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[PB-0080] Identification of a novel locus (C2) controlling canary yellow flesh in watermelons
Girim Park(Girim Park),Yunseo Choi(Yunseo Choi),Seoyeon Park(Seoyeon Park),Kaeun Jang(Kaeun Jang),Yongjae Kim(Yongjae Kim),Gibeom Kwon(Gibeom Kwon),Younghoon Park(Younghoon Park) 한국육종학회 2022 한국육종학회 공동학술발표집 Vol.2022 No.-
PDK4 Deficiency Suppresses Hepatic Glucagon Signaling by Decreasing cAMP Levels
Park, Bo-Yoon,Jeon, Jae-Han,Go, Younghoon,Ham, Hye Jin,Kim, Jeong-Eun,Yoo, Eun Kyung,Kwon, Woong Hee,Jeoung, Nam-Ho,Jeon, Yong Hyun,Koo, Seung-Hoi,Kim, Byung-Gyu,He, Ling,Park, Keun-Gyu,Harris, Robert American Diabetes Association 2018 Diabetes Vol.67 No.10
<P>In fasting or diabetes, gluconeogenic genes are transcriptionally activated by glucagon stimulation of the cAMP-protein kinase A (PKA)-CREB signaling pathway. Previous work showed pyruvate dehydrogenase kinase (PDK) inhibition in skeletal muscle increases pyruvate oxidation, which limits the availability of gluconeogenic substrates in the liver. However, this study found upregulation of hepatic PDK4 promoted glucagon-mediated expression of gluconeogenic genes, whereas knockdown or inhibition of hepatic PDK4 caused the opposite effect on gluconeogenic gene expression and decreased hepatic glucose production. Mechanistically, PDK4 deficiency decreased ATP levels, thus increasing phosphorylated AMPK (p-AMPK), which increased p-AMPK-sensitive phosphorylation of cyclic nucleotide phosphodiesterase 4B (p-PDE4B). This reduced cAMP levels and consequently p-CREB. Metabolic flux analysis showed that the reduction in ATP was a consequence of a diminished rate of fatty acid oxidation (FAO). However, overexpression of PDK4 increased FAO and increased ATP levels, which decreased p-AMPK and p-PDE4B and allowed greater accumulation of cAMP and p-CREB. The latter were abrogated by the FAO inhibitor etomoxir, suggesting a critical role for PDK4 in FAO stimulation and the regulation of cAMP levels. This finding strengthens the possibility of PDK4 as a target against diabetes.</P>
Multitask learning for virtual metrology in semiconductor manufacturing systems
Park, Chanhee,Kim, Younghoon,Park, Youngjoon,Kim, Seoung Bum Elsevier 2018 COMPUTERS & INDUSTRIAL ENGINEERING Vol.123 No.-
<P><B>Abstract</B></P> <P>Virtual metrology (VM) estimates the real metrology of wafers from process data collected from multiple chambers. In semiconductor manufacturing, independent models for each process chamber are limited because the number of sampled wafers measured at each chamber are too few to build a reliable model. One potential solution to this problem is to pool the data from all chambers to create a model capable of learning and serving as a global predictive model. However, even with chambers that perform the same operation, the condition of their semiconductor tools may vary because of various factors. This study uses, for the first time, various multitask methods to develop VM models. By learning multiple related tasks simultaneously, multitask methods effectively increase the number of observations included in the prediction model. In addition, by identifying the related task, the method can make a prediction using only similar tasks. This property of multitask learning can be useful to account for lack of information in a single chamber and for diversity among the chambers. The experimental results indicate that multitask models consistently outperformed independent and pooled models regardless of the size of the training set used. Among the multitask methods, a multitask tree-based ensemble model outperformed the others in every case. This implies that the problem of wafer quality prediction can be better addressed with a form of multitask learning.</P> <P><B>Highlights</B></P> <P> <UL> <LI> The usefulness of multitask learning for virtual metrology (VM) is examined. </LI> <LI> Multitask learning improves predictability by using shared information across tasks. </LI> <LI> Multitask methods deal with the sensor data by leveraging the chambers’ relatedness. </LI> <LI> Multitask VM models yield more accurate predictions than the existing VM models. </LI> </UL> </P>