Miracle-dz/UniFAR-FTUs huggingface dataset:
Topological charge density wave in monolayer NbSe2

Despite the progress made in successful prediction of many classes of weakly-correlated topological materials, it is not clear how a topological order can emerge from interacting orders and whether or not a charge ordered topological state can exist in a two-dimensional (2D) material. Here, through first-principles modeling and analysis, we identify a 2×2 charge density wave (CDW) phase in monolayer 2H-NbSe2 that harbors coexisting quantum spin Hall (QSH) insulator, topological crystalline insulator (TCI) and topological nodal line (TNL) semimetal states. The topology in monolayer NbSe2 is driven by the formation of the CDW and the associated symmetry-breaking periodic lattice distortions and not via a pre-existing topology. Our finding of an emergent triple-topological state in monolayer 2H-NbSe2 will offer novel possibilities for exploring connections between different topologies and a unique materials platform for controllable CDW-induced topological states for potential applications in quantum electronics and spintronics and Majorana-based quantum computing.
Multi Voxel-Point Neurons Convolution (MVPConv) for Fast and Accurate 3D Deep Learning

We present a new convolutional neural network, called Multi Voxel-Point Neurons Convolution (MVPConv), for fast and accurate 3D deep learning. The previous works adopt either individual point-based features or local-neighboring voxel-based features to process 3D model, which limits the performance of models due to the inefficient computation. Moreover, most of the existing 3D deep learning frameworks aim at solving one specific task, and only a few of them can handle a variety of tasks. Integrating both the advantages of the voxel and point-based methods, the proposed MVPConv can effectively increase the neighboring collection between point-based features and also promote the independence among voxel-based features. Simply replacing the corresponding convolution module with MVPConv, we show that MVPConv can fit in different backbones to solve a wide range of 3D tasks. Extensive experiments on benchmark datasets such as ShapeNet Part, S3DIS and KITTI for various tasks show that MVPConv improves the accuracy of the backbone (PointNet) by up to 36%, and achieves higher accuracy than the voxel-based model with up to 34 times speedup. In addition, MVPConv also outperforms the state-of-the-art point-based models with up to 8 times speedup. Notably, our MVPConv achieves better accuracy than the newest point-voxel-based model PVCNN (a model more efficient than PointNet) with lower latency.
Disentangling the Frequency Content in Optoacoustics

Signals acquired by optoacoustic tomography systems have broadband frequency content that encodes information about structures on different physical scales. Concurrent processing and rendering of such broadband signals may result in images with poor contrast and fidelity due to a bias towards low frequency contributions from larger structures. This problem cannot be addressed by filtering different frequency bands and reconstructing them individually, as this procedure leads to artefacts due to its incompatibility with the entangled frequency content of signals generated by structures of different sizes. Here we introduce frequency-band model-based (fbMB) reconstruction to separate frequency-band-specific optoacoustic image components during image formation, thereby enabling structures of all sizes to be rendered with high fidelity. In order to disentangle the overlapping frequency content of image components, fbMB uses soft priors to achieve an optimal trade-off between localization of the components in frequency bands and their structural integrity. We demonstrate that fbMB produces optoacoustic images with improved contrast and fidelity, which reveal anatomical structures in in vivo images of mice in unprecedented detail. These enhancements further improve the accuracy of spectral unmixing in small vasculature. By offering a precise treatment of the frequency components of optoacoustic signals, fbMB improves the quality, accuracy, and quantification of optoacoustic images and provides a method of choice for optoacoustic reconstructions.
Editable free-viewpoint video using a layered neural representation

Generating free-viewpoint videos is critical for immersive VR/AR experience, but recent neural advances still lack the editing ability to manipulate the visual perception for large dynamic scenes. To fill this gap, in this paper, we propose the first approach for editable free-viewpoint video generation for large-scale view-dependent dynamic scenes using only 16 cameras. The core of our approach is a new layered neural representation, where each dynamic entity, including the environment itself, is formulated into a spatio-temporal coherent neural layered radiance representation called ST-NeRF. Such a layered representation supports manipulations of the dynamic scene while still supporting a wide free viewing experience. In our ST-NeRF, we represent the dynamic entity/layer as a continuous function, which achieves the disentanglement of location, deformation as well as the appearance of the dynamic entity in a continuous and self-supervised manner. We propose a scene parsing 4D label map tracking to disentangle the spatial information explicitly and a continuous deform module to disentangle the temporal motion implicitly. An object-aware volume rendering scheme is further introduced for the re-assembling of all the neural layers. We adopt a novel layered loss and motion-aware ray sampling strategy to enable efficient training for a large dynamic scene with multiple performers, Our framework further enables a variety of editing functions, i.e., manipulating the scale and location, duplicating or retiming individual neural layers to create numerous visual effects while preserving high realism. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality, photo-realistic, and editable free-viewpoint video generation for dynamic scenes.
Deep Multi-View Stereo Gone Wild

Deep multi-view stereo (MVS) methods have been developed and extensively compared on simple datasets, where they now outperform classical approaches. In this paper, we ask whether the conclusions reached in controlled scenarios are still valid when working with Internet photo collections. We propose a methodology for evaluation and explore the influence of three aspects of deep MVS methods: network architecture, training data, and supervision. We make several key observations, which we extensively validate quantitatively and qualitatively, both for depth prediction and complete 3D reconstructions. First, complex unsupervised approaches cannot train on data in the wild. Our new approach makes it possible with three key elements: upsampling the output, softmin based aggregation and a single reconstruction loss. Second, supervised deep depthmap-based MVS methods are state-of-the art for reconstruction of few internet images. Finally, our evaluation provides very different results than usual ones. This shows that evaluation in uncontrolled scenarios is important for new architectures.
Induction of intrinsic apoptotic pathway and cell cycle arrest via baicalein loaded iron oxide nanoparticles as a competent nano-mediated system for triple negative breast cancer therapy

Magnetic nanoparticles have shown an increasing number of applications in the field of molecular medicine. In the present study we investigated the controlled synthesis of biocompatible polymer coated iron oxide nanoparticles (Fe2O3) loaded with baicalein and evaluated these for their drug loading and release behaviour. Moreover, the loaded particles were evaluated for their anti-proliferation, cell cycle arrest and apoptosis activation properties in triple negative breast cancer cells. The structure, morphology and magnetic properties of the prepared materials were studied by using scanning electronic microscopy, transmission electronic microscopy, Fourier transform infrared spectroscopy, X-ray diffraction analysis, thermogravimetric analysis, dynamic light scattering analysis and zeta potential analysis. DLS and TEM analysis confirms that the size of the synthesized iron oxide nanoparticles are about 90–100 nm and that they are extremely crystalline and spherical in nature. FTIR analysis confirms that the baicalein molecules were conjugated with PEG-coated iron oxide nanoparticles. X-ray diffraction patterns indicate that the magnetic nanoparticles were highly pure with a spinel structure. Baicalein loaded nanoparticles show a controlled release profile in response to various pH levels. We found significant cell cycle arrest at various phases in the treated group and subsequent apoptotic cell death to be evidenced by AO/EtBr, DAPI and PI of fluorescence microscopy analysis. The TUNEL assay evidences that DNA damage occurs in the treated cells. Further baicalein loaded iron oxide nanoparticles exhibit significant down-regulation of the anti-apoptotic protein Bcl-2 and up-regulation of pro-apoptotic proteins evidenced by Western blotting. Our findings clearly demonstrate that baicalein loaded iron oxide nanoparticles could efficiently deliver the drug of interest, and initiate and execute the apoptotic process in triple negative breast cancer cells. This could open a new window in polymer surface chemistry to develop metal oxide based drug delivery systems for all kinds of cancer therapeutics.
ace 2 expression is higher in intestines and liver while being tightly regulated in 1 development and disease in zebrafish 2

14 Human Angiotensin I Converting Enzyme 2 (ACE2) that acts as a receptor for SARS-CoV-2 entry 15 is highly expressed in human type II pneumocytes and enterocytes and similarly in other 16 mammals and zebrafish (Danio rerio). The zebrafish genome has a highly conserved, one-to-one 17 ortholog of ACE2, i.e., ace2, whose expression profile however has not yet been studied during 18 development or in pathologies relevant to COVID-19. Herein, we identified significant 19 development-, tissueand gender-specific modulations in ace2 expression based on meta20 analysis of zebrafish Affymetrix transcriptomics datasets (ndatasets=107, GPL1319 in GEO 21 database). Co-expression network analysis of ace2 revealed distinct positively correlated 22 (carboxypeptidase activity and fibrin clot formation), and negatively correlated (cilia 23 biogenesis/transport and chromatin modifications) STRING network modules. Using additional 24 transcriptomics datasets, we showed zebrafish embryos before 3 days post fertilization (dpf) 25 exhibited low levels of ace2 that increased significantly until 4 dpf implicating a role for ace2 in 26 organogenesis. Re-analysis of RNA-seq datasets from zebrafish adult tissues demonstrated ace2 27 was expressed highly in intestines, variably in liver, and at lower levels in other organs. In addition, 28 zebrafish females and males showed significant dimorphism in their age-dependent expression 29 . CC-BY-NC-ND 4.0 International license 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 made The copyright holder for this preprint this version posted December 24, 2020. ; https://doi.org/10.1101/2020.12. 24.424209 doi: bioRxiv preprint
Label‐free Electrochemical Impedance Detection of Rotavirus Based on Immobilized Antibodies on Gold Sononanoparticles

Currently, rotaviruses (RVs) are the leading cause of severe childhood gastroenteritis worldwide. This study proposes a sensitive and simple label-free assay for determining rotaviruses using, for the first time, electrochemical impedance spectroscopy (EIS). The immunosensor is fabricated by self-assembled monolayers (SAMs) of cysteamine on gold sononanoparticles (AuSNPs) modified glassy carbon electrode. The electron transfer resistance (Ret) of the electrode interface was adopted as a parameter for the detection of rotavirus. Using the proposed immunosensor, a good linear relationship (r2=0.993) between impedance change and logarithmic value of rotaviruses in a wide range of 4.6 to 4.6×104 PFU mL−1 was found, with a low limit of detection (LOD) 2.3 PFU mL−1 and a total analysis time of 55 min. Working parameters of the immunosensor, such as pH, antibodies concentration and immune-reaction time, have been investigated and optimized. No obvious interferences from non-target viruses such as hepatitis A virus (HAV) and enterovirus were found. In addition, the electrochemical impedance immunosensor has exhibited good sensitivity, selectivity and stability.
Neuroprotective response and efficacy of intravenous administration of mesenchymal stem cells in traumatic brain injury mice

Cellular transplantation of stem cells can be a beneficial treatment approach for neurodegenerative diseases such as traumatic brain injury (TBI). In this study, we investigated the proliferation and differentiation potential of infused mesenchymal stem cells (MSCs) after localisation at the injury site. We evaluated the appropriate homing of infused MSCs through immunohistochemistry, followed by Y‐chromosome–specific polymerase chain reaction and fluorescent in situ hybridisation analyses. The proliferation and differentiation of infused MSCs were analysed using exogenous cell tracer 5′‐bromo‐2′‐deoxyuridine (BrdU) labelling and neuronal specific markers, respectively. Structural and functional recovery in TBI mice were examined by performing magnetic resonance imaging and different behavioural assessments, respectively. Results demonstrated a significantly high number of BrdU‐positive cells in the lesion region in the MSC‐infused group compared with control and TBI groups. Infused MSCs were well differentiated into neural‐like cells and expressed significantly more neural markers (neuronal nuclear antigen [NeuN], microtubule‐associated protein 2 [MAP2] and glial fibrillary acid protein [GFAP]). Improved tissue abnormalities as well as functional behaviours were observed in MSC‐infused TBI mice, implying the substantial proliferation and differentiation of infused MSCs. Our findings support the neuroprotective response and efficacy of MSCs after transplantation in TBI mice, and MSCs may serve as potential therapeutic candidates in regenerative medicine.
Secretory pathway-dependent localization of the 1 yeast RhoGAP Rgd 1 p at growth sites

Secretory pathway-dependent localization of the 1 yeast RhoGAP Rgd1p at growth sites 2 3 Running title: Secretory pathway-dependent localization of Rgd1p 4 5 6 Authors: Fabien Lefèbvre#, Valérie Prouzet-Mauléon#, Michel Hugues, Marc Crouzet, Aurélie 7 Vieillemard, Derek McCusker, Didier Thoraval and François Doignon * 8 9 1 Université de Bordeaux, Institut de Biochimie et de Génétique Cellulaires and CNRS, UMR 5095, 10 Bordeaux France 11 2 Université de Bordeaux, Laboratoire Hématopoïèse Leucémique et Cibles Thérapeutiques, Unité 12 Inserm U1035, Bordeaux France 13 3 Université de Bordeaux, Chimie Biologie des Membranes et Nanoobjets and CNRS UMR 5248, 14 Bordeaux France 15 4 Université de Bordeaux, EA 4135 Biotechnologie des Protéines Recombinantes à Visée Santé and 16 Institut Polytechnique de Bordeaux, Bordeaux France 17 5 Université de Bordeaux, Laboratoire de Biogenèse Membranaire and CNRS, UMR 5200, 18 Bordeaux France 19 20 21 # Both of these authors contributed equally to this work 22 23 24 25 * Corresponding author: 26 F. Doignon 27 Université de Bordeaux, Laboratoire de Biogenèse Membranaire and CNRS, UMR 5200, 146 rue 28 Léo Saignat, 33076 Bordeaux cedex, FRANCE 29 Phone: +33 5 57 57 47 18 30 e-mail: doignon@u-bordeaux2.fr 31 32 Copyright © 2012, American Society for Microbiology. All Rights Reserved. Eukaryotic Cell doi:10.1128/EC.00042-12 EC Accepts, published online ahead of print on 23 March 2012
Dynamics of ligands on gold surfaces to obtain Janus nanoclusters : a theoretical and experimental investigation

We performed a joint computational – experimental investigation of the dynamics of ligand exchange on gold nanoclusters (GNC) surface with the aim to understand how to control the structural and optical properties of GNC through the design of their ligand shell. Our computational studies were carried out in the framework of the Kohn – Sham implementation of density functional theory in quantum chemistry. We analyzed the main features of UV – Vis spectra computed at the TD – DFT / CAMB3LYP level for the Au13, Au25, and Au28 metallic cores protected by thiolate, chloride, and phosphine ligands. Our results show that it is possible to tune the energy of the lowest absorption band of gold clusters by ligand shell engineering in order to control the charge redistribution between ligand shell and metallic core.In parallel we synthesized a set of Au25(ATP)x(TP)18 – x clusters with different ATP/TP ratios using an adapted Demessence protocol by combining 4ATP (4 – aminothiophenol) and TP (thiophenol) ligands. ESI – MS measurements evidence that for these mixed ligand shells the Au25 nuclearity is preserved. However, the addition of the DDT (1 – dodecanethiol) ligand in the mixture leads to nanoparticle formation. FT – IR spectroscopy confirms the absorption of two different ligands on the gold surface and SAXS shows that we have a good correlation between the distance between two clusters and the length of the ligand protecting them.Finally, we carried out a comparison of the mode of binding and the structural and optical properties of the fully ligated PH3 and NHC GNC with metallic cores of different nuclearities.
Analytical solution for a pollutant dispersion model with photochemical reaction in the atmospheric boundary layer

This paper presents an analytical solution for the three-dimensional advection-diffusion equation applied to the dispersion of pollutants that form in the Atmospheric Boundary Layer (ABL). Some substances, when emitted in ABL suffer photochemical reactions producing secondary pollutants, so that a source term is included in the advection-diffusion equation to represent this reaction. The model was applied to simulate the dispersion and transport of ozone (O3) produced by photochemical reactions from nitrogen dioxide (NO2), pollutant emitted by burning fossil fuels by automotive vehicles. The pollutant concentration fields obtained by the proposed solution are compared with mixing ratio data obtained by monitoring the air quality in the metropolitan area of Porto Alegre. From the analysis of the results one verifies that the inclusion of the term proposed to represent a photochemical reaction of a reactive pollutant allows to predict ozone concentrations in the ABL.
Bayesian analysis of heavy ion collisions with the heavy ion computational framework Trajectum

We introduce a model for heavy ion collisions named Trajectum , which includes an expanded initial stage with a variable free streaming velocity v fs and a hydrodynamic stage with three varying second order transport coeﬃcients. We describe how to obtain a Gaussian Emulator for this 20-parameter model and show results for key observables. This emulator can be used to obtain Bayesian posterior estimates on the parameters, which we test by an elaborate closure test as well as a convergence study. Lastly, we employ the optimal values of the parameters found in [1] to perform a detailed comparison to experimental data from PbPb and p Pb collisions. This includes both observables that have been used to obtain these values as well as wider transverse momentum ranges and new observables such as correlations of event-plane angles.
Fantastic Four: Differentiable Bounds on Singular Values of Convolution Layers

In deep neural networks, the spectral norm of the Jacobian of a layer bounds the factor by which the norm of a signal changes during forward/backward propagation. Spectral norm regularizations have been shown to improve generalization, robustness and optimization of deep learning methods. Existing methods to compute the spectral norm of convolution layers either rely on heuristics that are efficient in computation but lack guarantees or are theoretically-sound but computationally expensive. In this work, we obtain the best of both worlds by deriving {\it four} provable upper bounds on the spectral norm of a standard 2D multi-channel convolution layer. These bounds are differentiable and can be computed efficiently during training with negligible overhead. One of these bounds is in fact the popular heuristic method of Miyato et al. (multiplied by a constant factor depending on filter sizes). Each of these four bounds can achieve the tightest gap depending on convolution filters. Thus, we propose to use the minimum of these four bounds as a tight, differentiable and efficient upper bound on the spectral norm of convolution layers. We show that our spectral bound is an effective regularizer and can be used to bound either the lipschitz constant or curvature values (eigenvalues of the Hessian) of neural networks. Through experiments on MNIST and CIFAR-10, we demonstrate the effectiveness of our spectral bound in improving generalization and provable robustness of deep networks.
Weak value picture on quantum observables: gauge-invariant vector potentials

The conservation of physical quantities under coordinate transformations, known as gauge invariance, has been the foundation of theoretical frameworks in both quantum and classical theory. The finding of gauge-invariant quantities has enabled the geometric and topological interpretations of quantum phenomena with the Berry phase, or the separation of quark and gluon contributions in quantum chromodynamics. Here, with an example of quantum geometric quantities — Berry connection, phase, and curvature—we extract a new gauge-invariant quantity by applying a “weak value picture”. By employing different pre- and post-selections in the derivation of the Berry phase in the context of weak values, we derive the gauge-invariant vector potential from the Berry connection that is originally gauge-dependent, and show that the obtained vector potential corresponds to the weak value of the projected momentum operator. The local nature of this quantity is demonstrated with an example of the Aharonov-Bohm effect, proving that this gauge-invariant vector potential can be interpreted as the only source of the Berry curvature in the magnetic field. This weak value decomposition approach will lead to the extraction of new measurable quantities from traditionally unobservable quantities. A n R -space the sum of the ( A n S ( A n MP ) weak momenta in the R -space.