Occurrence of Medicinal Plant Pollen in \u3cem\u3eApis cerana\u3c/em\u3e Honeys of Khammam District, Andhra

A pollen analysis of 11 honey samples from Khammam district has been carried out. According to the pollen spectra found, most of them are unifloral (10); 1 sample multifloral. Thirty-two different pollen types were recorded, belonging to 20 families. Twenty-one plants recorded from the honey samples are used as medicinal plants in folklore and tribal medicine

Carbenoid addition to ɑ, β-ethylenic sulfones under PTC-A stereospecific synthesis

608-61

Amide-controlled, one-pot synthesis of tri-substituted purines generates structural diversity and analogues with trypanocidal activity

Anovel one-pot synthesis of tri-substituted purines and the discovery of purine analogues with trypanocidal activity are reported. The reaction is initiated by a metal-free oxidative coupling of primary alkoxides and diaminopyrimidines with Schiff base formation and subsequent annulation in the presence of large N,N-dimethylamides (e.g.N,N-dimethylpropanamide or larger). This synthetic route is in competition with a reaction previously-reported by our group1, allowing the generation of a combinatorial library of tri-substituted purines by the simple modification of the amide and the alkoxide employed. Among the variety of structures generated, two purine analogues displayed trypanocidal activity against the protozoan parasite Trypanosoma brucei with IC50 , 5 mM, being each of those compounds obtained through each of the synthetic pathways.J.J.D.M. thanks Spanish Ministerio de Economı´a y Competitividad for a Ramon y Cajal Fellowship. A.U.B. thanks MRC IGMM for an academic fellowship. This work was partially supported by Grant SAF2011-30528 to J.A.G.S.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein–protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio