A variational inference framework for inverse problems

A framework is presented for fitting inverse problem models via variational Bayes approximations. This methodology guarantees flexibility to statistical model specification for a broad range of applications, good accuracy and reduced model fitting times. The message passing and factor graph fragment approach to variational Bayes that is also described facilitates streamlined implementation of approximate inference algorithms and allows for supple inclusion of numerous response distributions and penalizations into the inverse problem model. Models for one- and two-dimensional response variables are examined and an infrastructure is laid down where efficient algorithm updates based on nullifying weak interactions between variables can also be derived for inverse problems in higher dimensions. An image processing application and a simulation exercise motivated by biomedical problems reveal the computational advantage offered by efficient implementation of variational Bayes over Markov chain Monte Carlo

Advancements in immobilization of cesium and strontium radionuclides in cementitious wasteforms—a review

The safe and secure encapsulation or immobilization of nuclear waste, particularly low to intermediate-level waste (accounting for ∼97% of the total volume of nuclear waste), has been a significant concern. Consequently, numerous studies have been conducted on various materials such as ordinary Portland cement-based, bitumen, and ceramics for the purpose of waste encapsulation/immobilization. However, these studies generally offer a broad overview of materials performance without focusing on specific radioisotopes of concern. Cesium (Cs) and strontium (Sr) are important radioactive nuclides to consider for encapsulation, but the existing studies on immobilizing these elements are fragmented and lack a comprehensive understanding. This critical review article offers a thorough qualitative and quantitative analysis to uncover the primary trends/knowledge gaps within the field. It comprehensively delves into waste classifications/management and leaching assessments, followed by an exploration of the immobilization performance and durability issues of various traditional and advanced cementitious materials including low-temperature chemically bonded ceramics such as alkali-activated matrices and Mg‒K phosphates for the immobilization of Cs and Sr. Furthermore, the review article provides fresh insights and perspectives, including recommendations for improvements, novel technologies, and future trends in this domain

RuvBL1/2 reduce toxic dipeptide repeat protein burden in multiple models of C9orf72-ALS/FTD

A G4C2 hexanucleotide repeat expansion in C9orf72 is the most common cause of amyotrophic lateral sclerosis and frontotemporal dementia (C9ALS/FTD). Bidirectional transcription and subsequent repeat-associated non-AUG (RAN) translation of sense and antisense transcripts leads to the formation of five dipeptide repeat (DPR) proteins. These DPRs are toxic in a wide range of cell and animal models. Therefore, decreasing RAN-DPRs may be of therapeutic benefit in the context of C9ALS/FTD. In this study, we found that C9ALS/FTD patients have reduced expression of the AAA+ family members RuvBL1 and RuvBL2, which have both been implicated in aggregate clearance. We report that overexpression of RuvBL1, but to a greater extent RuvBL2, reduced C9orf72-associated DPRs in a range of in vitro systems including cell lines, primary neurons from the C9-500 transgenic mouse model, and patient-derived iPSC motor neurons. In vivo, we further demonstrated that RuvBL2 overexpression and consequent DPR reduction in our Drosophila model was sufficient to rescue a number of DPR-related motor phenotypes. Thus, modulating RuvBL levels to reduce DPRs may be of therapeutic potential in C9ALS/FTD

Divergences and convergences across European musical preferences: how taste varies within and between countries

When investigating relational structures in culture, research in Europe has often either mapped the relationship between cultural tastes in a particular context, or mapped differences in cultural tastes (measured consistently) in different countries, without assessing how these differences can vary across them. Indeed, the idea of national homology (namely that the structures of cultural capital would be fairly similar in nations across Europe) has never been really tested, probably due to a lack of cross-national research on cultural preferences. Using data from the EUCROSS survey that took place in Denmark, Germany, Italy, Romania, Spain and the UK (2012–2013, n = 6016), we first use multiple correspondence analysis to estimate the relationships between a set of items on musical tastes. We then extend this through the use of class-specific analysis, to investigate how these relationships vary in each of the six countries. Finally, we analyse the relationships between the underlying dimensions of music tastes and different components of cosmopolitanism, compared with key demographic variables. We show that the musical field significantly varies across the nations represented in the survey, demonstrating that musical preferences remain largely anchored in national contexts. Cultural preferences are shaped by historical and social dynamics specific to each country, with significant variations in the symbolic value and demographic associations of music genres

Does online case-based learning foster clinical reasoning skills? A mixed methods study

Background Blended learning, integrating face-to-face and virtual methods, has become essential in clinical education, enhancing student satisfaction, engagement and knowledge outcomes. Particularly, online case-based learning emerges as a promising pedagogy to foster clinical reasoning skills. Despite the well-documented clinical reasoning cultivation through face-to-face case-based learning, the ability of online case-based learning to cultivate clinical reasoning remains unexplored. This study investigates the role of online case-based learning in fostering clinical reasoning skills among clinical-year medical students. Methods A mixed-methods sequential explanatory research study was adopted. In the first phase, quantitative data were gathered through a 16-item Likert scale questionnaire adapted from validated clinical reasoning questionnaires. In the second phase, focus group discussions were conducted to expand on the understanding of quantitative results. Results In total, 160 students completed the questionnaire (45% response rate), and 26 participated in focus group discussions. Participants agreed that online case-based learning fostered clinical reasoning skills (mean = 2.94) through different formats, such as clinical role play, simulated ward rounds and virtual consultation. Compared to face-to-face clinical teaching, the focus group revealed that participants were allowed to practise giving explanations to patients, engage in more in-depth discussions, and receive more comprehensive feedback on their clinical reasoning skills during online case-based learning. The barriers to online clinical reasoning skills development were poorer communication skills development and reduced student engagement. The lack of patient complexities of cases and the inability to perform physical examinations hindered students’ clinical reasoning ability. Suggestions to improve clinical reasoning cultivation include utilising actual patient cases, increasing case complexity and session interactivity. Conclusion This study highlights how online case-based learning can support the development of clinical reasoning skills in medical students, encouraging future educators to adopt a blended learning approach. Future research should focus on objective assessments, long-term impacts and innovative methods to improve clinical reasoning skill development continuously

Scene complexity and the detail trace of human long-term visual memory

Humans can remember a vast amount of scene images; an ability often attributed to encoding only low-fidelity gist traces of a scene. Instead, studies show a surprising amount of detail is retained for each scene image allowing them to be distinguished from highly similar in-category distractors. The gist trace for images can be relatively easily captured through both computational and behavioural techniques, but capturing detail is much harder. While detail can be broadly estimated at the categorical level (e.g. man-made scenes more complex than natural), there is a lack of both ground-truth detail data at the sample level and a way to operationalise it for measurement purposes. Here through three different studies, we investigate whether the perceptual complexity of scenes can serve as a suitable analogue for the detail present in a scene, and hence whether we can use complexity to determine the relationship between scene detail and visual long term memory for scenes. First we examine this relationship directly using the VISCHEMA datasets, to determine whether the perceived complexity of a scene interacts with memorability, finding a significant positive correlation between complexity and memory, in contrast to the hypothesised U-shaped relation often proposed in the literature. In the second study we model complexity via artificial means, and find that even predicted measures of complexity still correlate with the overall ground-truth memorability of a scene, indicating that complexity and memorability cannot be easily disentangled. Finally, we investigate how cognitive load impacts the influence of scene complexity on image memorability. Together, findings indicate complexity and memorability do vary non-linearly, but generally it is limited to the extremes of the image complexity ranges. The effect of complexity on memory closely mirrors previous findings that detail enhances memory, and suggests that complexity is a suitable analogue for detail in visual long-term scene memory

Return Predictability, Dividend Growth and the Persistence of the Price-Dividend Ratio

Empirical evidence shows that the order of integration of returns and dividend growth is approximately equal to the order of integration of the first differenced price-dividend ratio, which is about 0.8. Yet, the present-value identity implies the three series should be integrated of the same order. We reconcile this puzzle by showing that the aggregation of antipersistent expected returns and expected dividends gives rise to the price-dividend ratio with properties that mimic long memory in finite samples. In the empirical implementation, we extend and estimate the state-space present-value model by allowing for fractional integration in expected returns and expected dividend growth. This extension improves the model's forecasting power in-sample and out-of-sample. In addition, expected returns and expected dividend growth modelled as ARFIMA processes are more closely related to future macroeconomic variables, which makes them suitable as leading business cycle indicators

Using the ODD protocol and NetLogo to replicate agent-based models

Replicating existing models and their key results not only adds credibility to the original work, it also allows modellers to start model development from an existing approach rather than from scratch. New theory can then be developed by changing the assumptions or scenarios tested, or by carrying out more in-depth analysis of the model. However, model replication can be challenging if the original model description is incomplete or ambiguous. Here we show that the use of standards can facilitate and speed up replication: the ODD protocol for describing models, and NetLogo, an easy-to-learn but powerful software platform and language for implementing agent-based models. To demonstrate the benefits of this approach, we conducted a replication experiment on 18 agent-based models from different disciplines. The researchers doing the replications had no or little previous experience using ODD and NetLogo. Their task was to rewrite the original model description using ODD, implement the model in NetLogo and try to replicate at least one exemplary main result. They were also asked to produce, if time allowed, some initial new results with the replicated model, and to record the total time spent on the replication exercise. Replication was successful for 15 out of 18 models. The time taken varied between 2 and 12 days, with an average of 5 days. ODD helped to systematically scan the original model description, while NetLogo proved easy and quick to learn, but difficult to debug when implementation problems arose. Although most of the models replicated were relatively simple, we conclude that even for more complex models it can be useful to use ODD and NetLogo for replication, at least for developing a prototype to help decide how to proceed with the replicated model. Overall, the use of both, standard approaches such as ODD and easy to learn but powerful software such as NetLogo, can promote coherence and efficiency within and between different models and modelling communities. Imagine if all modellers spoke ODD and NetLogo as a common language or lingua franca

The effect of surface chemistry on the caking behaviour of sucrose crystals

The caking behaviour of organic crystals presents a significant challenge in both the food and pharmaceutical industries. This study aimed to investigate how surface impurities influence the caking behaviour of sucrose (sugar) crystals. Sucrose crystals were selected as the model material due to their relevance in various industries such as food processing and pharmaceuticals. The impact of surface chemistry was assessed for sucrose crystals with varying levels of impurities, namely white sugar, light brown sugar, and dark brown sugar. These crystals were subjected to controlled cycles of humidity exposure, compaction, and drying to induce caking, simulating real-world scenarios. The caking propensity was evaluated using a compression test, while the surface chemistry was characterized through Inverse Gas Chromatography (IGC). Moisture sorption properties were evaluated using a Dynamic Vapor Sorption (DVS) technique, and the formation of solid bridges was studied using scanning electron microscopy (SEM) and X-ray computed tomography (XCT). The results indicate that impurities play a crucial role in enhancing the caking behaviour of sucrose crystals by influencing their hygroscopicity and facilitating solid bridge formation. This study provides valuable insights into the relationship between surface chemistry, moisture sorption properties, and the caking behaviour of sucrose crystals, offering strategies to mitigate caking issues