BACKGROUND Hepatoid adenocarcinoma(HAC)occurs in extrahepatic organs such as the gastrointestinal tract,testes,ovaries,lungs,mediastinum and pancreas,and frequently produces a-fetoprotein(AFP).HAC of the lung(HAL)is r...BACKGROUND Hepatoid adenocarcinoma(HAC)occurs in extrahepatic organs such as the gastrointestinal tract,testes,ovaries,lungs,mediastinum and pancreas,and frequently produces a-fetoprotein(AFP).HAC of the lung(HAL)is rare,characterized by difficult treatment and poor prognosis.There are no reports of HAL in Yunnan-Guizhou Plateau,China.CASE S UMMARY A 60-year-old male patient was clinically diagnosed with HAL pT3 NOM0,stageⅡB.Chest computed tomography revealed a 7.5 cm x 7.2 cm soft tissue mass located in the right lung upper lobe and the adjacent superior mediastinum.Right upper lobectomy was performed.The diagnosis of HAL was confirmed by pathological examination,and the patient received paclitaxel and carboplatin as adjuvant chemotherapy after surgery.CONCL USION Clinical manifestations,pathological features,imaging findings,auxiliary examination,and treatment planning of HAL are presented to help clinicians improve their diagnosis and treatment.展开更多
It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement learning.The method uses Q-learning to find optimal control strategies based...It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement learning.The method uses Q-learning to find optimal control strategies based on the reward feedback from the environment that maximizes its performance.The optimal control strategies are recorded in a Q-table and then employed to implement controllers.The advantage of the method is that it does not require an explicit knowledge of the system,target states,and unstable periodic orbits.All that we need is the parameters that we are trying to control and an unknown simulation model that represents the interactive environment.To control the FK model,we employ the perturbation policy on two different kinds of parameters,i.e.,the pendulum lengths and the phase angles.We show that both of the two perturbation techniques,i.e.,changing the lengths and changing their phase angles,can suppress chaos in the system and make it create the periodic patterns.The form of patterns depends on the initial values of the angular displacements and velocities.In particular,we show that the pinning control strategy,which only changes a small number of lengths or phase angles,can be put into effect.展开更多
基金Supported by the Yunnan Health Science and Technology Plan Project Task Book,No.2017NS020Yunnan Provincial Health and Family Planning Commission Reserve Talent Project,No.H-2017013+2 种基金Yunnan Provincial Science and Technology Project,No.2017FE467(-142)2018 CSCOQilu Tumor Project,No.YQ201802-011the Educational Reform Project of Kunming Medical University,No.2018-JY-Y-046
文摘BACKGROUND Hepatoid adenocarcinoma(HAC)occurs in extrahepatic organs such as the gastrointestinal tract,testes,ovaries,lungs,mediastinum and pancreas,and frequently produces a-fetoprotein(AFP).HAC of the lung(HAL)is rare,characterized by difficult treatment and poor prognosis.There are no reports of HAL in Yunnan-Guizhou Plateau,China.CASE S UMMARY A 60-year-old male patient was clinically diagnosed with HAL pT3 NOM0,stageⅡB.Chest computed tomography revealed a 7.5 cm x 7.2 cm soft tissue mass located in the right lung upper lobe and the adjacent superior mediastinum.Right upper lobectomy was performed.The diagnosis of HAL was confirmed by pathological examination,and the patient received paclitaxel and carboplatin as adjuvant chemotherapy after surgery.CONCL USION Clinical manifestations,pathological features,imaging findings,auxiliary examination,and treatment planning of HAL are presented to help clinicians improve their diagnosis and treatment.
基金the National Natural Science Foundation of China(Grant Nos.12072262 and 11672231).
文摘It is shown that we can control spatiotemporal chaos in the Frenkel-Kontorova(FK)model by a model-free control method based on reinforcement learning.The method uses Q-learning to find optimal control strategies based on the reward feedback from the environment that maximizes its performance.The optimal control strategies are recorded in a Q-table and then employed to implement controllers.The advantage of the method is that it does not require an explicit knowledge of the system,target states,and unstable periodic orbits.All that we need is the parameters that we are trying to control and an unknown simulation model that represents the interactive environment.To control the FK model,we employ the perturbation policy on two different kinds of parameters,i.e.,the pendulum lengths and the phase angles.We show that both of the two perturbation techniques,i.e.,changing the lengths and changing their phase angles,can suppress chaos in the system and make it create the periodic patterns.The form of patterns depends on the initial values of the angular displacements and velocities.In particular,we show that the pinning control strategy,which only changes a small number of lengths or phase angles,can be put into effect.