Machine learning models of cerebral oxygenation (rcSO2) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The present study was designed to test the potential utility of regional cerebral oxygen saturation (rcSO2) in detecting term infants with brain injury. The study also examined whether quantitative rcSO2 features are associated with grade of hypoxic ischaemic encephalopathy (HIE). We analysed 58 term infants with HIE (>36 weeks of gestational age) enrolled in a prospective observational study. All newborn infants had a period of continuous rcSO2 monitoring and magnetic resonance imaging (MRI) assessment during the first week of life. rcSO2 Signals were pre-processed and quantitative features were extracted. Machine-learning and deep-learning models were developed to detect adverse outcome (brain injury on MRI or death in the first week) using the leave-one-out cross-validation approach and to assess the association between rcSO2 and HIE grade (modified Sarnat - at 1 h). The machine-learning model (rcSO2 excluding prolonged relative desaturations) significantly detected infant MRI outcome or death in the first week of life [area under the curve (AUC) = 0.73, confidence interval (CI) = 0.59-0.86, Matthew's correlation coefficient = 0.35]. In agreement, deep learning models detected adverse outcome with an AUC = 0.64, CI = 0.50-0.79. We also report a significant association between rcSO2 features and HIE grade using a machine learning approach (AUC = 0.81, CI = 0.73-0.90). We conclude that automated analysis of rcSO2 using machine learning methods in term infants with HIE was able to determine, with modest accuracy, infants with adverse outcome. De novo approaches to signal analysis of NIRS holds promise to aid clinical decision making in the future. KEY POINTS: Hypoxic-induced neonatal brain injury contributes to both short- and long-term functional deficits. Non-invasive continuous monitoring of brain oxygenation using near-infrared- spectroscopy offers a potential new insight to the development of serious injury. In this study, characteristics of the NIRS signal were summarised using either predefined features or data-driven feature extraction, both were combined with a machine learning approach to predict short-term brain injury. Using data from a cohort of term infants with hypoxic ischaemic encephalopathy, the present study illustrates that automated analysis of regional cerebral oxygen saturation rcSO2, using either machine learning or deep learning methods, was able to determine infants with adverse outcome.

Predicting Admission to Neonatal Care Unit at Mid-Pregnancy and Delivery Using Data from a General Obstetric Population.

OBJECTIVES: Development and validation of risk prediction models at mid-pregnancy and delivery to predict admission to the neonatal care unit. METHODS: We used data from all singleton deliveries at Cork University Maternity Hospital (CUMH), Ireland during 2019. Admission to the neonatal care unit was assumed if length of stay in the unit was > 24 h. Multivariable logistic regression with backward stepwise selection was used to develop the models. Discrimination was assessed using the ROC curve C-statistic, and internal validation was assessed using bootstrapping techniques. We conducted temporal external validation using data from all singleton deliveries at CUMH during 2020. RESULTS: Out of 6,077 women, 5,809 (95.6%) with complete data were included in the analyses. A total of 612 infants (10.54%) were admitted to the neonatal care unit for > 24 hours. Six variables were informative at mid-pregnancy: male infants, maternal smoking, advancing maternal age, maternal overweight/obesity, nulliparity and history of gestational diabetes (C-statistic: 0.600, 95% CI: 0.567, 0.614). Seven variables were informative at delivery: male infants, nulliparity, public antenatal care, gestational age < 39 weeks', non-spontaneous vaginal delivery, premature rupture of membranes and time of birth between 17:01-07.59 h (C-statistic: 0.738, 95% CI: 0.715, 0.760). Using these predictors, we developed nomograms to calculate individualised risk of neonatal care unit admission. Bootstrapping indicated good internal performance and external validation suggested good reproducibility. DISCUSSION: Our nomograms allow the user to quickly estimate individualised risk of neonatal care unit admission. Future research should aim to improve accuracy in early pregnancy to better assist counselling of parents.

Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set.

OBJECTIVE: To validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data. DESIGN: Secondary review of electronic health record data of term deliveries from January 2017 to December 2021. SETTING: A tertiary maternity hospital. PATIENTS: Infants >36 weeks' gestation with the following clinical variables available: Apgar Score at 1 min and 5 min, postnatal pH, base deficit, and lactate values taken within 1 hour of birth INTERVENTIONS: Previously trained open-source logistic regression and random forest (RF) prediction algorithms were used to calculate a probability index (PI) for each infant for the occurrence of HIE. MAIN OUTCOME: Validation of a machine learning algorithm to identify infants at risk of HIE in the immediate postnatal period. RESULTS: 1081 had a complete data set available within 1 hour of birth: 76 (6.95%) with HIE and 1005 non-HIE. Of the 76 infants with HIE, 37 were classified as mild, 29 moderate and 10 severe. The best overall accuracy was seen with the RF model. Median (IQR) PI in the HIE group was 0.70 (0.53-0.86) vs 0.05 (0.02-0.15), (p<0.001) in the non-HIE group. The area under the receiver operating characteristics curve for prediction of HIE=0.926 (0.893-0.959, p<0.001). Using a PI cut-off to optimise sensitivity of 0.30, 936 of the 1081 (86.5%) infants were correctly classified. CONCLUSION: In a large unseen data set an open-source algorithm could identify infants at risk of HIE in the immediate postnatal period. This may aid focused clinical examination, transfer to tertiary care (if necessary) and timely intervention.

The Bronchopulmonary Dysplasia score: A predictive model for bronchopulmonary dysplasia or death in high-risk preterm infants.

AIM: Progressive respiratory deterioration in infants at high risk of bronchopulmonary dysplasia (BPD) is associated with patent ductus arteriosus (PDA) exposure. This study aimed to design an early predictive model for BPD or death in preterm infants using early echocardiographic markers and clinical data. METHODS: Infants born with gestational age (GA) ≤ 29 weeks and/or birth weight (BW) < 1500 g at Cork University Maternity Hospital, Ireland were retrospectively evaluated. Those with echocardiography performed between 36 h and 7 days of life were eligible for inclusion. Exclusion criteria were pulmonary hypertension and major congenital anomalies. The primary outcome was a composite of BPD and death before discharge. RESULTS: The study included 99 infants. A predictive model for the primary outcome was developed, which included three variables (BW, Respiratory Severity Score and flow pattern across the PDA), and yielding an area under the curve of 0.98 (95% CI 0.96-1.00, p < 0.001). Higher scores were predictive of the primary outcome. A cut-off of -1.0 had positive and negative predictive values of 89% and 98%, and sensitivity and specificity of 98% and 88%, respectively. CONCLUSION: Our prediction model is an accessible bedside tool that predicts BPD or death in premature infants.

Regional variability in therapeutic hypothermia eligibility criteria for neonatal hypoxic-ischemic encephalopathy.

Early induced therapeutic hypothermia represents the cornerstone treatment in neonates with probable hypoxic-ischemic encephalopathy. The selection of patients for treatment usually involves meeting criteria indicating evidence of perinatal hypoxia-ischemia and the presence of moderate or severe encephalopathy. In this review, we highlight the variability that exists between some of the different regional and national eligibility guidelines. Determining the potential presence of perinatal hypoxia-ischemia may require either one, two or three signs amongst history of acute perinatal event, prolonged resuscitation at delivery, abnormal blood gases and low Apgar score, with a range of cutoff values. Clinical neurological exams often define the severity of encephalopathy differently, with varying number of domains required for determining eligibility and blurred interpretation of findings assigned to different severity grades in different systems. The role of early electrophysiological assessment is weighted differently. A clinical implication is that infants may receive different care depending on the location in which they are born. This could also impact epidemiological data, as inference of rates of moderate-severe encephalopathy based on therapeutic hypothermia rates are misleading and influenced by different eligibility methods used. We would advocate that a universally endorsed single severity staging of encephalopathy is vital for standardizing management and neonatal outcome. IMPACT: Variability exists between regional and national therapeutic hypothermia eligibility guidelines for neonates with probable hypoxic-ischemic encephalopathy. Differences are common in both criteria indicating perinatal hypoxia-ischemia and criteria defining moderate or severe encephalopathy. The role of early electrophysiological assessment is also weighted unequally. This reflects in different individual care and impacts research data. A universally endorsed single severity staging of encephalopathy would be crucial for standardizing management.

Assessment of Reader Technologies for Over-the-Counter Diagnostic Testing.

Background: Over-the-counter (OTC) diagnostic testing is on the rise with many in vitro diagnostic tests being lateral flow assays (LFAs). A growing number of these are adopting reader technologies, which provides an alternative to visual readouts for results interpretation, allowing for improved accessibility of OTC diagnostics. As the reader technology market develops, there are many technologies entering the market, but no clear, single solution has yet been identified. The purpose of this research is to identify and discuss important parameters for the assessment of LFA reader technologies for consideration by manufacturers or researchers. Methods: As part of The National Institute of Biomedical Imaging and Bioengineering's Rapid Acceleration of Diagnostics (RADx) Tech program, reader manufacturers were interviewed to investigate the current state of reader technology development through several parameters identified as important industry standards. Readers were categorized by technology type and parameters including cost, detection method, multiplex capabilities, assay type, maturity, and use case were all assessed. Results: Fifteen reader manufacturers were identified and interviewed, and information on a total of 19 technologies was assessed. Reader technology type was found to be predictive of other attributes, whether the reader is smart technology only, a standalone reader, a reader with smart technology required, or a reader with smart technology optional. Conclusions: Pairing reader technology with OTC diagnostic tests is important for improving existing COVID-19 tests and can be utilized in other diagnostics as the OTC use case grows in popularity. Reader technology type, which is predictive of core reader attributes, should be considered when selecting a reader technology for a specific LFA test within the context of regulatory guidance. As diagnostics increase in complexity, readers provide solutions to accessibility challenges, facilitate public health reporting, and ease the transition to multiplex testing, therefore increasing market availability.

Information Accessibility in the Form of Braille.

Braille is often proposed by the uninformed as the optimal solution to providing an alternative to visual information to the visually impaired. The purpose of this article is to highlight the complexity of the braille user population and discuss the importance of understanding the use of braille as a solution for equal access of information. As part of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) Rapid Acceleration of Diagnostics (RADx) Tech program and its goal to make home tests accessible to people with disabilities, a series of interviews with industry experts was conducted to better understand braille technologies and the braille user space. Published literature findings provided additional context and support to these interviews. It was found that expert consensus and data from published literature vary. The braille user population is complex and lacks consistent characterization. Visually printed media should not be solely relied on to communicate information. In conclusion, braille is one solution for improving access to information. Understanding the unique needs of braille users and how they engage with information in a world that is heavily reliant on visual content, is a critical step in developing and implementing non-visual alternatives that will collectively address information access.

Neuro-Specific and Immuno-Inflammatory Biomarkers in Umbilical Cord Blood in Neonatal Hypoxic-Ischemic Encephalopathy.

OBJECTIVES: The aim of the study was to evaluate neuronal injury and immuno-inflammatory biomarkers in umbilical cord blood (UCB) at birth, in cases with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE), compared with healthy controls and to assess their ability to predict HIE. STUDY DESIGN: In this case-control study, term infants with perinatal asphyxia were recruited at birth. UCB was stored at delivery for batch analysis. HIE was diagnosed by clinical Sarnat staging at 24 h. Glial fibrillary acidic protein (GFAP), the neuronal biomarkers tau and neurofilament light protein (NFL), and a panel of cytokines were analyzed in a total of 150 term neonates: 50 with HIE, 50 with asphyxia without HIE (PA), and 50 controls. GFAP, tau, and NFL concentrations were measured using ultrasensitive single-molecule array (Simoa) assays, and a cytokine screening panel was applied to analyze the immuno-inflammatory and infectious markers. RESULTS: GFAP, tau, NFL, and several cytokines were significantly higher in newborns with moderate and severe HIE compared to a control group and provided moderate prediction of HIE II/III (AUC: 0.681-0.827). Furthermore, the levels of GFAP, tau, interleukin-6 (IL-6), and interleukin-8 (IL-8) were higher in HIE II/III cases compared with cases with PA/HIE I. IL-6 was also higher in HIE II/III compared with HIE I cases. CONCLUSIONS: Biomarkers of brain injury and inflammation were increased in umbilical blood in cases with asphyxia. Several biomarkers were higher in HIE II/III versus those with no HIE or HIE I, suggesting that they could assist in the prediction of HIE II/III.

Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement From the American Heart Association and Neurocritical Care Society.

The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.

Establishing a regional registry for neonatal encephalopathy: impact on identification of gaps in practice.

BACKGROUND: Neonatal encephalopathy (NE) continues to be a significant risk for death and disability. To address this risk, regional guidelines were developed with the support of a malpractice insurance patient safety organization. A NE registry was also established to include 14 centers representing around 50% of deliveries in the state of Massachusetts. The aim of this study was to identify areas of variation in practice that could benefit from quality improvement projects. METHODS: This manuscript reports on the establishment of the registry and the primary findings to date. RESULTS: From 2018 to 2020, 502 newborns with NE were evaluated for Therapeutic Hypothermia (TH), of which 246 (49%) received TH, representing a mean of 2.91 per 1000 live births. The study reports on prenatal characteristics, delivery room resuscitation, TH eligibility screening, and post-natal management of newborns with NE who did and did not receive TH. CONCLUSIONS: The registry has allowed for the identification of areas of variation in clinical practices, which have guided ongoing quality improvement projects. The authors advocate for the establishment of local and regional registries to standardize and improve NE patient care. They have made the registry data collection tools freely available for other centers to replicate this work. IMPACT: Malpractice insurance companies can take an active role in supporting clinicians in establishing clinical practice guidelines and regional registries. Establishing a collaborative regional neonatal encephalopathy (NE) registry is feasible. Data Collection tools for a NE registry have been made publicly available to be adopted and replicated by other groups. Establishing a regional NE registry allowed for the identification of gaps in knowledge, variations in practice, and the opportunity to advance care through quality improvement projects.

Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement from the American Heart Association and Neurocritical Care Society.

The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.

Long-term developmental condition following neonatal arterial ischemic stroke: A systematic review.

BACKGROUND: Neonatal arterial ischemic stroke (NAIS) is the most frequent subtype of perinatal stroke. Its elusive pathophysiology, its abrupt and unexpected occurrence, and the uncertainty of the post-NAIS developmental condition may lead to parental emotional distress and psychological difficulties. The aim of this study was to summarize the current data on long-term developmental conditions following NAIS to support parental information given within the neonatal unit. METHODS: This systematic review included clinical studies of term infants with NAIS, who had a developmental assessment at ≥5 years of age. Studies were identified from the Medline and Embase databases on June 1, 2022. The Joanna Briggs Institute (JBI) appraisal tool was used to assess the risk of bias. Results were synthesized using a narrative approach. The 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was followed to report this work. RESULTS: Three cohort studies enrolling 205 children assessed from 5 to 7 years after NAIS were included. Most of the children presented long-term developmental conditions allowing them to be integrated into a regular school program, to participate in physical activities, and to have a good quality of life. Global intellectual deficiency and moderate-to-severe cerebral palsy occurred in less than 10% of the children. CONCLUSION: Physicians should not overestimate the incidence of moderate-to-severe developmental outcome following NAIS when discussing the prognosis with parents. A parental information sheet about NAIS and its long-term developmental conditions is provided.

Seasonal activity of spotted lanternfly (Hemiptera: Fulgoridae), in Southeast Pennsylvania.

The spotted lanternfly, Lycorma delicatula (White, 1845), is an invasive species in the United States. This pest causes damage to vineyards and has the potential to negatively affect other crops and industries. Information describing the seasonal timing of life stages can improve its management. In 2019 and 2020, spotted lanternfly seasonal activity was followed weekly from spring egg hatch to the first hard freeze. Weighted mean timing of activity for each nymphal instar, early adults, late adults, total adults, and egg mass deposition are presented for 2019 and 2020 on Acer rubrum and 2020 on Ailanthus altissima. Logistic equations describing the percentage completion of each activity period on these hosts were fitted using a start date of 1 January to calculate accumulated degree days (ADD). For the adult and egg mass deposition periods, we additionally used a biofix of the date adults were first observed to calculate ADD. ADD from 1 January adequately estimated the timing of nymphal instars but ADD from observation of the first adult better estimated the timing of adult activity and egg mass deposition. Late adult activity and egg mass deposition periods appeared to be influenced by another environmental cue, such as day length. Maps of season-long ADD show that spotted lanternflies are unlikely to reach adulthood in colder regions of the northeast United States, and therefore may not establish there. We also report a strong seasonal trend in sex ratio on A. rubrum, where the population shifted from over 80% male to over 80% female in October.

From early motor ability to global cognitive development seven years after neonatal arterial ischemic stroke.

The developmental condition of children after neonatal arterial ischemic stroke (NAIS) is characterized by cognitive and motor impairments. We hypothesized that independent walking age would be a predictor of later global cognitive functioning in this population. Sixty-one children with an available independent walking age and full-scale IQ score seven years after NAIS were included in this study. Full-scale IQ was assessed using the fourth edition of the Wechsler Intelligence Scale for Children (WISC-IV). Independent walking age was negatively correlated with full-scale IQ score at seven years of age (Pearson correlation coefficient of -0.27; 95% confidence interval from 0.48 to -0.01; p <0.05). Early motor function is correlated with later global cognitive functioning in children after NAIS. Assessing and promoting early motor ability is essential in this population.

Effects of long-term feeding by spotted lanternfly (Hemiptera: Fulgoridae) on ecophysiology of common hardwood host trees.

While the invasive spotted lanternfly, Lycorma delicatula (White) [Hemiptera: Fulgoridae], continues to expand its range in the United States, there remains a knowledge gap regarding the economic threat that this pest presents to forest ecosystems and production nurseries. L. delicatula uses several common hardwood trees as hosts and a previous study found that short-term feeding can reduce growth of young maple saplings. Herein, long-term feeding over 4 consecutive seasons significantly reduced diameter growth and below-ground starch storage in roots of young silver maples (Acer saccharinum L.), weeping willows (Salix babylonica L.), river birches (Betula nigra L.), and trees of heaven (Ailanthus altissima [Mill.] Swingle) in response to L. delicatula feeding pressure in a density-dependent manner. In Year 3 when feeding pressure was the lowest, silver maple and willow recovered with greater diameter growth than in Year 2. Nutrients essential for photosynthesis and growth (iron, sulfur, and phosphorus) were reduced in leaves of all tree species compared to controls in the second year. This 4-yr study represents a worst-case scenario in which L. delicatula fed on the same trees for 4 consecutive growing seasons. In the wild, population numbers can vary greatly from year to year on individual trees and they move frequently among hosts (until autumn when they settle on A. altissima or other late-season hosts that have not yet senesced). Thus, we would not expect negative impacts of unconfined L. delicatula in natural settings on forest or ornamental trees to be as marked as reported here.

Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.

PURPOSE: Stratifying patients with cancer according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to use machine learning to estimate probability of relapse in patients with early-stage non-small-cell lung cancer (NSCLC)? MATERIALS AND METHODS: For predicting relapse in 1,387 patients with early-stage (I-II) NSCLC from the Spanish Lung Cancer Group data (average age 65.7 years, female 24.8%, male 75.2%), we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHapley Additive exPlanations local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. RESULTS: Machine learning models trained on tabular data exhibit a 76% accuracy for the random forest model at predicting relapse evaluated with a 10-fold cross-validation (the model was trained 10 times with different independent sets of patients in test, train, and validation sets, and the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a held-out test set of 200 patients, calibrated on a held-out set of 100 patients. CONCLUSION: Our results show that machine learning models trained on tabular and graph data can enable objective, personalized, and reproducible prediction of relapse and, therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer.

Efficacy and nontarget effects of broadcast treatments to manage spotted lanternfly (Hemiptera: Fulgoridae) nymphs.

Management to control the spotted lanternfly, Lycorma delicatula (White), would ideally achieve managers' goals while limiting impacts on nontarget organisms. In a large-scale field study with 45 plots at least 711 m2, we tested foliar applications of dinotefuran and 2 formulations of Beauveria bassiana (Balsamo) Vuillemin, each applied from the ground and separately by helicopter. Applications targeted early instar nymphs. For both application methods, a single treatment with dinotefuran significantly reduced L. delicatula numbers, as measured by catch on sticky bands (91% reduction by air and 84% reduction by ground 19 days after application) and by timed counts (89% reduction by air and 72% reduction by ground 17 days after application). None of the B. bassiana treatments significantly reduced L. delicatula numbers, even after 3 applications. Beauveria bassiana infection in field-collected nymphs ranged from 0.4% to 39.7%, with higher mortality and infection among nymphs collected from ground application plots. Beauveria bassiana conidia did not persist for long on foliage which probably contributed to low population reduction. Nontarget effects were not observed among arthropods captured in blue vane flight intercept traps, San Jose Scale pheromone sticky traps or pitfall traps, but power analysis revealed that small reductions of less than 40% may not be detected despite extensive sampling of 48,804 specimens. These results demonstrate that dinotefuran can markedly reduce local abundance of L. delicatula with little apparent effect on nontarget insects when applied shortly after hatch, and that aerial applications can match or exceed the effectiveness of applications from the ground.

recountmethylation enables flexible analysis of public blood DNA methylation array data.

Summary: Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12 537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (i) study ID bias adjustment increased variation explained by biological and demographic variables, (ii) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions and (iii) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 38-46% of differentially methylated probes between sexes from two previously published epigenome-wide association studies. Availability and implementation: Source code to reproduce the main results are available on GitHub (repo: recountmethylation_flexible-blood-analysis_manuscript; url: https://github.com/metamaden/recountmethylation_flexible-blood-analysis_manuscript). All data was publicly available and downloaded from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). Compilations of the analyzed public data can be accessed from the website recount.bio/data (preprocessed HM450K array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/; preprocessed EPIC array data: https://recount.bio/data/remethdb_h5se-gm_epic_0-0-2_1589820348/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.