NLM IRP Seminar Schedule

UPCOMING SEMINARS

RECENT SEMINARS


The NLM IRP holds a public weekly seminar series for NLM trainees, staff scientists, and investigators to share details on current and exciting research projects at NLM. Seminars take place on Tuesdays at 11:00 AM, EST and some Thursdays at 3:00 PM, EST. Seminars are held in the B2 Library of Building 38A on the main NIH campus in Bethesda, MD. Due to the Covid-19 pandemic, all seminars are currently held virtually.

To schedule a seminar, click the “Schedule Seminar” button to the right, select an appropriate date on the calendar to sign up, and then complete the form. You will need an NIH PIV card to access the “Schedule Seminar” page.

Please include seminars by invited visiting scientists in the NLM IRP seminar series. These need not be on a Tuesday or Thursday.

If you would like to schedule a seminar by a visiting scientist, click the “Schedule Seminar” and complete the form. Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions. Please follow this link to subscribe/unsubscribe to/from the NLM IRP seminar mailing list.

Titles and Abstracts for Upcoming Seminars


(based on the current date)

Harutyun Saakyan
May 28, 2024 at 11 a.m.

Simulation of protein fold evolution with atomistic details

We developed a new approach that simulates protein fold evolution with atomistic details, providing insights into mechanisms of large-scale conformational changes during evolution. For many years, the origin and evolution of protein folds remained among the most challenging problems in biology. Although many hypotheses offer plausible scenarios of protein evolution, realistic simulation of this process was not feasible because of the lack of fast and reliable approaches for protein structure prediction, a situation that changed with the advent of AlphaFold. Our method introduces random mutations in a population of proteins, evaluates the effect of mutations on protein structure, and selects a new set of proteins for further mutagenesis. Repeating this process iteratively allows tracking the evolutionary trajectory of a changing protein fold that evolves under selective pressure, which can be protein fold stability, interaction with another protein, or other arbitrary features shaping the fitness landscape. We used protein fold evolution simulation (PFES) to demonstrate how protein folds could evolve from random amino acid sequences in a monomeric or homooligomeric state or in a complex with an interacting partner. We demonstrated the stability of the proteins that evolved in our simulations with physics-based methods even if these proteins do not exist in nature and their structure cannot be predicted with AlphaFold. PFES provides a complete evolutionary history from simulations that describes all intermediate states at the sequence and structure levels that can be used to test different hypotheses on protein fold evolution.

Deepak Gupta
May 30, 2024 at 3 p.m.

Towards Answering Health-related Questions from Medical Videos: Datasets and Approaches

The increase in the availability of online videos has transformed the way we access information and knowledge. A growing number of individuals now prefer instructional videos as they offer a series of step-by-step procedures to accomplish particular tasks. Instructional videos from the medical domain may provide the best possible visual answers to first aid, medical emergency, and medical education questions. This talk focuses on answering health-related questions asked by health consumers by providing visual answers from medical videos. The scarcity of large-scale datasets in the medical domain is a key challenge that hinders the development of applications that can help the public with their health-related questions. To address this issue, we first proposed a pipelined approach to create two large-scale datasets: HealthVidQA-CRF and HealthVidQA-Prompt. Leveraging the datasets, we developed monomodal and multimodal approaches that can effectively provide visual answers from medical videos to natural language questions. We conducted a comprehensive analysis of the results and outlined the findings, focusing on the impact of the created datasets on model training and the significance of visual features in enhancing the performance of the monomodal and multi-modal approaches for medical visual answer localization task.

Xiaofang Jiang
July 18, 2024 at 3 p.m.

Jiang Lab research updates

TBD