A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval

Research on the human microbiome, the microbiota that live in, on, and around the human person, has revolutionized our understanding of the complex interactions between microbial life and human health and disease. The microbiome may also provide a valuable tool in forensic death investigations by helping to reveal the postmortem interval (PMI) of a decedent that is discovered after an unknown amount of time since death. Current methods of estimating PMI for cadavers discovered in uncontrolled, unstudied environments have substantial limitations, some of which may be overcome through the use of microbial indicators. In this project, we sampled the microbiomes of decomposing human cadavers, focusing on the skin microbiota found in the nasal and ear canals. We then developed several models of statistical regression to establish an algorithm for predicting the PMI of microbial samples. We found that the complete data set, rather than a curated list of indicator species, was preferred for training the regressor. We further found that genus and family, rather than species, are the most informative taxonomic levels. Finally, we developed a k-nearest- neighbor regressor, tuned with the entire data set from all nasal and ear samples, that predicts the PMI of unknown samples with an average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations

The view of Czech students on issues of forced labour

The aim of this master's thesis is to ascertain knowledge of Czech students in secondary schools, or more precisely in Grammar school, about the issue of forced labour during World War II. The master's thesis is based primarily on literature and own research and is divided into two parts. The first part is the theoretical part where is briefly introduced the history of forced labour in the general level (definition, type of work, working conditions, division of workers, etc.) and the second part where is conduct social research which is oriented only to Czech students. This research is conducted through a questionnaire survey that has the form of mixed research (questions and its evaluation is both quantitative and qualitative). Based on this research is evaluated both the overall state of knowledge of this issue (it compares the first-years students and fourth -years students of secondary schools), and is also taken account of the issue of the topic processing in secondary school textbooks

A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval.

Research on the human microbiome, the microbiota that live in, on, and around the human person, has revolutionized our understanding of the complex interactions between microbial life and human health and disease. The microbiome may also provide a valuable tool in forensic death investigations by helping to reveal the postmortem interval (PMI) of a decedent that is discovered after an unknown amount of time since death. Current methods of estimating PMI for cadavers discovered in uncontrolled, unstudied environments have substantial limitations, some of which may be overcome through the use of microbial indicators. In this project, we sampled the microbiomes of decomposing human cadavers, focusing on the skin microbiota found in the nasal and ear canals. We then developed several models of statistical regression to establish an algorithm for predicting the PMI of microbial samples. We found that the complete data set, rather than a curated list of indicator species, was preferred for training the regressor. We further found that genus and family, rather than species, are the most informative taxonomic levels. Finally, we developed a k-nearest- neighbor regressor, tuned with the entire data set from all nasal and ear samples, that predicts the PMI of unknown samples with an average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations

The ten top performing models when ranked by validation error.

<p>The ten top performing models when ranked by validation error.</p

The ear equivalent of Table 3.

<p>The ear equivalent of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167370#pone.0167370.t003" target="_blank">Table 3</a>.</p

Some select high performing phyla, with ADD plotted against abundance.

<p>The vertical axis is normalized for each organism so that the relative abundances are on a similar scale.</p

A plot of ADD versus percentage composition for the bacterial genus <i>Vagococcus</i>.

<p>Each of the four sample cadavers has a corresponding curve, as indicated in the legend.</p

This table is similar to Table 3, but with joint datasets.

<p>This table is similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167370#pone.0167370.t003" target="_blank">Table 3</a>, but with joint datasets.</p