The Diversity and Determinants of Fungicide Programs for Hop Powdery Mildew.

Plant disease monitoring metadata is a rich source of information for predicting pesticide use and costs on individual fields, farms, or regionally. Pesticide use patterns for management of hop powdery mildew (Podosphaera macularis) in Oregon were summarized by frequency of use of active ingredients and Fungicide Resistance Action Committee (FRAC) code. There was extensive variation in the frequency of use of specific active ingredients and FRAC codes among growers, ranging from a mean of 0 to 2.

Fungicide Selection, Disease Risk, and Grower Switching Behavior.

Disease management may require multiple decisions by growers on whether to apply a pesticide, the frequency of applications, and also the type of pesticide to use. We developed models for estimating the probability of switching behavior by hop growers related to use of non-synthetic or synthetic fungicides, or mixtures thereof, for hop powdery mildew (Podosphaera macularis). Growers used non-synthetic fungicides alone in 61.

Identifying highly connected sites for risk-based surveillance and control of cucurbit downy mildew in the eastern United States.

Objective: Surveillance is critical for the rapid implementation of control measures for diseases caused by aerially dispersed plant pathogens, but such programs can be resource-intensive, especially for epidemics caused by long-distance dispersed pathogens. The current cucurbit downy mildew platform for monitoring, predicting and communicating the risk of disease spread in the United States is expensive to maintain. In this study, we focused on identifying sites critical for surveillance and treatment in an attempt to reduce disease monitoring costs and determine where control may be applied to mitigate the risk of disease spread.

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling.

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.

The Functional Biogeography of eDNA Metacommunities in the Post-Fire Landscape of the Angeles National Forest.

Wildfires have continued to increase in frequency and severity in Southern California due in part to climate change. To gain a further understanding of microbial soil communities’ response to fire and functions that may enhance post-wildfire resilience, soil fungal and bacterial microbiomes were studied from different wildfire areas in the Gold Creek Preserve within the Angeles National Forest using 16S, FITS, 18S, 12S, PITS, and COI amplicon sequencing. Sequencing datasets from December 2020 and June 2021 samplings were analyzed using QIIME2, ranacapa, stats, vcd, EZBioCloud, and mixomics.

Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses.

Background: There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts.

Methods: This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents.

Sparse, Predictive, and Interpretable Functional Connectomics with UoI.

Network formation from neural activity is a foundational problem in systems neuroscience. Functional networks, after downstream analysis, can provide key insights into the nature of neurobiological structure and computation. The validity of such insights hinges on accurate selection and estimation of the edges connecting nodes.

Prediction of Spread and Regional Development of Hop Powdery Mildew: A Network Analysis.

Dispersal is a fundamental aspect of epidemic development at multiple spatial scales, including those that extend beyond the borders of individual fields and to the landscape level. In this research, we used the powdery mildew of the hop pathosystem (caused by ) to formulate a model of pathogen dispersal during spring (May to June) and early summer (June to July) at the intermediate scale between synoptic weather systems and microclimate (mesoscale) based on a census of commercial hop yards during 2014 to 2017 in a production region in western Oregon. This pathosystem is characterized by a low level of overwintering of the pathogen as a result of absence of the ascigerious stage of the fungus and consequent annual cycles of localized survival via bud perennation and pathogen spread by windborne dispersal.