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ObjectiveLodging constitutes a severe crop-related catastrophe, resulting in a reduction in photosynthesis intensity, diminished nutrient absorption efficiency, diminished crop yield, and compromised crop quality. The utilization of unmanned aerial vehicles (UAV) to acquire agricultural remote sensing imagery, despite providing high-resolution details and clear indications of crop lodging, encounters limitations related to the size of the study area and the duration of the specific growth stages of the plants. This limitation hinders the acquisition of an adequate quantity of low-altitude remote sensing images of wheat fields, thereby detrimentally affecting the performance of the monitoring model. The aim of this study is to explore a method for precise segmentation of lodging areas in limited crop growth periods and research areas.MethodsCompared to the images captured at lower flight altitudes, the images taken by UAVs at higher altitudes cover a larger area. Consequently, for the same area, the number of images taken by UAVs at higher altitudes is fewer than those taken at lower altitudes. However, the training of deep learning models requires huge amount supply of images. To make up the issue of insufficient quantity of high-altitude UAV-acquired images for the training of the lodging area monitoring model, a transfer learning strategy was proposed. In order to verify the effectiveness of the transfer learning strategy, based on the Swin-Transformer framework, the control model, hybrid training model and transfer learning training model were obtained by training UAV images in 4 years (2019, 2020, 2021, 2023)and 3 study areas(Shucheng, Guohe, Baihe) under 2 flight altitudes (40 and 80 m). To test the model's performance, a comparative experimental approach was adopted to assess the accuracy of the three models for segmenting 80 m altitude images. The assessment relied on five metrics: intersection of union (IoU), accuracy, precision, recall, and F1-score.Results and DiscussionsThe transfer learning model shows the highest accuracy in lodging area detection. Specifically, the mean IoU, accuracy, precision, recall, and F1-score achieved 85.37%, 94.98%, 91.30%, 92.52% and 91.84%, respectively. Notably, the accuracy of lodging area detection for images acquired at a 40 m altitude surpassed that of images captured at an 80 m altitude when employing a training dataset composed solely of images obtained at the 40 m altitude. However, when adopting mixed training and transfer learning strategies and augmenting the training dataset with images acquired at an 80 m altitude, the accuracy of lodging area detection for 80 m altitude images improved, inspite of the expense of reduced accuracy for 40 m altitude images. The performance of the mixed training model and the transfer learning model in lodging area detection for both 40 and 80 m altitude images exhibited close correspondence. In a cross-study area comparison of the mean values of model evaluation indices, lodging area detection accuracy was slightly higher for images obtained in Baihu area compared to Shucheng area, while accuracy for images acquired in Shucheng surpassed that of Guohe. These variations could be attributed to the diverse wheat varieties cultivated in Guohe area through drill seeding. The high planting density of wheat in Guohe resulted in substantial lodging areas, accounting for 64.99% during the late mature period. The prevalence of semi-lodging wheat further exacerbated the issue, potentially leading to misidentification of non-lodging areas. Consequently, this led to a reduction in the recall rate (mean recall for Guohe images was 89.77%, which was 4.88% and 3.57% lower than that for Baihu and Shucheng, respectively) and IoU (mean IoU for Guohe images was 80.38%, which was 8.80% and 3.94% lower than that for Baihu and Shucheng, respectively). Additionally, the accuracy, precision, and F1-score for Guohe were also lower compared to Baihu and Shucheng.ConclusionsThis study inspected the efficacy of a strategy aimed at reducing the challenges associated with the insufficient number of high-altitude images for semantic segmentation model training. By pre-training the semantic segmentation model with low-altitude images and subsequently employing high-altitude images for transfer learning, improvements of 1.08% to 3.19% were achieved in mean IoU, accuracy, precision, recall, and F1-score, alongside a notable mean weighted frame rate enhancement of 555.23 fps/m2. The approach proposed in this study holds promise for improving lodging monitoring accuracy and the speed of image segmentation. In practical applications, it is feasible to leverage a substantial quantity of 40 m altitude UAV images collected from diverse study areas including various wheat varieties for pre-training purposes. Subsequently, a limited set of 80 m altitude images acquired in specific study areas can be employed for transfer learning, facilitating the development of a targeted lodging detection model. Future research will explore the utilization of UAV images captured at even higher flight altitudes for further enhancing lodging area detection efficiency.

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is one of the most destructive diseases that pose a great threat to wheat production. Wheat landraces represent a rich source of powdery mildew resistance. Here, we report the map-based cloning of powdery mildew resistance gene Pm24 from Chinese wheat landrace Hulutou. It encodes a tandem kinase protein (TKP) with putative kinase-pseudokinase domains, designated WHEAT TANDEM KINASE 3 (WTK3). The resistance function of Pm24 was validated by transgenic assay, independent mutants, and allelic association analyses. Haplotype analysis revealed that a rare 6-bp natural deletion of lysine-glycine codons, endemic to wheat landraces of Shaanxi Province, China, in the kinase I domain (Kin I) of WTK3 is critical for the resistance function. Transgenic assay of WTK3 chimeric variants revealed that only the specific two amino acid deletion, rather than any of the single or more amino acid deletions, in the Kin I of WTK3 is responsible for gaining the resistance function of WTK3 against the Bgt fungus.

The 2 K cryogenic system is a key subsystem of the new generation superconducting accelerator, and the 2 K heat exchanger is the key equipment. With the increasing scale of 2 K cryogenic system, the demand for high-flow 2 K heat exchanger becomes more and more urgent, and the current application of the tube and fin type, coil and tube structure of the heat exchanger is gradually replaced by a more efficient and compact platefin heat exchanger in high flow conditions, but the platefin heat exchanger has more structural parameters, and it is difficult to take into account the heat transfer efficiency, pressure drop and volume size in the design process. In this paper, based on the basic differential equations of heat transfer and pressure drop and the related flow heat transfer correlation equation, the design calculation of a 50 g/s flow rate 2 K heat exchanger supporting a 1 000 W@2 K cryogenic system was carried out using the distributed parameter method. Five parameters affecting the performance of the heat exchanger were analyzed and the comprehensive optimization index of the heat exchanger was proposed using the weighting coefficient method. Based on the proposed optimization indexes, a genetic algorithm was used to optimize the heat exchanger with multiple foci on high heat transfer efficiency, low pressure drop and small size according to the engineering needs, and a series of optimization solutions were obtained, from which the solution focusing on heat transfer efficiency was selected as the preliminary design result. Based on the parameter of this result, a threedimensional model of the heat exchanger heat transfer channel was established, and CFD numerical simulation was used for simulation calculation. The simulation results were in good agreement with the design results, verifying the accuracy of the preliminary design results, and the performance of the heat exchanger under different operating conditions was analyzed for this optimization result. The optimization result shows that the heat exchanger is selected with perforated plate type fins to cold-hot-cold (CHC) structure for the arrangement, it has 10 layers of CHC structure, its length is 760 mm, fin height is 4 mm, fin spacing is 7.9 mm, heat transfer efficiency of the heat exchanger is 84.4%, pressure drop is 222.16 Pa, and the volume is 0.022 1 m3. A variety of fin types of heat exchangers will be studied in the future to meet the demand of heat exchanger use.

A novel damage tolerance design method of discrete interleaved toughening for laminated composite was proposed. In this paper, discrete thermoplastic Polyamide-6,6 (PA 66) films were deployed at between the selected adjected layers, and three toughening dimensions were considered. Besides, three impact positions of specimens were tested under 5 J, 10 J, and 15 J, respectively. The impact behavior and compression after impact (CAI) of specimens were discussed and compared. Experimental results showed that delamination damage was suppressed in the process of propagation outwards across the toughened region, and the path of delamination crack propagation was changed and swerved. Therefore, the delamination damage projected area (DDPA) of all specimens were reduced by 21.09%–62.85%, compared with the non-toughened plate (base plate). In addition, the finding demonstrated that CAI strength is influenced by the delamination position and DDPA. The proposed method could improve the CAI strength by suppressing delamination and swerving the propagation path of delamination. Moreover, the CAI strength of all toughened plates were improved by 16.35%–61.94% in contrast to the related base plates. [ABSTRACT FROM AUTHOR]

Background. Testosterone deficiency is reportedly correlated with an elevation of cholesterol in plasma, but the mechanism remains unclear. Our objective was to investigate the effects of testosterone deficiency on cholesterol metabolism and the corresponding molecular changes in vivo and in vitro. Methods. SD rats were randomized into three groups: sham-operated (SHAM), subtotal orchiectomized (SO), and orchiectomized (ORX) and fed for 8 weeks. HepG2 cells were cultured with medium containing testosterone with the final concentrations of 0, 10, 30, and 300 nM. Method of isotope tracing and fluorescence labelling was adopted to investigate cholesterol metabolism. Several key molecules of cholesterol metabolism were also analyzed. Results. SO and ORX rats displayed dysfunctional liver uptake of cholesterol. HepG2 cells incubated with testosterone of lower and excessive level exhibited reduced capacity of cholesterol uptake. Further investigation revealed that lack of testosterone induced increased proprotein convertase subtilisin/kexin type 9 (PCSK9) and decreased low-density lipoprotein receptor (LDLR) both in vivo and in vitro. Moreover, the androgen receptor (AR) antagonist flutamide mimicked the effects of testosterone deficiency on PCSK9 and LDLR indicating the role of AR as a mediator in triggering attenuating liver cholesterol uptake in which testosterone instead of dihydrotestosterone (DHT) is the major functional form of androgen. Conclusion. Testosterone deficiency attenuated cholesterol liver uptake mediated by the PCSK9-LDLR pathway, in which AR and testosterone without transforming to DHT play important roles.