The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates : bioRxiv preprint against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

Over the past 40 years, China has made significant progress towards its poverty alleviation goals. The rural population under the current poverty line has decreased by 739.9 million. China has contributed to more than 70 per cent of world poverty reduction. To better promote the new anti‐poverty strategy and to serve as a reference for poverty alleviation in other developing countries, this paper summarises the main experiences of China’s poverty alleviation over the past 40 years and then discusses the challenges associated with implementing the targeted poverty alleviation policy in the new era. China’s experience with poverty alleviation includes development‐oriented poverty alleviation, improving self‐development capabilities of the poor population, encouraging multiple subjects to participate in poverty alleviation and focusing on innovation and ways to improve poverty alleviation. Although China’s poverty alleviation initiatives have achieved significant successes, there are still several challenges that should be of concern in the coming years, such as the diminishing marginal effect of financial inputs on poverty alleviation, the resulting negative incentives for the poor to improve their internal motivations and the insufficient participation of markets and social forces in poverty alleviation. Given these challenges, this paper provides suggestions for anti‐poverty policies beyond 2020.

Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2 Dear EditorTo date, tens of millions of people have been infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the outbreak of the respiratory disease named the coronavirus disease 2019 . As a newly emerged member of the coronavirus family, SARS-CoV-2 is an enveloped positive-strand RNA virus, which has probably the largest genome (approximately 30 kb) among all RNA viruses. The nucleocapsid (N) protein of SARS-CoV-2 is mainly responsible for recognizing and wrapping viral RNA into helically symmetric structures (Malik, 2020). It was also reported that N protein can boost the efficiency of transcription and replication of viral RNA, implying its vital and multifunctional roles in the life cycle of coronavirus (Surjit and Lal, 2008;Chang et al., 2014). Recently, several independent research teams have reported that N protein of SARS-CoV-2 is capable of undergoing liquid-liquid phase separation (LLPS) (Iserman et al., 2020;Perdikari et al., 2020;Savastano et al., 2020).Here, we comprehensively determined the characteristics of the phase separation driven by the N protein of SARS-CoV-2 (termed SARS-CoV-2 N), and found that LLPS is involved in the interplay between the N protein-viral RNA complex of SARS-CoV-2 (termed SARS-CoV-2 N-RNA) and other viral proteins, such as nsp12. Importantly, we identified two small molecules targeting the SARS-CoV-2 N protein, which can intervene the phase separation properties of the N protein-viral RNA-nsp12 (termed SARS-CoV-2 N-RNA-nsp12) complex, thus probably improving the accessibility of other antiviral drugs (e.g., remdesivir) to their viral targets (e. g., nsp12/RdRp).First, IUPred2 (Erdos and Dosztanyi, 2020) and PLAAC (Lancaster et al., 2014) programs showed that SARS-CoV-2 N is highly disordered, and contains three intrinsically disordered regions (IDRs), with one also displaying prion-like activity (Figs. 1A and S1). Then, we expressed and purified the recombinant SARS-CoV-2 N protein with an mEGFP-tag (a monomeric variant of EGFP, A206K) or a His-tag using a prokaryotic expression system to understand the properties of N-driven LLPS in vitro (Fig. S2A and S2B). Confocal fluorescence microscopy showed that SARS-CoV-2 N was readily self-associated to form numerous micron-sized spherical condensates (Fig. 1B and 1C). Further time-lapse observations revealed that the SARS-CoV-2 N condensates fused and coalesced into larger ones upon their intersections (Fig. 1D and Video S1), verifying the liquid-like properties of SARS-CoV-2 N condensates. We also used fluorescence recovery after photobleaching (FRAP) to deeply study the dynamics of internal molecules within the N protein condensates. Recovery of fluorescence within the bleached regions (Fig. 1E) showed that SARS-CoV-2 N can partially freely diffuse within the condensed phase, consistent with their liquid-like behavior. In addition, phase condensation of SARS-CoV-2 ...

Designing robust end-effector plays a crucial role in performance of a robot workcell. Design automation of industrial grippers' fingers/jaws is therefore of the highest interest in the robot industry. This paper systematically reviews the enormous studies performed in relevant research areas for finger design automation. Key processes for successfully achieving automatic finger design are identified and research contributions in each key process are critically reviewed. The proposed approaches in each key process are analyzed, verified and benchmarked. The most promising methods to accomplish finger design automation are highlighted and presented.

Intracellular accumulation of the hyperphosphorylated tau is a pathological hallmark in the brain of Alzheimer disease. Activation of extrasynaptic NMDA receptors (E-NMDARs) induces excitatory toxicity that is involved in Alzheimer's neurodegeneration. However, the intrinsic link between E-NMDARs and the tau-induced neuronal damage remains elusive. In the present study, we showed in cultured primary cortical neurons that activation of E-NMDA receptors but not synaptic NMDA receptors dramatically increased tau mRNA and protein levels, with a simultaneous neuronal degeneration and decreased neuronal survival. Memantine, a selective antagonist of E-NMDARs, reversed E-NMDARs-induced tau overexpression. Activation of E-NMDARs in wild-type mouse brains resulted in neuron loss in hippocampus, whereas tau deletion in neuronal cultures and in the mouse brains rescued the E-NMDARs-induced neuronal death and degeneration. The E-NMDARs-induced tau overexpression was correlated with a reduced ERK phosphorylation, whereas the increased MEK activity, decreased binding and activity of ERK phosphatase to ERK, and increased ERK phosphorylation were observed in tau knockout mice. On the contrary, addition of tau proteins promoted ERK dephosphorylation in vitro. Taking together, these results indicate that tau overexpression mediates the excitatory toxicity induced by E-NMDAR activation through inhibiting ERK phosphorylation.

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

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This paper presents a multidisciplinary design optimization (MDO) framework for automated design of a modular industrial robot. The developed design framework seamlessly integrates high level computer aided design (CAD) templates (HLCt) and physics based high fidelity models for automated geometry manipulation, dynamic simulation, and structural strength analysis. In the developed framework, methods such as surrogate models and multilevel optimization are employed in order to speed up the design optimization process. This work demonstrates how a parametric geometric model, based on the concept of HLCt, enables a multidisciplinary framework for multi-objective optimization of a modular industrial robot, which constitutes an example of a complex heterogeneous system.