A critical role for hepatic protein arginine methyltransferase 1 isoform 2 in glycemic control

Appropriate control of hepatic gluconeogenesis is essential for the organismal survival upon prolonged fasting and maintaining systemic homeostasis under metabolic stress. Here, we show protein arginine methyltransferase 1 (PRMT1), a key enzyme that catalyzes the protein arginine methylation process, particularly the isoform encoded by Prmt1 variant 2 (PRMT1V2), is critical in regulating gluconeogenesis in the liver. Liver‐specific deletion of Prmt1 reduced gluconeogenic capacity in cultured hepatocytes and in the liver. Prmt1v2 was expressed at a higher level compared to Prmt1v1 in hepatic tissue and cells. Gain‐of‐function of PRMT1V2 clearly activated the gluconeogenic program in hepatocytes via interactions with PGC1α, a key transcriptional coactivator regulating gluconeogenesis, enhancing its activity via arginine methylation, while no effects of PRMT1V1 were observed. Similar stimulatory effects of PRMT1V2 in controlling gluconeogenesis were observed in human HepG2 cells. PRMT1, specifically PRMT1V2, was stabilized in fasted liver and hepatocytes treated with glucagon, in a PGC1α‐dependent manner. PRMT1, particularly Prmt1v2, was significantly induced in the liver of streptozocin‐induced type 1 diabetes and high fat diet‐induced type 2 diabetes mouse models and liver‐specific Prmt1 deficiency drastically ameliorated diabetic hyperglycemia. These findings reveal that PRMT1 modulates gluconeogenesis and mediates glucose homeostasis under physiological and pathological conditions, suggesting that deeper understanding how PRMT1 contributes to the coordinated efforts in glycemic control may ultimately present novel therapeutic strategies that counteracts hyperglycemia in disease settings.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/10/fsb221018-sup-0005-FigS5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/9/fsb221018-sup-0001-FigS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/8/fsb221018-sup-0003-FigS3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/7/fsb221018-sup-0008-FigS8.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/6/fsb221018-sup-0002-FigS2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/5/fsb221018_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/4/fsb221018.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/3/fsb221018-sup-0007-FigS7.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/2/fsb221018-sup-0006-FigS6.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163465/1/fsb221018-sup-0004-FigS4.pd

Explaining the Attributes of a Deep Learning Based Intrusion Detection System for Industrial Control Networks

Intrusion detection is only the initial part of the security system for an industrial control system. Because of the criticality of the industrial control system, professionals still make the most important security decisions. Therefore, a simple intrusion alarm has a very limited role in the security system, and intrusion detection models based on deep learning struggle to provide more information because of the lack of explanation. This limits the application of deep learning methods to industrial control network intrusion detection. We analyzed the deep neural network (DNN) model and the interpretable classification model from the perspective of information, and clarified the correlation between the calculation process of the DNN model and the classification process. By comparing the normal samples with the abnormal samples, the abnormalities that occur during the calculation of the DNN model compared to the normal samples could be found. Based on this, a layer-wise relevance propagation method was designed to map the abnormalities in the calculation process to the abnormalities of attributes. At the same time, considering that the data set may already contain some useful information, we designed filtering rules for a kind of data set that can be obtained at a low cost, so that the calculation result is presented in a more accurate manner, which should help professionals lock and address intrusion threats more quickly

Methanol to Olefins (MTO): From Fundamentals to Commercialization

The methanol-to-olefins (MTO) reaction is an interesting and important reaction for both fundamental research and industrial application. The Dalian Institute of Chemical Physics (DICP) has developed a MTO technology that led to the successful construction and operation of the world's first coal to olefin plant in 2010. This historical perspective gives a brief summary on the key issues for the process development, including studies on the reaction mechanism, molecular sieve synthesis and crystallization mechanism, catalyst and its manufacturing scale up, reactor selection and reactor scale up, process demonstration, and commercialization. Further challenges on the fundamental research and the directions for future catalyst improvement are also suggested

Detecting Trivariate Associations in High-Dimensional Datasets

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC

Comparative investigation of the MTH induction reaction over HZSM-5 and HSAPO-34 catalysts

Methanol to hydrocarbons (MTH) induction reaction was comparatively investigated over HZSM-5 and HSAPO-34 catalysts combined with on line thermogravimetry analysis of catalyst weight increment by intelligent gravimetric analyzer (IGA) studies. The influence of catalyst topology and acidity and reaction temperature on the reaction performance were correlated with the confined organics formation and evolution over the catalysts. For the latter stage of the MTH induction period, methanol conversion over HZSM-5 catalyst was proved a well-defined autocatalysis process, while over HSAPO-34, the increasing rate of methanol conversion was retarded due to accumulation of methyladamantanes. There existed a similar deactivation behaviour for HZSM-5 catalysts with low Si/Al ratios and HSAPO-34 catalysts during the temperature-programmed MTH (TP-MTH) reaction. But the IGA studies showed that the change of the retained species amount was quite different: for TP-MTH reaction over HZSM-5, the amount firstly increased and then decreased to a stable value; while for HSAPO-34, the amount kept increasing until reached a constant value. MTH induction reaction over HZSM-5 catalyst with different Si/Al ratios and HSAPO-34 catalysts with different Si contents were also investigated. All these findings revealed the influence of catalyst topologies on the formation of retained species and then on the catalyst activity during the MTH induction reaction. (C) 2017 Elsevier B.V. All rights reserved