Search Results (6)

Accurate and rapid detection of tea shoots within the tea canopy is essential for achieving the automatic picking of famous tea. The current detection models suffer from two main issues: low inference speed and difficulty in deployment on movable platforms, which constrain the development of intelligent tea picking equipment. Furthermore, the detection of tea canopy shoots is currently limited to natural daylight conditions, with no reported studies on detecting tea shoots under artificial light during the nighttime. Developing an all-day tea picking platform would significantly improve the efficiency of tea picking. In view of these problems, the research objective was to propose an all-day lightweight detection model for tea canopy shoots (TS-YOLO) based on YOLOv4. Firstly, image datasets of tea canopy shoots sample were collected under low light (6:30–7:30 and 18:30–19:30), medium light (8:00–9:00 and 17:00–18:00), high light (11:00–15:00), and artificial light at night. Then, the feature extraction network of YOLOv4 and the standard convolution of the entire network were replaced with the lightweight neural network MobilenetV3 and the depth-wise separable convolution. Finally, to compensate for the lack of feature extraction ability in the lightweight neural network, a deformable convolutional layer and coordinate attention modules were added to the network. The results showed that the improved model size was 11.78 M, 18.30% of that of YOLOv4, and the detection speed was improved by 11.68 FPS. The detection accuracy, recall, and AP of tea canopy shoots under different light conditions were 85.35%, 78.42%, and 82.12%, respectively, which were 1.08%, 12.52%, and 8.20% higher than MobileNetV3-YOLOv4, respectively. The developed lightweight model could effectively and rapidly detect tea canopy shoots under all-day light conditions, which provides the potential to develop an all-day intelligent tea picking platform. Full article

It is important to describe lineages before they go extinct, as we can only protect what we know. This is especially important in the case of microendemic species likely to be relict populations, such as Hynobius salamanders in southern China. Here, we unexpectedly sampled Hynobius individuals in Fujian province, China, and then worked on determining their taxonomic status. We describe Hynobius bambusicolus sp. nov. based on molecular and morphological data. The lineage is deeply divergent and clusters with the other southern Chinese Hynobius species based on the concatenated mtDNA gene fragments (>1500 bp), being the sister group to H. amjiensis based on the COI gene fragment, despite their geographic distance. In terms of morphology, the species can be identified through discrete characters enabling identification in the field by eye, an unusual convenience in Hynobius species. In addition, we noted some interesting life history traits in the species, such as vocalization and cannibalism. The species is likely to be incredibly rare, over a massively restricted distribution, fitting the definition of Critically Endangered following several lines of criteria and categories of the IUCN Red List of Threatened Species. Full article

Uterine function during pregnancy is regulated mainly by progesterone (P4) and estrogen (E2). Serum P4 levels are known to fluctuate significantly over the course of pregnancy, especially during embryo implantation and labor. In this study, pregnant mice at E0.5, E4.5, E15.5, and E18.5 (n = 3/E) were used for an RNA-Seq-based analysis of mRNA and lncRNA expression. In this analysis, 1971 differentially expressed (DE) mRNAs, 493 known DE lncRNAs, and 1041 novel DE lncRNAs were found between E0.5 and E4.5 at the embryo implantation stage, while 1149 DE mRNAs, 192 known DE lncRNAs, and 218 novel DE lncRNAs were found between E15.5 and E18.5 at the labor stage. The expression level of lncRNA-MMP11 was significantly downregulated by P4 treatment on MSM cells, while lncRNA-ANKRD37 was significantly upregulated. Notably, 117 DE mRNAs, 19 known DE lncRNAs, and 31 novel DE lncRNAs were commonly expressed between the two stages, indicating that these mRNAs and lncRNAs may be directly or indirectly regulated by P4. Full article

Mycoplasma bovis (M. bovis) is an important pathogen of cattle responsible for huge economic losses in the dairy and beef industries worldwide. The proteins secreted by M. bovis are mainly related to its adhesion, invasion, virulence, and intracellular survival and play a role in mycoplasma–host interactions. In our previous study, we found MbovP0145, a secreted protein present in the M. bovis secretome, but little is known about its function. In this study, we assessed the inflammatory characteristics and underlined mechanism of this inflammation of recombinant MbovP0145 (rMbovP0145). For this, bovine lung epithelial cells (EBL) were stimulated by rMbovP0145 to see the IL-8 production in a time- and dose-dependent manner. We observed that rMbovP0145 increased the production of IL-8 via ERK1/2 and P38 pathway activation. Further, the effect of the M. bovis ΔMbov_0145 mutant and its complementary strain on IL-8 mRNA expression was also confirmed. A pulldown assay of the GST-tagged MbovP0145 protein with mass spectrometry demonstrated that β-actin could specifically interact with rMbovP0145 to mediate the IL-8 signaling. As knockdown of β-actin expression with RNA interference in EBL cells decreased the mRNA expression of IL-8 and the phosphorylated ERK1/2 and P38 proteins, whereas disrupted actin polymerization by cytochalasin D led to a significantly higher IL-8 expression and MAPK phosphorylation in rMbovP0145-stimulated cells. Compared to M. bovis HB0801 and its complementary strain, the culture supernatant of EBL cells infected with the M. bovis ΔMbov_0145 mutant induced less neutrophil migration to the lower chamber in a transwell system. In conclusion, MbovP0145 promoted IL-8 expression by interacting with β-actin through activation of the MAPK pathway, thus contributing to neutrophil migration. Full article

Afforestation in the Three North Region (TNR) of China has received wide concern due to the low survival rate and threats to water security associated with the lack of available precipitation for vegetation. It is crucial to provide a spatial layout for revegetation according to the available precipitation to achieve the vegetation cover target. This study investigated the spatial pattern of precipitation, determined the suitable vegetation distribution based on the ecological water requirements and precipitation, and proposed an optimized revegetation scheme by comparing the actual and suitable vegetation patterns. The results indicated that the actual vegetation that matched the pixel-level precipitation accounted for 67.24% of the total vegetation area in the TNR. However, 18.50% of the actual forest, 21.82% of the actual shrublands, and 19.95% of the actual grasslands were overloaded with respect to precipitation. The total suitable vegetation area was reduced slightly compared to the actual vegetation area. There is still some potential for the revegetation of forest and shrublands, mainly those in the eastern and south-eastern parts of the TNR. The optimized revegetation area in the TNR was 3.04 × 10⁶ km², including a maintenance management type of 2.19 × 10⁶ km², an upgrade type of 0.49 × 10⁶ km², and a degradation type of 0.37 × 10⁶ km². Maintenance management (natural restoration) and transformation to vegetation types with lower ecological water requirements were recognized as important revegetation practices in the TNR. This study provides guidelines to adjust the Three North Shelterbelt Project policies based on precipitation data to reduce the negative impact of revegetation on the hydrological cycle. Full article

Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF) of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF) based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF), and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring. Full article