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.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration

Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions: The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.

Electrochromic Switching Mechanism of Iron Hexacyanoferrates Molecular Compounds: The Role of Fe2+(CN)(6) Vacancies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Synthesis and Characterization of One-Dimensional and Molecular M(tren)InAsS4 (M = Mn, Co, and Zn) Compounds with a Noncondensed AsS33- Unit

Four transition-metal-amine complexes incorporating indium thioarsenates with the general formula M(tren)InAsS4 (M=Mn, Co, and Zn) and a noncondensed AsS33- unit have been prepared and characterized. Single-crystal X-ray diffraction analyses show that compound 1 (M=Mn) crystallizes in the triclinic crystal system (space group: P (1) over bar) and consists of a one-dimensional (1D) inorganic (1)(infinity){[InAsS4](2-)} chain and [Mn(tren)](2+) groups bonded to the opposite sides of an eight-membered In2As2S4 ring along the backbone of the infinite inorganic chains. Compounds 2 (M=Mn), 3 (M=Zn), and 4 (M=Co) are isomorphous molecular compounds. They all crystallize in the monoclinic crystal system (space group: P2(1)/c). The Mn2+ cation of [Mn(tren)](2+) in 1 has a distorted octahedral environment, while the transition-metal cations of [M(tren)](2+) in the other three compounds locate in trigonal-bipyramidal environments.

High pressure states in condensed matter: I. High pressure behavior of the iron-sulphur system with applications to the earth's core. II. Empirical equation of state for organic compounds at high pressures

Part I:

The earth's core is generally accepted to be composed primarily of iron, with an admixture of other elements. Because the outer core is observed not to transmit shear waves at seismic frequencies, it is known to be liquid or primarily liquid. A new equation of state is presented for liquid iron, in the form of parameters for the 4th order Birch-Murnaghan and Mie-Grüneisen equations of state. The parameters were constrained by a set of values for numerous properties compiled from the literature. A detailed theoretical model is used to constrain the P-T behavior of the heat capacity, based on recent advances in the understanding of the interatomic potentials for transition metals. At the reference pressure of 10⁵ Pa and temperature of 1811 K (the normal melting point of Fe), the parameters are: ρ = 7037 kg/m³, K_S0 = 110 GPa, K_S' = 4.53, K_S" = -.0337 GPa-1, and γ = 2.8, with γ α ρ^-1.17. Comparison of the properties predicted by this model with the earth model PREM indicates that the outer core is 8 to 10 % less dense than pure liquid Fe at the same conditions. The inner core is also found to be 3 to 5% less dense than pure liquid Fe, supporting the idea of a partially molten inner core. The density deficit of the outer core implies that the elements dissolved in the liquid Fe are predominantly of lower atomic weight than Fe. Of the candidate light elements favored by researchers, only sulfur readily dissolves into Fe at low pressure, which means that this element was almost certainly concentrated in the core at early times. New melting data are presented for FeS and FeS₂ which indicate that the FeS₂ is the S-hearing liquidus solid phase at inner core pressures. Consideration of the requirement that the inner core boundary be observable by seismological means and the freezing behavior of solutions leads to the possibility that the outer core may contain a significant fraction of solid material. It is found that convection in the outer core is not hindered if the solid particles are entrained in the fluid flow. This model for a core of Fe and S admits temperatures in the range 3450K to 4200K at the top of the core. An all liquid Fe-S outer core would require a temperature of about 4900 K at the top of the core.

Part II.

The abundance of uses for organic compounds in the modern world results in many applications in which these materials are subjected to high pressures. This leads to the desire to be able to describe the behavior of these materials under such conditions. Unfortunately, the number of compounds is much greater than the number of experimental data available for many of the important properties. In the past, one approach that has worked well is the calculation of appropriate properties by summing the contributions from the organic functional groups making up molecules of the compounds in question. A new set of group contributions for the molar volume, volume thermal expansivity, heat capacity, and the Rao function is presented for functional groups containing C, H, and O. This set is, in most cases, limited in application to low molecular liquids. A new technique for the calculation of the pressure derivative of the bulk modulus is also presented. Comparison with data indicates that the presented technique works very well for most low molecular hydrocarbon liquids and somewhat less well for oxygen-bearing compounds. A similar comparison of previous results for polymers indicates that the existing tabulations of group contributions for this class of materials is in need of revision. There is also evidence that the Rao function contributions for polymers and low molecular compounds are somewhat different.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Distributed mining of molecular fragments

In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Electrochemical capacitance spectroscopy and capacitive relaxation of the changeover process in iron hexacyanoferrate molecular compound

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Molecular-Based Magnetism in High-Spin Molecular Clusters and Three-Dimensional Networks Based on Cyanometalate Building Blocks

The field of molecule-based magnets is a relatively new branch of chemistry, which involves the design and study of molecular compounds that exhibit a spontaneous magnetic ordering below a critical temperature, Tc. One major goal involves the design of materials with tuneable Tc's for specific applications in memory storage devices. Molecule-based magnets with high magnetic ordering temperatures have recently been obtained from bimetallic and mixed-valence transition metal μ-cyanide complexes of the Prussian blue family. Since the μ-cyanide linkages permit an interaction between paramagnetic metal ions, cyanometalate building blocks have found useful applications in the field of molecule-based magnets. Our work involves the use of octacyanometalate building blocks for the self-assembly of two new classes of magnetic materials namely, high-spin molecular clusters which exhibit both ferromagnetic intra- and intercluster coupling, and specific extended network topologies which show long-range ferromagnetic ordering.

A Reactive Intermediate, Ni-5(C6H4N3)(6)(CO)(4)], in the Formation of Nonameric Clusters of Nickel, Ni-9(C6H4N3)(12)(CO)(6)] and Ni-9(C6H4N3)(12)(CO)(6)].2(C3H7NO)

Three new molecular compounds, Ni-5(bta)(6)(CO)(4)], I, Ni-9(bta)(12)(CO)(6)], II, Ni-9(bta)(12)(CO)(6)]. 2(C3H7NO), III, (bta = benzotriazole) were prepared employing solvothermal reactions. Of these, I have pentanuclear nickel, whereas II and III have nonanuclear nickel species. The structures are formed by the connectivity between the nickel and benzotriazole giving rise to the 5- and 9-membered nickel clusters. The structures are stabilised by extensive pi aEuro broken vertical bar pi and C-H... pi interactions. Compound II and III are solvotamorphs as they have the same 9-membered nickel clusters and have different solvent molecules. To the best of our knowledge, the compounds I-III represent the first examples of the same transition element existing in two distinct coordination environment in this class of compounds. The studies reveal that compound I is reactive and could be an intermediate in the preparation of II and III. Thermal studies indicate that the compounds are stable upto 350(a similar to)C and at higher temperatures (similar to 800(a similar to)C) the compounds decompose into NiO. Magnetic studies reveal that II is anti-ferromagnetic.

三聚氰胺的基质辅助激光解吸/电离飞行时间质谱研究

Oxidized carbon nanotubes are tested as the matrix for analysis of the melamine by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS). Traditional MALDI matrix are not suit for analysis of the low molecular compounds due to the interference associated to the matrix clusters. Oxidized carbon nanotubes can transfer energy to the analyte under the laser irradiation, which makes analyte well ionized or desorbed. Moreover, the interference of the intrinsic matrix ions can be eliminated. Melamine as the a toxic additive which had been added in the milk powder, then it is necessary to establish a new method for detection of the melamine rapid and sensitive.

Structuring reductive media containing protic ionic liquids and their application to the formation of metallic nanoparticles

Herein, we present a facile method for the formation of monodispersed metal nanoparticles (NPs) at room temperature from M^(III)Cl₃ (with M = Au, Ru, Mn, Fe or V) in different media based on N,N-dimethylformamide (DMF) or water solutions containing a protic ionic liquid (PIL), namely the octylammonium formate (denoted OAF) or the bis(2-ethyl-hexyl)ammonium formate (denoted BEHAF). These two PILs present different structures and redox-active structuring properties that influence their interactions with selected molecular compounds (DMF or water), as well as the shape and the size of formed metal NPs in these solutions. Herein, the physical properties, such as the thermal, transport and micellar properties, of investigated PIL solutions were firstly investigated in order to understand the relation between PILs structure and their properties in solutions with DMF or water. The formation of metal NPs in these solutions was then characterized by using UV–vis spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM) and dynamic light scattering (DLS) measurements. From our investigations, it appears that the PILs structure and their aggregation pathways in selected solvents affect strongly the formation, growths, the shape and the size of metal NPs. In fact by using this approach, the shape-/size-controlled metal NPs can be generated under mild condition. This approach suggests also a wealth of potential for these designer nanomaterials within the biomedical, materials, and catalysis communities by using designer and safer media based on PILs.