Today, the digital economy has pursued the required social and economic development, and enterprises can achieve long-term sustainability by relying on the digital platform ecosystem. However, the development of the digital economy and the rapid growth of platform ecosystems have also generated some problems. To study effective digital platform ecosystem governance, this study used the tripartite evolutionary game method and analyzed the changes and effects of platform regulation under different development stages from the perspectives of government, platforms, and enterprises. The results indicate that in the early stages, digital platforms primarily relied on the government’s obligation to regulate. With the digital platform ecosystem’s evolution, the platform organization and settled enterprises’ governance effects gradually appeared, establishing the three common constraints of governance mode. Finally, when the digital platform ecosystem is in its self-renewal period, the government effect is reduced, with the platform itself and settled enterprises taking primary supervisory roles. This study provides reasonable methods for effective platform governance at different stages and enriches the theoretical basis and management practices of platform ecosystems.

The polyomaviruses are small, non-enveloped DNA tumor viruses that cause serious disease in immunosuppressed people, including progressive multifocal leukoencephalopathy (PML) in patients infected with JC polyomavirus, but the molecular events mediating polyomavirus entry are poorly understood. Through genetic knockdown approaches, we identified phosphoinositide 3’ kinase γ (PI3Kγ) and its regulatory subunit PIK3R5 as cellular proteins that facilitate infection of human SVG-A glial cells by JCPyV, but not by adenovirus, an unrelated small, non-enveloped DNA tumor virus. PI3Kα appears less important for polyomavirus infection than PI3Kγ. CRISPR/Cas9-mediated knockout of PIK3R5 or PI3Kγ inhibited infection by authentic JCPyV and by JC pseudovirus. PI3Kγ knockout also inhibited infection by BK and Merkel Cell pseudoviruses, other pathogenic human polyomaviruses, and SV40, an important model polyomavirus. Reintroduction of the wild-type PI3Kγ gene into the PI3Kγ knock-out SVG-A cells rescued the JCPyV infection defect. Disruption of the PI3Kγ pathway did not block binding of JCPyV to cells or virus internalization, implying that PI3Kγ facilitates some intracellular step(s) of infection. These results imply that agents that inhibit PI3Kγ signaling may have a role in managing polyomavirus infections.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.