JAFSCD: Journal of Agriculture, Food Systems, and Community Development. Publication of the Thomas A. Lyson Center for Civic Agriculture and Food Systems

This study demonstrates the critical need for strategic transportation planning for sustainable food waste management in urban regions. The majority of food waste is generated from non-industrial sectors (i.e., residential, commercial, and institutional sectors that cluster in cities), while over two-thirds of discarded food could have been edible. Food recovery programs (FRPs) can help reduce food wastage, avoid the negative impacts of landfill disposal, and mitigate food insecurities. Notably, FRPs shift long-haul waste transport services destined for a central landfill location to local, short, and time-sensitive pick-up and delivery trips, which have not been carefully studied. This study quantifies the economic efficiency (i.e., cost) and environmental impact (i.e., emissions) of urban FRP scenarios by incorporating the characteristics of surplus food pick-up options, fleet, urban built environment, and donor program participation rates. The results of an empirical study in Chicago, U.S., show that urban FRPs can be both economically efficient and environmentally beneficial, although the preferred strategy for FRPs may vary by neighborhood characteristics. With strategic planning and management, the transportation cost could be as low as $ 0.06/lb of recovered food. Meanwhile, diverting 1 ton of edible food from landfills, on average, reduces greenhouse gas emissions by 97%, or 0.51 MTCO2e. If the estimated volume of 10,000 tons of surplus food in Chicago can be fully recovered, the regional net emission reduction can reach 5,100 MTCO2e, which is equivalent to removing 1,110 vehicles from the roads. This study is anticipated to provide planning insights into initiating and scaling up city-wide FRPs.

Breast cancer liver metastases (BCLM) are hypovascular lesions that resist intravenously administered therapies and have grim prognosis. Immunotherapeutic strategies targeting BCLM critically depend on the tumor microenvironment (TME), including tumor-associated macrophages. However, a priori characterization of the BCLM TME to optimize therapy is challenging because BCLM tissue is rarely collected. In contrast to primary breast tumors for which tissue is usually obtained and histologic analysis performed, biopsies or resections of BCLM are generally discouraged due to potential complications. This study tested the novel hypothesis that BCLM TME characteristics could be inferred from the primary tumor tissue. Matched primary and metastatic human breast cancer samples were analyzed by imaging mass cytometry, identifying 20 shared marker clusters denoting macrophages (CD68, CD163, and CD206), monocytes (CD14), immune response (CD56, CD4, and CD8a), programmed cell death protein 1, PD-L1, tumor tissue (Ki-67 and phosphorylated ERK), cell adhesion (E-cadherin), hypoxia (hypoxia-inducible factor-1α), vascularity (CD31), and extracellular matrix (alpha smooth muscle actin, collagen, and matrix metalloproteinase 9). A machine learning workflow was implemented and trained on primary tumor clusters to classify each metastatic cluster density as being either above or below median values. The proposed approach achieved robust classification of BCLM marker data from matched primary tumor samples (AUROC ≥ 0.75, 95% confidence interval ≥ 0.7, on the validation subsets). Top clusters for prediction included CD68+, E-cad+, CD8a+PD1+, CD206+, and CD163+MMP9+. We conclude that the proposed workflow using primary breast tumor marker data offers the potential to predict BCLM TME characteristics, with the longer term goal to inform personalized immunotherapeutic strategies targeting BCLM. Significance: BCLM tissue characterization to optimize immunotherapy is difficult because biopsies or resections are rarely performed. This study shows that a machine learning approach offers the potential to infer BCLM characteristics from the primary tumor tissue.