Syntheses of Some Sulfur‐Containing Polyunsaturated Fatty Acids as Potential Lipoxygenase Inhibitors

Starting from (all‐Z)‐eicosa‐5,8,11,14,17‐pentaenoic acid (EPA) and (all‐Z)‐4,7,10,13,16,19‐docosahexaenoic acid (DHA), several polyunsaturated fatty acids, containing a sulfur atom either in the chain or in a thiophene ring, have been synthesized as potential inhibitors of lipoxygenases.     

The Role of Entropy in Construct Specification Equations (CSE) to Improve the Validity of Memory Tests

Commonly used rating scales and tests have been found lacking reliability and validity, for example in neurodegenerative diseases studies, owing to not making recourse to the inherent ordinality of human responses, nor acknowledging the separability of person ability and item difficulty parameters according to the well-known Rasch model. Here, we adopt an information theory approach, particularly extending deployment of the classic Brillouin entropy expression when explaining the difficulty of recalling non-verbal sequences in memory tests (i.e., Corsi Block Test and Digit Span Test): a more ordered task, of less entropy, will generally be easier to perform. Construct specification equations (CSEs) as a part of a methodological development, with entropy-based variables dominating, are found experimentally to explain (r =R2 = 0.98) and predict the construct of task difficulty for short-term memory tests using data from the NeuroMET (n = 88) and Gothenburg MCI (n = 257) studies. We propose entropy-based equivalence criteria, whereby different tasks (in the form of items) from different tests can be combined, enabling new memory tests to be formed by choosing a bespoke selection of items, leading to more efficient testing, improved reliability (reduced uncertainties) and validity. This provides opportunities for more practical and accurate measurement in clinical practice, research and trials.     

Development of modular neural networks with fuzzy logic response integration for signature recognition

This paper describes a modular neural network (MNN) for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature, however this problem has proven to be a very difficult task. In this work, we propose an MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined by using a Sugeno fuzzy integral. The experimental results obtained by using a database of 30 individual’s shows that the modular architecture can achieve a very high 98% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system.     

OH(3) (-) and O(2)H(5) (-) double Rydberg anions: predictions and comparisons with NH(4) (-) and N(2)H(7) (-)

A low barrier in the reaction pathway between the double Rydberg isomer of OH(3) (-) and a hydride-water complex indicates that the former species is more difficult to isolate and characterize through anion photoelectron spectroscopy than the well known double Rydberg anion (DRA), tetrahedral NH(4) (-). Electron propagator calculations of vertical electron detachment energies (VEDEs) and isosurface plots of the electron localization function disclose that the transition state's electronic structure more closely resembles that of the DRA than that of the hydride-water complex. Possible stabilization of the OH(3) (-) DRA through hydrogen bonding or ion-dipole interactions is examined through calculations on O(2)H(5) (-) species. Three O(2)H(5) (-) minima with H(-)(H(2)O)(2), hydrogen-bridged, and DRA-molecule structures resemble previously discovered N(2)H(7) (-) species and have well separated VEDEs that may be observable in anion photoelectron spectra.     

Recognition of imidazoles by strapped zinc(II) porphyrin receptors: insight into the induced-fit mechanism

Imidazole-porphyrin coordination has become an important tool in the design of self-assembled materials. A combination of spectrophotometric and stopped-flow techniques has been used to gain insight into the control of imidazole binding in the distal pocket of phenanthroline-strapped porphyrins. The binding studies of a variety of imidazole substrates in combination with both hindered and accessible receptors have permitted the determination of the thermodynamic and kinetic parameters associated with the imidazole binding.     

Modulation of Mn3+ Spin State by Guest Molecule Inclusion

Spin state preferences for a cationic Mn³⁺ chelate complex in four different crystal lattices are investigated by crystallography and SQUID magnetometry. The [MnL₁]⁺ complex cation was prepared by complexation of Mn³⁺ to the Schiff base chelate formed from condensation of 4-methoxysalicylaldehyde and 1,2-bis(3-aminopropylamino)ethane. The cation was crystallized separately with three polyatomic counterions and in one case was found to cocrystallize with a percentage of unreacted 4-methoxysalicylaldehyde starting material. The spin state preferences of the four resultant complexes [MnL₁]CF₃SO₃·xH₂O, (1), [MnL₁]PF₆·xH₂O, (2), [MnL₁]PF₆·xsal·xH₂O, (2b), and [MnL₁]BPh₄, (3), were dependent on their ability to form strong intermolecular interactions. Complexes (1) and (2), which formed hydrogen bonds between [MnL₁]⁺, lattice water and in one case also with counterion, showed an incomplete thermal spin crossover over the temperature range 5–300 K. In contrast, complex (3) with the BPh₄⁻, counterion and no lattice water, was locked into the high spin state over the same temperature range, as was complex (2b), where inclusion of the 4-methoxysalicylaldehyde guest blocked the H-bonding interaction.     

Bio-inspired optimization of fuzzy logic controllers for robotic autonomous systems with PSO and ACO

In this paper we describe the use of bio-inspired optimization techniques, such as ant colony optimization and particle swarm optimization, for the design of optimal fuzzy logic controllers of autonomous wheeled mobile robots. The results obtained by the simulations with ant colony optimization and particle swarm optimization are statistically compared with previous optimization results obtained with genetic algorithms in order to find out the best optimization technique for a particular robotics problem.