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This article reports the construction of an articulated manipulator’s hybrid dynamic model and trajectory planning and optimization of the manipulator using deep reinforcement learning (RL) on the dynamic model. The hybrid model was composed of a physical-based reduced-order dynamic model, linear friction and damping terms, and a deep neural network model to compensate for the nonlinear characteristics of the manipulator. The hybrid model then served as the digital twin of the manipulator for trajectory planning to optimize energy efficiency and operation speed by using RL while taking obstacle avoidance into consideration. The proposed strategy was simulated and experimentally validated. The energy consumption along paths was reduced and the speed was increased so the manipulator could achieve more efficient motion.

Overexpression of anti-apoptotic proteins MCL1 and Bcl-xL are frequently observed in many cancers. Inhibitors targeting MCL1 are in clinical development, however numerous cancer models are intrinsically resistant to this approach. To discover mechanisms underlying resistance to MCL1 inhibition, we performed multiple flow-cytometry based genome-wide CRISPR screens interrogating two drugs that directly (MCL1i) or indirectly (CDK9i) target MCL1. Remarkably, both screens identified three components (CUL5, RNF7 and UBE2F) of a cullin-RING ubiquitin ligase complex (CRL5) that resensitized cells to MCL1 inhibition. We find that levels of the BH3-only pro-apoptotic proteins Bim and Noxa are proteasomally regulated by the CRL5 complex. Accumulation of Noxa caused by depletion of CRL5 components was responsible for re-sensitization to CDK9 inhibitor, but not MCL1 inhibitor. Discovery of a novel role of CRL5 in apoptosis and resistance to multiple types of anticancer agents suggests the potential to improve combination treatments.

When learning to write Chinese characters, it is essential for students to learn and maintain the correct order of the strokes. Chinese teachers often use computer-supported drill and practice to develop students' ability to write in the correct order, but such devices are rarely designed to stimulate learners' memory-manipulation in cognitive processes. To enhance the effect of the stimuli, a computer game called Chinese order of strokes was designed for students to practice their sensorimotor skill by providing a different color (i.e., red) to evoke learners' memory-manipulation cognitive processes. To understand the effect of this game, third-grade students from an elementary school in Taipei were invited to play Chinese order of strokes, and the correlates between their intrinsic cognitive load, gameplay interest, and flow experience were examined. The results showed that intrinsic cognitive load was negatively related to gameplay interest and flow, gameplay interest was positively related to flow, and flow was positively related to learning progress. The results imply that teachers can utilize a digital Chinese order of strokes to implement characters based on their own teaching materials and to facilitate the students' learning of the correct order of strokes.

The recent development of CRISPR-Cas systems as easily accessible and programmable tools for genome editing and regulation is spurring a revolution in biology. Paired with the rapid expansion of reference and personalized genomic sequence information, technologies based on CRISPR-Cas are enabling nearly unlimited genetic manipulation, even in previously difficult contexts, including human cells. Although much attention has focused on the potential of CRISPR-Cas to cure Mendelian diseases, the technology also holds promise to transform the development of therapies to treat complex heritable and somatic disorders. In this Review, we discuss how CRISPR-Cas can affect the next generation of drugs by accelerating the identification and validation of high-value targets, uncovering high-confidence biomarkers and developing differentiated breakthrough therapies. We focus on the promises, pitfalls and hurdles of this revolutionary gene-editing technology, discuss key aspects of different CRISPR-Cas screening platforms and offer our perspectives on the best practices in genome engineering.

As the international division of labour becomes more entrenched, the distance goods travel before they reach the final consumer increases; at least this was the case before the outbreak of the COVID-19 pandemic. International trade and cross-border cargo movements generate significant carbon emissions. Despite theoretical advances, empirical studies frequently draw contradictory conclusions and the influence of international trade on a country's decarbonization efforts is inconclusive. This study examines the carbon emissions caused by countries' transportation services on global value chains. The input–output (IO) model and the 2015 multi-regional environmental input–output table from the UNCTAD-Eora database are employed. The input–output approach was used to determine the carbon emissions generated by the transport sector, along global value chains, in 190 countries. Environmentally extended IO analysis then reallocates emissions responsibilities of the transport sector from production to consumption. The study identifies which country's transport sectors add more value or emit more CO2. Our findings indicated that: (1) the transportation industry of a country may have a detrimental effect on the environment while generating minimal economic benefit; and (2) a country's transport industry may be tightly related to global value chain operations, but does not create considerable environmental impact. Given the significant differences in emissions intensity, we propose not only calculating the production-based accounting (PBA) of CO2 emissions, but also the consumption-based accounting (CBA). If CBA emissions are lower than PBA emissions, a country's transport sector is in carbon-leakage credit. The top three countries with the highest carbon-leakage credit for the transport sector were China, Russia and USA. If carbon emissions are taken into consideration, some nations may cease to possess a comparative advantage in manufacturing and trade. [ABSTRACT FROM AUTHOR]

In this article, an innovative offline deep imitation learning algorithm for optimal trajectory planning is proposed. While many state‐of‐the‐art works achieved optimal trajectory planning, their systems were stable or quasistable, and their approaches rarely optimized the system's initial conditions (ICs). Here, a new unstable dynamic system task called "internal sliding object stabilization control" is proposed, modeled, and solved by deep imitation learning. Given the system's ICs, the neural networks (NNs) can imitate the iterative linear quadratic regulator (iLQR), generate optimal trajectories, and compute faster. A proportional–integral–derivative (PID) controller is used to track the unstable trajectories. Leveraging on the gradients of NNs, it can optimize the system's ICs, avoid obstacles stepwise, and ensure the worst bounds of NNs for safety. Subsequently, thorough simulations are conducted, including comparing the iLQR and PID controllers in the task, optimizing the system's different ICs by gradient descent, and finding the worst bound of the performance by gradient ascent. Results show that the proposed algorithm achieves considerably improved performance. Finally, experiments are conducted with a real manipulator to compare the proposed structure with the original iLQR. Results indicate that the proposed algorithm resembles the iLQR well. Program code and experiment results are in https://github.com/DanielYamChen/ISOSC.git. [ABSTRACT FROM AUTHOR]