Distributional effects of vehicle tax in the framework of transportation externalities

Figure S2.The relationship between perivascular CD4 infiltration and 12 months follow-up DLCO (p = 0.134, r = −0.205). (PPT 43 kb

Learning Nominal Regular Languages with Binders

The theories of formal language and automata are fundamental in several areas of computer science. During decades of development, these theories have been stretched out and reach many branches and application contexts, ranging from lexical analysis, natural language processing, model checking, and system design. Recently, the applications of machine learning are spreading out rapidly. One interesting application, learning automata, gains sustained attention crossing the disciplines.This dissertation investigates learning nominal automata, an extension of classical automata to alphabets featuring names. This class of automata capture nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders. These formalisms are amenable to abtract modelling resource-aware computations.We propose nL*, a learning algorithm on nominal regular languages with binders. Our algorithm generalises Angluin's algorithm L* with respect to nominal regular languages with binders. We show the correctness of nL* and study its theoretical complexity.We also develop a implementation of nL* that we use to experimentally analyse different strategies for providing counterexamples to the learner. These strategies are designed on the base of the rich algebraic structure provided by our nominal setting. Further, we evaluate the behaviours of the process and its efficiency according tothe different strategies with a set of experiments.</div

Structural modeling of human cardiac sodium channel pore domain

<p>The pore domain of human voltage-dependent cardiac sodium channel Na_v1.5 (hNa_v1.5) is the crucial binding targets for anti-arrhythmics drugs and some local anesthetic drugs but its three-dimensional structure is still lacking. This has affected the detailed studies of the binding features and mechanism of these drugs. In this paper, we present a structural model for open-state pore domain of hNa_v1.5 built using single template ROSETTA-membrane homology modeling with the crystal structure of Na_vMs. The assembled structural models are evaluated by rosettaMP energy and locations of binding sites. The modeled structures of the pore domain of hNa_v1.5 in open state will be helpful to explore molecular mechanism of a state-dependent drug binding and help designing new drugs.</p

Results of 21 antibody-antigen and 11 dockground complexes.Predicted by ASPDock, SRM+Correct binding site information and SRM+Wrong binding site information.

<p>a. Success rate of antibody-antigens. b. Hit count of antibody-antigens. c. Success rate of Dockground complexes. d. Hit count of Dockground complexes. e. Success rate of total complexes. f. Hit count of total complexes.</p

Success rates of three scoring functions on the benchmark constructed by Huang and Zou [28] using ZDOCK[30].

<p>Success rates of three scoring functions on the benchmark constructed by Huang and Zou [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref028" target="_blank">28</a>] using ZDOCK[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174662#pone.0174662.ref030" target="_blank">30</a>].</p

From Spirolactam Mixtures to Regioisomerically Pure 5- and 6-Rhodamines: A Chemodosimeter-Inspired Strategy

Inspired by the ring-open reaction of rhodamine spriolactams as typical chemodosimeters, a general strategy is proposed to conveniently and efficiently synthesize isomerically pure 5- and 6-R-tetramethylrhodamine on a larger scale

Score-LRMSD plots of the decoys of selected RNA-protein complexes.

<p>Score-LRMSD plots of the decoys of selected RNA-protein complexes.</p

Distribution of pair conformations.

<p>Upper: the distribution of the relative distances between the nucleotide and residue in class 9 of MET-G pair (left) and class 7 from ASN-U pair; Lower: comparison of two center conformations of ARG-G (left) and TYR-C (right) pairs whose nucleotide and residue have almost the same distances but their orientations are completely different.</p

Success rates of 3dRPC-Score over the training set for different values of the constant <i>lnP</i>_<i>v</i> in Eq (2).

<p><i>P</i>_<i>v</i> is the probability of class C in the whole conformational space of nucleotide-residue pairs in ideal state. Since the nucleotide-residue pairs are clustered into 10 classes, <i>P</i>_<i>v</i> = 1/10 or ln(<i>P</i>_<i>v</i>) ≈ -2.3 and so the success rate has a dramatic change between -2 and -3.</p

The distribution of RMSDs of nucleotide-residue conformations in relative to their center conformations of 800 classes.

<p>The distribution of RMSDs of nucleotide-residue conformations in relative to their center conformations of 800 classes.</p