http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Nocon, Justyna,Steiger, Matthias G.,Pfeffer, Martin,Sohn, Seung Bum,Kim, Tae Yong,Maurer, Michael,Rußmayer, Hannes,Pflü,gl, Stefan,Ask, Magnus,Haberhauer-Troyer, Christina,Ortmayr, Karin,Hann, Ste Academic Press 2014 Metabolic engineering Vol.24 No.-
<▼1><P>The production of recombinant proteins is frequently enhanced at the levels of transcription, codon usage, protein folding and secretion. Overproduction of heterologous proteins, however, also directly affects the primary metabolism of the producing cells. By incorporation of the production of a heterologous protein into a genome scale metabolic model of the yeast <I>Pichia pastoris</I>, the effects of overproduction were simulated and gene targets for deletion or overexpression for enhanced productivity were predicted. Overexpression targets were localized in the pentose phosphate pathway and the TCA cycle, while knockout targets were found in several branch points of glycolysis. Five out of 9 tested targets led to an enhanced production of cytosolic human superoxide dismutase (hSOD). Expression of bacterial β-glucuronidase could be enhanced as well by most of the same genetic modifications. Beneficial mutations were mainly related to reduction of the NADP/H pool and the deletion of fermentative pathways. Overexpression of the hSOD gene itself had a strong impact on intracellular fluxes, most of which changed in the same direction as predicted by the model. <I>In vivo</I> fluxes changed in the same direction as predicted to improve hSOD production. Genome scale metabolic modeling is shown to predict overexpression and deletion mutants which enhance recombinant protein production with high accuracy.</P></▼1><▼2><P><B>Highlights</B></P><P>•<P>Recombinant protein production in <I>P. pastoris</I> affects the central metabolism.</P>•<P>A genome scale metabolic model can predict these metabolic flux changes.</P>•<P>Mutations in central metabolic genes enhanced recombinant protein yield up to 40%.</P>•<P>These beneficial mutations were predicted by the metabolic model with high accuracy.</P></P></▼2>
Weighted zero-inflated Poisson mixed model with an application to Medicaid utilization data
Lee, Sang Mee,Karrison, Theodore,Nocon, Robert S.,Huang, Elbert The Korean Statistical Society 2018 Communications for statistical applications and me Vol.25 No.2
In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.