University of Missouri

4 related articles

medRxiv Oct 14, 2026
An agent emulates an elderly clopidogrel trial: 53 of 62 SQL checks met, calibrated risk ratio point estimate rises to 1.274
TrialCalibre1 links a clinical question, study design, and executable analysis into one reviewable chain, covering reference-trial replication, direct target-trial emulation, and BenchExCal, which joins benchmarking, extension, and calibration. On synthetic PCORnet-style claims it used ASCET as benchmark and extended the clopidogrel-versus-aspirin comparison to ages 81–90: 6,069 benchmark and 1,602 extension patients, extension risk ratio 0.848 (95% CI 0.569–1.184), calibrated to a point estimate of 1.274 (0.439–3.700) with a sensitivity interval still spanning 1. Two reviewers rated 53/62 and 50/62 assessable SQL checks met and 12/13 R checks met, with protocol and proxy disagreements remaining.
TrialCalibre1 links a clinical question, study design, and executable analysis into one reviewable chain, covering reference-trial replication, direct target-trial emulation, and BenchExCal, which joins benchmarking, extension, and calibration. On synthetic PCORnet-style claims it used ASCET as benchmark and extended the clopidogrel-versus-aspirin comparison to ages 81–90: 6,069 benchmark and 1,602 extension patients, extension risk ratio 0.848 (95% CI 0.569–1.184), calibrated to a point estimate of 1.274 (0.439–3.700) with a sensitivity interval still spanning 1. Two reviewers rated 53/62 and 50/62 assessable SQL checks met and 12/13 R checks met, with protocol and proxy disagreements remaining.
University of Missouri Xing Song Amir Habibdoust
Agentic Target Trial Emulation LLM Agent Workflow Benchmark, Expand, and Calibration (BenchExCal)
Nature Communications Oct 11, 2026
Cryo-ET membrane segmentation goes two-stage: Dice rises from 0.6381 to 0.8116, at 79.97 seconds per tomogram
Cell membrane segmentation in cryo-electron tomograms now runs through a two-stage fine-tuned pipeline: tomograms are sliced along Z and treated as video frames, and SAM2's memory encoder and memory attention track membranes across slices. Stage one produces an initial membrane prediction whose normalized logits are concatenated with the original slices before stage two emits the final mask. On 17 independent experimental tomograms with ground truth, Dice is 0.8116, IoU 0.7059, precision 0.8123 and recall 0.8430, all above Membrain-Seg and TARDIS, with AUPRC 0.8565; average runtime is 79.97 seconds per tomogram at 1312.1 MB of GPU memory.
Cell membrane segmentation in cryo-electron tomograms now runs through a two-stage fine-tuned pipeline: tomograms are sliced along Z and treated as video frames, and SAM2's memory encoder and memory attention track membranes across slices. Stage one produces an initial membrane prediction whose normalized logits are concatenated with the original slices before stage two emits the final mask. On 17 independent experimental tomograms with ground truth, Dice is 0.8116, IoU 0.7059, precision 0.8123 and recall 0.8430, all above Membrain-Seg and TARDIS, with AUPRC 0.8565; average runtime is 79.97 seconds per tomogram at 1312.1 MB of GPU memory.
University of Missouri Joel Selvaraj Jianlin Jack Cheng
bioRxiv Sep 18, 2026
An Open Field Phenomics Resource for Multimodal Maize Yield Prediction Across Divergent Environments
This work releases curated 2020-2021 Genomes to Fields imagery from 356 drone flights across 19 environments covering 1,180 maize hybrids, and uses functional principal components of vegetation index and weather trajectories together with genomic information for yield prediction and QTL mapping, finding that combined genomic and phenomic kernels raised held-out hybrid prediction to r = 0.501 when environments were represented in training and 0.408 when environments were also withheld, that accumulated growing degree days offered no consistent advantage over days after planting, that weather contributed modest task-dependent gains, and that NGRDI functional principal components repeatedly mapped to quantitative trait loci on chromosomes 3 and 7.
This work releases curated 2020-2021 Genomes to Fields imagery from 356 drone flights across 19 environments covering 1,180 maize hybrids, and uses functional principal components of vegetation index and weather trajectories together with genomic information for yield prediction and QTL mapping, finding that combined genomic and phenomic kernels raised held-out hybrid prediction to r = 0.501 when environments were represented in training and 0.408 when environments were also withheld, that accumulated growing degree days offered no consistent advantage over days after planting, that weather contributed modest task-dependent gains, and that NGRDI functional principal components repeatedly mapped to quantitative trait loci on chromosomes 3 and 7.
Donald Danforth Plant Science Center New York State College of Agriculture & Life Sciences University of Minnesota Texas A&M University University of Delaware University of Wisconsin–Madison Agricultural Research Service Purdue University West Lafayette Alejandro Castro Aviles Maninder P. Singh James C. Schnable Michael A. Gore Nick G. Duffield Natalia de León Jode W. Edwards M. Cinta Romay
Genomic Prediction Field Phenomics Quantitative Trait Locus Mapping Functional Principal Component Analysis Transformer Neural Process
medRxiv Sep 16, 2026
An AI Competency Framework for Emergency Medicine: A Multiphase Consensus Process
Using a Nominal Group Technique session (May 2025, n=6) and a two-round modified Delphi process (April–May 2026, expert panel n=13 across 12 academic medical centers, with 77% Round 1 and 100% Round 2 response rates), this study developed the first specialty-specific AI competency framework for a United States medical specialty, comprising 5 themes (Communicating about AI, Understanding appropriate use cases, Interacting with AI, AI risk management, Cognitive impacts of AI), 5 derived competencies (one-to-one theme-to-competency mapping endorsed by 12 of 13 panelists), 19 subthemes, and 10 retained clinical scenarios, with all themes, derived competencies, and individually rated subthemes meeting pre-specified consensus thresholds and 9 of 10 scenarios reaching consensus while 1 was retain
Using a Nominal Group Technique session (May 2025, n=6) and a two-round modified Delphi process (April–May 2026, expert panel n=13 across 12 academic medical centers, with 77% Round 1 and 100% Round 2 response rates), this study developed the first specialty-specific AI competency framework for a United States medical specialty, comprising 5 themes (Communicating about AI, Understanding appropriate use cases, Interacting with AI, AI risk management, Cognitive impacts of AI), 5 derived competencies (one-to-one theme-to-competency mapping endorsed by 12 of 13 panelists), 19 subthemes, and 10 retained clinical scenarios, with all themes, derived competencies, and individually rated subthemes meeting pre-specified consensus thresholds and 9 of 10 scenarios reaching consensus while 1 was retain
University of Kansas Stanford University University of Virginia University of California San Diego University of Chicago University of Florida University of Missouri University of South Florida Dania Abu‐Jubara RACHEL LIU Marta Rowh R. Andrew Taylor Mona Moukaddem Michael Makutonin Cassandra Mackey Carl M. Preiksaitis
Modified Delphi Consensus Nominal Group Technique AI Competency Framework Artificial Intelligence in Emergency Medicine

You’re all caught up

An agent emulates an elderly clopidogrel trial: 53 of 62 SQL checks met, calibrated risk ratio point estimate rises to 1.274

Cryo-ET membrane segmentation goes two-stage: Dice rises from 0.6381 to 0.8116, at 79.97 seconds per tomogram

An Open Field Phenomics Resource for Multimodal Maize Yield Prediction Across Divergent Environments

An AI Competency Framework for Emergency Medicine: A Multiphase Consensus Process