BackgroundComposition of microbial communities can be location specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the whole genome shotgun (WGS) metagenomics data from samples across 16 cities around the world and samples from another 8 cities were provided as the main and mystery datasets respectively as the part of the CAMDA 2019 MetaSUB “Forensic Challenge”. The feature selection, normalization, three methods of machine learning, PCoA (Principal Coordinates Analysis) and ANCOM (Analysis of composition of microbiomes) were conducted for both the main and mystery datasets.ResultsFeature selection, combined with the machines learning methods, revealed that the combination of the common features was effective for predicting the origin of the samples. The average error rates of 11.6% and 30.0% of three machine learning methods were obtained for main and mystery datasets respectively. Using the samples from main dataset to predict the labels of samples from mystery dataset, nearly 89.98% of the test samples could be correctly labeled as “mystery” samples. PCoA showed that nearly 60% of the total variability of the data could be explained by the first two PCoA axes. Although many cities overlapped, the separation of some cities was found in PCoA. The results of ANCOM, combined with importance score from the Random Forest, indicated that the common “family”, “order” of the main-dataset and the common “order” of the mystery dataset provided the most efficient information for prediction respectively.ConclusionsThe results of the classification suggested that the composition of the microbiomes was distinctive across the cities, which was also supported by the results from ANCOM and importance score from the RF. The analysis utilized in this study can be of great help in field of forensic science to efficiently predict the origin of the samples. And the accurate of the prediction could be improved by more samples and better sequencing depth.

Physical activity (PA) is associated with decreased signaling in the mechanistic target of rapamycin (mTOR) pathway in animal models of mammary cancer, which may indicate favorable outcomes. We examined the association between PA and protein expression in the mTOR signaling pathway in breast tumor tissue. Data on 739 breast cancer patients, among which 125 patients had adjacent-normal tissue, with tumor expression for mTOR, phosphorylated (p)-mTOR, p-AKT, and p-P70S6K were analyzed. Self-reported recreational PA levels during the year prior to diagnosis were classified using the Centers for Disease Control and Prevention guideline as sufficient (for moderate or vigorous) PA or insufficient PA (any PA but not meeting the guideline) or no PA. We performed linear models for mTOR protein and two-part gamma hurdle models for phosphorylated proteins. Overall, 34.8% of women reported sufficient PA; 14.2%, insufficient PA; 51.0%, no PA. Sufficient (vs. no) PA was associated with higher expression for p-P70S6K (35.8% increase, 95% CI, 2.6%–80.2%) and total phosphoprotein (28.5% increase, 95% CI, 5.8–56.3%) among tumors with positive expression. In analyses stratified by PA intensity, sufficient vs. no vigorous PA was also associated with higher expression levels of mTOR (beta = 17.7, 95% CI= 1.1–34.3) and total phosphoprotein (28.6% higher, 95% CI = 1.4%–65.0% among women with positive expression) in tumors. The study found that guideline-concordant PA levels were associated with increased mTOR signaling pathway activity in breast tumors. Studying PA in relation to mTOR signaling in humans may need to consider the complexity of the behavioral and biological factors.

<p>Supplemental Table 12 reported stratified analysis for low/intermediate-grade tumors and high-grade tumors.</p>

<p>Supplemental Table 15 reported stratified analysis for lymph node positive status and lymph node negative status.</p>

<p>Supplemental Table 13 reported stratified analysis for tumors <2cm and ≥2 cm in diameter.</p>