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.

Strength training with and without a vibration platform is capable to modulate heart rate variability?

The current research compared resting heart rate variability (VFC) before and after 10 weeks of strength training in groups that used and did not use a vibration platform. Seventeen healthy men were divided into conventional strength training (TF) or strength training using a vibration platform with a frequency of 30 Hz (TF+V30) training groups. One repetition maximum load (1-RM) on half squat exercise and VFC measurements were determined pre- and post-training program. Both groups had improved 1-RM load after the program (15.1% in TF group and 16.4% in TF+V30 group), although this increase was changed in the same extent for the two groups and there was no difference in 1-RM load between groups pre- and post-training program. No significant difference was observed in resting VFC measurements between groups pre and post-training program, however the magnitude of the effect size was moderated (ES = 0.50-0.80) for some variables (R-R interval, standard deviation of all R-R interval - SDNN, RMSSD, log-transformed of low frequency - InLF, and log-transformed of high frequency - InHF) in TF+V30 group. It was concluded that 10 weeks of strength training program with or without the vibration platform provided similar increase in 1-RM load in both groups, and although some evidences in this study indicate that vibration can increase vagal activity analyzed by ES, in neither groups the strength training was able to change VFC significantly.

Clinical evaluation of the failure rate of metallic brackets bonded with orthodontic composites

The purpose of the present study was to evaluate in vivo the failure rate of metallic brackets bonded with two orthodontic composites. Nineteen patients with ages ranging from 10.5 to 38.7 years needing corrective orthodontic treatment were selected for study. The enamel surfaces from second premolars to second premolars were treated with Transbond Plus-Self Etching Primer (3M Unitek). Next, 380 orthodontic brackets were bonded on maxillary and mandibular teeth, as follows: 190 with Transbond XT composite (3M Unitek) (control) and 190 with Transbond Plus Color Change (3M Unitek) (experimental) in contralateral quadrants. The bonded brackets were light cured for 40 s, and initial alignment archwires were inserted. Bond failure rates were recorded over a six-month period. At the end of the evaluation, six bond failures occurred, three for each composite. Kaplan-Meyer method and log-rank test (Mantel-Cox) was used for statistical analysis, and no statistically significant difference was found between the materials (p=0.999). Both Transbond XT and Transbond Plus Color Change composites had low debonding rates over the study period.