Longitudinal characterization of serum metabolome and lipidome reveals that the ceramide profile is associated with metabolic health in early postpartum cows experiencing different lipolysis

Reduced feed intake in early lactation prompts increased fat mobilization to meet dairy cows' energy needs for milk production. The increased lipolysis in cows presents significant health risks with unclear mechanisms. The objectives of our study were to compare the longitudinal profiles of metabolites and lipids of serum from high and low-lipolysis cows. Forty multiparous Holstein dairy cows were enrolled in the retrospective study. Serum samples were collected on d 7 before expected calving, as well as on d 5, d 7, d 14, and d 21 postpartum. Dairy cows were grouped according to mean serum nonesterified fatty acids on d 5 and 7 after parturition as low (<0.600 mmol/L; n = 8; LFM) and high (>0.750 mmol/L; n = 8; HFM), indicating fat mobilization during early lactation. Lactational performance and serum metabolic parameters related to glucose and lipid metabolism, liver functions, oxidative status, and inflammatory responses were determined. Serum samples were subjected to LC-MS-based metabolomics and lipidomics. Despite differences in postpartum BW change, there were no observed variations in milk yield and composition between 2 groups. Serum BHBA, glucose, leptin, aspartate aminotransferase, IL-6, and TNF-α were greater in cows with HFM than in LFM. Serum adiponectin, revised quantitative insulin sensitivity check index and albumin were lower in cows with HFM than LFM. Intensified fat mobilization in the HFM cows came along with reduced estimated insulin sensitivity, impaired liver functions, and increased oxidative stress and inflammatory responses. Differences in metabolic patterns were observed across the transition period when comparing serum blood matrices (e.g., in different amino acids, acylcarnitines, and sphingolipids). The serum metabolome of the HFM cows was characterized by higher concentrations of glycine, acylcarnitines, carnosine, Cer(d20:0/18:0), Cer(d18:1/16:0), and Cer(t18:0/24:0) compared with LFM. The differential serum metabolites and lipids at different sampling times during the peripartum period were enriched in the sphingolipid metabolism. Differences in serum metabolic status parameters suggest that cows adopt varied metabolic adaptation strategies to cope with energy deficits postpartum. Our investigation found a comprehensive remodeling of the serum metabolic profiles in transition dairy cattle, highlighting the significance of alterations in sphingolipid species, as they play a crucial role in insulin resistance and metabolic disorders.

Synergistic control potential of flue gas pollutants under Ultra-Low emission standards in waste incineration plants

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. NOx, SO2, 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/m3 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-SO2 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.

Recent advances in organic waste pyrolysis and gasification in a CO2 environment to value-added products

The persistent combustion of fossil fuels has resulted in a widespread greenhouse effect attributable to the continual elevation of carbon dioxide (CO2) levels in the atmosphere. Recent research indicates that utilizing CO2 as a pyrolysis gasification medium diminishes CO2 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 CO2 atmosphere. Meanwhile, the mechanisms of CO2 influence in the pyrolysis and gasification processes were also discussed. In comparison to noble gases, CO2 exhibits reactivity with char at≥710 °C, resulting in additional mass loss of the sample. In addition, CO2 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 CO2 was able to react with GPRs with some volatile products (e.g., light hydrocarbons) to increase biogas yield. Finally, CO2 also prevents catalyst deactivation by reducing secondary coke formation. We also recommend directing future attention toward utilizing unpurified CO2 in pyrolysis and gasification. This review aims to expand the utilization of CO2 and advocate for applying pyrolysis gasification products.

Roles of red mud in remediation of contaminated soil in mining areas: Mechanisms, advances and perspectives

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.

Enhancement of anaerobic digestion from food waste via inert substances based on metagenomic analysis: Oxidative phosphorylation and metabolism

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.

Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach

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.

Enhanced activation of peroxymonosulfate for advanced oxidation processes using solid waste: A novel and easy implement high-value utilization process of slag

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 Fe3O4 and Fe2O3 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 SO4•- 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.

Enhanced volatile fatty acid production from food waste via anaerobic fermentation: effect of irons with different sizes

ABSTRACTFood 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.

Diagnosis of invasive pulmonary fungal infections by a real-time panfungal PCR assay in non-neutropenic patients

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.

Transformer-based enhanced model for accurate prediction and comprehensive analysis of hazardous waste generation in Shanghai: Implications for sustainable waste management strategies

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 R2 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.

Data-driven models applying in household hazardous waste: Amount prediction and classification in Shanghai

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.

[Research advances on improving the therapeutic efficacy of mesenchymal stem cell-derived exosomes in wound repair]

How to promote high-quality wound healing is a common problem for plastic surgery and burn physicians. In recent years, numerous animal studies have demonstrated that mesenchymal stem cell-derived exosomes promote wound repair through multiple mechanisms and are promising cell-free therapeutic agents with broad prospect of application. How to enhance the therapeutic efficacy of exosomes, optimize their drug delivery strategy, and improve their biological properties are the challenges to be overcome in order to move from basic research to clinical application of exosome therapy for wound repair. This article focuses on methods to improve the wound repair potential of mesenchymal stem cell-derived exosomes, and reviews the recent research advances on improving the therapeutic efficacy of mesenchymal stem cell-derived exosomes in wound repair from three aspects, including pretreatment of parental mesenchymal stem cells, hydrogel bio-scaffold loaded with exosomes, and engineered exosomes, to provide a reference for further clinical studies.

Hi-MGT: A hybrid molecule graph transformer for toxicity identification

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.

Pyrolytic kinetics, reaction mechanisms and gas emissions of waste automotive paint sludge via TG-FTIR and Py-GC/MS

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 CH₄, CO₂, phenols, NH₃, H₂O, 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%).

MIST: multiple instance learning network based on Swin Transformer for whole slide image classification of colorectal adenomas

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.

Agglomeration-influenced transformation of heavy metals in gas-solid phases during simulated sewage sludge co-incineration: Effects of phosphorus and operating temperature

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 Na₂O-SiO₂. 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.

Highly-efficient molten NaOH-KOH for organochlorine destruction: Performance and mechanism

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 CO₂, 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.

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

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.

Minute pulmonary meningothelial-like nodules: associations between computed tomography and pathology features

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.

Combined multiomics analysis reveals the mechanism of CENPF overexpression-mediated immune dysfunction in diffuse large B-cell lymphoma in vitro

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.

Applying a deep residual network coupling with transfer learning for recyclable waste sorting

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.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai

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 (R² 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, R² 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.

Incineration disposal of organic waste bio-residue via a deep dewatering process using refuse incineration bottom ash: moisture transfer and low calorific value improvement

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)-K₂S₂O₈). 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 ZnCl₂ and ZnSO₄ 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.

The relationship of serum gastrin-17 and oral mucositis in head and neck carcinoma patients receiving radiotherapy

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.

Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer

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.

Characterizing the emission behaviors of cumulative VOCs from automotive solvent-based paint sludge

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.

Dissolved organic matter (DOM) was detected in MSWI plant: An investigation of DOM and potential toxic elements variation in the bottom ash and fly ash

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.

[Engineering application of aerobic methane oxidizing bacteria (methanotrophs): a review]

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.

A novel waste-recycled chelating agent for the stabilization of lead in municipal solid waste incineration fly ash: Preparation, feasibility, and mechanism analysis

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.

Pyrolytic characteristics of fine materials from municipal solid waste using TG-FTIR, Py-GC/MS, and deep learning approach: Kinetics, thermodynamics, and gaseous products distribution

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 H₂O, CO₂, CH₄, 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 R² 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.

Waste plastic resource recovery from landfilled refuse: A novel waterless cleaning method and its cost-benefit analysis

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.

Comprehensive understanding the transition behaviors and mechanisms of chlorine and metal ions in municipal solid waste incineration fly ash during thermal treatment

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.

How to predict emissions of volatile organic compounds from solid building materials? A critical review on mass transfer models

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.

SSR4 as a prognostic biomarker and related with immune infiltration cells in colon adenocarcinoma

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.

Sewage denitrification performance and sludge properties variation with the addition of liquid from perishable organic anaerobic fermentation

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.

Mechanistic insights into promoted dewaterability, drying behaviors and methane-producing potential of waste activated sludge by Fe2+-activated persulfate oxidation

Sludge management represents a critical challenge because of complex compositions and poor dewaterability. Fe²⁺-activated persulfate oxidation (Fe²⁺/S₂O₈^2-) is an effective, and widely investigated method for enhancing sludge dewatering. However, the potential effects of Fe²⁺/S₂O₈^2- 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 Fe²⁺/S₂O₈^2-, and step ii: drying-incineration or anaerobic digestion) was proposed and appraised comprehensively. Results showed that Fe²⁺/S₂O₈^2- 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-Fe²⁺/S₂O₈^2-/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 S₂O₈^2- 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.

Simultaneous annihilation of microorganisms and volatile organic compounds from municipal solid waste storage rooms with slightly acidic electrolyzed water

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)/m³ 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.

Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

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 (R² is 95.3%) and the mechanism of 1D-CLA is contributed by three former models following the order: LSTM>CNN>Attention.

Single-cell profiling of infiltrating B cells and tertiary lymphoid structures in the TME of gastric adenocarcinomas

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.

A novel additional carbon source derived from rotten fruits: Application for the denitrification from mature landfill leachate and evaluation the economic benefits

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 (NO3--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 €/kgNO3--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.

Simultaneous remediation and fertility improvement of heavy metals contaminated soil by a novel composite hydrogel synthesized from food waste

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.

TAFs contributes the function of PTPN2 in colorectal carcinogenesis through activating JAK/STAT signaling pathway

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.

Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. In coping with it, histopathology image analysis (HIA) provides key information for clinical diagnosis of CRC. Nowadays, the deep learning methods are widely used in improving cancer classification and localization of tumor-regions in HIA. However, these efforts are both time-consuming and labor-intensive due to the manual annotation of tumor-regions in the whole slide images (WSIs). Furthermore, classical deep learning methods to analyze thousands of patches extracted from WSIs may cause loss of integrated information of image. Herein, a novel method was developed, which used only global labels to achieve WSI classification and localization of carcinoma by combining features from different magnifications of WSIs. The model was trained and tested using 1346 colorectal cancer WSIs from the Cancer Genome Atlas (TCGA). Our method classified colorectal cancer with an accuracy of 94.6 %, which slightly outperforms most of the existing methods. Its cancerous-location probability maps were in good agreement with annotations from three individual expert pathologists. Independent tests on 50 newly-collected colorectal cancer WSIs from hospitals produced 92.0 % accuracy and cancerous-location probability maps were in good agreement with the three pathologists. The results thereby demonstrated that the method sufficiently achieved WSI classification and localization utilizing only global labels. This weakly supervised deep learning method is effective in time and cost, as it delivered a better performance in comparison with the state-of-the-art methods.

Characterization of sedimentary phosphorus in Lake Erie and on-site quantification of internal phosphorus loading

Lake Erie harmful algal blooms and hypoxia are two major environmental problems, and have severe impacts on human health, aquatic ecosystems, and the economy. However, little is known about internal loading of phosphorus (P) from sediments, which pose a challenge for assessing the efficacy of current conservation measures on the improvement of lake water quality. A modified Hedley's extraction procedure was employed to analyze representative sediment samples collected from the Lake Erie basin for assessing sedimentary P stock, potential availability for release into lake water, and internal P loading. Inorganic and organic P in the sediments were characterized by sequential extractions in H₂O, 0.5 M NaHCO₃, 0.1 M NaOH, and 1.0 M HCl, respectively. In the 0 - 10 cm sediment, total P stock was 172, 191, and 170 metric tons km^-2 in the western, central, and eastern basins, respectively. Sedimentary P seems unlikely to contribute to internal P loading in the western basin, while in the eastern basin it can potentially contribute to an internal loading of 359 metric tons P yr^-1. In the central basin, 41% of organic P, 15% of non-HCl extractable inorganic P, and 9.7% of residual P in the 0 - 10 cm sediment is potentially available for release into lake water; in the 10 - 20 cm sediment, organic P extracted by NaHCO₃ and NaOH is also partially available. The central basin potentially contributes to internal P loading at a total amount of 10,599 metric tons yr^-1. Internal P loading may not contribute to HABs in the western basin, but it can cause and maintain hypoxia in the central basin and delay the recovery of lake water quality for a lengthy time period in response to external P reduction measures.

Adsorption behavior of the antibiotic levofloxacin on microplastics in the presence of different heavy metals in an aqueous solution

In recent years, the composite pollution of microplastics with organic pollutants and heavy metal ions in the water environment, including their combined toxicity, has received increasing attention. However, the mechanism underlying the joint effect of antibiotics and heavy metals on the surface behavior of microplastics has not been reported. The primary purpose of this article was to analyze the adsorption of levofloxacin (OFL) onto polyvinyl chloride (PVC) in an aqueous solution. The adsorption behavior was studied using kinetics, thermodynamics, and isotherm models, and the effects of several environmental factors, such as ionic strength, fulvic acid, and heavy metals, were determined. The adsorption kinetics and isotherms models indicated that the whole adsorption process was controlled by both intraparticle and outer diffusion, as well as chemical adsorption, which was the dominant mechanism. Based on the results of the thermodynamic experiment, the adsorption process was a nonspontaneous and exothermic reaction process. Furthermore, the presence of Cu²⁺, Zn²⁺, and Cr³⁺ ions significantly promoted the adsorption of OFL, but the presence of Cd²⁺ and Pb²⁺ ions inhibited its adsorption. At the same time, the presence of the ionic strength and fulvic acid remarkably restricted the adsorption process. These findings confirmed that electrostatic interactions, ion exchange, intermolecular hydrogen bonds, and halogen bond cooperation were the main adsorption mechanisms. This paper mainly discusses the interaction between combinations of pollutants with microplastics, which provides theoretical guidance for the interface behavior, migration and transformation of marine microplastics in the actual environment.