The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,i...The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,in tumor pathology.The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology,including tumor diagnosis,subtyping,grading,staging,and prognostic prediction,as well as the identification of pathological features,biomarkers and genetic changes.The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks.In addition,AI is useful for pathologists to meet the requirements of precision oncology.However,there are still some challenges relating to the implementation of AI,including the issues of algorithm validation and interpretability,computing systems,the unbelieving attitude of pathologists,clinicians and patients,as well as regulators and reimbursements.Herein,we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.展开更多
Earthworm manure, the by-product obtained from the disposing of biowastes by earthworm breeding, is largely produced and employed as a feedstock for biochar preparation through pyrolysis. For repairing acidic soil or ...Earthworm manure, the by-product obtained from the disposing of biowastes by earthworm breeding, is largely produced and employed as a feedstock for biochar preparation through pyrolysis. For repairing acidic soil or acidic electroplating effluent, biochar physicochemical properties would suffer from some changes like an acidic washing process, which hence affected its application functions. Pristine biochar （UBC） from pyrolysis of earthworm manure at 700℃ and biochar treated by HCI （WBC） were comparatively investigated regarding their physicochemical properties, adsorption capability and adsorption mechanism of Cu2＋ and Cd2＋ from aqueous solution to explore the immobilization characteristics of biochar in acidic environment. After HCI treatment, the soluble ash content and phenolic-OH in the WBC sample was notably decreased against the increase of the carboxyl C=O, aromatic C=C and Si-O-Si, compared to that of UBC. All adsorption processes can be well described by Langmuir isotherm model. The calculated maximum adsorption capacity of Cu2＋ and Cd2＋ adsorption on UBC were 36.56 and 29.31 mg/g, respectively, which were higher than that of WBC （8.64 and 12.81 rag/g, respectively）, indicating that HCI treatment significantly decreased biochar adsorption ability. Mechanism analysis revealed that alkali and alkaline earth metallic, salts （carbonates, phosphates and silicates）, and surface functional groups were responsible for UBC adsorption, corresponding to ion exchange, precipitation and complexation, respectively. However, ion exchange made little contributions to WBC adsorption due to the great loss of soluble ash content. WBC adsorption was mainly attributed to the abundant exposure of silicates and surface functional groups （carboxyl C=O and aromatic C=C）.展开更多
基金National Nature Science Foundation of China,Grant/Award Numbers:81871990,81472263。
文摘The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence(AI),especially deep learning(DL)-based AI,in tumor pathology.The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology,including tumor diagnosis,subtyping,grading,staging,and prognostic prediction,as well as the identification of pathological features,biomarkers and genetic changes.The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks.In addition,AI is useful for pathologists to meet the requirements of precision oncology.However,there are still some challenges relating to the implementation of AI,including the issues of algorithm validation and interpretability,computing systems,the unbelieving attitude of pathologists,clinicians and patients,as well as regulators and reimbursements.Herein,we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology.
基金supported by the National Natural Science Foundation of China(No.51476034)the National Basic Research Program(973)of China(No.2012CB215306)supported by“Program for Changjiang Scholars and Innovative Research Team in University”from the Ministry and Education of China(No.IRT13083)
文摘Earthworm manure, the by-product obtained from the disposing of biowastes by earthworm breeding, is largely produced and employed as a feedstock for biochar preparation through pyrolysis. For repairing acidic soil or acidic electroplating effluent, biochar physicochemical properties would suffer from some changes like an acidic washing process, which hence affected its application functions. Pristine biochar （UBC） from pyrolysis of earthworm manure at 700℃ and biochar treated by HCI （WBC） were comparatively investigated regarding their physicochemical properties, adsorption capability and adsorption mechanism of Cu2＋ and Cd2＋ from aqueous solution to explore the immobilization characteristics of biochar in acidic environment. After HCI treatment, the soluble ash content and phenolic-OH in the WBC sample was notably decreased against the increase of the carboxyl C=O, aromatic C=C and Si-O-Si, compared to that of UBC. All adsorption processes can be well described by Langmuir isotherm model. The calculated maximum adsorption capacity of Cu2＋ and Cd2＋ adsorption on UBC were 36.56 and 29.31 mg/g, respectively, which were higher than that of WBC （8.64 and 12.81 rag/g, respectively）, indicating that HCI treatment significantly decreased biochar adsorption ability. Mechanism analysis revealed that alkali and alkaline earth metallic, salts （carbonates, phosphates and silicates）, and surface functional groups were responsible for UBC adsorption, corresponding to ion exchange, precipitation and complexation, respectively. However, ion exchange made little contributions to WBC adsorption due to the great loss of soluble ash content. WBC adsorption was mainly attributed to the abundant exposure of silicates and surface functional groups （carboxyl C=O and aromatic C=C）.
