This multi-site experimental study investigated the Hue-Heat Hypothesis (HHH), which posits that light hues can influence human thermal perception, as well as broader cross-modal interactions between visual and thermal domains. Across 464 experimental sessions in eight test rooms around the world, participants were exposed to varied thermal conditions (∼20 °C, ∼24 °C, ∼26 °C, and ∼28 °C) and typical white-light Correlated Color Temperatures (CCT, warm light: ∼3000 K; neutral: ∼4000 K; cool light: ∼6000 K) from LED sources (horizontal illuminance: ∼500 lx). The study assessed thermal, visual, and overall perceptions. Results revealed that thermal sensation and preference were predominantly influenced by thermal conditions, gender, and the laboratory setting, indicating that no statistically significant effects were found in support of the HHH. Similarly, visual perceptions were influenced by lighting conditions but not by the thermal environment. For instance, cool light was perceived as brighter than warm light, leading participants to prefer brighter light under warm light hues. Ultimately, this research revealed the significant challenges of interlaboratory experiments in this field, as local climate and test-room characteristics complicate both the conduct and the standardization of data analysis. Our findings highlight both the limited role of white-light CCT in shaping thermal sensations and the methodological challenges of multi-site comfort research, underscoring the need for careful data harmonization and context-aware analyses in future international collaborations.

The recent discovery of strong tidal dissipation in Saturn’s interior has radically changed our view of the Saturnian system. While some questions are naturally answered by the new paradigm, others are emerging and require further measurement. This article presents the next key questions to be addressed by future space missions and analysis. Suggestions for space measurements to discriminate between different scenarios concerning the formation, evolution and internal state of the Saturnian system are given.

Minds-On is a novel, theoretically-driven pedagogical approach to support the teaching and learning of science concepts and reasoning skills inupper primary science lessons. Minds-On uses a hands-on – minds-on app- roach interleaving doing experiments with creating interactive concept diagrams. Lessons are fully orchestrated in the online application. Learners complete the lessons independently and receive guidance and feedback from the software. Four lessons have been created combining the following concepts and reasoning skills: seasons/cause-effect, sound/cause-effect, fixings & animals/classificationand bicycle/systems thinking. The approach has been developed using an itera-tive design method with evaluations in each phase either in the classroom or in laboratory conditions. Results from one classroom evaluation (N = 490) showed that both teachers and learners enjoyed working with the lessons, that learners are easily able to complete the lessons within the allocated time and that the app- roach enables learners to effectively apply several types of scientific reasoning and do so more autonomously than in traditional science lessons. Results from the knowledge questionnaires in a later study of the sound lesson (N = 147) showed a significant increase in score compared to before the lesson. In this demonstration
paper, we present the Minds-On approach and associated application, discussing the design process, the theoretical principles on which our approach is based and the preliminary results from recent classroom evaluations.

INTRODUCTION: The sortilin-related receptor (SORL1) directs APP and Aβ trafficking within the retromer pathway. Cleavage at the cell surface releases soluble SORL1 (sSORL1) into cerebrospinal fluid (CSF). We examined whether CSF-sSORL1 can serve as an in vivo marker of genetically impaired SORL1. METHODS: CSF-sSORL1 was quantified by enzyme-linked immunosorbent assay (ELISA) in 218 participants: 90 carriers of SORL1 variants, 78 SORL1-wildtype (WT) AD patients, and 50 SORL1-WT controls. RESULTS: sSORL1 concentrations were significantly lower in carriers of protein-truncating and damaging missense variants. In SORL1-WT patients, CSF-sSORL1 correlated with pTau181 but not with Aβ42 among AD patients, and did not differ between patients and controls. DISCUSSION: These findings suggest that impaired SORL1 trafficking reduces receptor delivery to the cell surface and thereby decreases sSORL1 shedding, supporting its potential use as a pathway-specific biomarker. Highlights: Enzyme-linked immunosorbent assay (ELISA) enables quantitative measurement of soluble sortilin-related receptor (sSORL1) in cerebrospinal fluid (CSF). sSORL1 levels are reduced in CSF from carriers of a pathogenic SORL1 variant. CSF-sSORL1 levels correlate with tau pathology in Alzheimer's disease. sSORL1 levels represent an in vivo biomarker of SORL1 function.

People around the world seek climate risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate risk services should provide, many expect noncommercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: Only four percent of the most-cited peer-reviewed climate risk studies in recent years fully share their data and code although this is a widely accepted minimum standard for transparency. We highlight low-cost measures that noncommercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate risk management.

Wastewater treatment is a major source of global greenhouse gas (GHG) emissions, largely driven by nitrous oxide (N2O) release during nitrogen removal processes. While anaerobic ammonium-oxidizing (anammox) bacteria have become widely adopted to optimize nitrogen removal, the pathways governing N2O emissions in anammox systems remain insufficiently characterized. Findings revealed that in aerobic environments with low dissolved oxygen, nitrifying microorganisms showed pronounced upregulation of N2O synthesis genes, confirming their pivotal role in emissions. Conversely, heterotrophic denitrification dominated N2O production in anaerobic systems. Crucially, anammox-dominated biofilms displayed substantially lower expression of N2O-related genes compared to suspended sludge, suggesting reduced emission risks in biofilm configurations. Batch experiments demonstrated a 2.78-fold lower N2O release from biofilms than suspended biomass. This mitigation was linked to restricted substrate transport within biofilms, which curbed NO2− buildup and subsequently suppressed nitrifier-derived N2O formation. Additionally, the high-density colonization of anammox bacteria in biofilms efficient scavenging of nitric oxide (NO) and hydroxylamine (NH2OH), critical intermediates in N2O synthesis. Employing an integrated multi-omics approach, this study investigates N2O production and consumption pathways in diverse anammox systems and unveils the N2O mitigation mechanisms of biofilms, offering fundamental insights for reducing N2O emissions in wastewater treatment.

A comprehensive in-situ dataset of low-level Arctic clouds was collected in the Fram Strait during the HALO-(AC⁾³ campaign in spring 2022 using the research aircraft Polar 6. The clouds observed at altitudes below 1000 m were frequently in a mixed-phase state. We demonstrate that despite comparable optical properties, classic mixed-phase clouds (MPC) and mixed-phase haze (MPH) can be distinguished on the basis of their microphysical properties, with MPH observed about 8 times more frequently than MPC. While the thermodynamic phases of the particles within the MPH are similar to those in the MPC, the supercooled droplets observed in MPC are replaced by large (> 3 µm) wet aerosol particles in MPH. Furthermore, the particle number concentration measured in MPH is reduced by approximately 3 orders of magnitude compared to MPC. MPH is observed in subsaturated air with respect to water, suggesting that the small liquid particles are haze droplets and are in equilibrium below the activation threshold to form cloud droplets. Chemical analysis suggested that the haze particles contained significant amounts of sea salt. Additional in-situ measurements with an optical particle counter indicated that their number concentration was 2 times larger over the sea ice compared to the open ocean. Furthermore, measurements of the vertical distribution of the thermodynamic phases in low-level Arctic clouds revealed a characteristic structure, with a liquid regime frequently occurring at the top of the atmospheric boundary layer, followed by MPCs, and an MPH layer below. The findings from this study enhance our understanding of the microphysical composition of clouds in mixed-phase conditions.

Airbus, through its Tech Hub in the Netherlands and Airbus UpNext, is spearheading efforts to accelerate the maturation of critical cryogenic technologies. The ambition? Achieving hydrogen-powered flight in the next decades. As the Airbus Tech Hub aims to catalyse breakthroughs in aerospace technology, ICEFlight is one of its major projects that contributes to shaping the future of aviation. Project ICEFlight (Innovative Cryogenic Electric Flight) is supported by "Luchtvaart in Transitie", a Dutch public-private programme bringing together the aviation sector and local ecosystem. This presentation will detail how ICEFlight, in collaboration with a strong consortium including GKN Aerospace, Cryoworld B.V., Stirling Cryogenics B.V., Futura Composites B.V., the Royal NLR, Delft University of Technology, and University of Twente, will focus on the dual use of liquid hydrogen, as well as fuel as a source for cooling. Key aspects of ICEFlight include: – The development of a specialized cryogenic cooling and electrical distribution system. – Advancing the skills, competences, technologies, and products within the Netherlands for the next generation of aircraft. – The establishment of world-class testing facilities in the Netherlands, led by the Royal NLR, to ensure the reliability and validate the performance of cryogenic systems. Furthermore, the presentation will explain how ICEFlight is connected to Airbus UpNext's CRYOPROP demonstrator, highlighting the coordinated effort to mature superconducting and cryogenic technologies for future hydrogen-powered aircraft. ICEFlight is expected to significantly benefit the Dutch ecosystem by fostering innovation, developing local skills, and strengthening the local supply chain. These developments will all contribute to ultimately positioning the Netherlands as a key player when it comes to the future of aviation.

The rational design of functional materials through targeted element-selection strategies offers a promising route for developing next-generation catalysts. Here, we employ this strategy to tackle critical challenges in the Reverse Water Gas Shift (RWGS) reaction, including catalyst deactivation, low CO selectivity, and the high cost of conventional transition-metal catalysts. Through this approach, we designed and synthesized a novel class of metal-free boron-oxy-carbide (BO) catalysts. The catalyst exhibited 100 % CO selectivity and maintained equilibrium CO₂ conversion without deactivation for over 250 h at 600 °C. Advanced characterization techniques, combined with density functional theory (DFT) calculations suggested that the ‘B-O-C triad’ within the BO lattice is responsible for the RWGS activity. These findings demonstrate the potential of BO catalysts as robust, scalable, and sustainable alternatives to state-of-the-art transition-metal-based catalysts for CO₂ valorization. We anticipate that these findings will provide a foundation for the design and activity of metal-free catalysts applicable to a diverse range of chemical transformations.

Understanding sulfur confinement and chemical transformation in hybrid sulfur-carbon materials is critical for advancing metal-sulfur batteries. Here, we investigate the structural evolution of a sulfur-rich polymer into a hybrid sulfur-carbon via inverse vulcanization and thermal condensation. Multiscale analyses reveal a stepwise transformation, beginning with the emergence of sulfur radicals at ∼175°C, followed by the progressive development of a carbon matrix above 300°C that stabilizes the radical species. Around 450°C, a transitional phase forms, consisting of conjugated carbon clusters covalently bonded to sulfur chains. This hybrid structure confines sulfur within pseudo-graphitic nanodomains, effectively suppressing polysulfide dissolution and enhancing redox stability. DFT simulations show how sulfur confinement modulates Na-S reaction energetics, while electrochemical testing confirms high sulfur utilization, delivering ∼1000 mAh (Formula presented.) and 1200 Wh (Formula presented.), setting a new performance benchmark for room-temperature Na─S batteries. These findings provide critical insights into the correlation between structural evolution and electrochemical performance, offering design principles for next-generation sulfur-based electrodes.

Future space-based far infrared astronomical observations require background limited detector sensitivities and scalable focal plane array solutions to realize their vast potential in observation speed. In this work, a focal plane array of lens absorber coupled kinetic inductance detectors (KIDs) is proposed to fill this role. The figures of merit and design guidelines for the proposed detector concept are derived by employing a previously developed electromagnetic spectral modeling technique. Two designs operating at central frequencies of 6.98 and 12 THz are studied. A prototype array of the former is fabricated, and its performance is experimentally determined and validated. Specifically, the optical coupling of the detectors to incoherent distributed sources (i.e., normalized throughput) is quantified experimentally with good agreement with the estimations provided by the model. The coupling of the lens absorber prototypes to an incident plane wave, i.e., aperture efficiency, is also indirectly validated experimentally matching the expected value of 54% averaged over two linear polarizations. The noise equivalent power of the KIDs is also measured with limiting value of 8 × 10-20W\√Hz at the bath and radiator temperatures of 130 mK and 2.7 K, respectively, under negligible optical loading.

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e. from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

Magnon spintronics aims to harness spin waves in magnetic films for information technologies. Color center magnetometry is a promising tool for imaging spin waves, using electronic spins associated with atomic defects in solid-state materials as sensors. However, two main limitations persist: the magnetic fields required for spin-wave control detune the sensor-spin detection frequency, and this frequency is further restricted by the color center nature. Here, we overcome these limitations by decoupling the sensor spins from the spin-wave control fields –selecting color centers with intrinsic anisotropy axes orthogonal to the film magnetization– and by using color centers in diamond and hexagonal boron nitride to operate at complementary frequencies. We demonstrate isofrequency imaging of field-controlled spin waves in a magnetic half-plane and show how intrinsic magnetic anisotropies trigger bistable spin textures that govern spin-wave transport at device edges. Our results establish color center magnetometry as a versatile tool for advancing spin-wave technologies.

Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modeling communities, and statistical models. The following set of recommendations have been formulated:

(i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies;
(ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those;
(iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of simple features,
(iv) managing communities and fostering diversity is a necessary, on-going effort, and
(v) tools for cross-language development need more attention and support.

Studies have identified a link between specific microbiome-derived bacteria and immune checkpoint blockade (ICB) efficacy. However, these species lack consistency across studies, and their immunomodulatory mechanisms remain elusive. To understand the influence of the microbiome on ICB response, we studied its functional capacity. Using pan-cancer metagenomics data from ICB-treated patients, we showed that community-level metabolic pathways are stable across individuals, making them suitable for predicting ICB response. We identified several microbial metabolic processes significantly associated with response, including the methylerythritol 4-phosphate (MEP) pathway, which was associated with response and induced Vδ2 T cell–mediated antitumor responses in patient-derived tumor organoids. In contrast, riboflavin synthesis was associated with ICB resistance, and its intermediates induced mucosal-associated invariant T (MAIT) cell–mediated immune suppression. Moreover, gut metabolomics revealed that high riboflavin levels were linked to worse survival in patients with abundant intratumoral MAIT cells. Collectively, our results highlight the relevance of metabolite-mediated microbiome–immune cell cross-talk.

Microfold (M) cells are rare intestinal epithelial cells that reside in the follicle-associated epithelium of Peyer’s patches1. M cells transport luminal antigens to submucosal antigen-presenting cells2,3. These insights primarily derive from transmission electron microscopy and studies using genetically modified mice2, 3–4. Here we establish an intestinal organoid model to study human M cells and reconstruct the differentiation trajectory of M cells through transcriptome profiling. The results indicate that as well as facilitating luminal antigen transport, human M cells also directly present antigens via the class II major histocompatibility complex (MHC-II). Notably, the related enterocytes only express MHC-II in chronic inflammatory states and do not express typical dendritic cell markers. Human M cells physiologically express a gene profile that resembles that of dendritic cells. Similar to dendritic cells, M cell development is induced by RANKL and CSF2 and requires the transcription factors SPIB and RUNX2. HLA-DQ2.5 M cells process and present gluten antigen as demonstrated in organoid–T cell co-culture assays. These findings suggest that M cells may have a central role in coeliac disease.

This study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress–stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research.

Young drivers represent a high-risk group worldwide, with their overrepresentation in road trauma placing substantial pressure on health and economic systems. Their crashes are often linked to risky driving behaviors, accentuating the need for reliable instruments to assess these patterns. The Behavior of Young Novice Drivers Scale (BYNDS) was developed to comprehensively assess multiple dimensions of risky driving behavior in drivers aged 17–29 years; however, it has not yet undergone cross-cultural validation. Aim This study aimed to conduct a comprehensive cross-cultural validation of the BYNDS and examine differences in risky driving behaviors among young drivers from Low- and Middle-Income (LMIC) and High-Income (HIC) countries. Method Data were collected from a cross-sectional sample of n = 3,989 young drivers aged M = 22.25 years, of whom 52 % were male and 48 % female. Participants completed the BYNDS, a 44-item behavioral questionnaire administered across 12 countries (48.6 % LMICs; 51.4 % HICs) spanning five continents. Results The findings indicate that the BYNDS supports a five-factor structure with good fit indices, strong factor loadings, and acceptable reliability, and invariance between countries of different income levels. Furthermore, the validated BYNDS-42 (comprising 42 items distributed across five factors) also showed the ability to distinguish between drivers with and without self-reported crashes or traffic fines. Conclusion This study provides robust evidence supporting the cross-cultural validity and reliability of the BYNDS, reinforcing its value as a tool for assessing young driver behavior. These findings offer empirically grounded insights that can inform behavioral interventions aimed at improving young drivers’ road safety.

Control theory underpins the stabilization of dynamic systems, including cardiac tissue, where disruptions in electrical conduction cause arrhythmias. Current treatments either act rapidly but without precision or deliver targeted interventions that cannot adapt in real time. We present an integrated platform combining optical voltage mapping (OVM), machine learning (ML), and optogenetics for autonomous, real-time detection and correction of cardiac rhythm disorders in vitro. OVM provides high-resolution membrane potential visualization; the ML module identifies arrhythmic events and drives microLED-based light patterns restoring normal conduction; and optogenetics enables light-based modulation of excitable cells. This integration of electrical, optical, and bioelectrical domains through a unified computational control layer enables adaptive, closed-loop rhythm stabilization, a significant advance in real-time electrophysiological interventions. Because inference and actuation run in real time on modest hardware, the same control loop could be embedded into miniaturized devices or microcontrollers, accelerating the transition from in-vitro to in-vivo automated rhythm management.