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• Seagrass soils have been neglected in soil science but widely studied as sediments. • Published values for the main seagrass soil characteristics have been gathered. • Soil seagrass data is biased towards temperate species and high-income countries. • Soils under persistent seagrass species showed higher organic carbon and sandier textures. • Seagrass biological traits drive differences in soil physical and chemical properties. Seagrasses are a polyphyletic group of angiosperms that colonized marine environments more than 30 million years ago and currently inhabit coastal soft and rocky substrates in all continents except Antarctica. Due to their evolution from terrestrial plants, seagrasses have belowground organs that interact with the substrate, transforming it through chemo-physical processes analogous to terrestrial soil formation. Although seagrass substrates provide valuable ecosystem services including carbon and coastal stabilization, they have been largely regarded as sediments by marine scientists and neglected in soil science research. However, over the last decades, the increasing interest in carbon accumulation by seagrasses has generated multiple data on seagrass soil physical and chemical characteristics. Here, we review clay and silt content (<0.063 mm particle size), redox potential, pH, carbonate content, organic carbon or organic matter contents, dry bulk density, porosity and color of seagrass soils worldwide, summarizing data typically used for soil description, and looking for generalities in soil characteristics across seagrass habitats. The data gathered was biased towards temperate species and high-income countries, while data about color, porosity, redox potential and pH was scarce. Soil characteristics did not show significant differences among seagrass bioregions. Most seagrass substrates showed sandy textures, whereas one of the most sampled genera, Posidonia , was not present in muddy substrates. The soil C org content was significantly higher in meadows formed by persistent species (mean ± SD; 1.76 ± 2.17 %) than in meadows formed by species with opportunistic and colonizing life-strategies (1.52 ± 2.24 and 0.76 ± 0.95 %, respectively), while mud content was significantly higher in meadows formed by opportunistic and colonizing species (27.87 ± 29.58 and 21.23 ± 21.77 %, respectively) than in those formed by persistent species (11.83 ± 14.45 %). Redox potential was significantly lower in intertidal than in subtidal meadows, although caution is needed when interpreting these differences due to methodological limitations. This review provides an overview of current knowledge on seagrass soil characteristics, while identifying knowledge gaps in seagrass soil science, including geographical, species diversity and soil physico-chemical traits that limit our capacity to characterize and classify seagrass soils worldwide. [ABSTRACT FROM AUTHOR]

• Models of breathing motion using 1D convolutional neural networks and probabilistic autoencoders. • A novel semi-supervised algorithm based on Adversarial Autoencoders that allows joint classification and generation of breathing. • The models are trained with 4% and 12% of the labeled data and achieve a macro F1-score of 94.91% and 96.54% in a different test set. • A pre-processing step compresses 3-dimensional breathing time series into vectors. One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical time series based on a modified Adversarial Autoencoder (AAE) algorithm and one-dimensional convolutions. Our work is based on breathing time series, with specific motivation to capture breathing motion during radiotherapy lung cancer treatments. First, we explore the potential in using the Variational Autoencoder (VAE) and AAE algorithms to model breathing signals from individual patients. We then extend the AAE algorithm to allow joint semi-supervised classification and generation of different types of signals within a single framework. To simplify the modeling task, we introduce a pre-processing and post-processing compressing algorithm that transforms the multi-dimensional time series into vectors containing time and position values, which are transformed back into time series through an additional neural network. The resulting models are able to generate realistic and varied samples of breathing. By incorporating 4% and 12% of the labeled samples during training, our model outperforms other purely discriminative networks in classifying breathing baseline shift irregularities from a dataset completely different from the training set, achieving an average macro F1-score of 94.91 % and 96.54 % , respectively. To our knowledge, the presented framework is the first approach that unifies generation and classification within a single model for this type of biomedical data, enabling both computer aided diagnosis and augmentation of labeled samples within a single framework. [ABSTRACT FROM AUTHOR]

• Ethnic minorities are disproportionally affected by liver failure. • Hispanics have improved liver transplant outcomes. • African Americans have poorer survival after liver transplant. Racial and ethnic minorities are disproportionally affected by end-stage liver disease. Unfortunately, disparities in referrals to liver transplantation (LT), organ allocation, and posttransplant outcomes exist in this population. We performed a retrospective analysis of patients over the age of 18 years undergoing LT in the United States using the Scientific Registry of Transplant Recipients from 2002 to 2016. We evaluated factors associated with patient and graft outcomes and explored the effect of race and ethnicity along with social variables. During the study time period, 78,999 patients received LT. Of these, 60,102 were non-Hispanic White (NHW), 7988 were African American (AA), and 10,909 were Hispanic. AA had significantly lower patient survival, graft survival, and death-censored graft survival at both 1 and 5 years when compared to NHW. Conversely, at 1 and 5 years, patient survival and graft survival were significantly higher for Hispanics compared to NWH. In addition, AA had significantly lower survival outcomes compared to Hispanics. On multivariate analysis after controlling for race/ethnicity, age, AA race, diagnosis, and deceased donor were independent risk factors for patient death and graft failure. Despite socioeconomic disadvantages seen among Hispanics, this population appears to have improved short- and long-term survival after LT compared to NHW and AA. [ABSTRACT FROM AUTHOR]

Blue carbon ecosystems (BCE) include mangrove forests, tidal marshes, and seagrass meadows, all of which are currently under threat, putting their contribution to mitigating climate change at risk. Although certain challenges and trade-offs exist, remote sensing offers a promising avenue for transparent, replicable, and cost-effective accounting of many BCE at unprecedented temporal and spatial scales. The United Nations Framework Convention on Climate Change (UNFCCC) has issued guidelines for developing blue carbon inventories to incorporate into Nationally Determined Contributions (NDCs). Yet, there is little guidance on remote sensing techniques for monitoring, reporting, and verifying blue carbon assets. This review constructs a unified roadmap for applying remote sensing technologies to develop cost-effective carbon inventories for BCE – from local to global scales. We summarise and discuss (1) current standard guidelines for blue carbon inventories; (2) traditional and cutting-edge remote sensing technologies for mapping blue carbon habitats; (3) methods for translating habitat maps into carbon estimates; and (4) a decision tree to assist users in determining the most suitable approach depending on their areas of interest, budget, and required accuracy of blue carbon assessment. We designed this work to support UNFCCC-approved IPCC guidelines with specific recommendations on remote sensing techniques for GHG inventories. Overall, remote sensing technologies are robust and cost-effective tools for monitoring, reporting, and verifying blue carbon assets and projects. Increased appreciation of these techniques can promote a technological shift towards greater policy and industry uptake, enhancing the scalability of blue carbon as a Natural Climate Solution worldwide. • Blue carbon ecosystems (BCE) play a key role in climate change mitigation. • Remote sensing techniques are cost-effective for monitoring BCE extent and carbon stocks. • A tradeoff exists between spatial resolution and historical coverage of remotely sensed data. • New technologies can facilitate the inclusion of BCE in national inventories and Nationally Determined Contributions (NDCs). • Our decision tree guides the choice of remote sensing methods for monitoring BCE. [ABSTRACT FROM AUTHOR]

Seagrass meadows store significant carbon stocks at a global scale, but land-use change and other anthropogenic activities can alter the natural process of organic carbon (C org) accumulation. Here, we assessed the carbon accumulation history of two seagrass meadows in Zanzibar (Tanzania) that have experienced different degrees of disturbance. The meadow at Stone Town has been highly exposed to urban development during the 20th century, while the Mbweni meadow is located in an area with relatively low impacts but historical clearing of adjacent mangroves. The results showed that the two sites had similar sedimentary C org accumulation rates (22–25 g m−2 yr−1) since the 1940s, while during the last two decades (∼1998 until 2018) they exhibited 24–30% higher accumulation of C org , which was linked to shifts in C org sources. The increase in the δ13C isotopic signature of sedimentary C org (towards a higher seagrass contribution) at the Stone Town site since 1998 points to improved seagrass meadow conditions and C org accumulation capacity of the meadow after the relocation of a major sewage outlet in the mid–1990s. In contrast, the decrease in the δ13C signatures of sedimentary C org in the Mbweni meadow since the early 2010s was likely linked to increased C org run-off of mangrove/terrestrial material following mangrove deforestation. This study exemplifies two different pathways by which land-based human activities can alter the carbon storage capacity of seagrass meadows (i.e. sewage waste management and mangrove deforestation) and showcases opportunities for management of vegetated coastal C org sinks. [Display omitted] • The seagrass carbon accumulation rate was 22–25 g C org m−2 yr−1. • The rate of carbon accumulation had increased by 24–30% during the last ∼20 years. • The increase in carbon storage was likely due to land-use change. • This study highlights the importance of land-sea connectivity for blue carbon sinks. [ABSTRACT FROM AUTHOR]