Pseudo-merohedral twinning in the structure of the hydrated 1:1 proton-transfer compound of 5-sulfosalicylic acid with 4-aminopyridine

The structure of the pseudo-merohedrally twinned crystal of the 1:1 proton-transfer compound of 5-sulfosalicylic acid (3-carboxy-4-hydroxybenzenesulfonic acid) with 4-aminopyridine: 4-aminopyridinium 3-carboxy-4-hydroxybenzenesulfonate sesquihydrate has been determined at 180 K and the hydrogen-bonding pattern is described. Crystals of the compound are monoclinic with space group P21/c, with unit cell dimensions a = 35.2589(8), b = 7.1948(1), c = 24.5851(5) Å, β = 110.373(2)o, and Z = 16. The monoclinic asymmetric unit comprises four cation-anion pairs and six water molecules of solvation with only the pyridinium cations having pseudo-symmetry as a result of inter-cation aromatic ring π-π stacking effects. Extensive hydrogen bonding gives a three-dimensional framework structure.

The structure of the mineral leogangite Cu10(OH)6(SO4)(AsO4)4•8H2O–implications for arsenic accumulation and removal

The objective of this research is to determine the molecular structure of the mineral leogangite. The formation of the types of arsenosulphate minerals offers a mechanism for arsenate removal from soils and mine dumps. Raman and infrared spectroscopy have been used to characterise the mineral. Observed bands are assigned to the stretching and bending vibrations of (SO4)2- and (AsO4)3- units, stretching and bending vibrations of hydrogen bonded (OH)- ions and Cu2+-(O,OH) units. The approximate range of O-H...O hydrogen bond lengths is inferred from the Raman spectra. Raman spectra of leogangite from different origins differ in that some spectra are more complex, where bands are sharp and the degenerate bands of (SO4)2- and (AsO4)3- are split and more intense. Lower wavenumbers of  H2O bending vibration in the spectrum may indicate the presence of weaker hydrogen bonds compared with those in a different leogangite samples. The formation of leogangite offers a mechanism for the removal of arsenic from the environment.

Complexity, evolution, and the structure of demand

This chapter argues that evolutionary economics should be founded upon complex systems theory rather than neo-Darwinian analogies concerning natural selection, which focus on supply side considerations and competition amongst firms and technologies. It suggests that conceptions such as production and consumption functions should be replaced by network representations, in which the preferences or, more correctly, the aspirations of consumers are fundamental and, as such, the primary drivers of economic growth. Technological innovation is viewed as a process that is intermediate between these aspirational networks, and the organizational networks in which goods and services are produced. Consumer knowledge becomes at least as important as producer knowledge in determining how economic value is generated. It becomes clear that the stability afforded by connective systems of rules is essential for economic flexibility to exist, but that too many rules result in inert and structurally unstable states. In contrast, too few rules result in a more stable state, but at a low level of ordered complexity. Economic evolution from this perspective is explored using random and scale free network representations of complex systems.

The structure of Pavlovian fear conditioning in the amygdala

Do different brains forming a specific memory allocate the same groups of neurons to encode it? One way to test this question is to map neurons encoding the same memory and quantitatively compare their locations across individual brains. In a previous study, we used this strategy to uncover a common topography of neurons in the dorsolateral amygdala (LAd) that expressed a learning-induced and plasticity-related kinase (p42/44 mitogen-activated protein kinase; pMAPK), following auditory Pavlovian fear conditioning. In this series of experiments, we extend our initial findings to ask to what extent this functional topography depends upon intrinsic neuronal structure. We first showed that the majority (87 %) of pMAPK expression in the lateral amygdala was restricted to principal-type neurons. Next, we verified a neuroanatomical reference point for amygdala alignment using in vivo magnetic resonance imaging and in vitro morphometrics. We then determined that the topography of neurons encoding auditory fear conditioning was not exclusively governed by principal neuron cytoarchitecture. These data suggest that functional patterning of neurons undergoing plasticity in the amygdala following Pavlovian fear conditioning is specific to memory formation itself. Further, the spatial allocation of activated neurons in the LAd was specific to cued (auditory), but not contextual, fear conditioning. Spatial analyses conducted at another coronal plane revealed another spatial map unique to fear conditioning, providing additional evidence that the functional topography of fear memory storing cells in the LAd is non-random and stable. Overall, these data provide evidence for a spatial organizing principle governing the functional allocation of fear memory in the amygdala.

Effect of Al content on the structure of Al-substituted goethite : a micro-Raman spectroscopic study

The characterization of X-ray diffraction, X-ray fluorescence, and field emission scanning electron microscope were used to confirm the successful preparation of Al-substituted goethite with different Al content. The micro-Raman spectroscopy was utilized to investigate the effect of Al content on the goethite lattice. The results show that all the feature bands of goethite shifted to high wavenumbers after the occurrence of Al substitution for Fe in the structure of goethite. The shift of wavenumber shows a good linear relationship as a function of increasing Al content especially for the band at 299 cm−1 (R2 = 0.9992). The in situ Raman spectroscopy of thermally treated goethite indicated that the Al substitution not only hinders the transformation of goethite, but also retarded the crystallization of thermally formed hematite. All the results indicated that Raman spectrum displayed an excellent performance in characterizing Al-substituted goethite, which implied the promising application in other substituted metal oxides or hydroxides.

Time to face the fats : What can mass spectrometry reveal about the structure of lipids and their interactions with proteins?

Since the 1950s, X-ray crystallography has been the mainstay of structural biology, providing detailed atomic-level structures that continue to revolutionize our understanding of protein function. From recent advances in this discipline, a picture has emerged of intimate and specific interactions between lipids and proteins that has driven renewed interest in the structure of lipids themselves and raised intriguing questions as to the specificity and stoichiometry in lipid-protein complexes. Herein we demonstrate some of the limitations of crystallography in resolving critical structural features of ligated lipids and thus determining how these motifs impact protein binding. As a consequence, mass spectrometry must play an important and complementary role in unraveling the complexities of lipid-protein interactions. We evaluate recent advances and highlight ongoing challenges towards the twin goals of (1) complete structure elucidation of low, abundant, and structurally diverse lipids by mass spectrometry alone, and (2) assignment of stoichiometry and specificity of lipid interactions within protein complexes.

The influence of extracellular matrix on the generation of vascularized, engineered, transplantable tissue

In a recently described model for tissue engineering, an arteriovenous loop comprising the femoral artery and vein with interposed vein graft is fabricated in the groin of an adult male rat, placed inside a polycarbonate chamber, and incubated subcutaneously. New vascularized granulation tissue will generate on this loop for up to 12 weeks. In the study described in this paper three different extracellular matrices were investigated for their ability to accelerate the amount of tissue generated compared with a no-matrix control. Poly-D,L-lactic-co-glycolic acid (PLGA) produced the maximal weight of new tissue and vascularization and this peaked at two weeks, but regressed by four weeks. Matrigel was next best. It peaked at four weeks but by eight weeks it also had regressed. Fibrin (20 and 80 mg/ml), by contrast, did not integrate with the generating vascularized tissue and produced less weight and volume of tissue than controls without matrix. The limiting factors to growth appear to be the chamber size and the capacity of the neotissue to integrate with the matrix. Once the sides of the chamber are reached or tissue fails to integrate, encapsulation and regression follow. The intrinsic position of the blood supply within the neotissue has many advantages for tissue and organ engineering, such as ability to seed the construct with stem cells and microsurgically transfer new tissue to another site within the individual. In conclusion, this study has found that PLGA and Matrigel are the best matrices for the rapid growth of new vascularized tissue suitable for replantation or transplantation.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Conformational flexibility in androgenic steroids - the structure of a new form of (+)-17-beta-hydroxy-19-nor-4-androsten-3-one (19-nortestosterone), c18h26o2

The structure and conformation of a second crystalline modification of 19-nortestosterone has been determined by X-ray methods. M r = 274, monoclinic P2 l, a=9.755(2), b= 11.467(3), c= 14.196(3)/L fl=101.07(2) ° , V=1558.4 (8) A 3, Z=4, Ox= I. 168 g cm -3, Mo Ka, 2 = 0.7107 ,/k, ~ = 0.80 cm -l, F(000) = 600, T= 300 K. R = 0.060 for 2158 observed reflections. The two molecules in the asymmetric unit show significant differences in the A-ring conformation from that of the previously reported form of the title compound [Precigoux, Busetta, Courseille & Hospital (1975). Acta Cryst. B31, 1527-1532]. The l a,2fl-half-chair conformation of the A ring increases its conformational freedom compared with testosterone.

NMR spectra of oriented biologically important molecules - The structure of and the internal rotation in N,N'-dimethyluracil

From the proton NMR spectra of Nfl-dimethyluracil oriented in two different nematic solvents, the internal rotation of the methyl groups about the N-C bonds is studied. It has been observed that the preferred conformation of the methyl group having one carbonyl in the vicinity is the one where a C-H bond is in the ring plane pointing toward the carbonyl group. The results are not sensitive to the mode of rotation of the other methyl group. These data are interpreted in terms of the bond polarizations.

Computerized synthesis of the structure of geared kinematic chains

The paper presents for the first time a fully computerized method for structural synthesis of geared kinematic chains which can be used to derive epicyclic gear drives. The method has been formulated on the basis of representing these chains by their graphs, the graphs being in turn represented algebraically by their vertex-vertex incidence matrices. It has thus been possible to make advantageous use of concepts and results from graph theory to develop a method amenable for implementation on a digital computer. The computerized method has been applied to the structural synthesis of single-freedom geared kinematic chains with up to four gear pairs, and the results obtained thereform are presented and discussed.