Cranos Williams

The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

One of the grand challenges facing humanity is to secure sufficient and healthy food for the increasing world population. This requires maintaining sustainable cultivation of crop plants under changing climate conditions. Plant roots and soil microbes have been associated since the emergence of plants on land. Nevertheless, the mechanisms that coevolved to control and regulate microbiota associations with healthy plants are largely unexplored. The photosynthetically active green leaf tissues supply assimilated carbon to roots for development and also to feed its associated microbes. To maintain balanced growth, plants have to integrate this underground demand and regulate the rate of photosynthetic CO2 fixation, and sugar allocation needs to be coordinated between root and shoot. Research on plants and their naturally associated microorganisms is therefore in a prime position to provide new perspectives and concepts for understanding plant function, plant performance and plant growth under limited input conditions with a reduced environmental footprint and could also define breeding targets and develop microbial interventions. InRoot aims to: 1. Disentangle the effects of climate and soil type from the impact of root-microbe interactions through transplantation experiments and exploit natural variation to identify the plant genetic components responsible for adaptation to the local microbiota. 2. Identify key bacterial taxa governing the establishment of host-driven microbial networks in the rhizosphere by analysing the microbe-microbe and microbe-host interactions established in tailored synthetic communities (SynComs) with direct consequences on host performance. 3. Define the plant genetic components that control infection of plant roots by ubiquitous and host-specific endophytes using advanced genetic screens and new methods for quantifying root cellular responses to microbes 4. Understand molecular mechanisms integrating root-microbe interactions into whole-plant physiology by investigating systemic physiological responses induced by SynComs using whole plant phenotyping. 5. Predict plant performance as a function of plant and microbiota genotypes by building multiscale models based on genotype, phenotype, and mechanistic data thereby providing knowledge for application. InRoot perspective: Provide knowledge and tools for science-based development of new crop varieties and associated microbial interventions that will improve productivity, reduce the need for fertilizers and pesticides, and alleviate negative environmental impact.

Minimizing crop loss and increasing output, across the food supply chain, will increase the economic viability of US growers and the global economic competitiveness of industry and stakeholder partners. We have assembled a diverse team across different National and International Universities with faculty that have track records of convergent research, education, and outreach. We will be well positioned to implement a Networks of Networks with diverse backgrounds, ethnicities, genders, experiences, and disciplines to drive research and innovation. Students and postdocs will be exposed to hands-on learning, on-farm technology training, cooperative extension, commercialization, industry engagement, and transdisciplinary education to create a highly trained workforce that is equipped to address food security and safety challenges.

Title: Transcriptional and translational regulatory networks of hormone signal integration in tomato and Arabidopsis. PI: Jose M. Alonso (Plant Biology, NCSU), Co-PIs:Anna Stepanova (Plant Biology, NCSU), Steffen Heber (Computer Science, NCSU), Cranos Williams (Electric Engineering, NCSU). Overview: Plants, as sessile organisms, need to constantly adjust their intrinsic growth and developmental programs to the environmental conditions. These environmentally triggered ����������������adjustments���������������� often involve changes in the developmentally predefined patterns of one or more hormone activities. In turn, these hormonal changes result in alterations at the gene expression level and the concurrent alterations of the cellular activities. In general, these hormone-mediated regulatory functions are achieved, at least in part, by modulating the transcriptional activity of hundreds of genes. The study of these transcriptional regulatory networks not only provides a conceptual framework to understand the fundamental biology behind these hormone-mediated processes, but also the molecular tools needed to accelerate the progress of modern agriculture. Although often overlooked, understanding of the translational regulatory networks behind complex biological processes has the potential to empower similar advances in both basic and applied plant biology arenas. By taking advantage of the recently developed ribosome footprinting technology, genome-wide changes in translation activity in response to ethylene were quantified at codon resolution, and new translational regulatory elements have been identified in Arabidopsis. Importantly, the detailed characterization of one of the regulatory elements identified indicates that this regulation is NOT miRNA dependent, and that the identified regulatory element is also responsive to the plant hormone auxin, suggesting a role in the interaction between these two plant hormones. These findings not only confirm the basic biological importance of translational regulation and its potential as a signal integration mechanism, but also open new avenues to identifying, characterizing and utilizing additional regulatory modules in plants species of economic importance. Towards that general goal, a plant-optimized ribosome footprinting methodology will be deployed to examine the translation landscape of two plant species, tomato and Arabidopsis, in response to two plant hormones, ethylene and auxin. A time-course experiment will be performed to maximize the detection sensitivity (strong vs. weak) and diversity (early vs. late activation) of additional translational regulatory elements. The large amount and dynamic nature of the generated data will be also utilized to generate hierarchical transcriptional and translational interaction networks between these two hormones and to explore the possible use of these types of diverse information to identify key regulatory nodes. Finally, the comparison between two plant species will provide critical information on the conservation of the regulatory elements identified and, thus, inform research on future practical applications. Intellectual merit: The identification and characterization of signal integration hubs and cis-regulatory elements of translation will allow not only to better understand how information from different origins (environment and developmental programs) are integrated, but also to devise new strategies to control this flow for the advance of agriculture. Broader Impacts: A new outreach program to promote interest among middle and high school kids in combining biology, computers, and engineering. We will use our current NSF-supported Plants4kids platform (ref) with a web-based bilingual divulgation tools, monthly demos at the science museum and local schools to implement this new outreach program. Examples of demonstration modules will include comparison between simple electronic and genetic circuits.

At peanut buying stations across the U.S. South East, peanut grading is currently implemented using labor-intensive equipment. Many of the steps related to grading have been unchanged for decades. A critical reason for this involves political pressures against updating or expediting the grading process. However, like many other economic sectors, new labor-force pressures are requiring that more be done with fewer people. Given that (1) labor is more challenging to come by; and (2) political pressure exists to maintain the status quo, we propose to update key steps in the existing process to simplify and/or expedite data collection. This project���s goal is to develop automated imaging and weighing technologies that can serve as a bridge, toward more fully automated systems, by addressing key bottlenecks in the existing grading process. We will achieve this by the following objectives: (1) Automate the weighing and grading of peanuts either traveling down or entering the rollers during pod pre-sizing; and (2) Automate the detection of splits and, if possible, sound versus unsound splits, by adding vision systems to the existing sheller.

The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North Carolina������������������s sweet potato growers.

We propose to develop machine learning and deep learning approaches for predicting outcomes in crops relevant to BASF's breeding program. This process will include the development of novel ML and DL architectures that are capable of taking in diverse heterogeneous data sets. These data include environmental data, management practices data, and genotypic data. Our goal is to assess the extent to which specific ML and DL architectures are applicable for different ag data types.

Inconsistent quality and aesthetics in agricultural crops can result in increased consumer and producer food waste, reduced industry resiliency and decreased farmers������������������ and growers������������������ profit, poor consumer satisfaction, and inefficiencies across the supply chain. Although there are opportunities to characterize and quantify sources of phenotypic variability across the agricultural supply chain - from cultural practices of growers and producers to storage and handling by distributors - the data available to allow for assessment of horticultural quality drivers are disparate and disconnected. The absence of data integration platforms that link heterogeneous datasets across the supply chain precludes the development of strategies and solutions to constrain variability in produce quality. This project������������������s central hypothesis is that multi-dimensional produce data can be securely integrated and used to optimize management practices in the field while simultaneously adding value across the entire food supply chain. We propose to develop multi-modal sensing platform along with a trust-based, data management, integration, and analytics framework for systematic organization and dynamic abstraction of heterogeneous data across the supply chain of agricultural crops. The projects short term goals are to (1) engage growers to refine research and extension priorities; (2) develop a first-of-its-kind modular imaging system that responds to grower needs by analyzing existing and novel multi-dimensional data; (3) establish the cyberinfrastructure, including analytics and blockchain, to make meaningful inference of the acquired data as related to management practices while ensuring data security; (4) deploy the sensing system at NCSU������������������s Horticultural Crops Research Station in Clinton, NC and on a large-scale system at a major commercial farm and distribution facility, and (5) extend findings to producers and regulators through NC Cooperative Extension. The proposed sensing and cyberinfrastructure platforms will be crop-agnostic and our findings will be transferable to other horticultural crops produced in NC and beyond.

More than a third of crop yields are currently lost due to abiotic and biotic stressors such as drought, pests, and disease. These stressors are expected to worsen in a warmer, drier future, resulting in crop yields further declining ~25%; however, breeding is only expected to rescue 7-15% of that loss [1]. The plant microbiome is a new avenue of plant management that may help fill this gap. All plants have fungi living inside their leaves (����������������foliar fungal endophytes���������������). This is an ancient and intimate relationship in which the fungi affect plant physiology, biotic and abiotic stress tolerance, and productivity. For example, some foliar fungi prevent or delay onset of major yield-limiting diseases caused by pathogens such as Fusarium head blight [2]. Foliar endophytes also reduce plant water loss by up to half and delay wilting by several weeks [3, 4]. Endophyte effects on plants occur via diverse genes and metabolites, including genes involved in stress responses and plant defense [5]. Genes and metabolites also predict how interactions in fungal consortia affect host stress responses, which is important for developing field inoculations [6]. Because newly emergent leaves lack fungi, endophytes are also an attractive target for manipulation (particularly compared to soils, where competition with the existing microbial community inhibits microbial additives). We propose to address the role of endophytic ����������������mycobiomes��������������� in stress tolerance of five North Carolina food, fiber, and fuel crops (corn, hemp, soybean, switchgrass, wheat), and to develop tools that can push this field beyond its current limits. Our major objectives (Fig. 1) are to: 1. Identify key microbiome scales to optimally manage endophytes 2. Determine fungal mechanisms via greenhouse tests, modeling, and genetic engineering 3. Build tools for field detection of endophytes 4. Understand the regulatory environment and engage diverse stakeholders Results of these objectives will allow us to make significant progress in both understanding the basic biology of plant-fungal interactions and managing those interactions in real-world settings. Our extension efforts will also bring these ideas to the broader community. Finally, we will also be well positioned to pursue several future research endeavors supported by federal granting agencies.