Miracle-dz/UniFAR-FTUs huggingface dataset:
A graphical multi-fidelity Gaussian process model, with application to emulation of heavy-ion collisions

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss.
Prediction mismatch responses arise as corrections of a predictive spiking code

Prediction mismatch responses in cortex seem to signal the difference between an internal model of the animal and sensory observations. Often these responses are interpreted as evidence for the existence of error neurons, which guide inference in models of hierarchical predictive coding. Here we show that prediction mismatch responses also arise naturally in a spiking encoding of sensory signals, where spikes predict the future signal. In this model, the predictive representation has to be corrected when a mispredicted stimulus appears, which requires additional neural activity. This adaptive correction could explain why mismatch response latency can vary with mismatch detection difficulty, as the network gathers sensory evidence before committing to a correction. Prediction mismatch responses thus might not reflect the computation of errors per se, but rather the reorganization of the neural code when new information is incorporated.
Heteroplasmy is rare in plant mitochondria compared to plastids despite similar mutation rates

Plant cells harbor two membrane-bound organelles containing their own genetic material – plastids and mitochondria. Although the two organelles co-exist and co-evolve within the same plant cells, they differ in genome copy number, intracellular organization, and mode of inheritance. How these attributes determine the time to fixation, or conversely, loss of neutral alleles is currently unresolved. Here we show that mitochondria and plastids share the same mutation rate yet plastid alleles remain in a heteroplasmic state significantly longer compared to mitochondrial alleles. By analysing genetic variants across populations of the marine flowering plant Zostera marina and simulating organelle allele dynamics, we examine the determinants of allele segregation and fixation time. Our results suggest that bottleneck on the cell population, e.g., during branching and seeding, and stratification of the meristematic tissue, are important determinants of mitochondrial allele dynamics. Furthermore, we suggest that the prolonged plastid allele dynamics are due to a yet unknown active plastid partition mechanism. The dissimilarity between plastid and mitochondrial novel allele fixation at different levels of organization may figure into differences in adaptation processes. Our study uncovers fundamental principles of organelle population genetics that are essential for further investigations of long-term evolution and molecular dating of divergence events.
A novel antagonist of the CCL5/CCR5 axis suppresses the tumor growth and metastasis of triple-negative breast cancer by CCR5-YAP1 regulation

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) with a high mortality rate, and few effective therapeutic strategies are available. CCL5/CCR5 is an appealing immunotherapeutic target for TNBC. However, its signaling mechanism is poorly understood and its direct antagonists have not been reported. Here, we developed a high-throughput screening (HTS) assay for discovering its antagonists. Verteporfin was identified as a more selective and potent antagonist than the known CCR5 antagonist maraviroc. Without photodynamic therapy, verteporfin demonstrated significant inhibition on TNBC tumor growth through immune regulation, remarkable suppression of lung metastasis by cell-intrinsic mechanism, and a significant extension of overall survival in vivo. Mechanistically, CCR5 was found to be essential for expression of the key hippo effector YAP1. It promoted YAP1 transcription via HIF-1α and exerted further control over the migration of CD8+ T, NK, and MDSC immune cells through chemokines CXCL16 and CXCL8 which were identified from RNA-seq. Moreover, the CCR5-YAP1 axis played a vital role in promoting metastasis by modulating β-catenin and core epithelial-mesenchymal transition transcription factors ZEB1 and ZEB2. It is noteworthy that the regulatory relationship between CCR5 and YAP1 was observed across various BC subtypes, TNBC patients, and showed potential relevance in fifteen additional cancer types. Overall, this study introduced an easy-to-use HTS assay that streamlines the discovery of CCL5/CCR5 axis antagonists. Verteporfin was identified as a specific molecular probe of this axis with great potentials as a therapeutic agent for treating sixteen malignant diseases characterized by heightened CCR5 and YAP1 levels.
Enhancing Ligand-Based Virtual Screening with 3D Shape Similarity via a Distance-Aware Transformer Model

Following the assumption that chemically similar molecules exhibit similar biologcial properties, ligand-based virtual screening can be a valuable starting point in drug discovery projects. While 2D-based similarity metrics generally focus on similar scaffolds or substructures, 3D-based methods can capture the shape of a molecule, allowing for the identification of compounds with different scaffolds. We recently published a proof-of-concept study which demonstrated how a Transformer model can be adapted to preserve 2D similarities in latent space in the form of Euclidean distances. In this work, we extend this research and prove that the approach can be adapted to 3D similarities. We use pharmacophore-based shape similarity as 3D similarity measure. We show that the model is able to enrich the predicted most similar hits with compounds with different scaffolds that are indeed similar in 3D space. Whereas classical pharmacophore- or shape-based 3D similarity methods rely on expensive alignment processes, in our approach, we identify similar compounds directly by the Euclidean distances in latent space. This enables for the first time the 3D screening of ultra-large databases with high efficiency.
Alpha-synuclein null mutation exacerbates the phenotype of a model of Menkes disease in female mice

Genetic modifier screens provide a useful tool, in diverse organisms from Drosophila to C. elegans and mice, for recovering new genes of interest that may reduce or enhance a phenotype of interest. This study reports a modifier screen, based on N-ethyl-N-nitrosourea (ENU) mutagenesis and outcrossing, designed to increase understanding of the normal function of murine α-synuclein (Snca). Human SNCA was the first gene linked to familial Parkinson’s disease. Since the discovery of the genetic link of SNCA to Parkinson’s nearly three decades ago, numerous studies have investigated the normal function of SNCA protein with divergent roles associated with different cellular compartments. Understanding of the normal function of murine Snca is complicated by the fact that mice with homozygous null mutations live a normal lifespan and have only subtle synaptic deficits. Here, we report that the first genetic modifier (a sensitized mutation) that was identified in our screen was the X-linked gene, ATPase copper transporting alpha (Atp7a). In humans, mutations in Atp7a are linked to to Menkes disease, a disease with pleiotropic phenotypes that include a severe neurological component. Atp7a encodes a trans-Golgi copper transporter that supplies the copper co-factor to enzymes that pass through the ER-Golgi network. Male mice that carry a mutation in Atp7a die within 3 weeks of age regardless of Snca genotype. In contrast, here we show that Snca disruption modifies the phenotype of Atp7a in female mice. Female mice that carry the Atp7a mutation, on an Snca null background, die earlier (prior to 35 days) at a significantly higher rate than those that carry the Atp7a mutation on a wildtype Snca background ATPase copper transporting alpha. Thus, Snca null mutations sensitize female mice to mutations in Atp7a, suggesting that Snca protein may have a protective effect in females, perhaps in neurons, given the co-expression patterns. Although data has suggested diverse functions for human and mouse α-synuclein proteins in multiple cell compartments, this is the first demonstration via use of genetic screening to demonstrate that Snca protein may function in the ER-Golgi system in the mammalian brain in a sex-dependent manner. Author summary This study sought to probe the normal function(s) of a protein associated with Parkinson’s disease, the second most common neurodegenerative disease in humans. We used a genetic modifier approach to uncover aspects of normal protein function, via mutagenesis of mice and screening for neurological problems that are decreased or enhanced in mice that are null for α-synuclein (Snca). Through these studies, we identified the X-linked gene that is mutated in Menkes disease in humans as a modifier of the null Snca phenotype, specifically in female mice. The gene mutated in Menkes disease, ATP7a, encodes a copper transporter that is known to act in the trans-Golgi sub-cellular compartment. Genetic modifier effects suggest that Snca may also play a role in that compartment, potentially in the mammalian brain.
Do aposematic species have larger range sizes? A case study with Neotropical poison frogs

Aim Aposematic animals, i.e. those that are defended and warn potential predators through signals, are suggested to have resource-gathering advantages against non-aposematic ones. We here explore this in a biogeographic frame expecting that aposematic species are better dispersers, which translates into larger geographic range size. Location South America. Taxon Poison frogs (Amphibia; Aromobatidae and Dendrobatidae). Methods We use 43 toxic and 26 non-toxic poison frog species from the lowlands only as representatives of aposematic and non-aposematic study organisms, respectively. Realized and potential geographic ranges are calculated using minimum convex polygon and species distribution modelling methods, respectively. Accounting for species body size and phylogeny, we test if both range and aposematism are correlated using linear mixed models. Results Aposematic and non-aposematic species do neither differ in realized nor in potential geographic range size. There was no effect of body size. Main conclusions The role of aposematism yet is not as clear as suggested and determinants of poison frog range sizes are multifaceted. A more integrative approach is needed using information of behaviour, predation risk, and reproductive biology to assess the role of aposematism on observed species distributions. Such data are not yet available for most species, neither poison frogs nor other aposematic animals.
Video diffusion for early cellular apoptosis forecasting

Reliable and early prediction of cell death (apoptosis) is critically important in various areas of biology, particularly in characterizing the effectiveness of cell-based infusion products utilized for cancer immunotherapy. While deep Convolutional Neural Networks (CNNs) are often used in state-of-the-art approaches for apoptosis classification, they typically focus solely on individual cells and ignore cell-cell interaction. To address this limitation, we propose a novel generative approach based on a video diffusion model, which predicts future cellular behaviors for early detection of apoptosis events, even before molecular markers like Annexin-V or visual indications like membrane blebbing become apparent. Our approach accounts for the interactions of multiple target cells and their spatial and temporal relationships at each time frame. We condition our generative model on two starting frames and utilize an auto-regressive framework to predict the subsequent five frames. Our model achieves a 0.88 F1 score on the cell death event classification and 2.11 mean absolute death-time error, significantly outperforming state-of-the-art methods.
PRC1 directs PRC2-H3K27me3 deposition to shield adult spermatogonial stem cells from differentiation

Spermatogonial stem cell functionality resides in the slow-cycling and heterogeneous undifferentiated spermatogonia cell population. This pool of cells supports lifelong fertility in adult males by balancing self-renewal and differentiation to produce haploid gametes. However, the molecular mechanisms underpinning long-term stemness of undifferentiated spermatogonia during adulthood remain unclear. Here, we discover that an epigenetic regulator, Polycomb repressive complex 1 (PRC1), shields adult undifferentiated spermatogonia from differentiation, maintains slow cycling, and directs commitment to differentiation during steady-state spermatogenesis in adults. We show that PRC2-mediated H3K27me3 is an epigenetic hallmark of adult undifferentiated spermatogonia. Indeed, spermatogonial differentiation is accompanied by a global loss of H3K27me3. Disruption of PRC1 impairs global H3K27me3 deposition, leading to precocious spermatogonial differentiation. Therefore, PRC1 directs PRC2-H3K27me3 deposition to maintain the self-renewing state of undifferentiated spermatogonia. Importantly, in contrast to its role in other tissue stem cells, PRC1 negatively regulates the cell cycle to maintain slow cycling of undifferentiated spermatogonia. Our findings have implications for how epigenetic regulators can be tuned to regulate the stem cell potential, cell cycle, and differentiation to ensure lifelong fertility in adult males.
Noise reduction as a unified mechanism of perceptual learning in humans, macaques, and convolutional neural networks

It is established that perceptual learning enhances the signal-to-noise ratios of sensory processing. However, the specific mechanisms behind this improvement remain enigmatic. Here, we systematically investigated these mechanisms by analyzing population activity changes in deep convolutional neural networks (DCNNs), humans, and macaques. We first trained DCNNs on orientation and motion direction discrimination tasks. Our models successfully replicated the behavioral signature of enhanced signal-to-noise ratio, and the individual and population response changes observed in numerous empirical studies. We then devised a method to quantify all scenarios in which learning modifies the tuning and covariance structure of neuronal populations to enhance sensory discrimination. A groundbreaking revelation is that enhanced signal-to-noise ratios are primarily driven by reduced trial-by-trial response variability. This finding was strongly supported by population response changes measured in visual areas of both humans and macaques. These results elucidate the shared mechanisms of neural plasticity in biological and artificial visual systems.
Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis

How well a person responds to hypnosis is a stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order to identify brain patterns that differentiate between individuals high and low in susceptibility to hypnosis. In particular, we computed the periodic and aperiodic components of the EEG power spectrum, as well as graph theoretical measures derived from functional connectivity, from data acquired at rest (pre-induction) and under hypnosis (post-induction). We found that the 1/f slope of the EEG spectrum at rest was the best predictor of hypnotic susceptibility. Our findings support the idea that hypnotic susceptibility is a trait linked to the balance of cortical excitation and inhibition at baseline and offers novel perspectives on the neural foundations of hypnotic susceptibility. Future work can explore the contribution of background 1/f activity as a novel target to distinguish the responsiveness of individuals to hypnosis at baseline in the clinic. Significance Statement Hypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies greatly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction—a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.
Quantifying potential abiotic drivers of the ‘nurse-plant effect’ in two dominant shrub species of the northern Chihuahuan Desert

Aggregations of plants surrounded by areas without vegetation cover in dryland ecosystems are thought to arise when larger plants facilitate the recruitment and/or performance of smaller “protégé” plants—a phenomenon referred to as the “nurse-plant” effect. While numerous drivers can generate a nurse-plant effect, efforts to quantify multiple drivers simultaneously are rare. After verifying a higher density of protégés beneath the foundational shrubs Larrea tridentata and Prosopis glandulosa, multiple potential mechanisms underlying the nurse-plant effect were quantified in the Chihuahuan Desert of southern New Mexico. Comparisons of properties under shrub canopies relative to unvegetated interspaces revealed significantly greater concentrations of soil nutrients and lower photosynthetically active radiation and soil temperatures beneath shrubs but a consistently higher soil moisture in interspaces despite a greater water holding capacity in soils beneath shrubs. Nutrient concentrations were greater, on average, in soils beneath P. glandulosa than L. tridentata but protégé plant numbers did not significantly differ among the species. Further, in L. tridentata and P. glandulosa, canopy size was positively related to levels of understory shading, and canopy size of P. glandulosa was also positively related to soil nitrogen and microbial biomass. Results of this study suggest that a majority of the variance in the abiotic nurse-plant effect of this low-latitude system is explained by radiation interception and the concomitant reduction in temperatures experienced by protégé plants as opposed to direct effects of shrubs on soil water availability. As global change pressures intensify in drylands, a loss of perennial plant cover through mortality or dieback in canopies could have substantial, negative effects on soil biogeochemical pools and plant diversity. Additional quantification of the spatial and temporal variance in different mechanisms driving the nurse-plant effect across environmental and climatic gradients is needed to improve our understanding of plant community dynamics in dryland ecosystems.
Shared and diverging neural dynamics underlying false and veridical perception

We often mistake visual noise for meaningful images, which sometimes appear to be as convincing as veridical percepts. This suggests that there is considerable overlap between the mechanisms that underlie false and veridical perception. Yet, false percepts must arise at least in part from internally generated signals. Here, we apply multivariate analyses to human MEG data to study the overlap between veridical and false perception across two discrete stages of perceptual inference: discrimination of content (what did I see) and detection (did I see something? ). To this end, participants performed a visual discrimination task requiring them to indicate the orientation of a noisy grating, as well as their confidence in having seen a grating. Importantly, on 50% of trials no gratings were presented (noise-only trials). On a subset of these noise-only trials, participants reported seeing a grating with high confidence, dubbed here false percepts. We found that a sensory signal reflecting the content of these false percepts was present both before and after stimulus onset. Uniquely, high confidence false, but not veridical, percepts were associated with increased pre-stimulus high alpha/low beta [11-14Hz] power, potentially reflecting enhanced reliance on top-down signalling on false percept trials. Later on, a shared neural code reflecting confidence in stimulus presence emerged for both false and veridical percepts, as revealed by cross-decoding. These findings suggest that false percepts arise through sensory-like signals reflecting both content and detection signals, similar to veridical percepts, with an increase in pre-stimulus alpha/beta power uniquely contributing to false percepts. Significance statement The neural mechanisms underlying false percepts are likely different from those that underlie veridical perception, as the former are generated endogenously, whereas the latter are the result of an external stimulus. Yet, false percepts often get confused for veridical perception, suggesting a converging mechanism. This study explores the extent to which the mechanisms diverge and converge. We found that both high confidence false and veridical percepts were accompanied by content-specific stimulus-like orientation signals, as well as a shared signal reflecting perceptual confidence. In contrast, we found that false, but not veridical, percepts were preceded by increased high alpha/low beta [11-14 Hz] power, possibly reflecting a reliance on endogenous signals.
Development of A Fast Tsunami Simulation System Based on The Linear Boussinesq Theory

A fast tsunami simulation system was developed which utilized the superposition of point source computations stocked as a database. The single point source was represented by the Gaussian distribution with s=5 km. The propagation of the point source was computed by using the linear Boussinesq theory. The accuracy of the model was discussed in representing the actual tsunami source profile as well as in the dispersive tsunami propagation. The system was applied to a hypothetical Japan Sea tsunami used in the disaster mitigation planning of Tottori Prefecture. The system successfully displayed the sensitivity of local tsunami height to the tsunami source location and thus demonstrated the performance to assist specific evacuation planning in individual towns and cities.