The search for exudates from Eurasian watermilfoil and hydrilla

Secondary metabolites are produced by aquatic plants, and in some instances, exudation of these metabolites into the surrounding water has been detected. To determine whether infestations of Eurasian watermilfoil or hydrilla produce such exudates, plant tissues and water samples were collected from laboratory cultures and pond populations and were analyzed using solid phase extraction, HPLC, and various methods of mass spectrometry including electrospray ionization, GC/MS, electron impact and chemical ionization. Previously reported compounds such as tellimagrandin II (from Eurasian watermilfoil) and a caffeic acid ester (from hvdrilla), along with a newly discovered flavonoid, cyanidin 3 dimalonyl glucoside (from hydrilla), were readily detected in plant tissues used in this research but were not detected in any of the water samples. If compounds are being released, as suggested by researchers using axenic cultures, we hypothesize that they may be rapidly degraded by bacteria and therefore undetectable.

Using Google search Console to track impact and use of collections

Implementing resource discovery techniques at the National Fairground Archive and Special Collections, University of Sheffield Using Google search Console to track impact and use of collections

A combined statistical model for multiple motifs search

Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with de¯nite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite di®erent from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for ¯nding over- represented short sequence segments in di®erent kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from di®erent species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite di®erent, the results show that this model is rather general and sensible.