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Magnetic interference of the aircraft platform is one of the primary restrictions for aeromagnetic missions, like magnetic anomaly detection (MAD) and geomagnetic navigation. In recent years, two main paradigms have emerged for aeromagnetic compensation: model-driven methods based on the linear Tolles-Lawson (T-L) model and data-driven methods utilizing neural networks. However, model-driven methods struggle with nonlinear interference compensation, while data-driven methods exhibit a high dependence on the quality and quantity of available data. To address the limitations of existing paradigms, this article introduces a novel approach: an interference model-guided neural network for aeromagnetic compensation. This method leverages the strengths of both model-driven and data-driven techniques. The synergy is achieved through the development of a network initialization method based on T-L estimation and the incorporation of an interference model-embedded loss function. With the proposed method, aeromagnetic compensation can consider both linear and nonlinear interferences, and eliminate them even with limited and noisy data without significant accuracy degradation. The proposed method’s efficacy was validated through real flight tests. Results demonstrate that the proposed method outperforms both the model-driven and data-driven methods, with standard deviations (STDs) reaching 0.0550 nT, compared to 0.0812 and 0.1306 nT, respectively.

•Earliest data of human stable isotopes with direct AMS-14C dating (8,323 cal. BP–8,178 cal. BP) at the Houli site, in the Haidai region (Shandong), China.•Primitive millet agriculture originated under the broad-spectrum economic model of hunter-gatherer at c. 8,300 BP in Haidai.•A dietary shift with gradient millet consumption appeared from c. 8,300 BP to c. 6,000 BP in Haidai.•Millet-based agriculture became a dominant part of the production economy during 5,987 cal. BP–5,726 cal. BP in Haidai.

Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.