Validation of a triple-color pseudovirion-based neutralization assay for immunogenicity assessment of a 14-valent recombinant human papillomavirus vaccine.

Validation of bioanalytical methods is crucial, especially in the pharmaceutical industry, to determine their suitability for specific purposes and the accuracy of analytical results. The pseudovirion-based neutralization assay (PBNA) is considered the gold standard for detecting and quantifying neutralizing antibodies against human papillomavirus in vaccine development for disease prevention. This paper introduces an improved triple-color PBNA method, capable of simultaneous detection of two or three human papillomavirus (HPV types for use in the development of a 14-valent HPV vaccine candidate. The primary objective was to comprehensively validate the triple-color PBNA method for general vaccine immunogenicity assays. Results show that the method has good specificity, accuracy, precision, linearity, robustness, and applicability. This innovative triple-color PBNA offers an improved approach for large-scale immunogenicity assessment in vaccine development. This study lays a solid foundation that can serve as a guiding paradigm for assessing vaccine responses in preclinical and clinical phases, providing valuable insights to the field.

Lightweight strip steel defect detection algorithm based on improved YOLOv7.

The precise identification of surface imperfections in steel strips is crucial for ensuring steel product quality. To address the challenges posed by the substantial model size and computational complexity in current algorithms for detecting surface defects in steel strips, this paper introduces SS-YOLO (YOLOv7 for Steel Strip), an enhanced lightweight YOLOv7 model. This method replaces the CBS module in the backbone network with a lightweight MobileNetv3 network, reducing the model size and accelerating the inference time. The D-SimSPPF module, which integrates depth separable convolution and a parameter-free attention mechanism, was specifically designed to replace the original SPPCSPC module within the YOLOv7 network, expanding the receptive field and reducing the number of network parameters. The parameter-free attention mechanism SimAM is incorporated into both the neck network and the prediction output section, enhancing the ability of the model to extract essential features of strip surface defects and improving detection accuracy. The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance between detection accuracy and speed while maintaining a lightweight profile.

Steel Strip Surface Defect Detection Method Based on Improved YOLOv5s.

Steel strip is an important raw material for the engineering, automotive, shipbuilding, and aerospace industries. However, during the production process, the surface of the steel strip is prone to cracks, pitting, and other defects that affect its appearance and performance. It is important to use machine vision technology to detect defects on the surface of a steel strip in order to improve its quality. To address the difficulties in classifying the fine-grained features of strip steel surface images and to improve the defect detection rate, we propose an improved YOLOv5s model called YOLOv5s-FPD (Fine Particle Detection). The SPPF-A (Spatial Pyramid Pooling Fast-Advance) module was constructed to adjust the spatial pyramid structure, and the ASFF (Adaptively Spatial Feature Fusion) and CARAFE (Content-Aware ReAssembly of FEatures) modules were introduced to improve the feature extraction and fusion capabilities of strip images. The CSBL (Convolutional Separable Bottleneck) module was also constructed, and the DCNv2 (Deformable ConvNets v2) module was introduced to improve the model's lightweight properties. The CBAM (Convolutional Block Attention Module) attention module is used to extract key and important information, further improving the model's feature extraction capability. Experimental results on the NEU_DET (NEU surface defect database) dataset show that YOLOv5s-FPD improves the mAP50 accuracy by 2.6% before data enhancement and 1.8% after SSIE (steel strip image enhancement) data enhancement, compared to the YOLOv5s prototype. It also improves the detection accuracy of all six defects in the dataset. Experimental results on the VOC2007 public dataset demonstrate that YOLOv5s-FPD improves the mAP50 accuracy by 4.6% before data enhancement, compared to the YOLOv5s prototype. Overall, these results confirm the validity and usefulness of the proposed model.

Serum-Creatinine-to-Cystatin C-to-Waist-Circumference Ratios as an Indicator of Severe Airflow Limitation in Older Adults.

BACKGROUND: This study aimed to investigate the association between the serum-creatinine-to-cystatin C-to-waist-circumference (CCR/WC) ratio with lung function and severe airflow limitation (SAL). METHODS: The data were derived from the China Health and Retirement Longitudinal Study. Peak expiratory flow (PEF) was used as a measure of lung function parameter. Logistic and linear regression were utilized separately to evaluate the relationship between the CCR/WC ratio with PEF and SAL in baseline. Restricted cubic spline was used to explore potential non-linear associations between the CCR/WC ratio and SAL. Cox proportional-hazards models were used to assess the association between CCR/WC quartiles and the risk of new-onset SAL. RESULTS: A total of 6105 participants were included. This study revealed a positive association between the CCR/WC ratio and lung function (PEF: ß [partial coefficient]: 25.95, 95%CI: 12.72 to 39.18, p < 0.001; PEF/PEF prediction: ß = 0.08, 95%CI: 0.05 to 0.12, p < 0.001) and an inverse association relationship with SAL (OR [odds ratio]: 0.64, 95% confidence interval [CI]: 0.47 to 0.85, p = 0.003). Subgroup analysis showed a significant association between the CCR/WC ratio and SAL in males (OR: 0.58, 95% CI: 0.37 to 0.90, p = 0.017) but not in females (p = 0.059). Cox regression analysis revealed a decreased risk of SAL in the quartiles (Q2-4) compared to the first quartile of the CCR/WC ratio (hazard ratios [HRs]: 0.49 to 0.73, all p < 0.05). CONCLUSIONS: This study highlights a positive association between the CCR/WC ratio and lung function, with a potential protective effect against SAL.

Improved MobileNetV2 crop disease identification model for intelligent agriculture.

Using intelligent agriculture is an important way for the industry to achieve high-quality development. To improve the accuracy of the identification of crop diseases under conditions of limited computing resources, such as in mobile and edge computing, we propose an improved lightweight MobileNetV2 crop disease identification model. In this study, MobileNetV2 is used as the backbone network for the application of an improved Bottleneck structure. First, the number of operation channels is reduced using point-by-point convolution, the number of parameters of the model is reduced, and the re-parameterized multilayer perceptron (RepMLP) module is introduced; the latter can capture long-distance dependencies between features and obtain local a priori information to enhance the global perception of the model. Second, the efficient channel-attention mechanism is added to adjust the image-feature channel weights so as to improve the recognition accuracy of the model, and the Hardswish activation function is introduced instead of the ReLU6 activation function to further improve performance. The final experimental results show that the improved MobilNetV2 model achieves 99.53% accuracy in the PlantVillage crop disease dataset, which is 0.3% higher than the original model, and the number of covariates is only 0.9M, which is 59% less than the original model. Also, the inference speed is improved by 8.5% over the original model. The crop disease identification method proposed in this article provides a reference for deployment and application on edge and mobile devices.

Parameter-Efficient Densely Connected Dual Attention Network for Phonocardiogram Classification.

Cardiac auscultation, exhibited by phonocardiogram (PCG), is a non-invasive and low-cost diagnostic method for cardiovascular diseases (CVDs). However, deploying it in practice is quite challenging, due to the inherent murmurs and a limited number of supervised samples in heart sound data. To solve these problems, not only heart sound analysis based on handcrafted features, but also computer-aided heart sound analysis based on deep learning have been extensively studied in recent years. Though with elaborate design, most of these methods still use additional pre-processing to improve classification performance, which heavily relies on time-consuming experienced engineering. In this article, we propose a parameter-efficient densely connected dual attention network (DDA) for heart sound classification. It combines two advantages simultaneously of the purely end-to-end architecture and enriched contextual representations of the self-attention mechanism. Specifically, the densely connected structure can automatically extract the information flow of heart sound features hierarchically. Alongside, improving contextual modeling capabilities, the dual attention mechanism adaptively aggregates local features with global dependencies via a self-attention mechanism, which captures the semantic interdependencies across position and channel axes respectively. Extensive experiments across stratified 10-fold cross-validation strongly evidence that our proposed DDA model surpasses current 1D deep models on the challenging Cinc2016 benchmark with significant computational efficiency.

Coupling of microplastic contamination in organisms and the environment: Evidence from the tidal flat ecosystem of Hangzhou Bay, China.

Microplastics are a new type of contaminant, widely defined as fragmented plastics with the longest dimension or diameter less than 5 mm, that are widely distributed, difficult to degrade, and easily adsorb other pollutants. Estuaries are key habitats where terrestrial microplastics flow in water runoff and import into the ocean. The ubiquitous use of plastics has resulted in a massive amount of plastic waste that is released and accumulated in bay ecosystems, posing serious ecological impacts. The study of microplastic contamination in Hangzhou Bay, the estuary of the Qiantang River, has important theoretical value in ecology and environmental science. Microplastic contamination in the tidal flats and organisms of Hangzhou Bay is serious and microplastic characteristics (type, size, and polymer type) in organisms were significantly correlated with those in the environmental media. Spatial autocorrelation was found in the abundance of microplastics in marine and tidal flat sediments of Hangzhou Bay, China, but no spatial autocorrelation was found in the sediment environment as a whole. The microplastic abundance in each organism in this study was not statistically correlated by weight or by individual count with its corresponding trophic level (P = 0.239 > 0.05; P = 0.492 > 0.05, respectively). Our study suggests a coupling relationship of microplastic contamination between organisms and the environment and can provide essential data and a scientific foundation for the study of microplastics pollution in Hangzhou Bay, as well as provide important evidence for the ecological and health risk assessment of microplastics.

Optimization of a near-zero-emission energy system for the production of desalinated water and cooling using waste energy of fuel cells.

In the present study, a biomass-based multi-purpose energy system that can generate power, desalinated water, hydrogen, and ammonia is presented. The gasification cycle, gas turbine, Rankine cycle, PEM electrolyzer, ammonia production cycle using the Haber-Bosch process, and MSF water desalination cycle are the primary subsystems of this power plant. On the suggested system, a thorough thermodynamic and thermoeconomic evaluation has been conducted. For the analysis, the system is first modeled and investigated from an energy point of view, after which it is similarly studied from an exergy point of view before the system is subjected to economic analysis (exergoeconomic analysis). The system is evaluated and modeled using artificial intelligence to aid in the system optimization process after energy, exergy, and economic modeling and analysis. The resulting model is then optimized using a genetic algorithm to maximize system efficiency and reduce system expenses. EES software does the first analysis. After that, it sends the data to MATLAB program for optimization and to see how operational factors affect thermodynamic performance and overall cost rate. To find the best solution with the maximum energy efficiency and lowest total cost, multi-objective optimization is used. In order to shorten computation time and speed up optimization, the artificial neural network acts as a middleman in the process. In order to identify the energy system's optimal point, the link between the objective function and the choice factors has been examined. The results show that increasing the flow of biomass enhances efficiency, output, and cost while raising the temperature of the gas turbine's input decreases cost while simultaneously boosting efficiency. Additionally, according to the system's optimization results, the power plant's cost and energy efficiency are 37% and 0.3950$/s, respectively, at the ideal point. The cycle's output is estimated at 18900 kW at this stage.

Properties of Spherically Symmetric Black Holes in the Generalized Brans-Dicke Modified Gravitational Theory.

The many problems faced by the theory of general relativity (GR) have always motivated us to explore the modified theory of GR. Considering the importance of studying the black hole (BH) entropy and its correction in gravity physics, we study the correction of thermodynamic entropy for a kind of spherically symmetric black hole under the generalized Brans-Dicke (GBD) theory of modified gravity. We derive and calculate the entropy and heat capacity. It is found that when the value of event horizon radius r+ is small, the effect of the entropy-correction term on the entropy is very obvious, while for larger values r+, the contribution of the correction term on entropy can be almost ignored. In addition, we can observe that as the radius of the event horizon increases, the heat capacity of BH in GBD theory will change from a negative value to a positive value, indicating that there is a phase transition in black holes. Given that studying the structure of geodesic lines is important for exploring the physical characteristics of a strong gravitational field, we also investigate the stability of particles' circular orbits in static spherically symmetric BHs within the framework of GBD theory. Concretely, we analyze the dependence of the innermost stable circular orbit on model parameters. In addition, the geodesic deviation equation is also applied to investigate the stable circular orbit of particles in GBD theory. The conditions for the stability of the BH solution and the limited range of radial coordinates required to achieve stable circular orbit motion are given. Finally, we show the locations of stable circular orbits, and obtain the angular velocity, specific energy, and angular momentum of the particles which move in circular orbits.

Simulation of CO2 capture from natural gas by cyclic pressure swing adsorption process using activated carbon.

This work presented modeling and simulation of CO2 from natural gas. One of the most promising technologies is Pressure Swing Adsorption (PSA), which is an energy-efficient and cost-effective process for separating and capturing CO2 from industrial processes and power plants. This paper provides an overview of the PSA process and its application for CO2 capture, along with a discussion of its advantages, limitations, and future research directions. This process is pressure swing adsorption (PSA) with four adsorption beds. The adsorption bed columns fill with activated carbon as adsorbent. In this simulation momentum, mass and energy balance are solved simultaneously. The process was designed with two beds in adsorption conditions and the other two beds in desorption conditions. The desorption cycle includes blow-down and purge steps. The linear driving force (LDF) estimates the adsorption rate in modeling this process. The extended Langmuir isotherm is used for the equilibrium between solid and gas phases. The temperature changes by heat transfer from the gas phase to solid and axial heat dispersion. The set of partial differential equations is solved using implicit finite difference.

A Polysaccharide-RBD-Fc-Conjugated COVID-19 Vaccine, SCTV01A, Showed High Immunogenicity and Low Toxicity in Animal Models.

We previously developed a polysaccharide--RBD-conjugated nanoparticle vaccine which induced protective efficacy against SARS-CoV-2 in a mouse model. Here, we newly developed a vaccine, SCTV01A, by chemically conjugating recombinant SARS-CoV-2 RBD-Fc and PPS14 (Streptococcus pneumoniae serotype type 14 capsular polysaccharide). The immunogenicity and toxicity of SCTV01A were evaluated in animal models. The PPS14 conjugation enhanced the immunogenicity of RBD-Fc in C57BL/6 mice whether formulated with SCT-VA02B or Alum adjuvant. SCTV01A also induced high opsonophagocytic activity (OPA) against S. pneumoniae serotype 14. In addition, SCTV01A stimulated potent neutralizing titers in rhesus macaques and effectively reduced lung inflammation after SARS-CoV-2 infection with neither antibody-dependent enhancement (ADE) nor vaccine-enhanced diseases (VED) phenomenon. Importantly, the long-term toxicity study of SCTV01A in rhesus macaques did not cause any abnormal toxicity and was tolerated at the highest tested dose (120 µg). The existing immunogenicity and toxicological evaluation results have demonstrated the safety and efficacy of SCTV01A, which will be a promising and feasible vaccine to protect against SARS-CoV-2 infection.

Association Between Carbon Emission and Low Birth Weight in Mainland China.

OBJECTIVE: The aim of this study is to investigate the relationship between carbon emission and low birth weight (LBW). METHODS: A nested case-control study was contacted in mainland China. Multilevel logistic regression was used to estimate the effect of carbon emission on LBW. Generalized additive mixed effect model was performed to assess no-linear trend between LBW and carbon emission. RESULTS: Carbon emission was a risk factor for LBW (odds ratio, 1.182; 95% confidence interval, 1.011-1.383). Carbon emissions from power, residence, aviation, and transport department were risk factors for LBW (all P < 0.05). Moreover, generalized additive mixed effect model has shown that the risk of LBW decreased first and then increased as carbon emissions increased. CONCLUSIONS: Our study initially found that carbon emission may be a risk factor for LBW.

A spike-trimer protein-based tetravalent COVID-19 vaccine elicits enhanced breadth of neutralization against SARS-CoV-2 Omicron subvariants and other variants.

Multivalent vaccines combining crucial mutations from phylogenetically divergent variants could be an effective approach to defend against existing and future SARS-CoV-2 variants. In this study, we developed a tetravalent COVID-19 vaccine SCTV01E, based on the trimeric Spike protein of SARS-CoV-2 variants Alpha, Beta, Delta, and Omicron BA.1, with a squalene-based oil-in-water adjuvant SCT-VA02B. In the immunogenicity studies in naïve BALB/c and C57BL/6J mice, SCTV01E exhibited the most favorable immunogenic characteristics to induce balanced and broad-spectrum neutralizing potencies against pre-Omicron variants (D614G, Alpha, Beta, and Delta) and newly emerging Omicron subvariants (BA.1, BA.1.1, BA.2, BA.3, and BA.4/5). Booster studies in C57BL/6J mice previously immunized with D614G monovalent vaccine demonstrated superior neutralizing capacities of SCTV01E against Omicron subvariants, compared with the D614G booster regimen. Furthermore, SCTV01E vaccination elicited naïve and central memory T cell responses to SARS-CoV-2 ancestral strain and Omicron spike peptides. Together, our comprehensive immunogenicity evaluation results indicate that SCTV01E could become an important COVID-19 vaccine platform to combat surging infections caused by the highly immune evasive BA.4/5 variants. SCTV01E is currently being studied in a head-to-head immunogenicity comparison phase 3 clinical study with inactivated and mRNA vaccines (NCT05323461).

Tertiary treatment of bio-treated landfill leachate by a two-step electrochemical process including electrooxidation and electrocoagulation: a bench-scale trial.

A two-step electrochemical process including electrooxidation (EO) and electrocoagulation (EC) was proposed for the tertiary treatment of bio-treated landfill leachate (BTLL). The operating conditions of sole EO and EC technology were optimized via batch tests. Batch tests indicate that EO displayed superior removal efficiency towards color (89%) and UV254 (64%) under optimal experimental conditions. EC with the electrode combinations Fe-Fe-Fe-Fe (four plates, anode-cathode-anode-cathode) performed better than the other electrode combinations (Fe-Al-Fe-Al, Al-Fe-Al-Fe, Al-Al-Al-Al) and showed excellent removal efficiency towards COD (60%) and color (85%). In continuous-flow tests of 13 h, compared to sequential EC-EO process, the sequential EO-EC process was more effective than the sequential EC-EO process in reducing organic matters (COD, TOC) and residual chlorine. The sequential EO-EC process could remove 50% COD, 55% TOC, 72% UV254, and 96% color. The average concentration of residual chlorine in the final effluent of EO-EC process (147 mg/L) was significantly lower than that of EC-EO process (463 mg/L). UV-vis and GC-MS analyses indicate that the BTLL mainly contained humic acid and fulvic acid-like substances with unsaturated bonds. Conjugated unsaturated organics could be degraded into organic of small molecular weight after the sequential EO-EC process. EEM spectroscopic analysis revealed that soluble microbial byproducts became the predominant organics in the final effluent. This work verifies the synergism between EO and EC and provides some insights into the removal and degradation performance of organic substances in BTLL during the sequential EO-EC treatment.

Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome.

There generally exists a critical state or tipping point from a stable state to another in the development of colorectal cancer (CRC) beyond which a significant qualitative transition occurs. Gut microbiome sequencing data can be collected non-invasively from fecal samples, making it more convenient to obtain. Furthermore, intestinal microbiome sequencing data contain phylogenetic information at various levels, which can be used to reliably identify critical states, thereby providing early warning signals more accurately and effectively. Yet, pinpointing the critical states using gut microbiome data presents a formidable challenge due to the high dimension and strong noise of gut microbiome data. To address this challenge, we introduce a novel approach termed the specific network information gain (SNIG) method to detect CRC's critical states at various taxonomic levels via gut microbiome data. The numerical simulation indicates that the SNIG method is robust under different noise levels and that it is also superior to the existing methods on detecting the critical states. Moreover, utilizing SNIG on two real CRC datasets enabled us to discern the critical states preceding deterioration and to successfully identify their associated dynamic network biomarkers at different taxonomic levels. Notably, we discovered certain 'dark species' and pathways intimately linked to CRC progression. In addition, we accurately detected the tipping points on an individual dataset of type I diabetes.

Effects of Multimodal Analgesia Combined with Auricular Point Therapy on Physical and Mental Stress and Rehabilitation Quality of Patients with Meniscus Injury during the Perioperative Period.

Objective: To investigate the effect of multimodal analgesia combined with auricular point therapy on physical and mental stress and rehabilitation quality of patients with meniscus injury during the perioperative period. Methods: 148 patients in our hospital from October 2019 to October 2021 who were scheduled to undergo meniscus surgery were selected and grouped according to the order of file establishment, with 74 cases in each. The control group was given routine analgesia, and the observation group was given multimodal analgesia combined with auricular point therapy. The pain level (visual analogue scale (VAS)), physical and mental stress (heart rate (HR), mean arterial pressure (MAP), depression scale (PHQ-9), and anxiety scale (GAD-7)), complications, rehabilitation quality, and analgesia satisfaction were observed. Results: The VAS scores of pain in the observation group were lower than those in the control group at 6 hours before operation and at 6 hours, 24 hours, and 72 hours after operation (P < 0.05). The MAP, HR, PHQ-9, and GAD-7 scores of the observation group were lower than those of the control group 6 hours before operation (P < 0.05). There was no significant difference in MAP, HR, PHQ-9, and GAD-7 scores between the two groups at 6 hours and 24 hours after operation (P > 0.05). The analgesic satisfaction of the observation group was better than that of the control group (P < 0.05). The incidence of complications in the observation group was 8.11% compared with 12.16% in the control group, which was not statistically significant (P > 0.05). The first exhaust, getting out of bed, and hospital stay in the observation group were shorter than those in the control group (P < 0.05). Conclusion: Multimodal analgesia combined with auricular acupuncture therapy is effective in perioperative patients with meniscus injury. It can reduce perioperative pain, reduce physical and mental stress, and promote early postoperative recovery through a variety of analgesic mechanisms.

Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.

Objective: Preterm birth (PTB) was one of the leading causes of neonatal death. Predicting PTB in the first trimester and second trimester will help improve pregnancy outcomes. The aim of this study is to propose a prediction model based on machine learning algorithms for PTB. Method: Data for this study were reviewed from 2008 to 2018, and all the participants included were selected from a hospital in China. Six algorisms, including Naive Bayesian (NBM), support vector machine (SVM), random forest tree (RF), artificial neural networks (ANN), K-means, and logistic regression, were used to predict PTB. The receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess the performance of the model. Results: A total of 9550 pregnant women were included in the study, of which 4775 women had PTB. A total of 4775 people were randomly selected as controls. Based on 27 weeks of gestation, the area under the curve (AUC) and the accuracy of the RF model were the highest compared with other algorithms (accuracy: 0.816; AUC = 0.885, 95% confidence interval (CI): 0.873-0.897). Meanwhile, there was positive association between the accuracy and AUC of the RF model and gestational age. Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin were the main influence factors of PTB. Conclusion: The results indicated that the prediction model based on the RF algorithm had a potential value to predict preterm birth in the early stage of pregnancy. The important analysis of the RF model suggested that intervention for main factors of PTB in the early stages of pregnancy would reduce the risk of PTB.

Analysis on advances and characteristics of microplastic pollution in China's lake ecosystems.

China is the largest producer and consumer of plastics worldwide. Microplastic (MP) pollution has been a recent research hotspot in environmental science and ecology. This study collects and analyzes the statistical data for microplastics (MPs) 86 lakes in entire China's lake ecosystems in past five years (2016-2020), their range in area is 0.056-4543.000 km2 (average: 566.045 km2), and the water storage varies from 0.162 × 108 to 1050.000 × 108 m3 (average: 77.884 ×108 m3). The results showed (1) The MP abundance in lake surface water is significantly correlated with lake area (ρ = -0.562, p ＜0.01), provincial GDP (Gross Domestic Product, GDP) (ρ = 0.377, p = 0.002), GDP per capita (ρ = 0.346, p = 0.006), urban waste water discharge and ratio of agricultural land area (ρ = 0.369, p = 0.003). (2) The MP abundance in lake sediment is significantly correlated with per capita domestic volume of garbage disposal (ρ = -0.536, p ＜0.001), per capita urban waste water discharge (ρ = -0.544, p ＜0.001) and ratio of agricultural land area (ρ = 0.635, p ＜0.001). (3) Irrespective of whether the samples were from surface water or sediment, MPs were primarily transparent, and the dominant types were fragments, films, and fibers. In addition, the size of MPs samples was mostly less than 2 mm, and the major polymers were polyethylene (PE), polypropylene (PP), and polystyrene (PS). (4) The degree of MP pollution in organisms was related to the degree of environmental pollution. These findings could provide a theoretical basis for the control and management of MP pollution in China's lake ecosystems.

Response of substrate kinetics and biological mechanisms to various pH constrains for cultured Nitrobacter and Nitrospira in nitrifying bioreactor.

Nitrite (NO2-) oxidation is an essential step of biological nitrogen cycling in natural ecosystems, and is performed by chemolithoautotrophic nitrite-oxidizing bacteria (NOB). Although Nitrobacter and Nitrospira are regarded as representative NOB in nitrification systems, little attention has focused on kinetic characterisation of the coexistence of Nitrobacter and Nitrospira at various pH values. Here, we evaluate the substrate kinetics, biological mechanism and microbial community dynamics of an enrichment culture including Nitrobacter (17.5 ± 0.9%) and Nitrospira (7.2 ± 0.6%) in response to various pH constrains. Evaluation of the Monod equation at pH 6.0, 6.5, 7.0, 7.5, 8.0 and 8.5 showed that the enrichment had maximum rate (rmax) and maximum substrate affinity (KS) for NO2- oxidation at pH 7.0, which was also supported by the largest absolute abundance of Nitrobacter nxrA (5.26 × 107 copies per g wet sludge) and Nitrospira nxrB (1.975 × 109 copies per g wet sludge) genes. Moreover, the predominant species for the Nitrobacter-like nxrA were N. vulgaris and N. winogradskyi, while for the Nitrospira-like nxrB, the predominant species were N. japonica, N. calida and Ca. N. bockiana. Furthermore, the rmax was strongly and positively correlated with the abundance of the Nitrobacter nxrA or Nitrospira nxrB genes, or N. winogradsk, whereas KS was positively correlated with the abundance of Nitrobacter nxrA or Nitrospira nxrB genes or Ca. N. bockiana. Overall, this study could improve basis kinetic parameters and biological mechanism of NO2- oxidation in WWTPs.