Deep-learning architecture for PM_(2.5) concentration prediction: A review

Accurately predicting the concentration of fine particulate matter(PM_(2.5))is crucial for evaluating air pollution levels and public exposure.Recent advancements have seen a significant rise in using deep learning(DL)models for forecasting PM_(2.5) concentrations.Nonetheless,there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM_(2.5) prediction models.Here we extensively reviewed those DL-based hybrid models for forecasting PM_(2.5) levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines.We examined the similarities and differences among various DL models in predicting PM_(2.5) by comparing their complexity and effectiveness.We categorized PM_(2.5) DL methodologies into seven types based on performance and application conditions,including four types of DL-based models and three types of hybrid learning models.Our research indicates that established deep learning architectures are commonly used and respected for their efficiency.However,many of these models often fall short in terms of innovation and interpretability.Conversely,models hybrid with traditional approaches,like deterministic and statistical models,exhibit high interpretability but compromise on accuracy and speed.Besides,hybrid DL models,representing the pinnacle of innovation among the studied models,encounter issues with interpretability.We introduce a novel three-dimensional evaluation framework,i.e.,Dataset-MethodExperiment Standard(DMES)to unify and standardize the evaluation for PM_(2.5) predictions using DL models.This review provides a framework for future evaluations of DL-based models,which could inspire researchers to standardize DL model usage in PM_(2.5) prediction and improve the quality of related studies.

Fc fragment of immunoglobulin G receptor IIa(FCGR2A)as a new potential prognostic biomarker of esophageal squamous cell carcinoma

To the Editor: Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancer types and places a heavy burden on human health. The early diagnosis and prognosis monitoring of ESCC is important for therapy. Despite recent progress in treatment regimens for ESCC, the prognosis of ESCC remains poor.[1] Therefore, it is important to find new molecular therapeutic targets and prognostic monitoring biomarkers for ESCC patients. In this study, we aimed to explore new prognostic biomarkers for ESCC.