Introduction: Cervical cancer has become one of the most malignant tumors that threatens women's health worldwide. Liquid-based cytology (LBC) examination has become the most common screening method for detecting cervical cancer early and preventing it. Currently, nuclear segmentation technology for cervical clinical LBC images based on convolutional neural networks has become a vital means of assisting in the diagnosis of cervical cancer. However, the existing nuclear segmentation techniques fail to segment the nuclei of severely overlapping nuclei in highly aggregated cell clusters, which will inevitably lead to the misdiagnosis of cervical cancer pathology. Methods: Therefore, a novel bending loss and dual-task decoding network (Bloss-DDNet) is proposed for overlapping cell nucleus segmentation of cervical clinical LBC images. First, the network architecture search method is introduced to search and optimize the architecture of the decoding module in the dual-task branch, determining the mask and boundary decoding modules (dual-task decoding modules) of the Bloss-DDNet. Second, two feature maps, separately generated from dual-task decoding branches composed of a shared encoder module and dual-task decoder modules, are fused to enhance the sensitivity to cell nucleus boundaries. Third, a bending loss is introduced to the loss function to focus on the curvature variation characteristics of the intersection of overlapping cell nucleus boundaries, thereby constraining the training process of the dual-task decoding branch and increasing the constraint on the cell nucleus boundary. Results: The results show that all evaluation metrics of the proposed Bloss-DDNet achieved the best performance on public datasets. Discussion: Therefore, the proposed Bloss-DDNet can effectively address the segmentation problem of overlapping cell clusters and nuclei in clinical LBC images, providing strong support for subsequent clinical auxiliary diagnosis of cervical cancer.

Watershed ecological compensation (WEC), as a public policy for sustainability, aims at balancing the ecological environment and economic development within specific watersheds to achieve the harmonious coexistence between humans and nature. This study takes an innovative approach by constructing an effective WEC policy evaluation model for the Huai River Basin based on a comprehensive evaluation system and empirical research. By systematically integrating the fuzzy comprehensive evaluation of the analytic hierarchy process and descriptive statistical methods, the model provides a comprehensive evaluation of the WEC policy’s influence on the basin’s overall environment. The results indicate that each area related to the Huai River Basin presents a positive promotion of the ecological environment and economic development based on its comprehensive score calculated by the model. Besides, the results also reveal a noticeable reduction in major pollution elements in the Huai River Basin based on empirical research of the model, accompanied by effective improvements in water and air quality. However, the impact of WEC’s policy implementation on regional economic development is relatively limited. It is found that although the WEC’s policy implementation is in a favorable state, downstream cities of the Huai River Basin still exhibit a phenomenon of “free-riding”. Meanwhile, the WEC’s policy overlooks the focus on economic concerns and lacks robust legal safeguards. Therefore, this study further proposes constructive recommendations, including promoting collaborative governance between upstream and downstream areas, balancing environmental protection and economic development, and improving the legal system for the WEC.

Background: The knee joint is one of the largest and most complex joints in the human body, serving as the main support point for body weight, which allows the legs to bend and extend. Dynamic knee joint X-ray radiography provides the necessary imaging conditions for motion-function assessment of these key multi-object regions, including the patella, femur, tibia, and patellar tendon. An accurate, automatic segmentation model will not only assist radiologists and physicians in the diagnostic process but also further alleviate the significant labor they must invest. Meanwhile, the network architecture and the loss function are the primary factors influencing the segmentation model. Therefore, an optimal multi-object region segmentation model should be proposed for dynamic knee joint X-ray radiography to segment the patella, femur, tibia, and patellar tendon. Methods: First, a dual-level weighted cross-entropy loss function based on multi-object region areas for dynamic knee joint X-ray radiography is proposed to balance losses across the patella, femur, tibia, and patellar tendon. Second, two comprehensive evaluation metrics, constructed based on the characteristics of existing evaluation metrics, are developed to reduce the dimensionality of evaluation metrics and enable comprehensive evaluation of multi-object region segmentation models. Third, a novel scoring criterion is proposed based on the two constructed comprehensive evaluation metrics to determine the optimal multi-object region segmentation model, with an appropriate ratio for each loss function in the mixed loss function. Results: Compared to the traditional weighted cross-entropy loss function, the proposed dual-level weighted cross-entropy loss function improves the segmentation model's performance. Meanwhile, the multi-object region segmentation model with the optimal combination of network (DeepLabV3+_R50c) and mixed loss function ( τ 1 * L _{CE 2} + τ 2 * L _DICE + τ 3 * L _BD , τ ₁ : τ ₂ : τ ₃ = 0.50:0.25:0.25 ) is determined based on the proposed two comprehensive evaluation metrics and scoring criterion, achieves a mean IoU of 0.8921, a mean Dice of 0.9373, a mean Precision of 0.9316, a mean Recall of 0.9490, a mean HD95 of 2.9145, and a mean ASSD of 1.0309, respectively. Conclusion: The proposed multi-object region segmentation model has the potential to greatly enhance the accuracy and effectiveness of quantitative analysis of the knee joint motion.