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In addition we propose a response to this flux state, i.e. the lineage specific response (LSR), a large scale induction of lineage specific genes and regulatory factors for it.
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Since the linearity of the resonant frequency vs. tuning voltage (f V) relationship in an LC-resonant system is closely related to the C V response characteristic of the varactor, we propose a C V response tunable varactor to realize the linear frequency tuning.
We propose a revised response expectancy model, in which affective forecasts are a subgroup of response expectancies, referring to emotional outcomes.
For this reason, in this paper we propose a smart response mechanism that controls human operators' operational threats at all times.
On the basis of these observations, we propose a stress response pathway involving DNAJA1.
Second, we propose a flexible response model that allows for daily variation in behavior.
We propose a Bayesian response-adaptive randomization trial design for AHFS patients.
To these mails, a donor has to respond by making two decisions: "should I continue donating or not?" and "should I upgrade or not?" We propose a multi-response model that accommodates not only the differential effects of the two types of mail on the donors, but also the possible correlation across the two response functions for each donor.
Also, we propose that flux through early glycolysis or the TCA cycle is a more fundamental determining factor than growth rate for low growth rate protein production and we propose a novel eukaryotic response to this i.e. the lineage specific response (LSR).
Finally, we propose an engineering response surface methodology (RS M approach to predict the pressure drop.
In this study, we propose an emergency response system that can cope with leak accidents of a chemical plant by monitoring sensor data and track down the suspected leak source using machine learning: Deep-learning and Random Forest classifiers.
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CEO of Professional Science Editing for Scientists @ prosciediting.com