Water pollutant discharge permit allocation based on DEA and non-cooperative game theory.

Against the background of the ecological civilization system reform in the new era, the appropriate allocation of water pollutant discharge permits is an important policy for controlling the amount of wastewater discharge. Traditional allocation methods have disadvantages, such as high additional costs, an unfair allocation scheme, and market distortion. In the present study, a fixed-cost allocation model based on data envelopment analysis (DEA) and the Nash non-cooperative game theory is employed to allocate water pollutant discharge permits of totally 31 provinces in China from 2008 to 2017. The allocation scheme considers environmental efficiency. The results demonstrate regional differences in the allocation of water pollutant discharge permits. The eastern region has abundant allocations. The northeastern and central regions have insufficient allocations. Besides, the western region has a significant shortage of allocations. It indicates the higher the utilization efficiency of the water pollutant discharge permits, the higher the region's sustainable development is. Based on the analysis, we propose guidelines for industrial wastewater discharge reduction.

Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets.

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task, using a hierarchical Swin Transformer encoder to extract features at 5 resolution levels, and connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections. The models were trained and evaluated on open datasets, with data separation based on individual mice. Further evaluation on an external mouse dataset acquired on a different micro-CT with lower kVp and higher imaging noise was also employed to assess model robustness and generalizability. Results indicate that Swin UNETR consistently outperforms nnU-Net and AIMOS in terms of average dice similarity coefficient (DSC) and Hausdorff distance (HD95p), except in two mice of intestine contouring. This superior performance is especially evident in the external dataset, confirming the model's robustness to variations in imaging conditions, including noise and quality, thereby positioning Swin UNETR as a highly generalizable and efficient tool for automated contouring in pre-clinical workflows.

New alkaloids from Stemona tuberosa and structural revision of tuberostemonols P and R.

Three Stemona alkaloids named stemotuberines A-C (1-3) with unique C17N frameworks, presumably formed by elimination of the C-11-C-15 lactone ring of the stichoneurine skeleton, were isolated from the roots of Stemona tuberosa. Their structures were elucidated by spectroscopic analysis, X-ray diffraction, and computational methods. Compounds 2 and 3 showed inhibition (IC50 values of 37.1 and 23.2 µM, respectively) against LPS-induced nitric oxide production in RAW 264.7 cells. In addition, concern was expressed about the reported plant origin (S. sessilifolia) of the recently described alkaloids tuberostemonols O-R (4-7), which should be S. tuberosa. NMR calculations indicated structural misassignment of these compounds except for 6. Isolation of tuberostemonol P (5) from our material of S. tuberosa allowed for a close examination of the spectroscopic data leading to the revised structure 5a. Tuberostemonol R (7) was found to have identical 1H and 13C NMR data to the well-known alkaloid croomine, and therefore its structure including relative stereochemistry must be revised as 7a.

An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis.

BACKGROUND: Suppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological lung details but are impractical due to the extremely long processing time. Machine learning (ML) methods are computationally efficient but are limited by the available ground truth (GT) for effective and robust training, resulting in suboptimal results. PURPOSE: To improve bone shadow suppression, we propose a generalizable yet efficient workflow for CXR rib suppression by combining physical and ML methods. MATERIALS AND METHOD: Our pipeline consists of two stages: (1) pair generation with GT bone shadows eliminated by a physical model in spatially transformed gradient fields; and (2) a fully supervised image denoising network trained on stage-one datasets for fast rib removal from incoming CXRs. For stage two, we designed a densely connected network called SADXNet, combined with a peak signal-to-noise ratio and a multi-scale structure similarity index measure as the loss function to suppress the bony structures. SADXNet organizes the spatial filters in a U shape and preserves the feature map dimension throughout the network flow. RESULTS: Visually, SADXNet can suppress the rib edges near the lung wall/vertebra without compromising the vessel/abnormality conspicuity. Quantitively, it achieves an RMSE of ~0 compared with the physical model generated GTs, during testing with one prediction in <1 s. Downstream tasks, including lung nodule detection as well as common lung disease classification and localization, are used to provide task-specific evaluations of our rib suppression mechanism. We observed a 3.23% and 6.62% AUC increase, as well as 203 (1273 to 1070) and 385 (3029 to 2644) absolute false positive decreases for lung nodule detection and common lung disease localization, respectively. CONCLUSION: Through learning from image pairs generated from the physical model, the proposed SADXNet can make a robust sub-second prediction without losing fidelity. Quantitative outcomes from downstream validation further underpin the superiority of SADXNet and the training ML-based rib suppression approaches from the physical model yielded dataset. The training images and SADXNet are provided in the manuscript.

3D-engineered GelMA conduit filled with ECM promotes regeneration of peripheral nerve.

Autologous transplantation remains the golden standard for peripheral nerve repair. However, many drawbacks, such as the risk of reoperation or nerve injury remain associated with this method. To date, commercially available artificial nerve conduits comprise hollow tubes. By providing physical guiding and biological cues, tissue engineered conduits are promising for bridging peripheral nerve defects. The present study focuses on the preparation of artificial composite nerve conduits by 3D bio-printing. 3D-printed molds with a tubular cavity were filled with an Engelbreth-Holm-Swarm (EHS) Hydrogel mimicking the extracellular matrix (ECM) basement membrane. Chemically cross-linked gelatin methacryloyl (GelMA) was used to form the conduit backbone, while EHS Hydrogels improved nerve fiber growth while shortening repair time. Statistical significant difference had been found between the blank conduit and the composite conduit group on compound muscle action potential after 4 months. On the other hand, results between the composite conduit group and the autograft group were of no statistical differences. All results above showed that the composite conduit filled with EHS Hydrogel can promote the repair of peripheral nerve and may become a promising way to treat peripheral nerve defects.