methods for investigating bacterial antibiotic response and metabolic state are time-consuming and lack accuracy. To address these limitations, we propose a novel method for classifying bacterial virulence based on statistical analysis of nanomotion recordings. We demonstrated the method by classifying living Bordetella pertussis bacteria in the virulent or avirulence phase, and dead bacteria, based on their cellular nanomotion signal. Our method offers significant advantages over current approaches, as it is faster and more accurate. Additionally, its versatility allows for the analysis of cellular nanomotion in various applications beyond bacterial virulence classification.

Wenrong Cao, Ph.D., assistant professor, Department of Geological Sciences and Engineering, School of Science, Â鶹ӳ»­

Continental Magmatism Drives the Global Weathering Budget: New Results from a Spatiotemporally Explicit Perspective

Chemical weathering controls the long-term climate of the Earth. Rapidly eroding continental magmatic belts are the fastest weathering zones on the planet, but their contribution to global weathering and subsequently climate evolution over the Phanerozoic Eon remains elusive. Here, we collate existing datasets into a database of ~54,000 dated isotope samples from magmatic belts. We place these data into their proper paleogeographic and paleoclimatic context to reconstruct the evolution of the global flux and isotope composition from the weathering of continental magmatic belts. Our approach provides an interpretable framework to quantify the independent or coupled roles of tectonics, paleogeography and paleoclimate in shaping the long-term Earth's surface conditions.

Sujung Lim, Ph.D., post-doctoral fellow, School of Life Sciences, University of Â鶹ӳ»­, Las Vegas

Using curated HMMs to understand the diversity and distribution of microbial respiratory pathways

While modern high-throughput sequencing technology has provided us with vast quantities of data allowing us to probe the microbial diversity of many discrete environments, more granular observations are necessary to allow study of the evolutionary ecology of core processes such as microbial respiration. We developed curated hidden Markov models (HMMs) from experimentally validated respiratory protein sequences to search for homologs within metagenomic sequence data from diverse environments ranging from the human gut to terrestrial springs. In conjunction with geochemical context, these data give insight into the mechanisms of microbial adaptation to distinct environmental pressures.

Presented by guest speaker Chris Golias, Ph.D., Senior User Experience Researcher, Gemini Internationalization, Google

We examine LLM deployment through African lenses, utilizing and reflecting on ethnographic methods to engage with the continent’s unique technolinguistic landscape. Drawing from primary research in Ethiopia, Ghana, Kenya, Nigeria, and South Africa, we consider how LLMs are integrated and upheld within local contexts. We unearth instances where LLMs could perpetuate digital colonialism or exacerbate existing sociopolitical tensions, as well as how they are contested and adapted. We emphasize the imperative for ethnographic insights in LLM research and provide a detailed framework, both to counteract oversimplification and the misinterpretation of complex interactions and to support culturally informed deployments that enhance local agency.

Chris is also participating in the panel discussion “Career Paths in Data Science – Advice from Industry Experts” on Tuesday, Feb. 18 at 2:30 p.m.\

Golias is a technology anthropologist, currently with Google, who has conducted applied anthropological research across various areas including retail, healthcare, indigenous rights, substance use, ecommerce, governance, machine learning, localization and information technology. He holds a Ph.D. in anthropology from the University of Pennsylvania.