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Abstract Reducing wasted food has been identified as a key strategy to meet food security goals and attain human nutritional needs and food preferences in an equitable, sustainable, and resilient manner. Yet, mathematically modeling how reducing wasted food contributes to sustainability, equity, and resilience objectives, and the possible interactions and tradeoffs among these metrics, is limited by challenges to quantifying these characteristics. Using the process of convergent science, we develop a prototype wasted food model to evaluate how a set of common equity, sustainability, and resilience measures interact. We consider prevention (consumer education) and treatment (anaerobic digestion and composting) options for wasted food diversion from landfills. The model applies a convex nonlinear optimization to determine the allocation of wasted food to different management alternatives, optimizing for economic (net cost), sustainability (emissions reductions or energy savings), or equity (distribution of per-capita cost or emissions reduction impacts). The model developed in this research is available online as open-source code for others to replicate and build upon for future studies and analysis. Our findings illustrate that optimal wasted food management alternatives may vary when targeting different metrics and that strategies promoting cost-effectiveness may be in tension with sustainability or equity goals and vice versa. The implications of this study could be used by policy makers to evaluate how wasted food reduction measures will impact sustainability, equity, and resilience goals. 

Comparative transcriptomics has emerged as a powerful approach that allows us to unravel the genetic basis of organ morphogenesis and its diversification processes during evolution. However, the application of comparative transcriptomics in studying plant morphological diversity addresses challenges such as identifying homologous gene pairs, selecting appropriate developmental stages for comparison, and extracting biologically meaningful networks. Methods such as phylostratigraphy, clustering, and gene co-expression networks are explored to identify functionally equivalent genes, align developmental stages, and uncover gene regulatory relationships. In the current review, we highlight the importance of these approaches in overcoming the complexity of plant genomes, the impact of heterochrony on stage alignment, and the integration of gene networks with additional data for a comprehensive understanding of morphological evolution. 

Throughout history, coronaviruses have posed challenges to both public health and the global economy; nevertheless, methods to combat them remain rudimentary, primarily due to the absence of experiments to understand the function of various viral components. Among these, membrane (M) proteins are one of the most elusive because of their small size and challenges with expression. Here, we report the development of an expression system to produce tens to hundreds of milligrams of M protein per liter ofEscherichia coliculture. These large yields render many previously inaccessible structural and biophysical experiments feasible. Using cryo–electron microscopy and atomic force microscopy, we image and characterize individual membrane-incorporated M protein dimers and discover membrane thinning in the vicinity, which we validated with molecular dynamics simulations. Our results suggest that the resulting line tension, along with predicted induction of local membrane curvature, could ultimately drive viral assembly and budding. 

The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spurred unprecedented and concerted worldwide research to curtail and eradicate this pathogen. SARS-CoV-2 has four structural proteins: Envelope (E), Membrane (M), Nucleocapsid (N), and Spike (S), which self-assemble along with its RNA into the infectious virus by budding from intracellular lipid membranes. In this paper, we develop a model to explore the mechanisms of RNA condensation by structural proteins, protein oligomerization and cellular membrane–protein interactions that control the budding process and the ultimate virus structure. Using molecular dynamics simulations, we have deciphered how the positively charged N proteins interact and condense the very long genomic RNA resulting in its packaging by a lipid envelope decorated with structural proteins inside a host cell. Furthermore, considering the length of RNA and the size of the virus, we find that the intrinsic curvature of M proteins is essential for virus budding. While most current research has focused on the S protein, which is responsible for viral entry, and it has been motivated by the need to develop efficacious vaccines, the development of resistance through mutations in this crucial protein makes it essential to elucidate the details of the viral life cycle to identify other drug targets for future therapy. Our simulations will provide insight into the viral life cycle through the assembly of viral particles de novo and potentially identify therapeutic targets for future drug development.