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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. 

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. 

Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction.