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High molecular weight (HMW; >1 kDa) carbohydrates are a major component of dissolved organic matter (DOM) released by benthic primary producers. Despite shifts from coral to algae dominance on many reefs, little is known about the effects of exuded carbohydrates on bacterioplankton communities in reef waters. We compared the monosaccharide composition of HMW carbohydrates exuded by hard corals and brown macroalgae and investigated the response of the bacterioplankton community of an algae-dominated Caribbean reef to the respective HMW fractions. HMW coral exudates were compositionally distinct from the ambient, algae-dominated reef waters and similar to coral mucus (high in arabinose). They further selected for opportunistic bacterioplankton taxa commonly associated with coral stress (i.e.,Rhodobacteraceae,Phycisphaeraceae,Vibrionaceae, andFlavobacteriales) and significantly increased the predicted energy-, amino acid-, and carbohydrate-metabolism by 28%, 44%, and 111%, respectively. In contrast, HMW carbohydrates exuded by algae were similar to those in algae tissue extracts and reef water (high in fucose) and did not significantly alter the composition and predicted metabolism of the bacterioplankton community. These results confirm earlier findings of coral exudates supporting efficient trophic transfer, while algae exudates may have stimulated microbial respiration instead of biomass production, thereby supporting the microbialization of reefs. In contrast to previous studies, HMW coral and not algal exudates selected for opportunistic microbes, suggesting that a shift in the prevalent DOM composition and not the exudate type (i.e., coral vs algae)per se, may induce the rise of opportunistic microbial taxa. IMPORTANCEDissolved organic matter (DOM) released by benthic primary producers fuels coral reef food webs. Anthropogenic stressors cause shifts from coral to algae dominance on many reefs, and resulting alterations in the DOM pool can promote opportunistic microbes and potential coral pathogens in reef water. To better understand these DOM-induced effects on bacterioplankton communities, we compared the carbohydrate composition of coral- and macroalgae-DOM and analyzed the response of bacterioplankton from an algae-dominated reef to these DOM types. In line with the proposed microbialization of reefs, coral-DOM was efficiently utilized, promoting energy transfer to higher trophic levels, whereas macroalgae-DOM likely stimulated microbial respiration over biomass production. Contrary to earlier findings, coral- and not algal-DOM selected for opportunistic microbial taxa, indicating that a change in the prevalent DOM composition, and not DOM type, may promote the rise of opportunistic microbes. Presented results may also apply to other coastal marine ecosystems undergoing benthic community shifts. 

This dataset contains raw data for figures 5 (genus-level microbial community compositions) and 6 (predicted metabolic functions, pathway types), R code for PERMANOVAs (Table 3), DESeq2 and random forest (rfpermute) analyses, and R code to generate figures 5, 6b, S5 & S6. Overview of .txt files: Genus_16S_Counts.txt Counts data used for DESeq2 analysis (Fig. 5c). Genus_16S_relAbund.txt Relative abundance data used for Fig. 5a, b & d. MicFunPred_MetaCyc_types_all Predicted pathway abundance data for all pathway types used for DESeq2 (Fig. 6b), PERMANOVA (Table 3) and column clustering of Fig. 6b. MicFunPred_MetaCyc_AA_types.txt Amino acids (Fig. 6b) MicFunPred_MetaCyc_CH_types.txt Carbohydrates (Fig. 6b) MicFunPred_MetaCyc_EM _types.txt Energy metabolism (Fig. 6b) MicFunPred_MetaCyc_FAL _types.txt Fatty acids and lipids (Fig. 6b) MicFunPred_MetaCyc_SM _types.txt Secondary metabolism (Fig. 6b) MicFunPred_MetaCyc_OBiosyn _types.txt Other biosynthesis (Fig. S6) MicFunPred_MetaCyc_ODeg _types.txt Other degradation (Fig. S6) 

Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs; density of the CNT forests resulting in CNTs touching, crossing, and occluding each other; and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare training segmentation masks. In this paper, we propose CNTSegNet, a dual loss, orientation-guided, self-supervised, deep learning network for CNT forest segmentation in scanning electron microscopy (SEM) images. Our training labels consist of weak segmentation labels produced by intensity thresholding of the raw SEM images and self labels produced by estimating orientation distribution of CNTs in these raw images. The proposed network extends a U-net-like encoder-decoder architecture with a novel two-component loss function. The first component is dice loss computed between the predicted segmentation maps and the weak segmentation labels. The second component is mean squared error (MSE) loss measuring the difference between the orientation histogram of the predicted segmentation map and the original raw image. Weighted sum of these two loss functions is used to train the proposed CNTSegNet network. The dice loss forces the network to perform background-foreground segmentation using local intensity features. The MSE loss guides the network with global orientation features and leads to refined segmentation results. The proposed system needs only a few-shot dataset for training. Thanks to it’s self-supervised nature, it can easily be adapted to new datasets.