A mechanistic model for asymmetric alkylation of chiral dipole stabilized anions

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.

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The nature of metacognitive inefficiency in perceptual decision making

10.31234/osf.io/g9qzh ◽

2020 ◽

Author(s):

Medha Shekhar ◽

Dobromir Rahnev

Keyword(s):

Decision Making ◽

Mechanistic Model ◽

Perceptual Decision Making ◽

Large Dataset ◽

Substantial Body ◽

Confidence Levels ◽

Continuous Scale ◽

Competing Models ◽

Metacognitive Ability ◽

Confidence Ratings

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.

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Faculty Opinions recommendation of Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718014403.793477671 ◽

2013 ◽

Author(s):

Chih-hao Hsieh

Keyword(s):

Experimental Data ◽

Mechanistic Model ◽

Model Free

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Designing Experiments for Model Identification of the Nitrification Process

Water Science & Technology ◽

10.2166/wst.1991.0135 ◽

1991 ◽

Vol 24 (6) ◽

pp. 9-16 ◽

Cited By ~ 6

Author(s):

P. J. Ossenbruggen ◽

H. Spanjers ◽

H. Aspegren ◽

A. Klapwijk

Keyword(s):

Mechanistic Model ◽

Nonlinear Differential Equations ◽

Model Identification ◽

Reaction Rates ◽

Variable Coefficients ◽

Model Specification ◽

First Order ◽

Interactive Behavior ◽

Batch Tests ◽

Nitrification Process

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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Modelling heavy metal mobilisation in solid waste deposits - a predictive tool for environmental risk assessment

Water Science & Technology ◽

10.2166/wst.1999.0654 ◽

1999 ◽

Vol 39 (10-11) ◽

pp. 193-196

Author(s):

J. Petersen ◽

J. G. Petrie

Keyword(s):

Heavy Metal ◽

Solid Waste ◽

Environmental Risk ◽

Mechanistic Model ◽

Small Sample ◽

Waste Material ◽

Metal Species ◽

Model Parameters ◽

Assessment Methodology ◽

Predictive Tool

The release of heavy metal species from deposits of solid waste materials originating from minerals processing operations poses a serious environmental risk should such species migrate beyond the boundaries of the deposit into the surrounding environment. Legislation increasingly places the liability for wastes with the operators of the process that generates them. The costs for long-term monitoring and clean-up following a potential critical leakage have to be factored in the overall project plan from the outset. Thus assessment of the potential for a particular waste material to generate a harmful leachate is directly relevant for estimating the environmental risk associated with the planned disposal operation. A rigorous mechanistic model is proposed, which allows prediction of the time-dependent generation of a leachate from a solid mineral waste deposit. Model parameters are obtained from a suitably designed laboratory waste assessment methodology on a relatively small sample of the prospective waste material. The parameters are not specific to the laboratory environment in which they were obtained but are valid also for full-scale heap modelling. In this way the model, combined with the assessment methodology, becomes a powerful tool for meaningful assessment of the risks associated with solid waste disposal strategies.

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TEAD4 modulated LncRNA MNX1-AS1 contributes to gastric cancer progression partly through suppressing BTG2 and activating BCL2

Molecular Cancer ◽

10.1186/s12943-019-1104-1 ◽

2020 ◽

Vol 19 (1) ◽

Cited By ~ 12

Author(s):

You Shuai ◽

Zhonghua Ma ◽

Weitao Liu ◽

Tao Yu ◽

Changsheng Yan ◽

...

Keyword(s):

Gastric Cancer ◽

Cancer Progression ◽

Molecular Mechanisms ◽

Mechanistic Model ◽

Ectopic Expression ◽

Luciferase Reporter ◽

Migration And Invasion ◽

Tissue Samples ◽

Reporter Assays

Abstract Background Gastric cancer (GC) is the third leading cause of cancer-related mortality globally. Long noncoding RNAs (lncRNAs) are dysregulated in obvious malignancies including GC and exploring the regulatory mechanisms underlying their expression is an attractive research area. However, these molecular mechanisms require further clarification, especially upstream mechanisms. Methods LncRNA MNX1-AS1 expression in GC tissue samples was investigated via microarray analysis and further determined in a cohort of GC tissues via quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays. Cell proliferation and flow cytometry assays were performed to confirm the roles of MNX1-AS1 in GC proliferation, cell cycle regulation, and apoptosis. The influence of MNX1-AS1 on GC cell migration and invasion was explored with Transwell assays. A xenograft tumour model was established to verify the effects of MNX1-AS1 on in vivo tumourigenesis. The TEAD4-involved upstream regulatory mechanism of MNX1-AS1 was explored through ChIP and luciferase reporter assays. The mechanistic model of MNX1-AS1 in regulating gene expression was further detected by subcellular fractionation, FISH, RIP, ChIP and luciferase reporter assays. Results It was found that MNX1-AS1 displayed obvious upregulation in GC tissue samples and cell lines, and ectopic expression of MNX1-AS1 predicted poor clinical outcomes for patients with GC. Overexpressed MNX1-AS1 expression promoted proliferation, migration and invasion of GC cells markedly, whereas decreased MNX1-AS1 expression elicited the opposite effects. Consistent with the in vitro results, MNX1-AS1 depletion effectively inhibited the growth of xenograft tumour in vivo. Mechanistically, TEAD4 directly bound the promoter region of MNX1-AS1 and stimulated the transcription of MNX1-AS1. Furthermore, MNX1-AS1 can sponge miR-6785-5p to upregulate the expression of BCL2 in GC cells. Meanwhile, MNX1-AS1 suppressed the transcription of BTG2 by recruiting polycomb repressive complex 2 to BTG2 promoter regions. Conclusions Our findings demonstrate that MNX1-AS1 may be able to serve as a prognostic indicator in GC patients and that TEAD4-activatd MNX1-AS1 can promote GC progression through EZH2/BTG2 and miR-6785-5p/BCL2 axes, implicating it as a novel and potent target for the treatment of GC.

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