Comprehensive Set Research Articles

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

An integral methodological framework for the thermo-mechanical analysis and structural integrity assessment of the European TBM sets

Two European Test Blanket Modules (TBM) concepts are being developed by F4E, the Water-Cooled Lithium Lead (WCLL) and the Helium-Cooled Ceramic Pebbles concept (HCCP, in collaboration with ITER KOREA). For both concepts, the ESTEYCO Mechanics team is responsible for designing and analyzing the TBM-box, which serves as the plasma-facing module for tritium breeding, and the TBM Shield in the WCLL concept. The design-by-analysis activities for these TBM-Sets are particularly challenging due to several factors. These include diverse and complex loads such as thermal, electromagnetic, pressure, and seismic loads, intricate failure modes to be assessed, the use of a new structural material (EUROFER 97) with limited available information, and the need to work within tight compliance margins in a restricted design space. Over the years, ESTEYCO has developed advanced methodologies to virtually test various design solutions systematically, coherently, and efficiently, aligning with the applicable nuclear design code (RCC-MRx). This paper provides an overview of key developments in simulating the TBM-Set under a comprehensive set of loads and subsequently assessing its structural integrity in an exceptionally detailed and comprehensive manner. The authors have created novel automated tools that encompass the entire process. These tools address tasks like distributing nuclear thermal loads, simulating cooling system behavior under operational conditions, characterizing and distributing electromagnetic loads, implementing code assessment rules for numerous evaluation points, and developing robust visualization tools for result analysis and validation.

The greener the living environment, the better the health? Examining the effects of multiple green exposure metrics on physical activity and health among young students

The sedentary and less active lifestyle of modern college students has a significant impact on the physical and mental well-being of the college community. Campus Green Spaces (GSs) are crucial in promoting physical activity and improving students' health. However, previous research has focused on evaluating campuses as a whole, without considering the diverse spatial scenarios within the campus environment. Accordingly, this study focused on the young people’s residential scenario in university and constructed a framework including a comprehensive set of objective and subjective GSs exposure metrics. A systematic, objective exposure assessment framework ranging from 2D (GSs areas), and 2.5D (GSs visibility) to 3D (GSs volume) was innovatively developed using spatial analysis, deep learning technology, and unmanned aerial vehicle (UAV) measurement technology. Subjective exposure metrics incorporated GSs visiting frequency, GSs visiting duration, and GSs perceived quality. Our cross-sectional study was based on 820 university students in Nanjing, China. Subjective measures of GSs exposure, physical activity, and health status were obtained through self-reported questionnaires. The Generalized Linear Model (GLM) was used to evaluate the associations between GSs exposure, physical activity, and perceived health. Physical activity and social cohesion were considered as mediators, and path analysis based on Structural Equation Modeling (SEM) was used to disentangle the mechanisms linking GSs exposure to the health status of college students. We found that (1) 2D indicator suggested significant associations with health in the 100m buffer, and the potential underlying mechanisms were: GSs area → Physical activity → Social cohesion → Physical health → Mental health; GSs area → Physical activity → Social cohesion → Mental health. (2) Subjective GSs exposure indicators were more relevant in illustrating exposure-response relationships than objective ones. This study can clarify the complex nexus and mechanisms between campus GSs, physical activity, and health, and provide a practical reference for health-oriented campus GSs planning.

Image Encryption Algorithms: A Survey of Design and Evaluation Metrics

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications.

Electrochemically reactive colloidal nanobubbles by water splitting

Hypothesis: The existing literature reports have conflicting views on reactive oxygen species (ROS) generation by bulk nanobubbles. Consequently, we propose the hypothesis that (i) ROS may be generated during the process of nanobubble generation through water splitting, and (ii) bulk nanobubbles possess electrochemical reactivity, which could potentially lead to continuous ROS generation even after the cessation of nanobubble production.Experiments: A comprehensive set of experiments was conducted to generate nanobubbles in pure water using the water-splitting method. The primary aims of this study are as follows: (i) nanobubble generation by electrolysis and its characterization; (ii) to provide conclusive evidence that the nano-entities are indeed nanobubbles; (iii) to quantify the production of reactive oxygen species during the process of nanobubble generation and (iv) to establish evidence for the presence of electrochemically reactive nanobubbles.The findings of our experiment suggest that bulk nanobubbles possess the ability to generate reactive oxygen species (ROS) during the process of nanobubble nucleation. Additionally, our results indicate that bulk nanobubbles are electrochemically reactive after the cessation of nanobubble production. The electron spin spectroscopy (ESR) response and degradation of the dye compound over time confirm the electrochemical reactivity of bulk nanobubbles.

Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents.

Deep eutectic solvents (DESs) are emerging as environmentally friendly designer solvents for mass transport and heat transfer processes in industrial applications; however, the lack of accurate tools to predict and thus control their viscosities under both a range of environmental factors and formulations hinders their general application. While DESs may serve as designer solvents, with nearly unlimited combinations, this unfortunately makes it experimentally infeasible to comprehensively measure the viscosities of all DESs of potential industrial interest. To assist in the design of DESs, we have developed several new machine learning (ML) models that accurately and rapidly predict the viscosities of a diverse group of DESs at different temperatures and molar ratios using, to date, one of the most comprehensive data sets containing the properties of over 670 DESs over a wide range of temperatures (278.15-385.25 K). Three ML models, including support vector regression (SVR), feed forward neural networks (FFNNs), and categorical boosting (CatBoost), were developed to predict DES viscosity as a function of temperature and molar ratio and contrasted with multilinear and two-factor polynomial regression baselines. Quantum chemistry-based, COSMO-RS-derived sigma profile (σ-profile) features were used as inputs for the ML models. The CatBoost model is excellent at externally predicting DES viscosity, as indicated by high R2 (0.99) and low root-mean-square-error (RMSE) and average absolute relative deviations (AARD) (5.22%) values for the testing data sets, and 98% of the data points lie within the 15% of AARD deviations. Furthermore, SHapley additive explanation (SHAP) analysis was employed to interpret the ML results and rationalize the viscosity predictions. The result is an ML approach that accurately predicts viscosity and will aid in accelerating the design of appropriate DESs for industrial applications.

Systematic Review and Meta-analysis of Fenestrated and Chimney/Snorkel Techniques for Endovascular Repair of Juxtarenal Aortic Aneurysms.

Comparative effectiveness of fenestrated endovascular aneurysm repair (FEVAR) and chimney graft endovascular aneurysm repair (ChEVAR) for juxtarenal aortic aneurysms (JAAs) remains unclear. Our objective was to identify and analyze the current body of evidence comparing the effectiveness of both techniques for JAA. We performed a systematic review and meta-analysis comparing the effectiveness of FEVAR and ChEVAR for JAA repair. We searched MEDLINE, EMBASE, and Cochrane Register for Controlled Trials from January 1, 1990, for randomized and non-randomized studies assessing outcomes of FEVAR and ChEVAR for JAA repair. Screening, data extraction, risk of bias assessment, and GRADE (Grading of Recommendations, Assessments, Development, and Evaluations) certainty of evidence were performed in duplicate. Data were pooled statistically where possible. Nine retrospective cohort studies comparing the use of FEVAR and ChEVAR for juxtarenal aneurysm were included for meta-analysis. The FEVAR and ChEVAR arms of the meta-analysis consisted of 726 participants and 518 participants, respectively. There were 598 (86.8%) and 332 (81.6%) men in each arm. The mean diameter was larger in the ChEVAR arm (59 mm vs 52.5 mm). Both techniques had similar rates of postoperative 30-day mortality, 3.38% (8/237) versus 3.52% (8/227), acute kidney injury, 16.76% (31/185) versus 17.31% (18/104), and major adverse cardiac events, 7.30% (46/630) versus 6.60% (22/333). The meta-analysis supported the use of FEVAR for most outcomes, with significant advantage for technical success (odds ratio [OR]: 3.24, 95% CI: 1.24-8.42) and avoidance of type 1 endoleak (OR: 5.76, 95% CI: 1.94-17.08), but a disadvantage for spinal cord ischemia (OR: 10.21, 95% CI: 1.21-86.11), which had a very low number of events. The quality of evidence was "moderate" for most outcomes. Both endovascular techniques had good safety profiles. The evidence does not support superiority of either FEVAR or ChEVAR for JAA. While lack of equipoise has hampered the design of randomised trials of open versus endovascular repair of juxtarenal aortic aneurysms, concern about the durability of endovascular repair highlights the need for stronger evidence of the comparative efficacy of endovascular techniques. This review performed meta-analysis and evidence appraisal of recent data from large observational studies comparing fenestrated and chimney techniques, using a comprehensive outcome set. Superiority of either intervention could not be established due to differences in participants' baseline risk in each study arm. However, data suggests that both techniques are safe and suitable for use when indicated.

‘Greening’ an Oil Exporting Country: A Hydrogen, Wind and Gas Turbine Case Study

In the quest for achieving decarbonisation, it is essential for different sectors of the economy to collaborate and invest significantly. This study presents an innovative approach that merges technological insights with philosophical considerations at a national scale, with the intention of shaping the national policy and practice. The aim of this research is to assist in formulating decarbonisation strategies for intricate economies. Libya, a major oil exporter that can diversify its energy revenue sources, is used as the case study. However, the principles can be applied to develop decarbonisation strategies across the globe. The decarbonisation framework evaluated in this study encompasses wind-based renewable electricity, hydrogen, and gas turbine combined cycles. A comprehensive set of both official and unofficial national data was assembled, integrated, and analysed to conduct this study. The developed analytical model considers a variety of factors, including consumption in different sectors, geographical data, weather patterns, wind potential, and consumption trends, amongst others. When gaps and inconsistencies were encountered, reasonable assumptions and projections were used to bridge them. This model is seen as a valuable foundation for developing replacement scenarios that can realistically guide production and user engagement towards decarbonisation. The aim of this model is to maintain the advantages of the current energy consumption level, assuming a 2% growth rate, and to assess changes in energy consumption in a fully green economy. While some level of speculation is present in the results, important qualitative and quantitative insights emerge, with the key takeaway being the use of hydrogen and the anticipated considerable increase in electricity demand. Two scenarios were evaluated: achieving energy self-sufficiency and replacing current oil exports with hydrogen exports on an energy content basis. This study offers, for the first time, a quantitative perspective on the wind-based infrastructure needs resulting from the evaluation of the two scenarios. In the first scenario, energy requirements were based on replacing fossil fuels with renewable sources. In contrast, the second scenario included maintaining energy exports at levels like the past, substituting oil with hydrogen. The findings clearly demonstrate that this transition will demand great changes and substantial investments. The primary requirements identified are 20,529 or 34,199 km2 of land for wind turbine installations (for self-sufficiency and exports), and 44 single-shaft 600 MW combined-cycle hydrogen-fired gas turbines. This foundational analysis represents the commencement of the research, investment, and political agenda regarding the journey to achieving decarbonisation for a country.

The long‐term consequences of early school absences for educational attainment and labour market outcomes

AbstractSchool absences can negatively impact a child's schooling, including the loss of teacher‐led lessons, peer interactions, and, ultimately, academic achievement. However, little is known about the long‐term consequences of school absences for overall educational attainment and labour market outcomes. In this paper, we used data from the 1970 British Cohort Study to examine long‐term associations between school absences in late childhood and individuals’ educational attainment, social class, unemployment and earnings at age 42 while adjusting for a comprehensive set of confounders. Our findings show that school absences are associated with lower educational attainment but are not associated with social class destination except for increasing the likelihood of being out of the labour force after adjusting for confounders. Individuals who missed five days of school at age 10 were 5.2% more likely to have obtained no qualifications and 4.1% more likely to be out of the labour force. However, we did not find a significant association between school absences and individuals’ earnings or duration of unemployment. Our findings suggest that the detrimental consequences of school absences persist beyond schooling into adulthood. Overall, this study highlights the importance of addressing school absenteeism to promote educational and labour market participation over the life course.

Chemical modification of nanotubular Ti surfaces with calcium phosphate and strontium ranelate for biomedical applications

Titanium (Ti) and its alloys are widely used as dental implants due to their notable properties, including corrosion resistance, biocompatibility, and mechanical stability. The anodization process can create a unique nanotubular (NT) structure on the surface of Ti, which offers significant potential for improving the performance of this biomaterial. This study deals with the chemical modification of an NT Ti surface by the incorporation of calcium phosphate (CaP) and strontium ranelate (SrR). A comprehensive set of analytical techniques was used to study these surface modifications on Ti, including atomic force microscopy, water contact angle measurements, scanning electron microscopy, thermal diffusivity, X-ray diffraction, and X-ray photoelectron spectroscopy. Examination of the surface morphology after immersion revealed the presence of deposits, occasionally with partial blocking of the TiO2 nanotubes. Both surface morphology and chemical analysis demonstrated the successful incorporation of CaP and SrR. In addition, in vitro evaluations demonstrated the exceptional promise of the prepared samples for biomedical applications, exhibiting high cell viability, non-toxicity, and osteogenic properties. These results underscore the potential of these modified surfaces to significantly advance the field of dental implantology.

The hidden world of fossil larvae: description and morphological insights of an immature scorpionfly (Mecoptera: Panorpidae) from the Baltic amber

Abstract So far, there has been no information of the pre-imaginal stages in the well-preserved fossil record of the Mecoptera. The first and well-preserved mecopteran larva was discovered in Eocene Baltic amber. The application of synchrotron X-ray microtomography enabled the reconstruction of the body structure with high accuracy, providing a comprehensive set of morphological data that classical stereoscopic microscopy could not capture. The larva is eruciform, with distinct segmentation of the body. All the most important morphological structures are documented, including the chaetotaxy system. Shape of antenna and annulated processes on the abdominal segments point to the third/four stage of development. There is no doubt that the larva belongs to Panorpidae, the most abundant family of extant Mecoptera. The morphological characters point to the closest affinity to larvae of Cerapanorpa and Panorpa but the fossil larva cannot be assigned to any extant genus of Panorpidae. Based on the results of synchrotron X-ray microtomography, it is possible to discuss the assignement of this larva to a lower systematic level than the family, and draw conclusions about the nature of the habitat preferences of Eocene panorpid.

Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model

Abstract. A fundamental issue associated with the dynamical downscaling technique using limited-area models is related to the presence of a “spatial spin-up” belt close to the lateral boundaries where small-scale features are only partially developed. Here, we introduce a method to identify the distance from the border that is affected by the spatial spin-up (i.e., the spatial spin-up distance) of the precipitation field in convection-permitting model (CPM) simulations. Using a domain over eastern North America, this new method is applied to several simulations that differ on the nesting approach (single or double nesting) and the 3-D variables used to drive the CPM simulation. Our findings highlight three key points. Firstly, when using a single nesting approach, the spin-up distance from lateral boundaries can extend up to 300 km (around 120 CPM grid points), varying across seasons, boundaries and driving variables. Secondly, the greatest spin-up distances occur in winter at the western and southern boundaries, likely due to strong atmospheric inflow during these seasons. Thirdly, employing a double nesting approach with a comprehensive set of microphysical variables to drive CPM simulations offers clear advantages. The computational gains from reducing spatial spin-up outweigh the costs associated with the more demanding intermediate simulation of the double nesting. These results have practical implications for optimizing CPM simulation configurations, encompassing domain selection and driving strategies.

Comprehensive evaluation of privacy policies using the contextual integrity framework

AbstractOnline privacy policies are often lengthy and difficult to understand. This may lead many users to avoid reading them despite increasing concerns about how their personal information is managed. This article presents a structured approach to evaluate the transparency and comprehensiveness of privacy policies using a comprehensive set of evaluation questions within the contextual integrity (CI) framework. We use these questions to identify policies' responses to key privacy concerns. Applying the CI framework, we analyze the clarity and context of these responses, identifying any vagueness and contextual issues that could impede a user's understanding of the privacy policy. Using the CI analysis, we quantify the quality of policies' responses, thereby enabling users to make informed decisions about online services or products. We apply our methodology to two popular messaging apps, Telegram and WhatsApp, using them as case studies to systematically uncover the strengths and weaknesses of their privacy policies. The findings demonstrate that our proposed methodology can effectively identify transparency issues and assess the comprehensiveness of privacy policies. This suggests that our approach could serve as a practical alternative to subjective evaluations typically conducted by privacy experts.

Enhancing Soft Tissue Differentiation with Different Dual-Energy CT Systems: A Phantom Study

To quantitatively evaluate the possible advantages of quantifying and differentiating various soft tissues using virtual monochromatic images (VMI) derived from different dual-energy computed tomography (DECT) technologies. This study involved four DECT scanners with different technologies. CIRS phantom images were acquired in single-energy (SECT) and DECT modes with each scanner. The analysis focused on five equivalent soft-tissue inserts: adipose, breast, liver, muscle, and bone (200 mg). The signal-to-noise ratio (SNR) was calculated for each equivalent soft-tissue insert. Finally, the contrasts of tissue pairs between DECT and SECT images were compared using Wilcoxon signed-rank tests adjusted for multiple comparisons. Average CT numbers and noise showed a significant difference pattern between DECT with respect to SECT for each CT scanner. Generally, energy levels of 70 keV or higher led to improved SNR in VMI for most of the equivalent soft-tissue inserts. However, energy levels of 40–50 keV showed significantly higher contrasts in most of the equivalent soft-tissue insert pairs. DECT images at low energies, especially at 40–50 keV, outperform SECT images in discriminating soft tissues across all four DECT technologies. The combined use of DECT images reconstructed at different energy levels provides a more comprehensive set of information for diagnostic and/or radiotherapy evaluation compared to SECT. Some differences between scanners are evident, depending on the DECT acquisition technique and reconstruction method.

A statistically identified structural vector autoregression with endogenously switching volatility regime

We introduce a structural vector autoregressive model with endogenously switching conditional covariance matrix. The structural shocks are identified by simultaneously diagonalizing the reduced form error covariance matrices. It is not, however, always clear whether the condition for the full statistical identification is satisfied, and its validity is difficult to justify formally. Therefore, we provide general sets of conditions, that allow to combine sign and zero restrictions on the impact matrix, for identifying a subset of the shocks when the condition for statistical identification of the model fails. In an empirical application to the effects of the U.S. monetary policy shock, we find that a contractionary monetary policy shock significantly decreases output in a persistent hump-shaped pattern. Prices decrease permanently, but there is short-run inertia in their response. The accompanying R package gmvarkit provides a comprehensive set of tools for numerical analysis of the model.

Unlocking Precision Docking for Metalloproteins.

Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of high-affinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein-ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein-ligand interactions.

Predicting the role of the human gut microbiome in type 1 diabetes using machine-learning methods.

Gut microbes is a crucial factor in the pathogenesis of type 1 diabetes (T1D). However, it is still unclear which gut microbiota are the key factors affecting T1D and their influence on the development and progression of the disease. To fill these knowledge gaps, we constructed a model to find biomarker from gut microbiota in patients with T1D. We first identified microbial markers using Linear discriminant analysis Effect Size (LEfSe) and random forest (RF) methods. Furthermore, by constructing co-occurrence networks for gut microbes in T1D, we aimed to reveal all gut microbial interactions as well as major beneficial and pathogenic bacteria in healthy populations and type 1 diabetic patients. Finally, PICRUST2 was used to predict Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways and KO gene levels of microbial markers to investigate the biological role. Our study revealed that 21 identified microbial genera are important biomarker for T1D. Their AUC values are 0.962 and 0.745 on discovery set and validation set. Functional analysis showed that 10 microbial genera were significantly positively associated with D-arginine and D-ornithine metabolism, spliceosome in transcription, steroid hormone biosynthesis and glycosaminoglycan degradation. These genera were significantly negatively correlated with steroid biosynthesis, cyanoamino acid metabolism and drug metabolism. The other 11 genera displayed an inverse correlation. In summary, our research identified a comprehensive set of T1D gut biomarkers with universal applicability and have revealed the biological consequences of alterations in gut microbiota and their interplay. These findings offer significant prospects for individualized management and treatment of T1D.

Principles of logics for plausible reasoning

Abstract Plausible reasoning concerns situations that have an inherent lack of precision, which is not quantified; that is, there are no degrees or levels of precision, and hence no use of numbers like probabilities. A hopefully comprehensive set of principles that clarifies what it means for a formal logic to do plausible reasoning is presented. Several important plausible-reasoning examples that guide the development of some of the principles are also given. Versions of these principles and examples have appeared in Billington (2017) and chapter 4 of Billington (2019). Each principle in this article is the same as, or an improvement on, the corresponding principle in chapter 4 of Billington (2019); for a detailed comparison, see the Appendix. A propositional plausible logic that satisfies all these principles appears in Billington (2017, 2019). This shows that all the principles together are consistent.

The association between incarceration and housing insecurity and advanced immune age during late life

Emerging evidence suggests that psychosocial stress ages the immune system. Accordingly, immune aging may be an important potential mechanism linking psychosocial stress to aging-related decline and disease. Incarceration and housing insecurity represent severe and complex experiences of a multitude of psychosocial stressors, including discrimination, violence, and poverty. In this study, we investigated the association between incarceration and/or housing insecurity and advanced immune age in adults aged 55 and older. Our sample was derived from the Health and Retirement Survey (HRS), with n = 7003 individuals with valid housing insecurity data and n = 7523 with valid incarceration data. From 2016 Venous Blood Study data, we assessed immune aging using a comprehensive set of immune markers including inflammatory markers (IL-6, CRP, s-TNFR1), markers of viral control (CMV IgG antibodies), and ratios of T cell phenotypes (CD8+:CD4+, CD+ Memory: Naïve, CD4+ Memory: Naïve, CD8+ Memory: Naïve ratios). We found that both incarceration and housing insecurity were strongly associated with more advanced immune aging as indicated by increased inflammation, reduced viral control, and reduction in naïve T cells relative to memory T cells. Given that those who experienced incarceration, housing insecurity, and/or are racialized minorities were less likely to be included in this study, our results likely underestimated these associations. Despite these limitations, our study provided strong evidence that experiencing incarceration and/or housing insecurity may accelerate the aging of the immune system.

Beyond legal personhood for the Whanganui River: collaboration and pluralism in implementing the Te Awa Tupua Act

ABSTRACT There is now a large body of scholarly literature on the legal and governance arrangements for the Whanganui River in Aotearoa New Zealand, given the rights of a legal person under Te Awa Tupua (Whanganui River Claims Settlement) Act 2017. Most of this literature focuses on the innovation of legal personhood and rights of nature rather than situating the Act in its local context or examining its implementation in practice. This article analyzes two cases of implementation – Te Pūwaha (the revitalisation of the Port of Whanganui); and Te Kōpuka nā Te Awa Tupua, the collaborative group charged with developing a strategy for the river. We shift attention away from legal personality to three other elements: The centrality of Māori kawa (value-based, Indigenous law); the establishment of a comprehensive set of new institutions and practices; and the devolution of authority to hapū (subtribes) and communities. These elements represent a paradigm shift towards a relational and reciprocal form of governance. While Te Awa Tupua Act does have some alignment with rights of nature, it should primarily be understood as recognising Indigenous rights and the authority of Indigenous law.