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Non-productive binding of cellulolytic enzymes to various plant cell wall components, such as lignin and cellulose, necessitates high enzyme loadings to achieve efficient conversion of pretreated lignocellulosic biomass to fermentable sugars. Protein supercharging was previously employed as one of the strategies to reduce non-productive binding to biomass. However, various questions remain unanswered regarding the hydrolysis kinetics of supercharged enzymes towards pretreated biomass substrates and the role played by enzyme interactions with individual cell wall polymers such as cellulose and xylan. In this study, CBM2a (fromThermobifida fusca) fused with endocellulase Cel5A (fromT. fusca) was used as the model wild-type enzyme and CBM2a was supercharged using Rosetta, to obtain eight variants with net charges spanning −14 to +6. These enzymes were recombinantly expressed inE. coli, purified from cell lysates, and their hydrolytic activities were tested against pretreated biomass substrates (AFEX and EA treated corn stover). Although the wild-type enzyme showed greater activity compared to both negatively and positively supercharged enzymes towards pretreated biomass, thermal denaturation assays identified two negatively supercharged constructs that perform better than the wild-type enzyme (∼3 to 4-fold difference in activity) upon thermal deactivation at higher temperatures. To better understand the causal factor of reduced supercharged enzyme activity towards AFEX corn stover, we performed hydrolysis assays on cellulose-I/xylan/pNPC, lignin inhibition assays, and thermal stability assays. Altogether, these assays showed that the negatively supercharged mutants were highly impacted by reduced activity towards xylan whereas the positively supercharged mutants showed dramatically reduced activity towards cellulose and xylan. It was identified that a combination of impaired cellulose binding and lower thermal stability was the cause of reduced hydrolytic activity of positively supercharged enzyme sub-group. Overall, this study demonstrated a systematic approach to investigate the behavior of supercharged enzymes and identified supercharged enzyme constructs that show superior activity at elevated temperatures. Future work will address the impact of parameters such as pH, salt concentration, and assay temperature on the hydrolytic activity and thermal stability of supercharged enzymes. 

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work and offer directions for future research. Our work builds upon and extends earlier survey efforts by (a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and (b) offering constructive feedback and ideas that could inspire research in the field over the coming decade. 

The human-robot interaction community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench , a simulation framework for evaluating social navigation algorithms. SocNavBench comprises a simulator with photo-realistic capabilities and curated social navigation scenarios grounded in real-world pedestrian data. We also provide an implementation of a suite of metrics to quantify the performance of navigation algorithms on these scenarios. Altogether, SocNavBench provides a test framework for evaluating disparate social navigation methods in a consistent and interpretable manner. To illustrate its use, we demonstrate testing three existing social navigation methods and a baseline method on SocNavBench , showing how the suite of metrics helps infer their performance trade-offs. Our code is open-source, allowing the addition of new scenarios and metrics by the community to help evolve SocNavBench to reflect advancements in our understanding of social navigation. 

We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. Inspired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion planning. We conduct an extensive simulation study involving a series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables a robot to achieve statistically significantly higher safety and lower number of group intrusions than a series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.