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PINA is a novel ATPase and DNA helicase highly conserved in Archaea, the third domain of life. The PINA from Sulfolobus islandicus (SisPINA) forms a hexameric ring in crystal and solution. The protein is able to promote Holliday junction (HJ) migration and physically and functionally interacts with Hjc, the HJ specific endonuclease. Here, we show that SisPINA has direct physical interaction with Hjm (Hel308a), a helicase presumably targeting replication forks. In vitro biochemical analysis revealed that Hjm, Hjc, and SisPINA are able to coordinate HJ migration and cleavage in a concerted way. Deletion of the carboxyl 13 amino acid residues impaired the interaction between SisPINA and Hjm. Crystal structure analysis showed that the carboxyl 70 amino acid residues fold into a type II KH domain which, in other proteins, functions in binding RNA or ssDNA. The KH domain not only mediates the interactions of PINA with Hjm and Hjc but also regulates the hexameric assembly of PINA. Our results collectively suggest that SisPINA, Hjm and Hjc work together to function in replication fork regression, HJ formation and HJ cleavage.

Holliday junction (HJ) is a hallmark intermediate in DNA recombination and must be processed by dissolution (for double HJ) or resolution to ensure genome stability. Although HJ resolvases have been identified in all domains of life, there is a long-standing effort to search in prokaryotes and eukarya for proteins promoting HJ migration. Here, we report the structural and functional characterization of a novel ATPase, Sulfolobus islandicusPilT N-terminal-domain-containing ATPase (SisPINA), encoded by the gene adjacent to the resolvase Hjc coding gene. PINA is conserved in archaea and vital for S. islandicus viability. Purified SisPINA forms hexameric rings in the crystalline state and in solution, similar to the HJ migration helicase RuvB in Gram-negative bacteria. Structural analysis suggests that ATP binding and hydrolysis cause conformational changes in SisPINA to drive branch migration. Further studies reveal that SisPINA interacts with SisHjc and coordinates HJ migration and cleavage.

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs. [ABSTRACT FROM AUTHOR]

Weed control has always been one of the most important issues in agriculture. The research based on deep learning methods for weed identification and segmentation in the field provides necessary conditions for intelligent point-to-point spraying and intelligent weeding. However, due to limited and difficult-to-obtain agricultural weed datasets, complex changes in field lighting intensity, mutual occlusion between crops and weeds, and uneven size and quantity of crops and weeds, the existing weed segmentation methods are unable to perform effectively. In order to address these issues in weed segmentation, this study proposes a multi-scale convolutional attention network for crop and weed segmentation. In this work, we designed a multi-scale feature convolutional attention network for segmenting crops and weeds in the field called MSFCA-Net using various sizes of strip convolutions. A hybrid loss designed based on the Dice loss and focal loss is used to enhance the model's sensitivity towards different classes and improve the model's ability to learn from hard samples, thereby enhancing the segmentation performance of crops and weeds. The proposed method is trained and tested on soybean, sugar beet, carrot, and rice weed datasets. Comparisons with popular semantic segmentation methods show that the proposed MSFCA-Net has higher mean intersection over union (MIoU) on these datasets, with values of 92.64%, 89.58%, 79.34%, and 78.12%, respectively. The results show that under the same experimental conditions and parameter configurations, the proposed method outperforms other methods and has strong robustness and generalization ability. [ABSTRACT FROM AUTHOR]