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Observation of emergent scaling of spin–charge correlations at the onset of the pseudogap
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Any Light Particle Searches with ALPS II: first science campaign
No full textFrom February to May of 2024 the Any Light Particle Search II (ALPS II) conducted its first science campaign using the `light-shining-through-a-wall' technique to search for pseudo-Goldstone bosons that lie beyond the Standard Model of particle physics and which are inaccessible by accelerator-based experiments. The experimental setup consists of two strings of superconducting dipole magnets, each more than 100 m long, that are separated by a wall. Laser light is directed through the first magnet string and a heterodyne detection system is used to measure the electromagnetic power that traverses a wall via the conversion to and then from a bosonic field. After the wall, a high-finesse optical cavity resonantly enhances the signal power. Two searches were carried out, one with the laser polarized perpendicular to the magnetic field direction and another with its polarization state aligned parallel to the magnetic field. No evidence for the existence of new bosons was found. In its first science campaign, ALPS II reached photon-boson conversion probability sensitivities of a few . The ongoing upgrade of the optical system aims to increase this sensitivity by about four orders of magnitude
Tuning the circularly polarized reflection from cholesteric hydroxypropyl cellulose using molecular photoswitches
No full textControlling the ordering of molecular liquid crystals using light has found widespread applications in reflectors, sensors, and tunable optical filters. In chiral liquid crystals such as cholesterics, introducing a molecular switch can alter chiral interactions between mesogens and thus affect the periodicity and photonic band responsible for the structural color of the material. However, analogous photo-control of cellulose-based cholesteric polymers has not been reported. Here, we investigate the addition of achiral and chiral switches to cholesteric mesophases of structurally colored hydroxypropyl cellulose (HPC) and monitor changes in the photonic bandgap upon varying concentration, illumination, or temperature. While an achiral photoswitch enables reversible tuning of the reflected color upon trans-cis photoisomerization, chiral switches (with different pendant groups and R/S enantiomers) lead to substantial differences in pitch and polarized reflection during cycles of illumination or heating/cooling–attributed to the complex interplay between the chiralities of the switch and of the host mesophase. A final combination of chiral photoswitch and spiropyran enables independent control of reflected and transmitted color, which can be relevant for cellulose-based photonic displays, smart labels, or anti-counterfeiting technology
Hybrid learning enables reproducible >24% efficiency in autonomously fabricated perovskites solar cells
No full textAchieving high-performance perovskite solar cells (PSCs) with satisfactory reproducibility remains a major challenge due to their intrinsic susceptibility to processing variations and environmental fluctuations. To address this challenge, this study introduces an autonomous optimization framework that integrates hybrid machine learning and high-throughput experimentation with modified gradient ascent methods to optimize fabrication processes and minimize experimental variances. The framework successfully maps the complex, non-linear interdependencies between fabrication parameters and reveals the critical decoupling of photovoltaic metrics. Optimization across seven rounds and 144 parameter sets results in pronounced power conversion efficiency (PCE) and reproducibility enhancement on the platform. The optimized procedure delivers champion devices achieving PCEs exceeding 24%, surpassing the experience manual operator performance (20.6% PCE, CV >25%) and reducing the coefficient of variation (CV) to below 4.7%, with improvements consistently validated across independent trials. This work offers a practical strategy for improving PSC performance and reproducibility, while laying a foundation for scalable manufacturing and accelerated materials development
Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples
No full textHuman social interactions rely on the ability to reflect on one’s own and others’ internal states and traits—a process known as mentalizing. Impaired or altered mentalizing is a hallmark of multiple psychiatric and neurodevelopmental conditions. Yet, replicable and easily testable brain markers of mentalizing have so far been lacking. Here, we apply an interpretable machine learning approach to multiple datasets (total N=390) to train and validate fMRI brain signatures that predict i) mentalizing about the self, ii) mentalizing about another person, and iii) both types of mentalizing. Self-mentalizing and other-mentalizing classifiers had positive weights in anterior/medial and posterior/lateral brain areas, respectively, with accuracy rates of 82% and 77% for out-of-sample prediction. The classifier trained across both types of mentalizing showed 98% predictive accuracy and separated (mental) attributional from factual inferences. Classifier patterns revealed better self/other separation in healthy adults compared to individuals with schizophrenia and with increasing age in adolescence. Together, our findings reveal consistent and separable neural patterns subserving trait-based mentalizing about self and others—present at least from the age of adolescence and functionally altered in severe neuropsychiatric disorders. These mentalizing signatures hold promise as candidate neuromarkers of social-cognitive processes in different contexts and clinical conditions
Attention to alcohol advertising causes elevated consumption via increased alcohol-related craving
No full textObjective: Alcohol advertising can induce craving and increase the consumption of alcohol, though individuals vary in their susceptibility. Recent findings suggest that attentional allocation toward alcohol adverts predicts subsequent alcohol craving and consumption. However, methodological limitations leave key issues unresolved, including whether attention to alcohol adverts causally impacts craving and consumption. This study tested the hypothesis that attentional allocation to alcohol adverts increases their impact on alcohol consumption via craving using an attentional manipulation approach. Method: Seventy-one undergraduate students, who reported enjoying drinking beer, were exposed to beer and soft drink adverts in a dual advert viewing task designed to manipulate attentional allocation toward or away from beer adverts. Following advert viewing, relative craving for beer versus soft drinks and preferential beer consumption were assessed. A mediation model examined whether this attentional manipulation influenced consumption via craving. Results: The attentional manipulation was successful, with participants in the “attend beer adverts” condition displaying a disproportionate attentional allocation toward beer adverts and those in the “avoid beer adverts” condition showing a disproportionate attentional allocation away from beer adverts. Mediation analysis, employing bootstrapped confidence intervals, confirmed that the attentional manipulation influenced beer consumption following advert viewing via its effect on beer craving. Conclusions: These findings provide evidence that attentional allocation toward alcohol adverts causally influences alcohol consumption following advert exposure, mediated by its impact on alcohol craving. We discuss the implications of these findings for targeted interventions to mitigate the potentially harmful effects of alcohol advertising
Automated robust segmentation of the spinal canal on MRI
No full textBackground and ObjectiveAccurate segmentation of the spinal canal is important for quantitative analysis in various spinal pathologies. However, manual delineation is time-consuming and subject to inter- and intra-rater variability, especially in large multi-center studies. This work aimed to develop an automatic model to segment the spinal canal, defined as the dural sac, from T2-weighted (T2w) magnetic resonance imaging sequences.MethodsThis retrospective multi-site model-development study used a 3D convolutional neural network (nnUNetv2 framework) was trained using an active learning strategy on a multi-site dataset (n>18) with varying imaging parameters (resolution, field-of-view). The dataset included T2w images (TSE and 3D CISS) covering the cervical, thoracic, and lumbar spine from both healthy participants and patients with conditions like degenerative cervical myelopathy and traumatic spinal cord injury, including post-operative. The model was tested on unseen T2w images from two sites, including a range of acquisition protocols and clinical presentations, from mild to severe canal compression, to evaluate the performance and robustness. Comparisons were made against state-of-the-art (SOTA) models. Ethics approvals were obtained at each contributing institution; approval identifiers are reported in the main manuscriptResultsThe proposed model achieved strong and consistent quantitative performance (e.g. mean Dice similarity coefficient of 0.95 and mean Hausdorff distance of 1.17 mm² on a healthy thoraco-lumbar test cohort), outperforming the SOTA. Notably, it showed superior robustness in challenging cases of severe spinal canal stenosis, achieving Dice scores >0.9 where SOTA models dropped below 0.5.ConclusionsThe model generalized well across different MRI vendors, sequences, resolutions, and fields-of-view, for both healthy individuals and patients. The model is open-source and available in the Spinal Cord Toolbox v6.4 and higher