Excessive levels of organic matter in water threaten ecological safety and endanger human health. As the water resource environment is deteriorating, accurate and rapid determination of water quality parameters has become a current research hotspot. In recent years, the ultraviolet spectrometry method has been more widely used in the detection of chemical oxygen demand (COD), which is convenient and without chemical reagents. However, this method tends to use absorbance at 254 nm to measure COD. It has a good detection effect when the composition of pollutants is single, but in real life, the complex composition of pollutants will seriously affect the accuracy of measurement. Therefore, a COD prediction model based on ultraviolet-visible (UV-Vis) spectrometry and the convolutional neural network (CNN) is proposed. Compared with other traditional COD prediction models, this model makes full use of the absorbance of all ultraviolet and visible wavelengths, avoiding the information loss caused by using specific wavelengths. Meanwhile, this model is constructed based on the shallow CNN, using convolutional layers with different step lengths instead of the traditional pooling layers, which reduces computation and enhances the capture of spectral feature peaks. Additionally, with the powerful feature extraction capability of the CNN, this model reduces the reliance on pre-processing methods and improves the utilization of spectral information. Experiments have shown that our model has better fitting results and accuracy than other traditional COD prediction models such as the principal component analysis (PCA), partial least squares regression (PLSR), and backpropagation (BP) neural network. This study provides a better solution for improving the accuracy of UV-Vis water quality COD detection, which is conducive to real-time monitoring of the water quality, providing data support of water pollution and its development trend for the government’s water resource protection policy and promoting biodiversity development.
Ewing sarcoma and primitive neuroectodermal tumors (ES/PNETs) are rare tumors that belong to a family of round-cell neuroectodermally derived tumors, and their optimal treatment remains a great challenge. This study presented a case of ES/PNET, arising in the esophagus of a 21-year-old female patient presented with progressive dysphagia. Computed tomography and endoscopic ultrasonography showed a well-defined, submucosal solid mass in the superthoracic esophagus. The accurate diagnosis after surgery was obtained through immunohistochemistry and genetic studies, namely the CD99 immunopositivity as well as the EWSR1/FLI1 gene rearrangement associated with t(11;22)(q24;q12) in tumor cells. The patient underwent localized tumor resection followed by chemotherapy and chest radiotherapy. The patient is doing well with no evidence of tumor recurrence or metastasis 18 months after surgery<b>.</b> Although the esophagus is a rare site for ES/pPNET, we can speculate that the treatment protocol of ES/pPNET should include multi-agent chemotherapy, surgery, and local radiotherapy in order to improve the prognosis based on our report.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.