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Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020-2030 frames our ability to perform vital and far-reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1-4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant-based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid-response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non-invasive imaging, sensors, and plug-and-play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence-assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

Since its emergence in late 2019, more than 481 million COVID-19 cases have been confirmed worldwide (2) and >79 million cases reported in the United States (2). Numerous variants of [...]

Background: Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential. Objectives: This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants. Methods: Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority. Results: Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS- CoV-2 concentrations ranged from 2.9 to 5.1 [log.sub.10] gene copies per liter. Wastewater SARS-CoV-2 concentrations were more highly correlated (Pearson's r = 0.96; [R.sup.2] = 0.91) with community prevalence than were clinically reported cases of COVID-19 (Pearson's r = 0.85; [R.sup.2] = 0.73). Monte Carlo simulations indicated that wastewater SARS-CoV-2 concentrations were significantly better than clinically reported cases at estimating prevalence (p < 0.05). In addition, wastewater analyses determined neighborhood-level COVID-19 hot spots and identified SARS-CoV-2 variants (B.1 and B.1.399) at the neighborhood and city scales. Discussion: The greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289, Introduction Wastewater-based epidemiology (WBE) has emerged as an effective and sensitive approach for monitoring COVID-19 presence in a community through the detection of the novel coronavirus (SARS-CoV-2) shed by infected [...]

Abstract Background Pythium ultimum is a ubiquitous oomycete plant pathogen responsible for a variety of diseases on a broad range of crop and ornamental species. Results The P. ultimum genome (42.8 Mb) encodes 15,290 genes and has extensive sequence similarity and synteny with related Phytophthora species, including the potato blight pathogen Phytophthora infestans. Whole transcriptome sequencing revealed expression of 86% of genes, with detectable differential expression of suites of genes under abiotic stress and in the presence of a host. The predicted proteome includes a large repertoire of proteins involved in plant pathogen interactions, although, surprisingly, the P. ultimum genome does not encode any classical RXLR effectors and relatively few Crinkler genes in comparison to related phytopathogenic oomycetes. A lower number of enzymes involved in carbohydrate metabolism were present compared to Phytophthora species, with the notable absence of cutinases, suggesting a significant difference in virulence mechanisms between P. ultimum and more host-specific oomycete species. Although we observed a high degree of orthology with Phytophthora genomes, there were novel features of the P. ultimum proteome, including an expansion of genes involved in proteolysis and genes unique to Pythium. We identified a small gene family of cadherins, proteins involved in cell adhesion, the first report of these in a genome outside the metazoans. Conclusions Access to the P. ultimum genome has revealed not only core pathogenic mechanisms within the oomycetes but also lineage-specific genes associated with the alternative virulence and lifestyles found within the pythiaceous lineages compared to the Peronosporaceae.

The complete sequences of the mitochondrial genomes of the oomycetes of Phytophthora ramorum and P. sojae were determined during the course of their complete nuclear genome sequencing (Tyler, et al. 2006). Both are circular, with sizes of 39,314 bp for P. ramorum and 42,975 bp for P. sojae. Each contains a total of 37 identifiable protein-encoding genes, 25 or 26 tRNAs (P. sojae and P. ramorum, respectively) specifying 19 amino acids, and a variable number of ORFs (7 for P. ramorum and 12 for P. sojae) which are potentially additional functional genes. Non-coding regions comprise approximately 11.5 percent and 18.4 percent of the genomes of P. ramorum and P. sojae, respectively. Relative to P. sojae, there is an inverted repeat of 1,150 bp in P. ramorum that includes an unassigned unique ORF, a tRNA gene, and adjacent non-coding sequences, but otherwise the gene order in both species is identical. Comparisons of these genomes with published sequences of the P. infestans mitochondrial genome reveals a number of similarities, but the gene order in P. infestans differs in two adjacent locations due to inversions. Sequence alignments of the three genomes indicated sequence conservation ranging from 75 to 85 percent and that specific regions were more variable than others.