Evaluating the Quality, Readability, and Activity of Online Information on Brain Arteriovenous Malformations.

Introduction Brain arteriovenous malformations (AVMs) are vascular deformities created by improper connections between arteries and veins, most commonly in the brain and spinal cord. The management is complex and patient-dependent; further understanding of patient education activities is imperative. Internet access has become more ubiquitous, allowing patients to utilize a large database of medical information online. Using Google Trends (GT) (Google LLC, Mountain View, CA, USA), one can see the public interest in a particular topic over time. Further, when presented with numerous search results, patients may not be able to identify the highest-yielding resources, making objective measures of information quality and readability imperative. Methods A GT analysis was conducted for "hereditary hemorrhagic telangiectasia," "cerebral aneurysm," and "arteriovenous malformation". These relative search volumes (RSV) were compared with the 2017 to 2019 annual USA AVM diagnosis quantity for correlation. These RSVs were also compared with the 2017 to 2019 annual USA deaths due to cerebral hemorrhagic conditions. One search was conducted for "brain arteriovenous malformation". Since most users looking for health information online use only the first page of sources, the quality and readability analyses were limited to the first page of results on Google search. Five quality tools and six readability formulas were used. Results Pearson's correlation coefficients showed positive correlations between USA AVM RSVs and annual AVM deaths per capita from 2017 to 2019 (R2=0.932). The AVM annual diagnosis quantity and AVM RSVs showed a strong positive correlation as well (R2=0.998). Hereditary hemorrhagic telangiectasia and cerebral aneurysms had strong positive correlations between their RSVs and their corresponding annual diagnoses in the 2017 to 2019 time period (R2=0.982, R2=0.709). One-way ANOVA, for USA's 2004 to 2021 AVM RSVs and 2004 to 2019 deaths per capita, displayed no month-specific statistically significant repeating pattern (all p>0.483). The DISCERN tool had four websites that qualified as "poor" and five as "good." The average score for the tool was "good." The Journal of the American Medical Association (JAMA) benchmark scores were very low on average, as four websites achieved zero points. There was a wide variance in the currency, relevance, authority, accuracy, and purpose (CRAAP) scores, indicating an inconsistent level of webpage reliability across results. The patient education materials assessment tool (PEMAT) understandability (86.6%) showed much higher scores than the PEMAT actionability (54.6%). No readability score averaged at or below the American Medical Association (AMA)-recommended sixth-grade reading level. Conclusion These GT correlations may be due to patients and families with new diagnoses researching those same conditions online. The seasonality results reflect that no prior research has detected seasonality for AVM diagnosis or presentation. The quality study showed a wide variance in website ethics, treatment information quality, website/author qualifications, and actionable next steps regarding AVMs. Overall, this study showed that patients are routinely attempting to access information regarding these intracranial conditions, but the information available, specifically regarding AVMs, is not routinely reliable and the reading level required to understand them is too high.

Uncovering the environmental conditions required for Phyllachora maydis infection and tar spot development on corn in the United States for use as predictive models for future epidemics.

Phyllachora maydis is a fungal pathogen causing tar spot of corn (Zea mays L.), a new and emerging, yield-limiting disease in the United States. Since being first reported in Illinois and Indiana in 2015, P. maydis can now be found across much of the corn growing regions of the United States. Knowledge of the epidemiology of P. maydis is limited but could be useful in developing tar spot prediction tools. The research presented here aims to elucidate the environmental conditions necessary for the development of tar spot in the field and the creation of predictive models to anticipate future tar spot epidemics. Extended periods (30-day windowpanes) of moderate mean ambient temperature (18-23 °C) were most significant for explaining the development of tar spot. Shorter periods (14- to 21-day windowpanes) of moisture (relative humidity, dew point, number of hours with predicted leaf wetness) were negatively correlated with tar spot development. These weather variables were used to develop multiple logistic regression models, an ensembled model, and two machine learning models for the prediction of tar spot development. This work has improved the understanding of P. maydis epidemiology and provided the foundation for the development of a predictive tool for anticipating future tar spot epidemics.

Assessing Patterns in Childhood Obesity Patient Education: A Quality of Online Health Information and Google Trends Analysis.

Background: Childhood obesity (CO) is rapidly increasing in prevalence and developing into a health crisis of developed nations. The condition is associated with increased risk of developing various comorbidities later in life. Current treatment algorithms primarily target family education. Thus, this study aims to understand the quality of information online regarding CO and common comorbidities, determine the readability of online information, and report patterns in public interest over time using Google Trends. Methods: Four validated quality of information tools and 6 readability tools were implemented across 36 websites derived from 4 Google searches. Pearson's correlation coefficient was used to determine the associations between Google Trends' relative search volumes (RSVs) and biennial BMI-based cumulative proportion of CO. Results: Results showed variable information quality among the websites as scores ranged from "fair" to "very poor." Using six readability formulas, no website scored at or below the sixth grade reading level recommended by the American Medical Association. Google Trends' RSVs for the term "Childhood Obesity" were repeatedly increased in the months that fall in the US academic school year (October-November and February-May), and decreased within months in the US vacation periods (December-January and June-September). Search volumes were also negatively correlated with CO and pediatric type 2 diabetes prevalence. Conclusions: In summary, while Google Trends analysis showed that schools may play a role in increasing interest and awareness online, quality of information and readability analysis displayed that the information and its accessibility are far too variable to be reliable.

Google Trends™ and Quality of Information Analyses of Google™ Searches Pertaining to Concussion.

Sports-related concussions occur with high incidence in the United States. Google Trends™ (GT) analyses indicate changes of public interest in a topic over time, and can be correlated with incidence of health events such as concussion. Internet searches represent a primary means of patient education for many health topics, including concussion; however, the quality of medical information yielded by internet searches is variable and frequently of an inappropriate reading level. This study therefore aims to describe GT over time and evaluate the quality and readability of information produced by Google™ searches of the term "concussion." We identified a strong negative correlation from 2009 to 2016 between GT scores and total number of American high school football participants (R 2 = 0.8553) and participants per school (R 2 = 0.9533). Between 2004 and 2020, the monthly GT popularity score were variable (p = 3.193E-08), with September having the greatest scores, correlating with the height of American tackle football season. Applying five validated quality assessment scoring systems at two time points, it was confirmed that different sources yielded varying quality of information. Academic and non-profit healthcare sources demonstrated the highest quality metrics across two time points. There was significant variability of scores among the different scoring systems, however. The majority of searches at both time points yielded information that was rated as "fair" to "poor" in quality. Applying six readability tests, we revealed that only a single commercial website offered information written at or below the American Medical Association- recommended 6th-grade level for healthcare information. In summary, GT data analyses suggest that searches correlate with the American tackle football season and increased between 2009 and 2016, given that public interest in concussion increased and annual participation in football decreased. The quality of information yielded by Google™ searches and readability are inadequate, indicating the need for significant improvement.

Into the Trees: Random Forests for Predicting Fusarium Head Blight Epidemics of Wheat in the United States.

Constructing models that accurately predict Fusarium head blight (FHB) epidemics and are also amenable to large-scale deployment is a challenging task. In the United States, the emphasis has been on simple logistic regression (LR) models, which are easy to implement but may suffer from lower accuracies when compared with more complicated, harder-to-deploy (over large geographies) model frameworks such as functional or boosted regressions. This article examined the plausibility of random forests (RFs) for the binary prediction of FHB epidemics as a possible mediation between model simplicity and complexity without sacrificing accuracy. A minimalist set of predictors was also desirable rather than having the RF model use all 90 candidate variables as predictors. The input predictor set was filtered with the aid of three RF variable selection algorithms (Boruta, varSelRF, and VSURF), using resampling techniques to quantify the variability and stability of selected variable sets. Post-selection filtering produced 58 competitive RF models with no more than 14 predictors each. One variable representing temperature stability in the 20 days before anthesis was the most frequently selected predictor. This was a departure from the prominence of relative humidity-based variables previously reported in LR models for FHB. The RF models had overall superior predictive performance over the LR models and may be suitable candidates for use by the Fusarium Head Blight Prediction Center.

Transmission, localization, and infectivity of seedborne maize chlorotic mottle virus.

Maize lethal necrosis is a destructive virus disease of maize caused by maize chlorotic mottle virus (MCMV) in combination with a virus in the family Potyviridae. Emergence of MLN is typically associated with the introduction of MCMV or its vectors and understanding its spread through seed is critical for disease management. Previous studies suggest that although MCMV is detected on seed, the seed transmission rate of this virus is low. However, mechanisms influencing its transmission are poorly understood. Elucidating these mechanisms is crucial for informing strategies to prevent spread on contaminated seed. In this study, we evaluated the rate of MCMV seed transmission using seed collected from plants that were artificially inoculated with MCMV isolates from Hawaii and Kenya. Grow-out tests indicated that MCMV transmission through seed was rare, with a rate of 0.004% among the more than 85,000 seed evaluated, despite detection of MCMV at high levels in the seed lots. To understand factors that limit transmission from seed, MCMV distribution in seed tissues was examined using serology and immunolocalization. The virus was present at high levels in maternal tissues, the pericarp and pedicel, but absent from filial endosperm and embryo seed tissues. The ability to transmit MCMV from seed to uninfected plants was tested to evaluate virus viability. Transmission was negatively associated with both seed maturity and moisture content. Transmission of MCMV from infested seed dried to less than 15% moisture was not detected, suggesting proper handling could be important for minimizing spread of MCMV through seed.

A novel approach to interrogating the effects of chemical warfare agent exposure using organ-on-a-chip technology and multiomic analysis.

Organ-on-a-chip platforms are utilized in global bioanalytical and toxicological studies as a way to reduce materials and increase throughput as compared to in vivo based experiments. These platforms bridge the infrastructure and regulatory gaps between in vivo animal work and human systems, with models that exemplify active biological pathways. In conjunction with the advent of increased capabilities associated with next generation sequencing and mass spectrometry based '-omic' technologies, organ-on-a-chip platforms provide an excellent opportunity to investigate the global changes at multiple biological levels, including the transcriptome, proteome and metabolome. When investigated concurrently, a complete profile of cellular and regulatory perturbations can be characterized following treatment with specific agonists. In this study, global effects were observed and analyzed following liver chip exposure to the chemical warfare agent, VX. Even though the primary mechanism of action of VX (i.e. acetylcholinesterase inhibition) is well characterized, recent in vivo studies suggest additional protein binding partners that are implicated in metabolism and cellular energetic pathways. In addition, secondary toxicity associated with peripheral organ systems, especially in human tissues, is not well defined. Our results demonstrate the potential of utilizing an organ-on-a-chip platform as a surrogate system to traditional in vivo studies. This is realized by specifically indicating significant dysregulation of several cellular processes in response to VX exposure including but not limited to amino acid synthesis, drug metabolism, and energetics pathways.

Sampling Depth and Crop Growth Stage Affect Estimates of the Population Density of Lesion Nematodes (Pratylenchus spp.) in Corn Fields in Ohio.

The effects of sampling depth and crop growth stage on the population density of lesion nematodes were investigated in three commercial fields in Wayne and Fulton Counties, Ohio, during the 2016 and 2017 growing seasons. Soil samples were collected at five growth stages by removing 15 soil cores to a depth of 70 cm from each of 25 plots per field-year. Cores were divided into seven 10-cm sections, and nematodes were extracted from the soil and root fractions of each of them. Pratylenchus crenatus and P. thornei were detected in approximately 84 and 78% of the samples collected in Wayne and Fulton Counties, respectively. Depth significantly affected total population density of both species as well as densities in the soil and root factions in all field-years, but the effects of growth stage and its interaction with depth varied with field-year. In most cases, mean population densities were higher from 10 to 40 cm soil depth than at the reference 40 to 50 cm depth and lower from 50 to 70 cm. There were quadratic relationships between population density (on the log link scale) and depth, with the highest peaks in estimated predicted densities generally occurring between 20 and 40 cm, depending on crop growth stage and growing conditions. These findings suggest that the standard practice of sampling between growth stages V3 and V6 to a depth of 45 to 50 cm and using the entire core for extraction and enumeration could lead to underestimation of population densities of P. crenatus and P. thornei.

Sustaining productivity gains in the face of climate change: A research agenda for US wheat.

Wheat is a globally important crop and one of the "big three" US field crops. But unlike the other two (maize and soybean), in the United States its development is commercially unattractive, and so its breeding takes place primarily in public universities. Troublingly, the incentive structures within these universities may be hindering genetic improvement just as climate change is complicating breeding efforts. "Business as usual" in the US public wheat-breeding infrastructure may not sustain productivity increases. To address this concern, we held a multidisciplinary conference in which researchers from 12 US (public) universities and one European university shared the current state of knowledge in their disciplines, aired concerns, and proposed initiatives that could facilitate maintaining genetic improvement of wheat in the face of climate change. We discovered that climate-change-oriented breeding efforts are currently considered too risky and/or costly for most university wheat breeders to undertake, leading to a relative lack of breeding efforts that focus on abiotic stressors such as drought and heat. We hypothesize that this risk/cost burden can be reduced through the development of appropriate germplasm, relevant screening mechanisms, consistent germplasm characterization, and innovative models predicting the performance of germplasm under projected future climate conditions. However, doing so will require coordinated, longer-term, inter-regional efforts to generate phenotype data, and the modification of incentive structures to consistently reward such efforts.

Middle Meningeal Artery Aneurysm in a Giant Meningioma.

Nontraumatic perilesional aneurysms are thought to occur because of high-flow demands of feeding arteries and neoangiogenesis of hypervascular lesions, most commonly in vascular malformations, but they can rarely be associated with tumors as well. Most of these perilesional aneurysms are from intracranial vasculature associated with branches off the internal carotid artery. We report the unique case of a middle meningeal artery aneurysm associated with a giant convexity meningioma seen on a preoperative angiogram.

Environmental Conditions After Fusarium Head Blight Visual Symptom Development Affect Contamination of Wheat Grain with Deoxynivalenol and Deoxynivalenol-3-Glucoside.

Fusarium head blight (FHB) of wheat, caused by the fungus Fusarium graminearum, is associated with grain contamination with mycotoxins such as deoxynivalenol (DON). Although FHB is often positively correlated with DON, this relationship can break down under certain conditions. One possible explanation for this could be the conversion of DON to DON-3-glucoside (D3G), which is typically missed by common DON testing methods. The objective of this study was to quantify the effects of temperature, relative humidity (RH), and preharvest rainfall on DON, D3G, and the D3D/DON relationship. D3G levels were higher in grain from spikes exposed to 100% RH than to 70, 80, or 90% RH at 20 and 25°C across all tested levels of mean FHB index (percentage of diseased spikelets per spike). Mean D3G contamination was higher at 20°C than at 25 or 30°C. There were significantly positive linear relationships between DON and D3G. Rainfall treatments resulted in significantly higher mean D3G than the rain-free check and induced preharvest sprouting, as indicated by low falling numbers (FNs). There were significant positive relationships between the rate of increase in D3G per unit increase in DON (a measure of conversion) and sprouting. As FN decreased, the rate of D3G conversion increased, and this rate of conversion per unit decrease in FN was greater at relatively low than at high mean DON levels. These results provide strong evidence that moisture after FHB visual symptom development was associated with DON-to-D3G conversion and constitute valuable new information for understanding this complex disease-mycotoxin system.

Environment, Grain Development, and Harvesting Strategy Effects on Zearalenone Contamination of Grain from Fusarium Head Blight-Affected Wheat Spikes.

Fusarium head blight (FHB), caused by the fungus Fusarium graminearum, is associated with grain contamination with mycotoxins such as deoxynivalenol (DON) and zearalenone (ZEA). Unlike DON, less is known about factors affecting ZEA production during FHB epidemics. The objective of this study was to quantify ZEA contamination of wheat grain as influenced by temperature, relative humidity, FHB index (IND), grain maturation, simulated late-season rainfall, and harvest timing. Mean ZEA concentrations were low (<1.1 ppm) during the early stages of grain development (25 to 31 days after anthesis [DAA]) but rapidly increased 35 to 51 DAA in field experiments, particularly under rainy conditions. Five or ten consecutive days with simulated rainfall shortly before harvest greatly increased ZEA contamination. Similarly, extremely high levels of ZEA (51.8 to 468.6 ppm) were observed in grain from spikes exposed to 100% relative humidity (RH) at all tested temperatures and mean IND levels under controlled conditions. Interestingly, at RH ≤ 90%, ZEA concentrations were very low (0.1 to 3.6 ppm) at all tested temperatures, even at IND above 90%. At 100% RH, mean ZEA contamination was significantly higher at 20 and 25°C (235.1 and 278.2 ppm) than at 30°C (104.7 ppm). Grain harvested early and not exposed to rainfall had lower mean ZEA than grain harvested late and/or subjected to preharvest rainfall. This study was the first to associate ZEA contamination of grain from FHB-affected wheat spikes with temperature and moisture and show through designed experiments that early harvest could be a useful strategy for reducing ZEA contamination.

High Long-Term Mortality Rate in Elderly Patients with Mild Traumatic Brain Injury and Subdural Hematoma due to Ground-Level Fall: Neurosurgery's Hip Fracture?

BACKGROUND: Moderate-to-severe traumatic brain injury (TBI) is a major source of morbidity and mortality in elderly patients. Little is known about long-term mortality in elderly patients following mild, nonfatal TBI and how the injury mechanism predicts survival. This study aimed to compare long-term mortality in elderly patients with mild TBI and traumatic subdural hematoma (tSDH) due to ground-level fall (GLF) versus those with TBI and tSDH due to another cause (i.e., non-ground-level fall [nGLF]). METHODS: This retrospective study comprised 288 patients ≥60 years old from a single Level I trauma center with tSDH and Glasgow Coma Scale scores 13-15. RESULTS: Median follow-up after initial TBI presentation was 2.9 years for the GLF group and 2.4 years for the nGLF group. During follow-up, 98 patients died, and median survival for all elderly patients with mild TBI and tSDH was 4.6 years. The GLF group had a higher mortality rate than the nGLF group, with 93 patients in GLF group dying during follow-up compared with 5 in nGLF group (P < 0.0001). The annual death rate for patients in the GLF group was 12.5% per year. For patients 60-69 years old, 39% in GLF group died compared with 4% in nGLF group during follow-up (P = 0.0002). Likewise, for patients 70-79 years old, 29% in GLF group died compared with 7% in nGLF group (P = 0.021). Finally, 56% of patients >80 years old in GLF group compared with 18% in nGLF group (P = 0.11). CONCLUSIONS: Elderly patients with mild TBI and tSDH due to GLF have significantly higher long-term mortality than patients with injuries due to nGLF.

A Rapid, Whole Genome Sequencing Assay for Detection and Characterization of Novel Coronavirus (SARS-CoV-2) Clinical Specimens Using Nanopore Sequencing.

A new human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged at the end of 2019 in Wuhan, China that caused a range of disease severities; including fever, shortness of breath, and coughing. This disease, now known as coronavirus disease 2019 (COVID-19), quickly spread throughout the world, and was declared a pandemic by the World Health Organization in March of 2020. As the disease continues to spread, providing rapid characterization has proven crucial to better inform the design and execution of control measures, such as decontamination methods, diagnostic tests, antiviral drugs, and prophylactic vaccines for long-term control. Our work at the United States Army's Combat Capabilities Development Command Chemical Biological Center (DEVCOM CBC) is focused on engineering workflows to efficiently identify, characterize, and evaluate the threat level of any potential biological threat in the field and more remote, lower resource settings, such as forward operating bases. While we have successfully established untargeted sequencing approaches for detection of pathogens for rapid identification, our current work entails a more in-depth sequencing analysis for use in evolutionary monitoring. We are developing and validating a SARS-CoV-2 nanopore sequencing assay, based on the ARTIC protocol. The standard ARTIC, Illumina, and nanopore sequencing protocols for SARS-CoV-2 are elaborate and time consuming. The new protocol integrates Oxford Nanopore Technology's Rapid Sequencing Kit following targeted RT-PCR of RNA extracted from human clinical specimens. This approach decreases sample manipulations and preparation times. Our current bioinformatics pipeline utilizes Centrifuge as the classifier for quick identification of SARS-CoV-2 and RAMPART software for verification and mapping of reads to the full SARS-CoV-2 genome. ARTIC rapid sequencing results, of previous RT-PCR confirmed patient samples, showed that the modified protocol produces high quality data, with up to 98.9% genome coverage at >1,000x depth for samples with presumably higher viral loads. Furthermore, whole genome assembly and subsequent mutational analysis of six of these sequences identified existing and unique mutations to this cluster, including three in the Spike protein: V308L, P521R, and D614G. This work suggests that an accessible, portable, and relatively fast sample-to-sequence process to characterize viral outbreaks is feasible and effective.

Impact of Gibberella Ear Rot on Grain Quality and Yield Components in Maize as Influenced by Hybrid Reaction.

The impact of Gibberella ear rot (GER; caused by Fusarium graminearum) on deoxynivalenol (DON) contamination of grain and yield components in maize were investigated using data from 30 environments in Ohio (3 years by 10 locations). Fifteen hybrids, later classified as susceptible (SU), moderately susceptible (MS), or moderately resistant (MR), based on the magnitude of differences in mean arcsine square-root-transformed GER severity (arcSEV) and log-transformed DON (logDON) relative to a reference SU check, were planted in each environment, and 10 ears per hybrid were inoculated with a spore suspension of F. graminearum. Relationships between GER severity and DON were well described by a Kono-Sugino-type nonlinear equation. Estimated parameters representing height (A) and steepness (ß) of the curves were significantly higher for SU than MS and MR hybrids but A was not significantly different between MS and MR. Results from a surrogacy analysis showed that GER was a moderate trial- and individual-level surrogate for DON. Both grain weight per ear and ear diameter decreased with increasing arcSEV but the regression slopes varied among resistance classes. The rates of reduction in both yield components per unit increase in arcSEV were significantly greater for SU than for MS and MR. An estimated 50% reduction in grain weight occurred at 62% GER severity for SU, compared with 77% severity for MS and 83% for MR. These results show that GER severity can be used as a surrogate for early estimation of DON contamination and yield loss to help guide grain handling and marketing decisions.

Efficacy of Genetic Resistance and Fungicide Application Against Fusarium Head Blight and Mycotoxins in Wheat Under Persistent Pre- and Postanthesis Moisture.

Field experiments were conducted to investigate the efficacy of fungicide treatments in combination with genetic resistance against Fusarium head blight (FHB) and its associated mycotoxins under persistently wet pre- and postanthesis conditions in plots inoculated with Fusarium graminearum-colonized corn spawn. Treatments consisted of a single application of prothioconazole + tebuconazole at early anthesis (PA), or at 3 (P3), 6 (P6), or 9 (P9) days after early anthesis, or PA followed by a single application of metconazole at 3 (PA+C3), 6 (PA+C6), or 9 (PA+C9) days after early anthesis. PA and P3 were the most efficacious of the single-application treatments in terms of mean percentage control of FHB index (IND), deoxynivalenol (DON), zearalenone (ZEA), and mean increase in grain yield and test weight (TW) relative to the nontreated susceptible check (S_CK). The double-application treatments (PA+C3, PA+C6, and PA+C9) were the most effective of all tested fungicide programs. However, relative to S_CK, the highest overall mean percentage reduction in IND, DON, and ZEA and increase in grain yield and TW were observed when the double-application fungicide programs were integrated with genetic resistance. The estimated net cash income (NCI) of the integrated management (IM) programs was consistently higher than the NCI of other tested programs across different grain prices and fungicide application costs. Thus, the benefits of the two-treatment IM programs under highly favorable conditions for FHB development were enough to offset the cost of two applications, making these programs profitable.

Putative Phenotypically Neutral Genomic Insertion Points in Prokaryotes.

The barriers to effective genome editing in diverse prokaryotic organisms have been falling at an accelerated rate. As editing becomes easier in more organisms, quickly identifying genomic locations to insert new genetic functions without disrupting organism fitness becomes increasingly useful. When the insertion is noncoding DNA for applications such as information storage or barcoding, a neutral insertion point can be especially important. Here we describe an approach to identify putatively neutral insertion sites in prokaryotes. An algorithm (targetFinder) finds convergently transcribed genes with gap sizes within a specified range, and looks for annotations within the gaps. We report putative editing targets for 10 common synthetic biology chassis organisms, including coverage of available RNA-seq data, and provide software to apply to others. We further experimentally evaluate the neutrality of six identified targets in Escherichia coli through insertion of a DNA barcode. We anticipate this information and the accompanying tool will prove useful for synthetic biologists seeking neutral insertion points for genome editing.

Oxathiapiprolin Alone or Mixed with Metalaxyl Seed Treatment for Management of Soybean Seedling Diseases Caused by Species of Phytophthora, Phytopythium, and Pythium.

Species of Phytophthora, Phytopythium, and Pythium affect soybean seed and seedlings each year, primarily through reduced plant populations and yield. Oxathiapiprolin is effective at managing several foliar diseases caused by some oomycetes. The objectives of these studies were to evaluate oxathiapiprolin in a discriminatory dose assay in vitro; evaluate oxathiapiprolin as a soybean seed treatment on a moderately susceptible cultivar in 10 environments; compare the impact of seed treatment on plant populations and yields in environments with low and high precipitation; and compare a seed treatment mixture on cultivars with different levels of resistance in four environments. There was no reduction in growth in vitro among 13 species of Pythium at 0.1 µg ml-1. Soybean seed treated with the base fungicide plus oxathiapiprolin (12 and 24 µg a.i. seed-1) alone, oxathiapiprolin (12 µg a.i. seed-1) plus mefenoxam (6 µg a.i. seed-1), or oxathiapiprolin (24 µg a.i. seed-1) plus ethaboxam (12.1 µg a.i. seed-1) had greater yields in environments that received ≥50 mm of precipitation within 14 days after planting compared with those that received less. Early plant population and yield were significantly higher for seed treated with oxathiapiprolin (24 µg a.i. seed-1) + metalaxyl (13.2 µg a.i. seed-1) compared with nontreated for six of seven cultivars in at least one of four environments. Oxathiapiprolin combined with another Oomycota fungicide applied to seed has the potential to be used to protect soybean plant establishment and yield in regions prone to poor drainage after high levels of precipitation.

A ribosomal operon database and MegaBLAST settings for strain-level resolution of microbiomes.

Current methods to characterize microbial communities generally employ sequencing of the 16S rRNA gene (<500 bp) with high accuracy (∼99%) but limited phylogenetic resolution. However, long-read sequencing now allows for the profiling of near-full-length ribosomal operons (16S-ITS-23S rRNA genes) on platforms such as the Oxford Nanopore MinION. Here, we describe an rRNA operon database with >300 ,000 entries, representing >10 ,000 prokaryotic species and ∼ 150, 000 strains. Additionally, BLAST parameters were identified for strain-level resolution using in silico mutated, mock rRNA operon sequences (70-95% identity) from four bacterial phyla and two members of the Euryarchaeota, mimicking MinION reads. MegaBLAST settings were determined that required <3 s per read on a Mac Mini with strain-level resolution for sequences with >84% identity. These settings were tested on rRNA operon libraries from the human respiratory tract, farm/forest soils and marine sponges ( n = 1, 322, 818 reads for all sample sets). Most rRNA operon reads in this data set yielded best BLAST hits (95 ± 8%). However, only 38-82% of library reads were compatible with strain-level resolution, reflecting the dominance of human/biomedical-associated prokaryotic entries in the database. Since the MinION and the Mac Mini are both portable, this study demonstrates the possibility of rapid strain-level microbiome analysis in the field.

Characterizing Heterogeneity and Determining Sample Sizes for Accurately Estimating Wheat Fusarium Head Blight Index in Research Plots.

Because Fusarium head blight (FHB) intensity is usually highly variable within a plot, the number of spikes rated for FHB index (IND) quantification must be considered when designing experiments. In addition, quantification of sources of IND heterogeneity is crucial for defining sampling protocols. Field experiments were conducted to quantify the variability of IND ("field severity") at different spatial scales and to investigate the effects of sample size on estimated plot-level mean IND and its accuracy. A total of 216 7-row × 6-m-long plots of a moderately resistant and a susceptible cultivar were spray-inoculated with different Fusarium graminearum spore concentrations at anthesis to generate a range of IND levels. A one-stage cluster sampling approach was used to estimate IND, with an average of 32 spikes rated at each of 10 equally spaced points per plot. Plot-level mean IND ranged from 0.9 to 37.9%. Heterogeneity of IND, quantified by fitting unconditional hierarchical linear models, was higher among spikes within clusters than among clusters within plots or among plots. The projected relative error of mean IND increased as mean IND decreased, and as sample size decreased to <100 spikes per plot. Simple random samples were drawn with replacement 50,000 times from the original dataset for each plot and used to estimate the effects of sample sizes on mean IND. Samples of 100 or more spikes resulted in more precise estimates of mean IND than smaller samples. Poor sampling may result in inaccurate estimates of IND and poor interpretation of results.