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Characterizing the MARS community; physiological, behavioral, and genetic traits that shape the assembly and function of the plant-growth-promoting MAize Rhizosphere Synthetic community
No full textMicrobes are now understood to play a vital role in plant health and are being looked to as a way to promote plant growth to address current challenges in agriculture. Bacteria have been found to be able to alleviate a variety of abiotic stresses, including drought, heat, salinity, and nutrient stress, but the search for microbial inoculants is hindered by a lack of understanding of the mechanisms underlying microbiome assembly and maintenance. Studies using a single strain of bacteria lack the broader context of microbial interactions, leading to the identification of promising strains in the lab that then fail when faced with the native microbes in the field. Synthetic communities (SynComs) offer an experimentally tractable way for us to examine microbiome processes and answer mechanistic questions about how microbial communities are assembled and maintained. We created the MAize Rhizosphere Synthetic community (MARSc) as a model for studying how these processes shape the maize rhizosphere microbiome. We began by characterizing the growth and behaviour of individuals of MARSc followed by examining interactions of increasing complexity with the goal of identifying factors that contribute to community dynamics. Finally, we examined interactions between MARSc and maize to assess how factors influencing rhizosphere colonization and how MARSc affects plant growth.
MARSc members were isolated from the roots of maize grown in soil with a history of low inorganic nitrogen inputs, and were combined to represent the phylogenetic diversity of maize communities in the field. To this community, we added the model rhizosphere colonist and synthetic biology chassis Pseudomonas alloputida KT2440. Part of developing a SynCom is characterizing the isolates as individuals, as part of this we assembled de novo genome assemblies of all members using a hybrid consensus pipeline. Assembly against a reference can result in the loss of novel genetic elements and many of the MARSc members lacked quality reference genomes in the first place, thus we chose to perfom de novo assemblies to capture as much data as possible. A hybrid consensus pipeline combines long read assemblies from multiple assembly programs to create a consensus that is then polished with Illumina short reads, leading to accurate and complete genomes. With these genomes, we identified 13 members of MARSc as potential novel species based on whole genome phylogeny, re-capitulating our decision to assemble the genomes de novo. Our next step in characterizing MARSc was to look at the growth and behaviour of individuals and the effects of interactions within the community on said traits.
The majority of interactions in the community likely take place in the form of diffusible metabolites. Pairwise interactions between MARSc members were largely determined by nutrients, with low nutrient conditions leading to more inhibitory interactions and high nutrient conditions leading to fewer inhibitory interactions. Inhibitory interactions were mostly based on the production of inhibitory compounds while stimulatory interactions generally fit the pattern of cross-feeding. We then examined biofilm formation, as many rhizosphere bacteria live in multi-species biofilms and the ability to form biofilm has been associated with successful rhizosphere colonization. We found that root exudates, which contain metabolites that plants use to shape the rhizosphere microbiome, are broadly stimulatory to MARSc growth and biofilm formation. Filter plate assays, which allow for metabolite exchange without physical contact and for which we used the whole community as a metabolite source, showed community metabolites had significant effects on biofilm formation for 9 strains. Spent media assays in the same system revealed that reduction of biofilm formation was most likely due to the passive production of inhibitory compounds, but stimulation of biofilm formation was more variable and, in some cases, dependent on active metabolite exchange. Altogether, these interactions build on our understanding of how metabolite exchange shapes microbial communities.
Next, we sought to assess how well MARSc members colonize the rhizosphere and to see whether the behavioral, physiological, and genetic traits we collected could be linked to community dynamics in planta. To help in profiling the abundance of each member in communities established on roots, we developed a pipeline for full-length amplicon profiling: the Amplicon Consensus Taxonomy (ACT) pipeline. Full-length 16S rRNA amplicons give better phylogenetic resolution amplicons of the V3-V4 hypervariable regions but has only recently become feasible due to reductions in the error rates of long-read sequencing technologies. The ACT pipeline combines two popular taxonomic assignment pipelines to leverage the pros and cons of each: Emu, which has higher accuracy for named species but at the cost of rare and unnamed species, and Sintax, which has lower accuracy but can identify unnamed species. We also introduced the assignment of operational taxonomic units (OTUs) using the Long Amplicon Consensus Analysis (LACA) pipeline. The ACT pipeline has the accuracy of Emu in identifying known taxa but is also capable of classifying unnamed and low abundance species that would be dropped by Emu. The ACT pipeline supported the estimation of significantly greater alpha-diversities than Emu or Sintax alone when characterizing complex communities that contain novel taxa or taxa whose classification is not well resolved, such as the MARSc SynCom.
Finally, we used the ACT pipeline to address how MARSc colonizes the rhizosphere. In particular, we examined how MARSc influences the plant and vice versa by inoculating maize with MARSc. MARSc-treated plants showed significantly increased biomass 28 days after planting in multiple different nitrogen levels. The biofilm formation capabilities of strains in culture were not correlated with rhizosphere abundance, but genes related to surface attachment and exopolysaccharide production were. MARSc-treated roots differed significantly in root architecture from untreated roots, mostly due to increased root branching. Using single-strain inoculants, we identified MARSc strains that promote seminal and lateral root branching as well as root elongation. We also found significant correlations between the abundance of certain species in the rhizosphere and root architecture traits and identified two MARSc members, Acidovorax R-28 and Pedobacter R-06, with significant correlations with decreased average root diameter and increased plant weight and number of roots. Altogether, we have used the MARSc to begin to build a foundation for understanding interactions governing the maize rhizosphere microbiome
Communication-efficient personalization in federated learning for edge devices
No full textThis dissertation advances practical, privacy-preserving federated learning under real-world constraints of heterogeneity, limited resources, and diverse data modalities. It develops algorithms that make collaborative training more efficient, robust, and personalized without centralizing data.
First, we introduce a dataset-aware dynamic pruning strategy coupled with gradient control to curb overfitting on heterogeneous clients, stabilize convergence, and lower both computation and communication during local updates.
Next, we propose a multimodal federated framework with dual adapters: one larger adapter that is private to each client for personalization and a compact, shared adapter for knowledge transfer, augmented with selective pruning to balance local adaptation and global generalization for vision and language tasks.
Then, we present a lightweight, convolution-based approach to time-series forecasting that pairs learnable trend/seasonality decomposition with an efficient federated protocol, enabling accurate prediction across distributed, streaming signals on constrained devices.
Finally, we develop adaptive federated distillation with dual adapters and instance-wise fusion, aligning shared knowledge at the server while preserving client-specific representations to improve personalization under non-IID data.
Together, these contributions chart a cohesive path toward scalable, resource-aware, and personalization friendly federated learning across data types and tasks, closing the gap between theoretical promise and deployment reality while maintaining user privacy
Accessible Seed Germination and Storage Techniques for ex Situ Conservation of Yellow Necklacepod (Sophora tomentosa)
No full textYellow necklacepod (Sophora tomentosa L.) is a native shrub on Saint John, US Virgin Islands (USVI) and an important contributor to key ecological services, particularly after native vegetation was degraded during the 2017 hurricane season. A lack of relevant and accessible methods for seed propagation of native plants in the Caribbean region presents a barrier to ex situ production to restore landscapes. This study compared two dormancy-breaking practices of seedcoat puncture and gibberellic acid application and two storage conditions of indoor ambient and refrigerated environments on seed germination of S. tomentosa to provide species-specific germination and storage recommendations. Physical dormancy of S. tomentosa seeds was confirmed and seedcoat puncturing is an effective and accessible scarification strategy. A gibberellic acid application was found to increase germination speed and uniformity but is not required to induce germination. If conditions are not ideal for immediate germination, seeds of S. tomentosa can be stored for up to 6 months in a household refrigerator without a significant reduction in germination or viability. The effective germination and storage strategies identified in this study can assist in plant production of S. tomentosa for restoration efforts and landscape use in USVI, the Caribbean region, and other subtropical and tropical regions. Methods used in the study to assess the physical and/or non–deep physiological dormancy of seeds can be replicated in trials with other understudied native plant species.This article is published as Rogers, D. R., Nonnecke, G. R., & Goggi, A. S. (2026). Accessible Seed Germination and Storage Techniques for ex Situ Conservation of Yellow Necklacepod (Sophora tomentosa). HortScience, 61(1), 151–158. https://doi.org/10.21273/HORTSCI19062-2
Curiouser and Curiouser: The Macrocyclic Lactone, Abamectin, Is also a Potent Inhibitor of Pyrantel/Tribendimidine Nicotinic Acetylcholine Receptors of Gastro-Intestinal Worms
Full text linkNematode parasites may be controlled with drugs, but their regular application has given rise to concerns about the development of resistance. Drug combinations may be more effective than single drugs and delay the onset of resistance. A combination of the nicotinic antagonist, derquantel, and the macrocyclic lactone, abamectin, has been found to have synergistic anthelmintic effects against gastro-intestinal nematode parasites. We have observed in previous contraction and electrophysiological experiments that derquantel is a potent selective antagonist of nematode parasite muscle nicotinic receptors; and that abamectin is an inhibitor of the same nicotinic receptors. To explore these inhibitory effects further, we expressed muscle nicotinic receptors of the nodular worm, Oesophagostomum dentatum (Ode-UNC-29:Ode-UNC-63:Ode-UNC-38), in Xenopus oocytes under voltage-clamp and tested effects of abamectin on pyrantel and acetylcholine responses. The receptors were antagonized by 0.03 μM abamectin in a non-competitive manner (reduced Rmax, no change in EC50). This antagonism increased when abamectin was increased to 0.1 μM. However, when we increased the concentration of abamectin further to 0.3 μM, 1 μM or 10 μM, we found that the antagonism decreased and was less than with 0.1 μM abamectin. The bi-phasic effects of abamectin suggest that abamectin acts at two allosteric sites: one high affinity negative allosteric (NAM) site causing antagonism, and another lower affinity positive allosteric (PAM) site causing a reduction in antagonism. We also tested the effects of 0.1 μM derquantel alone and in combination with 0.3 μM abamectin. We found that derquantel on these receptors, like abamectin, acted as a non-competitive antagonist, and that the combination of derquantel and abamectin produced greater inhibition. These observations confirm the antagonistic effects of abamectin on nematode nicotinic receptors in addition to GluCl effects, and illustrate more complex effects of macrocyclic lactones that may be exploited in combinations with other anthelmintics.This article is published as Abongwa, Melanie, Samuel K. Buxton, Alan P. Robertson, and Richard J. Martin. "Curiouser and curiouser: the macrocyclic lactone, abamectin, is also a potent inhibitor of pyrantel/tribendimidine nicotinic acetylcholine receptors of gastro-intestinal worms." PLoS One 11, no. 1 (2016): e0146854. doi: https://doi.org/10.1371/journal.pone.0146854.Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Number R01AI047194 to RJM, R21AI092185-01A1 to APR and the Schlumberger Foundation to MA
AI for materials design: Generative AI with multi-fidelity strategies
No full textThe design of new molecules and materials is often hindered by the vastness of chemical and microstructural design spaces and the high cost of obtaining high-fidelity property labels through quantum or physics-based simulations. This dissertation introduces a unified framework that combines generative artificial intelligence (AI), hierarchical transfer learning, multi-fidelity modeling, and graph-driven voxel-based analysis to address four critical challenges in materials discovery: the need for syntactically valid and interpretable generative models, the data inefficiency of high-fidelity property prediction, the unreliability of models under distribution shift, and the lack of scalable tools for characterizing dynamic structural domains.
First, we present MolGen-Transformer, a transformer-based molecular language model trained on a dataset of 198 million molecules using the SELFIES representation. It achieves perfect reconstruction accuracy, generates chemically diverse and valid molecules, and offers a compact and interpretable latent space suitable for downstream design tasks. Second, we build on this well-trained model by developing a hierarchical property prediction framework that fuses MolGen-Transformer embeddings with both low- and high-fidelity labels. This multi-fidelity approach reduces the dependence on expensive density functional theory (DFT) data by up to fourfold. It also integrates uncertainty quantification via ensemble modeling to support reliable, property-driven molecular design through latent space path search. Third, we generalize the multi-fidelity paradigm beyond molecules by applying it to microstructure–property prediction for organic photovoltaics. We demonstrate that a combination of learned microstructure embeddings and limited high-fidelity simulations enables accurate prediction of device-level performance with data efficiency. Fourth, we introduce MDVoxelizer, a modular framework that integrates graph-based structural filtering with voxel-based spatial mapping to quantify local crystallinity in molecular dynamics simulations. This enables interpretable, time-resolved, and machine learning–compatible representations of complex, evolving molecular systems.
Collectively, these contributions establish a robust, generalizable approach to AI-guided design. By tightly integrating generative modeling, fidelity-aware learning, structural characterization, and uncertainty estimation, this work enables scalable exploration and optimization in chemical and materials spaces while mitigating computational cost and predictive risk
Alternative approach for the calibration of reliability-based regional resistance factors for axially loaded drilled shafts
No full textNational code recommended resistance factors for drilled shafts are not always reflective of the true level of design uncertainty at the regional levels. Moreover, several of these resistance factors still rely on factors of safety formerly used in the Allowable Stress Design approach, and, consequently, they do not comply with the Load and Resistance Factor Design principles and may be unable to achieve desired target levels of reliability consistently. Regional calibrations, while a solution to these issues, can be challenging to implement due to the limited quantity of quality load test data available at state levels. In this study, it is proposed and shown that a segmental procedure based on strain gauge data can be implemented in the resistance factor calibration framework to overcome the challenge in using a limited set of load test data to statistically characterize side resistance uncertainties. Using estimated statistical parameters, resistance factors are calibrated and observed differences with code recommended values are discussed.This article is published as Kalmogo, Philippe, Sri Sritharan, and Jeramy Ashlock. "Alternative approach for the calibration of reliability-based regional resistance factors for axially loaded drilled shafts." Soils and Foundations 66, no. 2 (2026): 101755. doi: https://doi.org/10.1016/j.sandf.2026.101755
A bi-stage data-driven process-based model for sorghum breeding and yield prediction: coupling explainable artificial intelligence and crop modeling
No full textWith the global population explosion, the increasing demand in food supply pushes the development of advanced breeding methods. This study presents a bi-stage data-driven and process-based crop model to provide breeding recommendations based on Genotype x Environment (GxE) effects for sorghum, a vital cereal crop with various plant types, such as Grain (G), Forage (F), Dual Purpose (DP), and Photoperiod-Sensitive (PS). The model combines traditional process-based crop modeling with explainable data-driven methods, which increases the general interpretability and flexibility of the model. The model considers extensive environmental data, including seven years of hourly weather records and soil factors from three research farms in Iowa, together with management practices and parental information from 651 males and 131 females. Additionally, the model predicts the hourly dry weight of sorghum’s leaves, stems and grain, and predicts final yield based on management practices. The final combined Relative Root mean squared error reached 16% to 19% across several environmental conditions, which demonstrating the robust predictive capabilities. Besides, the model effectively identified elite hybrids in four distinct sorghum types, which also demonstrated its utility in reducing the need for extensive field trials. Additionally, our analysis of genotype by environment interactions revealed significant variability in performance, which indicates the precise breeding strategies customized for the environmental conditions are important and vital. This research highlights that our explainable hybrid model framework can greatly improve crop modeling and plant breeding, making agriculture more efficient and sustainable.This article is published as Ni Z, Chang Y, Kemp J, Salas-Fernandez MG and Wang L (2026) A bi-stage data-driven process-based model for sorghum breeding and yield prediction: coupling explainable artificial intelligence and crop modeling. Front. Plant Sci. 16:1617753. doi: 10.3389/fpls.2025.1617753The author(s) declare financial support was received for the research and/or publication of this article. This work was partially supported by NSF and USDA (#1830478 and #2021-67021-35329) and the Plant Sciences Institute at Iowa State University. MS-F was supported by the United States Department of Agriculture, National Institute of Food and Agriculture (grant number IOW05768)
Introduction to the Minitrack on Digital Supply Chains: Technologies, Resilience and Sustainability
No full textThe following text sections provide an overview of the minitrack on the digital supply chain of the future. The minitrack addresses research questions concerning drivers and challenges of digital transformation, basic technologies, applications and services, digital platforms, as well as cultural and organizational change, etc. After a short introduction,
the different papers of the minitrack are described and embedded into an overall context. In the end, we discuss some recommendations concerning future research on digitalization of firms, business models and supply chains.This presentation is published as Bodendorf, F., Chen, H., Pflaum, A., Prockl, G., Introduction to the Minitrack on Digital Supply Chains: Technologies, Resilience and Sustainability. Presented at Proceedings of the 59th Hawaii International Conference on System Sciences. January 2026, https://hdl.handle.net/10125/11190
Lowering the Mo limit for nitrogen fixation by Mo-nitrogenase
No full textArchean ocean marine primary productivity may have been limited by biologically available nitrogen. Due to low molybdenum abundances, early biological nitrogen fixation is thought to have relied on alternative nitrogenases that incorporate vanadium or iron instead of molybdenum. Here, we examine nitrogen fixation in a Cyanobacteria-dominated, ferruginous, low-sulfate, low-molybdenum lake, which replicates biological and chemical conditions relevant to early marine primary productivity. Nitrogen fixation occurs even when molybdenum is <1 nM, 100x less than the abundance in modern oceans. Molybdenum additions did not increase nitrogen fixation rates, indicating that diazotrophs were not molybdenum limited. Only the molybdenum-iron nitrogenase was detected in metagenomes and metatranscriptomes, indicating that the alternative nitrogenases were not required. We suggest that low sulfate (<1 μM) and/or efficient uptake mitigated molybdenum limitation. These results indicate that molybdenum bioavailability may be strongly controlled by sulfate and that alternative nitrogenases are not essential for nitrogen fixation at low molybdenum.This article is published as Stevenson, Z., Schultz, D.L., Chamberlain, M. et al. Lowering the Mo limit for nitrogen fixation by Mo-nitrogenase. Commun Earth Environ 7, 169 (2026). https://doi.org/10.1038/s43247-026-03193-
Designing Janus particles for functional coating applications
No full textJanus particles (JPs) offer dual functionality within a single particle, yet scalable and practical fabrication routes remain limited. This dissertation introduces a one-step, one-pot synthesis of fully polymeric hydantoin-functionalized JPs with tunable size and morphology. When incorporated at only 5 wt% into waterborne coatings, the particles self-stratify to the surface and provide complete inactivation of S. aureus and E. coli upon chlorination, delivering a metal-free, durable, and rechargeable antimicrobial protection. This sustainable platform addresses the growing need for safe, cost-effective surface disinfection solutions in healthcare, food systems, and public environments. The work establishes a foundational route for multifunctional Janus materials with strong potential for future protective and smart-coating technologies.
Building on this strategy, the first demonstration of JPs in omniphobic coatings is presented. JPs featuring a unique raspberry-like morphology show improved colloidal stability, waterborne compatibility, and roughness-driven repellency. Preliminary coating studies on textile and glass substrates reveal enhanced oil contact angles and transparent surfaces, indicating a promising pathway for fluorine-free omniphobic technologies.
Collectively, this work establishes versatile, scalable Janus particle fabrication methods and advances their integration into sustainable antimicrobial and omniphobic coating systems