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● MSWNet was proposed to classify municipal solid waste. ● Transfer learning could promote the performance of MSWNet. ● Cyclical learning rate was adopted to quickly tune hyperparameters. An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.

Rapid industrialization of China generated a massive quantity of waste, among them industrial solid waste contributed the biggest flow to some 60 gigatonnes (Gt) in the past two decades. A complete tempo-spatial dataset of industrial waste, however, is absent in many areas in China, due to numerous waste producers and insufficient statistical coverage. To fill up the gap, we collected current available data from thousands of sources. We further developed six machine learning models to complete the dataset across all the 337 cities in China for the period 1990–2022. Bayesian optimization was employed to obtain the best estimation model for each city and to enhance its performance and resilience. In addition to the aggregate waste amount, generation of six major subcategories of industrial waste, i.e., metallurgical slags, fly ash, furnace slags, coal gangue, tailings, and desulfurization gypsum, are presented for more than half of the cities in 2022. This dataset can help researchers and policymakers recognize and address challenges brought by industrial waste.

Few studies reported using the convolutional neural network with transfer learning to finely classify the construction and demolition waste. This study aims to develop a highly efficient method to realize the finely sorting the construction and demolition waste, which is a key step for promoting the recycling system to realize carbon neutrality in the waste management sector. C&DWNet models, ResNet structures based on knowledge transfer and cyclical learning rate, were proposed to classify ten types of construction and demolition waste. Indexes (confusion metric, accuracy, precision, recall, F1 score, sensitivity, specificity and kappa) were adopted to evaluate the performance of various C&DWNet models. Knowledge transfer can reduce the training time and improve the performance of the C&DWNet model. The average training time is increased with the increase of the layer of C&DWNet architecture from C&DWNet-18 (946.7 s) to C&DWNet-152 (1186.6 s). The accuracy of various C&DWNet models is approximately 72–74%; the best accuracy is 73.6% in C&DWNet-152. C&DWNet-18 is more suitable for the classification of construction and demolition waste in terms of training time, accuracy, precision, and F1 score. Moreover, the t-distributed stochastic neighbor embedding can distinctly separate each type of construction and demolition waste. The environmental applications and limitations of the C&DWNet module were also discussed, which could provide a reference for the intelligent management of construction and demolition waste and promote the development of the circular economy.

Recyclable waste sorting has become a key step for promoting the development of a circular economy with the gradual realization of carbon neutrality around the world. This study aims to develop an intelligent and efficient method for recyclable waste sorting by the method of deep learning. Thus, RWNet models, which refers to various ResNet structures (ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152) based on transfer learning, were proposed to classify different types of recyclable waste. Cyclical learning rate and data augmentation were taken to improve the performance of RWNet models. In addition, accuracy, precision, recall, F1 score, and ROC were taken to evaluate the performance of RWNet models. Results showed that the accuracy of various RWNet models is almost at 88%, and the best accuracy is 88.8% in RWNet-152. The highest precision, recall, and F1 score in terms of weighted average value appeared in RWNet-101 (89.9%), RWNet-152 (88.8%), and RWNet-152 (88.9%), respectively. The area under the ROC curve (AUC) is higher than 0.9, except for the AUC value of plastic (0.85), which indicated that most of the recyclable waste can be well sorted by RWNet models. This study demonstrates the good performance of RWNet models that can be used to automatically sort most of the recyclable waste, which paves the way for better recyclable waste management.

Sepsis affects millions of hospitalized patients worldwide each year, but there are no sepsis-specific drugs, which makes sepsis therapies urgently needed. Suppression of excessive inflammatory responses is important for improving the survival of patients with sepsis. Our results demonstrate that DcR3 ameliorates sepsis in mice by attenuating systematic inflammation and modulating gut microbiota, and unveil the molecular mechanism underlying its anti-inflammatory effect.

Acute hepatopancreatic necrosis disease (AHPND), formerly called early mortality syndrome (EMS), causes high mortality in cultured penaeid shrimp, particularly Penaeus vannamei and Penaeus monodon . AHPND is mainly caused by Vibrio species carrying the pVA1 plasmid encoding the virulence genes Photorhabdus insect-related ( pir ) pir VP A and pir VP B . We developed a new molecular assay that combines recombinase polymerase amplification (RPA) and CRISPR/Cas12a technology (RPA-CRISPR/Cas12a) to detect pir VP A and pir VP B , with a fluorescent signal result. The fluorescence RPA-CRISPR/Cas12a assay had a detection limit of 20 copies/μL for pir VP A and pir VP B . To improve usability and visualize RPA-CRISPR/Cas12a assay results, a lateral flow strip readout was added. With the lateral flow strip, the RPA-CRISPR/Cas12a assay had a lower limit of detection of 200 copies/μL (0.3 fmol/L). The lateral flow assay can be completed in 2 h and showed no cross-reactivity with pathogens causing other shrimp diseases. In a field test of 60 shrimp samples, the RPA-CRISPR/Cas12a lateral flow assay showed 92.5% positive predictive agreement and 100% negative predictive agreement. As the new RPA-CRISPR/Cas12a assay is rapid, specific, and does not require complicated experimental equipment, it may have important field applications for detecting AHPND in farmed shrimp.