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.

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.

Characterisation of the compaction response of reinforcement fabrics is an important component in the design of composite manufacturing processes. To standardise a best practice method, 22 international organisations participated in an exercise to assess the viability and reproducibility of the method discussed in this work. All participants were supplied with the same multiaxial E-glass fibre non-crimp fabric and instructed to measure the compaction stress as a function of the specimen thickness following a set of guidelines. The scatter in results between participants was quantified in terms of the coefficient of variation (CV). The CV of the maximum compaction stress determined at a target specimen thickness of 3 mm (for 10 fabric layers) was 42 % for dry specimens and 46 % for wet specimens, however this was influenced by scatter in the thickness values, which deviated from the target. The CV of the specimen thickness at a compaction stress of 105 Pa was 4 %. In addition, a power law model and a model based on bending of beams were fitted to the compaction curves. Both generally produced fits with high values of the coefficient of determination. The observed level of scatter is thought to be caused by issues with the implementation of the procedures and by variability in the specimen properties, as well as the very steep variation of the force/thickness curve at the required target. The guidelines used here aim to minimise inaccuracies in the test method and will be proposed as a test protocol for standardisation.

Addressing non-idealities in Resistive Random Access Memories (RRAMs) is crucial for their successful commercialization. For example, the inherent resistance drift that occurs during consecutive read operations can induce Read Disturb Faults (RDF), leading to functional errors. This paper analyzes and characterizes the resistance drift and the RDF based on data measurements and presents a physics-based RRAM compact model that incorporates these non-idealities. Additionally, an in-field mitigation scheme is proposed, leveraging bidirectional read operations to balance the resistance. The scheme is implemented and validated through circuit simulations, both for RRAM used as memory and for RRAM-based computation-in-memory microarchitectures for deep neural networks. The results demonstrate that RRAM without any mitigation scheme can start failing after 8,000 consecutive reads, while our mitigation scheme ensures that the memory remains functional even after 10⁶ consecutive reads. Furthermore, the results indicate that using the MNIST dataset as a case study, the accuracy can drop significantly from 86% to as low as 12.5% without any mitigation scheme. In contrast, the proposed mitigation scheme improves this accuracy up to 84.2%.

In this paper, we present the Amsterdam Modeling Suite (AMS), a comprehensive software platform designed to support advanced molecular and materials simulations across a wide range of chemical and physical systems. AMS integrates cutting-edge quantum chemical methods, including Density Functional Theory (DFT) and time-dependent DFT, with molecular mechanics, fluid thermodynamics, machine learning techniques, and more, to enable multi-scale modeling of complex chemical systems. Its design philosophy allows for seamless coupling between components, facilitating simulations that range from small molecules to complex biomolecular and solid-state systems, making it a versatile tool for tackling interdisciplinary challenges, both in industry and in academia. The suite also emphasizes user accessibility, with an intuitive graphical interface, extensive scripting capabilities, and compatibility with high-performance computing environments.

The goal of the NEUROKIT2E project is to create an open-source Deep Learning framework for edge and embedded AI built around an established European value chain. This framework, called AIDGE, supports a wide range of application areas that operate independently and serve a global user community. It provides easy and fast full-stack solutions from Neural Network design and optimization to AI application development all the way down to hardware implementations while enabling code generation for application-specific targets. This platform provides flexibility for academic users in the AI domain to explore and innovate while allowing them the possibility to prototype systems, ensuring their work aligns well with industrial needs. This paper presents the results and achievements of the first part of this three-year project, along with its roadmap and expected outcomes.

Synthetic cells (SynCells) are artificial constructs designed to mimic cellular functions, offering insights into fundamental biology, as well as promising impact in the fields of medicine, biotechnology, and bioengineering. Achieving a functional SynCell from the bottom up, i.e. by assembling it from molecular components, requires a global collaboration to overcome the many challenges of engineering and assembling life-like modules while addressing biosafety, equity, and ethical concerns in order to guide responsible innovation. Here, we highlight major scientific hurdles, such as the integration of functional modules by ensuring compatibility across diverse synthetic subsystems, and we propose strategies to advance the field.

Tumors arise from uncontrolled cell proliferation driven by mutations in genes that regulate stem cell renewal and differentiation. Intestinal tumors, however, retain some hierarchical organization, maintaining both cancer stem cells (CSCs) and cancer differentiated cells (CDCs). This heterogeneity, coupled with cellular plasticity enabling CDCs to revert to CSCs, contributes to therapy resistance and relapse. Using genetically encoded fluorescent reporters in human tumor organoids, combined with our machine-learning-based cell tracker, CellPhenTracker, we simultaneously traced cell-type specification, metabolic changes, and reconstructed cell lineage trajectories during tumor organoid development. Our findings reveal distinctive metabolic phenotypes in CSCs and CDCs. We find that lactate regulates tumor dynamics, suppressing CSC differentiation and inducing dedifferentiation into a proliferative CSC state. Mechanistically, lactate increases histone acetylation, epigenetically activating MYC. Given that lactate's regulation of MYC depends on the bromodomain-containing protein 4 (BRD4), targeting cancer metabolism and BRD4 inhibitors emerge as a promising strategy to prevent tumor relapse.

Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart in vitro. Currently, multiple EHT platforms are used by both industry and academia for different applications, such as drug discovery, disease modelling, and fundamental research. The tissues’ contractile force, one of the main hallmarks of tissue function and maturation level of cardiomyocytes, can be read out from EHT platforms by optically tracking the movement of elastic pillars induced by the contractile tissues. However, existing optical tracking algorithms which focus on calculating the contractile force are customized and platform-specific, often not available to the broad research community, and thus hamper head-to-head comparison of the model output. Therefore, there is the need for robust, standardized and platform-independent software for tissues’ force assessment. To meet this need, we developed ForceTracker: a standalone and computationally efficient software for analyzing contractile properties of tissues in different EHT platforms. The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. In this way, we can obtain information about tissues’ contractile performance. ForceTracker is coded in Python and uses a multi-threading approach for time-efficient analysis of large data sets in multiple formats. The software efficiency to analyze circular and rectangular pillar shapes is successfully tested by analyzing different format videos from two EHT platforms, developed by different research groups. We demonstrate robust and reproducible performance of the software in the analysis of tissues over time and in various conditions. ForceTracker’s detection and tracking shows low sensitivity to common incidental defects, such as alteration of tissue shape or air bubbles. Detection accuracy is determined via comparison with manual measurements using the software ImageJ. We developed ForceTracker as a tool for standardized analysis of contractile performance in EHT platforms to facilitate research on disease modeling and drug discovery in academia and industry.

Significant efforts have been dedicated to the direct syngas conversion into ethanol, however, achieving a high ethanol yield remains a formidable task. In this study, we present the direct syngas-to-ethanol conversion over Li-promoted RhOx/MgO catalyst (RhOx/Li2O/MgO). The ethanol space-time yield (EtOH STY) and selectivity reached 12.2 mmol gcat–1 h–1 and 20%, respectively, at a 35% CO conversion over the RhOx/Li2O/MgO catalyst. The RhOx/Li2O/MgO catalyst demonstrated superior performance in terms of both ethanol selectivity and STY compared to Rh/Li2O catalysts on other support materials and Rh/MgO catalysts promoted with other alkali metals. In situ/operando spectroscopic techniques, combined with other characterisations and theoretical calculations, have elucidated the interactions between Li2O and Rh on the MgO surface. These interactions promote the formation of new active sites and weaken CO adsorption on the Rh surface, thereby enhancing ethanol production. This work provides a promising strategy for improving ethanol yield in syngas conversion processes.

Introduction
The field of forensic DNA analysis has undergone rapid advancements in recent decades. The integration of massively parallel sequencing (MPS) has notably expanded the forensic toolkit, moving beyond identity matching to predicting phenotypic traits and biogeographical ancestry. This shift is of particular significance in cases where conventional DNA profiling fails to identify a single suspect. Supplementing forensic analyses with estimated biological age may be valuable but involves a complex and time-consuming DNA methylation analysis. This study explores and validates the performance of a comprehensive forensic third-generation sequencing assay utilizing Oxford Nanopore Technologies (ONT) in an adaptive and direct sequencing approach. We incorporated the most widely used forensic markers, i.e., STRs, SNPs, InDels, mitochondrial DNA (mtDNA), and two methylation-based clock classifiers, thereby combining forensic genetic and epigenetic analysis in one single workflow.

Methods and results
In our investigation, DNA from six anonymous individuals was sequenced using the ONT standard adaptive direct sequencing approach, reaching a mean percentage of on-target reads ranging from 6.6 % to 7.7 % per sample. ONT data was compared to standard MPS data and Illumina EPIC DNA methylation profiles. Basecalling employed recommended ONT software packages. TREAT was used for ONT-based analysis of autosomal and Y-chromosome STRs, achieving 90–92 % correct calls depending on allelic read depth thresholds. InDel analyses for two lower-quality samples proved challenging due to inadequate read depth, while the remaining four samples significantly contributed to the observed percentage markers (60.9 %) and correct calls (97.8 %). SNP analysis achieved a 98 % call rate, with only two mismatches and two missed alleles. ONT-generated DNA methylation data demonstrated Pearson’s correlation coefficients with EPIC data ranging from 0.67 to 0.97 for Horvath’s clock. Additional age-associated markers exhibited Pearson’s correlation coefficients with chronological age between 0.14 (ELOVL2) and 0.96 (FHL2) at read depths of <30 and <20, respectively. Despite excluding mtDNA from our targeted sequencing approach, adaptive proof-reading fragments covered the complete mtDNA with an average read depth of 21–72, showing 100 % concordance with reference data.

Discussion
Our exploratory study using ONT adaptive sequencing for conventional forensic and age associated DNA methylation markers showed high sequencing accuracy for a significant number of markers, showcasing ONT as a promising (epi)genetic forensic method. Future studies must address three critical aspects: determining clear quantity and quality measures and detection thresholds for accuracy, optimizing input DNA quantity for forensic casework expectations, and addressing ethical considerations associated with phenotype and ancestry analysis to prevent ethnic biases.

Objective: To investigate whether local lesions created by stereo-electroencephalography (SEEG)–guided radiofrequency thermocoagulation (RFTC) affect distant brain connectivity and excitability in patients with focal, drug-resistant epilepsy (DRE). Methods: Ten patients with focal DRE underwent SEEG implantation and subsequently 1 Hz bipolar repetitive electrical stimulation (RES) for 30 s before and after RFTC. Root mean square (RMS) of cortico-cortical evoked potentials (CCEPs) was calculated for 15 ms to 300 ms post-stimulation with baseline correction. Contact pairs were categorized as both coagulated, hybrid, or both non-coagulated. The data were divided into nine categories based on the stimulating and recording contact pair combinations. RMS of CCEPs was compared before and after (<12 h) RFTC using a two-sample t test (Hochberg corrected, p < 0.05) for each patient. Boost score, indicating power increase during seizures before RFTC relative to baseline, was analyzed in 4 s windows with 1 s overlap during seizure duration. Results: RFTC altered connectivity across all categories. Of interest, decreases and increases in RMS were observed in connections between non-coagulated contacts distant from coagulation site (range: 1.09–85 mm, median = 17.7 mm, interquartile range [IQR] 10.1–32.3). Contact pairs involved in significantly altered non-coagulated connections showed a higher boost score correlation in the theta, beta, and gamma bands, as well as a stronger maximum correlation with coagulated sites in the delta band than contacts for which connectivity did not change after RFTC. Significance: This study highlights how local lesions alter distant brain connectivity, providing insights for future research on epilepsy network changes and seizure outcomes following RFTC.

This study investigates the microstructure evolution and mechanical behavior of bimodal-sized sintered copper (Cu) nanoparticles (NPs) under varying sintering pressures. Micro-pillar compression tests reveal a transition from collapse-dominated to compaction-driven deformation as sintering pressure increases. Transmission electron microscopy (TEM) and transmission Kikuchi diffraction (TKD) analyses identify a two-stage deformation mechanism—initial pore compaction followed by intragranular slip—fundamentally distinct from bulk Cu. Molecular dynamics (MD) simulations further reveal that large particles promote dislocation-mediated plasticity by accommodating intragranular slip, while small particles enhance load transfer through localized shear-compaction, together enabling uniform strain distribution and supporting the experimentally observed strain accommodation. The resulting microstructure achieves a combination of high yield strength (up to 320 MPa) and low elastic modulus (20 GPa), offering a compliant yet robust response. These findings elucidate a unique processing–structure–property relationship and provide a rational basis for designing porous metal interconnects capable of withstanding thermomechanical stresses in advanced electronic packaging.

Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who have already received the treatment the prediction model is meant to inform. Special attention to the causal role of those earlier treatments is required when interpreting the resulting predictions.

“Causal blind spots” were identified in 3 common approaches to handling treatment when developing a prediction model: including treatment as a predictor, restricting to persons taking a certain treatment, and ignoring treatment. Through several real examples, this article illustrates how the risks obtained from models developed using such approaches may be misinterpreted and can lead to misinformed decision making. The discussion covers issues attributable to confounding, selection, mediation, and changes in treatment protocols over time.

An extension of guidelines for the development, reporting, and evaluation of prediction models is advocated to avoid such misinterpretations. Developers must ensure that the intended target population for the model, and the treatment conditions under which predictions hold, are clearly communicated. When prediction models are intended to inform treatment decisions, they need to provide estimates of risk under the specific treatment (or intervention) options being considered, known as “prediction under interventions.” Next to suitable data, this requires causal reasoning and causal inference techniques during model development and evaluation. Being clear about what a given prediction model can and cannot be used for prevents misinformed treatment decisions and thereby prevents potential harm to patients.

The original publication of this article contained an incorrect author name. The incorrect and correct information is listed in this correction article. The original article has been updated.

Given the current ecological crisis, the HCI and design community is showing a growing interest in the adoption of more-than-human perspectives, challenging human-centric approaches. While this has sparked numerous research initiatives, many of them are still a far cry from providing practical solutions or transforming the industry. This also presents a hurdle for teaching more-than-human perspectives to design students, as they may feel powerless to practice those teachings in real-life industrial settings. To bring forth concrete examples of how more-than-human design practice can matter, we believe that it is now time to move beyond theorising about and advocating for the adoption of such perspectives and start a more-than-human design practice that transforms the industry. This workshop therefore aims to bring together educators, researchers, and designers to discuss and co-develop strategies for transitioning more-than-human perspectives from niche/speculation to mainstream/practice in HCI and design. The workshop also aims to develop ways to empower students to work with these perspectives to bring about this transformation of the industry.

BepiColombo, the joint ESA/JAXA mission to Mercury, was launched in October 2018 and is scheduled to arrive at Mercury in November 2026 after an 8-year cruise. Like other planetary missions, its scientific objectives focus mostly on the nominal, orbiting phase of the mission. However, due to the long duration of the cruise phase covering distances between 1.2 and 0.3 AU, the BepiColombo mission has been able to outstandingly contribute to characterise the solar wind and transient events encountered by the spacecraft, as well as planetary environments during the flybys of Earth, Venus, and Mercury, and contribute to the characterisation of the space radiation environment in the inner Solar System and its evolution with solar activity. In this paper, we provide an overview of the cruise observations of BepiColombo, highlighting the most relevant science cases, with the aim of demonstrating the importance of planetary missions to perform cruise observations, to contribute to a broader understanding of Space Weather in the Solar System, and in turn, increase the scientific return of the mission.

Biotite crystals are phyllosilicate trioctahedral micas with the general chemical formula K(Mg,Fe)₃AlSi₃O₁₀(OH)₂ that form a solid-solution series with iron-poor phlogopite and iron-rich annite endmembers. With a wide band gap energy and a layered structure with free surface charges, biotite nanosheets can be readily obtained by cleavage methods and used as dielectrics in nanodevice fabrication for the next generation of electronics and energy harvesting. Here, a comprehensive study of biotite samples with different iron concentrations and oxidation states is presented. Structural, optical, magneto-optical, and magnetic characterizations were performed using several experimental techniques, including state-of-the-art synchrotron-based techniques, to correlate the iron chemistry (content and oxidation state) with the macroscopic properties of both minerals. The study reveals a nanoscale-homogeneous Fe distribution via synchrotron X-ray fluorescence mapping, defect-mediated optical transitions modulated by Fe³⁺/Fe²⁺ ratios, and temperature-dependent magnetic transitions from paramagnetism to competing ferro−/antiferromagnetic interactions. Furthermore, the use of these biotite crystals as substrates for ultrathin heterostructures incorporating monolayer (ML) MoSe₂ is explored by magneto photoluminescence at cryogenic temperatures. The results show that the presence of iron impurities in different oxidation states significantly impacts the valley properties for ML-MoSe₂. Overall, these findings offer a comprehensive interpretation of the physical properties of bulk biotites in a correlative approach, serving as a robust reference for future studies aiming to explore biotites in their ultrathin form.

Neutrons, owing to their unique properties, serve as indispensable probes for investigating the structure and dynamics of materials across various length scales. The scientific community utilizing neutron research infrastructures encompasses a diverse range of disciplines, making it challenging to quantify its scientific and societal impact. To address this challenge, we apply Natural Language Processing (NLP) and machine learning techniques to analyze the scientific output of the European neutron science community. Leveraging open-source software toolkits, our method allows for the quantitative assessment of community evolution and research focus. Our analysis reveals consistent growth in the neutron community despite a reduction in sources, underscoring the enduring significance of neutron methods in scientific research. Furthermore, an increase in unique authors and an even distribution of publications across diverse scientific topics highlight the community’s interdisciplinary nature and collaborative spirit. While this study emphasizes neutron scattering, our methodology holds promise for a broad range of scientific communities reliant on Large Research Infrastructures (LRIs), offering opportunities for collaboration, optimization of experimental approaches, and informed decision-making by governmental and funding bodies.

UNC13A (rs12608932-CC) is associated with both amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), and shortens survival in ALS. We aim to describe the association for UNC13A and survival in FTD. We included 626 patients with FTD from Dutch memory clinics, including a subcohort of 150 patients with TDP-43 pathology. Survival analyses were performed using Cox proportional hazard models in a recessive manner. Homozygosity for rs12608932-C in UNC13A was associated with a shorter survival compared with other genotypes (hazard ratio [HR] = 1.28, 95% confidence interval [CI] = 1.02–1.60, p = 0.033), which has implications for patient counselling and trial design. ANN NEUROL 2025;97:1062–1066.