Ross Sozzani

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 major challenge for humankind is to feed the increasing human population in a sustainable manner. According to UN������������������s development programme extreme hunger and malnutrition is a major barrier to development in many countries: 795 million people are estimated to be chronically undernourished as of 2014, often as a direct consequence of environmental degradation, drought and loss of biodiversity. The Sustainable Development Goals (SDGs) aim to end hunger and malnutrition by 2030. Improved agricultural productivity is a critical part of achieving the SDG goal 2, Zero Hunger. Currently more than one third of crop yields are lost due to abiotic and biotic stress factors, such as drought, salinity, pests and disease. To minimize this yield gap and to simultaneously reduce the environmental impact of current agricultural practices, future crop production needs to be achieved on sub-optimal soils with reduced input of fertilizers and pesticides (���������������more with less������������������). These challenges have increased the awareness of the importance of the plant microbiome for improved agricultural practices. Plants are colonized by an astounding number of microorganisms that can have profound effects on seed germination, seedling vigour, plant growth and development, nutrition, diseases and productivity. Thus, the plants can be viewed as holobionts that benefits from its microbiome in terms of specific functions and traits. In return, plants transfer a substantial part of their photosynthetically fixed carbon directly into symbionts and into their immediate surroundings thereby supporting the microbial community and influencing its composition and activities. For the vast majority of plant-associated microorganisms, however, there is little knowledge of their specific impact on crop growth and crop resilience and the mechanisms underlying microbiome-plant interactions. Hence, a critical step in developing new microbiome-assisted approaches to quantitatively and predictably improve crop resilience management strategies is deciphering the hyperdiverse plant microbiome. In particular, we need to identify keystone microorganisms and mechanisms involved in plant growth promotion and protection against biotic and abiotic stresses. To that end, systems-based analyses combined with deep-learning and modelling are essential to decode the taxonomic diversity and functional potential of plant microbiomes. The overall aim of this multidisciplinary research program is to develop a scalable system-based strategy to harness the functional potential of plant microbiomes for improving crop resilience. More specifically, we will focus on experimental analyses and modelling of the phyllosphere microbiome of wheat (Triticum aestivum), one of the most important cereal food crops worldwide. The phyllosphere microbiome is defined here as the collective microbial communities inhabiting both the leaf surface as well as the internal leaf tissue. We will zoom in on the microbiome of flag leaves of wheat, as the flag leaf is a major determinant (up to 45%) of wheat yield. To do this, we combine renowned academic expertise in microbiology, chemistry, DNA and RNA sequencing, bioinformatics, machine-learning and modelling with company support in plant breeding and agronomy to deliver novel approaches and technologies.

ARF transcriptional activity is controlled by the large intrinsically disordered ����������������middle region��������������� and ARF activity in a yeast-based system has been well characterized. We will exploit the deep evolutionary conservation of ARF function, combined with the characterized middle region transcriptional activity to identify and characterize ARF ADs from 79 ARFs across a spectrum of species, including Arabidopsis thaliana (22), Zea mays (35), Physcomitrella patens (19), and Marchantia polymorpha (3). From this data, we will examine the conservation of number and positioning of ADs within ARF IDRs. We will validate these ADs by AD mutant variant analysis. We will use ML approaches to generate the ����������������rules��������������� for ARF AD features, then test our models on species such as tomato. This portion of the project will benefit from the highly characterized activity of ARF proteins from an evolutionarily diverse set of species.

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

Although an invaluable workhorse for research and training, the current 3D bioprinters available at NC State, such as the 3D Bioplotter (EnvisionTec), BioAssemblyBot (Advanced Solutions), BioX (Cellink), and Allevi 3 (3D Systems) are primarily based on the extrusion printing mechanisms. These systems are well suited for macro-geometric structures, but their micro-scale and cellular level control and precision are limited. Furthermore, these systems lack in-process monitoring abilities. This severely impedes fundamental research about cellular-level functional interactions in bioprinting and the potential to develop new manufacturing strategies and applications that can benefit from single cell-level control across layers of bulk constructs. The proposed multi-modal, high-resolution Next-Generation Bioprinter-Research (NGB-R) system [1] will address this gap and make a huge impact on on-going and future research and training at NC State. The comprehensive standalone BSL-2 system is equipped with micro-extrusion and inkjet bioprinting modalities along with the one-of-its-kind laser induced forward transfer (LIFT) mechanism. The uniqueness of the NGB-R system is further enhanced by the embedded microscope driven by machine learning algorithms, which enables high-throughput, in-line, real-time quality monitoring of bioprinted constructs.