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Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies.

Beginning in December of 2019, a novel coronavirus, SARS-CoV-2, emerged in China and is now a global pandemic with extensive morbidity and mortality. With the emergence of this threat, an unprecedented effort to develop vaccines against this virus began. As vaccines are now being introduced globally, we face the prospect of millions of people being vaccinated with multiple types of vaccines many of which use new vaccine platforms. Since medical events happen without vaccines, it will be important to know at what rate events occur in the background so that when adverse events are identified one has a frame of reference with which to compare the rates of these events so as to make an initial assessment as to whether there is a potential safety concern or not. Background rates vary over time, by geography, by sex, socioeconomic status and by age group. Here we describe two key steps for post-introduction safety evaluation of COVID-19 vaccines: Defining a dynamic list of Adverse Events of Special Interest (AESI) and establishing background rates for these AESI. We use multiple examples to illustrate use of rates and caveats for their use. In addition we discuss tools available from the Brighton Collaboration that facilitate case evaluation and understanding of AESI.

Usability evaluations are a powerful tool that can assist developers in their efforts to optimize the quality of their web environment. This underutilized, experimental method can serve to move applications toward true user-centered design. This article describes the usability methodology and illustrates its importance and application by describing a usability study undertaken at the Mayo Clinic for the purpose of improving an academic research web environment. Academic institutions struggling in an era of declining reimbursements are finding it difficult to maintain academic enterprises on the back of clinical revenues. This may result in declining amounts of time that clinical investigators have to spend in non-patient-related activities. For this reason, we have undertaken to design a web environment, which can minimize the time that a clinician-investigator needs to spend to accomplish academic instrumental activities of daily living. Usability evaluation is a powerful application of human factors engineering, which can improve the utility of web-based Informatics applications.

Hydrothermal explosions are one of the geological hazards most likely to impact people in Yellowstone National Park, but their frequency is poorly known. Infrasound and seismic sensors identified an explosion in Norris Geyser Basin on 15 April 2024, at 14:56 MDT (20:56 UTC)—the first instrumentally detected hydrothermal explosion in the Yellowstone region. The event affected an area tens of meters across, resulting in fractured ground, a shallow explosion crater, and a field of ejecta. There were no immediate geophysical precursors, but in the preceding years elevated discharge of thermal water altered the color, temperature, and level of a nearby small lake. Expanded seismo‐acoustic monitoring in Yellowstone National Park could be useful for detecting small hydrothermal explosions and constraining their frequency, magnitude, energy release, and locations—information that could be used to better assess and mitigate hazards for the millions of people that visit the park each year. Plain Language Summary Hydrothermal explosions occur when hot liquid water flashes to steam in the shallow subsurface, breaking the overlying rocks and ejecting steam, liquid water, rock, and mud into the air. Such explosions are one of the geological hazards most likely to impact people in Yellowstone National Park, but their occurrence rate is poorly known. In May 2024, scientists working in the Porcelain Terrace area of Norris Geyser Basin discovered an region of disrupted ground and a shallow crater surrounded by ejecta that had formed sometime during the preceding months. Using data from recently installed seismic and acoustic sensors, an explosion was confirmed to have occurred at 14:56 Mountain Daylight Time (20:56 UTC) on 15 April 2024—the first instrumentally detected hydrothermal explosion in the Yellowstone region. The event was preceded by several years of elevated hydrothermal activity, including discharge of thermal water that impacted the color, level, and temperature of a nearby small lake. Expansion of seismic and acoustic monitoring in the geyser basins of Yellowstone National Park could provide more information about the occurrence of small hydrothermal explosions—critical data for quantifying hydrothermal hazards that might impact the millions of people who visit the region every year. Key Points Seismic and infrasound monitoring detected a small explosion in Norris Geyser Basin, Yellowstone National Park, on 15 April 2024 This is the first hydrothermal explosion in Yellowstone National Park to be detected by instrumental monitoring Improved seismo‐acoustic monitoring in Yellowstone National Park hydrothermal areas could better characterize hazards from small explosions

Fusarium graminearum Schwabe (Fusarium head blight, FHB) and Puccinia triticina Eriks (leaf rust) are two major fungal pathogens posing a continuous threat to the wheat crop; consequently, identifying resistance genes from various sources is always of importance to wheat breeders. We identified tightly linked single nucleotide polymorphism (SNP) markers for the FHB resistance quantitative trait locus (QTL) Qfhs.pur-7EL and the leaf rust resistance locus Lr19 using genotyping-by-sequencing (GBS) in a wheat–tall wheatgrass introgression-derived recombinant inbred line (RIL) population. One thousand and seven hundred high-confidence SNPs were used to conduct the linkage and QTL analysis. Qfhs.pur-7EL was mapped to a 2.9 cM region containing four markers within a 43.6 cM segment of wheatgrass chromosome 7el₂ that was translocated onto wheat chromosome 7DL. Lr19 from 7el₁ was mapped to a 1.21 cM region containing two markers in the same area, in repulsion. Five lines were identified with the resistance-associated SNP alleles for Qfhs.pur-7EL and Lr19 in coupling. Two SNP markers in the Qfhs.pur-7EL region were converted into PCR-based KASP markers. Investigation of the genetic characteristics of the parental lines of this RIL population indicated that they are translocation lines in two different wheat cultivar genetic backgrounds instead of 7E–7D substitution lines in Thatcher wheat background, as previously reported in the literature.

A barrier to the adoption of genomic prediction in small breeding programs is the initial cost of genotyping material. Although decreasing, marker costs are usually higher than field trial costs. In this study we demonstrate the utility of stratifying a narrow‐base biparental oat population genotyped with a modest number of markers to employ genomic prediction at early and later generations. We also show that early generation genotyping data can reduce the number of lines for later phenotyping based on selections of siblings to progress. Using sets of small families selected at an early generation could enable the use of genomic prediction for adaptation to multiple target environments at an early stage in the breeding program. In addition, we demonstrate that mixed marker data can be effectively integrated to combine cheap dominant marker data (including legacy data) with more expensive but higher density codominant marker data in order to make within generation and between lineage predictions based on genotypic information. Taken together, our results indicate that small programs can test and initiate genomic predictions using sets of stratified, narrow‐base populations and incorporating low density legacy genotyping data. This can then be scaled to include higher density markers and a broadened population base.

Background Administrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices may confound efforts to benchmark hospital sepsis outcomes using claims data. Methods We evaluated the sensitivity of claims data for sepsis and organ dysfunction relative to clinical data from the electronic health records of 193 US hospitals. Sepsis was defined clinically using markers of presumed infection (blood cultures and antibiotic administrations) and concurrent organ dysfunction. Organ dysfunction was measured using laboratory data (acute kidney injury, thrombocytopenia, hepatic injury), vasopressor administrations (shock), or mechanical ventilation (respiratory failure). Correlations between hospitals’ sepsis incidence and mortality rates by claims (using “explicit” ICD-9-CM codes for severe sepsis or septic shock) versus clinical data were measured by the Pearson correlation coefficient (r) and relative hospital rankings using either data source were compared. All estimates were reliability-adjusted to account for random variation using hierarchical logistic regression modeling. Results The study cohort included 4.3 million adult hospitalizations in 2013 or 2014. The sensitivity of hospitals’ claims data for sepsis and organ dysfunction was low and variable: median sensitivity 30% (range 5–54%) for sepsis, 66% (range 26–84%) for acute kidney injury, 39% (range 16–60%) for thrombocytopenia, 36% (range 29–44%) for hepatic injury, and 66% (range 29–84%) for shock (Figure 1). There was only moderate correlation between claims and clinical data for hospitals’ sepsis incidence (r = 0.64) and mortality rates (r = 0.61), and relative hospital rankings for sepsis mortality differed substantially using either method (Figure 2). Of 48 (46%) hospitals, 22 ranked in the lowest sepsis mortality quartile by claims shifted to higher mortality quartiles using clinical data. Conclusion Variation in the completeness and accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospital sepsis rates and outcomes. Sepsis surveillance using objective clinical data may facilitate more meaningful hospital comparisons. Disclosures All authors: No reported disclosures.