This Swedish nationwide retrospective cohort study, utilizing national registries, investigated the fracture risk associated with recent (within two years) index fractures and existing (>2 years) fractures, comparing these risks to controls without a prior fracture. The study incorporated every Swedish person aged 50 or older who had been living in Sweden at any point from 2007 through 2010. Patients possessing a recent fracture were sorted into specific fracture groups, each group identified by the type of previous fracture. Recent fracture cases were categorized as either major osteoporotic fractures (MOF) – broken hip, vertebra, proximal humerus, and wrist – or non-MOF. From the start of the study to December 31, 2017, patients' progress was documented. Censoring was implemented for deaths and emigrations. The chances of fracturing in general and specifically of sustaining a hip fracture were subsequently determined. The study recruited 3,423,320 individuals. Of these, 70,254 experienced a recent MOF, 75,526 a recent non-MOF, 293,051 a past fracture, and 2,984,489 had not experienced a prior fracture. Each of the four groups had a different median follow-up time: 61 (interquartile range [IQR] 30-88), 72 (56-94), 71 (58-92), and 81 years (74-97), respectively. Patients with recent multiple organ failure (MOF), recent non-MOF conditions, and prior fractures presented with a significantly elevated risk of experiencing any fracture compared to healthy control subjects. The adjusted hazard ratios (HRs) considering age and sex were calculated as 211 (95% CI 208-214) for recent MOF, 224 (95% CI 221-227) for recent non-MOF, and 177 (95% CI 176-178) for prior fractures, respectively. Fractures, both recent and longstanding, including those involving metal-organic frameworks (MOFs) and non-MOFs, heighten the risk of further fracturing. This underscores the importance of encompassing all recent fractures in fracture liaison programs and warrants the exploration of targeted case-finding strategies for individuals with prior fractures to mitigate future breakages. Ownership of copyright rests with The Authors in 2023. Wiley Periodicals LLC, on behalf of the American Society for Bone and Mineral Research (ASBMR), publishes the Journal of Bone and Mineral Research.

The critical importance of developing sustainable, energy-efficient building materials lies in their ability to reduce thermal energy consumption and facilitate natural indoor lighting. Phase-change materials, strategically placed within wood-based materials, are suitable for thermal energy storage. However, the volume of renewable resources is typically limited, their energy storage and mechanical properties are often poor, and there is a significant gap in understanding their sustainability. A novel bio-based, transparent wood (TW) biocomposite for thermal energy storage, exhibiting excellent heat storage, adjustable optical transmission, and robust mechanical properties, is presented. Using a synthesized limonene acrylate monomer and renewable 1-dodecanol, a bio-based matrix is impregnated into mesoporous wood substrates, where it undergoes in situ polymerization. In comparison to commercial gypsum panels, the TW boasts a high latent heat (89 J g-1). This is accompanied by thermo-responsive optical transmittance up to 86% and mechanical strength up to 86 MPa. MK-8353 concentration Bio-based TW, according to a life cycle assessment, demonstrates a 39% lower environmental impact compared to transparent polycarbonate panels. The bio-based TW's potential as a scalable and sustainable transparent heat storage solution is substantial.

For energy-efficient hydrogen production, combining the urea oxidation reaction (UOR) with the hydrogen evolution reaction (HER) shows promise. However, the production of cheap and highly active bifunctional electrocatalysts for the entire urea electrolysis process continues to be a challenge. The one-step electrodeposition method is applied in this study to synthesize the metastable Cu05Ni05 alloy. The attainment of a 10 mA cm-2 current density for UOR and HER respectively necessitates only the potentials of 133 mV and -28 mV. MK-8353 concentration Superior performance is directly linked to the metastable alloy's properties. The alkaline environment supports the good stability of the Cu05 Ni05 alloy in the hydrogen evolution reaction; however, the oxygen evolution reaction results in rapid NiOOH formation due to the phase segregation of the Cu05 Ni05 alloy. The hydrogen generation system, energy-saving and coupled with hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), requires only 138 V of voltage at a current density of 10 mA cm-2. Furthermore, at a current density of 100 mA cm-2, the applied voltage decreases by 305 mV, compared to the conventional water electrolysis system (HER and OER). Compared to the recently published catalysts, the Cu0.5Ni0.5 catalyst shows enhanced electrocatalytic activity and greater resilience. Moreover, a straightforward, gentle, and expeditious approach to creating highly active bifunctional electrocatalysts for urea-assisted overall water splitting is detailed in this work.

In this paper's introduction, we delve into the concepts of exchangeability and their implications for Bayesian inference. Highlighting the predictive function of Bayesian models, we also examine the symmetry assumptions inherent in beliefs about an underlying exchangeable sequence of observations. Through a comparative analysis of the Bayesian bootstrap, Efron's parametric bootstrap, and a Doob-derived Bayesian inference framework based on martingales, a parametric Bayesian bootstrap is presented. Martingales are a cornerstone of fundamental importance. Presented are the illustrations and the relevant theoretical background. This article is incorporated into the theme issue, specifically 'Bayesian inference challenges, perspectives, and prospects'.

Defining the likelihood, for a Bayesian, can be just as baffling as defining the prior. Our emphasis is on cases where the parameter under scrutiny has been disentangled from the likelihood and is directly tied to the dataset through a loss function. Existing work in both Bayesian parametric inference employing Gibbs posteriors and Bayesian non-parametric inference is surveyed. A review of recent bootstrap computational techniques for approximating loss-driven posterior distributions follows. Importantly, we examine implicit bootstrap distributions, which are generated through a related push-forward map. Independent, identically distributed (i.i.d.) samplers, originating from approximate posteriors, are investigated, utilizing random bootstrap weights processed by a trained generative network. The deep-learning mapping's training allows for a negligible simulation cost when employing these independent and identically distributed samplers. The performance of deep bootstrap samplers is evaluated against exact bootstrap and MCMC methods, using examples like support vector machines and quantile regression. Theoretical insights into bootstrap posteriors are also provided, informed by connections to model mis-specification. This article falls under the thematic umbrella of 'Bayesian inference challenges, perspectives, and prospects'.

I examine the strengths of applying a Bayesian outlook (insisting on finding a Bayesian interpretation within seeming non-Bayesian models), and the weaknesses of a rigid Bayesian adherence (rejecting non-Bayesian methods as a matter of principle). I hold the belief that these ideas will prove instrumental to researchers exploring common statistical methods, encompassing confidence intervals and p-values, alongside educators and practitioners, who are keen to steer clear of the misdirection of excessively emphasizing philosophy over practical applications. This article is featured in the theme issue, specifically concerning 'Bayesian inference challenges, perspectives, and prospects'.

A critical examination of Bayesian causal inference is provided in this paper, drawing upon the potential outcomes framework. We delve into the causal estimands, the treatment assignment methodology, the comprehensive structure of Bayesian inference in causal effects, and the application of sensitivity analysis. Bayesian causal inference's distinctive features include considerations of the propensity score, the concept of identifiability, and the choice of prior distributions, applicable to both low-dimensional and high-dimensional datasets. The design stage, including covariate overlap, is of critical importance to the Bayesian approach to causal inference, as we demonstrate. We expand the conversation to include two complex assignment techniques: instrumental variables and time-variant treatments. We discern the strengths and weaknesses inherent in the Bayesian paradigm of causal inference. Examples are used throughout the text to illustrate the central concepts. As part of the 'Bayesian inference challenges, perspectives, and prospects' special issue, this article is presented.

The emphasis in Bayesian statistics and contemporary machine learning is on prediction, contrasting sharply with the more traditional emphasis on inference. MK-8353 concentration The uncertainty conveyed by the posterior distribution and credible intervals, within the context of random sampling and a Bayesian exchangeability perspective, can be understood in terms of predictive modeling. The predictive distribution serves as the focal point for the posterior law governing the unknown distribution; we establish its asymptotic Gaussian marginality, the variance of which relies on the predictive updates, i.e., how the predictive rule absorbs information with fresh observations. Through the application of the predictive rule, asymptotic credible intervals can be obtained without the need to specify a particular model or prior law. This highlights the relationship between frequentist coverage and predictive learning rules, and suggests a novel view of predictive efficiency requiring further research.