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Zadeh, P. B.

Publications and source records attributed to Zadeh, P. B..

2 recordsLinked to original sources

Do rhizobia buffer legumes against climate-mediated microbiome effects?

Climate change is altering both soil microbial communities and the ecological context of plant-microbe interactions. Heat, drought, and their legacies can alter soil microbiomes and potential plant symbionts, but the direct consequences of these microbial changes on plant performance and plant investment in symbiosis remain underexplored. Predicting how soil microbes modulate plant resilience to heat and drought is critical to mitigating the negative effects of climate change on ecosystems and agriculture. In this study, we conducted growth chamber experiments to isolate the microbially-mediated indirect effects of heat and drought on plant performance and symbiosis. In the first experiment, focused on drought, we found that drought and drought-treated microbes, along with their interaction, significantly decreased the biomass of Medicago lupulina plants compared to well-watered microbiomes and conditions. In a second experiment, we then tested how the addition of a well-known microbial mutualist, Sinorhizobium meliloti, affected heat- and drought-treated microbiomes impact on M. lupulina. We found that drought-adapted microbiomes negatively impacted legume performance by increasing mortality and reducing branch number, but that adding rhizobia erased differences in plant response to climate treated soils. In contrast, heat-adapted microbiomes did not differ significantly from control microbiomes in their effects on a legume. Our results suggest microbial legacy effects, mutualist partners, and their interactions are important in mediating plant responses to drought, with some mutualists equalizing plant responses across microbial legacies.

ecology↗

PHARAOH: A collaborative crowdsourcing platform for PHenotyping And Regional Analysis Of Histology

Deep learning has proven to be capable of automating key aspects of histopathologic analysis, but its continual reliance on large expert-annotated training datasets hinders widespread adoption. Here, we present an online collaborative portal that streamlines tissue image annotation to promote the development and sharing of custom computer vision models for PHenotyping And Regional Analysis Of Histology (PHARAOH; https://www.pathologyreports.ai/). PHARAOH uses a weakly supervised active learning framework whereby patch-level image features are leveraged to organize large swaths of tissue into morphologically-uniform clusters for batched human annotation. By providing cluster-level labels on only a handful of cases, we show how custom PHARAOH models can be developed and used to guide the quantification of cellular features that correlate with molecular, pathologic and patient outcome data. Both custom model design and feature extraction pipelines are amenable to crowdsourcing making PHARAOH a fully scalable systems-level solution for the systematic expansion and cataloging of computational pathology applications.

bioinformatics↗