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More Than Just a Magic Trick?:Exploring an Audience’s Supernatural Attributions for Magicians’ Performances
Get PDFPerforming magicians have long believed that some audience members attribute their magical effects to supernatural methods—to their use of “real magic”. In two samples of adults (n = 412 and 292), we explored personality traits and broader beliefs that might predict supernatural attributions for performance magic. Supernatural attributions were uncommon—many respondents wholly rejected the possibility that magicians’ magic was sometimes real—but nevertheless highly variable. In Study 1, a greater belief that magic tricks at least sometimes involve supernatural powers was associated with relatively higher extraversion, higher neuroticism, and lower openness to experience. In Study 2, random forests suggested that supernatural magic attributions were embedded in a family of paranormal and conspiratorial beliefs, particularly beliefs in psychic powers (reading and influencing thoughts) and precognition (perceiving the future). In both samples, gender, age, and education had small and inconsistent effects, but people who enjoyed performance magic were more likely to endorse supernatural attributions. Taken together, the findings suggest some truth to performing magicians’ suspicions and shed some light on who is more likely to explain magic tricks using supernatural causes
Effect of Thermal Processing by Spray Drying on Key Ginger Compounds
Get PDFBackground/Objectives: Spray drying is a technique widely employed in the food and nutraceutical industries to convert liquid extracts into stable powders, preserving their functional properties. Ginger (Zingiber officinale) is rich in bioactive compounds such as gingerols, shogaols, and zingerone, which contribute to its health benefits. This study aimed to investigate the impact of spray drying on the chemical profile of ginger, particularly focusing on the transformation of gingerols into shogaols and related compounds. Methods: Fresh ginger juice was spray-dried using various carrier agents, including Clear Gum (CO03), pea protein, and inulin. Mass spectra of the resulting powders were acquired using High-Resolution Flow Infusion Electrospray Ionisation Mass Spectrometry (HR-FIE-MS) to obtain fingerprint data. Key bioactive compounds were tentatively identified to Level 2, and their relative intensities were assessed to evaluate the effects of different carriers on the chemical composition of the ginger powders. Results: Spray drying with the commercial carrier CO03 resulted in an increase in shogaol analogues ([10]-, [8]-, and cis-[8]-shogaol), gingerenone B, and oxidation products such as 6-hydroxyshogaol, 6-dehydroshogaol, and zingerone. In contrast, natural carriers like pea protein and inulin led to lower relative intensities of these bioactives, suggesting limited capacity for promoting thermal transformations. Spray drying without a carrier produced a shogaol-dominant profile but resulted in powders with poor handling properties, such as stickiness and agglomeration. Antioxidant and total polyphenol assays showed that spray drying reduced antioxidant capacity, while total polyphenol content was more preserved; natural carriers such as inulin better maintained bioactivity compared to modified starch or pea protein. Conclusions: Among the five formulations evaluated—ginger juice with no carrier, with CO03 (two dilutions), pea protein, or inulin—CO03-based samples showed the greatest chemical transformation, while inulin and pea protein better preserved antioxidant capacity but induced fewer metabolite changes. Thus, choice of carrier in the spray-drying process influences the chemical profile and functional characteristics of resultant ginger powders. While CO03 effectively enhances the formation of bioactive shogaols and related compounds, its ultra-processed nature may not align with clean-label product trends. Natural carriers, although more label-friendly, may not create the desired chemical transformations. Therefore, optimising carrier selection is important to balance bioactivity, product stability, and consumer acceptability in the development of ginger-based functional products.</p
Evidence of Phylosymbiosis in the Microbiome of Conifer Roots
Get PDFPhylosymbiosis describes an association between the phylogenetic relationships among host species and the composition of their microbiomes. Conifers have long evolutionary histories and extensive opportunity for coevolution to have occurred among these host plants and their microbiomes. We tested for phylosymbiosis in the root microbiomes of conifer seedlings as an indicator of coevolutionary plant–microbiome selection processes or concurrent ecological filtering. We grew 23 species of Pinaceae and Cupressaceae in a common soil for 52 weeks and assessed whether similarity in bacterial and fungal root microbiome composition was based on host phylogenetic distances using correlation and topology-based methods. Relationships among soil physicochemical properties, host traits, and microbial community composition were also assessed. Phylosymbiosis was significant in both bacterial and fungal root microbiomes. Host taxonomic relationships consistently explained more variance in microbiome composition than host traits or soil physicochemical properties. Indirect host effects on soil physicochemical properties, specifically sulfate sulfur, explained variation in microbiome composition in most models. We have evidence for phylosymbiosis in the root systems of conifer seedlings in both bacterial and fungal communities. This represents an important step toward uncovering patterns of coevolution in long-lived organisms and their associated microbes and indicates the fundamental role of phylosymbiosis in root microbiome assembly.</p
A machine learning model to predict small molecules with antischistosomal activity
No full textCaused by parasitic worms that live in fresh water, schistosomasis is a disease that affects 251 million people world wide. This study proposes a computer-aided drug design system to predict small molecules that can inhibit schistosomal activity, and thus could be used as a treatment for this disease. We introduce a regression model that estimates the IC50 value for each molecule and a classification model that predicts whether a certain molecule is active or not against the Schistosoma mansoni parasite. We acquire a set of active and nonactive molecules from the ChemBL database and generate descriptors of those molecules using the rdkit library. The resulting features are preprocessed and fed into machine learning models to perform regression and classification tasks. In both cases, the artificial neural network scored the highest accuracy while tested on a set of 754 molecules using cross-validation techniques. Finally, we train a genetic algorithm using the proposed regression model within its fitness function and retrieve 3 molecules that have the best fitness values and have not been screened before
Monitoring the moratorium:assessing the demanding role of monitors
No full textThis article examines the role of the monitor in commencing and supporting the integrity of the company moratorium process. The standalone moratorium was introduced by the Corporate Insolvency and Governance Act 2020 as part of the permanent measures seeking to strengthen the UK’s rescue and restructuring regime. Entry into the moratorium requires an insolvency practitioner consenting to act as the monitor for the proposed moratorium. The process of obtaining, continuing, and terminating a moratorium is reliant on the co-operation of the monitor. Following the initial appointment, the monitor’s role is to ensure that the strict eligibility requirements for the moratorium are met and to assess the likelihood of the company being rescued as a going concern. Evidently, the demands placed on monitors are extensive and they are required to fulfil their monitoring obligations within the short timescales available. To date, there remains substantial uncertainties as to the role, reputational risks, and potential scope of liabilities and criminal penalties for monitors. Furthermore, the professional risks of acting as a monitor appear to be more significant compared to the risks of acting as an administrator. Such factors have impacted on the willingness of some insolvency practitioners from acting as monitors. These ongoing issues may provide a possible explanation as to why the standalone moratorium has seldom been used since its introduction. Consequently, this article will consider the onerous burdens placed on monitors in fulfilling their functions of assessing the prospects of a successful company rescue
Data visibility of cyclists:social justice implications of Strava Metro data in transport planning
No full textThis article investigates how the ‘datafication’ of cycling risks exacerbating existing mobility injustices associated with the privileging of data visibility over diversity. It focuses on the social justice implications of UK and US transport professionals’ perceptions of Strava Metro data, specifically around sample representativeness and bias. Our findings show how experts simultaneously recognise Strava Metro data as: (a) statistically valid and representative of observable cycling activities, including correlation with complementary cycle counter data. And as (b) demographically biased and unrepresentative of society, as Strava Metro data samples disproportionally capture journeys made by younger male cyclists. All expert interviewees emphasised the importance of considering a variety of data sources to support transport planning decisions, and emphasised Strava Metro data sample biases and limitations. However, our analysis of transport professional’s perceptions reveals how cycling app data, such as Strava Metro, has implications for social justice. These implications include distorting understandings of cycling at the detriment of diversifying cycling participation. Left unchecked, a privileging of hyper-visibility of select cyclists may slow efforts for socially sustainable and diverse cycling. The article closes with a discussion of the research and policy implications for emerging debates of artificial intelligence (AI) in transport.</p
Variation in Cell Wall Composition and Saccharification Potential of Seed-Based Miscanthus Hybrids Grown on Marginal Lands Across Six European Trial Locations
No full textMiscanthus breeding programs have focused on developing intraspecific (M. sinensis × M. sinensis) and interspecific (M. sinensis × M. sacchariflorus) seed-based hybrids with distinct cell wall characteristics for different biomass value chains. Here, we evaluated the performance of 13 novel hybrids (including seed-based intraspecific, seed-based interspecific, and one clonally propagated interspecific hybrid) relative to Miscanthus × giganteus (M × g). We compared the cell wall composition, saccharification efficiency, and yield after spring harvests in 2021 and 2022 across six European locations. Cell wall content and composition varied significantly among hybrids and were influenced by environmental conditions, yet differences due to parental background were largely consistent across locations. On average, seed-based interspecific hybrids (80.6%–84.0% neutral detergent fiber) had a lower total cell wall content than the other hybrids evaluated in this study (88.3%–90.8%). In contrast, cellulose was ~5.5% higher in hybrids with an M. sinensis × M. sacchariflorus background relative to the intraspecific hybrids, while hemicellulose averaged above 34% for intraspecific hybrids, 29.4% to 31.8% in the interspecific hybrids, and below 27% for M × g. Lignin content was highest in M × g (~13.8%), intermediate in the interspecific hybrids (11.0%–12.2%), and lowest in the intraspecific hybrids (~10%). These compositional traits translated into saccharification efficiencies that were 32.9% higher for the intraspecific hybrids and 9.8%–13.1% higher for the interspecific hybrids (seed-based and clonally propagated) compared to M × g. Accounting for biomass yield, either several seed-based hybrids or the novel clonally propagated hybrid exceeded the theoretical ethanol potential of M × g at all trial locations, indicating strong potential for their use in lignocellulosic biofuel production.</p
AEDMA-NDMAI:Automatic Extraction and Daily Monitoring of Algal Blooms Using Normalized Difference MODIS Algae Index
No full textAlgal blooms are ecological phenomena with long-lasting effects on the ecosystem and on the climate. Often, they reduce the oxygen level underwater, creating adverse circumstances for aquatic species’ survival, development, and reproduction. In this article, the mapping of algal bloom incidents and their daily monitoring is automated using Python script and the Earthdata website. The automation is carried out in eight separate modules and then integrated. Test site dictionary, configuration, query data, download MODIS data, open image data, clip data, implementing a novel Normalized Difference MODIS Algae Index (NDMAI), and threshold are the eight modules used for automating the extraction and daily monitoring. This automation requires two inputs: firstly, the bounding box, i.e., lower left coordinate (LLC) and upper right coordinate (URC) of the test site, and secondly, the date range. In this article, eight test sites are used to extract algal bloom incidents, and a ninth test site is used for the extraction and daily monitoring, which are reported by the NASA Earth Observatory (NEO). The proposed framework automates the process of enhancing algal bloom features in MODIS imagery, and daily monitoring is successfully accomplished, and the results perfectly match the algal bloom region in the test sites reported by the NEO
A Dynamic Multi-Scale Hypergraph Learning Framework Driven by Features and Structures for ceRNA-Disease Association Prediction
No full textCompetitive endogenous RNA (ceRNA) networks are pivotal for uncovering disease molecular mechanisms. Graph representation learning is a cornerstone for modeling biological regulatory networks and predicting disease-related biomarkers. However, current methods face challenges: traditional graph neural network (GNN) rely on low-order graph structures, which struggle to capture highorder molecular interactions, resulting in topological information loss; shallow GNN fail to model long-range dependencies, while deep architectures suffer from oversmoothing, limiting complex regulatory expression; static embeddings overlook dynamic molecular interactions, reducing biomarker accuracy. These limitations highlight the need for advanced graph learning frameworks. To address these challenges, we propose DMHLF, a Dynamic Multi-scale Hypergraph Learning Framework for predicting disease-associated ceRNA biomarkers. The framework first integrates multiple regulatory relationships among miRNAs, lncRNAs, circRNAs, mRNAs, and diseases to construct disease-specific ceRNA regulatory networks, capturing local and global regulatory patterns through multi-Hop hyperedges. Subsequently, we devise a HypergraphWeighted Dynamic Random Walk (HEDRW) method to dynamically extract node meta-embeddings that encode high-order regulatory information. Concurrently, we extend Eigen-GNN spectral analysis to hypergraph structures, incorporating a residual-enhanced hypergraph neural network to preserve the global topological properties of shallow hypergraphs. Finally, a cross-scale attention mechanism aligns and fuses multi-scale features to generate high-quality node embeddings for disease-ceRNA association prediction. Experiments on diverse datasets demonstrate that DMHLF significantly outperforms existing methods. Case study further validates the framework's efficacy in identifying disease-related ceRNA biomarkers, providing a reliable predictive tool for biomedical research.</p
Fiction Genre:From the Academy to the Library
No full textIt has been suggested that approaches to the provision of fiction, and the treatment of fiction, in the library world generally follow the attitudes adopted by the academy. To explore whether the treatment of fiction genre in librarianship follows the attitudes of the academy, this reflective paper examines the ways in which fiction genre has been approached in the discourse of literary and cultural studies and in the librarianship and information science domain with a view to determining similarities. Genre is an unusual organizing category, being fluid, permeable, changing, and often debated. In addition to the diachronically transformative nature of genre at the system level, the relationship between the individual generic text and the generic system offers some interesting challenges. All of this means that genre as a principle of knowledge organization is tricky; nevertheless it continues to be used in a range of knowledge organization systems relating to cultural documentation, from conventional library systems to user-led social media systems. Given the fluid nature of fiction genre, issues regarding the scope and range of genres are considered in relation to libraries as communities of practice, focusing on genrefication projects in school libraries.</p