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ABSTRACT Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state‐of‐the‐art Earth‐based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of these devices upon deployment on the targeted celestial bodies. The key misconception is that gravitational offset is necessary during theterramechanicstesting of rover and lander prototypes on Earth. The body of evidence supporting our argument is tied to a small number of studies conducted during parabolic flights and insights derived from newly revised scaling laws. We argue that what has prevented the community from fully diagnosing the problem at hand is the absence of effective physics‐based models capable of simulating terramechanics under low‐gravity conditions. We developed such a physics‐based simulator and utilized it to gauge the mobility of early prototypes of the Volatiles Investigating Polar Exploration Rover. This contribution discusses the results generated by this simulator, how they correlate with physical test results from the NASA‐Glenn SLOPE lab, and the fallacy of the gravitational offset in rover and lander testing. The simulator, which is open‐source and publicly available, also supports studies for in situ resource utilization activities, for example, digging, bulldozing, and berming, in low‐gravity environments. 

Natural killer (NK) cells have shown potential for allogeneic cell‐based cancer immunotherapies. For development of economical off‐the‐shelf allogeneic therapies, maximal expansion of the NK cells from each donor must be achieved while maintaining efficacy and uniformity of the cell product. The standard method for robust expansion utilizes weekly stimulation with engineered feeder cells derived from the K562 cell line. However, the effects of repeated stimulation on NK cell growth, metabolism, and function are not well understood. In this study, we demonstrated a distinct shift in growth kinetics and metabolism around week 3–4 of repeated K562 feeder cell stimulation, followed by a change in cytokine secretion and killing ability. Seahorse metabolic flux assays and transcriptomics suggested a transition from glycolytic metabolism to oxidative metabolism after the first week of stimulation, but the shift in growth kinetics generally correlated to reduced metabolic activity. Collectively, these results indicate that serial stimulation sustains large‐fold NK cell expansion that can be exploited for NK cell therapy; however, this expansion has important impacts on NK cell growth, metabolism, and function. Careful characterization is critical when developing large‐scale biomanufacturing processes to ensure efficacy of the final cellular product. 

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal dynamics modeling on mobility networks is a challenging task particularly considering scenarios where open-world events drive mobility behavior deviated from the routines. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes on mobility networks, nor adaptive to unprecedented volatility brought by potential open-world events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of open-world events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. What is more, experiments show generalization ability of EAST-Net to perform zero-shot inference over different open-world events that have not been seen. 

Mammalian cells are commonly used as hosts in cell culture for biologics production in the pharmaceutical industry. Structured mechanistic models of metabolism have been used to capture complex cellular mechanisms that contribute to varying metabolic shifts in different cell lines. However, little research has focused on the impact of temporal changes in enzyme abundance and activity on the modeling of cell metabolism. In this work, we present a framework for constructing mechanistic models of metabolism that integrate growth‐signaling control of enzyme activity and transcript dynamics. The proposed approach is applied to build models for three Chinese hamster ovary (CHO) cell lines using fed‐batch culture data and time‐series transcript profiles. Leveraging information from the transcriptome data, we develop a parameter estimation approach based on multi‐cell‐line (MCL) learning, which combines data sets from different cell lines and trains the individual cell‐line models jointly to improve model accuracy. The computational results demonstrate the important role of growth signaling and transcript variability in metabolic models as well as the virtue of the MCL approach for constructing cell‐line models with a limited amount of data. The resulting models exhibit a high level of accuracy in predicting distinct metabolic behaviors in the different cell lines; these models can potentially be used to accelerate the process and cell‐line development for the biomanufacturing of new protein therapeutics.