Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of Magnetohydrodynamic Normal Modes in the Earth's Magnetosphere

The Earth's magnetosphere supports a variety of Magnetohydrodynamic (MHD) normal modes with Ultra Low Frequencies (ULF) including standing Alfvén waves and cavity/waveguide modes. Their amplitudes and frequencies depend in part on the properties of the magnetosphere (size of cavity, wave speed distribution). In this work, we use ∼13 years of Time History of Events and Macroscale Interactions during Substorms satellite magnetic field observations, combined with linearized MHD numerical simulations, to examine the properties of MHD normal modes in the region L > 5 and for frequencies <80 mHz. We identify persistent normal mode structure in observed dawn sector power spectra with frequency‐dependent wave power peaks like those obtained from simulation ensemble averages, where the simulations assume different radial Alfvén speed profiles and magnetopause locations. We further show with both observations and simulations how frequency‐dependent wave power peaks at L > 5 depend on both the magnetopause location and the location of peaks in the radial Alfvén speed profile. Finally, we discuss how these results might be used to better model radiation belt electron dynamics related to ULF waves.
“Sing and make music to the Lord”: cultural difference in South Africa’s urban religious soundscapes

Scholars of contemporary Christianity have long noted the importance of sacred music in constructing faith communities and distinguishing religious actors from one another. Drawing on historical and ethnographic evidence gathered over 16 months in Johannesburg, South Africa, I examine the role of music in contributing to the Christian identity of conservative evangelicals and in mediating their relationship with religious others. For the interdenominational, multiracial network of conservative Christians with whom I worked, music sung during weekly church services was carefully curated to meet elders’ pastoral aims informed by the conventions of orthodox Christian theology, yet it was also a site of tension and innovation as efforts to appeal to wider demographics were implemented. The palpable contrast between this music style and that of more charismatic evangelical churches was a point of implicit social commentary, semiotically standing in for the spectre of dangerous religious difference. This ethnographic research illustrates the power of embodied, sonic “feeling” to maintain cultural boundaries in South Africa’s heterogeneous religious soundscapes.
The impact of speaker accent on discourse processing: a frequency investigation

Previous studies show that there are differences in native and foreign speech processing (Lev-Ari, 2018) while mixed evidence has been found regarding differences between dialectal and foreign accent processing (see: Adank et al., 2009; Floccia et al. 2006 but see also: Floccia et al., 2009; Girard et al., 2008). Within this field, two theories have been proposed. The Perceptual Distance Hypothesis states that the mechanisms underlying dialectal accent processing are attenuated versions of those of foreign (Clarke & Garrett, 2004). While, the Different Processes Hypothesis argues that the mechanisms of foreign and dialectal accent processing are qualitatively different (Floccia et al, 2009). A recent study looking at single-word EEG data, suggested that there may be flexibility in processing mechanisms (Thomas et al., 2022). The present study deepens this investigation by addressing in which frequency bands native, dialectal and foreign accent processing differ when listening to extended speech. Electroencephalographic data was recorded from 30 participants who listened to dialogues of approximately six minutes spoken in native, dialectal and foreign accents. Power spectral density estimation (1-35 hz) was performed. Linear mixed models were done in frequency windows of particular relevance to discourse processing. Frequency bands associated with phoneme [gamma], syllable [theta], and prosody [delta] were considered along with those of general cognitive mechanisms [alpha and beta]. Results show power differences in the Gamma frequency range. While in higher frequency ranges foreign accent processing is differentiated from power amplitudes of native and dialectal accent processing, in low frequencies we do not see any accent-related power amplitude modulations. This suggests that there may be a difference in phoneme processing for native accent types and foreign accent, while we speculate that top-down mechanisms during discourse processing may mitigate the effects observed with short units of speech.
Estimating Genome-wide Phylogenies Using Probabilistic Topic Modeling

Inferring the evolutionary history of species or populations with genome-wide data is gaining ground, but computational constraints still limit our abilities in this area. We developed an alignment-free method to infer the genome-wide species tree and implemented it in the Python package TopicContml. The method uses probabilistic topic modeling (specifically, Latent Dirichlet Allocation or LDA) to extract ‘topic’ frequencies from k-mers, which are derived from multilocus DNA sequences. These extracted frequencies then serve as an input for the program Contml in the PHYLIP package, which is used to generate a species tree. We evaluated the performance of our method with biological and simulated data sets: a data set with 14 DNA sequence loci from 78-92 haplotypes from two Australian bird species distributed in 9 populations; a second data set of 5162 loci from 80 mammal species; and a third data set of 67317 autosomal loci and 4157 X-chromosome loci of 6 species in the Anopheles gambiae complex, and several simulated data sets. Our empirical results and simulated data suggest that our method is efficient and statistically accurate. We also assessed the uncertainty of the estimated relationships among clades using a bootstrap procedure for aligned sequence data and for k-mer data.
Concurrent action observation but not motor imagery modulates interhemispheric inhibition during physical execution

Motor control relies on an inhibitory connection between the motor cortices of the brain, known as interhemispheric inhibition (IHI). This phenomenon is well established during the execution of unilateral motor tasks. It is unknown if the neurophysiological effects associated with IHI during physical execution (PE) also occur during action observation (AO) and motor imagery (MI) and/or if the addition of these covert processes to PE moderates IHI; speaking to differences in neurophysiology and functional equivalence. Participants (N=23) performed unilateral concentric wrist contractions (50% maximum voluntary contraction) under three conditions: PE alone, concurrent PE+AO, and concurrent PE+MI. To index IHI, we induced an ipsilateral silent period (iSP) and assessed iSP duration during each condition via neuro-navigated single-pulse transcranial magnetic stimulation (TMS) over the ipsilateral motor cortex. Relative to PE alone, iSP decreased during PE+AO, yet only when this condition preceded PE+MI. iSP duration was not modulated during PE+MI. Together, these data suggest that PE+AO promotes bilateral recruitment and ‘interhemispheric cooperation’ rather than inhibition. AO and MI differentially impact interhemispheric coordination, serving to suppress inhibition only when AO is primed by MI.
A Bayesian active learning platform for scalable combination drug screens

Large-scale combination drug screens are generally considered intractable due to the immense number of possible combinations. Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute novel combinations. Here we propose BATCHIE, an orthogonal approach that conducts experiments dynamically in batches. BATCHIE uses information theory and probabilistic modeling to design each batch to be maximally informative based on the results of previous experiments. On retrospective experiments from previous large-scale screens, BATCHIE designs rapidly discover highly effective and synergistic combinations. To validate BATCHIE prospectively, we conducted a combination screen on a collection of pediatric cancer cell lines using a 206 drug library. After exploring only 4% of the 1.4M possible experiments, the BATCHIE model was highly accurate at predicting novel combinations and detecting synergies. Further, the model identified a panel of top combinations for Ewing sarcomas, all of which were experimentally confirmed to be effective, including the rational and translatable top hit of PARP plus topoisomerase I inhibition. These results demonstrate that adaptive experiments can enable large-scale unbiased combination drug screens with a relatively small number of experiments, thereby powering a new wave of combination drug discoveries. BATCHIE is open source and publicly available (https://github.com/tansey-lab/batchie).
Event Detection and Classification from Multimodal Time Series with Application to Neural Data

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.
The Impact of Regulatory Approaches Targeting Collaborative Economy in the Tourism Accommodation Sector: Barcelona, Berlin, Amsterdam and Paris

The rise of peer-to-peer or collaborative economy tourism accommodation harnesses digital technologies to connect hosts and guests who would otherwise never have connected. In the process, new markets are created, access to new products and experiences are facilitated and new opportunities for tourism economic development are unlocked. This Impulse Paper assesses the scope and effects of the collaborative economy on the tourism accommodation sector in the cities of Barcelona, Berlin, Amsterdam and Paris. The objectives are:1. To compare and contrast the different regulatory measures that have been put in place in each of the four cities.2. To assess the impacts of regulatory approaches adopted in each city on different groups of stakeholders and on the destination in general.3. To analyse policy practices and make recommendations with respect to good practice.
Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

Large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine malignancy that, like the more common small cell lung cancer (SCLC), is associated with an absence of druggable oncogenic driver mutations, a clinically aggressive disease course, and dismal prognosis. In contrast to SCLC, however, there is little evidence to guide optimal treatment strategies which are, instead, often adapted from SCLC and non-small cell lung cancer (NSCLC) approaches. While there have been some efforts to describe the molecular landscape of LCNEC, to date there are few links between distinct biologic phenotypes of LCNEC and therapeutic vulnerabilities. Here, we demonstrate that the presence or absence of the transcription factor YAP1 distinguishes two roughly equal subsets of LCNEC. The YAP1-high subset is mesenchymal and inflamed and characterized, alongside TP53 mutations, by co-occurring alterations in CDKN2A/B and SMARCA4. Therapeutically, the YAP1-high subset demonstrates vulnerability to MEK and AXL targeting strategies, including a novel preclinical AXL CAR-T cell, as well as predicted vulnerability to SMARCA2 degraders and CDK4/6 inhibitors. Meanwhile, the YAP1-low subset is epithelial and immune-cold and more commonly features TP53 and RB1 co-mutations, similar to those observed in pure SCLC. Notably, the YAP1-low subset is also characterized by expression of SCLC subtype-defining transcription factors -especially ASCL1 and NEUROD1 - and, as expected given its transcriptional similarities to SCLC, exhibits putative vulnerabilities reminiscent of SCLC, including Delta-like ligand 3 (DLL3) and CD56 targeting, as with novel preclinical DLL3 and CD56 CAR T-cells, and DNA damage repair (DDR) inhibition. These findings highlight the potential for YAP1 to guide the first personalized treatment strategies for LCNEC.
A myristoyl switch at the plasma membrane triggers cleavage and oligomerization of Mason-Pfizer monkey virus matrix protein

For most retroviruses, including HIV, association with the plasma membrane (PM) promotes the assembly of immature particles, which occurs simultaneously with budding and maturation. In these viruses, maturation is initiated by oligomerization of polyprotein precursors. In contrast, several retroviruses, such as Mason-Pfizer monkey virus (M-PMV), assemble in the cytoplasm into immature particles that are transported across the PM. Therefore, protease activation and specific cleavage must not occur until the preassembled particle interacts with the PM. This interaction is triggered by a bipartite signal consisting of a cluster of basic residues in the matrix (MA) domain of Gag polyprotein and a myristoyl moiety N-terminally attached to MA. Here, we provide evidence that myristoyl exposure from the MA core and its insertion into the PM occurs in M-PMV. By a combination of experimental methods, we show that this results in a structural change at the C-terminus of MA allowing efficient cleavage of MA from the downstream region of Gag. This suggests that, in addition to the known effect of the myristoyl switch of HIV-1 MA on the multimerization state of Gag and particle assembly, the myristoyl switch may have a regulatory role in initiating sequential cleavage of M-PMV Gag in immature particles.
Plant metabolites modulate animal social networks and lifespan

Social interactions are prevalent in animals, with some individuals participating in more interactions than others. However, the mechanisms underlying this heterogeneity in social interaction frequency and its potential fitness consequences remain poorly characterized. Here, we examine the effects of non-nutritive plant metabolites (‘clerodanoids’) on social networks and lifespan in the turnip sawfly. The adult sawflies facultatively acquire clerodanoids from non-host plant species. We found high variation in clerodanoid amounts in both wild-caught and lab-reared individuals. Furthermore, sawflies could acquire clerodanoids by nibbling on conspecifics that had prior access to these metabolites, with asymmetric clerodanoid access leading to increased agonistic interactions. Network analysis revealed more social interactions in sawfly groups where some or all individuals had prior access to clerodanoids, while groups with no prior access had fewer interactions. The number of social interactions depended on the clerodanoid status of the focal individual and also of other conspecifics. Finally, we observed a shorter lifespan in adults with prior clerodanoid access when paired with individuals without prior access, suggesting that social interactions to obtain clerodanoids have fitness costs. Our findings demonstrate that individual variation in non-nutritional sequestered plant metabolites can skew social interactions, with potential fitness implications that impact individual social niches.
Dynamic predictive coding: A model of hierarchical sequence learning and prediction in the neocortex

We introduce dynamic predictive coding, a hierarchical model of spatiotemporal prediction and sequence learning in the neocortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using prediction errors. As a result, lower levels form representations that encode sequences at shorter timescales (e.g., a single step) while higher levels form representations that encode sequences at longer timescales (e.g., an entire sequence). We tested this model using a two-level neural network, where the top-down modulation creates low-dimensional combinations of a set of learned temporal dynamics to explain input sequences. When trained on natural videos, the lower-level model neurons developed spacetime receptive fields similar to those of simple cells in the primary visual cortex while the higher-level responses spanned longer timescales, mimicking temporal response hierarchies in the cortex. Additionally, the network’s hierarchical sequence representation exhibited both predictive and postdictive effects resembling those observed in visual motion processing in humans (e.g., in the flash-lag illusion). When coupled with an associative memory emulating the role of the hippocampus, the model allowed episodic memories to be stored and retrieved, supporting cue-triggered recall of an input sequence similar to activity recall in the visual cortex. When extended to three hierarchical levels, the model learned progressively more abstract temporal representations along the hierarchy. Taken together, our results suggest that cortical processing and learning of sequences can be interpreted as dynamic predictive coding based on a hierarchical spatiotemporal generative model of the visual world. Author Summary The brain is adept at predicting stimuli and events at multiple timescales. How do the neuronal networks in the brain achieve this remarkable capability? We propose that the neocortex employs dynamic predictive coding to learn hierarchical spatiotemporal representations. Using computer simulations, we show that when exposed to natural videos, a hierarchical neural network that minimizes prediction errors develops stable and longer timescale responses at the higher level; lower-level neurons learn space-time receptive fields similar to the receptive fields of primary visual cortical cells. The same network also exhibits several effects in visual motion processing and supports cue-triggered activity recall. Our results provide a new framework for understanding the genesis of temporal response hierarchies and activity recall in the neocortex.
Why Do Global Climate Models Struggle to Represent Low-Level Clouds in the West African Summer Monsoon?

AbstractClimate models struggle to realistically represent the West African monsoon (WAM), which hinders reliable future projections and the development of adequate adaption measures. Low-level clouds over southern West Africa (5°–10°N, 8°W–8°E) during July–September are an integral part of the WAM through their effect on the surface energy balance and precipitation, but their representation in climate models has received little attention. Here 30 (20) years of output from 18 (8) models participating in phase 5 of the Coupled Model Intercomparison Project (Year of Tropical Convection) are used to identify cloud biases and their causes. Compared to ERA-Interim reanalyses, many models show large biases in low-level cloudiness of both signs and a tendency to too high elevation and too weak diurnal cycles. At the same time, these models tend to have too strong low-level jets, the impact of which is unclear because of concomitant effects on temperature and moisture advection as well as turbulent mixing. Part o.
Specialization of the Human Hippocampal Long Axis Revisited

The hippocampus possesses anatomical differences along its long axis. Here the functional specialization of the human hippocampal long axis was explored using network-anchored precision functional MRI (N = 11) paired with behavioral analyses (N=266). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus was correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization using task data within the same intensively sampled individuals discovered a functional double dissociation between the anterior and posterior hippocampal regions. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest specialization along the long axis of the hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.
Gut microbiota influences foraging onset without affecting division of labor and associated physiological hallmarks in honeybees

Gut microbes can impact cognition and behavior, but whether they regulate division of labor in animal societies is unknown. We addressed this question using honeybees since they exhibit division of labor between nurses and foragers and because their gut microbiota can be manipulated. Using automated behavioral tracking and controlling for co-housing effects, we show that gut microbes influence the age at which bees start foraging but have no effects on the time spent in a foraging area and number of foraging trips. Moreover, the gut microbiota did not influence hallmarks of behavioral maturation such as body weight, cuticular hydrocarbon (CHC) profile, hypopharyngeal gland size, and the proportion of bees maturing into foragers. Overall, this study shows that the honeybee gut microbiota does not affect division of labor but rather plays an important function in controlling the onset of bee foraging.