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Priority monism and the emergence of spacetime
No full textThere has been a recent surge of interest in the idea that spacetime is not fundamental. Much of this interest has focused on the implications for physics. There has been less work investigating the implications of spacetime emergence for existing theories in metaphysics. This paper aims to fill this gap by considering the impact of spacetime emergence on priority monism. We argue that one prominent version of priority monism is incompatible with spacetime emergence. We go on to present a solution to this problem, which involves rethinking the nature of concreteness. This leads to a new version of priority monism that is compatible with emergent spacetime
A Graph Data Model for CityGML Utility Network ADE: A Case Study on Water Utilities
No full textModelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must be reconstructed through joins rather than represented as explicit relationships. This creates challenges when managing densely connected network structures. This study introduces the UNADE–Labelled Property Graph (UNADE-LPG) model, a graph-based representation that maps the classes, relationships, and constraints defined in the UNADE Unified Modelling Language (UML) schema into nodes, edges, and properties. A conversion pipeline is developed to generate UNADE-LPG instances directly from CityGML UNADE datasets encoded in GML, enabling the population of graph databases while maintaining semantic alignment with the original schema. The approach is demonstrated through two case studies: a schematic network and a real-world water system from Frankston, Melbourne. Validation procedures, covering structural checks, topological continuity, classification behaviour, and descriptive graph statistics, confirm that the resulting graph preserves the semantic structure of the UNADE schema and accurately represents the physical connectivity of the network. An analytical path-finding query is also implemented to illustrate how the UNADE-LPG structure supports practical network-analysis tasks, such as identifying connected pipeline sequences. Overall, the findings show that the UNADE-LPG model provides a clear, standards-aligned, and operationally practical foundation for representing utility networks within graph environments, supporting future integration into digital-twin and network-analytics applications
Antibiotic-Loaded Bone Cement and Risk of Infection After Knee Arthroplasty in High-Risk Patients: A Register Based Meta-Analysis
No full textBACKGROUND: The use of antibiotic-loaded bone cement (ALBC) in primary total knee arthroplasty (TKA) is debated. Some argue that ALBC might only be justified in high-risk patients. This study assessed the effectiveness of ALBC vs. plain bone cement (PBC) in reducing risk of revision for periprosthetic joint infection (PJI) in TKA patients considered to have a high risk of infection. METHODS: Cohort study of primary TKAs in 11 national or regional arthroplasty registries from 2010 to 2020. The 1-year risk of revision for PJI in TKAs with ALBC vs. PBC among patients with high American Society of Anesthesiologists (ASA) classification, body mass index (BMI), and/or diabetes was compared. Cumulative percent revision (1 minus Kaplan-Meier) based on 685,818 TKAs and Cox regression analyses (adjusted Hazard Rate Ratios [aHRRs]) were performed for TKAs with ALBC (reference) vs. PBC restricted to the following high-risk subgroups of patients: (1) ASA ≥3 (n = 335,612 vs. 35,997), (2) BMI ≥35 (n = 278,927 vs. 24,737), (3) ASA ≥3 and BMI ≥35 (n = 99,407 vs. 11,407), (4) diabetes (n = 38,341 vs. 21,838), and (5) ASA ≥3, BMI ≥35, and diabetes (n = 3,347 vs. 4,261). Advanced distributed meta-analyses were performed to combine all aggregate data and assess 1-year risk of revision for PJI. RESULTS: Each registry reported a 1-year cumulative percent revision of ≤1.6% for PJI following TKAs both for ALBC and PBC in all high-risk subgroups. Similar 1-year risks of revision for PJI were found in TKAs with ALBC (reference) and PBC among patients with ASA ≥3 (aHRR: 1.09; 95% CI, 0.90-1.31); BMI ≥35 (1.06; 0.54-2.12); ASA ≥3 and BMI ≥35 (1.12; 0.83-1.50); diabetes (0.95; 0.74-1.20); and ASA ≥3, BMI ≥35, and diabetes (1.40; 0.86-2.29). CONCLUSIONS AND RELEVANCE: Similar 1-year revision risk of PJI was found for TKAs with ALBC vs. PBC in high-risk patients. Confirmation of the efficacy of ALBC in high-risk TKA patients needs to be evaluated in clinical trials. LEVEL OF EVIDENCE: Level III. See Instructions for Authors for a complete description of levels of evidence
Optimization-based network partitioning for distributed and decentralized control
No full textControl of large-scale networks can be challenging due to difficulties in implementation of high-order control systems, data collection, and actuation. Distributed and decentralized control systems are therefore commonly used. This paper proposes an optimization-based partitioning approach for use in decentralized and distributed control. It factors in both computational and communication costs, while also taking into consideration the controllability of the subsystems. An efficient algorithm for solving the optimization problem is also provided. The proposed approach is demonstrated on case studies from water distribution systems
Ultra-massive fluid transfusion in adult liver transplant recipients: A single center observational study
No full textINTRODUCTION: Patients undergoing liver transplantation may require large volumes of fluid to maintain hemodynamic stability and treat coagulopathy. This study aimed to determine the prevalence of ultra-massive fluid transfusion and to examine its association with clinical outcomes. We defined an ultra-massive fluid transfusion a priori as a transfusion volume of >20 liters of crystalloids, colloids, blood and blood products administered intraoperatively and within the first 24 hours postoperatively. METHODS: This single-center retrospective observational study included all adult patients who underwent an orthotopic liver transplant and received an ultra-massive fluid transfusion. The primary aim was to determine the prevalence of ultra-massive fluid transfusion in patients undergoing liver transplantation. Secondary objectives included evaluating the effect of the total volume of fluid and packed red blood cell transfusions on postoperative complications, mechanical ventilation hours, intensive care unit and hospital length of stay, and mortality. RESULTS: Of the 844 liver transplantation procedures, 81 (9.6%) required an ultra-massive fluid transfusion with a median transfusion volume of 36.8 liters (IQR: 31.2-48.7). Each additional liter of fluid administered during surgery was associated with an additional stay of 0.47 days in intensive care (95%CI: 0.18-0.76, p = 0.003). Each additional unit of packed red blood cells administered during surgery was associated with an additional 12.8 hours of mechanical ventilation (95%CI: 3.12-22.43, p = 0.014) and 1.0 additional day in intensive care (95%CI: 0.27-1.79, p = 0.012). Neither ultra-massive fluid transfusion nor packed red blood cell transfusions were associated with increased complications. CONCLUSION: Approximately one in ten liver transplantation patients required an ultra-massive fluid transfusion. While ultra-massive fluid transfusion was associated with prolonged recovery, it was not associated with an increased risk of complications or mortality
N-terminomics profiling of naïve and inflamed murine colon reveals proteolytic signatures of legumain
No full textLegumain is a cysteine protease broadly associated with inflammation. It has been reported to cleave and activate protease-activated receptor 2 to provoke pain associated with oral cancer. Outside of gastric and colon cancer, little has been reported on the roles of legumain within the gastrointestinal tract. Using a legumain-selective activity-based probe, LE28, we report that legumain is activated within colonocytes and macrophages of the murine colon, and that it is upregulated in models of acute experimental colitis. We demonstrated that loss of legumain activity in colonocytes, either through pharmacological inhibition or gene deletion, had no impact on epithelial permeability in vitro. Moreover, legumain inhibition or deletion had no obvious impacts on symptoms or histological features associated with dextran sulfate sodium-induced colitis, suggesting its proteolytic activity is dispensable for colitis initiation. To gain insight into potential functions of legumain within the colon, we performed field asymmetric waveform ion mobility spectrometry-facilitated quantitative proteomics and N-terminomics analyses on naïve and inflamed colon tissue from wild-type and legumain-deficient mice. We identified 16 altered cleavage sites with an asparaginyl endopeptidase signature that may be direct substrates of legumain and a further 16 cleavage sites that may be indirectly mediated by legumain. We also analyzed changes in protein abundance and proteolytic events broadly associated with colitis in the gut, which permitted comparison to recent analyses on mucosal biopsies from patients with inflammatory bowel disease. Collectively, these results shed light on potential functions of legumain and highlight its potential roles in the transition from inflammation to colorectal cancer
Reimagining Social Value to Consider the Environment: How Should We Judge the Magnitude of Benefits in Health Research?
No full textGrowing recognition of intersections between our health and the environment, healthcare systems and the environment, and health research and the environment has led bioethics scholars to advocate that the field readopt a broader perspective that considers nature. As part of doing so, we urgently need to reimagine research ethics concepts and frameworks so that they account for the environment. This paper focuses on how we should reinterpret the ethical concept of social value in health research. The concept is understood in absolute and relative terms, and both must be revised. The absolute social value of health research is determined by judging its magnitude of benefits and likelihood of benefits. This paper aims to generate considerations for judging health research's magnitude of benefits that capture its environmental benefits. We start from the most comprehensive definition of absolute social value to-date and show how it falls short of adequately capturing the magnitude of potential benefits generated by health research that yields knowledge related to nature. Based on that analysis, we propose how to revise the definition of absolute social value to better account for the environment. To conclude, we highlight questions that our suggested revisions raise for making relative social value assessments that consider the environment
The biogeography and conservation of Earth's ‘dark’ ectomycorrhizal fungi
No full textBreakthroughs in DNA sequencing have upended our understanding of fungal diversity. Only ∼155,000 of the 2-3 million fungal species on the planet have been formally described and named, and 'dark taxa' - species known only from sequences - represent the vast majority of species within the fungal kingdom. The International Code of Nomenclature requires physical type specimens to officially recognize new fungal species, making it difficult to name dark taxa. This is a significant problem for conservation because, without names, species cannot be recognized for environmental and legal protection. Symbiotic ectomycorrhizal (EcM) fungi play a particularly important role in forest carbon drawdown, but at present we have little understanding of how many EcM fungal species exist, or where to prioritize research activities to survey and describe EcM fungal lineages. In this review, we use global soil metabarcoding databases (GlobalFungi and the Global Soil Mycobiome consortium) to evaluate current estimates of the total number of EcM fungal species on Earth, outline the current state of undescribed EcM dark taxa, and identify priority regions for future dark taxa exploration. The metabarcoding databases include up to 219,730 EcM fungal operational taxonomic units (OTUs) detected from almost 39,500 samples. Using Chao richness estimates corrected for extrapolating species numbers from metabarcoding datasets, we predict that the global diversity of EcM fungi could be ∼25,500-55,500 species. Dark taxa - those that do not match species-level identities - account for 79-83% of OTUs. Oceania contains the highest percentage of dark taxa (87%), and Europe the lowest (78%). Priority 'darkspots' for future research occur predominantly in tropical regions, but also in selected temperate forests at both southern and northern latitudes. We propose concrete steps to reduce the prevalence of EcM darkspots, including performing targeted field surveys, barcoding fungaria voucher specimens, and developing new ways to describe and conserve fungal taxa from DNA alone
Dissection of Neurochemical Pathways Across Complexity and Scale
No full textThe field of Neurochemistry spent decades trying to understand how the brain works, from nano to macroscale and across diverse species. Technological advancements over the years allowed researchers to better visualize and understand the cellular processes underpinning central nervous system (CNS) function. This review provides an overview of how novel models, and tools have allowed Neurochemistry researchers to investigate new and exciting research questions. We discuss the merits and demerits of different in vivo models (e.g., Caenorhabditis elegans, Drosophila melanogaster, Ratus norvegicus, and Mus musculus) as well as in vitro models (e.g., primary cells, induced pluripotent stem cells, and immortalized cells) to study Neurochemical events. We also discuss how these models can be paired with cutting-edge genetic manipulation (e.g., CRISPR-Cas9 and engineered viral vectors) and imaging techniques, such as super-resolution microscopy and new biosensors, to study cellular processes of the CNS. These technological advancements provide new insight into Neurochemical events in physiological and pathological contexts, paving the way for the development of new treatments (e.g., cell and gene therapies or small molecules) that aim to treat neurological disorders by reverting the CNS to its homeostatic state
Neural reparameterization for nonlocal metasurface topology optimization
No full textMetasurfaces are emerging as a paradigm for performing all-optical, energy efficient analog computing and image processing in an ultra-compact form-factor. While various operations have been demonstrated with simple metasurface geometries, broadening the range of available processing outputs will require non-trivial and non-intuitive designs. Topology optimization (TO) is a powerful inverse design technique capable of designing such metasurfaces with significantly enhanced optical properties. The two most important design considerations are the quality of the optical performance and the minimum feature size in the resulting device. Larger features can reduce the mismatch between design and realized device performance, increasing fabrication speed and extending the scalability of the fabrication. Metasurface TO is typically performed on a pixel grid to determine the material distribution at each point. Here, we compare two pixel-based methods to neural-network based parameterizations, where the parameters of a neural-network are trained to output a metasurface design. We produce 150 different computational metasurface design tasks to compare the performance of each parameterization with regard to the performance and feature sizes. In comparison to the best pixel-based approach, a hybrid neural-network and pixel method produced designs with a median minimum feature size 2.8 times larger with only a 1.4 times increase in RMS error. Compared to a pixel method capable of producing similar critical dimensions, the optical performance of the hybrid approach is improved by 39%. Finally, as a proof of concept, we use this neural-network based parameterization to design an angular bandpass filter with relatively large critical dimensions and non-trivial performance