Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Game-based Platforms for Artificial Intelligence Research

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-sourced games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the game-based platforms for artificial intelligence research, discusses the research trend induced by the evolution of those platforms, and gives an outlook

Tyrosine Phosphatase PTPRO Deficiency in ERBB2-Positive Breast Cancer Contributes to Poor Prognosis and Lapatinib Resistance

Despite the initial benefit from treating ERBB2-positive breast cancer with tyrosine kinase inhibitor lapatinib, resistance develops inevitably. Since the expression of protein tyrosine phosphatase receptor-type O (PTPRO), a member of the R3 subfamily of receptor protein tyrosine phosphatases (PTPs), is inversely correlated with the aggressiveness of multiple malignancies, we decided to explore the correlation between PTPRO and lapatinib resistance in ERBB2-positive breast cancer. Results of immunohistochemical (IHC) staining and the correlation analysis between the expression levels of PTPRO and the clinicopathological parameters indicate that PTPRO is downregulated in cancer tissues as compared with normal tissues and negatively associated with differentiation, tumor size, tumor depth, as well as the expression of ERBB2 and Ki67. Results from Kaplan–Meier analyses indicate that lower expression of PTPRO is correlated with shorter relapse-free survival for patients with ERBB2-positive breast cancer, and multivariable Cox regression analysis found that PTPRO can potentially serve as an independent prognostic indicator for ERBB2-positive breast cancer. Results from both human breast cancer cells with PTPRO knockdown or overexpression and mouse embryonic fibroblasts (MEFs) which derived from Ptpro ( +/+ ) and Ptpro ( −/− ) mice with then stably transfected plasmid FUGW-Erbb2 consistently demonstrated the essentiality of PTPRO in the lapatinib-mediated anticancer process. Our findings suggest that PTPRO is not only able to serve as an independent prognostic indicator, but upregulating PTPRO can also reverse the lapatinib resistance of ERBB2-positive breast cancer

Generation and Application of Inducible Chimeric RNA ASTN2-PAPPA(as) Knockin Mouse Model

Chimeric RNAs (chiRNAs) play many previously unrecognized roles in different diseases including cancer. They can not only be used as biomarkers for diagnosis and prognosis of various diseases but also serve as potential therapeutic targets. In order to better understand the roles of chiRNAs in pathogenesis, we inserted human sequences into mouse genome and established a knockin mouse model of the tamoxifen-inducible expression of ASTN2-PAPPA antisense chimeric RNA (A-P(as)chiRNA). Mice carrying the A-P(as)chiRNA knockin gene do not display any apparent abnormalities in growth, fertility, histological, hematopoietic, and biochemical indices. Using this model, we dissected the role of A-P(as)chiRNA in chemical carcinogen 4-nitroquinoline 1-oxide (4NQO)-induced carcinogenesis of esophageal squamous cell carcinoma (ESCC). To our knowledge, we are the first to generate a chiRNA knockin mouse model using the Cre-loxP system. The model could be used to explore the roles of chiRNA in pathogenesis and potential targeted therapies

Single-cell transcriptome sequencing reveals tumor heterogeneity in family neuroblastoma

Neuroblastoma(NB) is the most common extracranial solid tumor in childhood, and it is now believed that some patients with NB have an underlying genetic susceptibility, which may be one of the reasons for the multiplicity of NB patients within a family line. Even within the same family, the samples show great variation and can present as ganglioneuroblastoma or even benign ganglioneuroma. The genomics of NB is still unclear and more in-depth studies are needed to reveal its key components. We first performed single-cell RNA sequencing(sc-RNAseq) analysis on clinical specimens of two family neuroblastoma(FNB) and four sporadic NB cases. A complete transcriptional profile of FNB was constructed from 18,394 cells from FNB, and we found that SDHD may be genetically associated with FNB and identified a prognostic related CAF subtype in FNB: Fib-4. Single-cell flux estimation analysis (scFEA) results showed that malignant cells were associated with arginine spermine, oxaloacetate and hypoxanthine, and that malignant cells metabolize lactate at lower levels than T cells. Our study provides new resources and ideas for the development of the genomics of family NB, and the mechanisms of cell-to-cell interactions and communication and the metabolic landscape will provide new therapeutic targets

Repurposing dextromethorphan and metformin for treating nicotine-induced cancer by directly targeting CHRNA7 to inhibit JAK2/STAT3/SOX2 signaling

Smoking is one of the most impactful lifestyle-related risk factors in many cancer types including esophageal squamous cell carcinoma (ESCC). As the major component of tobacco and e-cigarettes, nicotine is not only responsible for addiction to smoking but also a carcinogen. Here we report that nicotine enhances ESCC cancer malignancy and tumor-initiating capacity by interacting with cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) and subsequently activating the JAK2/STAT3 signaling pathway. We found that aberrant CHRNA7 expression can serve as an independent prognostic factor for ESCC patients. In multiple ESCC mouse models, dextromethorphan and metformin synergistically repressed nicotine-enhanced cancer-initiating cells (CIC) properties and inhibited ESCC progression. Mechanistically, dextromethorphan non-competitively inhibited nicotine binding to CHRNA7 while metformin downregulated CHRNA7 expression by antagonizing nicotine-induced promoter DNA hypomethylation of CHRNA7. Since dextromethorphan and metformin are two safe FDA-approved drugs with minimal undesirable side-effects, the combination of these drugs has a high potential as either a preventive and/or a therapeutic strategy against nicotine-promoted ESCC and perhaps other nicotine-sensitive cancer types as well

Repurposing dextromethorphan and metformin for treating nicotine-induced cancer by directly targeting CHRNA7 to inhibit JAK2/STAT3/SOX2 signaling

Smoking is one of the most impactful lifestyle-related risk factors in many cancer types including esophageal squamous cell carcinoma (ESCC). As the major component of tobacco and e-cigarettes, nicotine is not only responsible for addiction to smoking but also a carcinogen. Here we report that nicotine enhances ESCC cancer malignancy and tumor-initiating capacity by interacting with cholinergic receptor nicotinic alpha 7 subunit (CHRNA7) and subsequently activating the JAK2/STAT3 signaling pathway. We found that aberrant CHRNA7 expression can serve as an independent prognostic factor for ESCC patients. In multiple ESCC mouse models, dextromethorphan and metformin synergistically repressed nicotine-enhanced cancer-initiating cells (CIC) properties and inhibited ESCC progression. Mechanistically, dextromethorphan non-competitively inhibited nicotine binding to CHRNA7 while metformin downregulated CHRNA7 expression by antagonizing nicotine-induced promoter DNA hypomethylation of CHRNA7. Since dextromethorphan and metformin are two safe FDA-approved drugs with minimal undesirable side-effects, the combination of these drugs has a high potential as either a preventive and/or a therapeutic strategy against nicotine-promoted ESCC and perhaps other nicotine-sensitive cancer types as well

Correction:Repurposing dextromethorphan and metformin for treating nicotine-induced cancer by directly targeting CHRNA7 to inhibit JAK2/STAT3/SOX2 signaling (Oncogene, (2021), 40, 11, (1974-1987), 10.1038/s41388-021-01682-z)

Only after the article was published online did the authors notice the misspelling of the second author’s name. It should be “Liang Du” instead of “Du Liang”. The authors sincerely apologize for any inconvenience this might have caused. The original article has been corrected