Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Reduction of invasive tests in chagasic patients with a modified self-organizing map

The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.

A new linear initialization in SOM for biomolecular data

In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relations among some intersting features of data input. Result of experimental tests, where the proposed algorithm is compared to the original initialization techniques, shows that our technique assures faster learning and better performance in terms of quantization error.

Self-assembled columns of fullerene

Columnar mesophases based on alternating triphenylene and hexaphenyltriphenylene moieties are exceptionally stable and able to accommodate bulky side-chain substituents within the alkyl chain continuum between the columns. This paper presents a system in which the triphenylene bears a fullerene on its side-chain and the hexaphenyltriphenylene equivalent is the aza-derivative hexakis(4-nonylphenyl)dipyrazino[2,3-f : 2'3'-h] quinoxalene, PDQ9. The mesophase formed was identified as hexagonal columnar (Col(h)) by X-ray diffraction (a = 25.2 angstrom and c = 3.5 angstrom) but, in addition to the expected peaks, there is indication of a two-dimensional hexagonal superlattice with d-spacing 59 angstrom. This superlattice is believed to arise from ordering of the fullerenes within the liquid crystal matrix. It can be explained on the assumption that, to maximise fullerene-fullerene contact, the fullerenes form chains which wrap around the central column in every group of seven columns of the triphenylene : PDQ9 Col(h) array.

Conformational and self-assembly studies of helix forming hexapeptides containing two alpha-amino isobutyric acids

Single crystal X-ray diffraction studies reveal that three hexapeptides with general formula Boc-Ile-Aib-Xx-Ile-Aib-Yy-OMe, where Xx and Yy are Leu in peptide I, Len and Phe in peptide II, and Phe and Leu in peptide III, respectively, adopt equivalent conformations that can be described as mixed 3(10)/alpha-helice with two 4 -> 1 and two 5 -> 1 intramolecular N-H center dot center dot center dot O=C H-bonds. The peptides do not generate any helixterminating Schellman motif despite having Aib at the penultimate position from C-terminus. In the crystalline state, the helices are packed in head-to-tail fashion through intermolecular hydrogen bonds to create supramolecular helical structures. The CD Studies of the three hexapeptides in acetonitrile indicate that they are folded in well-developed 3(10)-helical structures. NMR studies of peptide I in CDCl3 also suggest the formation of a homogeneous 3 m-helical structure. The field emission scanning electron microscopic (FE-SEM) images of peptide 11 in the solid state reveal a non-twisted ribbon-like morphology, which is formed through lateral association of non-twisted filaments. (c) 2007 Elsevier Ltd. All rights reserved.

Self-tuning property of state-space self tuners

The basic assumption from implicit self-tuning theory is that, for self tuning to occur, the control input obtained from the estimated system model converges to the value whic would be obtained if the system parameters were known. As as direct result of this, only certain control strategies are acceptable. Here a general rule for the self-tuning property of pole-placement self tuners is obtained, and previous strategies are shown to be special cases of this.

Decentralized PID self-tuning control of industrial robots

A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

Self-assembly studies of a chiral bisurea-based superhydrogelator

A chiral bisurea-based superhydrogelator that is capable of forming supramolecular hydrogels at concentrations as low as 0.2 mm is reported. This soft material has been characterized by thermal studies, rheology, X-ray diffraction analysis, transmission electron microscopy (TEM), and by various spectroscopic techniques (electronic and vibrational circular dichroism and by FTIR and Raman spectroscopy). The expression of chirality on the molecular and supramolecular levels has been studied and a clear amplification of its chirality into the achiral analogue has been observed. Furthermore, thermal analysis showed that the hydroACHTUNGTRENUNGgel- ACHTUNGTRENUNGation of compound 1 has a high response to temperature, which corresponds to an enthalpy-driven self-assembly process. These particular thermal characteristics make these materials easy to handle for soft-application technologies

A very rare self-assembled ribbon of fused cyclic water pentamers encapsulated in a Cu-II based metal-organic framework

A metal organic framework of Cu-II, tartarate (tar) and 2,2'-bipyridyl (2,2'-bipy)], {[Cu(tar)(2,2'-bipy)]center dot 5H(2)O}(n)} (1) has been synthesized at the mild ambient condition and characterized by single crystal X-ray crystallography. In the compound, the Cu(2,2'-bipy) entities are bridged by tartarate ions which are coordinated to Cu-II by both hydroxyl and monodentate carboxylate oxygen to form a one-dimensional chain. The non-coordinated water molecules form ID water chains by edge-sharing cyclic water pentamers along with dangling water dimers. It shows reversible water expulsion upon heating. The water chains join the ID coordination polymeric chains to a 31) network through hydrogen-bond interactions.

Deriving global flood hazard maps of fluvial floods through a physical model cascade

Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25×25 km grid, which is then reprojected onto a 1×1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25×25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.