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Global environmental governance (GEG) is one of the world’s major attempts to address climate change issues through mitigation and adaptation strategies. Despite a significant improvement in GEG’s structural, human, and financial capital, the global commons are decaying at an unprecedented pace. Among the global commons, land has the largest share in GEG. Land use change, which is rooted in increasing populations and urbanization, has a significant role in greenhouse gas (GHG) emissions. As a response, land governance and, consequently, good land governance, have arisen as normative concepts emerging from a series of success factors (notably economic development, environmental conservation, and social justice) to achieve greater sustainability. However, global land governance has shown little success in helping GEG due to the lack of intellectual and flexible thinking over governing the land sector. Consequently, reforming land governance “in a smart way” is one of the most critical actions that could contribute to achieving GEG goals. Hence, we propose a smart land governance (SLG) system that will be well addressed, understood, and modeled in a systemic and dynamic way. A smart system may be smart enough to adapt to different contexts and intellectual responses in a timely fashion. Accordingly, SLG is able to promote shared growth and solve many land sector problems by considering all principles of good land governance. Therefore, in order to enhance adaptive land governance systems, efficient land administration and management are required. This study’s outcomes will raise the comprehension of the problems of land management, providing an excellent framework to help land planners and policy-makers, as well as the development of strategic principles with respect to the principal multidimensional components of SLG.

Few studies have attempted to investigate the impact of airports on the ecological environment. This paper compares the effects of airports in China and Germany on the surrounding ecological environment and discusses the underlying institutional mechanisms that lead to these impacts. We used remote-sensing ecological indicators to assess ecological environment quality. A buffer analysis was used to determine the influence scopes of airports on the ecological environment. The institutional analysis development framework was adopted to investigate the functions of different institutions on the influence scopes and intensities. The results showed that the Chinese airport had obvious negative effects and that its impact scope was wide, while the impact intensity of the German airport was weaker. These significant differences stem from the distinct institutional systems that structure the two airports. Our findings ultimately provide insight into how to improve the relationship between infrastructure construction and environmental protection in China.

With the aim of improving farmland use efficiency without damaging the social function of farmland, Chinese policymakers have proposed the ‘trifurcation of land rights’ reform. When it comes to realization of the law, however, neither the Ownership Model nor the Bundle of Sticks Model can adequately explain this reform. The tree concept of property, which provides a new perspective in delineating property rights based on the function served by specific properties, is thus adopted. We find that this tree concept of property helps to better understand and realize the trifurcated rights on farmland in China. Also, a balance between the social and economic functions of farmland can be reached through reconstruction of the property rights involved, a process which comprises three steps: identification of the nature of the newly-established rights; configuration of the rights and duties of relevant entities; and state intervention in the enforcement of relevant rights with the aim of realizing certain social values. Finally, this paper argues that success of this trifurcated structure requires a systematic design of the Chinese Civil Code. In particular, it requests further improvements in legal rules on farmland lease.

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The source region of the Yellow River (SRYR), known as the “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes the sustainability of its alpine grasslands. Large-scale soil erosion monitoring poses a significant challenge, complicating global efforts to study soil erosion and land cover changes. Moreover, conventional methods for assessing soil erosion do not adequately address the variety of erosion types present in the SRYR. Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. By leveraging soil erosion data measured by 137Cs from 521 locations and employing the random forest (RF) algorithm, a new soil erosion model was formulated. Key findings include that: (1) The RF soil erosion model significantly outperformed the revised universal soil loss equation (RUSLE) model and revised wind erosion equation (RWEQ) model, achieving an R2 of 0.52 and an RMSE of 5.88. (2) The RF model indicated that from 2001 to 2020, the SRYR experienced an average annual soil erosion modulus (SEM) of 19.32 t·ha−1·y−1 with an annual total erosion in the SRYR of 225.18 × 106 t·y−1. Spatial analysis revealed that 78.64% of the region suffered low erosion, with erosion intensity declining from northwest to southeast. (3) The annual SEM in the SRYR demonstrated a downward trend from 2001 to 2020, with 83.43% of the study area showing improvement. Based on these findings, measures for soil erosion prevention and control in the SRYR were proposed. Future studies should refine the temporal analysis to better understand the influence of extreme climate events on soil erosion, while leveraging high-resolution data to enhance model accuracy. Insights into the drivers of soil erosion in the SRYR will support more effective policy development.

Upwelling, which mixes deep and surface waters, significantly enhances the productivity of surface waters and plays a critical role in marine ecosystems, especially in key fishing areas like Zhoushan. This study utilized merged sea surface temperature data and an upwelling edge detection algorithm based on temperature gradients to analyze the characteristics of upwelling in Zhoushan and the Yangtze River Estuary over the past 28 years. The results indicate that upwelling in Zhoushan begins in April, peaks in July, gradually weakens, and disappears by October. The phenomenon is most pronounced during the summer months (June to August), with significant spatial distribution differences in April and September. Notably, high probability values of upwelling centers and core areas are mainly concentrated near Ma’an Island, Zhongjieshan Island, and Taohua Island. In these areas, upwelling remains stable during the summer, forming a unique “footprint” distribution pattern, and these are also the locations of the Zhoushan National Marine Ranch. From April to August, the extent of the upwelling area gradually decreases and stabilizes. These findings emphasize the frequent upwelling activity around Zhoushan and its significant contribution to the formation of local fisheries. Additionally, considering that the formation of natural upwelling in the East China Sea depends on the southern monsoon, the study suggests establishing artificial upwelling systems during periods unfavorable for natural upwelling, based on high probability areas, to enhance fishery yields and support the development of local fisheries.

This article extracts the Qiantang River tidal bore, analyzing the water environment characteristics in front of the tidal line of the Qiantang River tidal bore and behind it. The Qiantang River tidal bore Index (QRI) was established using HY-1C, HY-1D, and Gao Fen-1 wide field-of-view (GF-1 WFV) satellite data to precisely determine the location and details of the Qiantang River tidal bore. Comparative analyses of the changes on the two sides of the Qiantang River tidal bore were conducted. The results indicate the following: (1) QRI enhances the visibility of tidal bore lines, accentuating their contrast with the surrounding river water, resulting in a more vivid character. QRI proves to be an effective extraction method, with potential applicability to similar tidal lines in different regions. (2) Observable roughness changes occur at the tidal bore location, with smoother surface textures observed in front of the tidal line compared to those behind it. There is a discernible increase in suspended sediment concentration (SSC) as the tidal bore passes through. (3) This study reveals the mechanism of water environment change induced by the Qiantang River tidal bore, emphasizing its significance in promoting vertical water body exchange as well as scouring the bottom sediments. This effect increases SSC and surface roughness.

This randomized, double-blind, placebo-controlled phase 1/2 trial aimed at evaluating the safety and immunogenicity of Ad5-nCoV via aerosolized or intramuscular or intramuscular–aerosolized routes in SARS-CoV-2-negative adults aged over 18 years. In the phase 1 trial, participants were sequentially enrolled into one of five regimen cohorts: Low-Dose (two doses of aerosolized Ad5-nCoV with 0.5 × 1010 viral particles [vps] per dose), Middle-Dose (two doses of aerosolized Ad5-nCoV with 1.0 × 1010 vps per dose), High-Dose (two doses of aerosolized Ad5-nCoV with 2.0 × 1010 vps per dose), Mixed (intramuscular Ad5-nCoV with 5.0 × 1010 vps [first dose] and aerosolized Ad5-nCoV with 2.0 × 1010 vps [second dose]), and Single-Dose (one dose of aerosolized Ad5-nCoV with 1.0 × 1010 vps). Eligible participants in the phase 2 trial were stratified by 18–59 years old or ≥60 years old and then were sequentially enrolled into one of six regimen cohorts: Low-Dose, Middle-Dose, High-Dose, Mixed, Single-Dose, and Intramuscular (one dose of intramuscular Ad5-nCoV with 1.0 × 1010 vps). The intervals between the two doses were 56 days. Participants were randomly allocated in 3:1 (phase 1) and 5:1 (phase 2) ratios to receive either Ad5-nCoV or the placebo in each cohort. This study is registered on ClinicalTrials.gov, NCT04840992. Most adverse reactions that occurred during the solicited period were mild and moderate. One serious adverse event (myelodysplastic syndrome) was considered potentially related to the aerosolized Ad5-nCoV. The GMTs of neutralizing antibodies in the Mixed group were the highest with 57.03 (95% CI: 23.95, 135.80) and 97.37 (95% CI: 74.30, 127.59) in phase 1 and 2 trials, respectively, 28 days after the second dose (p < 0.0001), which showed significantly higher immune responses compared to other regimens with aerosolized or intramuscular Ad5-nCoV alone.

The properties of faba bean (Vicia faba L.) protein isolate (FPI) gels depend on their starting protein material and can be modulated by the addition of polysaccharides. In order to investigate the interplay between these two factors, commercial FPI (FPI1) and FPI prepared in-house (FPI2) were used to fabricate glucono-delta-lactone-induced gels, with or without dextran (DX) addition. FPI1 exhibited lower solubility in water and a larger mean particle size, likely because it experienced extensive degradation due to the intense conditions involved in its preparation. The FPI1 gel showed a similar water-holding capacity as the FPI2 gel; however, its hardness was lower and viscoelasticity was higher. After DX addition, the hardness of both FPI gels decreased, while their water-holding capacity increased. Interestingly, DX addition decreased the viscoelasticity of the FPI1 gel but enhanced the viscoelasticity of the FPI2 gel. The microstructural analysis demonstrated that the density of the aggregation network decreased in the FPI1 gel after DX addition but increased in the FPI2 gel. This was consistent with the changes observed in the dominant protein interaction forces in these gels after DX addition. Overall, these findings have the potential to guide ingredient selection for the tailored preparation of FPI gels.

Two doses of the inactivated influenza vaccine (IIV) are generally recommended for children under 9 years old. This study assessed the necessity for a second dose of quadrivalent IIV (IIV4) in children aged 3–8 years. In this randomized, open-label, paralleled-controlled study, 400 children aged 3–8 years who were vaccine-unprimed were randomly assigned at a 1:1 ratio to receive a two-dose (Group 1) or one-dose (Group 2) regimen of IIV4, and 200 who were vaccine-primed received one dose of IIV4 (Group 3). A serum sample was collected before and 28 days after the last dose to determine the hemagglutination inhibition (HI) antibody level. Adverse events were collected within 28 days after each dose. One-dose or two-doses of IIV4 were well tolerated and safe in children aged 3–8 years, and no serious adverse events related to the vaccine were reported. The seroconversion rates (SCRs) of HI antibody ranged from 61.86% to 95.86%, and the post-vaccination seroprotection rates (SPRs) were all >70% in three groups against the four virus strains. The two-dose regimen in vaccine-unprimed participants (Group 1) achieved similar SPRs in comparison with the one-dose in the vaccine-primed group (Group 3), and the SPRs in Group 1 and Group 3 were higher in vaccine-unprimed participants of the one-dose regimen (Group 2). The present study supports the recommendations of a two-dose regimen for IIV4 use in children aged 3–8 years.

Waterlogging disasters severely restrict crop production. The middle and lower Yangtze River region (MLYRR) is an important grain-producing region in China but suffers from severe waterlogging disasters. In this study, an agriculture-specific index called the accumulative humidity index was introduced to analyze the spatiotemporal characteristics of waterlogging during different wheat and maize growth stages in the MLYRR from 1960 to 2020. Additionally, the relationships between waterlogging intensities and crop yield fluctuations were revealed. The results showed that over the past 60 years, the intensity of wheat and maize waterlogging in the central and eastern MLYRR have increased; crop waterlogging was more intense in the 1990s–2010s than during the 1960s–1980s, and waterlogging intensity peaked in the 1990s. For both crops, waterlogging was more intense during the early growth stages, but its yield-reducing impacts were more significant during middle and late growth stages. The southern MLYRR (especially southern Anhui) was the region where both crops were most prone to waterlogging, but yields in this region were not severely affected by waterlogging. Compared with wheat, maize was more prone to waterlogging, and its yield was more significantly reduced by waterlogging. In conclusion, this study provides guidance for agricultural waterlogging risk reduction in the MLYRR.

The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring.

The extraction of impacted third molars is one of the most common dental operations. When the impacted third molar is extracted, the operation plan is generally different because of the different impacted positions of the tooth. Therefore, judging the impacted type of the third molar is the basis of the third molar extraction operation. At present, oral health professionals usually analyze panoramic radiographs to determine the types of impacted third molars, but the diagnosis is easily affected by oral health professionals’ subjective consciousnesses. Computer vision technology can help doctors analyze medical images faster and more accurately, so it is very desirable to use computer vision to detect and classify the impacted third molars. Based on the panoramic radiographs of the School of Stomatology, Lanzhou University, this paper establishes an object detection dataset containing six types of impacted third molars. On the basis of this dataset, the lightweight third molar impacted detection and classification model is studied in this paper. This study introduces the method of knowledge distillation on the basis of YOLOv5s and uses YOLOv5x as the teacher’s model to guide YOLOv5s, which not only ensures the light weight of the model but also improves the accuracy of the model. Finally, the YOLOv5s-x model is obtained. The experimental results show that the introduction of knowledge distillation effectively improves the accuracy of the model while ensuring its light weight, the mAP of YOLOv5s-x is increased by 2.9% compared with the original model, and the amount of parameters and calculations is also reduced to a certain extent. Compared with mainstream object detection networks, including YOLOv8, YOLOv5s-x also has certain advantages, which can provide oral health professionals with better impacted third molar detection and classification services.

The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning. However, the actual plum orchard environment is complex, and the detection of plum fruits has many problems, such as leaf shading and fruit overlapping. The traditional method of manually estimating the number of fruits and the presence of pests and diseases used in the plum growing industry has disadvantages, such as low efficiency, a high cost, and low accuracy. To detect plum fruits quickly and accurately in a complex orchard environment, this paper proposes an efficient plum fruit detection model based on an improved You Only Look Once version 7(YOLOv7). First, different devices were used to capture high-resolution images of plum fruits growing under natural conditions in a plum orchard in Gulin County, Sichuan Province, and a dataset for plum fruit detection was formed after the manual screening, data enhancement, and annotation. Based on the dataset, this paper chose YOLOv7 as the base model, introduced the Convolutional Block Attention Module (CBAM) attention mechanism in YOLOv7, used Cross Stage Partial Spatial Pyramid Pooling–Fast (CSPSPPF) instead of Cross Stage Partial Spatial Pyramid Pooling(CSPSPP) in the network, and used bilinear interpolation to replace the nearest neighbor interpolation in the original network upsampling module to form the improved target detection algorithm YOLOv7-plum. The tested YOLOv7-plum model achieved an average precision (AP) value of 94.91%, which was a 2.03% improvement compared to the YOLOv7 model. In order to verify the effectiveness of the YOLOv7-plum algorithm, this paper evaluated the performance of the algorithm through ablation experiments, statistical analysis, etc. The experimental results showed that the method proposed in this study could better achieve plum fruit detection in complex backgrounds, which helped to promote the development of intelligent cultivation in the plum industry.

Tassel is an important part of the maize plant. The automatic detection and counting of tassels using unmanned aerial vehicle (UAV) imagery can promote the development of intelligent maize planting. However, the actual maize field situation is complex, and the speed and accuracy of the existing algorithms are difficult to meet the needs of real-time detection. To solve this problem, this study constructed a large high-quality maize tassel dataset, which contains information from more than 40,000 tassel images at the tasseling stage. Using YOLOv7 as the original model, a Tassel-YOLO model for the task of maize tassel detection is proposed. Our model adds a global attention mechanism, adopts GSConv convolution and a VoVGSCSP module in the neck part, and improves the loss function to a SIoU loss function. For the tassel detection task, the mAP@0.5 of Tassel-YOLO reaches 96.14%, with an average prediction time of 13.5 ms. Compared with YOLOv7, the model parameters and computation cost are reduced by 4.11 M and 11.4 G, respectively. The counting accuracy has been improved to 97.55%. Experimental results show that the overall performance of Tassel-YOLO is better than other mainstream object detection algorithms. Therefore, Tassel-YOLO represents an effective exploration of the YOLO network architecture, as it satisfactorily meets the requirements of real-time detection and presents a novel solution for maize tassel detection based on UAV aerial images.

The response of typical environmental factors in Zhoushan Fishery, including sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll a (Chl-a), before and after Typhoon In-fa was analyzed using satellite data and reanalysis data in this study. Additionally, this study simultaneously elucidated the mechanism by which the typhoon affected these factors. The results showed that: (1) the strong vertical mixing caused by In-fa provoked a decrease in SST, while the asymmetric typhoon wind stress and vertical difference in temperature structure before the typhoon caused a more robust cooling of SST on the right side of the In-fa track; (2) despite the strong mixing and inflow of hypersaline seawater increasing SSS, the combined effect of intense rainfall and diluted water inflow caused an overall decrease in SSS after In-fa’s landing; (3) In-fa caused the Chl-a concentration to decrease first and then increase. The high cloudiness and low Chl-a seawater inflow inhibited the phytoplankton growth during the typhoon, while the abundant light, rich surface nutrients under the upwelling effect, and transport of rich land-based substances induced rapid phytoplankton reproduction after the typhoon; and (4) the change in Chl-a concentration, current, temperature, and salinity induced by a typhoon are essential factors that affect fish behavior and community composition in fisheries. This study provides a point of reference to reveal the response of environmental factors to typhoons and their effects on fishery resources in fisheries located on nearshore estuarine shallow waters with intensive islands.

Antimony selenide (Sb2Se3) is emerging as a promising photovoltaic material owing to its excellent photoelectric property. However, the low carrier transport efficiency, and detrimental surface oxidation of the Sb2Se3 thin film greatly influenced the further improvement of the device efficiency. In this study, the introduction of tellurium (Te) can induce the benign growth orientation and the desirable Sb/Se atomic ratio in the Te-Sb2Se3 thin film. Under various characterizations, it found that the Te-doping tended to form Sb2Te3-doped Sb2Se3, instead of alloy-type Sb2(Se,Te)3. After Te doping, the mitigation of surface oxidation has been confirmed by the Raman spectra. High-quality Te-Sb2Se3 thin films with preferred [hk1] orientation, large grain size, and low defect density can be successfully prepared. Consequently, a 7.61% efficiency Sb2Se3 solar cell has been achieved with a VOC of 474 mV, a JSC of 25.88 mA/cm2, and an FF of 64.09%. This work can provide an effective strategy for optimizing the physical properties of the Sb2Se3 absorber, and therefore the further efficiency improvement of the Sb2Se3 solar cells.

Salinization of soil is a major environmental risk factor to plant functions, leading to a reduction of productivity of crops and forage. Epichloë gansuensis, seed-borne endophytic fungi, establishes a mutualistic symbiotic relationship with Achnatherum inebrians and confers salt tolerance in the host plants. In this study, analysis of transcriptome and metabolome was used to explore the potential molecular mechanism underlying the salt-adaptation of A. inebrians roots mediated by E. gansuensis. We found that E. gansuensis played an important role in the gene expression of the host’s roots and regulated multiple pathways involved in amino acid metabolism, carbohydrate metabolism, TCA cycle, secondary metabolism, and lipid metabolism in the roots of A. inebrians. Importantly, E. gansuensis significantly induced the biological processes, including exocytosis, glycolytic process, fructose metabolic process, and potassium ion transport in roots of host plants at transcriptional levels, and altered the pathways, including inositol phosphate metabolism, galactose metabolism, starch, and sucrose metabolism at metabolite levels under NaCl stress. These findings provided insight into the molecular mechanism of salt resistance in roots of A. inebrians mediated by E. gansuensis and could drive progress in the cultivation of new salt-resistance breeds with endophytes.

In this study, a group of large-scale biofloc shrimp culturing facilities were designed. The bioflocs were domesticated by continuously reducing the ratio of carbon to feed. The bacterial community diversity on the 20th, 40th, 60th, and 80th days was analyzed by high-throughput sequencing technology. The results showed that the biofloc content (0~16.8 ± 4.3) mL/L, TSS concentration (0~247.46 ± 27.3) mL/L, total ammonia nitrogen concentration (0~0.28 ± 0.052) mg/L, nitrite nitrogen concentration (0~4.13 ± 1.42) mg/L, nitrate nitrogen concentration (108.57 ± 19.6) mg/L were all within the safe concentration range of Litopenaeus vannamei. With the progress of reducing carbon input, the Chao1 index, the number of operational taxonomic units, and the Shannon index increased significantly. The number of OTUs of B80 (572.36 ± 13.26) was significantly higher than that of B60 (489.69 ± 12.97), B40 (423.35 ± 18.46) and B20 (407.67 ± 15.65) (p < 0.05). The Chao1 index of B80 (768.58 ± 36.96) was significantly higher than that of B60 (646.8 ± 52.53), B40 (569.7 ± 46.53) and B20 (516.3 ± 21.35) (p < 0.05). The Shannon index of B80 (5.63 ± 0.16) was higher than that of B60 (4.85 ± 0.13), B40 (4.68 ± 0.21) and B20 (3.65 ± 0.22), with significant difference (p < 0.05). At the end of the experiment, the domestication formed a micro-ecosystem with Proteobacteria as the carrier (46.98 ± 15.82%), Chloroflexi as the skeleton (2.2 ± 0.36%), Nitrospirae (1.35 ± 0.26%) as the main water treatment functional bacteria, and other bacteria as auxiliary nitrogen and phosphorus removal; At the genus level, unclassified_f_Rhodobacteracea (22.97 ± 3.82%), Ruegeria (10.35 ± 1.26%), Muricauda (5.73 ± 0.61%), Algoriphagus (3.75% ± 0.85%) and Nitrospira (1.56 ± 0.56%) are the dominant bacteria. Under the synergistic effect of the above bacteria, the biofloc system remains relatively stable. The survival rate and unit yield of shrimp were (65.32 ± 6.85)% and (4.15 ± 1.58) kg/m3, respectively.

Open-ditch controlled drainage is an important water management measure used to reduce drought and waterlogging stress in many areas in the world. Such measures are essential to promote the crop yield, make full use of rainfall resources, reduce regional drainage discharge (Q) and reduce water environmental pollution. To quantify its effects, an open-ditch controlled drainage and crop yield simulation model was developed in an area located in Northern Huaihe River Plain (NHRP), Anhui Province, China. The model was calibrated and validated. The changes in crop yield and Q were simulated under different main-ditch water-depth control schemes, field ditch layout and outlet weir height control schemes from 1991 to 2021. Compared with the current situation, the change in crop yield caused by the main ditch schemes was significantly higher than that caused by the field ditch schemes. The change in Q caused by the field ditch schemes was greater than that caused by the main ditch schemes, with values of 60% and 0.02%, respectively. Combined control schemes could further increase the crop yield and reduce the Q. The results have practical application value for ensuring good crop yields and reducing farmland drainage in the NHRP and other similar regions.

Plant functional traits at the community level (plant community traits hereafter) are commonly used in trait-based ecology for the study of vegetation–environment relationships. Previous studies have shown that a variety of plant functional traits at the species or community level can be successfully retrieved by airborne or spaceborne imaging spectrometer in homogeneous, species-poor ecosystems. However, findings from these studies may not apply to heterogeneous, species-rich ecosystems. Here, we aim to determine whether unmanned aerial vehicle (UAV)-based hyperspectral imaging could adequately estimate plant community traits in a species-rich alpine meadow ecosystem on the Qinghai–Tibet Plateau. To achieve this, we compared the performance of four non-parametric regression models, i.e., partial least square regression (PLSR), the generic algorithm integrated with the PLSR (GA-PLSR), random forest (RF) and extreme gradient boosting (XGBoost) for the retrieval of 10 plant community traits using visible and near-infrared (450–950 nm) UAV hyperspectral imaging. Our results show that chlorophyll a, chlorophyll b, carotenoid content, starch content, specific leaf area and leaf thickness were estimated with good accuracies, with the highest R2 values between 0.64 (nRMSE = 0.16) and 0.83 (nRMSE = 0.11). Meanwhile, the estimation accuracies for nitrogen content, phosphorus content, plant height and leaf dry matter content were relatively low, with the highest R2 varying from 0.3 (nRMSE = 0.24) to 0.54 (nRMSE = 0.20). Among the four tested algorithms, the GA-PLSR produced the highest accuracy, followed by PLSR and XGBoost, and RF showed the poorest performance. Overall, our study demonstrates that UAV-based visible and near-infrared hyperspectral imaging has the potential to accurately estimate multiple plant community traits for the natural grassland ecosystem at a fine scale.

Interspecific hybridization of rapeseed is an important way to innovate breeding resources. This research used Brassica napus and Brassica rapa for artificial synthesis interspecific hybridization of F1. The F1 self-fruiting rate was particularly low. By comparing the fertilization rate and seed setting rate of nine crosses and selfing combinations of interspecific hybrid progeny F1 and control B. napus, the results proved that the genetic stability of egg cells was greater than that of sperm cells, so the F1 could get seed by artificial pollination with other normal pollen. Based on these results, interspecific maternal inbred offspring (induced F1) from egg cells was obtained by emasculation and pollination with the pollen of DH inducer Y3380. It was found through morphological analysis, flow cytometry identification, and meiotic observation of induced F1, the plants had most normal fertile tetraploid and the meiosis was normal. The FISH results showed that the induced F1 were B. napus (2n = 4x = 38, AACC), 20 A and 19 C chromosomes. The results of SNP chip detection and genetic cluster analysis found that the genetic variation between interspecies could be preserved or broadened in the induced F1. The use of DH inducer created special breeding resources for interspecific hybridization and distant hybridization of rapeseed while shortening time, improving efficiency, and providing a new insight into innovate breeding resources.

Rapeseed is a significant oil-bearing cash crop. As a hybrid crop, Brassica napus L. produces a high yield, but it also has drawbacks such as a tall stalk, easy lodging, and is not suitable for mechanized production. To address these concerns, we created the DW871 rapeseed dwarf variety, which has a high yield, high oil content, and is suitable for mechanized production. To fully comprehend the dwarfing mechanism of DW871 and provide a theoretical foundation for future applications of the variety, we used transcriptome and proteome sequencing to identify genes and proteins associated with the dwarfing phenotype, using homologous high-stalk material HW871 as a control. By RNA-seq and iTRAQ, we discovered 8665 DEGs and 50 DAPs. Comprehensive transcription and translation level analysis revealed 25 correlations, 23 of which have the same expression trend, involving monolignin synthesis, pectin-lignin assembly, lignification, glucose modification, cell wall composition and architecture, cell morphology, vascular bundle development, and stalk tissue composition and architecture. As a result of these results, we can formulate a hypothesis about the DW871 dwarfing phenotype: plant hormone signal transduction, such as IAA and BRs, is linked to the formation of dwarf phenotypes, and metabolic pathways related to lignin synthesis, such as phenylpropane biosynthesis, also play a role. Our works will contribute to a better understanding of the genes and proteins involved in the rapeseed dwarf phenotype, and we will propose new insights into the dwarfing mechanism of Brassica napus L.

The distribution of Triplophysa yarkandensis is restricted to Xinjiang’s Tarim River basin. We collected 119 T. yarkandensis samples from nine geographic populations in the Tarim River basin and utilized the RAD-seq method for SNP genotyping. In this study, a total of 164.81 Gb bases were generated with the Illumina platform, and 129,873 candidate SNPs were obtained with the Stacks pipeline for population genetic analyses. High levels of genetic diversity were detected among nine populations. The AMOVA results showed that the majority of genetic variations originated from among populations (FST = 0.67), and the pairwise FST values ranged from 0.4579 to 0.8736, indicating high levels of genetic differentiation among these populations. The discriminate analysis of principal components (DAPCs) and neighbor joining (NJ) tree revealed that the nine populations could be separated into two clusters (i.e., south and north populations), and modest genetic differentiation between south and north populations was observed, while the individuals from several populations were not clustered together by geographical location. The evidence of two genetic boundaries between south and north populations (except TTM) was supported by barrier analysis. The Bayesian skyline plotting indicated that T. yarkandensis populations in the Tarim River basin had not experienced genetic bottlenecks, and the effective population size remained stable. This study first clarified the genetic diversity and differentiation of T. yarkandensis populations in the Tarim River basin, and it provided valuable molecular data for conservation and management of natural populations.

Accurate estimation of the aboveground biomass (AGB) of grassland is a key link in understanding the regional carbon cycle. We used 501 aboveground measurements, 29 environmental variables, and machine learning algorithms to construct and verify a custom model of grassland biomass in the Headwater of the Yellow River (HYR) and selected the random forest model to analyze the temporal and spatial distribution characteristics and dynamic trends of the biomass in the HYR from 2001 to 2020. The research results show that: (1) the random forest model is superior to the other three models (R2val = 0.56, RMSEval = 51.3 g/m2); (2) the aboveground biomass in the HYR decreases spatially from southeast to northwest, and the annual average value and total values are 176.8 g/m2 and 20.73 Tg, respectively; (3) 69.51% of the area has shown an increasing trend and 30.14% of the area showed a downward trend, mainly concentrated in the southeast of Hongyuan County, the northeast of Aba County, and the north of Qumalai County. The research results can provide accurate spatial data and scientific basis for the protection of grassland resources in the HYR.

The real-time detection and counting of rice ears in fields is one of the most important methods for estimating rice yield. The traditional manual counting method has many disadvantages: it is time-consuming, inefficient and subjective. Therefore, the use of computer vision technology can improve the accuracy and efficiency of rice ear counting in the field. The contributions of this article are as follows. (1) This paper establishes a dataset containing 3300 rice ear samples, which represent various complex situations, including variable light and complex backgrounds, overlapping rice and overlapping leaves. The collected images were manually labeled, and a data enhancement method was used to increase the sample size. (2) This paper proposes a method that combines the LC-FCN (localization-based counting fully convolutional neural network) model based on transfer learning with the watershed algorithm for the recognition of dense rice images. The results show that the model is superior to traditional machine learning methods and the single-shot multibox detector (SSD) algorithm for target detection. Moreover, it is currently considered an advanced and innovative rice ear counting model. The mean absolute error (MAE) of the model on the 300-size test set is 2.99. The model can be used to calculate the number of rice ears in the field. In addition, it can provide reliable basic data for rice yield estimation and a rice dataset for research.

Heat shock protein 47 (Hsp47) is a collagen-specific molecular chaperone that is indispensable for molecular maturation of collagen. In this study, hsp47 and hsp47-like cDNAs were cloned and characterized in grass carp (Ctenopharyngodon idella). The cDNAs were 1212 and 1218 base pairs long, respectively, and included an open reading frame encoding 403 and 405 amino acids. The molecular phylogeny based on the deduced amino acid sequences indicated that the correct sequences of the hsp47 and hsp47-like cDNA were obtained and the deduced proteins clustered distinctly into teleost clades. Primary structure analysis and characterization of Hsp47 and Hsp47-like shared the basic structure and biofunctions of Hsp47 in vertebrates. The spatial pattern of gene expression revealed that hsp47 and hsp47-like were relatively ubiquitous in different tissues and highly expressed in heart and skin. The expression levels of hsp47 and hsp47-like and type I collagen mRNAs varied similarly in different tissues. Type I collagen content increased significantly with the increase of water velocity in the muscle of grass carp in response to aerobic exercise. Among the gene expression changes of hsp47, hsp47-like, col1a1 and col1a2 in muscle that occurred in response to aerobic exercise, the change of type I collagen was most strongly correlated with hsp47 expression. Additionally, col1a1 showed the highest correlation with hsp47-like and col1a2 showed the highest correlation with hsp47. These findings suggest that grass carp Hsp47 and Hsp47-like are closely related to type I collagen synthesis. This study firstly suggests fish aerobic exercise can improve type I collagen content and Hsp47 gene expression in muscle of grass carp.

The resurrection plants Myrothamnus flabellifolia can survive long term severe drought and desiccation conditions and soon recover after rewatering. However, few genes related to such excellent drought tolerance and underlying molecular mechanism have been excavated. WRKY transcription factors play critical roles in biotic and abiotic stress signaling, in which WRKY70 functions as a positive regulator in biotic stress response but a negative regulator in abiotic stress signaling in Arabidopsis and some other plant species. In the present study, the functions of a dehydration-induced MfWRKY70 of M. flabellifolia participating was investigated in the model plant Arabidopsis. Our results indicated that MfWRKY70 was localized in the nucleus and could significantly increase tolerance to drought, osmotic, and salinity stresses by promoting root growth and water retention, as well as enhancing the antioxidant enzyme system and maintaining reactive oxygen species (ROS) homeostasis and membrane-lipid stability under stressful conditions. Moreover, the expression of stress-associated genes (P5CS, NCED3 and RD29A) was positively regulated in the overexpression of MfWRKY70 Arabidopsis. We proposed that MfWRKY70 may function as a positive regulator for abiotic stress responses and can be considered as a potential gene for improvement of drought and salinity tolerance in plants.

Fish muscle quality is an important parameter in the aquaculture industry. In this study, we analyzed and compared the muscle quality of grass carp (Ctenopharyngodon idellus) cultured at salinities of 0‰, 3‰, and 6‰ (GC0, GC3, GC6). There was no significant difference in crude protein and crude fat content of muscle between GC0 and GC3. Crude fat was significantly lower in GC6 compared to the other groups. GC3 and GC6 had higher hydroxyproline content, which suggested that these groups had higher collagen content. GC3 and GC6 had higher contents of free amino acids and umami amino acids than GC0, but there was no significant difference in sweet or sour amino-acid content among groups. GC3 and GC6 had better texture properties, including hardness, gumminess, chewiness, resilience, and springiness, than GC0. GC3 had the highest water-holding capacity among the groups. As the salinity increased, the diameter of muscle fibers decreased and the sarcolemma showed a thickening trend. These results suggest that a slight increase in salinity (i.e., 3‰) can effectively improve the muscle quality of grass carp.

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.