A mechanistic model for submerged aquatic macrophyte photosynthesis: Hydrilla in ambient and elevated CO2

Abstract Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.

Download Full-text

Propagation and production of an endangered aquatic macrophyte: Texas wildrice ( Zizania texana Hitchc.)

Native Plants Journal ◽

10.3368/npj.18.1.77 ◽

2017 ◽

Vol 18 (1) ◽

pp. 77-85 ◽

Cited By ~ 1

Author(s):

Jeffrey T Hutchinson

Keyword(s):

Aquatic Macrophyte

Download Full-text

A mechanistic model for dengue under the coexistence ofAedes aegyptiandAedes albopictusin endemic areas

10.1603/ice.2016.114997 ◽

2016 ◽

Author(s):

Osama Mekki Seidahmed

Keyword(s):

Mechanistic Model ◽

Endemic Areas

Download Full-text

Restriction of Access to Dark State: A New Mechanistic Model for Heteroatom-Containing AIE Systems

10.26434/chemrxiv.8275325.v1 ◽

2019 ◽

Author(s):

Yujie Tu ◽

Junkai Liu ◽

Haoke Zhang ◽

Qian Peng ◽

Jacky W. Y. Lam ◽

...

Keyword(s):

Excited States ◽

Radiative Decay ◽

Molecular Design ◽

Mechanistic Model ◽

Zinc Ion ◽

Easy Access ◽

Triplet States ◽

Dark State ◽

Ion Probe ◽

Π State

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.

Download Full-text

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.

Download Full-text