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The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 

The MYC oncogenic transcription factor is acetylated by the p300 and GCN5 histone acetyltransferases. The significance of MYC acetylation and the functions of specific acetylated lysine (AcK) residues have remained unclear. Here, we show that the major p300-acetylated K148(149) and K157(158) sites in human (or mouse) MYC and the main GCN5-acetylated K323 residue are reversibly acetylated in various malignant and nonmalignant cells. Oncogenic overexpression of MYC enhances its acetylation and alters the regulation of site-specific acetylation by proteasome and deacetylase inhibitors. Acetylation of MYC at different K residues differentially affects its stability in a cell type-dependent manner. Lysine-to-arginine substitutions indicate that although none of the AcK residues is required for MYC stimulation of adherent cell proliferation, individual AcK sites have gene-specific functions controlling select MYC-regulated processes in cell adhesion, contact inhibition, apoptosis, and/or metabolism and are required for the malignant cell transformation activity of MYC. Each AcK site is required for anchorage-independent growth of MYC-overexpressing cells in vitro, and both the AcK148(149) and AcK157(158) residues are also important for the tumorigenic activity of MYC transformed cells in vivo. The MYC AcK site-specific signaling pathways identified may offer new avenues for selective therapeutic targeting of MYC oncogenic activities.

 

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity. To increase farm efficiency and understand the tradeoffs of these new DA innovations we developed ROAM, which is a decision support system developed to find a Pareto optimal architectural design to build DA systems. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined metrics and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and to visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 

Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. Here, we present a computer-free, all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive diffractive surfaces are trained to all-optically reconstruct images of arbitrary objects that are completely covered by unknown, random phase diffusers. After the training stage, which is a one-time effort, the resulting diffractive surfaces are fabricated and form a passive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. We experimentally demonstrated this concept using coherent THz illumination and all-optically reconstructed objects distorted by unknown, random diffusers, never used during training. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusers can be extended to other wavelengths, and might fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others.