129,318 research outputs found
Optimal design for goodness-of-fit of the Michaelis-Menten enzyme kinetic function
No full textWe construct efficient designs for the Michaelis-Menten enzyme kinetic model capable of checking model assumption. An extended model, called EMAX model is also considered for this purpose. This model is widely used in pharmacokinetics and reduces to the Michaelis- Menten model for a specific choice of the parameter setting. Our strategy is to find efficient designs for estimating the parameters in the EMAX model and at the same time test the validity of the Michaelis-Menten model against the EMAX model by maximizing a minimum of the D- or D1-efficiencies taken over a range of values for the nonlinear parameters. In addition, we show that the designs obtained from maximizing the D-efficiencies are (i) efficient for estimating parameters in the EMAX model or the Michaelis-Menten model, (ii) efficient for testing the Michaelis-Menten model against the EMAX model and (iii) robust with respect to misspecification of the unknown parameters. --Chebyshev polynomials,EMAX model,goodness of fit test,locally D-optimal design,robust optimal design
Michaelis-Menten curves for cleavage reactions of Neu5Ac.
No full text(A) AsNAL (B) AsNAL N168A (C) AsNAL N168T and (D) EcNAL. Initial velocities at different substrate concentrations were fitted to the Michaelis-Menten equation.</p
Michaelis-Menten plots of enzymatic activity for GST-CBU1241 in relation to substrates OAA (A) and NADH (B).
No full textEnzyme specific activity (μmol/mg per mg protein) measured for varying concentrations of OAA concentrations at 0.5 mM NADH (A), and varying NADH concentrations at 0.6 mM OAA (B). Error bars represent standard deviation from the mean from at least three independent experiments. Linear regression line of best fit for Michaelis-Menten kinetic equation is plotted in grey for NADH (B).</p
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Michaelis-Menten kinetics parameters comparing the purified full-length protein (hTyr) to the purified truncated protein (hTyrC<sub>tr</sub>).
No full textMichaelis-Menten kinetics parameters comparing the purified full-length protein (hTyr) to the purified truncated protein (hTyrCtr).</p
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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No full textVarianten auf dem Titelblatt: Variante A "Cvra Ac Stvdio Io. Heinr. Michaelis", Variante B "Cvra Ac Stvdio D. Io. Heinr. Michaelis"Die Illustration ist ein Frontispi
Michaelis-Menten parameters for CO<sub>2</sub>, CH<sub>4</sub>, and C<sub>tot</sub> (Fig 2: Panels a-d and e-g).
No full textMichaelis-Menten parameters for CO2, CH4, and Ctot (Fig 2: Panels a-d and e-g).</p
Epicedia In Obitum Luctuosissimum Qvidem, Sed Beatissimum Pl. Reverendi Nec Non Clarissimi Viri Domini M. Joh. Tobiæ Bodini, Pastoris Ad B. Mar. Virg. Apud Wernigerodenses Vigilantissimi, Meritissimi, Qvi, Postqvam Inter Vivos Exegisset Annos Ferme LXX. d. 23. Junii Anno MDCCVII. Animam Suam Deo Reddidit, Corpvs Vero Nobis Reliqvit Dormitorio Svo ... Inferendvm, ... scripta á Nonnullis Fautoribus Ac Confratribus
No full textEnth. 7 Gedichte der BeiträgerVorlageform des Erscheinungsvermerks: Wenigerodæ, Literis Michaelis Antonii Strukii
Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
No full textNeuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware
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