Insect pest recognition has always been a significant branch of agriculture and ecology. The slight variance among different kinds of insects in appearance makes it hard for human experts to recognize. It is increasingly imperative to finely recognize specific insects by employing machine learning methods. In this study, we proposed a feature fusion network to synthesize feature presentations in different backbone models. Firstly, we employed one CNN-based backbone ResNet, and two attention-based backbones Vision Transformer and Swin Transformer to localize the important regions of insect images with Grad-CAM. During this process, we designed new architectures for these two Transformers to enable Grad-CAM to be applicable in such attention-based models. Then we further proposed an attention-selection mechanism to reconstruct the attention area by delicately integrating the important regions, enabling these partial but key expressions to complement each other. We only need part of the image scope that represents the most crucial decision-making information for insect recognition. We randomly selected 20 species of insects from the IP102 dataset and then adopted all 102 kinds of insects to test the classification performance. Experimental results show that the proposed approach outperforms other advanced CNN-based models. More importantly, our attention-selection mechanism demonstrates good robustness to augmented images.

Some potential markers of boar sperm freezability have been found in spermatozoa, but little attention has been paid to seminal plasma. The seminal plasma is composed of secretions from the testis, epididymis, and accessory sex glands. The exposure of spermatozoa to small molecules such as metabolites can affect sperm function. However, details and significance of the seminal plasma metabolome related to boar sperm freezability are unknown. Therefore, the main aim of this study was to explore the differences in the metabolic level of seminal plasma between boars with differential freezability and to explore the candidate biomarkers of semen freezability. A total of 953 metabolites were identified in boar semen plasma by UHPLC-qTOF-MS analysis, and 50 metabolites showed significant change between the GFE group and PFE group. Further, twelve metabolites were subjected to metabolic target analysis, and three metabolites (D-aspartic acid, N-acetyl-L-glutamate (NAG), and inosine) showed differences. In conclusion, there is significant difference in the metabolome of seminal plasma between GFE and PFE individuals. D-aspartic acid, NAG, and inosine in seminal plasma may be potential markers for assessing sperm cryopreservation resistance in boars.

Pre-trained natural language processing models on a large natural language corpus can naturally transfer learned knowledge to protein domains by fine-tuning specific in-domain tasks. However, few studies focused on enriching such protein language models by jointly learning protein properties from strongly-correlated protein tasks. Here we elaborately designed a multi-task learning (MTL) architecture, aiming to decipher implicit structural and evolutionary information from three sequence-level classification tasks for protein family, superfamily and fold. Considering the co-existing contextual relevance between human words and protein language, we employed BERT, pre-trained on a large natural language corpus, as our backbone to handle protein sequences. More importantly, the encoded knowledge obtained in the MTL stage can be well transferred to more fine-grained downstream tasks of TAPE. Experiments on structure- or evolution-related applications demonstrate that our approach outperforms many state-of-the-art Transformer-based protein models, especially in remote homology detection.