Dynamics (moving window analysis) of 7 subjects of the DGGE results.

<p>LA = left axilla; RA = right axilla. Left axis indicates the similarity (based on Pearson correlation) of the axillary sample compared to the previous axillary sample. The higher the curve, the more similar the samples. The axillary microbiome was relatively constant throughout time, even on a longer timescale (9 months). Two followed-up subjects experienced an community shift from one cluster to the other, after which the microbiome again was stable (subject 4 and 11).</p

Heatmap and clustering of the top 25 OTUs of the pyrosequenced samples.

<p>Data was ranked according to the total count of the OTU among all the samples and samples were clustered using hierarchical clustering (complete linkage) and Bray-Curtis distance measures. OTU0001 = <i>Corynebacterium</i> spp., OTU0005 = <i>Staphylococcus</i> spp., OTU0002 = <i>Moraxellaceae</i> (<i>Proteobacteria</i>). Full OTU description can be found in Table S6 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070538#pone.0070538.s001" target="_blank">File S1</a>.</p

Clustering of individual axillary samples analyzed by means of DGGE, where 69% of the subjects clustered into the <i>Staphylococcus</i> cluster and 31% into the <i>Corynebacterium</i> cluster.

<p>Right: Subject indices from S1 till S53 (not all subjects were shown); gender of the subject; pyrosequenced samples indicated with MID (multiplex identifiers). Above: Identified band: bands A were identified as <i>Staphylococcus epidermidis</i> (100% identity), bands B were identified as <i>Staphylococcus</i> spp. (99% identity), bands C were identified as <i>Staphylococcus hominis</i> (100% identity), bands D and E were identified as <i>Proteobacteria</i> (from pyrosequencing results), band G was identified as <i>Corynebacterium</i> spp. (99% identity), bands H were identified as <i>Corynebacterium</i> spp. (99% identity), and bands F, I, J and K were identified as <i>Corynebacterium</i> spp. (from pyrosequencing results). Left: Clustering of the samples, based on Pearson correlation and unweighted pair group with mathematical averages dendrogram method. Under: indication of GC% of the bacterial bands. <i>Firmicutes</i> have a low GC%, and bands are generally situated left on the gel; <i>Actinobacteria</i> have a high GC%, with bands situated generally on the right side of the gel.</p

Stacked bar sample-wise taxonomic distribution of the sequences on genus level of the nine pyrosequenced axillary samples.

<p>MID7 is a sample of a female person (34 y, S16) using deodorant 24 times per week; MID8 is a sample of a male person (24 y, S4) using no deodorant; MID10 is a sample of a male person (24 y, S28) using deodorant 7 times per week; MID11 is a sample of a male person (27 y, S11) using deodorant 5 times per week; MID13 is a sample of a male person (27 y, S27) using deodorant 3 times per week; MID14 is a sample of a male person (25 y, S38) using deodorant 3 times per week; MID15 is a sample of a male person (23 y, S6) using deodorant 7 times per week; MID16 is a sample of a male person (29 y, S31) using deodorant 10 times per week; MID17 is a sample of a male person (35 y, S1) using deodorant 7 times per week. All subjects were Belgian. Additional subject metadata description can be found in Table S7 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070538#pone.0070538.s001" target="_blank">File S1</a>.</p

Data_Sheet_1_Sniffing out safety: canine detection and identification of SARS-CoV-2 infection from armpit sweat.pdf

Detection dogs were trained to detect SARS-CoV-2 infection based on armpit sweat odor. Sweat samples were collected using cotton pads under the armpits of negative and positive human patients, confirmed by qPCR, for periods of 15–30 min. Multiple hospitals and organizations throughout Belgium participated in this study. The sweat samples were stored at −20°C prior to being used for training purposes. Six dogs were trained under controlled atmosphere conditions for 2–3 months. After training, a 7-day validation period was conducted to assess the dogs’ performances. The detection dogs exhibited an overall sensitivity of 81%, specificity of 98%, and an accuracy of 95%. After validation, training continued for 3 months, during which the dogs’ performances remained the same. Gas chromatography/mass spectrometry (GC/MS) analysis revealed a unique sweat scent associated with SARS-CoV-2 positive sweat samples. This scent consisted of a wide variety of volatiles, including breakdown compounds of antiviral fatty acids, skin proteins and neurotransmitters/hormones. An acceptability survey conducted in Belgium demonstrated an overall high acceptability and enthusiasm toward the use of detection dogs for SARS-CoV-2 detection. Compared to qPCR and previous canine studies, the detection dogs have good performances in detecting SARS-CoV-2 infection in humans, using frozen sweat samples from the armpits. As a result, they can be used as an accurate pre-screening tool in various field settings alongside the PCR test.</p

Image_1_Expert considerations and consensus for using dogs to detect human SARS-CoV-2-infections.PNG

The full text of this article can be freely accessed on the publisher's website

movie_s2.mp4

Placing changes in the microbiome in the context of the American Gut. We accumulated samples over sequencing runs to demonstrate the structural consistency in the data. We demonstrate that while the ICU dataset (https://www.ncbi.nlm.nih.gov/pubmed/27602409) falls within the American Gut samples, they do not fall close to most samples at any of the body sites. We then highlight samples from the United Kingdom, Australia, the United States and other countries to show that nationality does not overcome the variation in body site. We then highlight the utility of the American Gut in meta-analysis by reproducing results from (https://www.ncbi.nlm.nih.gov/pubmed/20668239) and (https://www.ncbi.nlm.nih.gov/pubmed/23861384), using the AGP dataset as the context for dynamic microbiome changes instead of the HMP dataset. We show rapid, complete recovery of C. diff patients following fecal material transplantation and also contextualized the change in an infant gut over time until it settles into an adult state. This demonstrates the power of the American Gut dataset, both as a cohesive study and as a context for other investigations

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The SEPP (Mirarab et al Pac Symp Biocomput 2012) fragment insertion tree used for phylogenetic analyses

Unweighted UniFrac distances

The unweighted UniFrac distance (Lozupone and Knight AEM 2005) matrix of the 9511 fecal samples used in the American Gut paper. UniFrac was computed using Striped UniFrac (https://github.com/biocore/unifrac). Prior to execution of UniFrac, Deblur (Amir et al mSystems 2017) was run on the samples, all bloom sOTUs were removed (Amir et al mSystems 2017), and samples were rarefied to a depth of 1250 reads (Weiss et al Microbiome 2017). For the phylogeny, fragments were inserted using SEPP (Mirarab et al Pac Symp Biocomput 2012) into the Greengenes 13_5 99% OTU tree (McDonald et al ISME 2012)

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Longitudinal samples from a large bowel resection. We place longitudinal samples collected prior to and following a large bowel resection in the context of samples from the AGP, the Earth Microbiome Project (https://www.ncbi.nlm.nih.gov/pubmed/29088705), intensive care unit patients (https://www.ncbi.nlm.nih.gov/pubmed/27602409), "extreme" diet samples from (https://www.ncbi.nlm.nih.gov/pubmed/24336217), and samples from the Hadza hunter-gatherers (https://www.ncbi.nlm.nih.gov/pubmed/28839072). Unweighted UniFrac was computed on this sample set, and principal coordinates were assessed. Using EMPeror (https://www.ncbi.nlm.nih.gov/pubmed/24280061), we then animate the plot by connect successive data points gut resection time series, while rotating the data frame. We first show the how the extent of change in the microbial community, and how the samples immediately following surgery resemble fecal samples from ICU patients. In the background of the animation, a black line connects a plant rhizosphere sample to a marine sediment sample, which have the same unweighted UniFrac distance (0.78) as the longitudinal sample immediately preceding and immediately following surgery