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Abstract Investigating the off-diagonal components of the conductivity and thermoelectric tensor of materials hosting complex antiferromagnetic structures has become a viable method to reveal the effects of topology and chirality on the electronic transport in these systems. In this respect, Mn5Si3 is an interesting metallic compound that exhibits several antiferromagnetic phases below 100 K with different collinear and noncollinear arrangements of Mn magnetic moments determined from neutron scattering. Previous electronic transport measurements have shown that the transitions between the various phases give rise to large changes of the anomalous Hall effect. Here, we report measurements of the anomalous Nernst effect of Mn5Si3 single crystals that also show clear transitions between the different magnetic phases. In the noncollinear phase, we observe an unusual sign change of the zero-field Nernst signal with a concomitant decrease of the Hall signal and a gradual reduction of the remanent magnetization. Furthermore, a symmetry analysis of the proposed magnetic structures shows that both effects should actually vanish. These results indicate a symmetry-breaking modification of the magnetic state with a rearrangement of the magnetic moments at low temperatures, thus questioning the previously reported models for the noncollinear magnetic structure obtained from neutron scattering.

Abstract Chemical reaction optimization (RO) is an iterative process that results in large, high-dimensional datasets. Current tools allow for only limited analysis and understanding of parameter spaces, making it hard for scientists to review or follow changes throughout the process. With the recent emergence of using artificial intelligence (AI) models to aid RO, another level of complexity has been added. Helping to assess the quality of a model’s prediction and understand its decision is critical to supporting human-AI collaboration and trust calibration. To address this, we propose CIME4R—an open-source interactive web application for analyzing RO data and AI predictions. CIME4R supports users in ( i ) comprehending a reaction parameter space, ( ii ) investigating how an RO process developed over iterations, ( iii ) identifying critical factors of a reaction, and ( iv ) understanding model predictions. This facilitates making informed decisions during the RO process and helps users to review a completed RO process, especially in AI-guided RO. CIME4R aids decision-making through the interaction between humans and AI by combining the strengths of expert experience and high computational precision. We developed and tested CIME4R with domain experts and verified its usefulness in three case studies. Using CIME4R the experts were able to produce valuable insights from past RO campaigns and to make informed decisions on which experiments to perform next. We believe that CIME4R is the beginning of an open-source community project with the potential to improve the workflow of scientists working in the reaction optimization domain. Scientific contribution To the best of our knowledge, CIME4R is the first open-source interactive web application tailored to the peculiar analysis requirements of reaction optimization (RO) campaigns. Due to the growing use of AI in RO, we developed CIME4R with a special focus on facilitating human-AI collaboration and understanding of AI models. We developed and evaluated CIME4R in collaboration with domain experts to verify its practical usefulness.

Abstract Background Aspergillus fumigatus is a major fungal pathogen that causes severe problems due to its increasing resistance to many therapeutic agents. Fludioxonil is a compound that triggers a lethal activation of the fungal-specific High Osmolarity Glycerol pathway. Its pronounced antifungal activity against A. fumigatus and other pathogenic molds renders this agent an attractive lead substance for the development of new therapeutics. The group III hydride histidine kinase TcsC and its downstream target Skn7 are key elements of the multistep phosphorelay that represents the initial section of the High Osmolarity Glycerol pathway. Loss of tcsC results in resistance to fludioxonil, whereas a Δskn7 mutant is partially, but not completely resistant. Results In this study, we compared the fludioxonil-induced transcriptional responses in the ΔtcsC and Δskn7 mutant and their parental A. fumigatus strain. The number of differentially expressed genes correlates well with the susceptibility level of the individual strains. The wild type and, to a lesser extend also the Δskn7 mutant, showed a multi-faceted stress response involving genes linked to ribosomal and peroxisomal function, iron homeostasis and oxidative stress. A marked difference between the sensitive wild type and the largely resistant Δskn7 mutant was evident for many cell wall-related genes and in particular those involved in the biosynthesis of chitin. Biochemical data corroborate this differential gene expression that does not occur in response to hyperosmotic stress. Conclusions Our data reveal that fludioxonil induces a strong and TcsC-dependent stress that affects many aspects of the cellular machinery. The data also demonstrate a link between Skn7 and the cell wall reorganizations that foster the characteristic ballooning and the subsequent lysis of fludioxonil-treated cells.

Abstract When people engage in rhythmic joint actions, they unintentionally increase their tempo. However, this phenomenon of joint rushing has so far been investigated only under very specific and somewhat artificial conditions. Therefore, it remains unclear whether joint rushing generalizes to other instances of rhythmic joint action. In this study our aim was to investigate whether joint rushing can also be observed in a wider range of naturalistic rhythmic social interactions. To achieve this, we retrieved videos of a wide range of rhythmic interactions from an online video-sharing platform. The data suggest that joint rushing indeed can also be observed in more naturalistic social interactions. Furthermore, we provide evidence that group size matters for how tempo unfolds in social interactions with larger groups showing a stronger tempo increase than smaller groups. Comparing the data from naturalistic interactions with data collected in a lab study further showed that unintended tempo changes in social interactions are reduced in naturalistic interactions compared to interactions in a lab context. It is an open question which factors led to this reduction. One possibility is that humans might have come up with strategies to reduce the effects of joint rushing.

Abstract Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.

Abstract Background Explainable artificial intelligence (XAI) methods have shown increasing applicability in chemistry. In this context, visualization techniques can highlight regions of a molecule to reveal their influence over a predicted property. For this purpose, some XAI techniques calculate attribution scores associated with tokens of SMILES strings or with atoms of a molecule. While an association of a score with an atom can be directly visually represented on a molecule diagram, scores computed for SMILES non-atom tokens cannot. For instance, a substring [N+] contains 3 non-atom tokens, i.e., [, $$+$$ + , and ], and their attributions, depending on the model, are not necessarily revealing an influence of the nitrogen atom over the predicted property; for that reason, it is not possible to represent the scores on a molecule diagram. Moreover, SMILES’s notation is complex, foregrounding the need for techniques to facilitate the analysis of explanations associated with their tokens. Results We propose XSMILES, an interactive visualization technique, to explore explainable artificial intelligence attributions scores and support the interpretation of SMILES. Users can input any type of score attributed to atom and non-atom tokens and visualize them on top of a 2D molecule diagram coordinated with a bar chart that represents a SMILES string. We demonstrate how attributions calculated for SMILES strings can be evaluated and better interpreted through interactivity with two use cases. Conclusions Data scientists can use XSMILES to understand their models’ behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users’ needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization.

Explosive reactive armor (ERA) is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets. Considering mobility, the aim is to design a protection system with a minimal amount of required mass. The efficiency of an ERA is sensitive to the impact position and the timing of the detonation. Therefore, different designs have to be tested for several impact scenarios to identify the best design. Since analytical models are not predicting the behavior of the ERA accurately enough and experiments, as well as numerical simulations, are too time-consuming, a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here. The ground truth for the artificial neural network (ANN) is numerical simulation results that are validated with experiments. The ANN approximates the plate positions for different materials, plate sizes, and detonation point positions with sufficient accuracy in real-time. In a future investigation, the results from the model can be used to estimate the interaction of the ERA with a given threat. Then, a measure for the effectiveness of an ERA can be calculated. Finally, an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time.

Candida species are a major cause of invasive fungal infections. While Candida albicans, C. glabrata, C. parapsilosis, and C. tropicalis are the most dominant species causing life-threatening candidiasis, C. auris recently emerged as a new species causing invasive infections with high rates of clinical treatment failures. To mimic initial phases of systemic Candida infections with dissemination via the bloodstream and to elucidate the pathogenic potential of C. auris, we used an ex vivo whole blood infection model. Similar to other clinically relevant Candida spp., C. auris is efficiently killed in human blood, but showed characteristic patterns of immune cell association, survival rates, and cytokine induction. Dual-species transcriptional profiling of C. auris-infected blood revealed a unique C. auris gene expression program during infection, while the host response proofed similar and conserved compared to other Candida species. C. auris-specific responses included adaptation and survival strategies, such as counteracting oxidative burst of immune cells, but also expression of potential virulence factors, (drug) transporters, and cell surface-associated genes. Despite comparable pathogenicity to other Candida species in our model, C. auris-specific transcriptional adaptations as well as its increased stress resistance and long-term environmental survival, likely contribute to the high risk of contamination and distribution in a nosocomial setting. Moreover, infections of neutrophils with pre-starved C. auris cells suggest that environmental preconditioning can have modulatory effects on the early host interaction. In summary, we present novel insights into C. auris pathogenicity, revealing adaptations to human blood and environmental niches distinctive from other Candida species.

Quantum phase transitions, occurring at zero temperature for a given system, can be induced by the application of physical or chemical pressure, and can help elucidate the underlying mechanisms of unconventional superconductivity. Here, using Raman spectroscopy, the authors report scaling properties indicative of a marginal Fermi liquid for an Fe-based superconductor tuned through a quantum critical point by chemical substitution.

Abstract The introduction of machine learning to small molecule research– an inherently multidisciplinary field in which chemists and data scientists combine their expertise and collaborate - has been vital to making screening processes more efficient. In recent years, numerous models that predict pharmacokinetic properties or bioactivity have been published, and these are used on a daily basis by chemists to make decisions and prioritize ideas. The emerging field of explainable artificial intelligence is opening up new possibilities for understanding the reasoning that underlies a model. In small molecule research, this means relating contributions of substructures of compounds to their predicted properties, which in turn also allows the areas of the compounds that have the greatest influence on the outcome to be identified. However, there is no interactive visualization tool that facilitates such interdisciplinary collaborations towards interpretability of machine learning models for small molecules. To fill this gap, we present CIME (ChemInformatics Model Explorer), an interactive web-based system that allows users to inspect chemical data sets, visualize model explanations, compare interpretability techniques, and explore subgroups of compounds. The tool is model-agnostic and can be run on a server or a workstation.