Getting at the Source of Distinctive Encoding Effects in the DRM Paradigm: Evidence From Signal-Detection Measures and Source Judgments

Studying Deese–Roediger–McDermott (DRM) lists using a distinctive encoding task can reduce the DRM false memory illusion. Reductions for both distinctively encoded lists and non-distinctively encoded lists in a within-group design have been ascribed to use of a distinctiveness heuristic by which participants monitor their memories at test for distinctive-task details. Alternatively, participants might simply set a more conservative response criterion, which would be exceeded by distinctive list items more often than all other test items, including the critical non-studied items. To evaluate these alternatives, we compared a within-group who studied 5 lists by reading, 5 by anagram generation, and 5 by imagery, relative to a control group who studied all 15 lists by reading. Generation and imagery improved recognition accuracy by impairing relational encoding, but the within group did not show greater memory monitoring at test relative to the read control group. Critically, the within group’s pattern of list-based source judgments provided new evidence that participants successfully monitored for distinctive-task details at test. Thus, source judgments revealed evidence of qualitative, recollection-based monitoring in the within group, to which our quantitative signal-detection measure of monitoring was blind

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Active Methane Venting Observed at Giant Pockmarks Along the U.S. Mid-Atlantic Shelf Break

Detailed near-bottom investigation of a series of giant, kilometer scale, elongate pockmarks along the edge of the mid-Atlantic continental shelf confirms that methane is actively venting at the site. Dissolved methane concentrations, which were measured with a commercially available methane sensor (METS) designed by Franatech GmbH mounted on an Autonomous Underwater Vehicle (AUV), are as high as 100 nM. These values are well above expected background levels (1–4 nM) for the open ocean. Sediment pore water geochemistry gives further evidence of methane advection through the seafloor. Isotopically light carbon in the dissolved methane samples indicates a primarily biogenic source. The spatial distribution of the near-bottom methane anomalies (concentrations above open ocean background), combined with water column salinity and temperature vertical profiles, indicate that methane-rich water is not present across the entire width of the pockmarks, but is laterally restricted to their edges. We suggest that venting is primarily along the top of the pockmark walls with some advection and dispersion due to local currents. The highest methane concentrations observed with the METS sensor occur at a small, circular pockmark at the southern end of the study area. This observation is compatible with a scenario where the larger, elongate pockmarks evolve through coalescing smaller pockmarks.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86052/1/knewman-18.pd