scholarly journals Adaptive protocols based on predictions from a mechanistic model of the effect of IL7 on CD4 counts

2018 ◽  
Vol 38 (2) ◽  
pp. 221-235
Author(s):  
Laura Villain ◽  
Daniel Commenges ◽  
Chloé Pasin ◽  
Mélanie Prague ◽  
Rodolphe Thiébaut
2012 ◽  
Vol 42 (13) ◽  
pp. 24
Author(s):  
DENISE NAPOLI
Keyword(s):  

2019 ◽  
Author(s):  
Yujie Tu ◽  
Junkai Liu ◽  
Haoke Zhang ◽  
Qian Peng ◽  
Jacky W. Y. Lam ◽  
...  

Aggregation-induced emission (AIE) is an unusual photophysical phenomenon and provides an effective and advantageous strategy for the design of highly emissive materials in versatile applications such as sensing, imaging, and theragnosis. "Restriction of intramolecular motion" is the well-recognized working mechanism of AIE and have guided the molecular design of most AIE materials. However, it sometimes fails to be workable to some heteroatom-containing systems. Herein, in this work, we take more than one excited state into account and specify a mechanism –"restriction of access to dark state (RADS)" – to explain the AIE effect of heteroatom-containing molecules. An anthracene-based zinc ion probe named APA is chosen as the model compound, whose weak fluorescence in solution is ascribed to the easy access from the bright (π,π*) state to the closelying dark (n,π*) state caused by the strong vibronic coupling of the two excited states. By either metal complexation or aggregation, the dark state is less accessible due to the restriction of the molecular motion leading to the dark state and elevation of the dark state energy, thus the emission of the bright state is restored. RADS is found to be powerful in elucidating the photophysics of AIE materials with excited states which favor non-radiative decay, including overlap-forbidden states such as (n,π*) and CT states, spin-forbidden triplet states, which commonly exist in heteroatom-containing molecules.


2020 ◽  
Author(s):  
Medha Shekhar ◽  
Dobromir Rahnev

Humans have the metacognitive ability to judge the accuracy of their own decisions via confidence ratings. A substantial body of research has demonstrated that human metacognition is fallible but it remains unclear how metacognitive inefficiency should be incorporated into a mechanistic model of confidence generation. Here we show that, contrary to what is typically assumed, metacognitive inefficiency depends on the level of confidence. We found that, across five different datasets and four different measures of metacognition, metacognitive ability decreased with higher confidence ratings. To understand the nature of this effect, we collected a large dataset of 20 subjects completing 2,800 trials each and providing confidence ratings on a continuous scale. The results demonstrated a robustly nonlinear zROC curve with downward curvature, despite a decades-old assumption of linearity. This pattern of results was reproduced by a new mechanistic model of confidence generation, which assumes the existence of lognormally-distributed metacognitive noise. The model outperformed competing models either lacking metacognitive noise altogether or featuring Gaussian metacognitive noise. Further, the model could generate a measure of metacognitive ability which was independent of confidence levels. These findings establish an empirically-validated model of confidence generation, have significant implications about measures of metacognitive ability, and begin to reveal the underlying nature of metacognitive inefficiency.


2020 ◽  
Author(s):  
Roseline Affi ◽  
Delphine Gabillard ◽  
Catherine Dunyach-Remy ◽  
Jean-Baptiste Ntakpe ◽  
Raoul Moh ◽  
...  
Keyword(s):  

1991 ◽  
Vol 24 (6) ◽  
pp. 9-16 ◽  
Author(s):  
P. J. Ossenbruggen ◽  
H. Spanjers ◽  
H. Aspegren ◽  
A. Klapwijk

A series of batch tests were performed to study the competition for oxygen by Nitrosomonas and Nitrobacter in the nitrification of ammonia in activated sludge. Oxygen uptake rate (OUR) and dynamic (compartment) models describing the process are proposed and tested. The OUR model is described by a Monod relationship and the biogradation process by a set of first order nonlinear differential equations with variable coefficients. The results show a mechanistic model and ten reaction rates are sufficient to capture the interactive behavior of the nitrification process. Methods for model specification, calibrating, and testing the model and the design of additional experiments are described.


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