A Scheme to fabricate magnetic graphene-like cobalt nitride CoN4monolayer proposed by first-principles calculations

We propose a scheme to fabricate the cobalt nitride CoN4 monolayer, a magnetic graphene-like two-dimensional material, in which all Co and N atoms are in a plane. Under the pressure above 40 GPa, the bulk CoN4 is stabilized in a triclinic phase. With the pressure decreasing, the triclinic phase of CoN4 is transformed into an orthorhombic phase, and the latter is a layered compound with large interlayer spacing. At ambient condition, the weak interlayer couplings are so small that single CoN4 layer can be exfoliated by the mechanical method

SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks

Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59\% and 76\% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance

Dunhuang murals contour generation network based on convolution and self-attention fusion

Dunhuang murals are a collection of Chinese style and national style, forming a self-contained Chinese-style Buddhist art. It has very high historical and cultural value and research significance. Among them, the lines of Dunhuang murals are highly general and expressive. It reflects the character's distinctive character and complex inner emotions. Therefore, the outline drawing of murals is of great significance to the research of Dunhuang Culture. The contour generation of Dunhuang murals belongs to image edge detection, which is an important branch of computer vision, aims to extract salient contour information in images. Although convolution-based deep learning networks have achieved good results in image edge extraction by exploring the contextual and semantic features of images. However, with the enlargement of the receptive field, some local detail information is lost. This makes it impossible for them to generate reasonable outline drawings of murals. In this paper, we propose a novel edge detector based on self-attention combined with convolution to generate line drawings of Dunhuang murals. Compared with existing edge detection methods, firstly, a new residual self-attention and convolution mixed module (Ramix) is proposed to fuse local and global features in feature maps. Secondly, a novel densely connected backbone extraction network is designed to efficiently propagate rich edge feature information from shallow layers into deep layers. Compared with existing methods, it is shown on different public datasets that our method is able to generate sharper and richer edge maps. In addition, testing on the Dunhuang mural dataset shows that our method can achieve very competitive performance

DiffSeer: Difference-based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based node reordering strategy is developed to group nodes with similar evolution patterns and highlight interesting graph structure details in each timeslice. We conducted two case studies on real-world graph datasets and in-depth interviews with 12 target users to evaluate DiffSeer. The results demonstrate its effectiveness in visualizing dynamic weighted graphs

Two-dimensional anisotropic Dirac materials PtN4C2 and Pt2N8C6 with quantum spin and valley Hall effects

We propose two novel two-dimensional topological Dirac materials, planar PtN4C2 and Pt2N8C6, which exhibit graphene-like electronic structures with linearly dispersive Dirac-cone states exactly at the Fermi level. Moreover, the Dirac cone is anisotropic, resulting in anisotropic Fermi velocities and making it possible to realize orientation-dependent quantum devices. Using the first-principles electronic structure calculations, we have systemically studied the structural, electronic, and topological properties. We find that spin-orbit coupling opens a sizable topological band gap so that the materials can be classified as quantum spin Hall insulators as well as quantum valley Hall insulators. Helical edge states that reside in the insulating band gap connecting the bulk conduction and valence bands are observed. Our work not only expands the Dirac cone material family, but also provides a new avenue to searching for more two-dimensional topological quantum spin and valley Hall insulators.Comment: 6 pages, 4 figure

Brain-Derived Microparticles (BDMPs) Contribute to Neuroinflammation and Lactadherin Reduces BDMP Induced Neuroinflammation and Improves Outcome After Stroke

Microparticles (MPs, ~size between 0.1 and 1 mm) are lipid encased containers derived from intact cells which contain antigen from the parent cells. MPs are involved in intercellular communication and regulate inflammation. Stroke increases secretion of brain derived MP (BDMP) which activate macrophages/microglia and induce neuroinflammation. Lactadherin (Milk fat globule–EGF factor-8) binds to anionic phospholipids and extracellular matrices, promotes apoptotic cell clearance and limits pathogenic antigen cross presentation. In this study, we investigate whether BDMP affects stroke-induced neuroinflammation and whether Lactadherin treatment reduces stroke initiated BDMP-induced neuroinflammation, thereby improving functional outcome after stroke. Middle aged (8–9 months old) male C57BL/6J mice were subjected to distal middle cerebral artery occlusion (dMCAo) stroke, and BDMPs were extracted from ischemic brain 24 h after dMCAo by ultracentrifugation. Adult male C57BL/6J mice were subjected to dMCAo and treated via tail vein injection at 3 h after stroke with: (A) +PBS (n = 5/group); (B) +BDMPs (1.5 × 108, n = 6/group); (C) +Lactadherin (400 μg/kg, n = 5/group); (D) +BDMP+Lactadherin (n = 6/group). A battery of neurological function tests were performed and mice sacrificed for immunostaining at 14 days after stroke. Blood plasma was used for Western blot assay. Our data indicate: (1) treatment of Stroke with BDMP significantly increases lesion volume, neurological deficits, blood brain barrier (BBB) leakage, microglial activation, inflammatory cell infiltration (CD45, microglia/macrophages, and neutrophils) into brain, inflammatory factor (TNFα, IL6, and IL1β) expression in brain, increases axon/white matter (WM) damage identified by decreased axon and myelin density, and increases inflammatory factor expression in the plasma when compared to PBS treated stroke mice; (2) when compared to PBS and BDMP treated stroke mice, Lactadherin and BDMP+Lactadherin treatment significantly improves neurological outcome, and decreases lesion volume, BBB leakage, axon/WM injury, inflammatory cell infiltration and inflammatory factor expression in the ischemic brain, respectively. Lactadherin treatment significantly increases anti-inflammatory factor (IL10) expression in ischemic brain and decreases IL1β expression in plasma compared to PBS and BDMP treated stroke mice, respectively. BDMP increases neuroinflammation and aggravates ischemic brain damage after stroke. Thus, Lactadherin exerts anti-inflammatory effects and improves the clearance of MPs to reduce stroke and BDMP induced neurological deficits

Structural, electronic, magnetic properties of Cu-doped lead-apatite Pb10−x_{10-x}Cux_x(PO4_4)6_6O

The recent report of superconductivity in the Cu-doped PbPO compound stimulates the extensive researches on its physical properties. Herein, the detailed atomic and electronic structures of this compound are investigated, which are the necessary information to explain the physical properties, including possible superconductivity. By the first-principles electronic structure calculations, we find that the partial replacement of Pb at $4f$ site by Cu atom, instead of Pb at $6h$ site, plays a crucial role in dominating the electronic state at Fermi energy. The $3d$ electronic orbitals of Cu atom emerge near the Fermi energy and exhibit strong spin-polarization, resulting in the local moment around the doped Cu atom. Particularly, the ground state of Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O (x = 1) is determined to be a semiconducting phase, in good agreement with the experimental measurements

Administration of Downstream ApoE Attenuates the Adverse Effect of Brain ABCA1 Deficiency on Stroke

The ATP-binding cassette transporter member A1 (ABCA1) and apolipoprotein E (ApoE) are major cholesterol transporters that play important roles in cholesterol homeostasis in the brain. Previous research demonstrated that specific deletion of brain-ABCA1 (ABCA1-B/-B) reduced brain grey matter (GM) and white matter (WM) density in the ischemic brain and decreased functional outcomes after stroke. However, the downstream molecular mechanism underlying brain ABCA1-deficiency-induced deficits after stroke is not fully understood. Adult male ABCA1-B/-B and ABCA1-floxed control mice were subjected to distal middle-cerebral artery occlusion and were intraventricularly infused with artificial mouse cerebrospinal fluid as vehicle control or recombinant human ApoE2 into the ischemic brain starting 24 h after stroke for 14 days. The ApoE/apolipoprotein E receptor 2 (ApoER2)/high-density lipoprotein (HDL) levels and GM/WM remodeling and functional outcome were measured. Although ApoE2 increased brain ApoE/HDL levels and GM/WM density, negligible functional improvement was observed in ABCA1-floxed-stroke mice. ApoE2-administered ABCA1-B/-Bstroke mice exhibited elevated levels of brain ApoE/ApoER2/HDL, increased GM/WM density, and neurogenesis in both the ischemic ipsilateral and contralateral brain, as well as improved neurological function compared with the vehicle-control ABCA1-B/-B stroke mice 14 days after stroke. Ischemic lesion volume was not significantly different between the two groups. In vitro supplementation of ApoE2 into primary cortical neurons and primary oligodendrocyte-progenitor cells (OPCs) significantly increased ApoER2 expression and enhanced cholesterol uptake. ApoE2 promoted neurite outgrowth after oxygen-glucose deprivation and axonal outgrowth of neurons, and increased proliferation/survival of OPCs derived from ABCA1-B/-B mice. Our data indicate that administration of ApoE2 minimizes the adverse effects of ABCA1 deficiency after stroke, at least partially by promoting cholesterol traffic/redistribution and GM/WM remodeling via increasing the ApoE/HDL/ApoER2 signaling pathway