X-ray diffraction and theoretical investigation of the Gedunin crystal structure

The Gedunin compound (C28H34O6) is a natural product extracted from Trichilia pallida that has shown a wide activity. The crystallographic structure shows two conformers in the asymmetric unit, which differ in a rotation of the furan group. To understand this molecular arrangement, the density functional calculations. Molecular Electrostatic Potential (MEP) and thermodynamic function calculation have been performed at the B3LYP/6-311++g(d,p) level. Both conformers were optimized and the agreement with the experimental structure was very good, making possible further theoretical analysis of the structure. The inter-conversion between two conformers depends on the energy barrier. This process is studied in the vacuum and shows two transition states with a low energetic barrier for a potential energy curve scanning rigid around furan group: 4.37 kcal/mol and 16.52 kcal/mol. As the first transition state has a notably lower energetic barrier, the preferred inter-conversion pathway between the conformers involves the first rather than the second transition state. Understanding this transition state in detail led us to perform its optimization, showing an energetic barrier around 3.66 kcal/mol. The negative free energy and low enthalpy confirm that the process is spontaneous and exothermic. The results show that this requirement makes the existence of the two conformers in the asymmetric unit possible. The structure of molecules in the asymmetric unit is better understood when the MEP is used on the interaction between molecules. For Gedunin, both molecules have shown MEP with well-defined regions, and this behavior contributes to the observed link between molecules and for the negative regions complementing positive regions of another molecule. (C) 2011 Elsevier B.V. All rights reserved.

Variability in light absorption and scattering of phytoplankton in Patagonian waters: Role of community size structure and pigment composition

Intense phytoplankton blooms were observed along the Patagonian shelf-break with satellite ocean color data, but few in situ optical observations were made in that region. We examine the variability of phytoplankton absorption and particulate scattering coefficients during such blooms on the basis of field data. The chlorophyll-a concentration, [Chla], ranged from 0.1 to 22.3 mg m−3 in surface waters. The size fractionation of [Chla] showed that 80% of samples were dominated by nanophytoplankton (N-group) and 20% by microphytoplankton (M-group). Chlorophyll-specific phytoplankton absorption coefficients at 440 and 676 nm, a*ph(440) and a*ph(676), and particulate scattering coefficient at 660 nm, b*p(660), ranged from 0.018 to 0.173, 0.009 to 0.046, and 0.031 to 2.37 m2 (mg Chla)−1, respectively. Both a*ph(440) and a*ph(676) were statistically higher for the N-group than M-group and also considerably higher than expected from global trends as a function of [Chla]. This result suggests that size of phytoplankton cells in Patagonian waters tends to be smaller than in other regions at similar [Chla]. The phytoplankton cell size parameter, Sf, derived from phytoplankton absorption spectra, proved to be useful for interpreting the variability in the data around the general inverse dependence of a*ph(440), a*ph(676), and b*p(660) on [Chla]. Sf also showed a pattern along the increasing trend of a*ph(440) and a*ph(676) as a function of the ratios of some accessory pigments to [Chla]. Our results suggest that the variability in phytoplankton absorption and scattering coefficients in Patagonian waters is caused primarily by changes in the dominant phytoplankton cell size accompanied by covariation in the concentrations of accessory pigments.

Electronic Structure Calculations in a 2D SixGe1-x Alloy Under an Applied Electric Field.

The recent advances and promises in nanoscience and nanotechnology have been focused on hexagonal materials, mainly on carbon-based nanostructures. Recently, new candidates have been raised, where the greatest efforts are devoted to a new hexagonal and buckled material made of silicon, named Silicene. This new material presents an energy gap due to spin-orbit interaction of approximately 1.5 meV, where the measurement of quantum spin Hall effect(QSHE) can be made experimentally. Some investigations also show that the QSHE in 2D low-buckled hexagonal structures of germanium is present. Since the similarities, and at the same time the differences, between Si and Ge, over the years, have motivated a lot of investigations in these materials. In this work we performed systematic investigations on the electronic structure and band topology in both ordered and disordered SixGe1-x alloys monolayer with 2D honeycomb geometry by first-principles calculations. We show that an applied electric field can tune the gap size for both alloys. However, as a function of electric field, the disordered alloy presents a W-shaped behavior, similarly to the pure Si or Ge, whereas for the ordered alloy a V-shaped behavior is observed.

Chromatin Remodeling by BRG1 and SNF2H : Biochemistry and Function

Chromatin is a highly dynamic, regulatory component in the process of transcription, repair, recombination and replication. The BRG1 and SNF2H proteins are ATP-dependent chromatin remodeling proteins that modulate chromatin structure to regulate DNA accessibility for DNA-binding proteins involved in these processes. The BRG1 protein is a central ATPase of the SWI/SNF complexes involved in chromatin remodeling associated with regulation of transcription. SWI/SNF complexes are biochemically hetero-geneous but little is known about the unique functional characteristics of the various forms. We have shown that SWI/SNF activity in SW13 cells affects actin filament organization dependent on the RhoA signaling pathway. We have further shown that the biochemical composition of SWI/SNF complexes qualitatively affects the remodeling activity and that the composition of biochemically purified SWI/SNF complexes does not reflect the patterns of chromatin binding of individual subunits. Chromatin binding assays (ChIP) reveal variations among subunits believed to be constitutive, suggesting that the plasticity in SWI/SNF complex composition is greater than suspected. We have also discovered an interaction between BRG1 and the splicing factor Prp8, linking SWI/SNF activity to mRNA processing. We propose a model whereby parts of the biochemical heterogeneity is a result of function and that the local chromatin environment to which the complex is recruited affect SWI/SNF composition. We have also isolated the novel B-WICH complex that contains WSTF, SNF2H, the splicing factor SAP155, the RNA helicase II/Guα, the transcription factor Myb-binding protein 1a, the transcription factor/DNA repair protein CSB and the RNA processing factor DEK. The formation of this complex is dependent on active transcription and links chromatin remodeling by SNF2H to RNA processing. By linking chromatin remodeling complexes with RNA processing proteins our work has begun to build a bridge between chromatin and RNA, suggesting that factors in chromatin associated assemblies translocate onto the growing nascent RNA.

Analyzing the dependence structure of microarray data: a copula–based approach

The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.