Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area...Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area to the left for road widening on horizontal curves. In order to provide safe and continuous shipment and transportation,appropriate curve widening areas should be provided for long vehicles along horizontal curves. In this study, a statistical model was developed to provide curve-widening solutions for long trucks（e.g., those with 18 wheels） considering various curve radius and deflection angles. The dynamic curve widening feature of Plateia 2013 program was employed to calculate curve widening for the specified vehicle. During the solution process, nine different horizontal curve diameters from 10 to 50 m（by 5 m intervals）and 17 different deflection angles from 90° to 170°（by 5°intervals） were evaluated to run horizontal curve-widening analysis. Using a multiple regression model, we made suitable predictions about curve widening. The curvewidening areas decrease as the horizontal curve radius increases, while increasing the deflection angle on horizontal curves increases curve widening areas. Clearly, the computer-based dynamic curve widening model developed in this study can be effectively used in determining optimum widening for horizontal curves by evaluating the number of alternatives that fit geometrical specifications and vehicle types.展开更多
Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between k...Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information （MI） to delineate specificity influencing residues （SIRs） from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods （direct information, PSICOV and adjusted mutual information） that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs for homeodomain （HI））, helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at http：//stormo.wustl.edu/SpecPred.展开更多
文摘Forest roads require important design specifications to ensure all-season access for various vehicles. Long and heavy log trucks can face serious maneuvering problems on forest roads due to insufficient amount of area to the left for road widening on horizontal curves. In order to provide safe and continuous shipment and transportation,appropriate curve widening areas should be provided for long vehicles along horizontal curves. In this study, a statistical model was developed to provide curve-widening solutions for long trucks（e.g., those with 18 wheels） considering various curve radius and deflection angles. The dynamic curve widening feature of Plateia 2013 program was employed to calculate curve widening for the specified vehicle. During the solution process, nine different horizontal curve diameters from 10 to 50 m（by 5 m intervals）and 17 different deflection angles from 90° to 170°（by 5°intervals） were evaluated to run horizontal curve-widening analysis. Using a multiple regression model, we made suitable predictions about curve widening. The curvewidening areas decrease as the horizontal curve radius increases, while increasing the deflection angle on horizontal curves increases curve widening areas. Clearly, the computer-based dynamic curve widening model developed in this study can be effectively used in determining optimum widening for horizontal curves by evaluating the number of alternatives that fit geometrical specifications and vehicle types.
文摘Transcription factors （TFs） are major modulators of transcription and subsequent cellular processes. The binding of TFs to specific regulatory elements is governed by their specificity. Considering the gap between known TFs sequence and specificity, specificity prediction frameworks are highly desired. Key inputs to such frameworks are protein residues that modulate the specificity of TF under consideration. Simple measures like mutual information （MI） to delineate specificity influencing residues （SIRs） from alignment fail due to structural constraints imposed by the three-dimensional structure of protein. Structural restraints on the evolution of the amino-acid sequence lead to identification of false SIRs. In this manuscript we extended three methods （direct information, PSICOV and adjusted mutual information） that have been used to disentangle spurious indirect protein residue-residue contacts from direct contacts, to identify SIRs from joint alignments of amino-acids and specificity. We predicted SIRs for homeodomain （HI））, helix-loop-helix, LacI and GntR families of TFs using these methods and compared to MI. Using various measures, we show that the performance of these three methods is comparable but better than MI. Implication of these methods in specificity prediction framework is discussed. The methods are implemented as an R package and available along with the alignments at http：//stormo.wustl.edu/SpecPred.
