Molecular Nature of X(3872)in B^(0)→K^(0)X(3872)and B^(+)→K^(+)X(3872)Decays

We investigate the decays of B^(0)→K^(0)X(3872)and B^(+)→K^(+)X(3872)based on the picture where the X(3872)resonance is strongly coupled to the DD*+c.c.channel.In addition to the decay mechanism where the X(3872)resonance is formed from the DD*pair hadronization with the short-distance interaction,we have also considered the DD*rescattering diagrams in the long-distance scale,where D andD*are formed from c andDD*separately.Because of the difference of the mass thresholds of charged and neutralDD*channels,and the rather narrow width of the X(3872)resonance,at the X(3872)mass,the loop functions ofD^(0)D*^(0)andD^(+)D*^(-)are much different.Taking this difference into account,the ratio of B[B^(0)→K^(0)X(3872)]/B[B+→K+X(3872)]■0.5 can be naturally obtained.Based on this result,we also evaluate the decay widths of B_s^(0)→η(η′)X(3872).It is expected that future experimental measurements of these decays can be used to elucidate the nature of the X(3872)resonance.

Mechanotransduction of stem cells for tendon repair

Tendon is a mechanosensitive tissue that transmits force from muscle to bone.Physiological loading contributes to maintaining the homeostasis and adaptation of tendon,but aberrant loading may lead to injury or failed repair.It is shown that stem cells respond to mechanical loading and play an essential role in both acute and chronic injuries,as well as in tendon repair.In the process of mechanotransduction,mechanical loading is detected by mechanosensors that regulate cell differentiation and proliferation via several signaling pathways.In order to better understand the stem-cell response to mechanical stimulation and the potential mechanism of the tendon repair process,in this review,we summarize the source and role of endogenous and exogenous stem cells active in tendon repair,describe the mechanical response of stem cells,and finally,highlight the mechanotransduction process and underlying signaling pathways.

Deep reinforcement learning:a survey

Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence research.It has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natural language dialogue systems.In this survey,we systematically categorize the deep RL algorithms and applications,and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods,model-free methods,and advanced RL methods.We thoroughly analyze the advances including exploration,inverse RL,and transfer RL.Finally,we outline the current representative applications,and analyze four open problems for future research.

Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.

Nucleolar protein 6 promotes cell proliferation and acts as a potential novel prognostic marker for hepatocellular carcinoma

Background:Nucleolar protein 6(NOL6)is a nucleolar RNA-associated protein that is highly conserved between species.It has been proved to be associated with the prognosis of liver cancer.However,the underlying mechanism has not been fully established.This study aimed to assess the relationship between NOL6 and liver cancer prognosis.Methods:We constructed anNOL6-short hairpin RNA(shRNA)-expressing lentivirus.Through viral transfection,cell growth assay and fluorescence-activated cell sorting,we evaluated the effect of shRNA-mediatedNOL6 knockdown on the proliferation,colony formation,and apoptosis of hepatocellular carcinoma(HCC)cells.The relationship betweenNOL6 expression and HCC patient survival has been established through bioinformatics analysis.We also explored the downstream molecular regulatory network ofNOL6 in HCC by performing an Ingenuity Pathway Analysis in the database.Results:IncreasedNOL6 expression was detected in HCC cells compared to normal controls;HCC patients with highNOL6 expression had poorer prognoses than those with low expression.NOL6 knockdown inhibited HCC cell proliferation,apoptosis,and colony formation.Also,MAPK8,CEBPA,andFOSL1 were selected as potential downstream genes ofNOL6.Conclusions:NOL6 up-regulates HCC cell proliferation and affects downstream expression of related genes.Moreover,NOL6 is considered to be associated with poor prognosis in HCC patients.