Sweet Dreams (Are Made of This): A Review and Perspectives on Aspartic Acid Production

Aspartic acid, or “aspartate,” is a non-essential, four carbon amino acid produced and used by the body in two enantiomeric forms: L-aspartic acid and D-aspartic acid. The L-configuration of amino acids is the dominant form used in protein synthesis; thus, L-aspartic acid is by far the more common configuration. However, D-aspartic acid is one of only two known D-amino acids biosynthesized by eukaryotes. While L-aspartic acid is used in protein biosynthesis and neurotransmission, D-aspartic acid is associated with neurogenesis and the endocrine system. Aspartic acid production and use has been growing in recent years. The purpose of this article is to discuss various perspectives on aspartic acid, including its industrial utility, global markets, production and manufacturing, optimization, challenges, and future outlook. As such, this review will provide a thorough background on this key biochemical.

Closed-Loop Digital Twin for the Product Lifecycle

Digital Twin advances have provided the conceptual ground for integrating a physical product with its digital representation. However, Digital Twin implementation has been focused on the beginning of life and manufacturing optimization, leaving space for developing a Digital Twin model that encompasses and connects different stages of the entire product lifecycle. In this scenario, the integration between company-internal data with real-time customers' data is still a challenge. Besides, implementing such a model in a multiplatform environment is also an open issue in the literature. This paper proposes the definition of a Closed-loop Digital Twin implemented as a middleware software that connects PLM, ERP, and MES data with customers' usage data. The proposed concept was implemented and tested in a learning factory. Results demonstrated the concept potential to consolidate product data, support data analyses, and provide insights for different stages of the product lifecycle.

A revised imperialist competition algorithm for cellular manufacturing optimization based on product line design

<p style='text-indent:20px;'>Due to the fierce market competition, enterprises try to satisfy customers' requirements for personalized products in order to maximize profit or market share of their products. This not only needs to determine the product variants through product line design, but also needs to pay attention to resource allocation in the manufacturing process. This paper proposes a cellular manufacturing optimization model that considers the market and production. If the company excessively pursues the satisfaction of customers' personalized needs, the manufacturing time and cost may increase accordingly. Of course, with the restriction of production capacity in manufacturing cells and the expectation of reducing cost, managers cannot design attributes' levels of a product line casually, which may result in its unstable marketing share and profit. Therefore, the product demand influenced by customers' preferences could be a key factor to link market and production. The objective of propose model is to maximize product profit which consists of revenue and miscellaneous costs (material, processing, transportation, final assembly and fixed costs). A revised imperialist competitive algorithm (RICA) is developed to optimize the discrete problem. Extensive numerical experiments and t-test are carried out to verify the effect of this method. The results demonstrate the proficiency of RICA over another imperialist competitive algorithm based method and genetic algorithm in terms of solution quality.</p>

P06.15 Highly multiplexed, single-cell functional proteomics of CAR-T products enables more predictive product characterization, cell manufacturing optimization, and cellular biomarkers across product types

Chimeric antigen receptor (CAR) T cell therapy has already paved the way for successful immunotherapies to fight against liquid tumors and is quickly expanding to solid tumors. Nevertheless, the biggest challenges are how to evaluate the quality of CAR-T cells and how to predict their in vivo behaviors once reinfused into a patient. In this report, we review single-cell polyfunctional profiling results obtained from several different sets of pre-infusion CAR-T samples, including CD19 CAR-T products from Novartis and Kite Pharma (Gilead), GoCAR-T cell products targeting Prostate Stem Cell Antigen from Bellicum, bispecific CD19/22 CAR-T cells from the NIH, trimeric APRIL-based CAR-T cells targeting both BCMA and TACI from MGH and CAR-T cells targeting glypican 3 in hepatocellular carcinoma from NIH. In each case, CD4+ and CD8+ CAR-T cells were stimulated and subsequently analyzed at a single-cell level using IsoPlexis’ IsoCode proteomic chips. Our single-cell data revealed highly polyfunctional and heterogeneous responses across each cohorts. The polyfunctional strength index (PSI) of the pre-infused CAR-T products is significantly associated with the clinical outcome of the patients after receiving the treatment, as well as post-infusion grade 3+ CRS. The CAR-T cells secreted a wide range of cytokines/chemokines in response to antigen specific stimulation and a significant portion of the CAR-T cells were polyfunctional (2+cytokines/cell). These results highlight the potential benefits of single-cell proteomics to comprehensively understand how CAR-T products behave in response to antigen-specific stimulation. Analyzing the single-cell polyfunctionality of CAR-T profiles also provides a valuable quality check for optimizing the manufacturing process and a powerful tool for next generation biomarker developments.Disclosure InformationD. Liu: None. P. Paczkowski: None. S. MacKay: None. J. Zhou: None.

Investigating Electrode Calendering and its Impact on Electrochemical Performance by Means of a New Discrete Element Method Model: Towards a Digital Twin of Li-Ion Battery Manufacturing

Lithium-ion battery (LIB) manufacturing optimization is crucial to reduce its CO2 fingerprint and cost, while improving their electrochemical performance. In this article, we present an experimentally validated calendering Discrete Element Method model for LiNi0.33Mn0.33Co0.33O2–based cathodes by considering explicitly both active material (AM) and carbon-binder domain (CBD). This model was coupled to a pre-existing Coarse-Grained Molecular Dynamics model describing the slurry equilibration and its drying and a 4D-resolved Finite Element Method model for predicting electrochemical performance. Our calendering model introduces important novelties versus the state of the art, such as the utilization of un-calendered electrode mesostructures resulting from validated simulations of the slurry and drying combined with the explicit consideration of the spatial distribution and interactions between AM and CBD particles, and its validation with both experimental micro-indentation and porosity vs. calendering pressure curves. The effect of calendering on the electrode mesostructure is analyzed in terms of pore size distribution, tortuosity and particles arrangement. In addition, the evolution of the macroscopic electrochemical behavior of the electrodes upon the degree of calendering is discussed, offering added insights into the links between the calendering pressure, the electrode mesostructure and its overall performance.<br>

Investigating Electrode Calendering and its Impact on Electrochemical Performance by Means of a New Discrete Element Method Model: Towards a Digital Twin of Li-Ion Battery Manufacturing

Lithium-ion battery (LIB) manufacturing optimization is crucial to reduce its CO2 fingerprint and cost, while improving their electrochemical performance. In this article, we present an experimentally validated calendering Discrete Element Method model for LiNi0.33Mn0.33Co0.33O2–based cathodes by considering explicitly both active material (AM) and carbon-binder domain (CBD). This model was coupled to a pre-existing Coarse-Grained Molecular Dynamics model describing the slurry equilibration and its drying and a 4D-resolved Finite Element Method model for predicting electrochemical performance. Our calendering model introduces important novelties versus the state of the art, such as the utilization of un-calendered electrode mesostructures resulting from validated simulations of the slurry and drying combined with the explicit consideration of the spatial distribution and interactions between AM and CBD particles, and its validation with both experimental micro-indentation and porosity vs. calendering pressure curves. The effect of calendering on the electrode mesostructure is analyzed in terms of pore size distribution, tortuosity and particles arrangement. In addition, the evolution of the macroscopic electrochemical behavior of the electrodes upon the degree of calendering is discussed, offering added insights into the links between the calendering pressure, the electrode mesostructure and its overall performance.<br>