Use of Whole-Genome Sequencing to Estimate the Contribution of Immune Evasion and Waning Immunity on Decreasing COVID-19 Vaccine Effectiveness.

BACKGROUND: The impact variant-specific immune evasion and waning protection have on declining coronavirus disease 2019 (COVID-19) vaccine effectiveness (VE) remains unclear. Using whole-genome sequencing (WGS), we examined the contribution these factors had on the decline that followed the introduction of the Delta variant. Furthermore, we evaluated calendar-period-based classification as a WGS alternative. METHODS: We conducted a test-negative case-control study among people tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between 1 April and 24 August 2021. Variants were classified using WGS and calendar period. RESULTS: We included 2029 cases (positive, sequenced samples) and 343 727 controls (negative tests). VE 14-89 days after second dose was significantly higher against Alpha (84.4%; 95% confidence interval [CI], 75.6%-90.0%) than Delta infection (68.9%; 95% CI, 58.0%-77.1%). The odds of Delta infection were significantly higher 90-149 than 14-89 days after second dose (P value = .003). Calendar-period-classified VE estimates approximated WGS-classified estimates; however, calendar-period-based classification was subject to misclassification (35% Alpha, 4% Delta). CONCLUSIONS: Both waning protection and variant-specific immune evasion contributed to the lower effectiveness. While calendar-period-classified VE estimates mirrored WGS-classified estimates, our analysis highlights the need for WGS when variants are cocirculating and misclassification is likely.

Predicting Soybean Yield and Sudden Death Syndrome Development Using At-Planting Risk Factors.

In the United States, sudden death syndrome (SDS) of soybean is caused by the fungal pathogen Fusarium virguliforme and is responsible for important yield losses each year. Understanding the risk of SDS development and subsequent yield loss could provide growers with valuable information for management of this challenging disease. Current management strategies for F. virguliforme use partially resistant cultivars, fungicide seed treatments, and extended crop rotations with diverse crops. The aim of this study was to develop models to predict SDS severity and soybean yield loss using at-planting risk factors to integrate with current SDS management strategies. In 2014 and 2015, field studies were conducted in adjacent fields in Decatur, MI, which were intensively monitored for F. virguliforme and nematode quantities at-planting, plant health throughout the growing season, end-of-season SDS severity, and yield using an unbiased grid sampling scheme. In both years, F. virguliforme and soybean cyst nematode (SCN) quantities were unevenly distributed throughout the field. The distribution of F. virguliforme at-planting had a significant correlation with end-of-season SDS severity in 2015, and a significant correlation to yield in 2014 (P < 0.05). SCN distributions at-planting were significantly correlated with end-of-season SDS severity and yield in 2015 (P < 0.05). Prediction models developed through multiple linear regression showed that F. virguliforme abundance (P < 0.001), SCN egg quantity (P < 0.001), and year (P < 0.01) explained the most variation in end-of-season SDS (R2 = 0.32), whereas end-of-season SDS (P < 0.001) and end-of-season root dry weight (P < 0.001) explained the most variation in soybean yield (R2 = 0.53). Further, multivariate analyses support a synergistic relationship between F. virguliforme and SCN, enhancing the severity of foliar SDS. These models indicate that it is possible to predict patches of SDS severity using at-planting risk factors. Verifying these models and incorporating additional data types may help improve SDS management and forecast soybean markets in response to SDS threats.

Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures.

Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and reduce hospital costs. This paper develops a postoperative complications prediction system by extracting data from the EHR and creating features. The analytic engine then provides model accuracy, calibration, feature ranking, and personalized feature responses. This allows clinicians to interpret the likelihood of an adverse event occurring, general causes for these events, and the contributing factors for each specific patient. The patient cohort considered was 5214 patients in Yale-New Haven Hospital undergoing major cardiovascular procedures. Cohort-specific models predicted the likelihood of postoperative respiratory failure and infection, and achieved an area under the receiver operating characteristic curve of 0.81 for respiratory failure and 0.83 for infection.