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Abstract Background The cultivation of bananas encounters substantial obstacles, particularly due to the detrimental effects of cold stress on their growth and productivity. A potential remedy that has gained attention is the utilization of ethyl mesylate (EMS)-induced mutagenesis technology, which enables the creation of a genetically varied group of banana mutants. This complex procedure entails subjecting the mutants to further stress screening utilizing L-Hyp in order to identify those exhibiting improved resistance to cold. This study conducted a comprehensive optimization of the screening conditions for EMS mutagenesis and L-Hyp, resulting in the identification of the mutant cm784, which exhibited remarkable cold resistance. Subsequent investigations further elucidated the physiological and transcriptomic responses of cm784 to low-temperature stress. Results EMS mutagenesis had a substantial effect on banana seedlings, resulting in modifications in shoot and root traits, wherein a majority of seedlings exhibited delayed differentiation and limited elongation. Notably, mutant leaves displayed altered biomass composition, with starch content exhibiting the most pronounced variation. The application of L-Hyp pressure selection aided in the identification of cold-resistant mutants among seedling-lethal phenotypes. The mutant cm784 demonstrated enhanced cold resistance, as evidenced by improved survival rates and reduced symptoms of chilling injury. Physiological analyses demonstrated heightened activities of antioxidant enzymes and increased proline production in cm784 when subjected to cold stress. Transcriptome analysis unveiled 946 genes that were differentially expressed in cm784, with a notable enrichment in categories related to ‘Carbohydrate transport and metabolism’ and ‘Secondary metabolites biosynthesis, transport, and catabolism’. Conclusion The present findings provide insights into the molecular mechanisms that contribute to the heightened cold resistance observed in banana mutants. These mechanisms encompass enhanced carbohydrate metabolism and secondary metabolite biosynthesis, thereby emphasizing the adaptive strategies employed to mitigate the detrimental effects induced by cold stress.

Aiming at the problem that the multi-object tracking algorithm is difficult to accurately design the object feature model and data association algorithm in the process of unmanned vehicle autonomous driving, a multi-object tracking algorithm based on online spatiotemporal feature correlation for unmanned vehicle autonomous driving scene (MOTA-BOSFCFUVADS) is proposed. Firstly, the algorithm performs object detection on the training samples, calibrates the coordinates of the detection results in the time dimension and the coordinates of the space dimension, eliminates the detection results whose confidence is less than the set value, and eliminates the overlapping boundaries in the detection through non-maximum suppression. Secondly, we use Kalman filter to predict the position of the tracking object in the current frame, then build the feature model of the object in the time dimension and the space dimension respectively, and fuse the temporal feature model of the tracking object with the spatial feature model, thereby, the spatiotemporal feature model of the tracking object is obtained. Finally, the spatiotemporal feature response of the object in the current frame is detected online, and the spatiotemporal feature response is correlated with the spatiotemporal object feature model of the tracking object, and then the similarity metric matching matrix obtained by fusion is calculated, and the tracking is solved by using the Hungarian algorithm. The optimal correlation pair between the object historical trajectory and the detection response, and update the parameters of the object spatiotemporal feature model. In addition, we use the MOT2015 database to test the effectiveness of the algorithm. The results show that the proposed algorithm has better tracking performance than the other two algorithms, and can effectively track multiple object continuously in time and space.

With the continuous acceleration of urbanization, the parking problem is becoming increasingly serious. How to better manage parking resources has become an urgent problem to be solved in urban development. In this context, according to the historical data and real-time video data collected by the parking camera, this paper proposes an algorithm for parking space detection and state recognition. Through image preprocessing, region of interest selection, Hough line detection, and parking information recognition of the input test image, an intelligent parking space detection model is constructed, which improves the utilization rate of parking space and reduces the management cost. On this basis, according to the free parking space data obtained by the detection algorithm, a short-term demand prediction algorithm for on-road parking based on Convolutional Neural Network (CNN) and Long Short-Term Memory Neural Network (LSTM) was proposed. Through the preprocessing of input parking space data, time vector transformation, data separation, model training, and prediction, the parking demand data is predicted and analyzed. By comparing the prediction results of multiple models, it was found that the CNN-LSTM prediction model had the best model stability and goodness of fit, the lowest Mean Error (MAE) and Root Mean Square Error (RMSE), the errors of working days were 13.301 and 21.156, and the errors of rest days were 12.573 and 20.739, respectively. It shows that CNN-LSTM can effectively capture the time and spatial feature information of parking lot free parking space data, and the prediction accuracy is good, which can be used to predict the number of free parking spaces in parking lots.

A patient-specific anatomical structure model has been widely used in many medical applications. However, in practical applications, to effectively construct a patient-specific anatomical structure model is a challenge, the reasons are: 1) the manual marking process for landmark points is time-consuming and is prone to have false points; 2) the correspondence establishment is difficult; and 3) the performance of the model is limited. Therefore, the purpose of this study is to automatically construct a patient-specific anatomical structure model to solve these difficulties. Firstly, the input data are preprocessed to enhance the region of interest in CT scan images. Then, the region of interest is regarded as a training specimen, and the triangle is used to mesh the training specimen. Meanwhile, vertices contraction strategy is introduced to iteratively contract triangle meshes, and the correspondences are established through improved B-spline free-form deformation. Finally, principal component analysis is used to generate the final patient-specific anatomical structure model. Experimental results on 30 pelvic CT scan images verify that the proposed method outperforms the compared methods.

Abstract Background Drought stress can severely affect plant growth and crop yield. The cloning and identification of drought-inducible promoters would be of value for genetically-based strategies to improve resistance of crops to drought. Results Previous studies showed that the MaPIP1;1 gene encoding an aquaporin is involved in the plant drought stress response. In this study, the promoter pMaPIP1;1, which lies 1362 bp upstream of the MaPIP1;1 transcriptional initiation site, was isolated from the banana genome..And the transcription start site(A) is 47 bp before the ATG. To functionally validate the promoter, various lengths of pMaPIP1;1 were deleted and fused to GUS to generate pMaPIP1;1::GUS fusion constructs that were then transformed into Arabidopsis to generate four transformants termed M-P1, M-P2, M-P3 and M-P4.Mannitol treatment was used to simulate drought conditions. All four transformants reacted well to mannitol treatment. M-P2 (− 1274 bp to − 1) showed the highest transcriptional activity among all transgenic Arabidopsis tissues, indicating that M-P2 was the core region of pMaPIP1;1. This region of the promoter also confers high levels of gene expression in response to mannitol treatment. Using M-P2 as a yeast one-hybrid bait, 23 different transcription factors or genes that interacted with MaPIP1;1 were screened. In an dual luciferase assay for complementarity verification, the transcription factor MADS3 positively regulated MaPIP1;1 transcription when combined with the banana promoter. qRT-PCR showed that MADS3 expression was similar in banana leaves and roots under drought stress. In banana plants grown in 45% soil moisture to mimic drought stress, MaPIP1;1 expression was maximized, which further demonstrated that the MADS3 transcription factor can synergize with MaPIP1;1. Conclusions Together our results revealed that MaPIP1;1 mediates molecular mechanisms associated with drought responses in banana, and will expand our understanding of how AQP gene expression is regulated. The findings lay a foundation for genetic improvement of banana drought resistance.