Topology Changes Research Articles

G-quadruplex forming regions in GCK and TM6SF2 are targets for differential DNA methylation in metabolic disease and hepatocellular carcinoma patients

The alarming increase in global rates of metabolic diseases (MetDs) and their association with cancer risk renders them a considerable burden on our society. The interplay of environmental and genetic factors in causing MetDs may be reflected in DNA methylation patterns, particularly at non-canonical (non-B) DNA structures, such as G-quadruplexes (G4s) or R-loops. To gain insight into the mechanisms of MetD progression, we focused on DNA methylation and functional analyses on intragenic regions of two MetD risk genes, the glucokinase (GCK) exon 7 and the transmembrane 6 superfamily 2 (TM6SF2) intron 2-exon 3 boundary, which harbor non-B DNA motifs for G4s and R-loops.Pyrosequencing of 148 blood samples from a nested cohort study revealed significant differential methylation in GCK and TM6SF2 in MetD patients versus healthy controls. Furthermore, these regions harbor hypervariable and differentially methylated CpGs also in hepatocellular carcinoma versus normal tissue samples from The Cancer Genome Atlas (TCGA). Permanganate/S1 nuclease footprinting with direct adapter ligation (PDAL-Seq), native polyacrylamide DNA gel electrophoresis and circular dichroism (CD) spectroscopy revealed the formation of G4 structures in these regions and demonstrated that their topology and stability is affected by DNA methylation. Detailed analyses including histone marks, chromatin conformation capture data, and luciferase reporter assays, highlighted the cell-type specific regulatory function of the target regions. Based on our analyses, we hypothesize that changes in DNA methylation lead to topological changes, especially in GCK exon 7, and cause the activation of alternative regulatory elements or potentially play a role in alternative splicing.Our analyses provide a new view on the mechanisms underlying the progression of MetDs and their link to hepatocellular carcinomas, unveiling non-B DNA structures as important key players already in early disease stages.

Numerical simulation of jet break-up in the second-wind induced regime using the local front reconstruction method

In the second wind-induced regime, the jet deformations are influenced by aerodynamic instabilities acting on the jet surface. In addition, velocity relaxation in the liquid is an important mechanism with a strong influence on the violent break-up of laminar jets (i.e., the bursting). The exact interplay between the aerodynamic forces and the velocity profile relaxation is insufficiently described in the literature. Thus, we investigate numerically cylindrical liquid jets with fully developed and uniform velocity inlet profiles for different liquid and gas properties to elucidate the physical mechanisms governing the bursting of the jet. For this study, the gas-liquid interface is tracked using a front tracking technique: the Local Front Reconstruction Method (LFRM). In contrast with traditional front tracking techniques, LFRM allows for complex topological changes (merging and break-up) while keeping a sharp representation of the interface. The comparison of the obtained results with literature suggests that LFRM is a suitable technique for capturing the complex morphological deformations of a bursting jet. Additionally, we analyzed the influence of air and liquid properties on the jet deformations, which evidenced that velocity profile relaxation plays an important role in the complex process of break-up of the jet, that in combination with the destabilizing effect of the aerodynamic forces lead to the bursting of the jet.

Quenched Pair Breaking by Interlayer Correlations as a Key to Superconductivity in La_{3}Ni_{2}O_{7}.

The recent discovery of superconductivity in La_{3}Ni_{2}O_{7} with T_{c}≃80 K under high pressure opens up a new route to high-T_{c} superconductivity. This material realizes a bilayer square lattice model featuring a strong interlayer hybridization unlike many unconventional superconductors. A key question in this regard concerns how electronic correlations driven by the interlayer hybridization affect the low-energy electronic structure and the concomitant superconductivity. Here, we demonstrate using a cluster dynamical mean-field theory that the interlayer electronic correlations (IECs) induce a Lifshitz transition resulting in a change of Fermi surface topology. By solving an appropriate gap equation, we further show that the leading pairing instability, s± wave, is enhanced by the IECs. The underlying mechanism is the quenching of a strong ferromagnetic channel, resulting from the Lifshitz transition driven by the IECs. Based on this picture, we provide a possible reason of why superconductivity emerges only under high pressure.

Pathologic Substrates of Structural Brain Network Resilience and Topology in Parkinson Disease Decedents.

In Parkinson disease (PD), α-synuclein spreading through connected brain regions leads to neuronal loss and brain network disruptions. With diffusion-weighted imaging (DWI), it is possible to capture conventional measures of brain network organization and more advanced measures of brain network resilience. We aimed to investigate which neuropathologic processes contribute to regional network topologic changes and brain network resilience in PD. Using a combined postmortem MRI and histopathology approach, PD and control brain donors with available postmortem in situ 3D T1-weighted MRI, DWI, and brain tissue were selected from the Netherlands Brain Bank and Normal Aging Brain Collection Amsterdam. Probabilistic tractography was performed, and conventional network topologic measures of regional eigenvector centrality and clustering coefficient, and brain network resilience (change in global efficiency upon regional node failure) were calculated. PSer129 α-synuclein, phosphorylated-tau, β-amyloid, neurofilament light-chain immunoreactivity, and synaptophysin density were quantified in 8 cortical regions. Group differences and correlations were assessed with rank-based nonparametric tests, with age, sex, and postmortem delay as covariates. Nineteen clinically defined and pathology-confirmed PD (7 F/12 M, 81 ± 7 years) and 15 control (8 F/7 M, 73 ± 9 years) donors were included. With regional conventional measures, we found lower eigenvector centrality only in the parahippocampal gyrus in PD (d = -1.08, 95% CI 0.003-0.010, p = 0.021), which did not associate with underlying pathology. No differences were found in regional clustering coefficient. With the more advanced measure of brain network resilience, we found that the PD brain network was less resilient to node failure of the dorsal anterior insula compared with the control brain network (d = -1.00, 95% CI 0.0012-0.0015, p = 0.018). This change was not directly driven by neuropathologic processes within the dorsal anterior insula or in connected regions but was associated with higher Braak α-synuclein staging (rs = -0.40, p = 0.036). Although our cohort might suffer from selection bias, our results highlight that regional network disturbances are more complex to interpret than previously believed. Regional neuropathologic processes did not drive regional topologic changes, but a global increase in α-synuclein pathology had a widespread effect on brain network reorganization in PD.

Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty

The modernization of power systems faces uncertainties due to fluctuating renewable energy sources, electric vehicle expansion, and demand response initiatives. These uncertainties require consideration in power flow analyses by calculating power flow across a spectrum of generation and load values. Traditional power flow methods, which rely on iterative solutions of non-linear equation sets, become computationally burdensome under these uncertain conditions. Surrogate models have been used to reduces the computational cost of power flow analysis under uncertainty. Recently, graph neural networks (GNNs) have gained increasing attention as surrogates for power system simulations. However, GNNs, similar to other machine learning models, require significant amounts of training data. This paper proposes an innovative approach combining a multi-fidelity methodology with a GNN-based surrogate model for efficient power flow calculations. This model is trained using both high-fidelity power flow simulations and low-fidelity power flow simulations. Our multi-fidelity GNN model not only reduces the cost of generating training data, but also outperforms both single-fidelity GNN models trained solely on high-fidelity data and conventional neural network models. Additionally, the proposed model exhibits robustness to minor topology changes and achieves reasonable performance with unseen topologies. The model’s effectiveness is validated through simulations on standard IEEE systems.

Tessellation and interactive visualization of four-dimensional spacetime geometries

This paper addresses two problems needed to support four-dimensional (3d+t) spacetime numerical simulations. The first contribution is a general algorithm for producing conforming spacetime meshes of moving geometries. Here, the surface points of the geometry are embedded in a four-dimensional space as the geometry moves in time. The geometry is first tessellated at prescribed time steps and then these tessellations are connected in the parameter space of each geometry entity to form tetrahedra. In contrast to previous work, this approach allows the resolution of the geometry to be controlled at each time step. The only restriction on the algorithm is the requirement that no topological changes to the geometry are made (i.e. the hierarchical relations between all geometry entities are maintained) as the geometry moves in time. The validity of the final mesh topology is verified by ensuring the tetrahedralizations represent a closed 3-manifold. For some analytic problems, the 4d volume of the tetrahedralization is also verified. The second problem addressed in this paper is the design of a system to interactively visualize four-dimensional meshes when the 4d view changes, including tetrahedra (embedded in 4d) and pentatopes. Algorithms that either include or exclude a geometry shader are described, and the efficiency of each approach is then compared. Overall, the results suggest that visualizing tetrahedra (either those bounding the domain, or extracted from a pentatopal mesh) using a geometry shader achieves the highest frame rate, realizing interactive frame rates of at least 15 frames per second for meshes with about 50 million tetrahedra.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function

BackgroundCocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. MethodsResting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22–55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics – betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) – were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. ResultsParticipants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. ConclusionsWidespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.

Structural basis for difunctional mechanism of m-AMSA against African swine fever virus pP1192R.

The African swine fever virus (ASFV) type II topoisomerase (Topo II), pP1192R, is the only known Topo II expressed by mammalian viruses and is essential for ASFV replication in the host cytoplasm. Herein, we report the structures of pP1192R in various enzymatic stages using both X-ray crystallography and single-particle cryo-electron microscopy. Our data structurally define the pP1192R-modulated DNA topology changes. By presenting the A2+-like metal ion at the pre-cleavage site, the pP1192R-DNA-m-AMSA complex structure provides support for the classical two-metal mechanism in Topo II-mediated DNA cleavage and a better explanation for nucleophile formation. The unique inhibitor selectivity of pP1192R and the difunctional mechanism of pP1192R inhibition by m-AMSA highlight the specificity of viral Topo II in the poison binding site. Altogether, this study provides the information applicable to the development of a pP1192R-targeting anti-ASFV strategy.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

In disaster-stricken areas monitoring, management, search, and rescue operations, unmanned aerial vehicles (UAVs) play a crucial role in disaster management and emergency communication due to their high mobility. To efficiently coordinate and plan UAVs and their carried sensor and base station resources, synchronization is essential to establish consistency, laying the foundation for high-level demands. In such scenarios, synchronization relies on request–response (RR) or publish–subscribe (PS) forms of information exchange. Existing research in the field typically focuses on higher-level applications and selects either RR or PS synchronization, thereby overlooking the potential advantages that could be gained from combining both methods to meet synchronization requirements. We propose a resource synchronization method based on the Data Distribution Service (DDS) and a linear time complexity subscription mechanism tailored to specific query demands, which considers the pros and cons of the above two information exchange forms and the bottom-layer network topology. Experimental results using open-source simulation tools demonstrate that the proposed method adapts to scene requirements and decreases bandwidth by at least 21.2% and packet rate by at least 3.7% compared to different baseline methods across three topologies, while satisfying delay and query success rate requirements. Furthermore, the method maintains robust performance in the face of dynamic changes in network topology, showcasing its robustness.

Observation of Topological Transition in Floquet Non-Hermitian Skin Effects in Silicon Photonics.

Non-Hermitian physics has greatly enriched our understanding of nonequilibrium phenomena and uncovered novel effects such as the non-Hermitian skin effect (NHSE) that has profoundly revolutionized the field. NHSE has been predicted in systems with nonreciprocal couplings which, however, are challenging to realize in experiments. Without nonreciprocal couplings, the NHSE can also emerge in systems with coexisting gauge fields and loss or gain (e.g., in Floquet non-Hermitian systems). However, such Floquet NHSE remains largely unexplored in experiments. Here, we realize the Floquet NHSEs in periodically modulated optical waveguides integrated on a silicon photonic platform. By engineering the artificial gauge fields induced by the periodical modulation, we observe various Floquet NHSE phases and unveil their rich topological transitions. Remarkably, we discover the transitions between the unipolar NHSE phases and an unconventional bipolar NHSE phase, which is accompanied by the directional reversal of the NHSEs. The underlying physics is revealed by the band winding in complex quasienergy space which undergoes a topology change from isolated loops with the same winding to linked loops with opposite windings. Our work unfolds a new route toward Floquet NHSEs originating from the interplay between gauge fields and dissipation effects, and thus offers fundamentally new ways for steering light and other waves.

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

Cardiac sympathetic neurons are additional cells affected in genetically determined arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (AC) is a familial cardiac disease, mainly caused by mutations in desmosomal genes, which accounts for most cases of stress-related arrhythmic sudden death, in young and athletes. AC hearts display fibro-fatty lesions that generate the arrhythmic substrate and cause contractile dysfunction. A correlation between physical/emotional stresses and arrhythmias supports the involvement of sympathetic neurons (SNs) in the disease, but this has not been confirmed previously. Here, we combined molecular, in vitro and ex vivo analyses to determine the role of AC-linked DSG2 downregulation on SN biology and assess cardiac sympathetic innervation in desmoglein-2mutant (Dsg2mut/mut) mice. Molecular assays showed that SNs express DSG2, implying that DSG2-mutation carriers would harbour the mutant protein in SNs. Confocal immunofluorescence of heart sections and 3-D reconstruction of SN network in clarified heart blocks revealed significant changes in the physiologialc SN topology, with massive hyperinnervation of the intact subepicardial layers and heterogeneous distribution of neurons in fibrotic areas. Cardiac SNs isolated from Dsg2mut/mut neonatal mice, prior to the establishment of cardiac innervation, show alterations in axonal sprouting, process development and distribution of varicosities. Consistently, virus-assisted DSG2 downregulation replicated, in PC12-derived SNs, the phenotypic alterations displayed by Dsg2mut/mut primary neurons, corroborating that AC-linked Dsg2 variants may affect SNs. Our results reveal that altered sympathetic innervation is an unrecognized feature of AC hearts, which may result from the combination of cell-autonomous and context-dependent factors implicated in myocardial remodelling. Our results favour the concept that AC is a disease of multiple cell types also hitting cardiac SNs. KEY POINTS: Arrhythmogenic cardiomyopathy is a genetically determined cardiac disease, which accounts for most cases of stress-related arrhythmic sudden death. Arrhythmogenic cardiomyopathy linked to mutations in desmoglein-2 (DSG2) is frequent and leads to a left-dominant form of the disease. Arrhythmogenic cardiomyopathy has been approached thus far as a disease of cardiomyocytes, but we here unveil that DSG2 is expressed, in addition to cardiomyocytes, by cardiac and extracardiac sympathetic neurons, although not organized into desmosomes. AC-linked DSG2 downregulation primarily affect sympathetic neurons, resulting in the significant increase in cardiac innervation density, accompanied by alterations in sympathetic neuron distribution. Our data supports the notion that AC develops with the contribution of several 'desmosomal protein-carrying' cell types and systems.

Kiphynet: an online network simulation tool connecting cellular kinetics and physiological transport.

Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at https://deepamahm.github.io/kiphynet_docs/ .

Microvascular plasticity in mouse stroke model recovery: Anatomy statistics, dynamics measured by longitudinal in vivo two-photon angiography, network vectorization.

This manuscript quantitatively investigates remodeling dynamics of the cortical microvascular network (thousands of connected capillaries) following photothrombotic ischemia (cubic millimeter volume, imaged weekly) using a novel in vivo two-photon angiography and high throughput vascular vectorization method. The results suggest distinct temporal patterns of cerebrovascular plasticity, with acute remodeling peaking at one week post-stroke. The network architecture then gradually stabilizes, returning to a new steady state after four weeks. These findings align with previous literature on neuronal plasticity, highlighting the correlation between neuronal and neurovascular remodeling. Quantitative analysis of neurovascular networks using length- and strand-based statistical measures reveals intricate changes in network anatomy and topology. The distance and strand-length statistics show significant alterations, with a peak of plasticity observed at one week post-stroke, followed by a gradual return to baseline. The orientation statistic plasticity peaks at two weeks, gradually approaching the (conserved across subjects) stroke signature. The underlying mechanism of the vascular response (angiogenesis vs. tissue deformation), however, is yet unexplored. Overall, the combination of chronic two-photon angiography, vascular vectorization, reconstruction/visualization, and statistical analysis enables both qualitative and quantitative assessments of neurovascular remodeling dynamics, demonstrating a method for investigating cortical microvascular network disorders and the therapeutic modes of action thereof.

Lifshitz Transition in a Single-Atom-Thick GdxYb1-xPb3 Kagome Compound on Si(111).

Lanthanide (Ln) elements Gd and Yb alloyed with a Pb monolayer on the Si(111) substrate form LnPb3 compounds having the same crystal structure. They comprise a single-atom-thick Pb layer arranged in a slightly distorted kagome lattice with Ln atoms filling the hexagonal voids. They have similar electronic band structures except for the Fermi level position, which varies between the divalent Yb- and trivalent Gd-containing compounds by ∼0.47 eV. The ability to create a 2D solid solution with the unified continuous Pb layer and hexagonal voids randomly filled with either Gd or Yb atoms allows precise control of the Fermi level position. Small alteration of the Fermi level triggers drastic changes in the Fermi surface topology due to the Lifshitz transition, hence in the physical properties. In particular, the sheet resistance of the GdxYb1-xPb3/Si(111) system can be controllably varied over an order of magnitude range.

Development of novel superconductivity with higher Tc via the suppression of magnetism in quasi-two-dimensional electride Y2C under high pressures

Discovery of superconductivity in electride materials has been a topic of interest as their intrinsic electron-rich properties might suggest a considerable electron-phonon interaction. LayeredY2Cis a ferromagnetic quasi-two-dimensional electride with polarized anionic electrons confined in the interlayer space. In this theoretical study, we reportY2Cundergoes a series of structural phase transitions into two superconducting phases with estimatedTcof 9.2 and 21.0 K at 19 and 80 GPa, respectively, via the suppression of magnetism. Our extensive first-principles swarm structure searches identify that these two high-pressure superconducting phases possess an orthorhombicPnmaand a tetragonalI4/mstructures, respectively, where thePnmaphase is found to be a one-dimensional electride characterized by electron confinements in channel spaces of the crystal lattice, while the electride property inI4/mphase has been completely destroyed. We attribute the development of an unprecedentedly highTcsuperconductivity in Y-C system to the destructions of magnetism and the delocalization of interlayered anionic electrons under pressures. This work provides a unique example of pressure-induced collapse of magnetism at the onset of superconductivity in electride materials, along with the dramatic changes of electron-confinement topology in crystal lattices.

EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points.

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and support topological changes. Input with an image sequence from a single camera, our network is trained automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3D) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art.