The generalized Langevin equation (GLE) is a useful framework for analyzing and modeling the dynamics of many-body systems in terms of low-dimensional reaction coordinates, where the specific form of the GLE depends on the choice of projection formalism. We compare parameters derived from different GLE formulations using molecular dynamics (MD) simulations of the dihedral angle dynamics of butane in water. Our analysis reveals non-negligible non-Gaussian contributions of the orthogonal force in all GLEs, being most significant for the Mori-GLE, where all non-linearities are relegated to the orthogonal force. By using the orthogonal-force trajectory extracted from MD simulations to perform simulations of the GLE, we show that the non-Gaussianity and higher-than-two-point autocorrelations of the orthogonal force are relevant for accurately reproducing the dihedral-angle distribution and dynamics. We find that the accuracy of GLE simulations depends significantly on the chosen GLE formalism. Surprisingly, the Mori-GLE offers the most accurate framework for capturing the dihedral angle dynamics as judged by comparing barrier-crossing times calculated from different GLE formulations, provided an accurate non-Gaussian orthogonal-force trajectory with the correct higher-than-two-point autocorrelations is used.

Synaptic vesicle (SV) fusion is not only tightly coordinated but also happens at a millisecond timescale. Competing models for fusion initiation and propagation suggest tight docking and hemifusion of SVs or localized lipid rearrangements leading to tip-like membrane contacts. Yet, a direct nanoscale examination of the full SV fusion sequence has been lacking. Here, we establish a workflow for timed in situ cryo-electron tomography of optogenetically stimulated mouse neurons to capture the complete SV fusion sequence – from SV recruitment to fusion pore formation, opening and collapse – with near-native structural preservation. Notably, tethered SVs directly undergo fusion initiation via stalk formation, without preceding tight docking or SV flattening. The plasma membrane forms a minimal dimple during fusion initiation, contradicting preceding models that invoke strong membrane bending prior to fusion. In addition, we observe filaments linking fusing SVs to adjacent SVs, indicating a physical link between fusion and SV resupply.

Termites are a lineage of social cockroaches abundant in tropical ecosystems where they are key decomposers of organic matter. Despite their ecological significance, only a handful of reference-quality termite genomes have been sequenced, which is insufficient to unravel the genetic mechanisms that have contributed to their ecological success. Here, we perform sequencing and hybrid assembly of 45 taxonomically and ecologically diverse termites and two cockroaches, resulting in haplotype-merged genome assemblies of 47 species, 22 of which were near-chromosome level. Next, we examine the link between termite dietary evolution and major genomic events. We find that Termitidae, which include ~80% of described termite species, have larger genomes with more genes and a higher proportion of transposons than other termites. Our analyses identify a gene number expansion early in the evolution of Termitidae, including an expansion of the repertoire of CAZymes, the genes involved in lignocellulose degradation. Notably, this expansion of genomes and gene repertoires coincided with the origin of soil-feeding in Termitidae and remained unchanged in lineages that secondarily reverted to a wood-based diet. Overall, our sequencing effort multiplies the number of available termite genomes by six and provides insights into the genome evolution of an ancient lineage of social insects.

Pharmacological studies revealed that the Balanophora species contains diverse phytochemicals which enable interesting biological activities and emphasize their pharmaceutical relevance. Previously, we identified significant xanthine oxidase (XO) inhibitory activity from extracts of the two Balanophora spp. ( Balanophora subcupularis P.C. Tam and Balanophora tobiracola Makino). However, the specific compounds responsible for this activity remain unidentified so far. Thus, in the present study, we focused on elucidating the compounds inducing the XO inhibitory effect of extracts from Balanophora species. Therefore, a combination of advanced liquid chromatography and mass spectrometry (LC-QToF-HRMS), virtual screening using machine learning (ML) models, and molecular docking simulation was applied. Using LC-QToF-HRMS, 23 and 21 compounds were identified in the ethyl acetate fractions of B. subcupularis and B. tobiracola , respectively. Next, a curated dataset of natural and synthetic compounds with known XO inhibitory activity was employed to train several ML models. Adducing five selected ML models, the virtual screening process identified the potentially active compounds 1-(3,4-dihydroxyphenyl)-6,7-dihydroxy-1,2-dihydro-2,3-naphthalenedicarboxylic acid, taxifolin, and 1- O -caffeoyl-6- O -(S)-brevifolincarboxyl- β -D-glucopyranose. All the compounds found in the two Balanophora spp. underwent docking simulations, in which MTE, FES, and AFH were retained in the active site of XO, ensuring reliable re-docking results. Finally, taxifolin emerged as the most promising novel XO inhibitor, demonstrating greater potential than the established drug allopurinol, as supported by both the virtual screening nomination and docking simuation. These findings contribute to the development of natural XO inhibitors and may open new opportunities for gout treatment and uric acid level control.

Weakly electric fish emit electric organ discharges (EODs) for both active electrolocation and electrocommunication. In African mormyrids, pulse-type EODs are produced at highly variable interdischarge intervals (IDIs), forming sequences that can convey contextual and behavioural information to conspecifics. Neighbouring fish frequently engage in time-locked interactive behaviours, such as fixed-latency echo responses and EOD synchronization. These behaviours have been proposed to function either as a jamming avoidance response (JAR), preventing simultaneous discharges and interference with electrolocation, or as a form of social signal. To test these hypotheses, we analysed interactions between pairs of Mormyrus rume proboscirostris, quantifying EOD synchronizations, echo events and instances of jamming. Our results show that jamming is rare in this species. We found no correlation between jamming frequency and echoing behaviour, nor evidence that synchronization and echo events are direct responses to recent jams. Instead, these interactive behaviours were associated with specific movement patterns and social contexts. Synchronizations typically occurred at slower swimming speeds and in the absence of a distinct leader–follower relationship, whereas echo events were more often associated with faster swimming speeds and initiated by fish actively following a social partner. Pairs with more frequent echoing also exhibited reduced interindividual distances, indicating that interactive electrical signalling promotes social cohesion. Rather than mitigating jamming, interactive electrical behaviours in mormyrids probably serve as communicative strategies to maintain group coherence or allocate social attention. These findings highlight the role of electrocommunication in structuring social interactions. Investigating the rules of these behaviours could eventually decipher which specific IDI patterns signal social intent and help to understand the underlying mechanisms of electrocommunication.

Understanding consciousness remains a significant challenge in science. What distinguishes conscious beings from unconscious systems, such as organoids, artificial intelligence or other non-sentient entities? Research on consciousness often focuses on identifying brain activity associated with conscious and non-conscious states, primarily in neurotypical human adults. However, this approach is limited in scope when applied to entities with developmental or evolutionary trajectories different from our own. How do we investigate consciousness in infants, whose brains are still maturing or in non-human animals, shaped by diverse ecological and evolutionary pressures? This opinion piece encourages consciousness studies to adopt a neuroethological perspective, drawing on Tinbergen’s framework for studying behaviour. By examining the (1) mechanisms, (2) development, (3) adaptive functions and (4) evolutionary origins of consciousness, we can move beyond a human-centric focus to explore its diversity across life forms. Most investigators now accept that consciousness is not confined to humans alone but that some other animals have it, and it is a continuum shaped by evolutionary pressures. By adopting this broader approach, consciousness studies can better investigate and understand consciousness in its various forms and contexts, with significant scientific, ethical and societal implications.

TLR7, TLR8 and TLR9 are endosomal immune receptors central to antiviral defense, autoimmunity and cancer immunotherapy. Recent structural insights have revealed distinct ligand-binding mechanisms and conformational dynamics, enabling the design of selective small-molecule agonists and antagonists. This review summarizes key advances in TLR7/8/9 biology, pharmacology and structure-guided drug discovery. We highlight clinical progress, delivery strategies and translational challenges including species-specific differences and immune-related toxicities. Novel approaches such as nanoparticle systems and endogenous RNA mimetics promise targeted modulation of TLR activity. Together, these developments emphasize the therapeutic potential of precision TLR modulation in immunologically complex diseases.

We explore the growing intersection of artificial intelligence (AI) and disinformation, examining its implications for journalism and mass communication. We propose a working definition of AI disinformation, highlighting its role in the production, dissemination, and perception of misleading content. While AI technologies enable hyper-realistic synthetic media and targeted influence campaigns, empirical evidence on their impact remains mixed. We critically assess both alarmist and dismissive narratives, calling for a nuanced, evidence-based approach. Finally, we explore how AI can also serve as a tool to detect and counter disinformation, emphasizing the dual-use nature of AI in today’s complex information environment.

The combination of two-dimensional materials into heterostructures offers new opportunities for the design of optoelectronic devices with tunable properties. However, computing electronic and optical properties of such systems using state-of-the-art methodology is challenging due to their large unit cells. This is particularly so for highly precise all-electron calculations within the framework of many-body perturbation theory, which come with high computational costs. Here, we extend an approach that allows for the efficient calculation of the noninteracting polarizability, previously developed for plane wave basis sets, to the (linearized) augmented plane wave method. This approach is based on an additive ansatz, which computes and superposes the polarizabilities of the individual components in their respective unit cells. We implement this formalism in the G(0)W(0) module of the exciting code and implement an analogous approach for BSE calculations. This allows the calculation of highly precise optical spectra at low cost. So-obtained results of the quasiparticle band structure and optical spectra are demonstrated for bilayer WSe2 and pyridine@MoS2 in comparison with exact reference calculations.

The ultrasonic vocalizations (USVs) of house mice (Mus musculus) have a surprisingly complex structure and have been actively studied as a model for applied questions in biomedical science. Despite this, the functional significance of different USV types and their use during natural social behaviour in diverse groups remains unclear. In this pilot long-term study, we examined the ultrasound activity of female mice housed in stable groups within an enriched environment, focusing on how behavioural context influences USV time-frequency characteristics, call type occurrence, and nonlinear phenomena. Our findings show that USVs are mainly produced in bouts, and bouts containing ten or more variable calls always accompanying direct social interactions. USV variables and types varied markedly depending on the interaction context. During aggressive encounters, USVs and entire bouts became longer, vocalization time and frequency modulation increased, while peak and minimum fundamental frequencies decreased. Additionally, aggressive context was characterized by a higher prevalence of multi-component and complex-low USVs (with peak frequency < 50 kHz), and increased nonlinear phenomena. These results suggest that USV features closely reflect emotional arousal (intensity) and probably also valence (positive/negative) in female mice during social hierarchy formation and maintenance. Notably, the relationship between arousal and nonlinear vocal phenomena in mice USVs follows patterns observed in audible vocalizations of other mammals (increase with arousal). In the future, complex-low USVs could potentially serve as non-invasive indicators of negative emotional expression in groups of female mice. This opens new possibilities for acoustic home-cage monitoring aimed at welfare assessment and other applied uses.

Working memory (WM) supports a range of higher order cognitive functions by enabling the short-term maintenance and manipulation of information through dynamic, distributed neural processes. In parallel to findings from the visual modality, tactile WM engages both sensory and higher-order cortical regions, but the temporal dynamics and functional significance of these areas remain incompletely understood. In this fMRI study, we used multivoxel pattern analysis to investigate how spatial features of tactile stimuli are represented and maintained across a short WM delay period. Our results reveal a dynamic engagement of contralateral primary somatosensory cortex (S1) and anterior superior parietal lobe (SPL) during initial encoding, with a shift toward bilateral posterior SPL involvement during later maintenance. Critically, decoding accuracy in the ipsilateral SPL correlated with individual task performance, suggesting that distinctiveness of WM-related representations in this region supports successful memory retention. These findings shed light on the hierarchical organization and temporal evolution of tactile spatial WM, indicating a transformation from concrete sensory to more abstract, distributed representations across parietal regions, modulated by behavioral demands.

The influence of long-range dust on soils and different ecological processes in deposition regions in the eastern Mediterranean depends primarily on the deposited amounts and therefore on transport dynamics, while its geochemical and mineralogical composition also plays an important role. As local distributions cannot be fully represented and analyzed by satellite data due to its relatively coarse spatial and temporal resolution, ground truth data is required. For this purpose, a network of eight sampling/monitoring stations was installed around the Lefka Ori mountains in western Crete (Greece), each equipped with a deposition sampler and an optical-particle-counter in order to detect the spatial variability in dust concentration and deposition. Measured dust concentrations for the period from March 2023 to April 2024 show significant differences between the individual site locations. By comparison with meteorological data, correlations with wind directions can be identified. Thereby, wind directions during dust events generally differ significantly between stations. Furthermore, we are able to differentiate between a fine-grained (< 1 μm in diameter) background dust load and event-based coarser-grained (1–10 μm) dust loads as well as between synoptically and regionally induced dust events. First results of mineralogical analysis clearly identify the deposited fine material to be of north African origin, indicated by the presence of kaolinite, sepiolite and palygorskite. In combination with high-resolution meteorological data, the measurement design allows for the differentiation between the influences of synoptic and local to regional conditions and therefore contributes to a detailed understanding of dust distribution in western Crete.

Interactions between humans and carnivores are widespread and shaped by diverse social-ecological factors. In Argentina, interactions with pumas (Puma concolor) primarily involve livestock depredation and hunting. Since both interaction types are threatening the coexistence between humans and pumas, understanding their spatial patterns is essential for promoting sustainable coexistence. We identified 90 interaction locations (51 depredation, 39 hunting) from online news and literature (2017–2022) and analyzed spatial social-ecological variables influencing these interactions using generalized linear models. Depredation interactions were associated with lower cattle density, higher human population density, and increased agriculture, while hunting interactions were linked to a higher number of settlements and greater small-livestock density. Within Social-Ecological Land Systems (SELS), these interactions occurred predominantly in “urbanized large-scale agricultural plains” and “low-diversity cold and temperate grassy rangelands”. These findings advance the understanding of interaction dynamics in socially and ecologically complex landscapes, highlighting the need for targeted policies for different regions.

Honeybee foragers explore the environment before they start foraging, following dances, or performing dances. Foragers are therefore familiar with the landscape surrounding the hive during their foraging career. Here, we ask whether dance-recruited honeybees expect the landscape features that the dancer experienced during its outbound foraging flights. If this were the case, the dance-recruited honeybees would behave differently according to whether the landscape features they experienced during their outbound flight matched the expected features. In our experiments, the dance followers (recruits) had explored the environment around the hive, and the dancers flew along an elongated ground structure (a gravel road) running approximately northward from the hive in the outbound condition. The flights of the recruits were recorded by harmonic radar. The recruits were released not only at the hive but also at two remote sites within the explored area, where they faced either a similar north-running gravel road or even grassland. We found that the recruits released from the remote sites performed flights more similar to those of the hive-released bees when they experienced a similar elongated ground structure. This behavior did not result from a spontaneous or learned tendency to follow elongated ground structures as documented by control experiments. We conclude that dance-recruited honeybees expect the salient landscape structures that the dancer experienced, although the dance message includes only vector information.

With the advancement of machine learning, many predictions and measurements in visual tasks can be achieved by convolutional neural networks (CNNs). Solidification hot cracking is a significant defect in laser beam welding, commonly encountered in practical applications. Existing theories indicate that the formation of cracks is closely related to strain accumulation near the solidification front. In this paper, we first leverage supervised regression networks to design CNNs that achieve real-time average strain estimation for each frame in the collected welding videos. Two different architectures are proposed and compared: the first model stacks two frames at a set interval and feeds them into the network, while the second model extracts image features individually and predicts the results by calculating the correlation between them. Each network has its own advantages in terms of computational efficiency and accuracy. Finally, we further train a multilayer perceptron (MLP) classification model that can detect the occurrence of cracks based on the predicted strain behaviors.

We study the synchronization behavior of discrete-time Markov chains on countable state spaces. Representing a Markov chain in terms of a random dynamical system, which describes the collective dynamics of trajectories driven by the same noise, allows for the characterization of synchronization via random attractors.

We establish the existence and uniqueness of a random attractor under mild conditions and show that forward and pullback attraction are equivalent in our setting. Additionally, we provide a sufficient condition for reaching the random attractor, or synchronization respectively, in a time of finite mean.

By introducing insulated and synchronizing sets, we structure the state space with respect to the synchronization behavior and characterize the size of the random attractor.

Statistical prediction models are ubiquitous in psychological research and practice. Increasingly, machine-learning models are used. Quantifying the uncertainty of such predictions is rarely considered, partly because prediction intervals are not defined for many of the algorithms used. However, generating and reporting prediction models without information on the uncertainty of the predictions carries the risk of overinterpreting their accuracy. Conventional methods for prediction intervals (e.g., those defined for ordinary least squares regression) are sensitive to violations of several distributional assumptions. In this tutorial, we introduce conformal prediction, a model-agnostic, distribution-free method for generating prediction intervals with guaranteed marginal coverage, to psychological research. We start by introducing the basic rationale of prediction intervals using a motivating example. Then, we proceed to conformal prediction, which is illustrated in three increasingly complex examples using publicly available data and R code.

It is well-known that the 2-Thief-Necklace-Splitting problem reduces to the discrete Ham Sandwich problem. In fact, this reduction was crucial in the proof of the -completeness of the Ham Sandwich problem [Filos-Ratsikas and Goldberg, STOC’19]. Recently, a variant of the Ham Sandwich problem called -Ham Sandwich has been studied, in which the point sets are guaranteed to be well-separated [Steiger and Zhao, DCG’10]. The complexity of this search problem remains unknown, but it is known to lie in the complexity class [Chiu, Choudhary and Mulzer, ICALP’20]. We define the analogue of this well-separation condition in the necklace splitting problem — a necklace is n - separable , if every subset A of the n types of jewels can be separated from the types by at most n separator points. Since this version of necklace splitting reduces to -Ham Sandwich in a solution-preserving way it follows that instances of this version always have unique solutions. We furthermore provide two FPT algorithms: The first FPT algorithm solves 2-Thief-Necklace-Splitting on -separable necklaces with n types of jewels and m total jewels in time . In particular, this shows that 2-Thief-Necklace-Splitting is polynomial-time solvable on n -separable necklaces. Thus, attempts to show hardness of -Ham Sandwich through reduction from the 2-Thief-Necklace-Splitting problem cannot work. The second FPT algorithm tests -separability of a given necklace with n types of jewels in time . In particular, n -separability can thus be tested in polynomial time, even though testing well-separation of point sets is -complete [Bergold et al., SWAT’22].

The present paper takes a broad perspective on the psychosocial functioning in adult patients with personality disorders. We start with a working definition, then we report on psychosocial functioning in personality disorders from both categorical and dimensional perspectives of personality disorder. We add a section on assessment tools which may be used in this context. We then address the question of how problematic psychosocial functioning may be addressed in psychotherapy and other treatment contexts, when it comes to supporting the person’s move towards sustained recovery. We add a lived and living experience perspective to psychosocial functioning and recovery. We end with recommendations for future research in the domain of psychosocial functioning.

Task-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Here, we propose DeepTaskGen, a deep-learning approach that synthesizes non-acquired task-based contrast maps from resting-state (rs-) fMRI. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals from UK Biobank. DeepTaskGen outperforms several benchmarks in generating synthetic task-contrast maps, achieving superior reconstruction performance while retaining inter-individual variation essential for biomarker development. We further show comparable or superior predictive performance of synthetic maps relative to actual maps and rs-connectomes across diverse demographic, cognitive, and clinical variables. This approach facilitates the study of individual differences and the generation of task-related biomarkers by enabling the generation of arbitrary functional cognitive tasks from readily available rs-fMRI data.