No abstract

In addition to its role as a TB vaccine, BCG has been shown to elicit heterologous protection against many other pathogens including viruses through a process termed trained immunity. Despite its potential as a broadly protective vaccine, little has been done to determine if BCG-mediated trained immunity levels can be optimized. Here we re-engineer BCG to express high levels of c-di-AMP, a PAMP recognized by stimulator of interferon genes (STING). We find that BCG overexpressing c-di-AMP elicits more potent signatures of trained immunity including higher pro-inflammatory cytokine responses, greater myeloid cell reprogramming toward inflammatory and activated states, and enhances epigenetic and metabolomic changes. In a model of bladder cancer, we also show that re-engineered BCG induces trained immunity and improved functionality. These results indicate that trained immunity levels and antitumor efficacy may be increased by modifying BCG to express higher levels of key PAMP molecules.

We present a novel discrete-time mixed-integer programming (MIP) formulation for the simultaneous batching and scheduling in multiproduct multistage processes under utility constraints. In addition to processing units and storage vessels, we consider utilities such as cooling water, steam, and electricity that are available in limited quantities. Since different tasks often share the limited utilities at the same time, we use a common time-grid approach. Further, the proposed method handles the batching decisions (the number and sizes of batches) seamlessly without the usage of explicit batch-selection variables. To preserve batch identity in storage vessels, we introduce a new class of inventory variables and constraints. Our approach is the first to address limited utilities for simultaneous batching and scheduling in multistage processes.

This paper presents a mixed-integer linear programming (MILP) model for the scheduling of a multistage process for the production of aluminum casts of different alloys, using parallel furnaces and casters. In contrast to the common approach in multistage models of considering a fixed set of orders being processed sequentially in the stages, the modeling approach in this paper accounts for actual material flows and, thus, provides flexibility with respect to the actual number of batches to be processed for meeting a given demand. This enables the model to handle parallel nonuniform units with variable capacities, where it is difficult to a priori decide on the number of batches that are required to satisfy the orders. The model also features a material balance over the furnace section, to capture processing details. A decomposition scheme that consists of a master problem and a sub-problem is developed and is used in an iterative algorithm to solve medium-to large-sized problems in reasonable computational time.