Overview of the COVID-19 text mining tool interactive demonstration track in BioCreative VII.

The coronavirus disease 2019 (COVID-19) pandemic has compelled biomedical researchers to communicate data in real time to establish more effective medical treatments and public health policies. Nontraditional sources such as preprint publications, i.e. articles not yet validated by peer review, have become crucial hubs for the dissemination of scientific results. Natural language processing (NLP) systems have been recently developed to extract and organize COVID-19 data in reasoning systems. Given this scenario, the BioCreative COVID-19 text mining tool interactive demonstration track was created to assess the landscape of the available tools and to gauge user interest, thereby providing a two-way communication channel between NLP system developers and potential end users. The goal was to inform system designers about the performance and usability of their products and to suggest new additional features. Considering the exploratory nature of this track, the call for participation solicited teams to apply for the track, based on their system's ability to perform COVID-19-related tasks and interest in receiving user feedback. We also recruited volunteer users to test systems. Seven teams registered systems for the track, and >30 individuals volunteered as test users; these volunteer users covered a broad range of specialties, including bench scientists, bioinformaticians and biocurators. The users, who had the option to participate anonymously, were provided with written and video documentation to familiarize themselves with the NLP tools and completed a survey to record their evaluation. Additional feedback was also provided by NLP system developers. The track was well received as shown by the overall positive feedback from the participating teams and the users. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-4/.

Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach.

There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed. Here, we present a set of criteria for evaluating these systems, called Hallmarks of Human-Machine Collaboration. The Hallmarks build on the success of heuristic evaluation used by the user interface community and past evaluation techniques used in the spoken language and chatbot communities. They consist of observable characteristics indicative of successful collaborative communication, grouped into eight high-level properties: robustness; habitability; mutual contribution of meaningful content; context-awareness; consistent human engagement; provision of rationale; use of elementary concepts to teach and learn new concepts; and successful collaboration. We present examples of how we used these Hallmarks in the DARPA Communicating with Computers (CwC) program to evaluate diverse activities, including story and music generation, interactive building with blocks, and exploration of molecular mechanisms in cancer. We used the Hallmarks as guides for developers and as diagnostics, assessing systems with the Hallmarks to identify strengths and opportunities for improvement using logs from user studies, surveying the human partner, third-party review of creative products, and direct tests. Informal feedback from CwC technology developers indicates that the use of the Hallmarks for program evaluation helped guide development. The Hallmarks also made it possible to identify areas of progress and major gaps in developing systems where the machine is an equal, creative partner.

Pathogen metadata platform: software for accessing and analyzing pathogen strain information.

BACKGROUND: Pathogen metadata includes information about where and when a pathogen was collected and the type of environment it came from. Along with genomic nucleotide sequence data, this metadata is growing rapidly and becoming a valuable resource not only for research but for biosurveillance and public health. However, current freely available tools for analyzing this data are geared towards bioinformaticians and/or do not provide summaries and visualizations needed to readily interpret results. RESULTS: We designed a platform to easily access and summarize data about pathogen samples. The software includes a PostgreSQL database that captures metadata useful for disease outbreak investigations, and scripts for downloading and parsing data from NCBI BioSample and BioProject into the database. The software provides a user interface to query metadata and obtain standardized results in an exportable, tab-delimited format. To visually summarize results, the user interface provides a 2D histogram for user-selected metadata types and mapping of geolocated entries. The software is built on the LabKey data platform, an open-source data management platform, which enables developers to add functionalities. We demonstrate the use of the software in querying for a pathogen serovar and for genome sequence identifiers. CONCLUSIONS: This software enables users to create a local database for pathogen metadata, populate it with data from NCBI, easily query the data, and obtain visual summaries. Some of the components, such as the database, are modular and can be incorporated into other data platforms. The source code is freely available for download at https://github.com/wchangmitre/bioattribution .

The role of climate adaptation in colonization success in Arabidopsis thaliana.

Understanding the genetic mechanisms that contribute to range expansion and colonization success within novel environments is important for both invasion biology and predicting species-level responses to changing environments. If populations are adapted to local climates across a species' native range, then climate matching may predict which genotypes will successfully establish in novel environments. We examine evidence for climate adaptation and its role in colonization of novel environments in the model species, Arabidopsis thaliana. We review phenotypic and genomic evidence for climate adaptation within the native range and describe new analyses of fitness data from European accessions introduced to Rhode Island, USA, in spring and fall plantings. Accessions from climates similar to the Rhode Island site had higher fitness indicating a potential role for climate pre-adaptation in colonization success. A genomewide association study (GWAS), and genotypic mean correlations of fitness across plantings suggest the genetic basis of fitness in Rhode Island differs between spring and autumn cohorts, and from previous fitness measurements in European field sites. In general, these observations suggest a scenario of conditional neutrality for loci contributing to colonization success, although there was evidence of a fitness trade-off between fall plantings in Norwich, UK, and Rhode Island. GWAS suggested that antagonistic pleiotropy at a few specific loci may contribute to this trade-off, but this conclusion depended upon the accessions included in the analysis. Increased genomic information and phenotypic information make A. thaliana a model system to test for the genetic basis of colonization success in novel environments.

Lagging adaptation to warming climate in Arabidopsis thaliana.

If climate change outpaces the rate of adaptive evolution within a site, populations previously well adapted to local conditions may decline or disappear, and banked seeds from those populations will be unsuitable for restoring them. However, if such adaptational lag has occurred, immigrants from historically warmer climates will outperform natives and may provide genetic potential for evolutionary rescue. We tested for lagging adaptation to warming climate using banked seeds of the annual weed Arabidopsis thaliana in common garden experiments in four sites across the species' native European range: Valencia, Spain; Norwich, United Kingdom; Halle, Germany; and Oulu, Finland. Genotypes originating from geographic regions near the planting site had high relative fitness in each site, direct evidence for broad-scale geographic adaptation in this model species. However, genotypes originating in sites historically warmer than the planting site had higher average relative fitness than local genotypes in every site, especially at the northern range limit in Finland. This result suggests that local adaptive optima have shifted rapidly with recent warming across the species' native range. Climatic optima also differed among seasonal germination cohorts within the Norwich site, suggesting that populations occurring where summer germination is common may have greater evolutionary potential to persist under future warming. If adaptational lag has occurred over just a few decades in banked seeds of an annual species, it may be an important consideration for managing longer-lived species, as well as for attempts to conserve threatened populations through ex situ preservation.

Controlled vocabularies for microbial virulence factors.

Knowledge about pathogenesis is increasing dramatically, and most of this information is stored in the scientific literature or in sequence databases. This information can be made more accessible by the use of ontologies or controlled vocabularies. Recently, several ontologies, controlled vocabularies and databases have been developed or adapted for virulence factors and their roles in pathogenesis. Here, we discuss these systems, how they are being used in research and the challenges that remain for developing and applying ontologies for virulence factors.

Fitness effects associated with the major flowering time gene FRIGIDA in Arabidopsis thaliana in the field.

To date, the effect of natural selection on candidate genes underlying complex traits has rarely been studied experimentally, especially under ecologically realistic conditions. Here we report that the effect of selection on the flowering time gene FRIGIDA (FRI) reverses depending on the season of germination and allelic variation at the interacting gene FLOWERING LOCUS C (FLC). In field studies of 136 European accessions of Arabidopsis thaliana, accessions with putatively functional FRI alleles had higher winter survival in one FLC background in a fall-germinating cohort, but accessions with deletion null FRI alleles had greater seed production in the other FLC background in a spring-germinating cohort. Consistent with FRI's role in flowering, selection analyses suggest that the difference in winter survival can be attributed to time to bolting. However, in the spring cohort, the fitness difference was associated with rosette size. Our analyses also reveal that controlling for population structure with estimates of inferred ancestry and a geographical restriction was essential for detecting fitness associations. Overall, our results suggest that the combined effects of seasonally varying selection and epistasis could explain the maintenance of variation at FRI and, more generally, may be important in the evolution of genes underlying complex traits.

A novel cost of R gene resistance in the presence of disease.

Resistance responses can impose fitness costs when pests are absent. Here, we test whether the induction of resistance can decrease fitness even in plants under attack; we call this potential outcome a net cost with attack. Using lines in which genetic background was controlled, we investigated whether susceptible Arabidopsis thaliana plants can outperform R gene resistant plants when infected with pathogens. For the R gene RPS2, there was a fitness benefit of resistance in the presence of intraspecific competition, but there was a net cost in the absence of competition: resistant plants produced less seed than susceptible plants even though infected with Pseudomonas syringae. This net cost was primarily due to overcompensation by susceptible plants, which occurred because of a developmental response to infection. For the R gene RPP5, there was no fitness effect of resistance without competition but a net cost when plants were infected with Peronospora parasitica in the presence of competition. This net cost was due to a reduction in the fitness of infected, resistant plants and complete compensation in susceptible plants. A spatially variable model suggests that a trade-off between net benefits and net costs with attack may help explain the persistence of individuals lacking R gene resistance to disease.

A developmental response to pathogen infection in Arabidopsis.

We present evidence that susceptible Arabidopsis plants accelerate their reproductive development and alter their shoot architecture in response to three different pathogen species. We infected 2-week-old Arabidopsis seedlings with two bacterial pathogens, Pseudomonas syringae and Xanthomonas campestris, and an oomycete, Peronospora parasitica. Infection with each of the three pathogens reduced time to flowering and the number of aerial branches on the primary inflorescence. In the absence of competition, P. syringae and P. parasitica infection also increased basal branch development. Flowering time and branch responses were affected by the amount of pathogen present. Large amounts of pathogen caused the most dramatic changes in the number of branches on the primary inflorescence, but small amounts of P. syringae caused the fastest flowering and the production of the most basal branches. RPS2 resistance prevented large changes in development when it prevented visible disease symptoms but not at high pathogen doses and when substantial visible hypersensitive response occurred. These experiments indicate that phylogenetically disparate pathogens cause similar changes in the development of susceptible Arabidopsis. We propose that these changes in flowering time and branch architecture constitute a general developmental response to pathogen infection that may affect tolerance of and/or resistance to disease.

Natural selection for polymorphism in the disease resistance gene Rps2 of Arabidopsis thaliana.

Pathogen resistance is an ecologically important phenotype increasingly well understood at the molecular genetic level. In this article, we examine levels of avrRpt2-dependent resistance and Rps2 locus DNA sequence variability in a worldwide sample of 27 accessions of Arabidopsis thaliana. The rooted parsimony tree of Rps2 sequences drawn from a diverse set of ecotypes includes a deep bifurcation separating major resistance and susceptibility clades of alleles. We find evidence for selection maintaining these alleles and identify the N-terminal part of the leucine-rich repeat region as a probable target of selection. Additional protein variants are found within the two major clades and correlate well with measurable differences among ecotypes in resistance to the avirulence gene avrRpt2 of the pathogen Pseudomonas syringae. Long-lived polymorphisms have been observed for other resistance genes of A. thaliana; the Rps2 data suggest that the long-term maintenance of phenotypic variation in resistance genes may be a general phenomenon and are consistent with diversifying selection acting in concert with selection to maintain variation.