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您的检索式：作者名="Patrick Henkel"
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| 1 | Expanding etiology of progressive familial intrahepatic cholestasis显示文摘BACKGROUND Progressive familial intrahepatic cholestasis(PFIC)refers to a disparate group of autosomal recessive disorders that are linked by the inability to appropriately form and excrete bile from hepatocytes,resulting in a hepatocellular form of cholestasis.While the diagnosis of such disorders had historically been based on pattern recognition of unremitting cholestasis without other identified molecular or anatomic cause,recent scientific advancements have uncovered multiple specific responsible proteins.The variety of identified defects has resulted in an ever-broadening phenotypic spectrum,ranging from traditional benign recurrent jaundice to progressive cholestasis and end-stage liver disease.AIM To review current data on defects in bile acid homeostasis,explore the expanding knowledge base of genetic based diseases in this field,and report disease characteristics and management.METHODS We conducted a systemic review according to PRISMA guidelines.We performed a Medline/PubMed search in February-March 2019 for relevant articles relating to the understanding,diagnosis,and management of bile acid homeostasis with a focus on the family of diseases collectively known as PFIC.English only articles were accessed in full.The manual search included references of retrieved articles.We extracted data on disease characteristics,associations with other diseases,and treatment.Data was summarized and presented in text,figure,and table format.RESULTS Genetic-based liver disease resulting in the inability to properly form and secrete bile constitute an important cause of morbidity and mortality in children and increasingly in adults.A growing number of PFIC have been described based on an expanded understanding of biliary transport mechanism defects and the development of a common phenotype.CONCLUSION We present a summary of current advances made in a number of areas relevant to both the classically described FIC1(ATP8B1),BSEP(ABCB11),and MDR3(ABCB4)transporter deficiencies,as well as more recently described gene mutations--TJP2(TJP2),FXR(NR1H4),MYO5B(MYO5B),and others which expand the etiology and understanding of PFIC-related cholestatic diseases and bile transport. | Sarah AF Henkel Judy H Squires Mary Ayers Armando Ganoza Patrick Mckiernan James E Squires | 2019 | World Journal of Hepatology2019，11，5： | 13 |
| 2 | Safe operation of online learning data driven model predictive control of building energy systems显示文摘Model predictive control is a promising approach to reduce the CO 2 emissions in the building sector.However,the vast modeling effort hampers the widescale practical application.Here,data-driven process models,like artificial neural networks,are well-suited to automatize the modeling.However,the underlying data set strongly determines the quality and reliability of artificial neural networks.In general,the validity domain of a machine learning model is limited to the data that was used to train it.Predictions based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control.Here,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller.By continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework.We compare controllers based on two data sets,one with extensive system excitation and one with baseline operation.The system is controlled to a fixed temperature set point in baseline operation.Therefore,the artificial neural networks trained on this data set tend to extrapolate in other operating points.We show that safe operation in combination with online learning significantly improves performance. | Phillip Stoffel Patrick Henkel Martin Ratz Alexander Kumpel Dirk Muller | 2023 | Energy and AI2023，14，4： | 0 |
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