The last known red wolves were captured in southwestern Louisiana and eastern Texas in 1980 to establish a captive breeding population. Before their extirpation, gene flow with coyotes resulted in the persistence of endangered red wolf genetic variation in local coyote populations. We assessed genomic ancestry and morphology of coyotes in southwestern Louisiana. We detected that 38 to 62% of the coyote genomes contained red wolf ancestry acquired in the past 30 years and have an admixture profile similar to that of the canids captured before the extirpation of red wolves. We further documented a positive correlation between ancestry and weight. Our findings highlight the importance of hybrids and admixed genomes as a reservoir of endangered species ancestry for innovative conservation efforts. Together, this work presents an unprecedented system that conservation can leverage to enrich the recovery program of an endangered species.
The intelligence of energy storage devices has led to a sharp increase in the amount of detection data generated. Data sharing among distributed energy storage networks can realize collaborative control and comprehensive analysis, which effectively improves the clustering and intelligence. However, data security problems have become the main obstacle for energy storage devices to share data for joint modeling and analysis. The security issues caused by information leakage far outweigh property losses. In this article, we first proposed a blockchain based machine learning scheme for secure data sharing in distributed energy storage networks. Then, we formulated the data sharing problem into a machine learning problem by incorporating secure federated learning. Innovative verification methods and consensus mechanisms were used to encourage participants to act honestly, and to use well designed incentive mechanisms to ensure the sustainable and stable operation of the system. We implemented the scheme of SFedChain and experimented on real datasets with different settings. The numerical results show that SFedChain is promising.
Hydroinformatics and water data science topics are increasingly common in university graduate settings through dedicated courses and programs as well as incorporation into traditional water science courses. The technical tools and techniques emphasized by hydroinformatics and water data science involve distinctive instructional styles, which may be facilitated by online formats and materials. In the broader hydrologic sciences, there has been a simultaneous push for instructors to develop, share, and reuse content and instructional modules, particularly as the COVID 19 pandemic necessitated a wide scale pivot to online instruction. The experiences of hydroinformatics and water data science instructors in the effectiveness of content formats, instructional tools and techniques, and key topics can inform educational practice not only for those subjects, but for water science generally. This paper reports the results of surveys and interviews with hydroinformatics and water data science instructors. We address the effectiveness of instructional tools, impacts of the pandemic on education, important hydroinformatics topics, and challenges and gaps in hydroinformatics education. Guided by lessons learned from the surveys and interviews and a review of existing online learning platforms, we developed four educational modules designed to address shared topics of interest and to demonstrate the effectiveness of available tools to help overcome identified challenges. The modules are community resources that can be incorporated into courses and modified to address specific class and institutional needs or different geographic locations. Our experience with module implementation can inform development of online educational resources, which will advance and enhance instruction for hydroinformatics and broader hydrologic sciences for which students increasingly need informatics experience and technical skills.