AI Research
TACC’s Supercomputers Power AI-driven Research Uncovering Rapid Genomic Shifts in Human Evolution
The mystery of how we became human still drives scientific inquiry, especially among researchers probing the ancient genetic shifts that gave rise to our complex brains, capacity for language, and upright posture—traits that set us apart from our closest ape relatives.
“What we found in our study was that a range of different traits—skeletal, neuropsychiatric, pigmentation, cholesterol synthesis, and so on—were accelerated at different points in the history of humans,” said Vagheesh Narasimhan, who co-authored a study published in Cell Genomics in January 2025. Narasimhan is an assistant professor in the College of Natural Sciences at The University of Texas at Austin (UT Austin).
The study integrated three powerful data sources: ancient DNA from fossils; 3D MRI scans from hundreds of thousands of participants in the UK Biobank revealing the structure of the brain, skeleton, and major organs; and comparative functional genomics that mapped how the human genome aligns—and diverges—from that of chimpanzees, orangutans, and other great apes. By layering these datasets, researchers were able to uncover where bursts of human-specific evolutionary changes and genetic mutations likely occurred.
“We looked at gene expression and gene regulation through embryo development between humans and other primates, particularly Rhesus macaque,” Narasimhan said. “We then carried out genomic enrichment analysis, which determines whether the overlap between our evolutionary annotation and our annotation associated with traits is more than we expect by chance compared to the genome wide average.”
Narasimhan and colleagues leveraged this method to look at whether sections of the human genome associated with traits had bursts at particular time intervals.
With advanced computing power from the Texas Advanced Computing Center (TACC), scientists were able to identify when key human traits may have undergone major evolutionary changes. TACC supported the research by awarding Narasimhan allocations on its Frontera and Lonestar6 supercomputers, along with data storage and management resources on the Corral system.
Lonestar6 helped the researchers process 80,000 3D MRI images of the heart, brain, liver, and pancreas, as well as hip, knee, spine, and whole-body X-ray scans from the UK Biobank.
“We trained AI models for segmentation and classification on the imaging data using TACC GPU (graphics processing unit) resources, particularly Lonestar6, which has a large number of GPUs that were capable of processing this type of data,” Narasimhan said.
“For carrying out genomic analyses, we are heavy users of the CPU (central processing unit) infrastructure on Frontera, largely because the genome is a very large data problem,” he added. “Having a large number of CPU nodes on a supercomputing cluster like Frontera was tremendously useful to shrink compute time from a linear process to a parallel process and allow the study to happen.”
The HIPAA protections on TACC’s Corral data storage allowed Narasimhan to simultaneously compute on two different environments, Lonestar6’s GPUs and Frontera’s CPUs.
“It’s impossible to do this work without this integrated enterprise at TACC,” Narasimhan said.
Narasimhan is excited about the new GPU computing capacity with TACC’s AI-focused Vista supercomputer, which entered production in November 2024.
“We’re hoping to use Vista soon and continue our work,” he said. “TACC’s vision to keep pace with new data generation is transformative.”
A more recent study by Narasimhan published in April 2025 in the journal Science also acknowledges TACC support. It found genetic correlations between pelvic proportions and traits such as osteoarthritis, walking speed, and back pain, giving insight into facets of the obstetrical dilemma—the biological tradeoffs between the size of a mother’s birth canal and the brain of her child.
“To truly understand change in the human genome, we need massive amounts of data from a vast number of individuals to look at what’s happening in each of these three billion DNA bases,” Narasimhan said. “It’s a monumental data problem that can only be tackled by using supercomputing infrastructure.”
This article was originally published by TACC and is reprinted with permission.
AI Research
Radiomics-Based Artificial Intelligence and Machine Learning Approach for the Diagnosis and Prognosis of Idiopathic Pulmonary Fibrosis: A Systematic Review – Cureus
Artificial intelligence may take over some tasks and transform others, but one thing is certain: it’s reshaping the job market. Researchers at USC’s Information Sciences Institute (ISI) analyzed LinkedIn job postings and AI-related patent filings to measure which jobs are most exposed, and where those changes are happening first.
The project was led by ISI research assistant Eun Cheol Choi, working with students in a graduate-level USC Annenberg data science course taught by USC Viterbi Research Assistant Professor Luca Luceri. The team developed an “AI exposure” score to measure how closely each role is tied to current AI technologies. A high score suggests the job may be affected by automation, new tools, or shifts in how the work is done.
Which Industries Are Most Exposed to AI?
To understand how exposure shifted with new waves of innovation, the researchers compared patent data from before and after a major turning point. “We split the patent dataset into two parts, pre- and post-ChatGPT release, to see how job exposure scores changed in relation to fresh innovations,” Choi said. Released in late 2022, ChatGPT triggered a surge in generative AI development, investment, and patent filings.
Jobs in wholesale trade, transportation and warehousing, information, and manufacturing topped the list in both periods. Retail also showed high exposure early on, while healthcare and social assistance rose sharply after ChatGPT, likely due to new AI tools aimed at diagnostics, medical records, and clinical decision-making.
In contrast, education and real estate consistently showed low exposure, suggesting they are, at least for now, less likely to be reshaped by current AI technologies.
AI’s Reach Depends on the Role
AI exposure doesn’t just vary by industry, it also depends on the specific type of work. Jobs like software engineer and data scientist scored highest, since they involve building or deploying AI systems. Roles in manufacturing and repair, such as maintenance technician, also showed elevated exposure due to increased use of AI in automation and diagnostics.
At the other end of the spectrum, jobs like tax accountant, HR coordinator, and paralegal showed low exposure. They center on work that’s harder for AI to automate: nuanced reasoning, domain expertise, or dealing with people.
AI Exposure and Salary Don’t Always Move Together
The study also examined how AI exposure relates to pay. In general, jobs with higher exposure to current AI technologies were associated with higher salaries, likely reflecting the demand for new AI skills. That trend was strongest in the information sector, where software and data-related roles were both highly exposed and well compensated.
But in sectors like wholesale trade and transportation and warehousing, the opposite was true. Jobs with higher exposure in these industries tended to offer lower salaries, especially at the highest exposure levels. The researchers suggest this may signal the early effects of automation, where AI is starting to replace workers instead of augmenting them.
“In some industries, there may be synergy between workers and AI,” said Choi. “In others, it may point to competition or replacement.”
From Class Project to Ongoing Research
The contrast between industries where AI complements workers and those where it may replace them is something the team plans to investigate further. They hope to build on their framework by distinguishing between different types of impact — automation versus augmentation — and by tracking the emergence of new job categories driven by AI. “This kind of framework is exciting,” said Choi, “because it lets us capture those signals in real time.”
Luceri emphasized the value of hands-on research in the classroom: “It’s important to give students the chance to work on relevant and impactful problems where they can apply the theoretical tools they’ve learned to real-world data and questions,” he said. The paper, Mapping Labor Market Vulnerability in the Age of AI: Evidence from Job Postings and Patent Data, was co-authored by students Qingyu Cao, Qi Guan, Shengzhu Peng, and Po-Yuan Chen, and was presented at the 2025 International AAAI Conference on Web and Social Media (ICWSM), held June 23-26 in Copenhagen, Denmark.
Published on July 7th, 2025
Last updated on July 7th, 2025
The Spanish Society of Medical Radiology (SERAM) has collaborated with six other scientific societies to develop an AI-supported urology clinical decision-making project called Uro-Oncogu(IA)s.
The initiative produced an algorithm that will “reduce time and clinical variability” in the management of urological patients, the society said. SERAM’s collaborators include the Spanish Urology Association (AEU), the Foundation for Research in Urology (FIU), the Spanish Society of Pathological Anatomy (SEAP), the Spanish Society of Hospital Pharmacy (SEFH), the Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM), and the Spanish Society of Radiation Oncology (SEOR).
SERAM Secretary General Dr. MaríLuz Parra launched the project in Madrid on 3 July with AEU President Dr. Carmen González.
On behalf of SERAM, the following doctors participated in this initiative:
- Prostate cancer guide: Dr. Joan Carles Vilanova, PhD, of the University of Girona,
- Upper urinary tract guide: Dr. Richard Mast of University Hospital Vall d’Hebron in Barcelona,
- Muscle-invasive bladder cancer guide: Dr. Eloy Vivas of the University of Malaga,
- Non-muscle invasive bladder cancer guide: Dr. Paula Pelechano of the Valencian Institute of Oncology in Valencia,
- Kidney cancer guide: Dr. Nicolau Molina of the University of Barcelona.
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