A mathematical model of inter-terminal transportation

We present a novel integer programming model for analyzing inter-terminal transportation (ITT) in new and expanding sea ports. ITT is the movement of containers between terminals (sea, rail or otherwise) within a port. ITT represents a significant source of delay for containers being transshipped, which costs ports money and affects a port’s reputation. Our model assists ports in analyzing the impact of new infrastructure, the placement of terminals, and ITT vehicle investments. We provide analysis of ITT at two ports, the port of Hamburg, Germany and the Maasvlakte 1 & 2 area of the port of Rotterdam, The Netherlands, in which we solve a vehicle flow combined with a multi-commodity container flow on a congestion based time–space graph to optimality. We introduce a two-step solution procedure that computes a relaxation of the overall ITT problem in order to find solutions faster. Our graph contains special structures to model the long term loading and unloading of vehicles, and our model is general enough to model a number of important real-world aspects of ITT, such as traffic congestion, penalized late container delivery, multiple ITT transportation modes, and port infrastructure modifications. We show that our model can scale to real-world sizes and provide ports with important information for their long term decision making.     

On the approximation by neural networks with bounded number of neurons in hidden layers

In the current note, we show that a two hidden layer neural network with d inputs, d   neurons in the first hidden layer, 2d+2$2d+2$ neurons in the second hidden layer and with a specifically constructed sigmoidal and infinitely differentiable activation function can approximate any continuous multivariate function with arbitrary accuracy.     

Cyclodextrin–[RuCl2(Arene)]2 conjugates: another way to enhance the enantioselectivity of aromatic ketones reduction by aromatic ligands' volume

Eight amino alcohol-modified β-CDs CD-1–CD-8 have been synthesized in acceptable yields and were employed to form artificial metalloenzymes with [RuCl₂(Benzene)]₂ and [RuCl₂(Mesitylene)]₂, respectively. All the conformations of CD-1–CD-8, the complexes between CD-1–CD-8 and [RuCl₂(Arene)]₂, and the inclusion complexes between CD-1–CD-8 and acetophenone were characterized by UV, ¹H NMR, ¹H ROESY NMR, and quantum calculation. The catalytic activity of the formed artificial metalloenzymes in the asymmetric hydrogenation of aromatic ketones, especially the effect of the aromatic ligands' volume on the enantioselectivity were investigated in detail, in which it was obvious that the enantioselectivity increased as the increase in the aromatic ligands' volume. For the best artificial metalloenzyme constructed from the complex between CD-8 and [RuCl₂(Mesitylene)]₂, which not only exhibits a good tolerance to a wide range of substrates but also demonstrates some substrate selectivity, 76.39% ee was obtained for acetophenone and 79.67% ee for 2-acetylnaphthalene. A strategy to improve the enantioselectivity in the asymmetric reactions catalyzed by the artificial metalloenzymes based on CDs has been provided.     

A Palladium-mediated DNA Base Pair of a β-C-Nucleoside Possessing a 2-Aminophenol as the Nucleobase

Abstract

An approach we have used in this study for the incorporation of metal ions into DNA, is the direct modification of a DNA base itself, turning it into a metal-chelating nucleobase wherein two nucleobases are paired through metal coordination. Herein we report the X-ray crystal structure of a synthetic intermediate 6 for the aminophenol bearing nucleoside 3 and its metal coordination properties with Pd²⁺. The anomeric configuration of the nucleoside was unequivocally determined to be β-form by the X-ray analysis of 6; the structure has been resolved by direct methods (S1R97) and expanded using Fourier techniques (DIRDIF94) using 2628 independent reflections with I>2.00 [sgrave](I) and 425 parameters. Final R (R_w) was 0.037 (0.043): orthorhombic, space group P2₁2₁2₁ (#19) with a=16.562(1) Å, b=16.933(1) Å, c=11.205(1) Å, and V=3142.2(4) ų; D_c =1.369g/cm³ for Z=4, and molecular weight 647.65. This result is consistent with the tentative assignment by our previous ¹H NOE differentiation experiments. Detailed ¹H NMR studies showed that the nucleoside forms a stable 2:1 complex with Pd²⁺ with concomitant deprotonation of its phenolic proton. Although there are two possible structures (cis or trans) for the square-planar Pd²⁺ complex, the ratio of cis to trans was approximately 1:1. The electrospray ionization time-of-flight mass spectrum of the complex also provided clear evidence for the 2:1 complexation.     

Variable selection for QSAR by artificial ant colony systems

Derivation of quantitative structure-activity relationships (QSAR) usually involves computational models that relate a set of input variables describing the structural properties of the molecules for which the activity has been measured to the output variable representing activity. Many of the input variables may be correlated, and it is therefore often desirable to select an optimal subset of the input variables that results in the most predictive model. In this paper we describe an optimization technique for variable selection based on artificial ant colony systems. The algorithm is inspired by the behavior of real ants, which are able to find the shortest path between a food source and their nest using deposits of pheromone as a communication agent. The underlying basic self-organizing principle is exploited for the construction of parsimonious QSAR models based on neural networks for several classical QSAR data sets.     

Protein structure prediction and biomolecular recognition: From protein sequence to peptidomimetic design with the human β 3 integrin

Computational tools can bridge the gap between sequence and protein 3D structure based on the notion that information is to be retrieved from the databases and that knowledge-based methods can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network-based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, the topology of membrane proteins and porins and stable f -helical segments suited for protein design. Moreover we have developed methods for predicting contact maps in proteins and the probability of finding a cysteine in a disulfide bridge, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called ab initio prediction). All our predictors take advantage of evolution information derived from the structural alignments of homologous (evolutionary related) proteins and taken from the sequence and structure databases. When it is necessary to build models for proteins of unknown spatial structure, which have very little homology with other proteins of known structure, non-standard techniques need to be developed and the tools for protein structure predictions may help in protein modeling. The results of a recent simulation performed in our lab highlights the role of high performing computing technology and of tools of computational biology in protein modeling and peptidomimetic design.     

An approach to the interpretation of backpropagation neural network models in QSAR studies

An approach to the interpretation of backpropagation neural network models for quantitative structure-activity and structure-property relationships (QSAR/QSPR) studies is proposed. The method is based on analyzing the first and second moments of distribution of the values of the first and the second partial derivatives of neural network outputs with respect to inputs calculated at data points. The use of such statistics makes it possible not only to obtain actually the same characteristics as for the case of traditional "interpretable" statistical methods, such as the linear regression analysis, but also to reveal important additional information regarding the non-linear character of QSAR/QSPR relationships. The approach is illustrated by an example of interpreting a backpropagation neural network model for predicting position of the long-wave absorption band of cyane dyes.     

The Learned Symmetry Concept in Revealing Quantitative Structure-Activity Relationships With Artificial Neural Networks

Abstract

A method to build QSAR models based on substituent constants for congeneric sets of compounds having several topologically equivalent substituent positions was proposed. The approach is based on the application of artificial neural networks (learning to construct nonlinear structure-activity relationships taking into account necessary symmetry properties of training set structures) to a training set expanded by adding the copies of compounds with the same activity values but with permuted assignment of equivalent substituent positions. The better predictive power of these constructed models, as compared with the performances of neural network models for non-expanded sets was demonstrated for the calcium channel blockers of 1,4-dihydropyridine type and for hallucinogenic phenylalkylamines.     

Neural Networks Predict Protein Folding and Structure: Artificial Intelligence Faces Biomolecular Complexity

Abstract

In the genomic era DNA sequencing is increasing our knowledge of the molecular structure of genetic codes from bacteria to man at a hyperbolic rate. Billions of nucleotides and millions of aminoacids are already filling the electronic files of the data bases presently available, which contain a tremendous amount of information on the most biologically relevant macromolecules, such as DNA. RNA and proteins. The most urgent problem originates from the need to single out the relevant information amidst a wealth of general features. Intelligent tools are therefore needed to optimise the search. Data mining for sequence analysis in biotechnology has been substantially aided by the development of new powerful methods borrowed from the machine learning approach. In this paper we discuss the application of artificial feedforward neural networks to deal with some fundamental problems tied with the folding process and the structure-function relationship in proteins.