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The nitrogen (N) isotopic composition of coral tissue provides insight into N sources and cycling on reefs, and coral skeleton-bound organic matter (CS-δ¹⁵N) can extend these insights into the past. Across the Bermuda platform, we measured the δ¹⁵N of four coral species and their potential N sources, as well as an asymbiotic filter feeder as a comparative heterotroph and benthic macroalgae as a comparative autotroph. Organisms and organic N pools from the coral reefs exhibit a δ¹⁵N increase toward the Bermuda coast, likely due to anthropogenic N inputs. At all sites, the δ¹⁵N of bulk coral tissue is consistent with corals feeding dominantly on zooplankton-sized organic matter and some smaller suspended particulate N. The corals lack the trophic δ¹⁵N elevation that characterizes serpulids; this is consistent with internal recycling and retention of low-δ¹⁵N metabolic N by symbiont-bearing corals. The data are inconsistent with corals’ reliance on the dissolved inorganic N used by macroalgae at the same sites. Among coral species, two species with smaller polyps (1-2 mm) have ~1‰ lower bulk tissue δ¹⁵N than two counterparts with larger polyps (5-10 mm), perhaps due to differences in food source. Taxon-specific δ¹⁵N differences are also observed between coral tissue and skeleton-bound N, with larger differences in the two small-polyp species. In net, however, CS-δ¹⁵N mean values and spatial gradients were similar in the four species studied. 

The resilience of coral reefs in oligotrophic, (sub)tropical oceans is largely due to the symbiotic relationship between scleractinian corals and Symbiodiniaceae algae, which enables efficient internal nutrient recycling. Investigating the history of this coral symbiosis can provide insights into its role in sustaining the health of both present and future coral reefs. The isotopic composition of organic nitrogen (¹⁵N/¹⁴N or δ¹⁵N) bound within coral skeletons has been utilized to trace the existence of symbiosis in fossil corals, suggesting that coral symbiosis dates back to at least 210 million years ago. The basis of this proxy is that symbiotic corals are expected to exhibit lower δ¹⁵N compared to their non-symbiotic (aposymbiotic) counterparts within the same environments, owing to internal nitrogen recycling between the coral host and algal symbiont, and reduced leakage of low-δ¹⁵N ammonium into seawater. However, this hypothesis has not been adequately tested in contemporary settings. In a laboratory experiment, we examined the δ¹⁵N differences between the symbiotic and aposymbiotic branches within the same genetic backgrounds of the facultatively symbiotic coralOculina arbusculaunder well-fed conditions. Across five different genotypes in two separate experiments, symbiotic branches consistently showed lower δ¹⁵N than their aposymbiotic counterparts. These findings corroborate the use of δ¹⁵N as a proxy for identifying coral symbiosis in the past, particularly when multiple species of corals coexisted in the same environments. 

Accurately reconstructing 3D hand poses is a pivotal element for numerous Human-Computer Interaction applications. In this work, we propose SonicHand, the first smartphone-based 3D hand pose reconstruction system using purely inaudible acoustic signals. SonicHand incorporates signal processing techniques and a deep learning framework to address a series of challenges. First, it encodes the topological information of the hand skeleton as prior knowledge and utilizes a deep learning model to realistically and smoothly reconstruct the hand poses. Second, the system employs adversarial training to enhance the generalization ability of our system to be deployed in a new environment or for a new user. Third, we adopt a hand tracking method based on channel impulse response estimation. It enables our system to handle the scenario where the hand performs gestures while moving arbitrarily as a whole. We conduct extensive experiments on a smartphone testbed to demonstrate the effectiveness and robustness of our system from various dimensions. The experiments involve 10 subjects performing up to 12 different hand gestures in three distinctive environments. When the phone is held in one of the user’s hands, the proposed system can track joints with an average error of 18.64 mm.