In autonomous driving systems, high-speed and real-time image processing, along with object recognition, are crucial technologies. This paper builds upon the research achievements in in-dustrial item sorting systems and proposes an object recognition and sorting system for auton-omous driving. In industrial sorting lines, goods sorting robots often need to work at high speeds to efficiently sort large volumes of items. This poses a challenge to the robot's real-time vision and sorting capabilities, making it both practical and economically viable to implement a real-time and low-cost sorting system in a real-world industrial sorting line. Existing sorting systems have limitations such as high cost, high computing resource consumption, and high power consump-tion. These issues lead to the fact that existing sorting systems are typically used only in large industrial plants. In this paper, we design a high-speed, low-cost, low-resource-consumption FPGA (Field-Programmable Gate Array) based item sorting system that achieves similar perfor-mance to current mainstream sorting systems at a lower cost and consumption than existing sorting systems. The recognition part employs a morphological recognition method, which segments the image using a frame difference algorithm and then extracts the color and shape features of the items. To handle sorting, a six-degree-of-freedom robotic arm is introduced in the sorting segment. The improved cubic B-spline interpolation algorithm is employed to plan the motion trajectory and consequently control the robotic arm to execute the corresponding actions. Through a series of experiments, this system achieves an average recognition delay of 25.26ms, ensures smooth operation of the gripping motion trajectory, minimizes resource consumption, and reduces implementation costs.

Artificial intelligence algorithms have gained widespread adoption in the field of air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. Training samples that lack relevance to the predicted moments can introduce interference into the neural network's learning process, potentially leading to a state of local convergence during its iterative process. This, in turn, diminishes the robustness and generalization capabilities of the prediction model. To enhance the prediction accuracy of air conditioning load prediction models based on artificial intelligence algorithms, this study presents an artificial intelligence algorithm prediction model based on the method of sample similarity sample screening. Initially, the comprehensive similarity coefficient between samples is computed using the gray correlation analysis method, enriched with enhancements in information entropy. Subsequently, a subset of closely related samples is extracted from the original dataset and employed as the training dataset for the artificial intelligence prediction model. Finally, the trained artificial intelligence algorithm prediction model is deployed for air conditioning load prediction. The results illustrate that the method of screening training samples based on sample similarity effectively improves the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is important to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Lipid dysregulation is critically involved in hepatocellular carcinoma (HCC). Further, male predi-lection and Ras pathway hyperactivation are distinct characteristics of HCC. However, mecha-nisms underlying their connections remain unknown. The aim of the present study was to perform a comprehensive lipidomics analysis of a Hras12V transgenic mice (Ras-Tg) model of HCC induced by hepatocyte-specific Ras pathway activation and characterized by male predilection and a dis-rupted lipid metabolism. A total of 3437 lipids were identified in HCC (T) and peri-tumor tissues (P) of Ras-Tg mice and liver tissues of wild-type mice (W) of both sexes. Longitudinal comparisons of W, P, and T yielded 359 differentially expressed lipids (DELs) in male mice and 306 DELs in female mice. Generally, glycerolipid accumulation, glycerophospholipid reduction and monounsaturated fatty acid synthesis improvement were more frequent in T compared to P. The expression change pattern analysis revealed common and characteristic DELs positively/negatively associated with HCC or the Ras oncogene. Further lipid metabolism pathway investigations revealed that disordered lipid and fatty acid biosynthesis contributed to glycerolipid accumulation and glycerophospholipid re-duction in T. Comparisons between P and W suggests that different responses to the Ras oncogene in mice of different sexes, as well as higher amounts of aberrantly regulated lipids in males, may contribute to male-biased hepatocarcinogenesis. However, lateral comparisons between sexes showed a converging trend during hepatocarcinogenesis, explaining the poor efficacy for gen-der-specific therapies. In conclusion, the common and characteristic DELs and lipid metabolism pathways in HCC initiated by the Ras oncogene from sexually dimorphic hepatocytes provide novel insights into the clinical diagnosis and management of HCC.

Aborycin is a type I lasso peptide with a stable interlocked structure, offering a favorable framework for drug development. The aborycin biosynthetic gene cluster gul from marine sponge-associated Streptomyces sp. HNS054 was cloned and integrated into the chromosome of S. coelicolor hosts with different copies. The 3-copy gul-integration strains S. coelicolor M1346::3gul showed better production than one-copy or 2-copy gul-integration strains, and the total titer reached approximately 10.4 mg/L, i.e., 2.1 times that of the native strain. Then, five regulatory genes, phoU (SCO4228), wblA (SCO3579), SCO1712, orrA (SCO3008) and gntR (SCO1678), which were reported to have negative effects on secondary metabolism, were further knocked out from the M1346::3gul genome by CRISPR/Cas9 technology. While the ΔSCO1712 mutant showed a significant decrease (4.6 mg/L) and the ΔphoU mutant showed no significant improvement (12.1 mg/L) in aborycin production, the ΔwblA, ΔorrA and ΔgntR mutations significantly improved the aborycin titers to approximately 23.6 mg/L, 56.3 mg/L and 48.2 mg/L, respectively, which were among the highest heterologous yields for lasso peptides in both Escherichia coli systems and Streptomyces systems. Thus, this study provided important clues for future studies on enhancing antibiotic production in Streptomyces systems.

The Yangtze River is cross-linked with numerous lakes within its floodplain and is a worldwide biodiversity hotspot. There is no evidence indicating when this unique river-lake system developed. The endemic East Asian cyprinid clade has evolved diverse spawning adaptations to different flow conditions. Our ancestral egg-type reconstruction showed an ancestral state of adhesive eggs and later demersal eggs origination (both stream adaptations). Semi-buoyant eggs emerged ~18 Mya as a fast-flowing river adaptation, with increased hydration via three yolk protein degradation pathways, ion transport pathways and egg envelope permeability transition pores. Adhesive eggs evolved secondarily ~14 Mya with the egg envelope increasing to four layers and an adhesive layer, along with an increase in adhesiveness via microfilament/adhesive-related protein crosslinking and enhanced glycosaminoglycan biosynthesis, improving adherence to submerged lake plants, indicating that the cross-linked river-lake system formed in the mid-Miocene. This study provides a unique biological evidence for large-scale water system evolution.