Examination of the Nitric Oxide Production-Suppressing Component in Tinospora tuberculata

The component of aqueous Tinospora tuberculata extract that inhibits nitric oxide (NO) production was examined using macrophages activated by the addition of lipopolysaccharide. The aqueous extract was partitioned with ethyl acetate. The aqueous layer was fractionated with a Diaion column. The residue of the aqueous extract was extracted with methanol, and partitioned with ethyl acetate. The ethyl acetate layer was found to be associated with a distinct decrease in the NO level and inducible NO synthase. On further fractionation, the subfraction of E-3 showed high anti-NO activity. N-trans-Feruloyltyramine isolated from E-3 was identified as exhibiting strong anti-NO activity. This compound is the most active component of Tinospora tuberculata with respect to the suppression of NO production

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

Galaxy bulges and their massive black holes: a review

With references to both key and oft-forgotten pioneering works, this article starts by presenting a review into how we came to believe in the existence of massive black holes at the centres of galaxies. It then presents the historical development of the near-linear (black hole)-(host spheroid) mass relation, before explaining why this has recently been dramatically revised. Past disagreement over the slope of the (black hole)-(velocity dispersion) relation is also explained, and the discovery of sub-structure within the (black hole)-(velocity dispersion) diagram is discussed. As the search for the fundamental connection between massive black holes and their host galaxies continues, the competing array of additional black hole mass scaling relations for samples of predominantly inactive galaxies are presented.Comment: Invited (15 Feb. 2014) review article (submitted 16 Nov. 2014). 590 references, 9 figures, 25 pages in emulateApJ format. To appear in "Galactic Bulges", E. Laurikainen, R.F. Peletier, and D.A. Gadotti (eds.), Springer Publishin

Impaired lung function and Health Status in Adult Survivors of Bronchopulmonary Dysplasia

[Abstract]: In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment. As a new technology, Microarray data presents some fresh challenges to scientists since Microarray data contains a large number of genes (around tens thousands) with a small number of samples (around hundreds). Both filter and wrapper gene selection methods aim to select the most informative genes among the massive data in order to reduce the size of the expression database. Gene selection methods are used in both data preprocessing and classification stages. We have conducted some experiments on different existing gene selection methods to preprocess Microarray data for classification by benchmark algorithms SVMs and C4.5. The study suggests that the combination of filter and wrapper methods in general improve the accuracy performance of gene expression Microarray data classification. The study also indicates that not all filter gene selection methods help improve the performance of classification. The experimental results show that among tested gene selection methods, Correlation Coefficient is the best gene selection method for improving the classification accuracy on both SVMs and C4.5 classification algorithms

Applying Gaussian distribution-dependent criteria to decision trees for high-dimensional microarray data

Microarray data presents an interesting problem to machine learning algorithms due to their highdimension and small number of samples. Since algorithms such as support vector machines (SVM) typically achieve high prediction accuracies, other methods garner a relatively small amount of attention even though they possess other characteristics which are useful for microarray analysis

Using Rules to Analyse Bio-medical Data: A Comparison between C4.5 and PCL

For easy comprehensibility, rules are preferrable to non-linear kernel functions in the analysis of bio-medical data. In this paper, we describe two rule induction approaches---C4.5 and our PCL classifier---for discovering rules from both traditional clinical data and recent gene expression or proteomic profiling data. C4.5 is a widely used method, but it has two weaknesses, the single coverage constraint and the fragmentation problem, that affect its accuracy. PCL is a new rule-based classifier that overcomes these two weaknesses of decision trees by using many significant rules. We present a thorough comparison to show that our PCL method is much more accurate than C4.5, and it is also superior to Bagging and Boosting in general