JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom.

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

Predicted structural proteome of Sphagnum divinum and proteome-scale annotation.

MOTIVATION: Sphagnum-dominated peatlands store a substantial amount of terrestrial carbon. The genus is undersampled and under-studied. No experimental crystal structure from any Sphagnum species exists in the Protein Data Bank and fewer than 200 Sphagnum-related genes have structural models available in the AlphaFold Protein Structure Database. Tools and resources are needed to help bridge these gaps, and to enable the analysis of other structural proteomes now made possible by accurate structure prediction. RESULTS: We present the predicted structural proteome (25 134 primary transcripts) of Sphagnum divinum computed using AlphaFold, structural alignment results of all high-confidence models against an annotated nonredundant crystallographic database of over 90,000 structures, a structure-based classification of putative Enzyme Commission (EC) numbers across this proteome, and the computational method to perform this proteome-scale structure-based annotation. AVAILABILITY AND IMPLEMENTATION: All data and code are available in public repositories, detailed at https://github.com/BSDExabio/SAFA. The structural models of the S. divinum proteome have been deposited in the ModelArchive repository at https://modelarchive.org/doi/10.5452/ma-ornl-sphdiv.

Sulfur speciation in Sphagnum peat moss modified by mutualistic interactions with cyanobacteria.

Peat moss (Sphagnum spp.) develops mutualistic interactions with cyanobacteria by providing carbohydrates and S compounds in exchange for N-rich compounds, potentially facilitating N inputs into peatlands. Here, we evaluate how colonization of Sphagnum angustifolium hyaline cells by Nostoc muscorum modifies S abundance and speciation at the scales of individual cells and across whole leaves. For the first time, S K-edge X-ray Absorption Spectroscopy was used to identify bulk and micron-scale S speciation across isolated cyanobacteria colonies, and in colonized and uncolonized leaves. Uncolonized leaves contained primarily reduced organic S and oxidized sulfonate- and sulfate-containing compounds. Increasing Nostoc colonization resulted in an enrichment of S and changes in speciation, with increases in sulfate relative to reduced S and sulfonate. At the scale of individual hyaline cells, colonized cells exhibited localized enrichment of reduced S surrounded by diffuse sulfonate, similar to observations of cyanobacteria colonies cultured in the absence of leaves. We infer that colonization stimulates plant S uptake and the production of sulfate-containing metabolites that are concentrated in stem tissues. Sulfate compounds that are produced in response to colonization become depleted in colonized cells where they may be converted into reduced S metabolites by cyanobacteria.

Temperature and CO2 interactively drive shifts in the compositional and functional structure of peatland protist communities.

Microbes affect the global carbon cycle that influences climate change and are in turn influenced by environmental change. Here, we use data from a long-term whole-ecosystem warming experiment at a boreal peatland to answer how temperature and CO2 jointly influence communities of abundant, diverse, yet poorly understood, non-fungi microbial Eukaryotes (protists). These microbes influence ecosystem function directly through photosynthesis and respiration, and indirectly, through predation on decomposers (bacteria and fungi). Using a combination of high-throughput fluid imaging and 18S amplicon sequencing, we report large climate-induced, community-wide shifts in the community functional composition of these microbes (size, shape, and metabolism) that could alter overall function in peatlands. Importantly, we demonstrate a taxonomic convergence but a functional divergence in response to warming and elevated CO2 with most environmental responses being contingent on organismal size: warming effects on functional composition are reversed by elevated CO2 and amplified in larger microbes but not smaller ones. These findings show how the interactive effects of warming and rising CO2 levels could alter the structure and function of peatland microbial food webs-a fragile ecosystem that stores upwards of 25% of all terrestrial carbon and is increasingly threatened by human exploitation.

A research agenda for nonvascular photoautotrophs under climate change.

Nonvascular photoautotrophs (NVP), including bryophytes, lichens, terrestrial algae, and cyanobacteria, are increasingly recognized as being essential to ecosystem functioning in many regions of the world. Current research suggests that climate change may pose a substantial threat to NVP, but the extent to which this will affect the associated ecosystem functions and services is highly uncertain. Here, we propose a research agenda to address this urgent question, focusing on physiological and ecological processes that link NVP to ecosystem functions while also taking into account the substantial taxonomic diversity across multiple ecosystem types. Accordingly, we developed a new categorization scheme, based on microclimatic gradients, which simplifies the high physiological and morphological diversity of NVP and world-wide distribution with respect to several broad habitat types. We found that habitat-specific ecosystem functions of NVP will likely be substantially affected by climate change, and more quantitative process understanding is required on: (1) potential for acclimation; (2) response to elevated CO2 ; (3) role of the microbiome; and (4) feedback to (micro)climate. We suggest an integrative approach of innovative, multimethod laboratory and field experiments and ecophysiological modelling, for which sustained scientific collaboration on NVP research will be essential.

Climate drivers alter nitrogen availability in surface peat and decouple N2 fixation from CH4 oxidation in the Sphagnum moss microbiome.

Peat mosses (Sphagnum spp.) are keystone species in boreal peatlands, where they dominate net primary productivity and facilitate the accumulation of carbon in thick peat deposits. Sphagnum mosses harbor a diverse assemblage of microbial partners, including N2 -fixing (diazotrophic) and CH4 -oxidizing (methanotrophic) taxa that support ecosystem function by regulating transformations of carbon and nitrogen. Here, we investigate the response of the Sphagnum phytobiome (plant + constituent microbiome + environment) to a gradient of experimental warming (+0°C to +9°C) and elevated CO2 (+500 ppm) in an ombrotrophic peatland in northern Minnesota (USA). By tracking changes in carbon (CH4 , CO2 ) and nitrogen (NH4 -N) cycling from the belowground environment up to Sphagnum and its associated microbiome, we identified a series of cascading impacts to the Sphagnum phytobiome triggered by warming and elevated CO2 . Under ambient CO2 , warming increased plant-available NH4 -N in surface peat, excess N accumulated in Sphagnum tissue, and N2 fixation activity decreased. Elevated CO2 offset the effects of warming, disrupting the accumulation of N in peat and Sphagnum tissue. Methane concentrations in porewater increased with warming irrespective of CO2 treatment, resulting in a ~10× rise in methanotrophic activity within Sphagnum from the +9°C enclosures. Warming's divergent impacts on diazotrophy and methanotrophy caused these processes to become decoupled at warmer temperatures, as evidenced by declining rates of methane-induced N2 fixation and significant losses of keystone microbial taxa. In addition to changes in the Sphagnum microbiome, we observed ~94% mortality of Sphagnum between the +0°C and +9°C treatments, possibly due to the interactive effects of warming on N-availability and competition from vascular plant species. Collectively, these results highlight the vulnerability of the Sphagnum phytobiome to rising temperatures and atmospheric CO2 concentrations, with significant implications for carbon and nitrogen cycling in boreal peatlands.

Divergent selection and climate adaptation fuel genomic differentiation between sister species of Sphagnum (peat moss).

BACKGROUND AND AIMS: New plant species can evolve through the reinforcement of reproductive isolation via local adaptation along habitat gradients. Peat mosses (Sphagnaceae) are an emerging model system for the study of evolutionary genomics and have well-documented niche differentiation among species. Recent molecular studies have demonstrated that the globally distributed species Sphagnum magellanicum is a complex of morphologically cryptic lineages that are phylogenetically and ecologically distinct. Here, we describe the architecture of genomic differentiation between two sister species in this complex known from eastern North America: the northern S. diabolicum and the largely southern S. magniae. METHODS: We sampled plant populations from across a latitudinal gradient in eastern North America and performed whole genome and restriction-site associated DNA sequencing. These sequencing data were then analyzed computationally. KEY RESULTS: Using sliding-window population genetic analyses we find that differentiation is concentrated within 'islands' of the genome spanning up to 400 kb that are characterized by elevated genetic divergence, suppressed recombination, reduced nucleotide diversity and increased rates of non-synonymous substitution. Sequence variants that are significantly associated with genetic structure and bioclimatic variables occur within genes that have functional enrichment for biological processes including abiotic stress response, photoperiodism and hormone-mediated signalling. Demographic modelling demonstrates that these two species diverged no more than 225 000 generations ago with secondary contact occurring where their ranges overlap. CONCLUSIONS: We suggest that this heterogeneity of genomic differentiation is a result of linked selection and reflects the role of local adaptation to contrasting climatic zones in driving speciation. This research provides insight into the process of speciation in a group of ecologically important plants and strengthens our predictive understanding of how plant populations will respond as Earth's climate rapidly changes.

Clonality, local population structure and gametophyte sex ratios in cryptic species of the Sphagnum magellanicum complex.

BACKGROUND AND AIMS: Sphagnum (peatmoss) comprises a moss (Bryophyta) clade with ~300-500 species. The genus has unparalleled ecological importance because Sphagnum-dominated peatlands store almost a third of the terrestrial carbon pool and peatmosses engineer the formation and microtopography of peatlands. Genomic resources for Sphagnum are being actively expanded, but many aspects of their biology are still poorly known. Among these are the degree to which Sphagnum species reproduce asexually, and the relative frequencies of male and female gametophytes in these haploid-dominant plants. We assess clonality and gametophyte sex ratios and test hypotheses about the local-scale distribution of clones and sexes in four North American species of the S. magellanicum complex. These four species are difficult to distinguish morphologically and are very closely related. We also assess microbial communities associated with Sphagnum host plant clones and sexes at two sites. METHODS: Four hundred and five samples of the four species, representing 57 populations, were subjected to restriction site-associated DNA sequencing (RADseq). Analyses of population structure and clonality based on the molecular data utilized both phylogenetic and phenetic approaches. Multi-locus genotypes (genets) were identified using the RADseq data. Sexes of sampled ramets were determined using a molecular approach that utilized coverage of loci on the sex chromosomes after the method was validated using a sample of plants that expressed sex phenotypically. Sex ratios were estimated for each species, and populations within species. Difference in fitness between genets was estimated as the numbers of ramets each genet comprised. Degrees of clonality [numbers of genets/numbers of ramets (samples)] within species, among sites, and between gametophyte sexes were estimated. Sex ratios were estimated for each species, and populations within species. Sphagnum-associated microbial communities were assessed at two sites in relation to Sphagnum clonality and sex. KEY RESULTS: All four species appear to engage in a mixture of sexual and asexual (clonal) reproduction. A single ramet represents most genets but two to eight ramets were dsumbers ansd text etected for some genets. Only one genet is represented by ramets in multiple populations; all other genets are restricted to a single population. Within populations ramets of individual genets are spatially clustered, suggesting limited dispersal even within peatlands. Sex ratios are male-biased in S. diabolicum but female-biased in the other three species, although significantly so only in S. divinum. Neither species nor males/females differ in levels of clonal propagation. At St Regis Lake (NY) and Franklin Bog (VT), microbial community composition is strongly differentiated between the sites, but differences between species, genets and sexes were not detected. Within S. divinum, however, female gametophytes harboured two to three times the number of microbial taxa as males. CONCLUSIONS: These four Sphagnum species all exhibit similar reproductive patterns that result from a mixture of sexual and asexual reproduction. The spatial patterns of clonally replicated ramets of genets suggest that these species fall between the so-called phalanx patterns, where genets abut one another but do not extensively mix because of limited ramet fragmentation, and the guerrilla patterns, where extensive genet fragmentation and dispersal result in greater mixing of different genets. Although sex ratios in bryophytes are most often female-biased, both male and female biases occur in this complex of closely related species. The association of far greater microbial diversity for female gametophytes in S. divinum, which has a female-biased sex ratio, suggests additional research to determine if levels of microbial diversity are consistently correlated with differing patterns of sex ratio biases.

Extensive Genome-Wide Phylogenetic Discordance Is Due to Incomplete Lineage Sorting and Not Ongoing Introgression in a Rapidly Radiated Bryophyte Genus.

The relative importance of introgression for diversification has long been a highly disputed topic in speciation research and remains an open question despite the great attention it has received over the past decade. Gene flow leaves traces in the genome similar to those created by incomplete lineage sorting (ILS), and identification and quantification of gene flow in the presence of ILS is challenging and requires knowledge about the true phylogenetic relationship among the species. We use whole nuclear, plastid, and organellar genomes from 12 species in the rapidly radiated, ecologically diverse, actively hybridizing genus of peatmoss (Sphagnum) to reconstruct the species phylogeny and quantify introgression using a suite of phylogenomic methods. We found extensive phylogenetic discordance among nuclear and organellar phylogenies, as well as across the nuclear genome and the nodes in the species tree, best explained by extensive ILS following the rapid radiation of the genus rather than by postspeciation introgression. Our analyses support the idea of ancient introgression among the ancestral lineages followed by ILS, whereas recent gene flow among the species is highly restricted despite widespread interspecific hybridization known in the group. Our results contribute to phylogenomic understanding of how speciation proceeds in rapidly radiated, actively hybridizing species groups, and demonstrate that employing a combination of diverse phylogenomic methods can facilitate untangling complex phylogenetic patterns created by ILS and introgression.

amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool.

SUMMARY: Antimicrobial peptides (AMPs) are promising alternative antimicrobial agents. Currently, however, portable, user-friendly and efficient methods for predicting AMP sequences from genome-scale data are not readily available. Here we present amPEPpy, an open-source, multi-threaded command-line application for predicting AMP sequences using a random forest classifier. AVAILABILITY AND IMPLEMENTATION: amPEPpy is implemented in Python 3 and is freely available through GitHub (https://github.com/tlawrence3/amPEPpy). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Plant carbohydrate storage: intra- and inter-specific trade-offs reveal a major life history trait.

Trade-offs among carbon sinks constrain how trees physiologically, ecologically, and evolutionarily respond to their environments. These trade-offs typically fall along a productive growth to conservative, bet-hedging continuum. How nonstructural carbohydrates (NSCs) stored in living tree cells (known as carbon stores) fit in this trade-off framework is not well understood. We examined relationships between growth and storage using both within species genetic variation from a common garden, and across species phenotypic variation from a global database. We demonstrate that storage is actively accumulated, as part of a conservative, bet-hedging life history strategy. Storage accumulates at the expense of growth both within and across species. Within the species Populus trichocarpa, genetic trade-offs show that for each additional unit of wood area growth (in cm2 yr-1 ) that genotypes invest in, they lose 1.2 to 1.7 units (mg g-1 NSC) of storage. Across species, for each additional unit of area growth (in cm2 yr-1 ), trees, on average, reduce their storage by 9.5% in stems and 10.4% in roots. Our findings impact our understanding of basic plant biology, fit storage into a widely used growth-survival trade-off spectrum describing life history strategy, and challenges the assumptions of passive storage made in ecosystem models today.

Habitat-adapted microbial communities mediate Sphagnum peatmoss resilience to warming.

Sphagnum peatmosses are fundamental members of peatland ecosystems, where they contribute to the uptake and long-term storage of atmospheric carbon. Warming threatens Sphagnum mosses and is known to alter the composition of their associated microbiome. Here, we use a microbiome transfer approach to test if microbiome thermal origin influences host plant thermotolerance. We leveraged an experimental whole-ecosystem warming study to collect field-grown Sphagnum, mechanically separate the associated microbiome and then transfer onto germ-free laboratory Sphagnum for temperature experiments. Host and microbiome dynamics were assessed with growth analysis, Chla fluorescence imaging, metagenomics, metatranscriptomics and 16S rDNA profiling. Microbiomes originating from warming field conditions imparted enhanced thermotolerance and growth recovery at elevated temperatures. Metagenome and metatranscriptome analyses revealed that warming altered microbial community structure in a manner that induced the plant heat shock response, especially the HSP70 family and jasmonic acid production. The heat shock response was induced even without warming treatment in the laboratory, suggesting that the warm-microbiome isolated from the field provided the host plant with thermal preconditioning. Our results demonstrate that microbes, which respond rapidly to temperature alterations, can play key roles in host plant growth response to rapidly changing environments.

Phylogenomic structure and speciation in an emerging model: the Sphagnum magellanicum complex (Bryophyta).

Sphagnum magellanicum is one of two Sphagnum species for which a reference-quality genome exists to facilitate research in ecological genomics. Phylogenetic and comparative genomic analyses were conducted based on resequencing data from 48 samples and RADseq analyses based on 187 samples. We report herein that there are four clades/species within the S. magellanicum complex in eastern North America and that the reference genome belongs to Sphagnum divinum. The species exhibit tens of thousands (RADseq) to millions (resequencing) of fixed nucleotide differences. Two species, however, referred to informally as S. diabolicum and S. magni because they have not been formally described, are differentiated by only 100 (RADseq) to 1000 (resequencing) of differences. Introgression among species in the complex is demonstrated using D-statistics and f4 ratios. One ecologically important functional trait, tissue decomposability, which underlies peat (carbon) accumulation, does not differ between segregates in the S. magellanicum complex, although previous research showed that many closely related Sphagnum species have evolved differences in decomposability/carbon sequestration. Phylogenetic resolution and more accurate species delimitation in the S. magellanicum complex substantially increase the value of this group for studying the early evolutionary stages of climate adaptation and ecological evolution more broadly.

PDKit: A data science toolkit for the digital assessment of Parkinson's Disease.

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson's Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license.

Monitoring COVID-19 on Social Media: Development of an End-to-End Natural Language Processing Pipeline Using a Novel Triage and Diagnosis Approach.

BACKGROUND: The COVID-19 pandemic has created a pressing need for integrating information from disparate sources in order to assist decision makers. Social media is important in this respect; however, to make sense of the textual information it provides and be able to automate the processing of large amounts of data, natural language processing methods are needed. Social media posts are often noisy, yet they may provide valuable insights regarding the severity and prevalence of the disease in the population. Here, we adopt a triage and diagnosis approach to analyzing social media posts using machine learning techniques for the purpose of disease detection and surveillance. We thus obtain useful prevalence and incidence statistics to identify disease symptoms and their severities, motivated by public health concerns. OBJECTIVE: This study aims to develop an end-to-end natural language processing pipeline for triage and diagnosis of COVID-19 from patient-authored social media posts in order to provide researchers and public health practitioners with additional information on the symptoms, severity, and prevalence of the disease rather than to provide an actionable decision at the individual level. METHODS: The text processing pipeline first extracted COVID-19 symptoms and related concepts, such as severity, duration, negations, and body parts, from patients' posts using conditional random fields. An unsupervised rule-based algorithm was then applied to establish relations between concepts in the next step of the pipeline. The extracted concepts and relations were subsequently used to construct 2 different vector representations of each post. These vectors were separately applied to build support vector machine learning models to triage patients into 3 categories and diagnose them for COVID-19. RESULTS: We reported macro- and microaveraged F1 scores in the range of 71%-96% and 61%-87%, respectively, for the triage and diagnosis of COVID-19 when the models were trained on human-labeled data. Our experimental results indicated that similar performance can be achieved when the models are trained using predicted labels from concept extraction and rule-based classifiers, thus yielding end-to-end machine learning. In addition, we highlighted important features uncovered by our diagnostic machine learning models and compared them with the most frequent symptoms revealed in another COVID-19 data set. In particular, we found that the most important features are not always the most frequent ones. CONCLUSIONS: Our preliminary results show that it is possible to automatically triage and diagnose patients for COVID-19 from social media natural language narratives, using a machine learning pipeline in order to provide information on the severity and prevalence of the disease for use within health surveillance systems.

The physiological acclimation and growth response of Populus trichocarpa to warming.

Plant metabolic acclimation to thermal stress remains underrepresented in current global climate models. Gaps exist in our understanding of how metabolic processes (i.e., photosynthesis, respiration) acclimate over time and how aboveground versus belowground acclimation differs. We measured the thermal acclimation of Populus trichocarpa, comparing aboveground versus belowground physiology over time. Ninety genetically identical ramets were propagated in mesocosms that separated root and microbial components. After establishment at 25°C for 6 weeks, 60 clones were warmed +4 or +8°C and monitored for 10 weeks, measuring photosynthesis (A), leaf respiration (R), soil respiration (Rs ), root plus soil respiration (Rs+r ), and root respiration (Rr ). We observed thermal acclimation in both A and R, with rates initially increasing, then declining as the thermal photosynthetic optimum (Topt ) and the temperature-sensitivity (Q10 ) of respiration adjusted to warmer conditions. Photosynthetic acclimation was constructive, based on an increase in both Topt and peak A. Belowground, Rs+r decreased linearly with warming, while Rs rates declined abruptly, then remained constant with additional warming. Plant biomass was greatest at +4°C, with 30% allocated belowground. Rates of mass-based Rr were similar among treatments; however, root nitrogen declined at +8°C leading to less mass nitrogen-based Rr in that treatment. The Q10 -temperature relationship of Rr was affected by warming, leading to differing values among treatments. Aboveground acclimation exceeded belowground acclimation, and plant nitrogen-use mediated the acclimatory response. Results suggest that moderate climate warming (+4°C) may lead to acclimation and increased plant biomass production but increases in production could be limited with severe warming (+8°C).

Phylogenomics reveals convergent evolution of red-violet coloration in land plants and the origins of the anthocyanin biosynthetic pathway.

The flavonoids, one of the largest classes of plant secondary metabolites, are found in lineages that span the land plant phylogeny and play important roles in stress responses and as pigments. Perhaps the most well-studied flavonoids are the anthocyanins that have human health benefits and help plants attract pollinators, regulate hormone production, and confer resistance to abiotic and biotic stresses. The canonical biochemical pathway responsible for the production of these pigments is well-characterized for flowering plants yet its conservation across deep divergences in land plants remains debated and poorly understood. Many early land plants such as mosses, liverworts, and ferns produce flavonoid pigments, but their biosynthetic origins and homologies to the anthocyanin pathway remain uncertain. We conducted phylogenetic analyses using full genome sequences representing nearly all major green plant lineages to reconstruct the evolutionary history of the anthocyanin biosynthetic pathway then test the hypothesis that genes in this pathway are present in early land plants. We found that the entire pathway was not intact until the most recent common ancestor of seed plants and that orthologs of many downstream enzymes are absent from seedless plants including mosses, liverworts, and ferns. Our results also highlight the utility of phylogenetic inference, as compared to pairwise sequence similarity, in orthology assessment within large gene families that have complex duplication-loss histories. We suggest that the production of red-violet flavonoid pigments widespread in seedless plants, including the 3-deoxyanthocyanins, requires the activity of novel, as-yet discovered enzymes, and represents convergent evolution of red-violet coloration across land plants.

Tradeoffs between phenotypic plasticity and local adaptation influence the ecophysiology of the moss, Sphagnum magellanicum.

Bryophytes are a diverse plant group and are functionally different from vascular plants. Yet, their peculiarities are rarely considered in the theoretical frameworks for plants. Currently, we lack information about the magnitude and the importance of intraspecific variability in the ecophysiology of bryophytes and how these might translate to local adaptation-a prerequisite for adaptive evolution. Capitalizing on two ecologically distinct (hummock and hollow) phenotypes of Sphagnum magellanicum, we explored the magnitude and pattern of intraspecific variability in this species and asked whether the environmental-mediated changes in shoot and physiological traits are due to phenotypic plasticity or local adaptation. Size, pigmentation, and habitat type that distinguished the phenotypes in the field did not influence the trait responses under a transplant and factorial experiment. In addition, the magnitude and pattern of trait variability (e.g., branch, stem and capitulum mass) changed with the treatments, which suggest that trait responses were due largely to phenotypic plasticity. The trait responses also suggest that the ecophysiological needs for mosses to grow in clumps, where they maintain a uniform growth may have an overriding effect over the potential for a fixed adaptive response to environmental heterogeneity, which would constrain local adaptation. We conclude that extending the trait-based framework to mosses or making comparisons between mosses and vascular plants under any theoretical framework would only be meaningful to the extent that growth form and dispersal strategies are considered.

Learning structured medical information from social media.

Our goal is to summarise and aggregate information from social media regarding the symptoms of a disease, the drugs used and the treatment effects both positive and negative. To achieve this we first apply a supervised machine learning method to automatically extract medical concepts from natural language text. In an environment such as social media, where new data is continuously streamed, we need a methodology that will allow us to continuously train with the new data. To attain such incremental re-training, a semi-supervised methodology is developed, which is capable of learning new concepts from a small set of labelled data together with the much larger set of unlabelled data. The semi-supervised methodology deploys a conditional random field (CRF) as the base-line training algorithm for extracting medical concepts. The methodology iteratively augments to the training set sentences having high confidence, and adds terms to existing dictionaries to be used as features with the base-line model for further classification. Our empirical results show that the base-line CRF performs strongly across a range of different dictionary and training sizes; when the base-line is built with the full training data the F1 score reaches the range 84%-90%. Moreover, we show that the semi-supervised method produces a mild but significant improvement over the base-line. We also discuss the significance of the potential improvement of the semi-supervised methodology and found that it is significantly more accurate in most cases than the underlying base-line model.

Correction to: Diel rewiring and positive selection of ancient plant proteins enabled evolution of CAM photosynthesis in Agave.

Following publication of the original article.