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1.
Sci Total Environ ; : 174871, 2024 Jul 20.
Article in English | MEDLINE | ID: mdl-39038672

ABSTRACT

Vegetated coastal ecosystems (VCE), encompassing tidal marshes, mangroves, and seagrass, serve as significant 'blue' carbon (C) sinks. Improving our understanding of VCE soils and their spatial and temporal dynamics is essential for conservation efforts. Conventional methods to characterise the dynamics and provenance of VCE soils and measure their total organic carbon (TOC) and inorganic carbon (TIC) contents are cumbersome and expensive. We recorded the MIR spectra and measured the TOC and TIC content of 323 subsamples across consistent depths from 106 soil core samples. Using the spectra of each VCE, we determined their mineral and organic composition by depth. We then used a regression tree algorithm, cubist, to model TOC and TIC contents. We rigorously validated the models to test their performance with a 10-fold cross-validation, bootstrapping, and a separate random test dataset. Our analysis revealed distinct mineralogical and organic MIR signatures in VCE soils correlated with their position within the seascape. The spectra showed decreased clay minerals and increased quartz and carbonate with distance from freshwater inputs. The mineralogy of tidal marsh and mangrove soils differed with depth, showing larger absorptions due to carbonate and quartz and weakening clay minerals and organics absorptions. The mineralogy of the seagrass soils remained the same with depth. The cubist models to estimate TOC and TIC content were accurate (Lin's concordance correlation, ρc≥ 0.92 and 0.93 respectively) and interpretable, confirming our understanding of C in these systems. These findings shed light on the provenance of the soils and help quantify the flux and accumulation of TOC and TIC, which is crucial for informing VCE conservation. Moreover, our results show that MIR spectroscopy could help scale the measurements cost-effectively, for example, in carbon crediting schemes and to improve inventories. The approach could advance blue C science and contribute to their conservation and protection.

2.
ArXiv ; 2024 Jul 01.
Article in English | MEDLINE | ID: mdl-39010876

ABSTRACT

Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in large foundation models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, a fully automated treatment planning framework that harnesses prior radiation oncology knowledge encoded in multi-modal large language models, such as GPT-4Vision (GPT-4V) from OpenAI. GPT-RadPlan is made aware of planning protocols as context and acts as an expert human planner, capable of guiding a treatment planning process. Via in-context learning, we incorporate clinical protocols for various disease sites as prompts to enable GPT-4V to acquire treatment planning domain knowledge. The resulting GPT-RadPlan agent is integrated into our in-house inverse treatment planning system through an API. The efficacy of the automated planning system is showcased using multiple prostate and head & neck cancer cases, where we compared GPT-RadPlan results to clinical plans. In all cases, GPT-RadPlan either outperformed or matched the clinical plans, demonstrating superior target coverage and organ-at-risk sparing. Consistently satisfying the dosimetric objectives in the clinical protocol, GPT-RadPlan represents the first multimodal large language model agent that mimics the behaviors of human planners in radiation oncology clinics, achieving remarkable results in automating the treatment planning process without the need for additional training.

3.
Sci Total Environ ; 934: 173219, 2024 Jul 15.
Article in English | MEDLINE | ID: mdl-38750738

ABSTRACT

Although seaweeds rank among the most productive vegetated habitats globally, their inclusion within Blue Carbon frameworks is at its onset, partially because they usually grow in rocky substrates and their organic carbon (Corg) is mostly exported and stored beyond their habitat and thus, demonstrating its long-term storage is challenging. Here, we studied the sedimentary Corg storage in macroalgal forests dominated by Gongolaria barbata and in adjacent seagrass Cymodocea nodosa mixed with Caulerpa prolifera algae meadows, and bare sand habitats in Mediterranean shallow coastal embayments. We characterized the biogeochemistry of top 30 cm sedimentary deposits, including sediment grain-size, organic matter and Corg contents, Corg burial rates and the provenance of sedimentary Corg throughout stable carbon isotopes (δ13Corg) and pyrolysis analyses. Sediment Corg stocks and burial rates (since 1950) in G. barbata forests (mean ± SE, 3.5 ± 0.2 kg Corg m-2 accumulated at 15.5 ± 1.6 g Corg m-2 y-1) fall within the range of those reported for traditional Blue Carbon Ecosystems. Although the main species contributing to sedimentary Corg stocks in all vegetated habitats examined was C. nodosa (36 ± 2 %), macroalgae contributed 49 % (19 ± 2 % by G. barbata and 30 ± 3 % by C. prolifera) based on isotope mixing model results. Analytical pyrolysis confirmed the presence of macroalgae-derived compounds in the sediments, including N-compounds and α-tocopherol linked to G. barbata and C. prolifera, respectively. The sedimentary Corg burial rate linked to macroalgae within the macroalgal forests examined ranged from 5.4 to 9.5 g Corg m-2 y-1 (7.4 ± 2 g Corg m-2 y-1). This study provides empirical evidence for the long-term (∼70 years) sequestration of macroalgae-derived Corg within and beyond seaweed forests in Mediterranean shallow coastal embayments and thereby, supports the inclusion of macroalgae in Blue Carbon frameworks.


Subject(s)
Forests , Seaweed , Carbon Sequestration , Carbon/analysis , Mediterranean Sea , Environmental Monitoring , Ecosystem , Geologic Sediments/chemistry
4.
Environ Pollut ; 351: 124017, 2024 Jun 15.
Article in English | MEDLINE | ID: mdl-38685553

ABSTRACT

Nutrient reduction is an essential environmental policy for water quality remediation, but climate change can offset the ecological benefits of nutrient reduction and lead to the difficulty of environmental evaluation. Here, based on the records of three lipid microalgal biomarkers and stable isotopes of carbon and nitrogen in two sediment cores from the embayment of Perth, Australia, we reconstructed the microalgal biomasses (diatoms, dinoflagellates and coccolithophores) over the past century and evaluated the ecological effects of nutrient reduction on them, using Change Point Modeling (CPM) and redundancy analysis (RDA). The CPM result showed that total microalgal biomarkers increased by 25% and 51% in deep and shallow areas, respectively, due to nutrient enrichment caused by industrial wastewater in the 1950s and the causeway construction in the 1970s, and dinoflagellates were beneficiaries of eutrophication. The nutrient reduction policy since the 1980s had not decreased total microalgal biomass, and diatoms were beneficiaries of this period. RDA based on time series of sediment cores and water monitoring data revealed that the increase of sea-surface temperature and the decrease of rainfall since the 1980s may be important factors sustaining the high total microalgal biomass and increasing the degree of diatom dominance. The result also indicated that the variations of microalgal assemblages may better explain the effect of nutrient reduction rather than total microalgal biomass.


Subject(s)
Environmental Restoration and Remediation , Eutrophication , Microalgae , Water Quality , Environmental Restoration and Remediation/methods , Australia , Environmental Monitoring/methods , Diatoms , Biomass , Dinoflagellida , Seawater/chemistry , Climate Change , Nitrogen/analysis , Geologic Sediments/chemistry , Water Pollutants, Chemical/analysis
5.
Transplantation ; 108(8): e181-e186, 2024 Aug 01.
Article in English | MEDLINE | ID: mdl-38419160

ABSTRACT

BACKGROUND: Nonalcoholic steatohepatitis (NASH) is the fastest-growing indication for liver transplantation (LT). Sex disparities among patients with cirrhosis on the LT waitlist are well known. We wanted to understand these disparities further in women with end-stage liver disease patients listed for NASH cirrhosis in a contemporary cohort. METHODS: We used data from the Scientific Registry of Transplant Recipients to assess sex racial, and ethnic differences in NASH patients listed for LT. Adults transplanted from August 1997 to June 2021 were included. Inferential statistics were used to evaluate differences with univariate and multivariate comparisons, including competitive risk analysis. RESULTS: During the study time period, we evaluated 12 844 LT for NASH cirrhosis. Women were transplanted at a lower rate (46.5% versus 53.5%; P  < 0.001) and higher model for end-stage liver disease (MELD) (23.8 versus 22.6; P < 0.001) than men. Non-White women were transplanted at a higher MELD (26.1 versus 23.1; P  < 0.001) than White women and non-White male patients (26.1 versus 24.8; P  < 0.001). Graft and patient survivals were significantly different ( P  < 0.001) between non-White women and White women and men (White and non-White). CONCLUSIONS: Evaluation of LT candidates in the United States demonstrates women with NASH cirrhosis have a higher MELD than men at LT. Additional disparities exist among non-White women with NASH as they have higher MELD and creatinine at LT compared with White women. After LT, non-White women have worse graft and patient survival compared with men or White women. These data indicate that non-White women with NASH are the most vulnerable on the LT waitlist.


Subject(s)
Healthcare Disparities , Liver Transplantation , Non-alcoholic Fatty Liver Disease , Waiting Lists , Humans , Liver Transplantation/adverse effects , Non-alcoholic Fatty Liver Disease/surgery , Non-alcoholic Fatty Liver Disease/ethnology , Female , Middle Aged , Male , Waiting Lists/mortality , Sex Factors , United States/epidemiology , Graft Survival , Adult , Registries , Risk Factors , End Stage Liver Disease/surgery , End Stage Liver Disease/mortality , End Stage Liver Disease/diagnosis , Liver Cirrhosis/surgery , Liver Cirrhosis/mortality , Liver Cirrhosis/diagnosis , Treatment Outcome , Risk Assessment , Retrospective Studies , Aged , Time Factors
6.
Transplant Proc ; 55(8): 1793-1798, 2023 Oct.
Article in English | MEDLINE | ID: mdl-37487863

ABSTRACT

BACKGROUND: There is a paucity of evidence on the risk of donor-recipient transmission of the SARS-CoV-2 in solid organ transplant recipients. Initial impressions suggest non-lung solid organs may be safely transplanted from SARS-CoV-2-positive donors without risk of viral transmission. METHODS: We reviewed clinical results of transplants in which SARS-CoV-2-negative recipients received non-lung solid organs from SARS-CoV-2-positive donors at a single transplant center. No prisoners were used in this study, and participants were neither coerced nor paid. The manuscript was created in compliance with the Helsinki Congress and the Declaration of Istanbul. RESULTS: Between June 2021 and January 2023, we transplanted 26 solid organs, including 13 kidneys, 8 livers, 3 hearts, and 1 simultaneous heart and kidney, from 23 SARS-CoV-2-positive donors into 25 SARS-CoV-2 negative recipients. Two of the recipients had a positive SARS-CoV-2 real-time polymerase chain reaction after transplantation, but otherwise, patients had no SARS-CoV-2-related complications, and all patients to date are alive with excellent allograft function. CONCLUSION: Transplantation of non-lung solid organs from SARS-CoV-2-positive donors into uninfected recipients can be safely performed without adverse effects from SARS-CoV-2.


Subject(s)
COVID-19 , Organ Transplantation , Transplants , Humans , SARS-CoV-2 , Organ Transplantation/adverse effects , Tissue Donors , Transplant Recipients
7.
New Phytol ; 239(6): 2126-2137, 2023 09.
Article in English | MEDLINE | ID: mdl-37366062

ABSTRACT

The response of Posidonia oceanica meadows to global warming of the Eastern Mediterranean Sea, where the increase in sea surface temperature (SST) is particularly severe, is poorly investigated. Here, we reconstructed the long-term P. oceanica production in 60 meadows along the Greek Seas over two decades (1997-2018), using lepidochronology. We determined the effect of warming on production by reconstructing the annual and maximum (i.e. August) SST, considering the role of other production drivers related to water quality (i.e. Chla, suspended particulate matter, Secchi depth). Grand mean (±SE) production across all sites and the study period was 48 ± 1.1 mg DW per shoot yr-1 . Production over the last two decades followed a trajectory of decrease, which was related to the concurrent increase in annual SST and SSTaug . Annual SST > 20°C and SSTaug > 26.5°C was related to production decline (GAMM, P < 0.05), while the rest of the tested factors did not help explain the production pattern. Our results indicate a persistent and increasing threat for Eastern Mediterranean meadows, drawing attention to management authorities, highlighting the necessity of reducing local impacts to enhance the resilience of seagrass meadows to global change threats.


Subject(s)
Alismatales , Global Warming , Mediterranean Sea , Temperature , Ecosystem
8.
Sci Total Environ ; 886: 163957, 2023 Aug 15.
Article in English | MEDLINE | ID: mdl-37164078

ABSTRACT

The implementation of climate change mitigation strategies based on the conservation and restoration of Blue Carbon ecosystems requires a deep understanding of the magnitude and variability in organic carbon (Corg) storage across and within these ecosystems. This study explored the variability in soil Corg stocks and burial rates across and within intertidal estuarine habitats of the Atlantic European coast and its relation to biotic and abiotic drivers. A total of 136 soil cores were collected across saltmarshes located at different tidal zones (high marsh, N = 45; low marsh, N = 30), seagrass meadows (N = 17) and tidal flats (N = 44), and from the inner to the outer sections of five estuaries characterized by different basin land uses. Soil Corg stocks were higher in high-marsh communities (65 ± 3 Mg ha-1) than in low-marsh communities (38 ± 3 Mg ha-1), seagrass meadows (40 ± 5 Mg ha-1) and unvegetated tidal flats (46 ± 3 Mg ha-1) whereas Corg burial rates also tended to be higher in high marshes (62 ± 13 g m-2 y-1) compared to low marshes (43 ± 15 g m-2 y-1) and tidal flats (35 ± 9 g m-2 y-1). Soil Corg stocks and burial rates decreased from inner to outer estuarine sections in most estuaries reflecting the decrease in the river influence towards the estuary mouth. Higher soil Corg stocks were related to higher content of silt and clay and higher proportion of forest and natural land within the river basin, pointing at new opportunities for protecting coastal natural carbon sinks based on the conservation and restoration of upland ecosystems. Our study contributes to the global inventory of Blue Carbon by adding data from unexplored regions and habitats in Europe, and by identifying drivers of variability across and within estuaries.


Subject(s)
Carbon , Ecosystem , Geologic Sediments , Wetlands , Carbon Sequestration , Soil
9.
Sci Total Environ ; 885: 163699, 2023 Aug 10.
Article in English | MEDLINE | ID: mdl-37149169

ABSTRACT

Seaweed (macroalgae) has attracted attention globally given its potential for climate change mitigation. A topical and contentious question is: Can seaweeds' contribution to climate change mitigation be enhanced at globally meaningful scales? Here, we provide an overview of the pressing research needs surrounding the potential role of seaweed in climate change mitigation and current scientific consensus via eight key research challenges. There are four categories where seaweed has been suggested to be used for climate change mitigation: 1) protecting and restoring wild seaweed forests with potential climate change mitigation co-benefits; 2) expanding sustainable nearshore seaweed aquaculture with potential climate change mitigation co-benefits; 3) offsetting industrial CO2 emissions using seaweed products for emission abatement; and 4) sinking seaweed into the deep sea to sequester CO2. Uncertainties remain about quantification of the net impact of carbon export from seaweed restoration and seaweed farming sites on atmospheric CO2. Evidence suggests that nearshore seaweed farming contributes to carbon storage in sediments below farm sites, but how scalable is this process? Products from seaweed aquaculture, such as the livestock methane-reducing seaweed Asparagopsis or low carbon food resources show promise for climate change mitigation, yet the carbon footprint and emission abatement potential remains unquantified for most seaweed products. Similarly, purposely cultivating then sinking seaweed biomass in the open ocean raises ecological concerns and the climate change mitigation potential of this concept is poorly constrained. Improving the tracing of seaweed carbon export to ocean sinks is a critical step in seaweed carbon accounting. Despite carbon accounting uncertainties, seaweed provides many other ecosystem services that justify conservation and restoration and the uptake of seaweed aquaculture will contribute to the United Nations Sustainable Development Goals. However, we caution that verified seaweed carbon accounting and associated sustainability thresholds are needed before large-scale investment into climate change mitigation from seaweed projects.


Subject(s)
Ecosystem , Seaweed , Carbon Dioxide , Climate Change , Carbon Sequestration , Carbon
10.
Phys Med Biol ; 68(9)2023 05 03.
Article in English | MEDLINE | ID: mdl-37068492

ABSTRACT

Objective.In this work, we propose a content-based image retrieval (CBIR) method for retrieving dose distributions of previously planned patients based on anatomical similarity. Retrieved dose distributions from this method can be incorporated into automated treatment planning workflows in order to streamline the iterative planning process. As CBIR has not yet been applied to treatment planning, our work seeks to understand which current machine learning models are most viable in this context.Approach.Our proposed CBIR method trains a representation model that produces latent space embeddings of a patient's anatomical information. The latent space embeddings of new patients are then compared against those of previous patients in a database for image retrieval of dose distributions. All source code for this project is available on github.Main results.The retrieval performance of various CBIR methods is evaluated on a dataset consisting of both publicly available image sets and clinical image sets from our institution. This study compares various encoding methods, ranging from simple autoencoders to more recent Siamese networks like SimSiam, and the best performance was observed for the multitask Siamese network.Significance.Our current results demonstrate that excellent image retrieval performance can be obtained through slight changes to previously developed Siamese networks. We hope to integrate CBIR into automated planning workflow in future works.


Subject(s)
Algorithms , Software , Humans , Machine Learning , Information Storage and Retrieval , Databases, Factual
11.
Sci Total Environ ; 874: 162518, 2023 May 20.
Article in English | MEDLINE | ID: mdl-36870497

ABSTRACT

Vegetated coastal ecosystems, in particular mangroves, tidal marshes and seagrasses are highly efficient at sequestering and storing carbon, making them valuable assets for climate change mitigation and adaptation. The state of Queensland, in northeastern Australia, contains almost half of the total area of these blue carbon ecosystems in the country, yet there are few detailed regional or state-wide assessments of their total sedimentary organic carbon (SOC) stocks. We compiled existing SOC data and used boosted regression tree models to evaluate the influence of environmental variables in explaining the variability in SOC stocks, and to produce spatially explicit blue carbon estimates. The final models explained 75 % (for mangroves and tidal marshes) and 65 % (for seagrasses) of the variability in SOC stocks. Total SOC stocks in the state of Queensland were estimated at 569 ± 98 Tg C (173 ± 32 Tg C, 232 ± 50 Tg C, and 164 ± 16 Tg C from mangroves, tidal marshes and seagrasses, respectively). Regional predictions for each of Queensland's eleven Natural Resource Management regions revealed that 60 % of the state's SOC stocks occurred within three regions (Cape York, Torres Strait and Southern Gulf Natural Resource Management regions) due to a combination of high values of SOC stocks and large areas of coastal wetlands. Protected areas in Queensland play an important role in conserving SOC assets in Queensland's coastal wetlands. For example, ~19 Tg C within terrestrial protected areas, ~27 Tg C within marine protected areas and ~ 40 Tg C within areas of matters of State Environmental Significance. Using multi-decadal (1987-2020) mapped distributions of mangroves in Queensland; we found that mangrove area increased by approximately 30,000 ha from 1987 to 2020, which led to temporal fluctuations in mangrove plant and SOC stocks. We estimated that plant stocks decreased from ~45 Tg C in 1987 to ~34.2 Tg C in 2020, while SOC stocks remained relatively constant from ~107.9 Tg C in 1987 to 108.0 Tg C in 2020. Considering the level of current protection, emissions from mangrove deforestation are potentially very low; therefore, representing minor opportunities for mangrove blue carbon projects in the region. Our study provides much needed information on current trends in carbon stocks and their conservation in Queensland's coastal wetlands, while also contributing to guide future management actions, including blue carbon restoration projects.

12.
Phys Med Biol ; 68(8)2023 04 10.
Article in English | MEDLINE | ID: mdl-36958058

ABSTRACT

Objective. In radiotherapy, the internal movement of organs between treatment sessions causes errors in the final radiation dose delivery. To assess the need for adaptation, motion models can be used to simulate dominant motion patterns and assess anatomical robustness before delivery. Traditionally, such models are based on principal component analysis (PCA) and are either patient-specific (requiring several scans per patient) or population-based, applying the same set of deformations to all patients. We present a hybrid approach which, based on population data, allows to predict patient-specific inter-fraction variations for an individual patient.Approach. We propose a deep learning probabilistic framework that generates deformation vector fields warping a patient's planning computed tomography (CT) into possible patient-specific anatomies. This daily anatomy model (DAM) uses few random variables capturing groups of correlated movements. Given a new planning CT, DAM estimates the joint distribution over the variables, with each sample from the distribution corresponding to a different deformation. We train our model using dataset of 312 CT pairs with prostate, bladder, and rectum delineations from 38 prostate cancer patients. For 2 additional patients (22 CTs), we compute the contour overlap between real and generated images, and compare the sampled and 'ground truth' distributions of volume and center of mass changes.Results. With a DICE score of 0.86 ± 0.05 and a distance between prostate contours of 1.09 ± 0.93 mm, DAM matches and improves upon previously published PCA-based models, using as few as 8 latent variables. The overlap between distributions further indicates that DAM's sampled movements match the range and frequency of clinically observed daily changes on repeat CTs.Significance. Conditioned only on planning CT values and organ contours of a new patient without any pre-processing, DAM can accurately deformations seen during following treatment sessions, enabling anatomically robust treatment planning and robustness evaluation against inter-fraction anatomical changes.


Subject(s)
Deep Learning , Prostatic Neoplasms , Male , Humans , Radiotherapy Planning, Computer-Assisted/methods , Tomography, X-Ray Computed/methods , Models, Statistical , Prostatic Neoplasms/diagnostic imaging , Prostatic Neoplasms/radiotherapy
13.
Med Phys ; 50(5): 3159-3171, 2023 May.
Article in English | MEDLINE | ID: mdl-36669122

ABSTRACT

BACKGROUND: Fast dose calculation is critical for online and real-time adaptive therapy workflows. While modern physics-based dose algorithms must compromise accuracy to achieve low computation times, deep learning models can potentially perform dose prediction tasks with both high fidelity and speed. PURPOSE: We present a deep learning algorithm that, exploiting synergies between transformer and convolutional layers, accurately predicts broad photon beam dose distributions in few milliseconds. METHODS: The proposed improved Dose Transformer Algorithm (iDoTA) maps arbitrary patient geometries and beam information (in the form of a 3D projected shape resulting from a simple ray tracing calculation) to their corresponding 3D dose distribution. Treating the 3D CT input and dose output volumes as a sequence of 2D slices along the direction of the photon beam, iDoTA solves the dose prediction task as sequence modeling. The proposed model combines a Transformer backbone routing long-range information between all elements in the sequence, with a series of 3D convolutions extracting local features of the data. We train iDoTA on a dataset of 1700 beam dose distributions, using 11 clinical volumetric modulated arc therapy (VMAT) plans (from prostate, lung, and head and neck cancer patients with 194-354 beams per plan) to assess its accuracy and speed. RESULTS: iDoTA predicts individual photon beams in ≈50 ms with a high gamma pass rate of 97.72 ± 1.93 % $97.72\pm 1.93\%$ (2 mm, 2%). Furthermore, estimating full VMAT dose distributions in 6-12 s, iDoTA achieves state-of-the-art performance with a 99.51 ± 0.66 % $99.51\pm 0.66\%$ (2 mm, 2%) pass rate and an average relative dose error of 0.75 ± 0.36%. CONCLUSIONS: Offering the millisecond speed prediction per beam angle needed in online and real-time adaptive treatments, iDoTA represents a new state of the art in data-driven photon dose calculation. The proposed model can massively speed-up current photon workflows, reducing calculation times from few minutes to just a few seconds.


Subject(s)
Radiotherapy Planning, Computer-Assisted , Radiotherapy, Intensity-Modulated , Male , Humans , Radiotherapy Planning, Computer-Assisted/methods , Neural Networks, Computer , Radiotherapy, Intensity-Modulated/methods , Algorithms , Prostate , Radiotherapy Dosage
14.
Plant Physiol ; 191(2): 946-956, 2023 02 12.
Article in English | MEDLINE | ID: mdl-36315095

ABSTRACT

The CO2-fixing enzyme Ribulose bisphosphate carboxylase-oxygenase (Rubisco) links the inorganic and organic phases of the global carbon cycle. In aquatic systems, the catalytic adaptation of algae Rubiscos has been more expansive and followed an evolutionary pathway that appears distinct to terrestrial plant Rubisco. Here, we extend this survey to differing seagrass species of the genus Posidonia to reveal how their disjunctive geographical distribution and diverged phylogeny, along with their CO2 concentrating mechanisms (CCMs) effectiveness, have impacted their Rubisco kinetic properties. The Rubisco from Posidonia species showed lower carboxylation efficiencies and lower sensitivity to O2 inhibition than those measured for terrestrial C3 and C4-plant Rubiscos. Compared with the Australian Posidonia species, Rubisco from the Mediterranean Posidonia oceanica had 1.5-2-fold lower carboxylation and oxygenation efficiencies, coinciding with effective CCMs and five Rubisco large subunit amino acid substitutions. Among the Australian Posidonia species, CCM effectiveness was higher in Posidonia sinuosa and lower in the deep-living Posidonia angustifolia, likely related to the 20%-35% lower Rubisco carboxylation efficiency in P. sinuosa and the two-fold higher Rubisco content in P. angustifolia. Our results suggest that the catalytic evolution of Posidonia Rubisco has been impacted by the low CO2 availability and gas exchange properties of marine environments, but with contrasting Rubisco kinetics according to the time of diversification among the species. As a result, the relationships between maximum carboxylation rate and CO2- and O2-affinities of Posidonia Rubiscos follow an alternative path to that characteristic of terrestrial angiosperm Rubiscos.


Subject(s)
Carbon Dioxide , Ribulose-Bisphosphate Carboxylase , Ribulose-Bisphosphate Carboxylase/metabolism , Carbon Dioxide/metabolism , Australia , Phylogeny , Plants/metabolism , Photosynthesis , Kinetics
15.
Transplant Proc ; 54(8): 2263-2269, 2022 Oct.
Article in English | MEDLINE | ID: mdl-36243574

ABSTRACT

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Despite socioeconomic disadvantages seen among Hispanics, this population appears to have improved short- and long-term survival after LT compared to NHW and AA.


Subject(s)
Liver Transplantation , United States , Humans , Adult , Middle Aged , Liver Transplantation/adverse effects , Retrospective Studies , Ethnic and Racial Minorities , Hispanic or Latino , Graft Survival
16.
Transpl Infect Dis ; 24(4): e13872, 2022 Aug.
Article in English | MEDLINE | ID: mdl-35642883

ABSTRACT

Utilization of Hepatitis B virus (HBV)-infected kidney allografts represents an opportunity to bridge the gap between organ supply and demand. Highly efficacious vaccines and antiviral therapies allow these allografts to be transplanted with negligible risk to the recipient. The purpose of this study was to describe the prophylactic strategies and related clinical outcomes of kidney transplant recipients who received a kidney from an HBV viremic donor. Eight patients received an allograft from an HBV viremic deceased kidney donor between January 1, 2017 and December 4, 2020. All recipients were immune to hepatitis B with a surface antibody titer greater than or equal to 10 mIU/ml (range: 12 - > 1000 mIU/ml). After transplant, 62.5% demonstrated HBV core antibody seroconversion at an average of 47.4 ± 28.5 days post-transplant. Anti-viral prophylaxis was initiated in 87.5% of patients; 62.5% preemptively during the transplant admission (range 1-3 days post-transplant) and 25% following HBcAb seroconversion (range 45-304 days post-transplant). Of the four patients who were started on entecavir preemptively, two subsequently core converted. These two patients had an HBV surface antibody less than 100 mIU/ml at the time of transplant. None of the recipients converted to HBV surface antigen positivity. The average estimated glomerular filtration rate was 41 ± 19 ml/min/1.73m2 , and there were no significant elevations in liver enzymes through one year post-transplant. The use of HBV viremic kidney allografts may represent an additional source of transplant organs; however, more studies are needed to better elucidate the optimal protective strategies for these recipients.


Subject(s)
Hepatitis B , Kidney Transplantation , Hepatitis B/drug therapy , Hepatitis B/prevention & control , Hepatitis B Antibodies , Hepatitis B Core Antigens , Hepatitis B Surface Antigens , Hepatitis B virus , Humans , Kidney Transplantation/adverse effects , Retrospective Studies , Tissue Donors , Viremia
17.
Sci Total Environ ; 836: 155598, 2022 Aug 25.
Article in English | MEDLINE | ID: mdl-35500709

ABSTRACT

There is a need for tools to determine the origin of organic matter (OM) in Blue Carbon Ecosystems (BCE) and marine sediments to (1) facilitate the implementation of Blue Carbon strategies into carbon accounting and crediting schemes and (2) decipher changes in ecosystem condition over decadal to millennial time scales and thus to understand and predict the stability of BCE in a changing world. Pyrolysis-GC-compound specific isotope analysis (Py-CSIA) is applied for the first time in marine environments and BCE research. We studied Australian mangrove, tidal marsh and seagrass sediments, in addition to potential sources of OM (Avicennia, Posidonia, Zostera, Sarcocornia, Ecklonia and Ulva species and seagrass epiphytes), to identify precursors of different biomacromolecule constituents (lignin, polysaccharides and aliphatic structures). Firstly, the link between bulk δ13C and δ13C reconstructed from compound-specific δ13C showed that the pyrolysis approach allows for the isotopic screening of a representative portion of the OM. Secondly, for all samples, the C isotope fingerprint of the carbohydrate products (plant polysaccharides) was the heaviest (13C enriched), followed by lignin and aliphatic products. The differences in δ13C among macromolecules and the overlap in δ13C among putative sources reflect the limitations of bulk δ13C analyses for deciphering OM provenance. Thirdly, phanerogams specimen had the heaviest carbohydrate and lignin, confirming that seagrass-derived lignocellulose can be traced based on δ13C. Consistent differences for individual compounds were identified between seagrasses and between Avicennia and Sarcocornia using Py-CSIA. Fourth, ecosystem shifts (colonization of seagrass habitats by mangrove) on millenary time scales, hypothesized in previous studies on the basis of bulk δ13C and Py-GC-MS, were confirmed by Py-CSIA. We conclude that Py-CSIA is useful in Blue Carbon research to decipher OM sources in marine sediments, identify ecosystem transitions in palaeoenvironmental records, and to understand the role of different OM compounds in Blue Carbon storage.


Subject(s)
Carbon , Ecosystem , Australia , Carbohydrates , Carbon/analysis , Carbon Isotopes/analysis , Isotopes/analysis , Lignin/analysis , Pyrolysis
18.
Phys Med Biol ; 67(10)2022 05 09.
Article in English | MEDLINE | ID: mdl-35447605

ABSTRACT

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.


Subject(s)
Deep Learning , Proton Therapy , Algorithms , Humans , Monte Carlo Method , Phantoms, Imaging , Proton Therapy/methods , Protons , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted/methods
19.
Nat Commun ; 13(1): 1348, 2022 03 15.
Article in English | MEDLINE | ID: mdl-35292644

ABSTRACT

Wildfire magnitude and frequency have greatly escalated on a global scale. Wildfire products rich in biogenic elements can enter the ocean through atmospheric and river inputs, but their contribution to marine phytoplankton production is poorly understood. Here, using geochemical paleo-reconstructions, a century-long relationship between wildfire magnitude and marine phytoplankton production is established in a fire-prone region of Kimberley coast, Australia. A positive correlation is identified between wildfire and phytoplankton production on a decadal scale. The importance of wildfire on marine phytoplankton production is statistically higher than that of tropical cyclones and rainfall, when strong El Niño Southern Oscillation coincides with the positive phase of Indian Ocean Dipole. Interdecadal chlorophyll-a variation along the Kimberley coast validates the spatial connection of this phenomenon. Findings from this study suggest that the role of additional nutrients from wildfires has to be considered when projecting impacts of global warming on marine phytoplankton production.


Subject(s)
Phytoplankton , Wildfires , Chlorophyll A , El Nino-Southern Oscillation , Indian Ocean , Oceans and Seas
20.
Mar Environ Res ; 176: 105608, 2022 Apr.
Article in English | MEDLINE | ID: mdl-35358909

ABSTRACT

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 (Corg) 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 Corg 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 Corg, which was linked to shifts in Corg sources. The increase in the δ13C isotopic signature of sedimentary Corg (towards a higher seagrass contribution) at the Stone Town site since 1998 points to improved seagrass meadow conditions and Corg 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 Corg in the Mbweni meadow since the early 2010s was likely linked to increased Corg 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 Corg sinks.


Subject(s)
Carbon Sequestration , Urban Renewal , Carbon , Ecosystem , Geologic Sediments , Humans , Sewage
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