scholarly journals Reactions of DOPA (3,4-dihydroxyphenylalanine) decarboxylase with DOPA

1979 ◽  
Vol 183 (2) ◽  
pp. 361-368 ◽  
Author(s):  
A Minelli ◽  
A T Charteris ◽  
C B Voltattorni ◽  
R A John
Keyword(s):  
Steady State ◽  
Time Scales ◽  
Direct Observation ◽  
Equilibrium Constants ◽  
Active Part ◽  
Stopped Flow ◽  
Step Rate ◽  
Multiple Reactions ◽  
Absorbing Material ◽  
Different Time Scales

The study of DOPA (3,4-dihydroxyphenylalanine) decarboxylase by steady-state methods is difficult because multiple reactions occur. The reaction with DOPA was studied at enzyme concentrations between 20 and 50 micrometer by direct observation of the bound coenzyme by using stopped-flow and conventional spectrophotometry. Four processes were observed on different time scales and three of these were attributed to stages in the decarboxylation. The fourth was attributed to an accompanying transamination that renders the enzyme inactive. It was clear that much, if not all, of the 330 nm-absorbing coenzyme present in the free enzyme plays an active part in the decarboxylation, since it is converted into 420 nm-absorbing material in the first observable step. An intermediate absorbing maximally at 390 nm is formed in a slower step. Rate and equilibrium constants have been determined and the ratio of decarboxylation to transamination was estimated to be 1200:1.

Atomic Migration as a Mechanism of Superstructure Formation in Intermetallic Compounds

2005 ◽  
Vol 237-240 ◽  
pp. 609-620
Author(s):  
Rafal Abdank-Kozubski ◽  
Mirosław Kozłowski ◽  
S. Czekaj ◽  
Veronique Pierron-Bohnes ◽  
Wolfgang Pfeiler
Keyword(s):  
Steady State ◽  
Time Scales ◽  
Vacancy Concentration ◽  
Simulation Studies ◽  
Atomic Configuration ◽  
State Diffusion ◽  
Nonsteady State ◽  
Defect Mechanism ◽  
Steady State Diffusion ◽  
Different Time Scales

“Order-order” relaxations driven by atomic migration in superstructures proceed in nonsteady- state of a system, which relaxes to the equilibrium atomic configuration. Hence, the corresponding studies are complementary to standard steady-state diffusion investigations. Two time scales operating in “order-order” relaxations in L12-ordered (Ni3Al) and L10-ordered (FePd, FePt) binary intermetallics were experimentally observed. On the other hand, in B2-ordered NiAl – known of a giant vacancy concentration, “order-order” relaxations appeared surprisingly slow. Definite relationships between the activation energies for diffusion ( ) D A E and “order-order” relaxations ( ) O O A E − were revealed: ( ) D A E < ( ) O O A E − in L12-type superstructure; ( ) D A E ³ ( ) O O A E − in L10- and in B2-type superstructures. Corresponding simulation studies elucidated the specific atomistic mechanism of the processes. It has been shown that different time scales active in “order-order” relaxations in L12 and L10-ordered systems follow from specific atomic-jump correlations, which result from non-steady-state conditions and particular superlattice geometries: the availability of easy diffusion channels. A model of “order-order” kinetics in NiAl as controlled by a triple-defect mechanism is proposed.

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

Green markets integration in different time scales: A regional analysis

2021 ◽  
Vol 98 ◽  
pp. 105254 ◽  
Author(s):  
Christian Urom ◽  
Hela Mzoughi ◽  
Ilyes Abid ◽  
Mariem Brahim
Keyword(s):  
Time Scales ◽  
Regional Analysis ◽  
Different Time Scales

scholarly journals Measuring chromosome dynamics on different time scales using resolvases with varying half-lives

2005 ◽  
Vol 56 (4) ◽  
pp. 1049-1061 ◽  
Author(s):  
Richard A. Stein ◽  
Shuang Deng ◽  
N. Patrick Higgins
Keyword(s):  
Time Scales ◽  
Chromosome Dynamics ◽  
Different Time Scales

scholarly journals Reservoir ecological operation considering outflow variations across different time scales

2021 ◽  
Vol 125 ◽  
pp. 107582 ◽  
Author(s):  
Yibo Wang ◽  
Pan Liu ◽  
Ming Dou ◽  
He Li ◽  
Bo Ming ◽  
...  
Keyword(s):  
Time Scales ◽  
Different Time Scales

scholarly journals Stellar noise and planet detection. I. Oscillations, granulation and sun-like spots

2010 ◽  
Vol 6 (S276) ◽  
pp. 527-529
Author(s):  
Xavier Dumusque ◽  
Nuno C. Santos ◽  
Stéphane Udry ◽  
Cristophe Lovis ◽  
Xavier Bonfils
Keyword(s):  
Radial Velocity ◽  
Time Scales ◽  
High Precision ◽  
Habitable Zone ◽  
Planet Detection ◽  
Solar Type ◽  
Physical Phenomena ◽  
Instrumental Precision ◽  
Observational Strategy ◽  
Different Time Scales

AbstractSpectrographs like HARPS can now reach a sub-ms−1 precision in radial-velocity (RV) (Pepe & Lovis 2008). At this level of accuracy, we start to be confronted with stellar noise produced by 3 different physical phenomena: oscillations, granulation phenomena (granulation, meso- and super-granulation) and activity. On solar type stars, these 3 types of perturbation can induce ms−1 RV variation, but on different time scales: 3 to 15 minutes for oscillations, 15 minutes to 1.5 days for granulation phenomena and 10 to 50 days for activity. The high precision observational strategy used on HARPS, 1 measure per night of 15 minutes, on 10 consecutive days each month, is optimized, due to a long exposure time, to average out the noise coming from oscillations (Dumusque et al. 2011a) but not to reduce the noise coming from granulation and activity (Dumusque et al. 2011a and Dumusque et al. 2011b). The smallest planets found with this strategy (Mayor et al. 2009) seems to be at the limit of the actual observational strategy and not at the limit of the instrumental precision. To be able to find Earth mass planets in the habitable zone of solar-type stars (200 days for a K0 dwarf), new observational strategies, averaging out simultaneously all type of stellar noise, are required.

scholarly journals Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018

2021 ◽  
Vol 13 (4) ◽  
pp. 811
Author(s):  
Hao Liu ◽  
Zexun Wei
Keyword(s):  
Time Scales ◽  
Correlation Coefficients ◽  
Dominant Mode ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Ocean Data Assimilation ◽  
Multiple Datasets ◽  
Different Time Scales

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.

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