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  1. Yavitt, Joseph B. (Ed.)
    As global change shifts the species composition of forests, we need to understand which species characteristics affect soil organic matter cycling to predict future soil carbon (C) storage. Recently, whether a tree species forms a symbiosis with arbuscular (AM) versus ectomycorrhizal (EcM) fungi has been suggested as a strong predictor of soil carbon storage, but there is wide variability within EcM systems. In this study, we investigated how mycorrhizal associations and the species composition of canopy trees and mycorrhizal fungi relate to the proportion of soil C and nitrogen (N) in mineral-associations and soil C:N across four sites representing distinct climates and tree communities in the Eastern U.S. broadleaf forest biome. In two of our sites, we found the expected relationship of declining mineral-associated C and N and increasing soil C:N ratios as the basal area of EcM-associating trees increased. However, across all sites these soil properties strongly correlated with canopy tree and fungal species composition. Sites where the expected pattern with EcM basal area was observed were 1) dominated by trees with lower quality litter in the Pinaceae and Fagaceae families and 2) dominated by EcM fungi with medium distance exploration type hyphae, melanized tissues, and the potential to produce peroxidases. This observational study demonstrates that differences in soil organic matter between AM andEcM systems are dependent on the taxa of trees and EcM fungi involved. Important information is lost when the rich mycorrhizal symbiosis is reduced to two categories. 
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  2. Wu, Joseph T. (Ed.)
    The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real-time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities—a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number. 
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  3. Yavitt, Joseph B. (Ed.)
    Conspecific negative density dependence (CNDD) promotes tree species diversity by reducing recruitment near conspecific adults due to biotic feedbacks from herbivores, pathogens, or competitors. While this process is well-described in tropical forests, tests of temperate tree species range from strong positive to strong negative density dependence. To explain this, several studies have suggested that tree species traits may help predict the strength and direction of density dependence: for example, ectomycorrhizal-associated tree species typically exhibit either positive or weaker negative conspecific density dependence. More generally, the strength of density dependence may be predictably related to other species-specific ecological attributes such as shade tolerance, or the relative local abundance of a species. To test the strength of density dependence and whether it affects seedling community diversity in a temperate forest, we tracked the survival of seedlings of three ectomycorrhizal-associated species experimentally planted beneath conspecific and heterospecific adults on the Prospect Hill tract of the Harvard Forest, in Massachusetts, USA. Experimental seedling survival was always lower under conspecific adults, which increased seedling community diversity in one of six treatments. We compared these results to evidence of CNDD from observed sapling survival patterns of 28 species over approximately 8 years in an adjacent 35-ha forest plot. We tested whether species-specific estimates of CNDD were associated with mycorrhizal association, shade tolerance, and local abundance. We found evidence of significant, negative conspecific density dependence (CNDD) in 23 of 28 species, and positive conspecific density dependence in two species. Contrary to our expectations, ectomycorrhizal-associated species generally exhibited stronger (e.g., more negative) CNDD than arbuscular mycorrhizal-associated species. CNDD was also stronger in more shade-tolerant species but was not associated with local abundance. Conspecific adult trees often have a negative influence on seedling survival in temperate forests, particularly for tree species with certain traits. Here we found strong experimental and observational evidence that ectomycorrhizal-associating species consistently exhibit CNDD. Moreover, similarities in the relative strength of density dependence from experiments and observations of sapling mortality suggest a mechanistic link between negative effects of conspecific adults on seedling and sapling survival and local tree species distributions. 
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  4. Jez, Joseph (Ed.)
  5. Matoba, Osamu ; Valenta, Christopher R. ; Shaw, Joseph A. (Ed.)
    Free, publicly-accessible full text available May 22, 2024
  6. Matoba, Osamu ; Valenta, Christopher R. ; Shaw, Joseph A. (Ed.)
    Free, publicly-accessible full text available May 22, 2024
  7. Wu, Joseph T. (Ed.)
    Colombia announced the first case of severe acute respiratory syndrome coronavirus 2 on March 6, 2020. Since then, the country has reported a total of 5,002,387 cases and 127,258 deaths as of October 31, 2021. The aggressive transmission dynamics of SARS-CoV-2 motivate an investigation of COVID-19 at the national and regional levels in Colombia. We utilize the case incidence and mortality data to estimate the transmission potential and generate short-term forecasts of the COVID-19 pandemic to inform the public health policies using previously validated mathematical models. The analysis is augmented by the examination of geographic heterogeneity of COVID-19 at the departmental level along with the investigation of mobility and social media trends. Overall, the national and regional reproduction numbers show sustained disease transmission during the early phase of the pandemic, exhibiting sub-exponential growth dynamics. Whereas the most recent estimates of reproduction number indicate disease containment, with R t <1.0 as of October 31, 2021. On the forecasting front, the sub-epidemic model performs best at capturing the 30-day ahead COVID-19 trajectory compared to the Richards and generalized logistic growth model. Nevertheless, the spatial variability in the incidence rate patterns across different departments can be grouped into four distinct clusters. As the case incidence surged in July 2020, an increase in mobility patterns was also observed. On the contrary, a spike in the number of tweets indicating the stay-at-home orders was observed in November 2020 when the case incidence had already plateaued, indicating the pandemic fatigue in the country. 
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  8. Bondy-Denomy, Joseph (Ed.)
    ABSTRACT Chemical communication between bacteria and between bacteria and the bacteriophage (phage) viruses that prey on them can shape the outcomes of phage-bacterial encounters. Quorum sensing (QS) is a bacterial cell-to-cell communication process that promotes collective undertaking of group behaviors. QS relies on the production, release, accumulation, and detection of signal molecules called autoinducers. Phages can exploit QS-mediated communication to manipulate their hosts and maximize their own survival. In the opportunistic pathogen Pseudomonas aeruginosa , the LasI/R QS system induces the RhlI/R QS system, and in opposing manners, these two systems control the QS system that relies on the autoinducer called PQS. A P. aeruginosa Δ lasI mutant is impaired in PQS synthesis, leading to accumulation of the precursor molecule HHQ, and HHQ suppresses growth of the P. aeruginosa Δ lasI strain. We show that, in response to a phage infection, the P. aeruginosa Δ lasI mutant reactivates QS, which, in turn, restores pqsH expression, enabling conversion of HHQ into PQS. Moreover, downstream QS target genes encoding virulence factors are induced. Additionally, phage-infected P. aeruginosa Δ lasI cells transiently exhibit superior growth compared to uninfected cells. IMPORTANCE Clinical isolates of P. aeruginosa frequently harbor mutations in particular QS genes. Here, we show that infection by select temperate phages restores QS, a cell-to-cell communication mechanism in a P. aeruginosa QS mutant. Restoration of QS increases expression of genes encoding virulence factors. Thus, phage infection of select P. aeruginosa strains may increase bacterial pathogenicity, underscoring the importance of characterizing phage-host interactions in the context of bacterial mutants that are relevant in clinical settings. 
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  9. Dubrovsky, Joseph (Ed.)
    Abstract

    A fundamental question in developmental biology is how the progeny of stem cells become differentiated tissues. The Arabidopsis root is a tractable model to address this question due to its simple organization and defined cell lineages. In particular, the zone of dividing cells at the root tip—the root apical meristem—presents an opportunity to map the gene regulatory networks underlying stem cell niche maintenance, tissue patterning, and cell identity acquisition. To identify molecular regulators of these processes, studies over the last 20 years employed global profiling of gene expression patterns. However, these technologies are prone to information loss due to averaging gene expression signatures over multiple cell types and/or developmental stages. Recently developed high-throughput methods to profile gene expression at single-cell resolution have been successfully applied to plants. Here, we review insights from the first published single-cell mRNA sequencing and chromatin accessibility datasets generated from Arabidopsis roots. These studies successfully reconstruct developmental trajectories, phenotype cell identity mutants at unprecedented resolution, and reveal cell type-specific responses to environmental stimuli. The experimental insight gained from Arabidopsis paves the way to profile roots from additional species.

     
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  10. Turkan, Yelda ; Louis, Joseph ; Leite, Fernanda ; Ergan, Semiha (Ed.)
    Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers’ activities. HAR has many applications in human-robot interaction research to enable robots’ understanding of human counterparts’ activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction. 
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