In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target tracking algorithms. Additionally, traditional target-tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target-tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and Transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample-convolve-interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into sub-sequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target-tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.

When ground-penetrating radar is used to detect targets within concrete, the location of the targets, the identification of different shapes, properties and less obvious echoes all greatly increase the interpretation time of the staff and can easily cause misjudgment of the echo images. In this paper, the ground-penetrating radar echo images (B-scan) after processing are mean filtered to eliminate the direct waves that interfere greatly with the echoes. The RFB-s structure is added to the YOLOv3-SPP network structure, while the Anchor value is optimized and the EIOU loss function is introduced. For four types of data with different shapes and properties at random target locations, three models, YOLOv3, YOLOv3-SPP and the improved YOLOv3-SPP, are used for classification and identification, and the proposed algorithm models are comprehensively evaluated using model evaluation metrics. The experimental results show that the algorithm models proposed in this paper have good recognition effect in ground-penetrating radar echo image target detection.

Sphingolipids are ubiquitous in eukaryotes and certain prokaryotes, where they serve as vital components of biological membranes and as bioactive molecules. Chloroplasts have complex membrane structures that play crucial roles in photosynthesis, but their specific sphingolipidome remains unreported. In this study, we used high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS) to analyze the sphingolipidome of purified Arabidopsis thaliana chloroplasts. We detected 92 chloroplast sphingolipids. The chloroplast sphingolipidome differed from total leaf (TL) samples, having a higher content of free long-chain bases and hydroxyceramides, and a greater proportion of complex sphingolipids with 16C fatty acid (FA) forms. Notably, chloroplast glucosylceramides were predominantly the d18:1 h16:0 and t18:1 h16:0 forms, rather than the 24C FA form found in TL and other cellular structures. Comparing the sphingolipidomes of different cellular structures underscores the inhomogeneity of intracellular distribution of sphingolipids. This provides a robust reference for further elucidating the function of sphingolipids in plant cells.

Both lactic acid and short chain fatty acid (SCFA) play important roles in maintenance of intestinal epithelial structure and function. Trilactic glyceride (TLG) obtains both excellences of lactic acid and SCFA. This study was to investigate the effects of trilactic glyceride on growth performance, blood parameters, liver function, intestinal morphology and intestine function of piglets. Twelve weaned piglets (21±2 d) were randomly allocated to two treatment groups: 1) control group, piglets fed the basal diet; 2) TLG group, piglets fed the basal diet supplemented with 0.5 % TLG. On day 21 of the trial, D-xylose (0.1 g/kg·BW) was orally administrated to all piglets and blood samples were collected 1 h thereafter. Then, all the piglets were sacrificed to examine intestinal mucosal morphology and collect fatty tissue, liver and intestinal mucosa for further analysis. The results showed that: compared with the control group, TLG group decreased blood ALB and GGT on day 10 and 20, TLG group decreased blood TP and increased blood TG on day 20 of the trail (p < 0.05); TLG group decreased blood D-xylose and LDL, increased blood HDL (p < 0.05). These data suggested that supplementing trilactic glyceride had beneficial impacts on promoting nutrients’ metabolism, maintaining intestinal integrity, and alleviating oxidative stress and diarrhoea. Further research of molecular mechanisms showed changing expression levels of related proteins and genes, suggesting that these could be involved in the regulation of the impact. The community composition of the gut microbiota was also found to be altered in several operational taxonomic units within the genus, Prevotella (order Bacteroidales), and the order, Clostridiales.