Dexmedetomidine post-treatment attenuates cardiac ischaemia/reperfusion injury by inhibiting apoptosis through HIF-1α signalling.

Hypoxia-inducible factor 1α (HIF-1α) plays a critical role in the apoptotic process during cardiac ischaemia/reperfusion (I/R) injury. This study aimed to investigate whether post-treatment with dexmedetomidine (DEX) could protect against I/R-induced cardiac apoptosis in vivo and in vitro via regulating HIF-1α signalling pathway. Rat myocardial I/R was induced by occluding the left anterior descending artery for 30 minutes followed by 6-hours reperfusion, and cardiomyocyte hypoxia/reoxygenation (H/R) was induced by oxygen-glucose deprivation for 6 hours followed by 3-hours reoxygenation. Dexmedetomidine administration at the beginning of reperfusion or reoxygenation attenuated I/R-induced myocardial injury or H/R-induced cell death, alleviated mitochondrial dysfunction, reduced the number of apoptotic cardiomyocytes, inhibited the activation of HIF-1α and modulated the expressions of apoptosis-related proteins including BCL-2, BAX, BNIP3, cleaved caspase-3 and cleaved PARP. Conversely, the HIF-1α prolyl hydroxylase-2 inhibitor IOX2 partly blocked DEX-mediated cardioprotection both in vivo and in vitro. Mechanistically, DEX down-regulated HIF-1α expression at the post-transcriptional level and inhibited the transcriptional activation of the target gene BNIP3. Post-treatment with DEX protects against cardiac I/R injury in vivo and H/R injury in vitro. These effects are, at least in part, mediated via the inhibition of cell apoptosis by targeting HIF-1α signalling

Determining the local dark matter density with LAMOST data

Measurement of the local dark matter density plays an important role in both Galactic dynamics and dark matter direct detection experiments. However, the estimated values from previous works are far from agreeing with each other. In this work, we provide a well-defined observed sample with 1427 G \& K type main-sequence stars from the LAMOST spectroscopic survey, taking into account selection effects, volume completeness, and the stellar populations. We apply a vertical Jeans equation method containing a single exponential stellar disk, a razor thin gas disk, and a constant dark matter density distribution to the sample, and obtain a total surface mass density of $\rm {78.7 ^{+3.9}_{-4.7}\ M_{\odot}\ pc^{-2}}$up to 1 kpc and a local dark matter density of$0.0159^{+0.0047}_{-0.0057}\,\rm M_{\odot}\,\rm pc^{-3}$. We find that the sampling density (i.e. number of stars per unit volume) of the spectroscopic data contributes to about two-thirds of the uncertainty in the estimated values. We discuss the effect of the tilt term in the Jeans equation and find it has little impact on our measurement. Other issues, such as a non-equilibrium component due to perturbations and contamination by the thick disk population, are also discussed.Comment: 11 pages, 10 figure

History and future of business ecosystem: a bibliometric analysis and visualization

The business ecosystem theory has developed rapidly in recent years and has become a hottopic in the field of business and management. However, the use of this concept is con-troversial. This study systematically reviewed literature published spanning nearly threedecades from 1993 to 2022. In this paper, researchers designed an improved traceabilitymethod to retrieve literature based on data sources form Web of Science. VOSviewerand CiteSpace are adopted as two scientific atlas tools for information processing andvisualization to evaluate the relationship between sub fields of business ecosystem. Thefindings show that the four branches of business ecosystem, i.e., innovation, platform, en-trepreneurship and service, absorb theoretical ideas to varying degrees. Among them, thetheoretical inheritance relationship of innovation branch is most clear, and gradually growsinto the backbone of ecosystem research. Major contribution of this study is reflected inthree aspects: Firstly, the improved traceability method provides a repeatable quantitativedescription process on the basis of significantly reducing researchers’ subjective participa-tion. Secondly, from perspective of bibliometrics, the branch direction and key nodes oftheory development are identified. Thirdly, the study helps identify the future developmentdirections of business ecosystem, including innovation, digitalization, entrepreneurship,self-organization and the strategic transformation guided by emerging technologie

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to poor generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to effectively integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes and eliminates the use of explicit prompts for improved robustness and a simpler pipeline. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning, further enhancing the discrimination of the class prototypes for more accurate class prompting. The results of extensive experiments on both EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves state-of-the-art performance while only requiring a small number of tunable parameters. The source code will be released at https://github.com/wenxi-yue/SurgicalSAM.Comment: Technical Report. The source code will be released at https://github.com/wenxi-yue/SurgicalSA

Trust in Software Supply Chains: Blockchain-Enabled SBOM and the AIBOM Future

Software Bill of Materials (SBOM) serves as a critical pillar in ensuring software supply chain security by providing a detailed inventory of the components and dependencies integral to software development. However, challenges abound in the sharing of SBOMs, including potential data tampering, hesitation among software vendors to disclose comprehensive information, and bespoke requirements from software procurers or users. These obstacles have stifled widespread adoption and utilization of SBOMs, underscoring the need for a more secure and flexible mechanism for SBOM sharing. This study proposes a novel solution to these challenges by introducing a blockchain-empowered approach for SBOM sharing, leveraging verifiable credentials to allow for selective disclosure. This strategy not only heightens security but also offers flexibility. Furthermore, this paper broadens the remit of SBOM to encompass AI systems, thereby coining the term AI Bill of Materials (AIBOM). This extension is motivated by the rapid progression in AI technology and the escalating necessity to track the lineage and composition of AI software and systems. Particularly in the era of foundational models like large language models (LLMs), understanding their composition and dependencies becomes crucial. These models often serve as a base for further development, creating complex dependencies and paving the way for innovative AI applications. The evaluation of our solution indicates the feasibility and flexibility of the proposed SBOM sharing mechanism, positing a new solution for securing (AI) software supply chains

Dynamic evolution of MADS-box genes in extant ferns via large-scale phylogenomic analysis

IntroductionSeveral studies of MADS-box transcription factors in flowering plants have been conducted, and these studies have indicated that they have conserved functions in floral organ development; MIKC-type MADS-box genes has been proved to be expanded in ferns, however, few systematic studies of these transcription factors have been conducted in non-seed plants. Although ferns and seed plants are sister groups, they exhibit substantial morphological differences.MethodsHere, we clarified the evolution of MADS-box genes across 71 extant fern species using available transcriptome, genome, and gene expression data.ResultsWe obtained a total of 2,512 MADS-box sequences, ranging from 9 to 89 per species. The most recent common ancestor (MRCA) of ferns contained approximately three type I genes and at least 5–6 type II MADS-box genes. The domains, motifs, expression of type I and type II proteins, and the structure of the both type genes were conserved in ferns as to other land plants. Within type II genes, MIKC*-type proteins are involved in gametophyte development in ferns; MIKCC-type proteins have broader expression patterns in ferns than in seed plants, and these protein sequences are likely conserved in extant seed plants and ferns because of their diverse roles in diploid sporophyte development. More than 90% of MADS-box genes are type II genes, and MIKCC genes, especially CRM1 and CRM6-like genes, have undergone a large expansion in leptosporangiate ferns; the diverse expression patterns of these genes might be related to the fuctional diversification and increased complexity of the plant body plan. Tandem duplication of CRM1 and CRM6-like genes has contributed to the expansion of MIKCC genes.Conclusion or DiscussionThis study provides new insights into the diversity, evolution, and functions of MADS-box genes in extant ferns