Communications Biology

The development of novel frameworks to understand the properties of unconscious representations and how they differ from the conscious counterparts may be critical to make progress in the neuroscience of vision consciousness. Here we re-analysed data from a within-subject, high-precision, highly-sampled fMRI study (N=7) coupled with model-based representational similarity analysis (RSA) in order to provide an information-based approach to study the representation of conscious and unconscious visual contents The standard whole-brain searchlight RSA revealed that the hidden representations of convolutional neural network models explained brain activity patterns in response to unconscious contents in the ventral visual pathway in the majority of the observers, particularly for models that ranked high in explaining the variance of the visual cortex (i.e., VGGNet and ResNet50). Also five of seven subjects showed brain activity patterns that correlated with the model in frontoparietal areas in the unconscious trials. However, the results of an encoding-based RSA analyses in the unconscious condition were mixed and somehow difficult to interpret, including negative correlations between the representations of the computer vision models and the brain activity in frontal areas in a substantial amount of the observers.

The rapid loss of tissue viability during hypothermic storage frequently results in organ failure and high discard rates in transplantation. In contrast, ectothermic animals can tolerate prolonged exposure to low temperatures in their natural environments. Elucidating the mechanisms underlying this cold tolerance may therefore inform improved strategies for tissue preservation. Here, we investigate proteomic and metabolomic responses to cold exposure in the livers of two frog species from distinct habitats: the cold-sensitive African clawed frog (Xenopus laevis) and the cold-tolerant Northeastern Asian brown frog (Rana dybowskii). Cold exposure induced lipid mobilization in X. laevis, whereas it promoted phospholipid mono-unsaturation in R. dybowskii. Notably, treatment with monounsaturated fatty acids (MUFAs) or overexpression of stearoyl-CoA desaturase (SCD) reduced cell death during cold storage. Moreover, MUFA infusion significantly improved cell viability in mouse liver tissue under hypothermic conditions. Together, these findings suggest that lipid mono-unsaturation, an adaptive feature of cold-tolerant frogs, can be leveraged to enhance tissue preservation during cold storage.

The observation of others actions activates a network of temporal, parietal and premotor/prefrontal areas in macaque monkeys and humans. This action-observation network (AON) has been shown to play important roles in understanding the actions of others, learning by imitation, and social cognition in both species. It is unclear whether a similar network exists in New World primates, which separated from Old Word Primates [~] 35 million years ago. Here we used ultra-high field fMRI at 9.4T in awake common marmosets (Callithrix jacchus) while they watched videos depicting the upper-limb of conspecifics performing goal-directed (grasping food) or non-goal-directed actions. We found that the observation of goal-directed actions, compared to non-goal directed ones, activated a temporo-parieto-frontal network, including areas 6 and 45 in premotor and prefrontal cortices, areas PGa-IPa, FST and the TE complex in occipito-temporal region and areas V6A, MIP, LIP and PG in the occipito-parietal cortex. These results show remarkable overlap with the AON observed in humans and macaques. These results demonstrate the existence of an evolutionarily conserved AON that likely predates the separation of Old and New World primates.

Coral reef systems are under global threat due to warming and acidifying oceans1. Understanding the response of the coral holobiont to environmental change is crucial to aid conservation efforts. The most pressing problem is "coral bleaching", usually precipitated by prolonged thermal stress that disrupts the algal symbiosis sustaining the holobiont2,3. We used metabolomics to understand how the coral holobiont metabolome responds to heat stress with the goal of identifying diagnostic markers prior to bleaching onset. We studied the heat tolerant Montipora capitata and heat sensitive Pocillopora acuta coral species from the Hawaiian reef system in K[a]neohe Bay, Oahu. Untargeted LC-MS analysis uncovered both known and novel metabolites that accumulate during heat stress. Among those showing the highest differential accumulation were a variety of co-regulated dipeptides present in both species. The structures of four of these compounds were determined (Arginine-Glutamine, Lysine-Glutamine, Arginine-Valine, and Arginine-Alanine). These dipeptides also showed differential accumulation in symbiotic and aposymbiotic (alga free) individuals of the sea anemone model Aiptasia4, suggesting their animal provenance and algal symbiont related function. Our results identify a suite of metabolites associated with thermal stress that can be used to diagnose coral health in wild samples.

TACAN is an ion channel involved in sensing mechanical pain. It has recently been shown to represent a novel and evolutionarily conserved class of mechanosensitive channels. Here, we present the cryoelectron microscopic structure of human TACAN (hTACAN). hTACAN forms a dimer in which each protomer consists of a transmembrane globular domain (TMD) that is formed of six helices and an intracellular domain (ICD) that is formed of two helices. Molecular dynamic simulations suggest that a putative ion conduction pathway is located inside each protomer. Single point mutation of the key residue Met207 significantly increased the surface tension activated currents. Moreover, cholesterols were identified at the flank of each subunit. Our data show the molecular assembly of hTACAN and suggest that the wild type hTACAN is in a closed state, providing a basis for further understanding the activation mechanism of the hTACAN channel.

Injuries in elite sports disrupt team performance, shorten careers, and incur significant financial costs, highlighting the critical need for accurate predictions to inform optimal decisions that effectively prevent injuries. Existing approaches to injury prediction fail to account for cumulative risk, overlook injury severity, lack reliable probability calibration, and omit statistically guided decision thresholds. Here, we present a novel injury prediction framework integrating risk accumulation via survival analysis with machine learning, probability beta calibration, and statistical decision theory. Using a unique dataset spanning four seasons from FC Barcelonas womens team, we demonstrate that our framework outperforms standard classifiers, yielding superior discrimination ability. Our framework identifies fatigue-related measures as key injury predictors and incorporates flexible thresholds based on match importance and decision-maker certainty, improving player availability. Scalable and transferable to other sports, this framework bridges academic research and practical deployment, empowering sports organizations to optimize player performance and long-term outcomes.

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Understanding the default-mode network (DMN) in the common marmoset (Callithrix jacchus) has been challenging due to inconsistencies with human and marmoset DMNs. By analyzing task-negative activation in fMRI studies, we identified medial prefrontal cortical areas, rostral auditory areas, entorhinal cortex, posterior cingulate cortex area 31, hippocampus, hypothalamus, and basomedial amygdala as marmoset DMN components. Notable, medial and posterior parietal areas that were previously hypothesized to be part of the DMN were activated during visual task blocks. Seed analysis using resting-state fMRI showed strong connectivity between task-negative areas, and tracer data supported a structural network aligning with this functional DMN. These findings challenge previous definition of the marmoset DMN and reconcile many inconsistencies with the DMNs observed in humans, macaque monkeys, and even rodents. Overall, these results highlight the marmoset as a powerful model for DMN research, with potential implications for studying neuropsychiatric disorders where DMN activity and connectivity are altered.

General anesthetics are routinely used to induce unconsciousness. While much is known about their effects on receptor function and the activity of individual neurons, much less is known about how these local effects are manifest at the level of largescale, distributed brain networks. Using functional magnetic resonance imaging (fMRI) with the common marmoset (Callithrix jacchus) we investigated the effects of the anaesthetic isoflurane on functional brain networks and their temporal dynamics, comparing network measures during wakeful rest and induced unconsciousness. The anaesthetic condition was characterised by weak functional networks that were more similar to anatomical structure and more fragmented than during wakeful rest. Conversely, the awake condition was characterised by coordinated network reconfiguration and more distinct subnetwork composition. Our findings are consistent with the view that consciousness is an emergent property of the dynamics of functional brain networks, and that anaesthetics impoverish these dynamics by reducing the efficacy of synaptic transmission.

Substantial changes in behavior, physiology, and brain function occur when alertness decreases 1- 5. These changes in brain function involve increased synchronization between cortical areas 6,7 as well as alterations in sensory processing pathways and networks connecting the thalamus and cortex 5,8-11. Cognitive tasks engage overlapping functional networks with sensory pathways facilitating information processing 12,13, and thalamocortical and corticocortical networks supporting task performance 14,15. Frontoparietal circuits play a crucial role in cognitive tasks 16 and states of decreased consciousness 17. To develop an integrated framework of consciousness and cognition, it is important to understand how fluctuations in alertness and cognitive processing interact in these shared circuits 18. Our hypothesis is that during periods of low alertness, individuals who actively maintain task engagement would recruit additional frontoparietal and sensory processing networks, while thalamocortical dynamics that typically change during sleep onset would remain unaffected. Our findings demonstrated that as alertness decreased, passively listening to auditory tones led to increased synchronization in the parietal lobe, whereas actively performing an auditory task resulted in increased long-range frontoparietal synchronization. During decreasing alertness, passive listening (but not active task engagement) was associated with widespread increased synchronization between the thalamus and cortex. In contrast, active task engagement (but not passive listening) led to increased synchronization between the auditory cortex and the rest of the brain. These results reveal the functional mechanisms of the brains flexible reorganization during transitions of consciousness when individuals are actively engaged in cognitive processes.

Understanding cellular function within 3D multicellular spheroids is essential for advancing cancer research, particularly in studying cell-stromal interactions as potential targets for novel drug therapies. However, accurate single-cell segmentation in 3D cultures is challenging due to dense cell clustering and the impracticality of manual annotations. We present a high-throughput (HT) 3D single-cell analysis pipeline that combines optimized wet-lab conditions, deep learning (DL)-based segmentation models, and Bayesian optimization to address these challenges. By using live-cell nuclear and cytoplasmic dyes, we achieved clear and uniform staining of cell populations in renal cancer and immune T-cell monocultures and cocultures, improving single-cell detection in spheroids. Our pipeline integrates image preprocessing and DL models based on 3DUnet architecture, enabling robust segmentation of densely packed 3D structures. Bayesian optimization, guided by a custom objective function, was employed to refine segmentation parameters and improve quality based on biologically relevant criteria. The pipeline successfully segments cells under various drug treatments, revealing drug-induced changes not detectable by bulk conventional assays. This approach has potential for application to more complex biological samples, including, organoid co-cultures, diverse drug treatments, and integration with additional immunostaining assays, paving the way for detailed HT analyses of single-cell responses.

How the human brain supports accurate navigation of a learned environment has been an active topic of research for nearly a century1-5. In rodents, the theta rhythm within the medial temporal lobe (MTL) has been proposed as a neural basis for fragmenting incoming information and temporally organizing experiences and is thus widely implicated in spatial and episodic memory6. In addition, high-frequency theta (~8Hz) is associated with navigation, and loss of theta results in spatial memory deficits in rats 7. Recently, high-frequency theta oscillations during ambulatory movement have been identified in humans8,9, though their relationship to spatial memory remains unexplored. Here, we were able to record MTL activity during spatial memory and navigation in freely moving humans immersed in a room-scale virtual reality (VR) environment. Naturalistic movements were captured using motion tracking combined with wireless VR in participants implanted with an intracranial electroencephalographic (iEEG) recording system for the treatment of epilepsy. We found that prevalence of theta oscillations across brain sites during both learning and recall of spatial locations during ambulatory navigation is critically linked to memory performance. This finding supports the reinstatement hypothesis of episodic memory--thought to underlie our ability to recreate a prior experience10-12--and suggests that theta prevalence within the MTL may act as a potential representational state for memory reinstatement during spatial navigation. Additionally, we found that theta power is hexadirectionally modulated13-15 as a function of the direction of physical movement, most prominently after learning has occurred. This effect bears a resemblance to the rodent grid cell system16 and suggests an analog in human navigation. Taken together, our results provide the first characterization of neural oscillations in the human MTL during ambulatory spatial memory tasks and provide a platform for future investigations of neural mechanisms underlying freely moving navigation in humans.

We recently reported that the membrane associated progesterone receptor (MAPR) protein family (mammalian members: PGRMC1, PGRMC2, NEUFC and NENF) originated from a new class of prokaryotic cytochrome b5 (cytb5) domain proteins, called cytb5M (MAPR-like). Relative to classical cytb5 proteins, MAPR and ctyb5M proteins shared unique sequence elements and a distinct heme binding orientation at an approximately 90 rotation relative to classical cytb5, as demonstrated in the archetypal crystal structure of a cytb5M protein (PDB accession number 6NZX). Here, we present the second crystal structure of an archaeal cytb5M domain (Methanococcoides burtonii WP_011499504.1, PDB:6VZ6). It exhibits similar heme-binding to the 6NZX cytb5M, supporting the deduction that MAPR-like heme orientation was inherited from the prokaryotic ancestor of the original eukaryotic MAPR gene.

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing--a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brains intrinsic functional architecture.

The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and missing cells are common segmentation errors making the subsequent cell tracking even more difficult. In 2020, we successfully participated as team KIT-Sch-GE (1) in the 5th edition of the ISBI Cell Tracking Challenge. With our deep learning-based distance map regression segmentation and our graph-based cell tracking, we achieved multiple top 3 rankings on the diverse data sets. In this manuscript, we show how our approach can be further improved by using another optimizer and by fine-tuning training data augmentation parameters, learning rate schedules, and the training data representation. The fine-tuned segmentation in combination with an improved tracking enabled to further improve our performance in the 6th edition of the Cell Tracking Challenge 2021 as team KIT-Sch-GE (2).

Cattle are broadly classified into two subspecies: Bos taurus, adapted to temperate climates, and Bos indicus, adapted to tropical environments. Indicine cattle show better heat tolerance and stronger disease resistance, whereas taurine cattle are known for higher milk yield and better meat quality. Improving milk yield while maintaining resistance to heat stress and infectious diseases is an important objective in cattle breeding. Marker-assisted selection is an effective approach for improving economically important traits, highlighting the need to understand genetic differences between taurine and indicine cattle. However, genome-wide comparisons between European taurine and Indian indicine cattle remain limited. To address this gap, whole-genome sequencing of 48 Indian indicine cattle was performed and combined with publicly available data. This enabled a comparative genomic analysis of 74 Indian indicine and 83 European taurine individuals to identify genes involved in heat tolerance and immune response. Genome-wide analyses using Fst and XP-CLR identified 4,343 and 1,457 differentiated genes, respectively, with 826 genes common to both methods. These genes were mainly associated with immune response, protein stability, and cytoskeletal structure. Strong selection signals were observed in three heat shock protein genes (DNAJC11, DNAJC5, and DNAJB11) and 229 immune-related genes. To examine the inheritance of these genes through crossbreeding, a haplotype-resolved genome assembly was generated for the Indian crossbreed Sunandini, which showed predominantly taurine ancestry (69.13-96.04%), with a smaller indicine contribution (1.22-1.87%). Several genes related to heat tolerance and immune response were inherited exclusively from indicine cattle, highlighting their importance for environmental adaptation and future breeding programs.

The infection course of Mycobacterium tuberculosis is highly dynamic and comprises sequential stages that require damaging and crossing of several membranes to enable the translocation of the bacteria into the cytosol or their escape from the host. Many important breakthroughs such as the restriction of vacuolar and cytosolic mycobacteria by the autophagy pathway and the recruitment of sophisticated host repair machineries to the Mycobacterium-containing vacuole have been gained in the Dictyostelium discoideum/M. marinum system. Despite the availability of well-established light and advanced electron microscopy techniques in this system, a correlative approach that integrates both methodologies with almost native ultrastructural preservation is still lacking at the moment. This is most likely due to the low ability of D. discoideum to adhere to surfaces, which results in cell loss even after fixation. To address this problem, we improved the adhesion of cells and developed a straightforward and convenient workflow for 3D-correlative light and electron microscopy. This approach includes high-pressure freezing, which is an excellent technique for preserving membranes. Thus, our method allows to monitor the ultrastructural aspects of vacuole escape which is of central importance for the survival and dissemination of bacterial pathogens.

This work is based on the manifold-embedding approach to study biological molecules exhibiting continuous conformational changes. Previous work established a method capable of reconstructing 3D movies and accompanying energetics of atomic-level structures from single-particle cryo-EM images of macromolecules displaying multiple conformational degrees of freedom. Here, we introduce an unsupervised geometric machine learning approach that is informed by detailed heuristic analysis of manifolds formed by simulated heterogeneous cryo-EM datasets generated from an atomic structure. These simulated data were generated with increasing complexity to account for multiple conformational motions, state occupancies and typical microscope parameters in a wide range of signal-to-noise ratios. Using these datasets as ground-truth, we provide detailed exposition of our findings using several conformational motions while exploring the available parameter space. Guided by these insights, we build a framework to leverage the high-dimensional geometric information obtained towards reconstituting a quasi-continuum of conformational states in the form of a free-energy landscape and respective 3D density maps for all states therein. As shown by a direct comparison of results, this framework offers substantial improvements relative to the previous work.

Motor learning alters activities in multiple brain areas. While learning-induced activity change in these areas has been investigated, how the information flow changes in the network across those areas remains to be thoroughly examined. We analysed wide-field calcium imaging data spanning from the premotor cortex to the parietal cortex of marmosets while they learned a two-target forelimb-reaching task. We applied non-negative matrix factorization (NMF) to the activity data and extracted about 30 localized activity components. Encoding model analysis indicated that learning was associated with a decrease in activity components related to hand movements, and an increase in those related to external and reward signals. Causality analysis by embedding entropy (EE) revealed increases in causal links across activity components in different areas and stabilization of the network structure with behavioural improvements. These results indicate that motor learning entails both a redistribution of task-related activity and a reorganization of large-scale cortical network interactions.

Cryo-electron microscopy (cryo-EM) has become a pivotal tool for determining the atomic structures of biological macromolecules. In this study, we introduce a robust hierarchical linear (RHL) model to estimate key atom-specific parameters: the amplitude and width of Gaussian functions, which are typically simplified using uniform widths and amplitudes scaled by atomic number in cryo-EM map related studies. Our RHL framework incorporates minimum density power divergence estimation (MDPDE) to account for heteroscedasticity and enhance robustness against outliers. Through both simulation studies and real data analysis, we demonstrate that the proposed method effectively reduces the influence of outliers and yields reliable parameter estimates. When applied to cryo-EM data of human apoferritin (PDB ID: 6Z6U; EMDB ID: 11103), our model reveals that the estimated Gaussian parameters are stable across most amino acids, with nitrogen atoms consistently displaying lower amplitude and width values than predicted by conventional Gaussian modeling. These results underscore the need for a systematic analysis of paired cryo-EM maps and atomic models from the EMDB and PDB to gain deeper insights into atom-specific features embedded in cryo-EM data.