488 research outputs found

    Census 2010 Demographic Profile: Madison County

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    This demographic profile describes characteristics of the local and state population based on results from the 2010 Census. The decennial census is an official enumeration, or count, of all residents on April 1st of the census year. The results of the census provide us with information about basic demographic characteristics of the population, including age, race, ethnicity, household composition, housing occupancy, and housing tenure

    Probing the Hyperconjugative Aromaticity of Cyclopentadiene and Pyrroliums Containing Group 7 Transition Metal Substituents

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    Aromaticity and hyperconjugation are two fundamental concepts in chemistry. Combining them together led to the proposal of the concept of hyperconjugative aromaticity by Mulliken in 1939. Now, it has been attracting considerable attention from both theoretical and experimental chemists. Recently, the concept of hyperconjugative aromaticity has been extended from main-group substituents to transition metal systems including groups 9, 10, and 11 transition metal substituents. Here, we report the hyperconjugative aromaticity in cyclopentadienes and pyrroliums containing group 7 transition metal substituents through density functional theory (DFT) calculations. It is found that the metal–metal bonding interaction can significantly reduce the aromaticity in cyclopentadienes, whereas the stronger σ-donor ligand, bridged hydride, and carbonyls can enhance aromaticity. All these findings expand the scope of the concept of hyperconjugative aromaticity, enriching aromatic organometallic chemistry

    Probing the Hyperconjugative Aromaticity of Cyclopentadiene and Pyrroliums Containing Group 7 Transition Metal Substituents

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    Aromaticity and hyperconjugation are two fundamental concepts in chemistry. Combining them together led to the proposal of the concept of hyperconjugative aromaticity by Mulliken in 1939. Now, it has been attracting considerable attention from both theoretical and experimental chemists. Recently, the concept of hyperconjugative aromaticity has been extended from main-group substituents to transition metal systems including groups 9, 10, and 11 transition metal substituents. Here, we report the hyperconjugative aromaticity in cyclopentadienes and pyrroliums containing group 7 transition metal substituents through density functional theory (DFT) calculations. It is found that the metal–metal bonding interaction can significantly reduce the aromaticity in cyclopentadienes, whereas the stronger σ-donor ligand, bridged hydride, and carbonyls can enhance aromaticity. All these findings expand the scope of the concept of hyperconjugative aromaticity, enriching aromatic organometallic chemistry

    Why Does Activation of the Weaker CS Bond in CS<sub>2</sub> by P/N-Based Frustrated Lewis Pairs Require More Energy Than That of the CO Bond in CO<sub>2</sub>? A DFT Study

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    The sequestration of carbon disulfide (CS<sub>2</sub>), a common pollutant in environmental systems, is of great importance due to its physical harm to human beings. Compared with CO<sub>2</sub> capture, that of CS<sub>2</sub> is much less developed. The use of P/N-based frustrated Lewis pairs (FLPs) has been proven, both experimentally and theoretically, to be an alternative strategy to efficiently sequestrate CO<sub>2</sub>. Therefore, we pose the question of whether the analogue CS<sub>2</sub> could also be sequestrated by the same FLPs, given that the CS bond in CS<sub>2</sub> is weaker than the CO bond in CO<sub>2</sub>. Herein, we carry out a thorough DFT study to theoretically examine this hypothesis for a series of P/N-based FLPs. Our results reveal unexpectedly higher reaction barriers in CS<sub>2</sub> capture by most of the P/N-based FLPs, although the bond dissociation energy of the CS bond in CS<sub>2</sub> (105.3 kcal mol<sup>–1</sup>) is smaller than that of the CO bond in CO<sub>2</sub> (127.2 kcal mol<sup>–1</sup>). The unexpected higher energy required for CS<sub>2</sub> activation can be rationalized by its larger bond distortion and its reverse bond polarization, as revealed by energy decomposition analysis and natural bond orbital analysis, respectively. Our findings could be helpful for experimentalists investigating the sequestration of CS<sub>2</sub> with P/N-based FLPs

    Supplement 1. Annotated computer code in Matlab and SAS for performing the simulations and analyses in examples 1–4.

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    <h2>File List</h2><blockquote> <p><b><i>Text file</i></b></p> <blockquote> <p><a href="fullV.txt">fullV.txt</a></p> </blockquote> <p><b><i>Matlab executable files</i></b></p> <blockquote> <p><a href="IZ_GLSeg.m">IZ_GLSeg.m</a></p> <p><a href="IZ_SPACEeg.m">IZ_SPACEeg.m</a></p> <p><a href="Space_MLfunct.m">Space_MLfunct.m</a></p> <p><a href="IZ_TIMEeg.m">IZ_TIMEeg.m</a></p> <p><a href="Time_MLfunct.m">Time_MLfunct.m</a></p> <p><a href="IZ_MIXEDeg.m">IZ_MIXEDeg.m</a></p> <p><a href="IZ_MIXEDegRep.m">IZ_MIXEDegRep.m</a></p> </blockquote> <p><b><i>SAS executable files</i></b></p> <blockquote> <p><a href="SASMIXEDeg.sas">SASMIXEDeg.sas</a></p> <p><a href="SASMIXEDegREG.sas">SASMIXEDegREG.sas</a></p> </blockquote> <p><b><i>File descriptions</i></b></p> <blockquote> <p>IZ_GLSeg.m – Matlab code simulating and analyzing phylogenetic data as in example #1</p> <p>fullV.txt – Text file containing covariance matrix needed for IZ_GLSeg.m</p> <p>IZ_SPACEeg.m – Matlab code simulating and analyzing spatial data as in example #2</p> <p>Space_MLfunct.m – Matlab function giving the likelihood function called by IZ_SPACEeg.m</p> <p>IZ_TIMEeg.m – Matlab code simulating and analyzing time-series data as in example #3</p> <p>Time_MLfunct.m – Matlab function giving the likelihood function called by IZ_TIMEeg.m</p> <p>IZ_MIXEDeg.m – Matlab code simulating a single data set with spatial and temporal corelations as in example #4</p> <p>IZ_MIXEDegRep.m – Matlab code simulating multiple data sets with spatial and temporal corelations as in example #4</p> <p>SASMIXEDeg.sas – SAS code using REML to analyze a single data set produced by IZ_MIXEDeg.m</p> <p>SASMIXEDegREG.sas – SAS code using REML to analyze multiple data sets produced by IZ_MIXEDegRep.m</p> </blockquote> <p> </p></blockquote><h2>Description</h2><blockquote> <p>Matlab ".m" files are text files that should run under Matlab version 5.0 and 6.5 (MathWorks 1996). The two programs used for example #4, IZ_MIXEDeg.m and IZ_MIXEDegRep.m, generate text files called 'mixedeg.txt'and 'mixedegrep.txt', respecitvely. These text files are then analyzed by the two SAS programs, SASMIXEDeg.sas and SASMIXEDegREG.sas, respectively, which are written in SAS version 6.12 (SAS 1996). To run the SAS programs, the infile line will have to be changed to set the path to the files mixedeg.txt and mixedegrep.txt. The current infile line in SASMIXEDeg.sas is</p> <p>infile 'Macintosh HD:mixedeg.txt';</p> <p>which finds the file mixedeg.txt on the hard drive 'Macintosh HD'.</p> <p>All files should run under Macintosh, Windows, and Unix operating systems.</p> </blockquote> <p> </p

    Table_6_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC

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    <p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p

    Table_5_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC

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    <p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p

    Table_7_Dominance and Epistasis Interactions Revealed as Important Variants for Leaf Traits of Maize NAM Population.DOC

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    <p>Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, non-additive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38∼47 highly significant loci (-log<sub>10</sub>P<sub>EW</sub> > 5), while estimated total heritability were 64.32∼79.06% with large contributions due to dominance and dominance related epistasis effects (16.00∼56.91%). Analysis with additive model obtained smaller total heritability (hT2 ≙ 18.68∼29.56%) and detected fewer loci (30∼36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.</p
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