A direct comparison of one- and two-component dendritic self-assembled materials: elucidating molecular recognition pathways

This paper compares and contrasts, for the first time, one- and two-component gelation systems that are direct structural analogues and draws conclusions about the molecular recognition pathways that underpin fibrillar self-assembly. The new one-component systems comprise L-lysine-based dendritic headgroups covalently connected to an aliphatic diamine spacer chain via an amide bond, One-component gelators with different generations of headgroup (from first to third generation) and different length spacer chains are reported. The self-assembly of these dendrimers in toluene was elucidated using thermal measurements, circular dichroism (CD) and NMR spectroscopies, scanning electron microscopy (SEM), and small-angle X-ray scattering (SAXS). The observations are compared with previous results for the analogous two-component gelation system in which the dendritic headgroups are bound to the aliphatic spacer chain noncovalently via acid-amine interactions. The one-component system is inherently a more effective gelator, partly as a consequence of the additional covalent amide groups that provide a new hydrogen bonding molecular recognition pathway, whereas the two-component analogue relies solely on intermolecular hydrogen bond interactions between the chiral dendritic headgroups. Furthermore, because these amide groups are important in the assembly process for the one-component system, the chiral information preset in the dendritic headgroups is not always transcribed into the nanoscale assembly, whereas for the two-component system, fiber formation is always accompanied by chiral ordering because the molecular recognition pathway is completely dependent on hydrogen bond interactions between well-organized chiral dendritic headgroups.

Synthesis, structural analysis, and magnetic behaviour of three fumarate bridged coordination polymers: five-fold interpenetrated diamond-like net of Ni-II, sheets of Ni-II and Co-II

The hydrothermal reactions of Ni(NO3)(2).6H(2)O, disodium fumarate (fum) and 1,2-bis(4-pyridyl)ethane (bpe)/1,3-bis(4-pyridyl) propane (bpp) in aqueous-methanol medium yield one 3-D and one 2-D metal-organic hybrid material, [Ni(fum)(bpe)] (1) and [Ni(fum)(bpp)(H2O)] (2), respectively. Complex 1 possesses a novel unprecedented structure, the first example of an "unusual mode" of a five-fold distorted interpenetrated network with metal-ligand linkages where the four six-membered windows in each adamantane-type cage are different. The structural characterization of complex 2 evidences a buckled sheet where nickel ions are in a distorted octahedral geometry, with two carboxylic groups, one acting as a bis-chelate, the other as a bis-monodentate ligand. The metal ion completes the coordination sphere through one water molecule and two bpp nitrogens in cis position. Variable-temperature magnetic measurements of complexes 1 and 2 reveal the existence of very weak antiferromagnetic intramolecular interactions and/or the presence of single-ion zero field splitting (D) of isolated Ni-II ions in both the compounds. Experimentally, both the J parameters are close, comparable and very small. Considering zero-field splitting of Ni-II, the calculated D values are in agreement with values reported in the literature for Ni-II ions. Complex 3, [{Co(phen)}(2)(fum)(2)] (phen=1,10-phenanthroline) is obtained by diffusing methanolic solution of 1,10-phenanthroline on an aqueous layer of disodium fumarate and Co(NO3)(2).6H(2)O. It consists of dimeric Co-II(phen) units, doubly bridged by carboxylate groups in a distorted syn-syn fashion. These fumarate anions act as bis-chelates to form corrugated sheets. The 2D layer has a (4,4) topology, with the nodes represented by the centres of the dimers. The magnetic data were fitted ignoring the very weak coupling through the fumarate pathway and using a dimer model.

Ditopic redox-active polyferrocenyl zinc(II) dithiocarbamate macrocyclic receptors: synthesis, coordination and electrochemical recognition properties

The synthesis of a range of ditopic polyferrocenyl zinc(II) dithiocarbamate macrocyclic receptors containing ferrocene groups on the macrocycle's periphery and/or as part of the cyclic cavity is reported. The assemblies have been characterised by a range of spectroscopic techniques, electrochemical studies and in two cases by X-ray structure determination. The ability of these host systems to bind and sense electrochemically anionic guest species, isonicotinate and benzoate, and neutral 4-picoline guest was examined by H-1 NMR and cyclic voltammetric titration studies. The strongest association was found between the isonicotinate anion and a dinuclear zinc(II) receptor whose macrocyclic cavity is of complementary size to complex this bidentate guest species in a cooperative manner. Cyclic voltammetric studies demonstrated that all receptors can electrochemically sense the binding of isonicotinate and benzoate via significant cathodic perturbations of the respective ferrocene redox couple.

Utilisation of dendritic architectures in molecular recognition and self-assembly processes

This mini-review outlines recent key developments in the use of dendritic architectures in self-assembly processes via utilisation of molecular recognition motifs.

The relative success of recognition-based inference in multichoice decisions

The utility of an "ecologically rational" recognition-based decision rule in multichoice decision problems is analyzed, varying the type of judgment required (greater or lesser). The maximum size and range of a counterintuitive advantage associated with recognition-based judgment (the "less-is-more effect") is identified for a range of cue validity values. Greater ranges of the less-is-more effect occur when participants are asked which is the greatest of to choices (m > 2) than which is the least. Less-is-more effects also have greater range for larger values of in. This implies that the classic two-altemative forced choice task, as studied by Goldstein and Gigerenzer (2002), may not be the most appropriate test case for less-is-more effects.

False recognition of abstract versus common objects in older and younger adults: testing the semantic categorization account

Older adults often demonstrate higher levels of false recognition than do younger adults. However, in experiments using novel shapes without preexisting semantic representations, this age-related elevation in false recognition was found to be greatly attenuated. Two experiments tested a semantic categorization account of these findings, examining whether older adults show especially heightened false recognition if the stimuli have preexisting semantic representations, such that semantic category information attenuates or truncates the encoding or retrieval of item-specific perceptual information. In Experiment 1, ambiguous shapes were presented with or without disambiguating semantic labels. Older adults showed higher false recognition when labels were present but not when labels were never presented. In Experiment 2, older adults showed higher false recognition for concrete but not abstract objects. The semantic categorization account was supported.

Rich and famous: recognition-based judgment in the Sunday Times rich list

Recognition as a cue to judgment in a novel, multi-option domain (the Sunday Times Rich List) is explored. As in previous studies, participants were found to make use of name recognition as a cue to the presumed wealth of individuals. Names that were recognized were judged to be the richest name from amongst the set presented at above chance levels. This effect persisted across situations in which more than one name was recognized; recognition was used as an inclusion criterion for the sub-set of names to be considered the richest of the set presented. However, when the question was reversed, and a “poorest” judgment was required, use of recognition as an exclusion criterion was observed only when a single name was recognized. Reaction times when making these judgments also show a distinction between “richest” and “poorest” questions with recognition of none of the options taking the longest time to judge in the “richest” question condition and full recognition of all the names presented taking longest to judge in the “poorest” question condition. Implications for decision-making using simple heuristics are discussed.

An analysis of real time implementation of Fourier transform-based frequency recognition algorithms

Frequency recognition is an important task in many engineering fields such as audio signal processing and telecommunications engineering, for example in applications like Dual-Tone Multi-Frequency (DTMF) detection or the recognition of the carrier frequency of a Global Positioning, System (GPS) signal. This paper will present results of investigations on several common Fourier Transform-based frequency recognition algorithms implemented in real time on a Texas Instruments (TI) TMS320C6713 Digital Signal Processor (DSP) core. In addition, suitable metrics are going to be evaluated in order to ascertain which of these selected algorithms is appropriate for audio signal processing(1).

A combined Bayesian Markovian approach for behaviour recognition

Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.