DNA damage is presumed to be one type of stochastic macromolecular damage that contributes to aging, yet little is known about the precise mechanism by which DNA damage drives aging. Here, we attempt to address this gap in knowledge using DNA repair-deficient C. elegans and mice. ERCC1-XPF is a nuclear endonuclease required for genomic stability and loss of ERCC1 in humans and mice accelerates the incidence of age-related pathologies. Like mice, ercc-1 worms are UV sensitive, shorter lived, display premature functional decline and they accumulate spontaneous oxidative DNA lesions (cyclopurines) more rapidly than wild-type worms. We found that ercc-1 worms displayed early activation of DAF-16 relative to wild-type worms, which conferred resistance to multiple stressors and was important for maximal longevity of the mutant worms. However, DAF-16 activity was not maintained over the lifespan of ercc-1 animals and this decline in DAF-16 activation corresponded with a loss of stress resistance, a rise in oxidant levels and increased morbidity, all of which were cep-1 / p53 dependent. A similar early activation of FOXO3A (the mammalian homolog of DAF-16), with increased resistance to oxidative stress, followed by a decline in FOXO3A activity and an increase in oxidant abundance was observed in Ercc1 -/- primary mouse embryonic fibroblasts. Likewise, in vivo , ERCC1-deficient mice had transient activation of FOXO3A in early adulthood as did middle-aged wild-type mice, followed by a late life decline. The healthspan and mean lifespan of ERCC1 deficient mice was rescued by inactivation of p53 . These data indicate that activation of DAF-16/FOXO3A is a highly conserved response to genotoxic stress that is important for suppressing consequent oxidative stress. Correspondingly, dysregulation of DAF-16/FOXO3A appears to underpin shortened healthspan and lifespan, rather than the increased DNA damage burden itself.
Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of optimization. Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. We perform experiments on retargeting Chinese sign language, which involves two arms and two hands, with additional requirements on the relative relationships among joints. Experiments include retargeting various human demonstrations to YuMi, NAO, and Pepper in the simulation environment and to YuMi in the real-world environment. Both efficiency and accuracy of the proposed method are verified.
During bioanalytical assay development and validation, maintaining the stability of the parent drug and metabolites of interest is critical. While stability of the parent drug has been thoroughly investigated, the stability of unanalyzed metabolites is often overlooked. When an unstable metabolite is known or suspected to interfere with measurement of the parent drug or other metabolites of interest through back-conversion or other routes, additional tests with these unstable metabolites should be conducted. Here, the development and validation of two assays for quantification of rosuvastatin, one in human plasma and one in human urine, was reported. To this end, additional sets of quality control samples were added during assay validation to ensure the reliability of the assays. Acid treatment of samples is shown to be necessary for rosuvastatin quantification. In this regard, stability issues caused by the metabolite, rosuvastatin lactone, may have been overlooked if assay development and validation had only considered the parent drug, rosuvastatin. These assays represent a case study for how to develop and validate assays with unstable metabolites. Taken together, unstable metabolites should be included in all applicable stability tests.
Motion retargeting from a human demonstration to a robot is an effective way to reduce the professional requirements and workload of robot programming, but faces the challenges resulting from the differences between humans and robots. Traditional optimization-based methods are time-consuming and rely heavily on good initialization, while recent studies using feedforward neural networks suffer from poor generalization to unseen motions. Moreover, they neglect the topological information in human skeletons and robot structures. In this paper, we propose a novel neural latent optimization approach to address these problems. Latent optimization utilizes a decoder to establish a mapping between the latent space and the robot motion space. Afterward, the retargeting results that satisfy robot constraints can be obtained by searching for the optimal latent vector. Alongside with latent optimization, neural initialization exploits an encoder to provide a better initialization for faster and better convergence of optimization. Both the human skeleton and the robot structure are modeled as graphs to make better use of topological information. We perform experiments on retargeting Chinese sign language, which involves two arms and two hands, with additional requirements on the relative relationships among joints. Experiments include retargeting various human demonstrations to YuMi, NAO, and Pepper in the simulation environment and to YuMi in the real-world environment. Both efficiency and accuracy of the proposed method are verified.
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. A reward function considering social conventions is designed to guide the training of the policy. Specifically, the policy model takes laser scan sequence and robot's own state as input and outputs steering command. The laser sequence is further transformed into stacked local obstacle maps disentangled from robot's ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that the policy using our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation tasks compared with other methods. Experiments are conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation.
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. The model takes laser scan sequence and robot's own state as input and outputs steering control. The laser sequence is further transformed into stacked local obstacle maps disentangled from robot's ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation task compared with other methods. Experiments were conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully while avoiding pedestrians in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability.
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