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With the rapid progress of intelligent vehicle technology, the accurate recognition of road surface types and conditions has emerged as a crucial technology for improving the safety and comfort levels in autonomous driving. This paper puts forward a multi-feature fusion approach for road surface identification. Relying on a 24 GHz millimeter-wave radar, statistical features are combined with wavelet transform techniques. This combination enables the efficient classification of diverse road surface types and conditions. Firstly, the discriminability of radar echo signals corresponding to different road surface types is verified via statistical analysis. During this process, six-dimensional statistical features that display remarkable differences are extracted. Subsequently, a novel radar data reconstruction approach is presented. This method involves fitting discrete echo signals into coordinate curves. Then, discrete wavelet transform is utilized to extract both low-frequency and high-frequency features, thereby strengthening the spatio-temporal correlation of the signals. The low-frequency information serves to capture general characteristics, whereas the high-frequency information reflects detailed features. The statistical features and wavelet transform features are fused at the feature level, culminating in the formation of a 56-dimensional feature vector. Four machine learning models, namely the Wide Neural Network (WNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Kernel methods, are employed as classifiers for both training and testing purposes. Experiments were executed with 8865 samples obtained from a real-vehicle platform. These samples comprehensively represented 12 typical road surface types and conditions. The experimental outcomes clearly indicate that the proposed method is capable of attaining a road surface type identification accuracy as high as 94.2%. As a result, it furnishes an efficient and cost-efficient road perception solution for intelligent driving systems. This research validates the potential application of millimeter-wave radar in intricate road environments and offers both theoretical underpinning and practical support for the advancement of autonomous driving technology.

3D object-detection based on LiDAR point clouds can help driverless vehicles detect obstacles. However, the existing point-cloud-based object-detection methods are generally ineffective in detecting small objects such as pedestrians and cyclists. Therefore, a small-object-detection algorithm based on clustering is proposed. Firstly, a new segmented ground-point clouds segmentation algorithm is proposed, which filters out the object point clouds according to the heuristic rules and realizes the ground segmentation by multi-region plane-fitting. Then, the small-object point cloud is clustered using an improved DBSCAN clustering algorithm. The K-means++ algorithm for pre-clustering is used, the neighborhood radius is adaptively adjusted according to the distance, and the core point search method of the original algorithm is improved. Finally, the detection of small objects is completed using the directional wraparound box model. After extensive experiments, it was shown that the precision and recall of our proposed ground-segmentation algorithm reached 91.86% and 92.70%, respectively, and the improved DBSCAN clustering algorithm improved the recall of pedestrians and cyclists by 15.89% and 9.50%, respectively. In addition, visualization experiments confirmed that our proposed small-object-detection algorithm based on the point-cloud clustering method can realize the accurate detection of small objects.

In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

Lactic acid bacteria are a kind of microorganisms that can ferment carbohydrates to produce lactic acid, and are currently widely used in the fermented food industry. In recent years, with the excellent role of lactic acid bacteria in the food industry and probiotic functions, their microbial metabolic characteristics have also attracted more attention. Lactic acid bacteria can decompose macromolecular substances in food, including degradation of indigestible polysaccharides and transformation of undesirable flavor substances. Meanwhile, they can also produce a variety of products including short-chain fatty acids, amines, bacteriocins, vitamins and exopolysaccharides during metabolism. Based on the above-mentioned metabolic characteristics, lactic acid bacteria have shown a variety of expanded applications in the food industry. On the one hand, they are used to improve the flavor of fermented foods, increase the nutrition of foods, reduce harmful substances, increase shelf life, and so on. On the other hand, they can be used as probiotics to promote health in the body. This article reviews and prospects the important metabolites in the expanded application of lactic acid bacteria from the perspective of bioengineering and biotechnology.

Lead-free double perovskites are emerging as an excellent candidate material for optoelectronic devices, owing to their unique properties such as widely adjustable band gap and markedly lower toxicity than the lead-halide counterparts. In this work, a series of Mn2+-doped Cs2NaInCl6 (Mn2+:Cs2NaInCl6) DP crystals with tailored Mn2+-doping concentrations are synthesized. The effects of the reaction temperatures and Mn/(In + Na) ratios in the raw materials on the Mn2+ doping concentrations and their photoluminescence (PL) properties are investigated systematically. The resultant DP crystals exhibit well-resolved Mn2+4T1 → 6A1 d − d emission with an optimal PL quantum yield of 16% and an extremely long excited state lifetime up to ~ 17 ms, which is the longest one of Mn2+ emission among those of Mn2+-doped II–VI semiconductors or perovskite materials ever reported. Furthermore, the temperature-dependent PL and PL decay are studied in the temperatures range from 120 to 480 K. The decay lifetime decreased monotonously with the rise in temperatures, while the PL intensity gradually reaches a maximum and then keeps nearly constant, followed by a sharp decrease. Such abnormal phenomenon is mainly attributed to the combined effect of intrinsic property of octahedrally coordinated Mn2+ in perovskite lattice and enhanced electron–phonon coupling at elevated temperature.

Aurantiochytrium sp., a heterotrophic microorganism, has received increasing attention for its high production of polyunsaturated fatty acids and has been widely applied in various industries. This study intended to optimize the carotenoid synthesis pathway in Aurantiochytrium sp. by metabolic engineering to increase the carotenoid content. Multi-sourced key enzyme genes involved in lipid synthesis (LPAAT and DGAT) and astaxanthin synthesis (crtZ and crtW) were selected to construct single-gene expression vectors and transformed into Aurantiochytrium sp. The results showed that the overexpression of LPAAT of Phaeodactylum tricornutum in Aurantiochytrium sp. caused an increase of 39.3% in astaxanthin, 424.7% in β-carotene, 901.8% in canthaxanthin, and 575.9% in lutein, as well as a down-regulation of 15.3% in the fatty acid content. Transcriptomics analysis revealed enhanced expression of genes involved in purine and amino acid metabolism in the transformed strains, and the down-regulation of the citric acid cycle led to an increase in the source of acetyl coenzyme A for the production of fatty acids. This study provides strong experimental evidence to support the application of increasing carotenoid levels in Aurantiochytrium sp.

Background Ventilator-induced lung injury (VILI) is a clinical complication of mechanical ventilation observed in patients with acute respiratory distress syndrome. It is characterized by inflammation mediated by inflammatory cells and their secreted mediators. Methods To investigate the mechanisms underlying VILI, a C57BL/6J mouse model was induced using high tidal volume (HTV) mechanical ventilation. Mice were pretreated with Clodronate liposomes to deplete alveolar macrophages or administered normal bone marrow-derived macrophages or Group V phospholipase A2 (gVPLA2) intratracheally to inhibit bone marrow-derived macrophages. Lung tissue and bronchoalveolar lavage fluid (BALF) were collected to assess lung injury and measure Ca2 + concentration, gVPLA2, downstream phosphorylated cytoplasmic phospholipase A2 (p-cPLA2), prostaglandin E2 (PGE2), protein expression related to mitochondrial dynamics and mitochondrial damage. Cellular experiments were performed to complement the animal studies. Results Depletion of alveolar macrophages attenuated HTV-induced lung injury and reduced gVPLA2 levels in alveolar lavage fluid. Similarly, inhibition of alveolar macrophage-derived gVPLA2 had a similar effect. Activation of the cPLA2/PGE2/Ca2 + pathway in alveolar epithelial cells by gVPLA2 derived from alveolar macrophages led to disturbances in mitochondrial dynamics and mitochondrial dysfunction. The findings from cellular experiments were consistent with those of animal experiments. Conclusions HTV mechanical ventilation induces the secretion of gVPLA2 by alveolar macrophages, which activates the cPLA2/PGE2/Ca2 + pathway, resulting in mitochondrial dysfunction. These findings provide insights into the pathogenesis of VILI and may contribute to the development of therapeutic strategies for preventing or treating VILI.

Acute lung injury (ALI) is a pneumonic response characterized by neutrophil infiltration. Macrophage efferocytosis is the process whereby macrophages remove apoptotic cells, and is required for ALI inflammation to subside. The glycoprotein ulinastatin (UTI) has an anti‐inflammatory effect during the acute stages of ALI, but its effect on efferocytosis and the subinflammatory stage of ALI is unclear. Extracellular signal‐regulated kinase 5 (ERK5) is a key protein in efferocytosis, and we thus hypothesized that it may be activated by UTI to regulate efferocytosis and the resolution of pneumonia. To test this hypothesis, here we monitored phagocytosis of macrophages through in vivo and in vitro experiments. Pulmonary edema, neutrophil infiltration, protein exudation, and inflammatory factor regression were observed on days 1, 3, 5, and 7 in vivo. RAW264.7 cells were pretreated with different concentrations of UTI and ERK5 inhibitors, and the expression of tyrosine‐protein kinase Mer (Mer) protein on macrophage membrane was detected. UTI increased the phagocytosis of apoptotic neutrophils by macrophages in vitro and in vivo, and promoted the resolution of pneumonia. The protein expression of ERK5 and Mer increased with UTI concentration, while the expression of Mer was down‐regulated by ERK5 inhibitors. Therefore, our results suggest that UTI enhances efferocytosis and reduces lung inflammation and injury through the ERK5/Mer signaling pathway, which may be one of the targets of UTI in the treatment of lung injury. Ulinastatin (UTI) has an anti‐inflammatory effect during the acute stage of ALI, but its effect on efferocytosis and the subinflammatory stage of ALI is unclear. ERK5 is a key protein in efferocytosis, it is unknown whether it is activated by UTI to regulate resolution of efferocytosis and pneumonia. Our study suggests that UTI enhances efferocytosis and reduces lung inflammation and injury through the ERK5/Mer signaling pathway.