Miracle-dz/UniFAR-FTUs huggingface dataset:
DYNAMICAL BLACK HOLES

For time dependent black hole spacetimes the event horizon cannot be described by a Killing horizon. We show that when the spacetime admits a timelike conformal Killing field which generates a hypersurface, called the conformal Killing horizon (CKH), one can locally describe the event horizon by using this hypersurface. When the CKH does not coincide with the conformal stationary limit surface, on which the conformal Killing field becomes null, a generalization of the weak rigidity theorem which establishes the conformal Killing property of the event horizon and the rigidity of its rotation, is obtained. A physical definition of surface gravity for CKHs is given, which is then used to formulate a generalized zeroth law of black hole physics.
Enhanced cutoff energies for direct and rescattered strong-field photoelectron emission of plasmonic nanoparticles

Abstract The efficient generation, accurate detection, and detailed physical tracking of energetic electrons are of applied interest for high harmonics generation, electron-impact spectroscopy, and femtosecond time-resolved scanning tunneling microscopy. We here investigate the generation of photoelectrons (PEs) by exposing plasmonic nanostructures to intense laser pulses in the infrared (IR) spectral regime and analyze the sensitivity of PE spectra to competing elementary interactions for direct and rescattered photoemission pathways. Specifically, we measured and numerically simulated emitted PE momentum distributions from prototypical spherical gold nanoparticles (NPs) with diameters between 5 and 70 nm generated by short laser pulses with peak intensities of 8.0 × 1012 and 1.2 × 1013 W/cm2, demonstrating the shaping of PE spectra by the Coulomb repulsion between PEs, accumulating residual charges on the NP, and induced plasmonic electric fields. Compared to well-understood rescattering PE cutoff energies for strong-field photoemission from gaseous atomic targets (10× the ponderomotive energy), our measured and simulated PE spectra reveal a dramatic cutoff-energy increase of two orders of magnitude with a significantly higher contribution from direct photoemission. Our findings indicate that direct PEs reach up to 93 % of the rescattered electron cutoff energy, in contrast to 20 % for gaseous atoms, suggesting a novel scheme for the development of compact tunable tabletop electron sources.
The cognitive mechanisms of state transformation during the multi-state maintenance of visual working memory

Visual working memory (VWM) system serves as a cornerstone for high-level cognition. The state-based models of working memory delineated a hierarchy of functional states. Memory representations in the passive state are robustly maintained while the active representations were effectively processed at the same time. The memory representations are dynamically transferred between the two states according to task demands; however, it was still unclear how the state transformation process implemented to achieve a perfect storage-dissociation. To explore the property of transformation process, we adopted a sequential presentation paradigm where two memory arrays were presented sequentially; that effectively directs memory items for retention in the two distinct states. We modulated the temporal context concerned with the state transformation process, presentation time of the second array and retention interval between memory arrays. These results indicated that state transformation reflected a consolidation process of memory representations from the active into the passive state; this transformation process is subject to cognitive control. Moreover, we found that sufficient temporal context facilitated a smooth state transformation, thus minimizing the memory loss. These findings lead to a further understanding for the storage mechanism of working memory representations during the dynamic processing.
Tree-based QTL mapping with expected local genetic relatedness matrices

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide Association Studies (GWAS) are a powerful way to find genetic loci associated with phenotypes. GWAS are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix given the ARG (local eGRM). Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to identify a large-effect BMI locus, the CREBRF gene, in a sample of Native Hawaiians in which it was not previously detectable by GWAS because of a lack of population-specific imputation resources. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.
Responses of globally important phytoplankton species to olivine dissolution products and implications for carbon dioxide removal via ocean alkalinity enhancement

Anthropogenic greenhouse gas emissions are leading to global temperature increases, ocean acidification, and significant ecosystem impacts. Given current emissions trajectories, the IPCC reports indicate that rapid abatement of CO2 emissions and development of carbon dioxide removal (CDR) strategies are needed to address legacy and difficult to abate emissions sources. These CDR methods must efficiently and safely sequester gigatons of atmospheric CO2. Coastal Enhanced Weathering (CEW) via the addition of the common mineral olivine to coastal waters is one promising approach to enhance ocean alkalinity for large-scale CDR. As olivine weathers, it releases several biologically active dissolution products, including alkalinity, trace metals, and the nutrient silicate. Released trace metals can serve as micronutrients but may also be toxic at high concentrations to marine biota including phytoplankton that lie at the base of marine food webs. We grew six species representing several globally important phytoplankton species under elevated concentrations of olivine dissolution products via a synthetic olivine leachate (OL) based on olivine elemental composition. We monitored their physiological and biogeochemical responses, which allowed us to determine physiological impacts and thresholds at elevated olivine leachate concentrations, in addition to individual effects of specific constituents. We found both positive and neutral responses but no evident toxic effects for two silicifying diatoms, a calcifying coccolithophore, and three cyanobacteria. In both single and competitive co-cultures, silicifiers and calcifiers benefited from olivine dissolution products like iron and silicate or enhanced alkalinity, respectively. The non-N2-fixing picocyanobacterium could use synthetic olivine-derived iron for growth, while N2-fixing cyanobacteria could not. However, other trace metals like nickel and cobalt supported cyanobacterial growth across both groups. Growth benefits to phytoplankton groups in situ will depend on species-specific responses and ambient concentrations of other required nutrients. Results suggest olivine dissolution products appear unlikely to cause negative physiological effects for any of the phytoplankton examined, even at high concentrations, and may support growth of particular taxa under some conditions. Future studies can shed light on long-term eco-evolutionary responses to olivine exposure and on the potential effects that marine microbes may in turn have on olivine dissolution rates and regional biogeochemistry.
Rats emit aversive 44-kHz ultrasonic vocalizations during prolonged Pavlovian fear conditioning

Rats are believed to communicate their emotional state by emitting two distinct types of ultrasonic vocalizations. The first is long “22-kHz” vocalizations (>300 ms, <32 kHz) with constant frequency, signaling aversive states and the second, is short “50-kHz” calls (<150 ms, >32 kHz), often frequency-modulated, in appetitive situations. Here we are first to describe a new-type of aversive vocalizations emitted at a higher pitch by Wistar and spontaneously hypertensive rats (SHR) in an intensified aversive state – prolonged fear conditioning. These calls, which we named “44-kHz” vocalizations, are long (>150 ms), generally at a constant frequency (usually within 35-50 kHz range) and have an overall spectrographic image similar to 22-kHz calls. Some 44-kHz vocalizations are comprised of both 22-kHz-like and 44-kHz-like elements. Furthermore, two separate clustering methods confirmed that these 44-kHz calls are distinct from other vocalizations. We observed 44-kHz calls to be associated with freezing behavior during fear conditioning, during which they constituted up to 19.4% of all calls. We also show that some of rats’ responses to the playback of 44-kHz calls were more akin to that of aversive calls, e.g., heart rate changes, whereas other responses were at an intermediate level between aversive and appetitive calls. Our results suggest that rats have a wider vocal repertoire than previously believed, and current definitions of major call types may require reevaluation. We hope that future investigations of 44-kHz calls in rat models of human diseases will contribute to expanding our understanding and therapeutic strategies related to human psychiatric conditions.
The Rous sarcoma virus Gag polyprotein forms biomolecular condensates driven by intrinsically-disordered regions

Biomolecular condensates (BMCs) play important roles in cellular structures including transcription factories, splicing speckles, and nucleoli. BMCs bring together proteins and other macromolecules, selectively concentrating them so that specific reactions can occur without interference from the surrounding environment. BMCs are often made up of proteins that contain intrinsically disordered regions (IDRs), form phase-separated spherical puncta, form liquid-like droplets that undergo fusion and fission, contain molecules that are mobile, and are disrupted with phase-dissolving drugs such as 1,6-hexanediol. In addition to cellular proteins, many viruses, including influenza A, SARS-CoV-2, and human immunodeficiency virus type 1 (HIV-1) encode proteins that undergo phase separation and rely on BMC formation for replication. In prior studies of the retrovirus Rous sarcoma virus (RSV), we observed that the Gag protein forms discrete spherical puncta in the nucleus, cytoplasm, and at the plasma membrane that co-localize with viral RNA and host factors, raising the possibility that RSV Gag forms BMCs that participate in the virion intracellular assembly pathway. In our current studies, we found that Gag contains IDRs in the N-terminal (MAp2p10) and C-terminal (NC) regions of the protein and fulfills many criteria of BMCs. Although the role of BMC formation in RSV assembly requires further study, our results suggest the biophysical properties of condensates are required for the formation of Gag complexes in the nucleus and the cohesion of these complexes as they traffic through the nuclear pore, into the cytoplasm, and to the plasma membrane, where the final assembly and release of virus particles occurs.
Experimental validation of computational models for the prediction of phase distribution during multi-channel transcranial alternating current stimulation

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. Neural oscillations exhibit phase-dependent associations with cognitive functions, and tools to manipulate local oscillatory phases can affect communication across remote brain regions. A recent study demonstrated that multi-channel tACS can generate electric fields with a phase gradient or traveling waves in the brain. Computational simulations using phasor algebra can predict the phase distribution inside the brain and aid in informing parameters in tACS experiments. However, experimental validation of computational models for multi-phase tACS is still lacking. Here, we develop such a framework for phasor simulation and evaluate its accuracy using in vivo recordings in nonhuman primates. We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues’ conductivity. Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.
Multi-scale Effects of Habitat Loss and the Role of Trait Variation

Habitat loss (HL) is a major cause of species extinctions. Although effects of HL beyond the directly impacted area have been previously observed, they are not very well understood, especially in an eco-evolutionary context. To start filling this gap, we study a two-patch deterministic consumer-resource model, with one of the patches experiencing loss of resources. Our model allows foraging and mating within a patch as well as between patches. We then introduce heritable variation in consumer traits to investigate eco-evolutionary dynamics and compare results with constant or no trait variation scenarios. Our results show that HL indeed reduces consumer densities in the neighboring patch, but when the resources are overexploited, HL in one patch can increase the consumer densities in the neighbouring patch. Yet at the landscape scale, the effect of HL on consumer densities is consistently negative. In presence of HL, patch isolation has positive effects on consumer density in the patch experiencing HL and mostly negative effects on the neighbouring patch. The landscape level pattern depends on which of these effects are dominant at the local scale. Evolution always increased resistance of consumers in the affected patch to HL, with varied effects at the landscape level. Finally, we also show a possibility of landscape level consumer extinction due to HL in a local patch when the cross-patch dependence is high, and foraging and mating preferences are coupled. Eco-evolutionary dynamics can rescue consumers from such extinction in some cases if their death rates are sufficiently small. Our findings show that HL at a local scale can affect the neighbouring patch and the landscape as a whole, and that heritable trait variation can provide some resistance against HL. We thus suggest joint consideration of multiple spatial scales and trait variation when assessing and predicting the impacts of HL.
Pan-cancer analysis of the ion permeome reveals functional regulators of glioblastoma aggression

Ion channels, transporters, and other ion-permeating proteins, collectively comprising the ion permeome (IP), are common drug targets. However, their roles in cancer are understudied. Our integrative pan-cancer analysis shows that IP genes display highly-elevated expression patterns in subsets of cancer samples significantly more often than expected transcriptome-wide. To enable target identification, we identified 410 survival-associated IP genes in 29 cancer types using a machine learning approach. Notably, GJB2 and SCN9A show prominent expression in neoplastic cells and associate with poor prognosis in glioblastoma (GBM), the most common and aggressive brain cancer. GJB2 or SCN9A knockdown in patient-derived GBM cells induces transcriptome-wide changes involving neural projection and proliferation pathways, impairs cell viability and tumor sphere formation, mitigates tunneling nanotube formation, and extends the survival of GBM-bearing mice. Thus, aberrant activation of IP genes appears as a pan-cancer feature of tumor heterogeneity that can be exploited for mechanistic insights and therapy development.
Root-exuded secondary metabolites can alleviate negative plant-soil feedbacks

Plants can suppress the growth of other plants by modifying soil properties. These negative plant-soil feedbacks are often species-specific, suggesting that some plants possess resistance strategies. However, the underlying mechanisms remain largely unknown. Here, we investigated if and how benzoxazinoids, a class of dominant secondary metabolites that are exuded into the soil by maize and other cereals, help plants to resist negative plant-soil feedbacks. We find that three out of five tested crop species suppress maize performance relative to the mean across species. This effect is partially alleviated by the plant’s capacity to produce benzoxazinoids. Soil complementation of benzoxazinoid-deficient mutants with purified benzoxazinoids is sufficient to restore the protective effect. Sterilization and reinoculation experiments suggest that benzoxazinoid-mediated protection acts via changes in soil microbes. Substantial variation of the protective effect between experiments and soil types illustrates that the magnitude of the protective effect of benzoxazinoids against negative plant-soil feedbacks is context dependent. In summary, our study demonstrates that plant secondary metabolites can confer resistance to negative plant-soil feedbacks. These findings expand the functional repertoire of plant secondary metabolites, reveal a mechanism by which plants can resist suppressive soils, and may represent a promising avenue to stabilize plant performance in crop rotations in the future.
Doxorubicin inhibits phosphatidylserine decarboxylase and confers broad-spectrum antifungal activity

As phospholipids of cell membranes, phosphatidylethanolamine (PE) and phosphatidylserine (PS) play crucial roles in glycerophospholipid metabolism. Broadly, some phospholipid biosynthesis enzymes serve as potential fungicide targets. Therefore, revealing the functions and mechanism of PE biosynthesis in plant pathogens would provide potential targets for crop disease control. We performed analyses including phenotypic characterizations, lipidomics, enzyme activity, site-directed mutagenesis, and chemical inhibition assays to study the function of PS decarboxylase-encoding gene MoPSD2 in rice blast fungus Magnaporthe oryzae. The Mopsd2 mutant was defective in development, lipid metabolism and plant infection. The PS level increased while PE decreased in Mopsd2, consistent with the enzyme activity. Furthermore, chemical doxorubicin inhibited the enzyme activity of MoPsd2 and showed antifungal activity against ten phytopathogenic fungi including M. oryzae and reduced disease severity of two crop diseases in the field. Three predicted doxorubicin-interacting residues are important for MoPsd2 functions. Our study demonstrates that MoPsd2 is involved in de novo PE biosynthesis and contributes to the development and plant infection of M. oryzae and that doxorubicin shows broad-spectrum antifungal activity as a fungicide candidate. The study also implicates that bacterium Streptomyces peucetius, which biosynthesizes doxorubicin, could be potentially used as an eco-friendly biocontrol agent.
Cell cycle regulation of the psoriasis associated gene CCHCR1 by transcription factor E2F1

The coiled-coil alpha-helical rod protein 1 (CCHCR1) was first identified as a candidate gene in psoriasis and has lately been associated with COVID-19 susceptibility. Located within P-bodies and centrosomes, its exact cellular role and transcriptional control remain largely unknown. Here, we showed that CCHCR1 shares a bidirectional promoter with its neighboring gene, TCF19. This bidirectional promoter is activated by the G1/S-regulatory transcription factor E2F1, and both genes are co-induced during the G1/S transition of the cell cycle. A luciferase reporter assay suggests that the short intergenic sequence, only 287 bp in length, is sufficient for the G1/S induction of both genes, but the expression of CCHCR1 is further enhanced by the presence of exon 1 from both TCF19 and CCHCR1. This research uncovers the transcriptional regulation of the CCHCR1 gene, offering new perspectives on its function. These findings contribute to the broader understanding of diseases associated with CCHCR1 and may serve as a foundational step for future research in these vital medical fields.
SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Directed at Cell Signaling Networks

The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.
Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis—describing the nonlinear interaction between mutations and their phenotypic consequences—manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into “subspaces” corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.