Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of primate protomusicality via locomotion

Animals communicate acoustically to report location and identity to conspecifics. More complex patterning of calls can also function as displays to potential mates and as territorial advertisement. Music and song are terms often reserved only for humans and birds, but elements of both forms of acoustic display are also found in non-human primates. While theories on proximate functions abound, ultimate drivers of specific call structures are less well understood. We hypothesized that spatio-temporal precision in landing during perilous arboreal locomotion favored the evolution of musical calling in early primates—vastly preceding the origin of more music-like behavior in hominoids and subsequent emergence of music in later hominids. We test this locomotion based hypothesis on the origins of proto-musicality using spectrographic depictions of vocal repertoires of modern day primates and corresponding estimates of locomotor activity. Phylogenetically controlled regression analysis of 54 primate species reveals that arboreal locomotion and monogamy are robust influences on complex calling patterns while controlling for other socioecological variables. Given that these findings rest primarily upon a handful of deep branching points in the primate tree, we conclude that this coevolution likely occurred very slowly, occupying on the order of tens of millions of years.
Improving chromatin-interaction prediction using single-cell open-chromatin profile and making insight about the cis-regulatory landscape of the human brain

Single-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell-type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin-interactions despite the fact that long-range interaction among genomic sites plays a significant role in gene regulation. Here, we propose a method that predicts both local and long-range interactions among genomic sites using single-cell open chromatin profiles. Using our method’s better sensitivity, we could predict almost 0.7 million interactions among genomic sites across 7 cell-types in the human brain. The chromatin-interactions estimated in the human brain revealed surprising but useful insight about target genes of human-accelerated-elements and disease-associated mutations.
Fast end-to-end learning on protein surfaces

Proteins’ biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science. In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features. To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.
Spectrum Degradation of Hippocampal LFP During Euthanasia

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary stage when the LFP spectrum exhibited power-law feature while the frequency components over 20 Hz exhibited a power decay with a similar decay rate. This stage was followed by a rapid collapse of power spectrum towards the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex or visual cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.
A Constitutively Closed Pannexin1 Channel in Lipid Bilayer Nanodiscs Assembles as a Large-Pore Heptamer

Pannexin 1 (Panx1) channels are widely expressed and play important roles in apoptotic cell clearance, inflammation, blood pressure regulation, neurological disorders, opiate withdrawal, and cancer progression and metastasis. We performed (1) physicochemical analysis on a constitutively closed Panx1 channel (designated fPanx1ΔC) to examine the entire population of particles to detect multiple oligomeric states and (2) cryoEM in the membrane mimetics amphipol A8-35 and lipid bilayer nanodiscs. Our results reveal that the dominant if not exclusive oligomeric state of fPanx1ΔC is a heptamer, in solution and by cryoEM. The Panx1 heptamer provides further structural diversity within the family of large-pore channels, including hexameric LRRC8 (SWELL1) channels and connexin hemichannels, octameric CALHM1 channels and innexin hemichannels, and undecameric CALHM2 channels. Conserved structural themes are a large cytoplasmic vestibule with a diameter that corresponds roughly with the oligomeric state and a 4-helix bundle protomer, albeit with noncanonical helical packing for CALHM1 and CALHM2. In Brief The 4-helix bundle protomer of a constitutively closed pannexin1 channel assembles as a heptamer in solution and by cryoEM.
Female Dispersion Is Necessary, But Not Sufficient, For The Evolution of Monogamy in Mammals

Explanations for the evolution of monogamy in mammals typically emphasise one of two possibilities: monogamy evolves when females are overdispersed (such that males cannot defend more than one female at a time) or when males provide a service to the female. However, the first claim has never been directly tested. I test it directly at three levels using data from primates and ungulates. First, I show that the females of monogamous genera do not have territories that are significantly larger, either absolutely or relatively, than those of polygamous genera. Second, using both the Mitani-Rodman and Lowen-Dunbar inequalities, I show that, given their typical day journey lengths, males of most monogamous species could easily defend an area large enough to allow them to monopolise as many as 5-10 females if these ranged solitarily. Finally, I use a model of male mate searching strategies to show that, unlike the males of socially-living polygamous species, the opportunity cost that monogamous males incur is typically more than five times the reproductive success they have by being obligately monogamous. This suggests that the selection pressure dissuading them from pursuing a roving male strategy must be very considerable.
Disrupted dynamic functional network connectivity among cognitive control networks in the progression of Alzheimer’s disease

Background Alzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression. Results Progression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD. Impact Statement By assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.
The DAXX-SREBP axis promotes oncogenic lipogenesis and tumorigenesis

De novo lipogenesis produces lipids for membrane biosynthesis and cell signaling. Elevated lipogenesis is a major metabolic feature in cancer cells. In breast and other cancer types, genes involved in lipogenesis are highly upregulated, but the mechanisms that control their expression remain poorly understood. DAXX modulates gene expression through binding to diverse transcription factors although the functional impact of these diverse interactions remains to be defined. Our recent analysis indicates that DAXX is overexpressed in diverse cancer types. However, mechanisms underlying DAXX’s oncogenic function remains elusive. Using global integrated transcriptomic and lipidomic analyses, we show that DAXX plays a key role in lipid metabolism. DAXX depletion attenuates, while its overexpression enhances, lipogenic gene expression, lipid synthesis and tumor growth. Mechanistically, DAXX interacts with SREBP1 and SREBP2 and activates SREBP-mediated transcription. DAXX associates with lipogenic gene promoters through SREBPs. Underscoring the critical roles for the DAXX-SREBP interaction for lipogenesis, SREBP2 knockdown attenuates tumor growth in cells with DAXX overexpression, and a DAXX mutant unable to bind SREBPs are incapable of promoting lipogenesis and tumor growth. Our results identify the DAXX-SREBP axis as an important pathway for tumorigenesis.
Sliding of kinetochore fibers along bridging fibers helps center the chromosomes on the spindle

Chromosome alignment at the spindle equator during metaphase is the most remarkable feature of mitosis, which promotes proper chromosome segregation and depends on the forces exerted at the plus end of kinetochore microtubules and polar ejection forces. However, forces arising from lateral mechanical coupling of kinetochore fibers with non-kinetochore microtubules play a role in chromosome alignment, but the mechanism is unclear. Here we develop a speckle microscopy assay to measure the poleward flux of individual microtubules in spindles of human cells and show that bridging microtubules slide apart and undergo poleward flux at a higher rate than kinetochore microtubules. Depletion of the microtubule coupler NuMa increased the difference in the flux velocity of kinetochore and bridging microtubules, suggesting that sliding forces from the bridging fiber are transmitted largely through NuMa onto the associated kinetochore fibers. Depletions of Kif18A/kinesin-8, Kif4A/kinesin-4, as well as double depletions of Kif18A together with Kif4A or Kif18A together with the crosslinker of antiparallel microtubules PRC1 increased the flux velocity of kinetochore fibers up to the velocity of bridging fibers, due to the increased coupling resulting from the extended antiparallel overlap regions. We found severe kinetochore misalignment after double Kif18A and Kif4A as well as Kif18A and PRC1 depletions compared to a single Kif18A depletion, suggesting that forces within the bridging fiber have a centering effect on the kinetochores. We propose that lateral length-dependent sliding forces that the bridging fiber exerts onto kinetochore fibers drive the movement of kinetochores towards the spindle center, thereby promoting chromosome alignment. CC-BY-NC-ND 4.0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted January 1, 2021. ; https://doi.org/10.1101/2020.12. 30.424837 doi: bioRxiv preprint
Wnt/beta-catenin signalling is dispensable for adult neural stem cell homeostasis and activation

Adult mouse hippocampal neural stem cells (NSCs) generate new neurons that integrate into existing hippocampal networks and modulate mood and memory. These NSCs are largely quiescent and are stimulated by niche signals to activate and produce neurons. Wnt/β-catenin signalling acts at different steps along the hippocampal neurogenic lineage and has been shown to promote the proliferation of intermediate progenitor cells. However, whether it has a direct role in the regulation of NSCs still remains unclear. Here we used Wnt/β-catenin reporters and transcriptomic data from in vivo and in vitro models to show that both active and quiescent adult NSCs respond to Wnt/β-catenin signalling. Wnt/β-catenin stimulation instructed neuronal differentiation of active NSCs and promoted the activation or differentiation of quiescent NSCs in a dose-dependent manner. However, we found that inhibiting NSCs response to Wnt, by conditionally deleting β-catenin, did not affect their activation or maintenance of their stem cell characteristics. Together, our results indicate that whilst NSCs do respond to Wnt/β-catenin stimulation in a dose-dependent and state-specific manner, Wnt/β-catenin signalling is not cell-autonomously required to maintain NSC homeostasis, which could reconcile some of the contradictions in the literature as to the role of Wnt/β-catenin signalling in adult hippocampal NSCs.
Multiple timescales of sensory-evidence accumulation across the dorsal cortex

Cortical areas seem to form a hierarchy of intrinsic timescales, but whether this is causal to cognitive behavior remains unknown. In particular, decisions requiring the gradual accrual of sensory evidence over time recruit widespread areas across this hierarchy. Here, we causally tested the hypothesis that this recruitment is related to the intrinsic integration timescales of these widespread areas. We trained mice to accumulate evidence over seconds while navigating in virtual reality, and optogenetically silenced the activity of many cortical areas during different brief trial epochs. We found that the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes. Specifically, we observed selective changes in the weighting of evidence over time, such that frontal inactivations led to deficits on longer timescales than posterior cortical ones. Likewise, large-scale cortical Ca2+ activity during task performance displayed different temporal integration windows matching the effects of inactivation. Our findings suggest that distributed cortical areas accumulate evidence by leveraging their hierarchy of intrinsic timescales.
Glycolysis downregulation is a hallmark of HIV-1 latency and sensitizes infected cells to oxidative stress

HIV-1 infects lymphoid and myeloid cells, which can harbor a latent proviral reservoir responsible for maintaining lifelong infection. Glycolytic metabolism has been identified as a determinant of susceptibility to HIV-1 infection, but its role in the development and maintenance of HIV-1 latency has not been elucidated. By combining transcriptomic, proteomic and metabolomic analysis, we here show that transition to latent HIV-1 infection downregulates glycolysis, while viral reactivation by conventional stimuli reverts this effect. Decreased glycolytic output in latently infected cells is associated with downregulation of NAD+/NADH. Consequently, infected cells rely on the parallel pentose phosphate pathway and its main product, the antioxidant NADPH, fueling antioxidant pathways maintaining HIV-1 latency. Of note, blocking NADPH downstream effectors, thioredoxin and glutathione, favors HIV-1 reactivation from latency in lymphoid and myeloid cellular models. This provides a “shock and kill effect” decreasing proviral DNA in cells from people-living-with-HIV/AIDS. Overall, our data show that downmodulation of glycolysis is a metabolic signature of HIV-1 latency that can be exploited to target latently infected cells with eradication strategies.
An Optimal Relay Number Selection Algorithm for Balancing Multiple Performance in Flying Ad Hoc Networks

The rapid development of aviation technology has made the application of unmanned aerial vehicles (UAVs) more popular in recent years. Due to the inadequate capability of a single UAV, flying ad hoc networks (FANETs), which consist of multiple different UAVs, not only break the limits of single UAV by introducing the cooperation between UAVs, but also complete more complex missions by extending the communication range at infrastructure-less areas. One of the most important design issues for FANETs is the communication and cooperation between UAVs, and the number of UAV relays has been proved to be closely related to the improvement of system performance. However, few FANET protocols or algorithms have been proposed from the perspective of scheduling the number of relays. Inspired by the idea, this paper proposes an optimal relay number selection algorithm based on a more realistic network model which includes a novel cooperative (n+2)-node system model considering the distance metrics between relays and a Nakagami-m short-term static fading channel model more suitable for UAV operation environments. The system outage probability is calculated by introducing the Meijer-G function, and a three-dimensional discrete time Markov chain (DTMC) model is established and analyzed to derive the closed-form binary expressions of throughput, energy efficiency and average transmission delay. In order to balance the three performance so as to achieve a comprehensive system performance, a trade-off factor named EDT is further proposed and maximized to evaluate the optimal number of relays. Finally, simulation results assess the impact of network parameters on system performance, and verify the superiority of the proposed algorithm over its fixed relay number counterpart. INDEX TERMS FANETs, outage probability, relay number selection, DTMC, system performance
Deep Learning-Based Vibration Signal Personnel Positioning System

In this work, we present a person localization system based on ground vibration caused by walking persons. The system is designed for production plants and large buildings to track the movement of workers. Position and movement in these settings are especially safety-relevant in emergencies. Our approach is privacy-preserving, because it requires neither video nor sound. Instead, piezo sensors on the floor measure vibrations, which are analyzed with machine learning to derive a person’s position from the vibration signals. This way, our system can determine where a person is moving, but it is not straightforward to attach names to the detected persons. Due to the anisotropic characteristic of the ground vibration wave, classical analysis methods are not applicable. We show that a deep learning-based approach is feasible. Our experiments show that we can determine the position with an average F1 score of 0.95. INDEX TERMS Vibration Signal; Localization; Pattern Recognition; Deep Learning; Privacy Protection; Robustness; Piezo Sensor
Derivative-Based Optimization with a Non-Smooth Simulated Criterion

Indirect inference requires simulating realizations of endogenous variables from the model under study. When the endogenous variables are discontinuous functions of the model parameters, the resulting indirect inference criterion function is discontinuous and does not permit the use of derivative-based optimization routines. Using a specific class of measure changes, we propose a novel simulation algorithm that alleviates the underlying discontinuities inherent in the indirect inference criterion function, permitting the application of derivative-based optimization routines to estimate the unknown model parameters. Unlike competing approaches, this approach does not rely on kernel smoothing or bandwidth parameters. Several Monte Carlo examples that have featured in the literature on indirect inference with discontinuous outcomes illustrate the approach. These examples demonstrate that this new method gives superior performance over existing alternatives in terms of bias, variance and coverage.