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The Central lapetus Magmatic Province (CIMP) is a large igneous province (LIP) emplaced in the Baltican and Laurentian paleocontinents that marks the onset of the Caledonian Wilson Cycle. Paleozoic magmatism of the CIMP is preserved in both northeastern America and northern Europe. This study investigates rocks belonging to the hyper-extended margin of Baltica currently found in the Seve Nappe Complex of the Scandinavian Caledonides. Specifically, U-Pb zircon geochronology and whole-rock geochemistry are applied to a migmatitic variety of the Vierrucohkka amphibolite of the Marma Terrane, to the Aurek gabbro, and amphibolite of the Aurek Assemblage exposed in the Seve Nappe Complex in the Kebnekaise region, northern Swedish Caledonides. U-Pb zircon geochronology yields crystallization ages of 626 ±7 Ma for the protolith of the Vierrucohkka amphibolite, and 614 ±2 Ma and 609 ±1 Ma for the emplacement of the Aurek gabbro and amphibolite protolith, respectively. A younger age of 599 ±3 Ma is recorded in the Vierrucohkka amphibolite and is interpreted as the age of partial melting and migmatization. The geochemical signatures of the rocks demonstrate crustal assimilation during the emplacement of their protoliths and modification due to prograde metamorphic processes during Caledonian subduction. The Vierrucohkka amphibolite and the Aurek Assemblage samples display upper and lower crustal assimilation, respectively. Trace elements (Dy, Sm, Lu, and Y) record the growth of metamorphic garnet, while elevated TiO2contents record the crystallization of metamorphic rutile. Nevertheless, high field strength elements (HSFE) and ANb suggesta depleted mantle source forthe magmas of the protoliths of the investigated rocks. Altogether, geochemical data indicate that the igneous activity recorded in the Vierrucohkka amphibolite and the Aurek Assemblage between c. 626-609 Ma is related to continental rifting processes associated with the opening of the lapetus Ocean. [ABSTRACT FROM AUTHOR]

Accurate segmentation of the stem of pumpkin seedlings has a great influence on the modernization of pumpkin cultivation, and can provide detailed data support for the growth of pumpkin plants. We collected and constructed a pumpkin seedling point cloud dataset for the first time. Potting soil and wall background in point cloud data often interfere with the accuracy of partial cutting of pumpkin seedling stems. The stem shape of pumpkin seedlings varies due to other environmental factors during the growing stage. The stem of the pumpkin seedling is closely connected with the potting soil and leaves, and the boundary of the stem is easily blurred. These problems bring challenges to the accurate segmentation of pumpkin seedling point cloud stems. In this paper, an accurate segmentation algorithm for pumpkin seedling point cloud stems based on CPHNet is proposed. First, a channel residual attention multilayer perceptron (CRA-MLP) module is proposed, which suppresses background interference such as soil. Second, a position-enhanced self-attention (PESA) mechanism is proposed, enabling the model to adapt to diverse morphologies of pumpkin seedling point cloud data stems. Finally, a hybrid loss function of cross entropy loss and dice loss (HCE-Dice Loss) is proposed to address the issue of fuzzy stem boundaries. The experimental results show that CPHNet achieves a 90.4% average cross-to-merge ratio (mIoU), 93.1% average accuracy (mP), 95.6% average recall rate (mR), 94.4% F1 score (mF1) and 0.03 plants/second (speed) on the self-built dataset. Compared with other popular segmentation models, this model is more accurate and stable for cutting the stem part of the pumpkin seedling point cloud. [ABSTRACT FROM AUTHOR]

Evaporation from the soil is an important component of evapotranspiration, and mulching greatly affects soil evaporation. We conducted numerical simulations to study the effect of the thickness of sand mulch on soil evaporation. We tested nine treatments: mulching with sand thicknesses of 1, 3, 5, 6, 8, 10, 15 and 20 cm plus an unmulched control (CK). Accumulated evaporation was significantly lower, and the resistance to evaporation was significantly higher, for the mulched treatments than CK. The volumetric soil water content (SWC) was significantly higher for the mulched treatments than CK, but SWC varied little for thicknesses >5 cm. Heating was slower and more uniform for the mulched treatments than for CK. With the increase of the thickness of sand, the temperature transmission was slowed down. The change of soil temperature was not obvious at thicknesses >15 cm. A thickness of 5 cm was the most effective for storing water and preserving heat. Our results provide a theoretical basis and technical guidance for the effective use and management of soil water in farmland in arid regions.

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize's original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method's ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland. [ABSTRACT FROM AUTHOR]

Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model's computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model's generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting. [ABSTRACT FROM AUTHOR]

Objective. Microfold cells (M cells) are specific intestinal epithelial cells for monitoring and transcytosis of antigens, microorganisms, and pathogens in the intestine. However, the mechanism for M-cell development remained elusive. Materials and Methods. Real-time polymerase chain reaction, immunofluorescence, and western blotting were performed to analyze the effect of sorbitol-regulated M-cell differentiation in vivo and in vitro, and luciferase and chromatin Immunoprecipitation were used to reveal the mechanism through which sorbitol-modulated M-cell differentiation. Results. Herein, in comparison to the mannitol group (control group), we found that intestinal M-cell development was inhibited in response to sorbitol treatment as evidenced by impaired enteroids accompanying with decreased early differentiation marker Annexin 5, Marcksl1, Spib, sox8, and mature M-cell marker glycoprotein 2 expression, which was attributed to downregulation of receptor activator of nuclear factor kappa-В ligand (RANKL) expression in vivo and in vitro. Mechanically, in the M-cell model, sorbitol stimulation caused a significant upregulation of phosphodiesterase 4 (PDE4) phosphorylation, leading to decreased protein kinase A (PKA)/cAMP-response element binding protein (CREB) activation, which further resulted in CREB retention in cytosolic and attenuated CREB binds to RANKL promoter to inhibit RANKL expression. Interestingly, endogenous PKA interacted with CREB, and this interaction was destroyed by sorbitol stimulation. Most importantly, inhibition of PDE4 by dipyridamole could rescue the inhibitory effect of sorbitol on intestinal enteroids and M-cell differentiation and mature in vivo and in vitro. Conclusion. These findings suggested that sorbitol suppressed intestinal enteroids and M-cell differentiation and matured through PDE4-mediated RANKL expression; targeting to inhibit PDE4 was sufficient to induce M-cell development. [ABSTRACT FROM AUTHOR]