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Editorial Perspective:Interdisciplinary Research in HRM for Impact—Rethinking, Redefining and Reshaping Practices
No full textThe aim of this editorial perspective is to highlight the importance and relevance of interdisciplinary research (IDR) in HRM, offer insights into the core aspects of IDR (i.e., what, why, when, and how), and offer guidance and tools necessary to conduct IDR in HRM. It incorporates the input of leading scholars on the key components of IDR, such as paradigms, theory, methodological plurality, contributions and impact and an integrative framework to facilitate IDR in HRM. This perspective seeks to move the field of HRM forward by offering theoretical and methodological alternatives that are needed to effectively engage in IDR, and practical tools and techniques for conducting IDR research in HRM
LATENT DIFFUSION-BASED 3D MOLECULAR RECOVERY FROM VIBRATIONAL SPECTRA
No full textInfrared (IR) spectroscopy, a type of vibrational spectroscopy, is widely used for molecular structure determination and provides critical structural information for chemists. However, existing approaches for recovering molecular structures from IR spectra typically rely on one-dimensional SMILES strings or two-dimensional molecular graphs, which fail to capture the intricate relationship between spectral features and three-dimensional molecular geometry. Recent advances in diffusion models have greatly enhanced the ability to generate molecular structures in 3D space. Yet, no existing model has explored the distribution of 3D molecular geometries corresponding to a single IR spectrum. In this work, we introduce IR-GeoDiff, a latent diffusion model that recovers 3D molecular geometries from IR spectra by integrating spectral information into both node and edge representations of molecular structures. We evaluate IR-GeoDiff from both spectral and structural perspectives, demonstrating its ability to recover the molecular distribution corresponding to a given IR spectrum. Furthermore, an attention-based analysis reveals that the model is able to focus on characteristic functional group regions in IR spectra, qualitatively consistent with common chemical interpretation practices
A Digital Intervention for Capturing Real-Time Health Data for Epilepsy Seizure Forecasting:Protocol for the ATMOSPHERE Study
No full textBackground: Epilepsy is a chronic neurological disorder marked by recurrent and apparently unpredictable seizures and associated with premature death, injury, and diminished quality of life. The unpredictability of seizures is a major concern for people with epilepsy. Thus, developing tools for seizure prediction is a research priority. The Artificial Intelligence to Optimise Seizure Prediction to Empower People With Epilepsy (ATMOSPHERE) project focuses on the development and evaluation of seizure forecasting technology involving mobile technology and machine learning to provide personalized seizure forecasting (risk of seizure in the near future). The project is informed by complex intervention frameworks, which recommend phases of development, feasibility study, clinical evaluation, and implementation.Objective: Objective 1 aims to conduct a feasibility study to test and refine the trial methods for a future clinical trial. Objective 2 aims to test and refine the data collection technology, considering usability and technical performance. Objective 3 aims to collect longitudinal data on seizures and their precipitants to refine seizure forecasting.Methods: This study is a single-arm, mixed methods feasibility study, testing a prototype of the data collection technology, with phase 2 testing a minimum viable product. In total, 60 participants will be recruited via specialist National Health Service epilepsy clinics. Inclusion criteria are adults with epilepsy, experiencing seizures twice per month, able to consent, and engage with technology. Clinicians will screen and gain consent to contact, with researchers obtaining full consent. Participants will be invited to complete the following study procedures: (1) onboarding, (2) use the data collection technology (phase 1 or 2) in their lived context for up to 6 months, (3) complete patient-reported outcome measures and capture clinical-reported outcome measures at baseline and 3 months, and (4) complete a qualitative interview exploring their views of the data collection technology. A study flow diagram will report recruitment rates (outcome 1), diversity of the recruited sample (outcome 2), barriers and facilitators to recruitment (outcome 3), retention rates (outcome 4), and barriers and facilitators to retention (outcome 5). To assess the data collection technology, quantitative technology use data and qualitative interview data will be analyzed to assess usability (outcome 6) and technical performance (outcome 7) of the data collection technology. These outcomes will inform iterative minimum viable product development and testing cycles with stakeholders.Results: Recruitment is planned to begin in quarter 1 of 2026, with data collection expected to be completed by quarter 2 of 2027. Data analysis will take place during quarter 3, and the results will be published in quarter 4.Conclusions: This project aims to improve clinical outcomes for people with epilepsy through seizure forecasting technology. To evaluate clinical outcomes, robust trial methodology is critical. This feasibility study will optimize methods for a future full-scale clinical trial as well as refine the seizure forecasting intervention.International Registered Report Identifier (IRRID): PRR1-10.2196/85993.<div/
Random Permutation Circuits Beyond Qubits are Quantum Chaotic
No full textRandom permutation circuits were recently introduced as minimal models for local many-body dynamics that can be interpreted both as classical and quantum. Standard dynamical complexity indicators such as damage spreading and out-of-time-order correlators (OTOCs), show that these systems exhibit sensitivity to initial conditions in the classical setting and operator scrambling in the quantum setting. Here, we address their quantum chaoticity - a stricter property - by studying the time evolution of local operator entanglement (LOE). We show that the behaviour of LOE in random permutation circuits depends on the dimension of the local configuration space q. When q = 2, i.e. the circuits act on qubits, random permutations are Clifford and the LOE of any local operator is bounded by a constant, indicating that they are not truly chaotic. On the other hand, when the dimension of the local configuration space exceeds two, the LOE grows linearly in time. We prove this in the limit of large q and present numerical evidence that a three-dimensional local configuration space is sufficient for a linear growth of LOE. Our findings highlight that quantum chaos can be produced by essentially classical dynamics. Moreover, we show that LOE can be defined also in the classical realm and put it forward as a universal indicator chaos, both quantum and classical
Indoor air quality and its impacts on asthma and COPD
No full textBackground: Though indoor air pollution is associated with high mortality and economic impact globally, it is relatively understudied. Knowledge gaps remain regarding exposure to peak pollutant concentrations and their effects, especially among patients with respiratory diseases who are susceptible to a greater impact.Methods: This 2-week cohort study monitored indoor air quality and symptoms in patients with asthma and chronic obstructive pulmonary disease. Statistical process control charts were used to track hourly pollutant peaks, while notched box plots visualised significant particulate matter 2.5 (PM2.5) peaks over 6-hour periods. Linear mixed-effects and autoregressive models were used to assess the impact of PM2.5 on symptoms. Results: The analyses included 30 participants. Hourly plots revealed that 43.3% experienced PM2.5 and PM1 peaks above the upper control limit between 6 pm and 9 pm, with 33.3% occurring specifically at 19:00 hours, consistent with cooking as a source of particulates. There were also a few peaks between 10 am and 12 noon. Peaks recorded between midnight and 5 am were minimal, corresponding to low activity during sleep. Smokers exhibited higher average pollutant levels than non-smokers. On average, participants experienced four to six pollutant peak periods exceeding the WHO 2021 air quality guidelines. No statistically significant association was found between PM2.5 and asthma symptoms (p>0.05), although a weak relationship was observed visually. Conclusion: The data suggest that human activities significantly influence indoor air quality for PM, indicating that behavioural interventions could help optimise it.</p
Abnormal acoustoplasticity originating from ultrasound-controlled evolution of multiscale ordering in complex concentrated alloys
No full textContrary to the traditional Blaha effect in ordinary metals and alloys, where ultrasonic excitations induce softening, this study unveils an abnormal acoustoplasticity effect in the CrCoNi complex concentrated alloy (CCA), where ultrasound induces hardening up to 52% by increasing the ultrasonic amplitude. Two effects contribute to this unusual phenomenon in CrCoNi. First, as ultrasound amplitude increases, dislocations multiply, leading to work hardening, as opposed to annihilation leading to softening as in ordinary metals and alloys, due to the very low stacking fault energy in CrCoNi, which makes dipole annihilation on stress reversal difficult. Secondly, ultrasound vibrations trigger novel disordering-ordering transitions in CrCoNi, progressing from short- to long-range ordering, resulting in phases including L12 and Cr2O3 nano-precipitates as well as large (∼2.37 μm) Cr2O3 precipitates. Combined experiments and Monte Carlo/molecular dynamics (MC/MD) simulations suggest that the enhanced ordering stems from two mechanisms: (i) the multiplied dislocations attract atomic segregation at their cores, and (ii) the incomplete dipole annihilation driven by stress reversals elevates the concentration of vacancies, which enhances atomic diffusion for ordering. These ordered phases may facilitate higher dislocation nucleation rates and hinder dislocation mobility, further promoting dislocation multiplication in the CrCoNi alloy, which has low cross-slip potential. Quantitative analysis reveals that the contribution of ordering to hardening even surpasses Taylor hardening at an ultrasound amplitude of 11 μm. This work shows an unexpected acoustoplastic response in CCAs arising from the coupling of dislocation-based mechanisms and ultrasound-induced ordering, offering a novel pathway for customizing the mechanical properties in these alloys
Antipsychotic-induced weight gain in psychosis:causal mediation analysis and feasibility study of causal actionable prediction model development using counterfactuals to target obesity
No full textBackground: People with psychosis have a life expectancy that is reduced by 15 years, mainly owing to preventable physical illnesses of which obesity is a precursor. Obesity is three times more common in individuals with psychosis, and antipsychotics are an important cause. Prediction could individualise obesity treatment, but current models are not fully actionable for individuals. Aims: To test whether antipsychotic-induced weight increase at 1 year is causally mediated by weight change in the first 12 weeks of treatment, and then develop and internally validate a causal actionable prediction pathway to prevent antipsychotic-induced obesity. Method: This was a post hoc analysis of a clinical trial of olanzapine versus haloperidol which recruited 263 participants with first-episode psychosis. We conducted two distinct analyses: causal mediation and prediction modelling, within which there were two sequential models (a baseline model to predict 12-week outcome and a 12-week model to predict 1-year outcome), followed by counterfactual prediction. In the first analysis, we used parallel causal mediation analysis to determine the natural direct and indirect and total effects of antipsychotic choice on weight in 97 participants, considering two mediators: weight change from 0 to 12 weeks, and weight change from 12 to 52 weeks. In the second analysis, we first developed a baseline causal actionable prediction model to predict weight gain at 12 weeks in 172 participants and then a 12-week model to predict obesity at 1 year in 97 of the participants. Finally, we demonstrated counterfactual prediction. Results: Antipsychotic-induced weight gain at 1 year appeared to be causally mediated by weight change during the first 12 weeks of treatment (indirect effect 5.70; 95% CI 2.83 to 8.66). At internal validation, the discrimination c-statistic for the baseline causal actionable prediction model was 0.728 (95% CI 0.661 to 0.801), and the calibration slope was 0.768 (95% CI 0.436 to 1.21). For the 12-week model, the c-statistic was 0.904 (95% CI 0.820 to 0.961), and the calibration slope was 0.601 (95% CI −0.0633 to 1.21). We used the models to predict the counterfactual outcomes of antipsychotic choice and 12-week weight change. Conclusions: Our results show that it may be early rather than later weight change that causally mediates antipsychotic-induced weight gain at 1 year. They also demonstrate the potential for causal actionable prediction of counterfactuals for true precision medicine, although this is tempered by the feasibility scope of this study and small sample size. Our results are hypothesis-generating and not yet clinically deployable
Chemical cues from <i>Agrilus biguttatus</i> beetle larvae trigger proliferation and putative virulence gene expression of the tree pathogen <i>Brenneria goodwinii</i>
No full textAIMS: Agricultural crop productivity and global forest biomes are coming under increasing threat from insect pests and microbial pathogens. This impact is worsened by inter-kingdom insect-microbe interactions that can increase transmission and disease severity in affected plants. Whilst bacterial chemical cues have been shown to directly influence insect behaviour, the impact of insect-derived compounds on phytopathogens is poorly understood. Here, we investigated the chemical basis for interactions between beetle larvae and bacteria in acute oak decline (AOD), a disease characterised by inner bark necrosis of Quercus robur and Q. petraea involving a polymicrobial consortium including Brenneria goodwinii and larval galleries of Agrilus biguttatus.METHODS AND RESULTS: : We found that A. biguttatus larval extractable metabolites increase bacterial growth rate and final cell density during in vitro culture, and stimulate the differential expression of ∼600 genes, including the type III secretion system and its effectors, which are major virulence factors in plant pathogens. Chemical compounds from closely related insect species did not have this effect.CONCLUSIONS: These findings highlight the importance of inter-kingdom interactions in plant disease and suggest a role for insect-derived chemical elicitors in facilitating the virulence of phytopathogens.</p
Written evidence submitted by Professor Martin Coppack and Professor Adele Atkinson: CHASM, University of Birmingham
No full textFinancial inclusion in the UK is prevented by deep-rooted structural and market failures. While the Strategy contains a number of welcome initiatives, it is overly narrow in scope, heavily reliant on industry goodwill, and lacks the government and regulatory ambition, targets, and accountability mechanisms required to deliver a significant reduction in financial exclusion
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