LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

Effect of golden needle mushroom (Flammulina velutipes) stem waste on laying performance, calcium utilization, immune response and serum immunity at early phase of production.

OBJECTIVE: This experiment was conducted to evaluate the effects of golden needle mushroom (Flammulina velutipes) stem waste (FVW), on organic eggs production, calcium utilization, antibody response, serum immunoglobulin, and serum cytokine concentration at early phase of production in laying hens. METHODS: A total 210, 19 weeks old aged ISA Brown layers were randomly assigned into 5 equal treatment groups, with 7 replications of 6 hens each. Dietary treatment included a standard basal diet as control; antibiotic (0.05% flavomycin); 2% FVW; 4% FVW; and 6% FVW. The experimental duration was 10 weeks. RESULTS: There was no significant differences (p>0.05) on hen day egg production, egg weight, egg mass, feed intake, and feed conversion ratio (FCR) among experimental groups. Unmarketable eggs were significantly lower (p<0.05) both in 4% FVW and 6% FVW fed groups than control group. The calcium retention and calcium in egg shell deposition were significantly higher (p<0.05) in FVW inclusion groups than control and antibiotic groups. Antibody titers against Newcastle diseases were significantly higher (p<0.05) in 6% FVW fed group (except combined with 4% FVW at day 147) and infectious bronchitis were significantly higher (p< 0.05) in FVW fed groups (except 2% FVW and 4% FVW at day 161) than control and antibiotic groups. Serum immunoglobulin sIgA was significantly higher (p<0.05) in all levels of FVW and IgG was significantly higher (p<0.05) in 4% FVW than control and antibiotic groups. Serum cytokine concentration interleukin-2 (IL-2) was significantly higher (p<0.05) in 6% FVW; IL-6 and tumor necrotic factor-α were significantly higher (p<0.05) both in 4% FVW and 6% FVW than control and antibiotic groups; IL-4 was significantly higher (p<0.05) in antibiotic, 2% FVW and 4% FVW fed groups than control. CONCLUSION: F. velutipes mushroom waste can be used as a novel substitute for antibiotic for organic egg production and sound health status in laying hens.

A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.

Background: Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets. Methods: This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases. Results: In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively. Conclusions: Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.

Partial Purification and Antioxidant Activities of Oligosaccharides from Hericium caput-medusae (Agaricomycetes).

Fermentation concentrate of Hericium caput-medusae (HFC) was used in this study and two different molecular weight oligosaccharides (HFCO1, HFCO2) were obtained by dialysis methods. Furthermore, DEAE Sepharose Fast Flow and Sephadex G-25 column chromatography were used to obtain the two purified components (HFCO1-1, HFCO2-1). The two components were partially characterized, and analysis of monosaccharide composition through Fourier-transform infrared spectroscopy was performed. The sugar-containing components in HFC were mainly neutral oligosaccharides, and the monosaccharide composition was mainly glucose, with the configuration of α-pyranose. Their chemical and cellular antioxidant activities were compared, and the results showed that antioxidant activities of HFCO2 were higher than HFCO2-1 and antioxidant activities of crude oligosaccharides were higher than those of neutral oligosaccharides. By both in vitro and in vivo studies of scavenging activities toward the free radicals and increasing the activity of antioxidant enzymes in cells, HFCO2 and HFCO2-1 could suppress H2O2-induced reduction in cell viability.

Purification, partial characterization and inducing tumor cell apoptosis activity of a polysaccharide from Ganoderma applanatum.

In this study, a homogeneous polysaccharide (designated as GAP-3S) with an average molecular weight of 6.82×105 Da was purified from Ganoderma applanatum. GAP-3S was composed of glucose, galactose, fucose and xylose in the molar ratio of 7.1:2.6:1.1:1. It could significantly inhibit the proliferation of MCF-7 cells in a dose- and time-dependent manner. Flow cytometry analysis indicated that GAP-3S could also induce cell apoptosis. Treatment of MCF-7 cells with GAP-3S lead to the collapse of mitochondrial membrane potential and the accumulation of ROS, up-regulated expression of Bax, cleavage of PARP, p-p38 and p-JNK, and the down-regulated expression of Bcl-2, PARP and p-ERK. The activities of caspase-3 and caspase-9 were also increased in GAP-3S-treated MCF-7 cells compared to untreated cells. These findings suggested that GAP-3S probably induced apoptosis in MCF-7 cells through intrinsic mitochondrial apoptosis and MAPK signaling pathways.