SCANNING ELECTRON MICROSCOPY AND ELECTRON MICROPROBE ANALYSIS OF SILICIFICATION PATTERNS IN INFLORESCENCE BRACTS OF AVENA SATIVA

Using scanning electron microscopy, we determined the kinds and distribution of epidermal cell types in Avena inflorescence bracts (glume, lemma, and palea). Electron microprobe analysis of silica deposition in these epidermal cells showed that silica cells constitute one of the important deposition sites. Probe ratio data indicate that the silica deposited is 74 % pure. Significant amounts of silica also become deposited in the trichomes and lesser amounts in the walls of long epidermal cells. None could be detected in the stomata. The possible functional significance of silica deposition in epidermal cells of these bracts is discussed.     

On the PAM matrix model of protein evolution

The internal consistency of the PAM matrix model of protein evolution is here investigated. The 1 PAM matrix has been constructed from amino acid replacements observed in closely related sequences. Such replacements are of two types, those that do not require an intermediate amino acid replacement and those that do. The second type of replacement must generally be produced by a repetition of the first. This allows data on the first type to be used in predicting data on the second type so that some elements of the 1 PAM matrix may be used to predict others. A discrepancy of more than two orders of magnitude is found between the predictions and the data when this is carried out. This is partly accounted for by an error in constructing the matrix. However, it also seems necessary that the basic model be modified. Several possibilities are considered. One of these is to incorporate a site-dependent spectrum of mutabilities associated with each amino acid.      

PubMed related articles: a probabilistic topic-based model for content similarity

Background

We present a probabilistic topic-based model for content similarity called pmra that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ^® in MEDLINE ^®.

Results

The pmra retrieval model was compared against bm25, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of pmra over bm25 in terms of precision.

Conclusion

Our experiments suggest that the pmra model provides an effective ranking algorithm for related article search.