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Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal relationship between target variables and AD performance, this study developed four machine learning models to predict biogas yield and consider the interaction among various factors. Results indicated that the highest prediction accuracy of AD performance was achieved by adding bacterial genera dataset with environmental factors. Random forest model exhibited the highest accuracy, with the testing coefficient of determination equal to 0.9879. Among two types of input features, the bacterial genera accounted for 89.9 % of the impact on biogas yield, followed by environmental factors. The results revealed Keratinibaculum and Acetomicrobium as critical bacteria. The volatile fatty acid controlled below 2000 mg/L and the improved stirring system in AD process were recommended to achieve maximum biogas yield., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The thermochemical conversion technology for anaerobic digestate from food waste (ADFW) can reduce waste volume, eliminate pathogens, and recover energy through incineration, pyrolysis, gasification, and hydrothermal transformation. This paper comprehensively reviews the physicochemical features of anaerobically fermented digestate from food waste (FW), digestate treatment methods, and their advantages and disadvantages. In addition, the analysis and application of associated by-products from ADFW thermochemical conversion are also discussed. The main products include biochar, bio-oil, and biogas. Biochar can be used for soil improvement and biomedicine and bio-oil can be used forliquid fuel. Meanwhile, biogas mainly consists of CH 4 , CO 2 , and H 2 and CO, which can be used in petrochemicals, metallurgy, and other fields. The catalytic pyrolysis/gasification for plastic-containing ADFW is proposed by adding iron-based industrial waste (red mud/copper) as catalysts under the CO 2 /CH 4 atmosphere. This review helps to provide new guidelines for the ADFW utilization of desired products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The substantial generation of textile waste (TW) and red mud (RM) has resulted in significant resource wastage and environmental challenges. Co-utilization technology of solid waste is an effective approach to improve waste utilization efficiency. In this study, RM catalytic pyrolysis experiments of TW were conducted using TG-FTIR and Py-GC-MS for liquid fuel production, and TW and RM were recycled simultaneously. At the optimal experimental conditions (temperature of 600 °C and feed catalyst ratio of 2:1), the tar yield and higher heating value (HHV) of TW pyrolysis catalyzed by RM were 73.43 wt% and 32.34 kJ/g, respectively. Additionally, experiments on the pyrolysis of various TW types revealed that LDPE and PP are suitable for tar production, while cotton, nylon, and PET are more suitable as feedstock for syngas production. The RM catalytic pyrolysis mechanism of textile waste is that Fe 2 O 3 in RM exhibits significant catalytic activity in enhancing tar and syngas yields. However, during the catalytic process, Fe 2 O 3 undergoes reduction to Fe 3 O 4 , resulting in diminished catalytic performance of the RM. After five cycles of use, the RM essentially lost its catalytic activity due to the accumulation of char and tar. All experimental findings of this study could offer an effective guideline for TW recycle and promoting RM utilization toward the waste-to-energy circular economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Here, the pyrolysis decomposition behavior and kinetics of four kinds of organic hazardous wastes (OHWs) and their blends were investigated. The four types of OHWs are oil sludge (OS), paint sludge (PS), waste print circuit boards (WPCBs), and penicillin fermentation residue (PFR). A combination of thermogravimetry (TG) and derivative thermogravimetry (DTG) analysis was employed with Gaussian multi-peak fitting and Flynn-Wall-Ozawa (FWO) method. The results showed that OS was mainly pyrolyzed at 550-900 °C while PS, WPCBs, and PFR were mainly pyrolyzed at 150-550 °C. During co-pyrolysis, the PS/PFR exhibited the best pyrolysis performance among all the blends due to the high volatiles content in PS and PFR. The interaction analysis revealed that OS/PS exhibited the lowest coefficient of residue percentage compared to other blending samples, with OS/PFR following behind. This observation indicates a synergistic promotional effect between OS and PS, as well as between OS and PFR, during the co-pyrolysis process. Gaussian fitting showed that the carbonate decomposition peak of OS shifted towards lower temperatures during its co-pyrolysis with PS and PFR., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Sorting out plastic waste (PW) from municipal solid waste (MSW) by material type is crucial for reutilization and pollution reduction. However, current automatic separation methods are costly and inefficient, necessitating an advanced sorting process to ensure high feedstock purity. This study introduces a Swin Transformer-based model for effectively detecting PW in real-world MSW streams, leveraging both morphological and material properties. And, a dataset comprising 3560 optical images and infrared spectra data was created to support this task. This vision-based system can localize and classify PW into five categories: polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS). Performance evaluations reveal an accuracy rate of 99.75% and a mean Average Precision (mAP 50 ) exceeding 91%. Compared to popular convolutional neural network (CNN)-based models, this well-trained Swin Transformer-based model offers enhanced convenience and performance in five-category PW detection task, maintaining a mAP 50 over 80% in the real-life deployment. The model's effectiveness is further supported by visualization of detection results on MSW streams and principal component analysis of classification scores. These results demonstrate the system's significant effectiveness in both lab-scale and real-life conditions, aligning with global regulations and strategies that promote innovative technologies for plastic recycling, thereby contributing to the development of a sustainable circular economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na 3 AlF 6 , AlF 3 , CaF 2 , and SiF 4 . Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 μg/m 3 and 9.0 μg/m 3 , respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In silico models for screening substances of healthy and ecological concern are essential for effective chemical management. However, current data-driven toxicity prediction models confront formidable challenges related to expressive capacity, data scarcity, and reliability issues. Thus, this study introduces TOX-BERT, a SMILES-based pretrained model for screening health and ecological toxicity. Results show that masked atom recovery pretraining and multi-task learning offer promising solutions to enhance model capacity and address data scarcity issues. Two novel application domain (AD) parameters, termed PCA-AD and LDS, were proposed to improve prediction reliability of TOX-BERT with accuracy surpassing 90 % and mean absolute error (MAE) below 0.52. TOX-BERT was applied to 18,905 IECSC chemicals, revealing distinct toxicity relationships that align with experimental studies such as those between cardiotoxicity and acute ecotoxicity. In addition to previous PBT screening, 156 potential high-risk chemicals for specific endpoint were identified covering 7 categories. Furthermore, a SMILES-based toxicity site detection approach was developed for structural toxicity analysis. These advancements carry profound implications to address challenges faced by current data-driven toxicity prediction models. TOX-BERT emerges as a valuable tool for more comprehensive, reliable, and applicable predictions of health and ecological toxicity in chemical risk assessment and management., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Doping inorganic acid ions represents a promising pathway to improving the photocatalytic activity of TiO 2 , and oxygen vacancy has been regarded as the determinant factor for photocatalytic activity. A series of samples doped with Cl - , NO 3 - , and SO 4 2- was prepared via a simple sol-gel method. Two different oxygen vacancies in the crystal layer of NO 3 - /TiO 2 and Cl - /TiO 2 were found, and those are [Ti 3+ ]-V 0 -[Ti 3+ ] and [Ti 3+ ]-Cl, respectively. The photocurrent of NO 3 - /TiO 2 with [Ti 3+ ]-V 0 -[Ti 3+ ] is significantly greater than that of Cl - /TiO 2 with [Ti 3+ ]-Cl. The least oxygen vacancy is in the gel layer of SO 4 2- /TiO 2 , and the negligible photocurrent is due to difficulty in forming a stable sol. Furthermore, the process conditions for the application of TiO 2 were investigated in this work. The optimal process parameters are to adjust the solution to pH = 3 during sol-gel preparation, to adopt 550 °C as the calcination temperature, and to use an alkaline electrolyte, while the rest of the preparation conditions remain unchanged. This work reveals a new avenue for designing efficient photocatalysts for air pollutant degradation.

The persistent combustion of fossil fuels has resulted in a widespread greenhouse effect attributable to the continual elevation of carbon dioxide (CO 2 ) levels in the atmosphere. Recent research indicates that utilizing CO 2 as a pyrolysis gasification medium diminishes CO 2 emissions and concurrently augments the value of the resultant pyrolysis gasification products. This paper reviews recent advancements in the pyrolysis gasification of organic solid wastes under a CO 2 atmosphere. Meanwhile, the mechanisms of CO 2 influence in the pyrolysis and gasification processes were also discussed. In comparison to noble gases, CO 2 exhibits reactivity with char at≥710 °C, resulting in additional mass loss of the sample. In addition, CO 2 was able to increase the specific surface area and stability of biochar and reduce biooil toxicity by lowering the content of cyclic compounds in the biooil, while CO 2 was able to react with GPRs with some volatile products (e.g., light hydrocarbons) to increase biogas yield. Finally, CO 2 also prevents catalyst deactivation by reducing secondary coke formation. We also recommend directing future attention toward utilizing unpurified CO 2 in pyrolysis and gasification. This review aims to expand the utilization of CO 2 and advocate for applying pyrolysis gasification products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

As the dominant waste disposal process, incineration is regarded as the main incentive for the "not-in-my-backyard" syndrome, and faces an inescapable pressures of ultra-low emissions (ULE). Establishing precise response relationships between emission factors (EFs) and full-process influencing factors can provide guidance for the synergistic mitigation of flue gas pollutants (FGPs). In this work, the multi-dimensional EFs of FGPs were identified by initially integrating FGPs concentration monitoring data of existing 1,226 processing lines in China, technologies applied and operational experience (OE), local economic and political characteristics. Significant regional imbalance performance was observed, which EFs in the coastal regions were 3.55-92.39 % lower than those of the inland areas. NO x , SO 2 , HCl were identified as critical components requiring further reduction under the ULE standards, with exceedance rates recorded at 73.07 %, 38.90 %, and 56.69 %, respectively. An indicative value of 20 mg/m 3 for PM is recommended for the control of heavy metals of Cd + Tl and Sb + As + Pb + Cr + Co + Cu + Mn + Ni based on the correlation coefficients of r = 0.28 (p < 0.001) and r = 0.20 (p = 0.002), respectively. Waste composition and OE were quantified as the main contributors of EFs' disparities by the tree-branching controlled variable approach established in this study. Predictive models for FGPs control process and corresponding EFs were constructed. EFs of nine FGPs in 2030 would decrease by 0.97-65.42 %, due to more complex purification processes employed to meet ULE's limitations, such as the application of five-stage processes growing from 45.60 % to 58.28 %. While regional imbalance in EFs-SO 2 and EFs-HCl were extended with increases from 25.83 % to 33.07 % and 9.91 % to 32.32 %, respectively, due to the consistent disparities of OE and growing heterogeneity of control policies. Enhancing interregional empirical exchanges, reducing the regional market monopolies, and formulating technical guidelines would be beneficial to synergize the reduction of FGPs emissions and alleviate regional imbalance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Red mud (RM) is a kind of strong alkaline solid waste produced from the aluminum industry, which contributes significantly to environmental pollution and can cause severe health issues.Currently, RM is widely recognized as a potential material for soil remediation because of its rich metal oxide content, such as Fe/Al oxides. However, there is no comprehensive description on the roles of RM in passivation remediation of contaminated soil in mining areas. This review summarizes the mechanisms of passivation of heavy metals (HMs) in contaminated soil by RM, including precipitation, adsorption and ion exchange. Besides the effects of adding RM on soil physicochemical properties, heavy metal forms and ecological environment are further elaborated. Moreover, using the co-hydrothermal carbonization of RM and biomass for enhancing the efficiency of contaminated soil remediation is proposed as the main prospective research. This paper provides technical references for the resource utilization of RM and the treatment of heavy metal-contaminated soil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The application of anaerobic digestion (AD) in the treatment of food waste (FW) has become widespread. However, the presence of inert substances, such as bones, ceramics, and shells, within FW introduces a degree of uncertainty into the AD process. To clarify this intricate issue, this study conducted an in-depth investigation into the influence of inert substances on AD. The results revealed that when inert substances were present at a concentration of 0.08 g/g VSS, methane productivity in the AD process was significantly augmented by 86%. Subsequent investigations suggested that this positive effect was primarily evident in various biochemical processes, including solubilization, hydrolysis acidification, methanogenesis, and the accumulation of extracellular polymeric substances. Metagenomic analysis showed that inert substances enhance the relative abundance of hydrolytic bacteria and have a pronounced impact on the relative abundance of hydrogenotrophic methanogens (Methanosarcina) and acetotrophic methanogens (Methanobacterium). Additionally, inert substances significantly increased the relative abundance of functional genes in oxidative phosphorylation, a pivotal pathway for ATP synthesis. Furthermore, inert substances had a substantial effect on the functional genes related to the metabolic pathways associated with methanogenesis (both hydrogenotrophic and acetotrophic). This comprehensive study shed light on the substantial impact of inert substances on the AD of food waste, contributing to an enhanced understanding of the underlying mechanisms of anaerobic fermentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

A simple, efficient and low energy-consuming process available to generate resultful radicals from PMS for organic pollutants removal had been employed in this study. Slag had been used as the activator for organic pollutants degradation under slag/PMS advanced oxidation process. In this work, effects of slag with or without pretreatment on pollutant removal were studied and radical species generated by slag were measured. Calcination pretreatment is one efficient method to enhance the degradation efficiency significantly. Due to Fe 3 O 4 and Fe 2 O 3 became the dominant phases after calcination, it was about 8.6-flods increasing after comparing the pollutant removal efficiency for different slag/PMS system with calcination pretreatment or not. Organic pollutant neither degraded in PMS system at 25 °C nor being absorbed by slag system for 60 min. On the contrary, up to 90% pollutant concentration reduction achieved in the slag/PMS process. During this process, both • OH and SO 4 •- had been detected once slag and PMS interaction in wastewater. Through the free radicals quenching tests, • OH should be the key free radical in this advanced oxidation process for the organic pollutant removal under this alkaline condition. In general, organic degradation rate was determined by the slag dosage, and the maximum degradation efficiency was mainly controlled by the PMS usage. This work is expected to broaden the high-value reutilization way for industrial solid waste., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

ABSTRACT Food waste is an excellent organic matter for anaerobic fermentation. This study provided a cost-effective and highly efficient volatile fatty acid (VFA) production strategy by the addition of zero-valent iron (ZVI). Results showed that VFA concentration of 44.6 g/L was obtained with the optimized conditions of 200-mesh iron powder at a dosage of 20.0 g, fermentation time of 11 d, total solids (TS) of 10 wt.%, and fermentation temperature of 37 ℃. Further, the iron of different particle sizes (iron scraps, 200-mesh iron powder, and 800-mesh iron powder) had a differential influence on total organic carbon (TOC), total nitrogen (TN), and VFA concentrations. For the reactor containing 200-mesh iron powder, the conversion rate of organic compound into VFA increased with the increase of dosage, which reached 58.4% at the 40.0 g dosage. The mechanism revealed that the VFA production was enhanced by micro-electrolysis, which can rapidly inactivate bacteria and increase the conversion of macromolecular organics into micromolecular organics.

This study explored the utility of quantitative real-time panfungal PCR assay in diagnosing invasive pulmonary fungal diseases (IPFD) in non-neutropenic patients. Panfungal PCR assay was performed on respiratory tract specimens from patients whose clinical signs could not exclude fungal infection. At the same time, the samples were subjected to bacterial and fungal culture, microscopic examination and galactomannan antigen (GM) test in order to find the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the 4 diagnostic methods in proven and probable cases. 518 specimens were collected while 63 respiratory tract specimens tested by PCR had positive results. According to diagnostic criteria, 40 patients were diagnosed with IPFD, with 12 proven, 20 probable and 8 possible cases. Among these, 33 patients of PCR results were positive, most of which were from BALF samples (44.12%). 23 cases were caused by Aspergillus species, with Aspergillus fumigatus was the major cause. Other Aspergillus species, including Aspergillus flavus, Aspergillus terreus and Aspergillus nidulans were found in 1 sample respectively. Candida species were found in 5 samples, Pneumocystis jeroveci pneumonia (PJP) in 4 samples and Mucormycosis in 1 sample. An analysis of proven/probable diagnosis showed a sensitivity of 78.13%, specificity of 92.18%, PPV of 39.68% and NPV of 98.46% for PCR and 50%, 85.27%, 35.7%, 95.65% for GM test respectively. The Ct value difference between proven/probable and possible cases had no statistical significance (P = .824). Fungal culture showed a sensitivity of 17.5% while microscopic examination sensitivity of 32.5%. Through stratified analysis, no apparent correlation was found between the Ct value of the PCR assay and GM value (r: 0.223, P = .294). But a conjunction of the 2 tests raised the PPV of Aspergillus to 90%. As shown in this study, the panfungal RT-PCR assay has high sensitivity and consistency with serological test and culture. Its high PPV in the detection of Aspergillus and PJP were also evident., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which can be achieved through the application of advanced models. The Transformer model, known for its ability to capture complex nonlinear processes, proves invaluable in extracting essential features and making precise HW generation predictions. To enhance comprehension of the key factors influencing HW generation, visualization techniques such as SHapley Additive exPlanations (SHAP) provide insightful explanations. In this study, a novel approach combining classical deep learning algorithms with the Transformer model is proposed, yielding impressive results with an R 2 value of 0.953 and an RMSE of 7.284 for HW prediction. Notably, among the five key fields considered-demographics, socio-economics, industrial production, environmental governance, and medical health-industrial production emerges as the primary contributor, accounting for over 50% of HW generation. Moreover, a high rate of industrial development is anticipated to further accelerate this process., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing this approach for HHW prediction and classification due to the scarcity of available data. To address this, the current study employed the prophet model to forecast HHW quantities based on the Integration of Two Networks systems in Shanghai. HHW classification was performed using HVGGNet structures, which were based on VGG and transfer learning. To expedite the process of finding the optimal global learning rate, the method of cyclical learning rate was adopted, thus avoiding the need for repeated testing. Results showed that the average rate of HHW generation was 0.1 g/person/day, with the most significant waste categories being fluorescent lamps (30.6 %), paint barrels (26.1 %), medicine (26.2 %), battery (15.8 %), thermometer (0.03 %), and others (1.22 %). Recovering rare earth element (18.85 kg), Cd (3064.10 kg), Hg (15643.43 kg), Zn (14239.07 kg), Ag (11805.81 kg), Ni (4956.64 kg) and Li (1081.45 kg) from HHW can help avoid groundwater pollution, soil contamination and air pollution. HVGGNet-11 demonstrated 90.5 % precision and was deemed most suitable for HHW sorting. Furthermore, the prophet model predicted that HHW in Shanghai would increase from 794.43 t in 2020 to 2049.67 t in 2025., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

The present study experimentally quantified the pyrolysis behaviors of waste solvent-based automotive paint sludge (OAPS) and water-based automotive paint sludge (WAPS) at four different heating rates using thermogravimetric-Fourier transform infrared (TG-FTIR) spectrometry and pyrolysis-gas chromatography-mass (Py-GC/MS) spectrometry analyses. Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods combined with the master-plots method were employed to investigate the pyrolysis kinetics and reaction mechanisms of waste automotive paint sludge. Three reaction stages and three reaction peaks in stage 2 were distinguished for both OAPS and WAPS degradation. The average activation energy (E a ) estimates for OAPS (FWO: 179.09 kJ/mol; KAS: 168.28 kJ/mol) were slightly higher than WAPS (FWO: 175.90 kJ/mol; KAS: 164.80 kJ/mol) according to FWO and KAS methods. The main pyrolysis reaction mechanisms of both OAPS and WAPS closely matched with the order-based model corresponding to 3rd and 2nd order random nucleation on an individual particle. The evolved gas species of CH 4 , CO 2 , phenols, NH 3 , H 2 O, and CO from OAPS and WAPS pyrolysis were identified by TG-FTIR. According to Py-GC/MS, hydrocarbons (47.2%) and O-components (42.7%) were relatively large after OAPS and WAPS pyrolysis, respectively. Melamine was the most abundant N-component product after pyrolysis of OAPS (5.8%) and WAPS (4.8%)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Colorectal adenoma is a recognized precancerous lesion of colorectal cancer (CRC), and at least 80% of colorectal cancers are malignantly transformed from it. Therefore, it is essential to distinguish benign from malignant adenomas in the early screening of colorectal cancer. Many deep learning computational pathology studies based on whole slide images (WSIs) have been proposed. Most approaches require manual annotation of lesion regions on WSIs, which is time-consuming and labor-intensive. This study proposes a new approach, MIST - Multiple Instance learning network based on the Swin Transformer, which can accurately classify colorectal adenoma WSIs only with slide-level labels. MIST uses the Swin Transformer as the backbone to extract features of images through self-supervised contrastive learning and uses a dual-stream multiple instance learning network to predict the class of slides. We trained and validated MIST on 666 WSIs collected from 480 colorectal adenoma patients in the Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School. These slides contained six common types of colorectal adenomas. The accuracy of external validation on 273 newly collected WSIs from Nanjing First Hospital was 0.784, which was superior to the existing methods and reached a level comparable to that of the local pathologist's accuracy of 0.806. Finally, we analyzed the interpretability of MIST and observed that the lesion areas of interest in MIST were generally consistent with those of interest to local pathologists. In conclusion, MIST is a low-burden, interpretable, and effective approach that can be used in colorectal cancer screening and may lead to a potential reduction in the mortality of CRC patients by assisting clinicians in the decision-making process. © 2022 The Pathological Society of Great Britain and Ireland., (© 2022 The Pathological Society of Great Britain and Ireland.)

Phosphorus and operating temperature not only affect the agglomeration behavior but also the transformation and migration of heavy metals. Accordingly, this study examined the effect of temperature and phosphorus in a fluidized bed combustion process to understand the emission and distribution of heavy metals by both experimental and thermodynamic calculations. The experimental results indicated that the sodium-phosphate reactions occur before the sodium-silicate reaction in the solid phase when the ratio of P/Na was 1/2. A low-melting-point sodium phosphate component, such as NaPO 3, leads to easier particle agglomeration than Na 2 O-SiO 2 . In terms of the emissions of heavy metals, Pb and Cd show a similar trend: both the amount of emission smaller than that without adding phosphorus and the amount of emission share an upward trend with the operating time increased during MSS fluidized bed combustion. However, with the presence of phosphorus, the emission of Cr shows slightly decreased, and then sharply dropped, after that, increasing with operating time increased. Generally speaking, the maximum amount of Pb and Cd emitted was at 900 °C, followed by 800 °C and 700 °C. The higher temperature would promote the volatilization of Pb and Cd to emit. On the other hand, Cr emitted at the beginning tended to increase but later decreases when the temperatures were 700 and 900 °C, which may be due to the emission of Cr being influenced by the different affinities of both Al and Cr, reacting with Na in a fluidized bed incinerator. As for the distribution of heavy metals in the solid phase, a higher concentration of heavy metals was found in both the coarsest and finest particles during the process of agglomeration/defluidization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Molten salt has been increasingly acknowledged to be useful in the destruction of chlorine-containing organic wastes (COWs), e.g., organochlorine. However, the operational temperatures are usually high, and local structure and thermodynamic property of the molten salt remain largely unclear. In this study, novel molten NaOH-KOH is developed for organochlorine destruction, and its eutectic point can be lowered to 453 K with 1:1 mol ratio of NaOH to KOH. Further experiment shows that this molten NaOH-KOH is highly-efficient towards the destructions of both trichlorobenzene and dichlorophenol, acquiring the final dechlorination efficiencies as 88.2% and 94.1%, respectively. The organochlorine destruction and chloride salt enrichment are verified by fourier-transform infrared spectrometer. Molten NaOH-KOH not only eliminates the C-Cl and CC bonds, but also traps generated CO 2 , other acidic gases, and possibly particulate matters as a result of the high surface area and high viscosity. This makes it possibly advantageous over incineration for organic waste destruction for carbon neutrality. To sufficiently reveal the inherent mechanism for the temperature dependent performance, molecular dynamics simulation is further adopted. Results show that the radial distance between ions increases with temperature, causing larger molar volume and lower resistance to shear deformation. Moreover, thermal expansion coefficient, specific heat capacity, and ion self-diffusion coefficient of the molten NaOH-KOH are found to increase linearly with temperature. All these microscopic alterations contribute to the organochlorine destruction. This study benefits to develop highly-efficient molten system for COWs treatment via a low-carbon approach., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

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., Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users., (© Higher Education Press 2023.)

Background: Increased use of multislice computed tomography (CT) scans has revealed that minute pulmonary meningothelial-like nodules (MPMNs) showed as ground-glass nodules (GGNs) are frequent in patients. However, little is known about the incidence and fate of nodules. By using a cross-sectional design, this study compared the multislice CT signs and pathological results of MPMNs, and further used pathological results to explain the formation mechanism of the CT signs of MPMNs to improve the clinical understanding of the disease., Methods: The clinicopathological data of 93 cases diagnosed as MPMNs in the Jiangsu Province Hospital from January 2016 to September 2019 and the Nanjing First Hospital from January 2017 to December 2019 were examined. The related literature was reviewed, and each case's age, gender, medical history, and preoperative CT examinations were classified. Based on CT signs, this study analyzed the imaging features, including size, shape, boundary, distribution, opacity, and their relationship with pulmonary blood vessels., Results: A total of 13 cases had immunohistochemistry results among which the lesions showed consistent positive expression of vimentin (100%), followed by epithelial cell membrane antigen (92.3%) and progesterone (8%). The MPMNs mainly occurred in individuals aged 50-59 years (32.6%). Most patients (82.6%) had neoplastic disease. All nodules (100%) manifested with a round shape and well-demarcated borders on images. The size of the nodules on CT scans ranged from 2.5 to 5.0 mm, with an average size of 3.04±1.12 mm. Most nodules were subpleural (89.1%) and showed ground-glass opacity (97.8%). The follow-up results of postoperative clinical manifestations and chest CT examination were negative in 12 patients., Conclusions: This study suggested that the pathological findings of MPMNs could explain the formation mechanism of the CT signs. The results can provide guidance for the diagnosis of the disease in the future., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1250/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Diffuse large B-cell lymphoma (DLBCL) is one of the most common aggressive B-cell lymphomas with significant heterogeneity. More than half of patients are cured, but 40%-45% still face relapse or develop drug resistance, and the mechanism is not yet known. In this study, Centrimeric protein F (CENPF) overexpression was found in several DLBCL patients with relapsed or refractory disease compared to patients with complete remission. Thus, the human DLBCL cell line SU-DHL-4 was chosen for this study, and CENPF was upregulated in that cell line by using an adenovirus in vitro . Mass spectrometry-based quantitative proteome analysis was first performed, and the results showed that the expression levels of various proteins were increased when CENPF was upregulated, and these proteins are mainly involved in cellular processes, biological regulation, immune system processes and transcriptional regulator activity. Bioinformatics data analysis revealed that the main enriched proteins, including UBE2A, UBE2C, UBE2S, TRIP12, HERC2, PIRH2, and PIAS, were involved in various ubiquitin-related kinase activities and ubiquitination processes. Thus, ubiquitinome analysis was further performed, and the results demonstrated that proteins in many immune-related cellular pathways, such as natural killer cell-mediated cytotoxicity, the T-cell receptor signaling pathway and the B-cell receptor signaling pathway, were significantly deubiquitinated after CENPF was upregulated in DLBCL cells. Furthermore, TIMER2.0 was also used to reveal the association between CENPF and immune infiltration in DLBCL. The results showed that CENPF expression was positively correlated with CD8 + T cells, NK cells and B lymphocytes in DLBCL samples but negatively correlated with regulatory T cells. Aberrant activation of CENPF may induce immune dysregulation in DLBCL cells by mediating protein deubiquitination in various immune signaling pathways, which leads to tumor escape of DLBCL, but further experimental validation is still needed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yang, Wang, Hu, Li, Yang, Zhao, Xu, Zhang, Tang and Zhang.)

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., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Bio-residue is a by-product from organic waste anaerobic digestion process with high moisture, high organic matter, low calorific value and poor biological stability, and may be incinerated after deep dewatering. The moisture existence forms of bio-residue were clarified firstly, finding that adsorbed water, capillary water and bound water occupied 75.61%, 23.81% and 0.58%, respectively. Furthermore, refuse incineration bottom ash was used as a typical deep dewatering reagent, when compared with calcium oxide (CaO), refuse incineration fly ash and iron powder-potassium persulfate (Fe(0)-K 2 S 2 O 8 ). Results showed that the addition of bottom ash powder could decrease the bio-residue's moisture from 80.0% to 37.9% at the optimal dosage of 2.0%, and corresponding low calorific value reached 10.5 MJ/kg at the 10 th d. It was suggested that ZnCl 2 and ZnSO 4 present in bottom ash powder could react with moisture in bio-residue, forming hydrates of inorganic salt and breaking the polyacrylamide colloid, and lead to the release of adsorbed water in the bio-residue., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R 2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R 2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Objective: The aim of this study was to analyze the relationship of serum gastrin-17 (G-17) and oral mucositis in head and neck carcinoma (HNC) patients receiving radiotherapy., Methods: Serum G-17 were detected in patients before and after radiotherapy. Patients were divided into high G-17 group (baseline serum G-17 ≥ 5pmol/L) and low G-17 group (baseline serum G-17 < 5pmol/L). The severity of oral mucositis was analyzed between the two groups. Other complications such as dysphagia, salivary gland, mandible, thyroid function, larynx, pain, and weight loss were also investigated., Results: Forty-two patients were analyzed in this study. The level of serum G-17 had a significant decrease after radiotherapy (7.29 ± 5.70pmol/L versus 4.93 ± 4.46pmol/L, P = 0.038). In low serum G-17 group, the incidences of grade 0, 1-2 and 3-4 of oral mucositis were 0%, 30.4%, and 69.6%, respectively. In high serum G-17 group, the incidences of grade 0, 1-2 and 3-4 of oral mucositis were 0%, 63.2%, and 36.8%, respectively. Pearson correlation analysis showed that serum G-17 was negatively correlated with oral mucositis (r=-0.595, P < 0.01). Weight loss of low G-17 group was more serious than that of high G-17 group., Conclusion: Serum G-17 has a close relationship with oral mucositis. Baseline serum G-17 may be a potential predictor for the severity of oral mucositis in HNC patients receiving radiotherapy., (© 2022. The Author(s).)

The sorting of Construction and Demolition (C&D) waste is a critical step to linking the recycling system and to the macro prediction, which helps to promote the development of the circular economy. Moreover, the effective classification and automated separation process will also help to stop the spreading of pathogenic organisms, such as virus and bacteria, by minimizing human intervention in the sorting process, while also helping to prevent further contamination by COVID-19 virus. This study aims to develop an efficient method to sort C&D waste through deep learning combined with knowledge transfer approach. In this paper, CVGGNet models, that is four VGG structures (VGGNet-11, VGGNet-13, VGGNet-16, and VGGNet-19), based on knowledge transfer combined with the technology of data augmentation and cyclical learning rate, are proposed to classify ten types of C&D waste images. Results show that 2.5 × 10 -4 , 1.8 × 10 -4 , 0.8 × 10 -4 , and 1.0 × 10 -4 are the optimum learning rate for CVGGNet-11, CVGGNet-13, CVGGNet-16, and CVGGNet-19, respectively. Knowledge transfer helped shorten the training time from 1039.45 s to 991.05 s, and while it improved the performance of the CVGGNet-11 model in training, validation, and test datasets. The average training time increases as the number of the layers in the CVGGNet architecture rises: CVGGNet-11 (991.05 s) ˂ CVGGNet-13 (1025.76 s) ˂ CVGGNet-16 (1090.48 s) ˂ CVGGNet-19 (1337.81 s). Compared to other CVGGNet models, CVGGNet-16 showed an excellent performance in various C&D waste types, in terms of accuracy (76.6%), weighted average precision (76.8%), weighted average recall (76.6%), weighted average F1-score (76.6%) and micro average ROC (87.0%). In addition, the t-distributed Stochastic Neighbor Embedding (t-SNE) approach can reduce the dataset to a lower dimension and distinctly separate each type of C&D waste. This study demonstrates the good performance of CVGGNet models that can be used to automatically sort most of the C&D waste, paving the way for better C&D waste management., (Copyright © 2022. Published by Elsevier Ltd.)

Volatile organic compounds (VOCs) diffused from paint sludge are potential hazard contributing significantly to environmental pollution and exposure to them can cause severe health issues. In this paper, a diffusion-controlled model was firstly developed for characterizing the emission behaviors of cumulative VOCs from automotive solvent-based paint sludge based on the worst field management scenario. The presented model is characterized by two key parameters: the diffusion coefficient (D m ) and the initial emittable concentration (C m,0 ), which can be simultaneously obtained by our proposed ER-history method. Four major components were detected including 1-butanol, butyl acetate and 1,2,4-trimethylbenzene and 1-ethyl-4-methylbenzene. In addition, the model was validated by using environmental data in a ventilated test chamber, proving that the model is reliable and convincing. However, relative deviations of 1-butanol and butyl acetate are larger than those of 1,2,4-trimethylbenzene and 1-ethyl-4-methylbenzene, indicating that the model is more accurate for predicting hydrophobic VOCs release than those of hydrophilic VOCs. Besides, an increase in C m,0 and D m tends to enhance VOCs cumulation release. Our studies provide new insight into experimental designs for rapid model parameters measurement and a sound basis for estimating VOCs cumulative release from paint sludge as well as for hazardous waste., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The content of dissolved organic matter (DOM) and potentially toxic elements (PTEs) were investigated in the bottom ash (BA) and fly ash (FA) of different sections of the municipal solid waste incineration (MSWI) plant. BA and FA were collected from the dry (BA1-BA2), burn (BA3-BA4), and burn-out (BA5) sections of the grate incinerator; FA was collected after denitration (DNFA), and from the deacidification tower (FA1) and bag-type dust remover (FA2), respectively. The DOM concentration in BA was higher than that in FA, the highest concentration was in BA3 (556.18 mg/kg), while the lowest concentration was in DNFA (17.53 mg/kg). DOM in BA was mainly composed of protein-like, fulvic-like, tryptophan-like, and humic-like substances, of which humic-like substances accounted for more than 40%. DOM in FA consisted of tryptophan-like and humic-like substances, of which humic-like substances accounted for more than 80%. DOM still existed in BA which may be related to the incomplete combustion, and the influence of microbes, while DOM was increased in FA1, which might be due to the addition of lime slurry. PTEs were analyzed by the Tessier extraction method, Fe-Mn hydroxide-bound fraction of PTEs increased in FA1 in which DOM concentration (137.22 mg/kg) was 7.83 times that in DNFA. The increase of DOM may lead to a higher risk of PTEs in FA. FTIR results indicated that DOM can bond to PTEs in BA and FA. The contents of humus-like substances in DOM were positively correlated with the effective fraction of As, Cu, Pb, Cr, and Cd. This paper investigated the risk of DOM existing in BA and FA in MSWI plant, which can provide a new perspective on how to deal with BA and FA, and reduce their environmental risks., Competing Interests: Declaration of competing interest The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Aerobic methane oxidizing bacteria (methanotrophs) can use methane as carbon source and energy source, eliminating 10%-20% of global methane. Methanotrophs can also effectively synthesize valuable methane-derived products. This article introduced the methane oxidizing mechanism of methanotrophs, and summarized the practical application and research hotspots of methanotrophs in the field of methane emission reduction in the landfill, ventilation air methane mitigation in coal mines, valuable chemicals biosynthesis, as well as oil and gas reservoir exploration. Main factors influencing the pollutant removal and the biosynthesis efficiency in various applications were also discussed. Based on the study of large-scale cultivation of methanotrophs, some measures to benefit the application and promotion of aerobic methane oxidizing biotechnology were proposed. This includes investigating the effect of intermediate metabolites on methanotrophs activity and population structure, and exploiting economical and efficient alternative culture media and culture techniques.

There are two key issues in the area of waste management: one is stabilizing heavy metal, lead (Pb), in municipal solid waste incineration fly ash (MSWI-FA), the other is enhancing nitrogen utilization efficiency from organic waste. However, the connection between both issues was limited. In this study, a novel organic chelating agent based on food waste, FW-CA, was produced for immobilizing Pb. A maximum 86.22% of polypeptide in hydrolysis liquid of FW was utilized for preparing FW-CA with a yield of 8.22 g/L. Results indicated that FW-CA-stabilized fly ash satisfied the criteria of GB 18598-2019 with a dosage of 4.6%, lower than the demand of pure chemicals and industrially applied chelating agents. After treating with FW-CA, the exchangeable and bound to carbonate fraction of Pb decreased by 5.08% and 18.57%, respectively, contributing to a low environmental risk class of the Pb assessment code. FW-CA effectively chelated Pb at a wider range of leaching pH (3.19-11.24), and the leachability was hardly affected by curing time, which were attributed to the presence of dithiocarbamate group and formation of cross-linked structure between Pb and sulfur. Overall, the unique waste-utilized chelating agent was a suitable alternative for Pb stabilization in MSWI-FA., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (<138 °C), de-volatilization (138-570 °C), and decomposition stage (≥570 °C above). The de-volatilization can further be divided into stage 2 and stage 3, with values of activation energy estimated by Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods as 123.35 and 172.95 kJ/mol, respectively. The gas products like H 2 O, CO 2 , CH 4 , and CO, as well as functional groups like phenols and carbonyl (CO), were all detected during the process of FM pyrolysis by thermogravimetric-fourier transform infrared spectrometry at a heating rate of 10  ° C/min. The main species detected by pyrolysis-gas chromatography-mass spectrometry analyzer included acid (41.98%) and aliphatic hydrocarbon (22.44%). Finally, the 1D-CNN-LSTM algorithm demonstrated an outstanding generalization capability to predict the relationship between FM composition and temperature, with R 2 reaching 93.91%. In sum, this study provided a reference for the treatment of FM from MSW classification as well as the feasibility and practicability of deep learning applied in pyrolysis., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The efficient classification and resource conversion of landfilled refuse can simultaneously realize the secondary pollution prevention and land replacement. This work aimed to investigate the waste plastic resource recovery from landfilled refuse via a novel waterless cleaning method. Composition analysis showed that the content of waste plastic from landfilled refuse had differences between different regions, and the specific weight content range was between 15-40%. Moreover, when the landfill time was less than 7 years, the high calorific value (>35 MJ/kg) and mechanical property (declining range <3%) of waste plastic were basically the same as those of the original waste plastic, indicating a high resource attribute. However, due to the high degree of pollution, it was difficult to directly make high-value utilization. Furthermore, a waterless device was designed for waste plastic cleaning, and 94% cleaning rate was obtained with the optimal conditions of the sand medium particle size of 4-14 mesh, stirring speed of 50 r/min, and the operation time of 45 min. The actual on-site pilot test was carried out under the best conditions, and the results showed that the cleaning rate of waste plastic could reach 90%. On the basis, cost-benefit analysis of different waste plastic recycling methods was carried out. Compared with the other four methods, direct extrusion molding after multi-effect cleaning had lower resource consumption (cost of 88.64 RMB), higher economic benefit (net income of 311.36 RMB), and was more in line with the characteristics of raw materials. This research can provide scientific basis and technical support for the cleaning and upgrading and resource utilization of plastic in refuse., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Municipal solid waste incineration fly ash is classified as the hazardous waste because of its high levels of heavy metals alkali chlorides, and polychlorinated dibenzo-p-dioxins. Thermal treatment is widely used for fly ash treatment because of its advantages of reduction and harmless. The transformation behaviors of chlorine and metal ions during the thermal treatment of fly ash has a significant impact on the harmless and resource of fly ash. At present, the migration behaviors of chlorine and metal ions during thermal treatment of fly ash is not clearly demonstrated. In this manuscript, the phase compositions, transformation behaviors, the variation of mass and content of chlorine and various metal ions were analyzed through diverse characterization methods under different sintering temperatures to understand the migration behaviors of chlorine and metal ions during thermal treatment. Roasting experiments showed that the migration behaviors of heavy metals and chlorides were consistent. The chlorine, sodium, potassium and heavy metal ions can be removed sharply while the calcium, aluminum, magnesium and iron were decreased slightly when the roasting temperature was above 750 °C. The findings also suggested that removed chlorides were soluble chlorides and unstable crystals in municipal solid waste incineration fly ash were inclined to formed steady structure under high temperature. The structure of roasted fly ash became denser and generated ceramic-like particle due to thermal agglomeration and chemical reactions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Background: Signal sequence receptor subunit delta (SSR4) gene is reported to encode the translocon-associated protein δ and related with the human immune regulation. However, the expression of SSR4 in colon adenocarcinoma (COAD) and its correlation with clinical treatment remains unclear., Research Design and Methods: SSR4 mRNA expression level and its relationship with tumor infiltrating lymphocytes (TILs) in COAD were evaluated through several databases. Furthermore, the study collected 238 cases of COAD tissue samples to detect the association of SSR4 protein expression level in TILs with clinical pathological information, and the prognosis of COAD., Results: SSR4 mRNA was significantly highly expressed in COAD tissues and significantly correlated with several types of TILs in COAD. Moreover, SSR4 highly expressed in many types of TILs, especially highly expressed in plasma cell from COAD patients with advanced TNM stage. High SSR4 protein expression in TILs was associated with lymph node metastasis, distant metastasis, American Joint Committee on Cancer (AJCC) staging, and Response Evaluation Criteria in Solid Tumors (RECIST) efficacy. COAD patients with high SSR4 expression in TILs had better overall survival., Conclusions: In conclusion, high SSR4 mRNA and protein expression in TILs can be used as a prognostic biomarker for predicting better overall survival and treatment efficacy in COAD patients.

Volatile organic compounds (VOCs) emitting from solid building materials can cause adverse human health and environmental climate effects. It's more cost effective and powerful for mass-transfer emission models to describe the emission characteristic of VOCs than emission chamber studies. In this review, the existing main physical mechanism-based models for predicting VOCs emissions from dry solid building materials have been discussed, as well as their differences and similarities. Ignoring internal diffusion and porosity of solid materials, single-phase model is generally quite safe for use in actual condition. Conversely, porous media model is good for understanding VOC-transfer principles in porous materials. Additionally, the porous media model and the single-phase model can be transformed mutually because their model parameters are correlative. The availability of emission models is largely determined by the reliable and useful model parameters. Therefore, substantial technologies and novel methods have been developed for parameter estimation, which have also been reviewed in this paper. How to readily and rapidly obtain model parameters is a future development direction. In addition, applying emission models to predict and control VOCs emission from other solid waste materials is another future research prospect., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The organics in the classified wet waste were deficiently utilized while sewage denitrification requires abundant carbon sources. Herein, the fermentation of perishable organic waste (POW) and the denitrification process with obtained liquid were investigated. The most volatile fatty acids (VFAs) production was realized with the fermentation liquid of food waste (FL-FW). Increasing substrate tended to lower the proportion of VFAs and acetic acid in FL-FW. Under the optimum conditions of FL-FW carbon source, carbon to nitrogen ratio 7, and temperature 30 ℃, the removal efficiency of nitrate nitrogen reached 99.23% within 4 h. The sludge settleability and microbial activity were significantly enhanced, contributing to the actual sewage a promotional removal of organics (95.84%) and nitrogen (70.31%) with the supplementation of FL-FW. High addition ratio would cause more degradation of refractory organics, which confirmed the feasibility of using FL-FW as a cost-efficient carbon source for the denitrification., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Sludge management represents a critical challenge because of complex compositions and poor dewaterability. Fe 2+ -activated persulfate oxidation (Fe 2+ /S 2 O 8 2- ) is an effective, and widely investigated method for enhancing sludge dewatering. However, the potential effects of Fe 2+ /S 2 O 8 2- on sludge drying efficiency, anaerobic biodegradation behaviors and potential recycling of sludge residua are not yet well-known. In this study, a new sludge disposal route (step i: enhanced dewatering via Fe 2+ /S 2 O 8 2- , and step ii: drying-incineration or anaerobic digestion) was proposed and appraised comprehensively. Results showed that Fe 2+ /S 2 O 8 2- oxidation destroyed extracellular polymeric substances, lysed sludge cells and enhanced the dewaterability greatly. Capillary suction time and mechanical filtration time at 2.0/1.6 mmol-Fe 2+ /S 2 O 8 2- /g-VS decreased by 88.0% and 79.6%, respectively. Moreover, 89.8% of micro-pollutants (e.g., methylbenzene, ethylbenzene, p-m-xylene and o-xylene) in sludge were removed. Besides, the pretreatment was able to alter sludge drying behaviors and methane-producing potential. Pretreated sludge exhibited faster drying rate and shorter lag-time for methane production. Incineration residua of dewatered sludge could be re-coupled with S 2 O 8 2- as the conditioner to enhance sludge dewaterability, thereby reducing the chemical input and disposal cost. This study provides a novel, self-sustainable strategy for sludge management, reutilization and final safe disposal., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Great deal pathogenic bacteria and malodorous gases are hidden in municipal solid waste (MSW), which poses excellent environmental sanitation risks for sanitation workers and residents, and preventive measures should be implemented. In this study, the simultaneous annihilation of microorganisms and volatile organic compounds (VOCs) with slightly acidic electrolyzed water (SAEW) was investigated in an MSW storage room of a residential community in Shanghai, China. The microbial population of airborne, surfaces and handles of waste bins, hands of sanitation workers and the main components of VOCs were measured. The results indicated that the bacterial reduction efficiencies of SAEW with an available chlorine concentration (ACC) of 50-100 mg/L on surfaces and handles of waste bins and sanitation workers' hands were 22.7%-84.1%. Also, SAEW effectively reduced the average population of airborne bacteria and fungi by 358 and 378 colony-forming units (CFU)/m 3 and decreased the detection rates of coliforms by 14.2%-51.9%. The concentrations of most VOCs were reduced by 21.4%-88.3% after spraying SAEW. And the accumulated values of carcinogenic and noncarcinogenic risks also tended to decrease with spraying SAEW. These findings imply that SAEW has significant application potential to control environmental sanitation risks in MSW storage rooms., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Municipal solid waste (MSW) amount has direct influence on MSW management, policy-decision making, and MSW treatment methods. Machine learning has great potential for prediction, but few studies apply the approaches of deep learning to forecast the quantity of MSW. Therefore, the aim of this study is to evaluate the feasibility and practicability of employing the methods of supervised learning, including Attention, one-dimension Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) to predict the MSW Amount in Shanghai. Integrated 1D-CNN and LSTM with Attention model, the new structure model (1D-CNN-LSTM-Attention, 1D-CLA), is designed to forecast MSW amount. In addition, the influence of socioeconomic factors on MSW amount, the structure and layers distribution of Attention, 1D-CNN, LSTM and 1D-CLA are also discussed. The results indicate that the correlation coefficients of Attention, one-dimension CNN, LSTM, and proposed 1D-CLA model to predict the MSW in Shanghai are 78%, 86.6%, 90%, and 95.3%, respectively, suggesting the feasible and practicable. The values of 24, 0.01, 50 and 25 for the number of neurons, dropout, the value of epoch number and Batch size best fit 1D-CLA to predict the amount of MSW in Shanghai. Furthermore, the performance of 1D-CLA is better than any single model or two model's combination (R 2 is 95.3%) and the mechanism of 1D-CLA is contributed by three former models following the order: LSTM>CNN>Attention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The occurrence and development of gastric adenocarcinoma (gADC) is closely related to the interaction between tumor cells and immune cells in the tumor microenvironment (TME). Our objective was to characterize the repertoire of immune cells in the TME of gADC. To analyze the transcriptomic, immune, and spatial information of TME in gADC, we constructed single-cell RNA sequencing, 10 × Genomics V(D)J analysis, multiple immunofluorescence techniques, and OSCmap analysis of 49,765 single cells in seven samples from four gADC patients. Our integrative analysis of B cells demonstrated that a large number of mucosal associated lymphoid tissue (MALT)-B cells were detected in the gADC tissues, which have mature tertiary lymphatic structures (mTLSs), and almost no MALT-B cells in peripheral blood sample. Moreover, MALT-B cells are a class of IgA + plasma cells, which are characterized with high expression of complement pathway activation-related genes. Next, natural killer T (NKT) cells mainly exist in gADC tissues accompanied by mTLSs. This study also classified monocytes/macrophages and epithelial cells into benign and malignant types. Interestingly, CSOmap ( q < .05) and multiple immunofluorescence ( p < .05) results indicated more types of immune cells can be enriched in tissues with mTLSs than normal TLSs, and the density of mTLSs were higher than normal TLSs. Our findings provide novel insights for the signature of immune cells and tumor cells in the TME of gADC with TLSs and highlight the potential importance of IgA-mediated humoral immunity in gADC patients with TLSs., Competing Interests: The author(s) declare no competing financial interests., (© 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Rotten fruits could be used as an available resource due to the high organic content and low pollution introduction. In this study, four kinds of rotten fruits including banana, apple, pear and grape, were utilized as additional carbon source to improve the nitrogen removal from mature landfill leachate. With the optimal condition of carbon-nitrogen ratio 6.5 and operation time 2 d, the rotten banana group had a higher denitrification rate of 11.78 mg/(gVSS·h) than that of other groups, corresponding the 99.55% of nitrate nitrogen (NO 3 - -N), 99.36% of total nitrogen and 94.60% of organics removal. High carbon-nitrogen ratio would contribute to more degradation of organic and humus matters, and the low cost of 0.65 €/kgNO 3 - -N was obtained. Biodiversity analysis indicated that denitrificans and organic-degrading bacterial were enriched after the addition of rotten banana. Overall, the novel carbon source of rotten banana was a cost-efficient choice for the denitrification., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Soil contamination by heavy metals constitutes a serious global environmental problem, and numerous remediation technologies have been developed. In this study, a novel soil remediation agent, namely composite hydrogel (leftover rice-g-poly(acrylic acid)/montmorillonite/Urea, LR-g-PAA/MMT/urea), was prepared based on free radical polymerization cross-linking technology. Experimental results indicated that the LR-g-PAA/MMT/urea dosage increased from 0% to 10%, the oxidizable state proportions of Cd, Cu, Pb and Zn in contaminated soil increased from 8.3%, 23.7%, 54.0% and 11.4%-71.3%, 61.0%, 76.5%, and 27.9%, respectively. Compared with control experiment, the residue state growth rate were 56.6%, 23.4% and 39.8% for Cu, Pb and Zn respectively with 10% dosage of composite hydrogel. Simultaneously, the LR-g-PAA/MMT/urea was also seen to enhance soil fertility, including organic matter content, cation exchange capacity, and N and P contents. Pot experiments for biological toxicity suggested that the addition of hydrogel weakened the toxic effect of heavy metals on cotton seeds, and the action effect was increasingly visible with the increase of hydrogel dosage. The analysis of the mechanism involved suggested that the organic matter and its possessed characteristic functional groups could weaken the biological toxicity via complexation, adsorption, and ion exchange. Overall, the synthesized composite hydrogel exhibits great potential for the simultaneous remediation and fertility improvement of heavy metal contaminated soil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The morbidity and mortality of colorectal cancer (CRC) ranks fourth worldwide, moreover, the tumor microenvironment (TME) of CRC is quite complex, and is one of the necessary factors affecting promotion of tumor metastasis. PTPN2 is a tumor suppressor which plays an important role in cancer-related downstream molecular pathway. FSP-1 is highly-expressed in multiple types of tumor tissues and is a biomarker of stromal fibroblasts. To examine the function of PTPN2 in the metastasis of CRC, the study evaluated the co-expression level of PTPN2 and FSP-1 in CRC tissues by double staining, and demonstrated the relationship with clinical information about each patient. The roles of PTPN2 and FSP-1 were detected in vitro by proliferation and transwell assay through knockdown of expression level of PTPN2. Lower PTPN2 with higher FSP-1 expression was correlated with poor survival outcomes in CRC. TAFs contribute to the migration function of PTPN2 in CRC in vitro through inducing changes in the level of TGF-β1. Western blot and qRT-PCR assays were used to detect the mechanism of PTPN2 regulation of migration with TAFs in the JAK/STAT signaling pathway, moreover, TAFs contributed the function of PTPN2 in colorectal carcinogenesis in vivo . In summary, the study shed light on the effect of TAFs contributes the function of PTPN2 in colorectal carcinogenesis through activating JAK/STAT signaling pathway. In addition, double-staining assay could give us a unique perspective from which to study TME in CRC., Competing Interests: None., (AJCR Copyright © 2021.)