Future carbon emissions from global mangrove forest loss

Mangroves have among the highest carbon densities of any tropical forest. These 'blue carbon' ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land-uses can prevent future greenhouse gas emissions and increase CO₂ sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land-use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under 'business as usual' rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO_2 eq by the end of the century, or 3392 Tg CO_2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO_2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them.

Ecological impact assessment of climate change and habitat loss on wetland vertebrate assemblages of the Great Barrier Reef catchment and the influence of survey bias

Wetlands are among the most vulnerable ecosystems, stressed by habitat loss and degradation from expanding and intensifying agricultural and urban areas. Climate change will exacerbate the impacts of habitat loss by altering temperature and rainfall patterns. Wetlands within Australia's Great Barrier Reef (GBR) catchment are not different, stressed by extensive cropping, urban expansion, and alteration for grazing. Understanding how stressors affect wildlife is essential for the effective management of biodiversity values and minimizing unintended consequences when trading off the multiple values wetlands support. Impact assessment is difficult, often relying on an aggregation of ad hoc observations that are spatially biased toward easily accessible areas, rather than systematic and randomized surveys. Using a large aggregate database of ad hoc observations, this study aimed to examine the influence of urban proximity on machine-learning models predicting taxonomic richness and assemblage turnover, relative to other habitat, landscape, and climate variables, for vertebrates dwelling in the wetlands of the GBR catchment. The distance from the nearest city was, by substantial margins, the most influential factor in predicting the richness and assemblage turnover of all vertebrate groups, except fish. Richness and assemblage turnover was predicted to be greatest nearest the main urban centers. The extent of various wetland habitats was highly influential in predicting the richness of all groups, while climate (predominately the rainfall in the wettest quarter) was highly influential in predicting assemblage turnover for all groups. Bias of survey records toward urban centers strongly influenced our ability to model wetland-affiliated vertebrates and may obscure our understanding of how vertebrates respond to habitat loss and climate change. This reinforces the need for randomized and systematic surveys to supplement existing ad hoc surveys. We urge modelers in other jurisdictions to better portray the potential influence of survey biases when modeling species distributions.

Systematic Review and Meta-Analysis of Peak Wall Stress and Peak Wall Rupture Index in Ruptured and Asymptomatic Intact Abdominal Aortic Aneurysms

Background Prior studies have suggested aortic peak wall stress (PWS) and peak wall rupture index (PWRI) can estimate the rupture risk of an abdominal aortic aneurysm (AAA), but whether these measurements have independent predictive ability over assessing AAA diameter alone is unclear. The aim of this systematic review was to compare PWS and PWRI in participants with ruptured and asymptomatic intact AAAs of similar diameter. Methods and Results Web of Science, Scopus, Medline, and The Cochrane Library were systematically searched to identify studies assessing PWS and PWRI in ruptured and asymptomatic intact AAAs of similar diameter. Random-effects meta-analyses were performed using inverse variance-weighted methods. Leave-one-out sensitivity analyses were conducted to assess the robustness of findings. Risk of bias was assessed using a modification of the Newcastle-Ottawa scale and standard quality assessment criteria for evaluating primary research papers. Seven case-control studies involving 309 participants were included. Meta-analyses suggested that PWRI (standardized mean difference, 0.42; 95% CI, 0.14-0.70; P=0.004) but not PWS (standardized mean difference, 0.13; 95% CI, -0.18 to 0.44; P=0.418) was greater in ruptured than intact AAAs. Sensitivity analyses suggested that the findings were not dependent on the inclusion of any single study. The included studies were assessed to have a medium to high risk of bias. Conclusions Based on limited evidence, this study suggested that PWRI, but not PWS, is greater in ruptured than asymptomatic intact AAAs of similar maximum aortic diameter.

MnO2 Nanoflowers Induce Immunogenic Cell Death under Nutrient Deprivation: Enabling an Orchestrated Cancer Starvation-Immunotherapy

MnO2 nanoparticles have been widely employed in cancer immunotherapy, playing a subsidiary role in assisting immunostimulatory drugs by improving their pharmacokinetics and/or creating a favorable microenvironment. Here, the stereotype of the subsidiary role of MnO2 nanoparticles in cancer immunotherapy is challenged. This study unravels an intrinsic immunomodulatory property of MnO2 nanoparticles as a unique nutrient-responsive immunogenic cell death (ICD) inducer, capable of directly modulating immunosurveillance toward tumor cells. MnO2 nanoflowers (MNFs) constructed via a one pot self-assembly approach selectively induce ICD to nutrient-deprived but not nutrient-replete cancer cells, which is confirmed by the upregulated damage associated molecular patterns in vitro and a prophylactic vaccination in vivo. The underlying mechanism of the MNFs-mediated selective ICD induction is likely associated with the concurrently upregulated oxidative stress and autophagy. Built on their unique immunomodulatory properties, an innovative nanomaterials orchestrated cancer starvation-immunotherapy is successfully developed, which is realized by the in situ vaccination with MNFs and vascular disrupting agents that cut off intratumoral nutrient supply, eliciting potent efficacy for suppressing local and distant tumors. These findings open up a new avenue toward biomedical applications of MnO2 materials, enabling an innovative therapeutics paradigm with great clinical significance.

Metabolic changes in the developing sugarcane culm associated with high yield and early high sugar content

Sugarcane, with its exceptional biomass and sugar yield, has a high potential for the production of bioenergy, biomaterials, and high-value products. Currently, the link between metabolic changes in the developing internodes in sugarcane and final yield and sugar characteristics is not well understood. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 360 samples across 24 sugarcane cultivars at five different developmental stages. A combination of metabolome profiling and trait co-expression analyses were conducted to reveal the interaction between the metabolome and essential agronomic traits, including Brix (total sugar), polarity (sucrose content), purity (sucrose purity), commercially extractable sucrose, fiber, and tons of cane per hectare (TCH). Metabolomic analysis revealed significant differences in metabolic patterns mainly correlated with developmental stage. Hierarchical clustering of genotypes and traits revealed clear partitioning of groups of early-, mid- and late-season sugar content, with secondary segregation by the yield trait, TCH, and fiber content. The study identified co-expression and specific metabolites associated with metabolic pathways correlated with Brix and fiber content. Knowledge of the correlation between co-expressed metabolites and diverse agronomic traits will allow more deliberate selection of genotypes for early or late sugar development and fiber content and biomass yield.