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Mangroves have evolved at least 27 times across ~20 plant families to survive coastal. To environments characterized by high salinity, inundation, intense light, and strong winds survive these extreme conditions, mangroves exhibit a variety of physiological strategies to tolerate the low osmotic potentials associated with saltwater inundation. Because low osmotic potentials are counterbalanced by high turgor pressure, saltwater exposure exerts mechanical demands on cells. Analyzing 34 mangrove species and 33 closely related inland taxa from 17 plant families, we show that compared to their inland relatives, mangroves have unusually small leaf epidermal pavement cells and thicker cell walls, which together confer greater mechanical strength and tolerance to low osmotic potentials. However, mangroves do not exhibit smaller, more numerous stomata that enable higher photosynthetic rates , suggesting selection on biomechanical integrity rather than on gas exchange capacity. Notably, mangroves break the allometric scaling between the sizes of epidermal pavement cells and stomata typically seen in land plants, highlighting that strong selection in saline habitats can override genome size–mediated scaling rules. Phylogenetic comparative analyses revealed repeated convergent evolution of cell traits across independent transitions from inland to coastal habitats. These anatomical changes constitute a simple but effective adaptation to salt stress. Our findings underscore the role of biomechanics in driving convergent evolution of cell traits and suggest that manipulating cell size and wall properties could be a promising strategy to engineering salt-tolerant plants. 

Abstract Although high-performance computing (HPC) systems have been scaled to meet the exponentially growing demand for scientific computing, HPC performance variability remains a major challenge in computer science. Statistically, performance variability can be characterized by a distribution. Predicting performance variability is a critical step in HPC performance variability management. In this article, we propose a new framework to predict performance distributions. The proposed framework is a modified Gaussian process that can predict the distribution function of the input/output (I/O) throughput under a specific HPC system configuration. We also impose a monotonic constraint so that the predicted function is nondecreasing, which is a property of the cumulative distribution function. Additionally, the proposed model can incorporate both quantitative and qualitative input variables. We predict the HPC I/O distribution using the proposed method for the IOzone variability data. Data analysis results show that our framework can generate accurate predictions, and outperform existing methods. We also show how the predicted functional output can be used to generate predictions for a scalar summary of the performance distribution, such as the mean, standard deviation, and quantiles. Our prediction results can further be used for HPC system variability monitoring and optimization. This article has online supplementary materials. 

Complex systems can exhibit sudden transitions or regime shifts from one stable state to another, typically referred to as critical transitions. It becomes a great challenge to identify a robust warning sufficiently early that action can be taken to avert a regime shift. We employ landscape-flux theory from nonequilibrium statistical mechanics as a general framework to quantify the global stability of ecological systems and provide warning signals for critical transitions. We quantify the average flux as the nonequilibrium driving force and the dynamical origin of the nonequilibrium transition while the entropy production rate as the nonequilibrium thermodynamic cost and thermodynamic origin of the nonequilibrium transition. Average flux, entropy production, nonequilibrium free energy, and time irreversibility quantified by the difference in cross-correlation functions forward and backward in time can serve as early warning signals for critical transitions much earlier than other conventional predictors. We utilize a classical shallow lake model as an exemplar for our early warning prediction. Our proposed method is general and can be readily applied to assess the resilience of many other ecological systems. The early warning signals proposed here can potentially predict critical transitions earlier than established methods and perhaps even sufficiently early to avert catastrophic shifts. 

ABSTRACT This note describes improvements of UV oxidation method that is used to measure carbon isotopes of dissolved organic carbon (DOC) at the National Ocean Sciences Accelerator Mass Spectrometry Facility (NOSAMS). The procedural blank is reduced to 2.6 ± 0.6 μg C, with Fm of 0.42 ± 0.10 and δ 13 C of –28.43 ± 1.19‰. The throughput is improved from one sample per day to two samples per day.