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Abstract Illegal trade in sharks and rays continues to undermine global conservation efforts, with enforcement often hampered by the inability to identify products to the species level. Here, we present a portable, cost-effective High-Resolution melt (HRM) assay for rapid DNA-based identification of elasmobranch species in trade. Using a reference library of 669 vouchered tissue samples collected from field operations and international market surveys, we validated the assay’s capacity to accurately differentiate at least 55 shark and ray species based on melt curve profiles, including 38 species listed under the Convention on International Trade in Endangered Species of Wild Fauna and Flora. Automated image classification enabled high-throughput identification with 99.2% accuracy. The assay yields results within two hours at a per-sample cost of $1.50, and is compatible with portable qPCR platforms, making it suitable for on-site applications. This approach represents a scalable molecular enforcement tool that can empower local authorities to monitor trade more effectively, support compliance with international regulations, and enhance global efforts to combat wildlife trafficking. 

Classifying whether collected information related to emerging topics and domains is fake/incorrect is not an easy task because we do not have enough labeled data in the domains. Given labeled data from source domains (e.g., gossip and health) and limited labeled data from a newly emerging target domain (e.g., COVID-19 and Ukraine war), simply applying knowledge learned from source domains to the target domain may not work well because of different data distribution. To solve the problem, in this paper, we propose an energy-based domain adaptation with active learning for early misinformation detection. Given three real world news datasets, we evaluate our proposed model against two baselines in both domain adaptation and the whole pipeline. Our model outperforms the baselines, improving at least 5% in the domain adaptation task and 10% in the whole pipeline, showing effectiveness of our proposed approach.