DataSheet_1_Geography, niches, and transportation influence bovine respiratory microbiome and health.docx

Bovine respiratory disease (BRD), one of the most common and infectious diseases in the beef industry, is associated with the respiratory microbiome and stressors of transportation. The impacts of the bovine respiratory microbiota on health and disease across different geographic locations and sampling niches are poorly understood, resulting in difficult identification of BRD causes. In this study, we explored the effects of geography and niches on the bovine respiratory microbiome and its function by re-analyzing published metagenomic datasets and estimated the main opportunistic pathogens that changed after transportation. The results showed that diversity, composition, structure, and function of the bovine nasopharyngeal microbiota were different across three worldwide geographic locations. The lung microbiota also showed distinct microbial composition and function compared with nasopharyngeal communities from different locations. Although different signature microbiota for each geographic location were identified, a module with co-occurrence of Mycoplasma species was observed in all bovine respiratory communities regardless of geography. Moreover, transportation, especially long-distance shipping, could increase the relative abundance of BRD-associated pathogens. Lung microbiota from BRD calves shaped clusters dominated with different pathogens. In summary, geography, sampling niches, and transportation are important factors impacting the bovine respiratory microbiome and disease, and clusters of lung microbiota by different bacterial species may explain BRD pathogenesis, suggesting the importance of a deeper understanding of bovine respiratory microbiota in health.</p

Bland-Altman plots of thickness measurements determined with the automated segmentation algorithm on UHR-OCT and RTVue100 images.

<p>Only the images along the horizontal meridian were analyzed. The horizontal full lines represent the mean of thickness differences, and the horizontal dashed lines represent the mean differences ±1.96 standard deviation.</p

Boundaries of intra-retinal layers in OCT macular images.

<p>As seen in this image taken by UHR-OCT in the horizontal meridian, nine boundaries of intra-retinal layers were visualized. Images taken in the vertical meridian by UHR-OCT and in both meridians by the RTVue100 were similar to this.</p

The detailed sequence in the boundary segmentation process.

<p>(a) Original image. (b) Image smoothing. (c) Gradient image. (d) The ILM and the boundary between the RPE and choroid layers were first segmented. (e) Limiting detection area and search the minimum-weighted path. (f) Segmented image.</p

profiles of eight intra-retinal layers determined from the UHR-OCT and RTVue100 images in the horizontal meridian.

<p>Thickness profiles of eight intra-retinal layers along the horizontal meridian were averaged for 20 normal healthy eyes. Error bars represent standard deviation.</p

Configuration of the two OCT instruments.

<p>Configuration of the two OCT instruments.</p

Comparison of the thickness measurements for eight intra-retinal layers and total retina between UHR-OCT and RTVue100 devices.

<p>P value: tested by paired-t test.</p

Repeatability and reproducibility of thickness measurements for eight intra-retinal layers measured by the RTVue100.

<p>T1: mean thickness for the first measurement by examiner 1; T2: mean thickness for the second measurement by examiner 1; T3: mean thickness for the first measurement by examiner 2; ICCa: intraclass correlation coefficients of repeatability; ICCb: intraclass correlation coefficients of reproducibility; CORa: coefficients of repeatability; CORb: coefficients of reproducibility; n = 20 eyes.</p

Segmentation errors in scans of lower image quality and corresponding corrected segmentation after applying the semi-automated approach.

<p>(A) The algorithm mistakenly identified the OPL/ONL interface. (B) Corrected segmentation corresponding (A) after applying the semi-automated approach. (C) The algorithm mistakenly identified the RNFL/GCL boundary. (D) Corrected segmentation corresponding (C) after applying the semi-automated approach.</p

Thickness profiles of eight intra-retinal layers determined from the UHR-OCT and RTVue100 images in the vertical meridian.

<p>Thickness profiles of eight intra-retinal layers along the vertical meridian were averaged for 20 normal healthy eyes. Error bars represent standard deviation.</p