AI-enabled biomanufacturing

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Advanced manufacturing process design for Mesenchymal Stromal Cell therapies

For decades, the potential immunomodulatory effects of Mesenchymal Stromal Cells (MSCs) have prompted numerous cell-therapy clinical investigations targeting various diseases such as graft-versus-host disease and autoimmune diseases. Despite their ubiquitous usage in clinical trials, significant challenges related to their manufacturing and biological variabilities have led to poorly reproducible outcomes of therapeutic efficacy. Therefore, identification of validated critical quality attributes (CQAs) correlative to therapeutic function is of great interest to the MSC community. Such CQAs would also permit identification of critical process parameters (CPPs) to achieve and maintain MSC quality while producing a high yield. In this study, we designed and tested a “smart” feedback-controlled hollow fiber-based bioreactor for maintaining nutrient and waste levels for human umbilical cord tissue-derived MSC expansions. The bioreactor platform is a semi-autonomous system complete with in-line sensors, modeling, data-driven controllers, and an automated sampling platform. The small-scale system reduced costs, labor, time, and perturbations and improved yields of MSC products using a hollow fiber cartridge that closely models the basic design of the large-scale Quantum Cell Expansion System. Our feedback-controlled bioreactor responded to in-line glucose and lactate levels while recorded pH and dissolved oxygen measurements. This information was fed into a controller, which auto-calculates cell growth rates based on our developed mathematical model, and subsequently regulated media feed rates to support cell growth and nutrient requirements. Compared to the manual expansion process, the automated expansion processes showed higher yields and comparative therapeutic potency of MSCs, indicated by indolamine 2,3-dioxygenase assay and T cell proliferation assay. Future directions of our study propose to correlate metabolites and secreted proteins in culture media as putative CQAs that can be used as in-line predictors of MSC yield and therapeutic potency. Moreover, we aim to maintain a metabolic and secretory profile throughout MSC expansions enabled by real-time modulation of CPPs and scale up of the “smart” bioreactor. The proposed bioprocess for MSC products can be adapted and applied to industrial cell therapy manufacturing and can enable high-yield and high-quality products while minimizing variabilities. Please click Additional Files below to see the full abstract

Towards a robot task ontology standard

Ontologies serve robotics in many ways, particularly in de- scribing and driving autonomous functions. These functions are built around robot tasks. In this paper, we introduce the IEEE Robot Task Representation Study Group, including its work plan, initial development efforts, and proposed use cases. This effort aims to develop a standard that provides a comprehensive on- tology encompassing robot task structures and reasoning across robotic domains, addressing both the relationships between tasks and platforms and the relationships between tasks and users. Its goal is to develop a knowledge representation that addresses task structure, with decomposition into subclasses, categories, and/or relations. It includes attributes, both common across tasks and specific to particular tasks and task types

Vehicle Level World Model Manager Interface Description

Developed over the course of two decades at the National Institute of Standards and Technology (NIST) and elsewhere, the Real-time Control System (RCS) provides a reference architecture and engineering methodology to aid in the design of complex control systems. In order to accomplish complex behaviors, RCS provides guidelines for the decomposition of control problems into a set of hierarchical control nodes each of which follows a “sense-model-act ” paradigm. In this paradigm, sensory processing (SP) functions feed information into a world model (WM) and a behavior generation (BG) module makes decisions or “acts ” based on commands from the level above and input from a value judgement (VJ) module in cooperation with the WM. RCS may be further specialized into application-specific versions. One such version is 4-D/RCS [Albus, 1997] that is aimed at the design and implementation of control systems for intelligent autonomous vehicles for military scout missions. An important part of the 4-D/RCS hierarchy is the world model. The goal of this paper is to describe the world model manager system that provides client applications access to a comprehensive world model. These client applications may perform such tasks as planning or display generation. This paper concentrates on the details of a “vehicle level node ” implementation of the world model for the Demo III program [Shoemaker et al., 1998]. Emphasis is placed on interface flexibility and modularity

Planning for OnRoad Driving through Incrementally Created Graphs

Abstract — A general-purpose hierarchical planning framework that allows for cost-optimal, logic-constrained plans will be presented. This framework will be applied to planning an on-road path for an autonomous vehicle traveling amongst moving and stationary objects. Through this application, the ability to implement both hard and soft constraints, as well as cope with dynamic environments and user objectives will be demonstrated

Search Graph Formation for Minimizing the Complexity of Planning

A large number of path planning problems are solved by the use of graph based search algorithms. There are a variety of techniques available to optimize the search within these graphs as well as thorough studies of the complexity involved in searching through them. However, little e#ort has been dedicated to constructing the graphs so that the results of searching will be optimized