Morphoanatomical and physicochemical studies on Ailanthus excelsa roxb. stem bark: a tree of heaven

Ailanthus excelsa Roxb. (Simaroubaceae) is a traditional medicinal plant used widely in India and China in various health conditions. The morphology and microscopical evaluation are most preferred quality control parameter, in order to establish its quality and purity, we report some important pharmacognostic profile of A. excelsa stem-bark for the purpose of its identification and differentiation from related species. The study of the fresh, powdered and anatomical sections of the stem bark were carried out to determine the morphological, microscopical, some physicochemical and phytochemical parameters. Presence of lignified multicellular trichrome, stone cells, scleroids, lignified pericyclic fibre, phloem fibre, prismatic calcium oxalate, starch grains and uni to multiserrate non-lignified medullary rays observed as distinguishing microscopical characteristics in transverse section and powder studies. The result of preliminary phytochemical screening indicated presence of alkaloids, glycoside, steroids, carbohydrates, proteins, phenolic compounds, tannins, flavonoids and saponins. In addition, quantitative phytochemical analysis revealed significant amount of total phenolic and flavonoid content. The present study will be useful for its identification prior to carrying out further research work. Keywords: Ailanthus excelsa; stem-bark; pharmacognostic; quantitative phytochemical analysis

Inviability of a DNA2 deletion mutant is due to the DNA damage checkpoint

Dna2 is a dual polarity exo/endonuclease, and 5' to 3' DNA helicase involved in Okazaki Fragment Processing (OFP) and Double-Strand Break (DSB) Repair. In yeast, DNA2 is an essential gene, as expected for a DNA replication protein. Suppression of the lethality of dna2Δ mutants has been found to occur by two mechanisms: overexpression of RAD27^(scFEN1), encoding a 5' to 3' exo/endo nuclease that processes Okazaki fragments (OFs) for ligation, or deletion of PIF1, a 5' to 3' helicase involved in mitochondrial recombination, telomerase inhibition and OFP. Mapping of a novel, spontaneously arising suppressor of dna2Δ now reveals that mutation of rad9 and double mutation of rad9 mrc1 can also suppress the lethality of dna2Δ mutants. Interaction of dna2Δ and DNA damage checkpoint mutations provides insight as to why dna2Δ is lethal but rad27Δ is not, even though evidence shows that Rad27^(ScFEN1) processes most of the Okazaki fragments, while Dna2 processes only a subset

From START to FINISH : the influence of osmotic stress on the cell cycle

Peer reviewedPublisher PD

Displacement of polarons by vibrational modes in doped conjugated polymers

Organic pi-conjugated polymers are deemed to be soft materials with strong electron-phonon coupling, which results in the formation of polarons, i.e., charge carriers dressed by self-localized distortion of the nuclei. Universal signatures for polarons are optical resonances below the band gap and intense vibrational modes (IVMs), both found in the infrared (IR) spectral region. Here, we study p-doped conjugated homo-and copolymers by combining first-principles modelling and optical spectroscopy from the far-IR to the visible. Polaronic IVMs are found to feature absorption intensities comparable to purely electronic transitions and, most remarkably, show only loose resemblance to the Raman or IR-active modes of the neutral polymer. The IVM frequency is dramatically scaled down (up to 50%) compared to the backbone carbon-stretching modes in the pristine polymers. The very large intensity of IVMs is associated with displacement of the excess positive charge along the backbone driven by specific vibrational modes. We propose a quantitative picture for the identification of these polaron shifting modes that solely based on structural information, directly correlates with their IR intensity. This finding finally discloses the elusive microscopic mechanism behind the huge IR intensity of IVMs in doped polymeric semiconductors

A Systematic Analysis of Cell Cycle Regulators in Yeast Reveals That Most Factors Act Independently of Cell Size to Control Initiation of Division

Upstream events that trigger initiation of cell division, at a point called START in yeast, determine the overall rates of cell proliferation. The identity and complete sequence of those events remain unknown. Previous studies relied mainly on cell size changes to identify systematically genes required for the timely completion of START. Here, we evaluated panels of non-essential single gene deletion strains for altered DNA content by flow cytometry. This analysis revealed that most gene deletions that altered cell cycle progression did not change cell size. Our results highlight a strong requirement for ribosomal biogenesis and protein synthesis for initiation of cell division. We also identified numerous factors that have not been previously implicated in cell cycle control mechanisms. We found that CBS, which catalyzes the synthesis of cystathionine from serine and homocysteine, advances START in two ways: by promoting cell growth, which requires CBS's catalytic activity, and by a separate function, which does not require CBS's catalytic activity. CBS defects cause disease in humans, and in animals CBS has vital, non-catalytic, unknown roles. Hence, our results may be relevant for human biology. Taken together, these findings significantly expand the range of factors required for the timely initiation of cell division. The systematic identification of non-essential regulators of cell division we describe will be a valuable resource for analysis of cell cycle progression in yeast and other organisms

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.</p> <p>Results</p> <p>This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|H_i), which is used as confidence level. The unit network with higher P(D|H_i) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|H_i), which is a unique property of the proposed algorithm.</p> <p>The algorithm is evaluated with synthetic and <it>Saccharomyces cerevisiae </it>expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm.</p> <p>The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and <it>Saccharomyces cerevisiae </it>expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.</p> <p>Conclusion</p> <p>From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.</p

At the bottom of the differential diagnosis list: unusual causes of pediatric hypertension

Hypertension affects 1–5% of children and adolescents, and the incidence has been increasing in association with obesity. However, secondary causes of hypertension such as renal parenchymal diseases, congenital abnormalities and renovascular disorders still remain the leading cause of pediatric hypertension, particularly in children under 12 years old. Other less common causes of hypertension in children and adolescents, including immobilization, burns, illicit and prescription drugs, dietary supplements, genetic disorders, and tumors will be addressed in this review