Correction to 'Ancient DNA and the tropics: a rodent's tale'.

The present erratum is in regards to our article entitled 'Ancient DNA and the tropics: a rodent's tale'. We were made aware of problems with some of the ancient sequences submitted to GenBank and conducted a systematic review of all the files used in our study. We discovered that, unfortunately, an incorrect file was sent to GenBank and was also used in some of our downstream analyses. We immediately contacted GenBank, explained the situation and corrected the file. We have redone some analyses with the correct file and describe these changes below.

Ancient DNA and the tropics: a rodent's tale.

Most genetic studies of Holocene fauna have been performed with ancient samples from dry and cold regions, in which preservation of fossils is facilitated and molecular damage is reduced. Ancient DNA work from tropical regions has been precluded owing to factors that limit DNA preservation (e.g. temperature, hydrolytic damage). We analysed ancient DNA from rodent jawbones identified as Ototylomys phyllotis, found in Holocene and Late Pleistocene stratigraphic layers from Loltún, a humid tropical cave located in the Yucatan peninsula. We extracted DNA and amplified six short overlapping fragments of the cytochrome b gene, totalling 666 bp, which represents an unprecedented success considering tropical ancient DNA samples. We performed genetic, phylogenetic and divergence time analyses, combining sequences from ancient and modern O. phyllotis, in order to assess the ancestry of the Loltún samples. Results show that all ancient samples fall into a unique clade that diverged prior to the divergence of the modern O. phyllotis, supporting it as a distinct Pleistocene form of the Ototylomys genus. Hence, this rodent's tale suggests that the sister group to modern O. phyllotis arose during the Miocene-Pliocene, diversified during the Pleistocene and went extinct in the Holocene.

Practical eight-frame algorithms for fringe projection profilometry.

In this paper we present several eight-frame algorithms for their use in phase shifting profilometry and their application for the analysis of semi-fossilized materials. All algorithms are obtained from a set of two-frame algorithms and designed to compensate common errors such as phase shift detuning and bias errors.

Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods.

The main cause of death in Mexico and the world is heart disease, and it will continue to lead the death rate in the next decade according to data from the World Health Organization (WHO) and the National Institute of Statistics and Geography (INEGI). Therefore, the objective of this work is to implement, compare and evaluate machine learning algorithms that are capable of classifying normal and abnormal heart sounds. Three different sounds were analyzed in this study; normal heart sounds, heart murmur sounds and extra systolic sounds, which were labeled as healthy sounds (normal sounds) and unhealthy sounds (murmur and extra systolic sounds). From these sounds, fifty-two features were calculated to create a numerical dataset; thirty-six statistical features, eight Linear Predictive Coding (LPC) coefficients and eight Cepstral Frequency-Mel Coefficients (MFCC). From this dataset two more were created; one normalized and one standardized. These datasets were analyzed with six classifiers: k-Nearest Neighbors, Naive Bayes, Decision Trees, Logistic Regression, Support Vector Machine and Artificial Neural Networks, all of them were evaluated with six metrics: accuracy, specificity, sensitivity, ROC curve, precision and F1-score, respectively. The performances of all the models were statistically significant, but the models that performed best for this problem were logistic regression for the standardized data set, with a specificity of 0.7500 and a ROC curve of 0.8405, logistic regression for the normalized data set, with a specificity of 0.7083 and a ROC curve of 0.8407, and Support Vector Machine with a lineal kernel for the non-normalized data; with a specificity of 0.6842 and a ROC curve of 0.7703. Both of these metrics are of utmost importance in evaluating the performance of computer-assisted diagnostic systems.