Structural and mechanistic investigations on CC bond forming α-oxoamine synthase allowing L-glutamate as substrate.

Carbon­carbon (C-C) bonds serve as the fundamental structural backbone of organic molecules. As a critical CC bond forming enzyme, α-oxoamine synthase is responsible for the synthesis of α-amino ketones by performing the condensation reaction between amino acids and acyl-CoAs. We previously identified an α-oxoamine synthase (AOS), named as Alb29, involved in albogrisin biosynthesis in Streptomyces albogriseolus MGR072. This enzyme belongs to the α-oxoamine synthase family, a subfamily under the pyridoxal 5'-phosphate (PLP) dependent enzyme superfamily. In this study, we report the crystal structures of Alb29 bound to PLP and L-Glu, which provide the atomic-level structural insights into the substrate recognition by Alb29. We discover that Alb29 can catalyze the amino transformation from L-Gln to L-Glu, besides the condensation of L-Glu with ß-methylcrotonyl coenzyme A. Subsequent structural analysis has revealed that one flexible loop in Alb29 plays an important role in both amino transformation and condensation. Based on the crystal structure of the S87G mutant in the loop region, we capture two distinct conformations of the flexible loop in the active site, compared with the wild-type Alb29. Our study offers valuable insights into the catalytic mechanism underlying substrate recognition of Alb29.

An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks.

To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation.

A Multiscale Lightweight and Efficient Model Based on YOLOv7: Applied to Citrus Orchard.

With the gradual increase in the annual production of citrus, the efficiency of human labor has become the bottleneck limiting production. To achieve an unmanned citrus picking technology, the detection accuracy, prediction speed, and lightweight deployment of the model are important issues. Traditional object detection methods often fail to achieve balanced effects in all aspects. Therefore, an improved YOLOv7 network model is proposed, which introduces a small object detection layer, lightweight convolution, and a CBAM (Convolutional Block Attention Module) attention mechanism to achieve multi-scale feature extraction and fusion and reduce the number of parameters of the model. The performance of the model was tested on the test set of citrus fruit. The average accuracy (mAP@0.5) reached 97.29%, the average prediction time was 69.38 ms, and the number of parameters and computation costs were reduced by 11.21 M and 28.71 G compared with the original YOLOv7. At the same time, the Citrus-YOLOv7 model's results show that it performs better compared with the current state-of-the-art network models. Therefore, the proposed Citrus-YOLOv7 model can contribute to solving the problem of citrus detection.