Comparison of the larynx-associated lymphoid tissue in the false vocal cords and subglottis in pediatric autopsies

The aim this work was to compare the distribution of cellular phenotypes of the LF in the FVC to the ones in the subglottic region in pediatric autopsy, relating this distribution to age and different causes of death. We analyzed 60 larynges of newborns and children autopsied in the period from 1993 to 2003. The fragments were prepared in order to perform histochemical and immunohistochemical techniques. The morphological analysis showed cases that presented LF only in FVC (35%), LF only in the subglottic region (20%), lack of LF in FVC (30%) and lymphoid aggregates, which did not characterize an LF (15%). The cases of LF in the subglottic region were significantly younger compared to the ones that presented LF in the FVC (p = 0.017). The LF in the subglottic region was bigger than the LF in the FVC (p = 0.020). There was no significant difference between the cause of death and cellular phenotype for both FVC and the subglottic region. In conclusion, the cells that make up the LF in the FVC in newborns and children younger than one year have functional characteristics similar to LF cells in the subglottic region, suggesting that there are similarities with LALT. (c) 2012 Elsevier GmbH. All rights reserved.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.