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Abstract The electrical outputs of single-layer antiferromagnetic memory devices relying on the anisotropic magnetoresistance effect are typically rather small at room temperature. Here we report a new type of antiferromagnetic memory based on the spin phase change in a Mn-Ir binary intermetallic thin film at a composition within the phase boundary between its collinear and noncollinear phases. Via a small piezoelectric strain, the spin structure of this composition-boundary metal is reversibly interconverted, leading to a large nonvolatile room-temperature resistance modulation that is two orders of magnitude greater than the anisotropic magnetoresistance effect for a metal, mimicking the well-established phase change memory from a quantum spin degree of freedom. In addition, this antiferromagnetic spin phase change memory exhibits remarkable time and temperature stabilities, and is robust in a magnetic field high up to 60 T.

A1 polarization of astrocytes mediated prolonged inflammation contributing to brain injury in ischemic stroke. We have previously shown that AD16 protects against neonatal hypoxic-ischemic brain damage in vivo and oxygen-glucose deprivation in vitro. More recently, AD16 has demonstrated safety, tolerability, and favorable pharmacokinetics in a randomized controlled phase I trial. In this study, we utilized a rat model of transient middle cerebral artery occlusion (tMCAO) to explore whether the anti-inflammatory compound AD16 protects against ischemic brain injury by regulating A1 polarization and its underlying mechanisms. Our results showed that AD16 treatment significantly reduced the brain infarcted volume and improved neurological function in tMCAO rats. GO analysis results show that differential genes among the Sham, tMCAO and AD16 treatment groups are involved in the regulation of cytokine and inflammatory response. KEGG enrichment pathways analysis mainly enriched in cytokine-cytokine receptor interaction, viral protein interaction with cytokine-cytokine receptor, TNF, chemokine, NF-κB and IL-17 signaling pathway. Furthermore, AD16 treatment decreased the permeability of the blood-brain barrier and suppressed neuroinflammation. AD16 treatment also significantly reduced the polarization of A1 and inhibited NF-κB and JAK2/STAT3 signaling pathways. This study demonstrates that AD16 protects against brain injury in ischemic stroke by reducing A1 polarization to suppress neuroinflammation through downregulating NF-κB and JAK2/STAT3 signaling. Our findings uncover a potential molecular mechanism for AD16 and suggest that AD16 holds promising therapeutic potential against cerebral ischemia.

In order to explore the effect of hydrate formation and loading-unloading condition on the strain and permeability of gas hydrate bearing coal, the authors first measured the permeability of coal before and after gas hydrate formation, by using the triaxial testing machine for coupling action of stress, seepage, and chemical effect, as well as measured the strain and permeability of coal (three particle sizes: 0.425−0.850 mm (20−40 mesh), 0.250−0.425 mm (40−60 mesh), 0.180−0.250 mm (60−80 mesh) and three saturations: 40%, 60%, 80%) under the axial loading and confining unloading conditions. Then, the stress-strain curve was used to determine the deformation characteristic of coal during under loading and unloading conditions, the effect of hydrate formation, hydrate saturation and deviator stress on the coal permeability, the extend of coal permeability and volume expansion effect were quantitatively characterized by permeability loss rate formula and deformation angle formula. Furthermore, the influence mechanism of hydrate distribution mode was preliminarily discussed on the coal permeability with the theoretical permeability models. The results show that ① the influence of saturation on the variation law of coal permeability is more complicated. In general, with the increase of hydrate saturation, the greater the percentage of permeability reduction, the more significant the degree of blockage. The coal permeability obviously reduces after the formation of gas hydrate, with the range of reductions being 58.3%−83.3% (20−40 mesh), 61.5%−95.0% (40−60 mesh) and 81.8%−90.9% (60−80 mesh). The coal permeability decreases with the increase of hydrate saturation with a reduction between 55.6% and 86.1%. ② The axial strains of the coal exhibit three-stage process, including stability increasing, slowly increasing, and rapidly increasing with the increase of time. There is a certain correlation between the permeability and the strain of coal, and the permeability varies with the increase of deviator stress, in terms of quadratically increasing, decreasing and first increasing and then decreasing, the quadratic function can better predict the permeability variation law of gas hydrate bearing coal under mining stress disturbance. ③ Introducing the permeability loss rate, the permeability loss rate increases with the increase of the deviator stress applied on the coal under the condition of the same hydrate saturation. ④ Introducing the expansion deformation angle, the coal expansion deformation angle decreases from 19.0°−63.9° to 0.2°−38.2° with the increase of hydrate saturation under the condition of the same deviator stress, which indicates that the lower the hydrate saturation, the more significant effect of the coal expansion.

[Purpose/Significance] Breeding 4.0, characterized by "biotechnology + artificial intelligence + big data information technology," has brought new requirements for the digital management and intelligent utilization of germplasm resources. In order to meet the diverse support needs for knowledge service forms under an intelligent background, this article aims to propose an effective method for knowledge organization and deep semantic association. This is essential to address the inconveniences that discrete germplasm resource data bring to researchers when collaborating across regions and institutions. Therefore, the article presents a method that integrates fragmented domain data into a systematic knowledge system, which is particularly important. [Method/Process] By analyzing the domain data descriptions and the current organizational status, the ontology construction was performed using the seven-step method developed by Stanford University Hospital. First, existing ontologies such as the Crop Ontology, Gene Ontology, and Darwin Core were referenced and reused, and then integrated with the knowledge framework from the "Technical Specifications for Crop Germplasm Resources" series and example datasets. Consequently, an ontology model was successfully constructed, which covers five major categories of crops: cereals, cash crops, vegetables, fruit trees, and forage and green manure crops. This model defines 11 core classes including phenotypes and genotypes, as well as identification methods and evaluation standards, along with 10 object properties and 56 data properties. [Results/Conclusions] Based on the ontology model, the article proposes a methodology for constructing a knowledge graph of crop germplasm resources. Using rice as an example, a domain-specific fine-grained knowledge graph is developed to facilitate semantic association and querying across multiple knowledge dimensions. The article also outlines prospective designs for new intelligent knowledge service scenarios driven by the knowledge graph, such as intelligent question and answer and knowledge computation, aiming to meet the knowledge service needs of researchers, breeding companies, and the general public. This is intended to provide more accurate and efficient support for computational breeding efforts. Currently, the research focuses only on rice as an example of a cereal crop, with economic crops, vegetables, and other types of crop germplasm resources not yet included in the study. Future work will expand the scope of the study and add new classes and properties specific to different germplasm resources to better address the diverse and personalized knowledge needs of users in the eraa of big data. This approach aims to promote the contextualization, ubiquity, and intelligence of knowledge services, and to further integrate them into different academic disciplines related to the development of new quality digital productivity.

[Purpose/Significance] Knowledge Graph has become a major research hotspot in the era of artificial intelligence due to its ability to provide a new means of organization and representation of knowledge. As the field continues to evolve, numerous scholars have proposed advanced algorithms and technologies for each core stage of constructing a knowledge graph, and many large domestic and foreign enterprises have also developed their independent knowledge graph management systems. However, the majority of these graph tools developed are designed for commercial use and are often too expensive and difficult to deploy locally for small and medium-sized research teams. This presents a challenge for information organizations such as research libraries with massive resources, which require a more adaptable, universal, and efficient tool to build and manage knowledge graphs. To meet this need, it is important to develop an open-source, user-friendly, and customizable knowledge graph management system that can be easily deployed by small and medium-sized research teams. [Method/Process] In summary, this article offers a thorough and informative analysis of six mainstream knowledge graph management systems, both domestically and internationally. It delves into the unique characteristics of each system within the business process and provides an in-depth comparative analysis based on several important factors, including system functionality, technology selection, open-source availability, and application domains. The article refers to the standard construction process of knowledge graphs and highlights the platform characteristics of each system during the construction process while also examining their limitations based on current data characteristics. In response to practical needs, the article focuses on multi-path, multi-engine, distributed, and collaborative construction, integrating advanced graph algorithms and considering a well-developed underlying graph storage strategy. [Results/Conclusions] As a result,the article presents an in-depth analysis of the construction model for a collaborative development and management system of an integrated knowledge graph. It not only investigates the current state of knowledge graph management systems but also proposes novel optimization ideas. These ideas include distributed collaborative construction, which allows for simultaneous contributions from multiple sources, and parallel management of multiple graphs, enabling efficient organization and retrieval. Additionally, some suggestions are put forward: developing multi-path knowledge extraction techniques to enhance the knowledge acquisition process, and using specialized multi-graph storage engines for optimized storage and retrieval. Last, the article emphasizes the importance of incorporating cross-media and multimodal knowledge into the graph for a comprehensive representation of information.