Relating the carbon sources to denitrifying community in full-scale wastewater treatment plants.

Carbon source is a key factor determining the denitrifying effectiveness and efficiency in wastewater treatment plants (WWTPs). Whereas, the relationships between diverse and distinct denitrifying communities and their favorable carbon sources in full-scale WWTPs were not well-understood. This study performed a systematic analysis of the relationships between the denitrifying community and carbon sources by using 15 organic compounds from four categories and activated sludge from 8 full-scale WWTPs. Results showed that, diverse denitrifying bacteria were detected with distinct relative abundances in 8 WWTPs, such as Haliangium (1.98-4.08%), Dechloromonas (2.00-3.01%), Thauera (0.16-1.06%), Zoogloea (0.09-0.43%), and Rhodoferax (0.002-0.104%). Overall, acetate resulted in the highest denitrifying activities (1.21 to 4.62 mg/L/h/gMLSS), followed by other organic acids (propionate, butyrate and lactate, etc.). Detectable dissimilatory nitrate reduction to ammonium (DNRA) was observed for all 15 carbon sources. Methanol and glycerol resulted in the highest DRNA. Acetate, butyrate, and lactate resulted in the lowest DNRA. Redundancy analysis and 16S cDNA amplicon sequencing suggested that carbon sources within the same category tended to correlate to similar denitrifiers. Methanol and ethanol were primarily correlated to Haliangium. Glycerol and amino acids (glutamate and aspartate) were correlated to Inhella and Sphaerotilus. Acetate, propionate, and butyrate were positively correlated to a wide range of denitrifiers, explaining the high efficiency of these carbon sources. Additionally, even within the same genus, different amplicon sequence variants (ASVs) performed distinctly in terms of carbon source preference and denitrifying capabilities. These findings are expected to benefit carbon source formulation and selection in WWTPs.

Thermochromic Hydrogels with Adjustable Transition Behavior for Smart Windows.

With the fast economic development and accelerating urbanization, more and more skyscrapers made entirely of concrete and glass are being constructed. To keep a comfortable indoor environment, massive energy for air conditioning or heating appliances is consumed. A huge amount of heat (>30%) is gained or released through glass windows. Using smart windows with the capability to modulate light is an effective way to reduce building energy consumption. Thermochromic hydrogel is one of the potential smart window materials due to its excellent thermal response, high radiation-blocking efficiency, cost-effectiveness, biocompatibility, and good uniformity. In this work, polyhydroxypropyl acrylate (PHPA) hydrogels with controllable lower critical solution temperature (LCST) were prepared by photopolymerization. The transition temperature and transition rate under "static transition" conditions were investigated. Unlike "static" conditions in which the transition temperature was not affected by the initial and final temperature and heating/cooling ramp, the transition temperature varied with the rate of temperature change under dynamic conditions. The "dynamic" transition temperature of the PHPA hydrogel gradually increased with the increase of the heating rate. It was the result of the movement of the molecular chains lagging behind the temperature change when the temperature change was too fast. The results of the solar irradiation experiment by filling PHPA hydrogels into double glazing windows showed that the indoor temperature was about 15 °C lower than that of ordinary glass windows, indicating that it can significantly reduce the energy consumption of air conditioning. In addition, a wide range of adjustable transition temperatures and fast optical response make PHPA hydrogels potentially applicable to smart windows.

Development and Application of a Duplex RT-RPA Assay for the Simultaneous Detection of Cymbidium mosaic virus and Odontoglossum ringspot virus.

Cymbidium mosaic virus (CymMV) and Odontoglossum ringspot virus (ORSV) are among the world's most serious and widespread orchid viruses; they often infect orchids, causing devastating losses to the orchid industry. Therefore, it is critical to establish a method that can rapidly and accurately detect viruses in the field using simple instruments, which will largely reduce the further spread of viruses and improve the quality of the orchid industry and is suitable for mass promotion and application at grassroots agrotechnical service points. In this investigation, we established a rapid amplification method for virus detection at 39 °C for 35 min to detect the presence of CymMV and ORSV simultaneously, sensitively, and specifically in orchids. Primers for the capsid protein (CP)-encoding genes of both viruses were designed and screened, and the reaction conditions were optimized. The experimental amplification process was completed in just 35 min at 39 °C. There were no instances of nonspecific amplification observed when nine other viruses were present. The RPA approach had detection limits of 104 and 103 copies for pMD19T-CymMV and pMD19T-ORSV, respectively. Moreover, the duplex RT-RPA investigation confirmed sensitivity and accuracy via a comparison of detection results from 20 field samples with those of a gene chip. This study presents a precise and reliable detection method for CymMV and ORSV using RT-RPA. The results demonstrate the potential of this method for rapid virus detection. It is evident that this method could have practical applications in virus detection processes.

Automated and reusable deep learning (AutoRDL) framework for predicting response to neoadjuvant chemotherapy and axillary lymph node metastasis in breast cancer using ultrasound images: a retrospective, multicentre study.

Background: Previous deep learning models have been proposed to predict the pathological complete response (pCR) and axillary lymph node metastasis (ALNM) in breast cancer. Yet, the models often leveraged multiple frameworks, required manual annotation, and discarded low-quality images. We aimed to develop an automated and reusable deep learning (AutoRDL) framework for tumor detection and prediction of pCR and ALNM using ultrasound images with diverse qualities. Methods: The AutoRDL framework includes a You Only Look Once version 5 (YOLOv5) network for tumor detection and a progressive multi-granularity (PMG) network for pCR and ALNM prediction. The training cohort and the internal validation cohort were recruited from Guangdong Provincial People's Hospital (GPPH) between November 2012 and May 2021. The two external validation cohorts were recruited from the First Affiliated Hospital of Kunming Medical University (KMUH), between January 2016 and December 2019, and Shunde Hospital of Southern Medical University (SHSMU) between January 2014 and July 2015. Prior to model training, super-resolution via iterative refinement (SR3) was employed to improve the spatial resolution of low-quality images from the KMUH. We developed three models for predicting pCR and ALNM: a clinical model using multivariable logistic regression analysis, an image model utilizing the PMG network, and a combined model that integrates both clinical and image data using the PMG network. Findings: The YOLOv5 network demonstrated excellent accuracy in tumor detection, achieving average precisions of 0.880-0.921 during validation. In terms of pCR prediction, the combined modelpost-SR3 outperformed the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.833 vs 0.822 vs 0.806 vs 0.790 vs 0.712, all p < 0.05) in the external validation cohort (KMUH). Consistently, the combined modelpost-SR3 exhibited the highest accuracy in ALNM prediction, surpassing the combined modelpre-SR3, image modelpost-SR3, image modelpre-SR3, and clinical model (AUC: 0.825 vs 0.806 vs 0.802 vs 0.787 vs 0.703, all p < 0.05) in the external validation cohort 1 (KMUH). In the external validation cohort 2 (SHSMU), the combined model also showed superiority over the clinical and image models (0.819 vs 0.712 vs 0.806, both p < 0.05). Interpretation: Our proposed AutoRDL framework is feasible in automatically predicting pCR and ALNM in real-world settings, which has the potential to assist clinicians in optimizing individualized treatment options for patients. Funding: National Key Research and Development Program of China (2023YFF1204600); National Natural Science Foundation of China (82227802, 82302306); Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (JNU1AF-CFTP-2022-a01201); Science and Technology Projects in Guangzhou (202201020022, 2023A03J1036, 2023A03J1038); Science and Technology Youth Talent Nurturing Program of Jinan University (21623209); and Postdoctoral Science Foundation of China (2022M721349).

Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs.

RATIONALE AND OBJECTIVES: Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included. A total of 1180 cases were used for training and 145 cases were used for testing. We proposed a novel broad-learning system (BLS) and then compared the performance of BLS models using radiomic features and deep features as a source of input. The deep features were extracted using ResNet18 and VGG11, respectively. The diagnostic performances of these BLS models were evaluated with the area under the curve (AUC), sensitivity, and specificity. RESULTS: The incidence rate of osteoporosis in the training and test sets was 35.9% and 37.9%, respectively. The radiomic feature-based BLS model achieved higher testing AUC (0.802 vs. 0.654 vs. 0.632, both P = .002), sensitivity (78.2% vs. 56.4% vs. 50.9%), and specificity (82.2% vs. 74,4% vs. 75.6%) than the two deep feature-based BLS models. CONCLUSION: Our proposed radiomic feature-based BLS model has the potential to expand osteoporosis screening to a broader population by identifying osteoporosis on lumbar spine X-ray radiographs.

Establishment of a triplex TaqMan quantitative real-time PCR assay for simultaneous detection of Cymbidium mosaic virus, Odontoglossum ringspot virus and Cymbidium ringspot virus.

Orchids are significant ornamental plants whose viral infection results in substantial economic damage. Cymbidium mosaic virus (CymMV), Odontoglossum ringspot virus (ORSV), and Cymbidium ringspot virus (CymRSV) represent three important and prevalent orchid viruses. The detection system proposed in this study uses a triplex TaqMan quantitative real-time PCR assay to identify CymMV, ORSV, and CymRSV in a simultaneous manner. We designed specific primers and probes for CymMV, ORSV, and CymRSV, with amplified sequences of 156 bp, 148 bp, and 145 bp, respectively. The minimum detection limit of the triplex qRT-PCR assay for CymMV and CymRSV was 1 copy/assay, and the minimum detection limit was 10 copies/assay for ORSV. The minimum stable detection limits for CymMV, ORSV, and CymRSV were 10, 102, and 102 copies/assay, respectively. Therefore, this system exhibited higher sensitivity (approximately 10 to 104-fold) than RT-PCR. The intra-and interassay CVs of Cq values are less than 0.55 and 0.95%, respectively, indicating that the triplex assay is highly reliable and accurate. In addition, 66 samples from five different orchid genera were analyzed using the established assay and gene chip. The detection results demonstrated that the triplex probe qRT-PCR demonstrated higher sensitivity than the gene chip, indicating that the triplex real-time PCR assay could be used for the detection of field samples. Our findings suggest that the triplex real-time RT-PCR detection system represents a rapid, simple, and accurate tool for detecting CymMV, ORSV, and CymRSV on orchids.

Adsorption Characterization of Cu(II) and Cd(II) by a Magnetite-Chitosan Composite: Kinetic, Thermodynamic and Equilibrium Studies.

Optimizing the use of magnetite-chitosan composites for heavy metal adsorption has been of great interest due to their environmental friendliness. To gain insights into their potential with green synthesis, this study analyzed one of these composites through X-ray diffraction, Fourier-transform infrared spectroscopy and scanning electron microscopy. Adsorption properties were then explored via static experiments to evaluate the pH dependence, isotherms, kinetics, thermodynamics and regeneration adsorption of Cu(II) and Cd(II). Results disclosed that the optimum pH of adsorption was 5.0, the equilibrium time was about 10 min, and the capacity for Cu(II) and Cd(II) reached 26.28 and 18.67 mg/g, respectively. The adsorption amount of cations increased with temperature from 25 °C to 35 °C and decreased with further increase in temperature from 40 °C to 50 °C, which might be related to the unfolding of chitosan; the adsorption capacity was above 80% of the initial value after two regenerations and about 60% after five regenerations. The composite has a relatively rough outer surface, but its inner surface and porosity are not obvious; it has functional groups of magnetite and chitosan, and chitosan might dominate the adsorption. Consequently, this research proposes the value of maintaining green synthesis research to further optimize the composite system of heavy metal adsorption.

Silver Nanoparticles Cause Neural and Vascular Disruption by Affecting Key Neuroactive Ligand-Receptor Interaction and VEGF Signaling Pathways.

Introduction: Silver nanoparticles (AgNP) are widely used as coating materials. However, the potential risks of AgNP to human health, especially for neural and vascular systems, are still poorly understood. Methods: The vascular and neurotoxicity of various concentrations of AgNP in zebrafish were examined using fluorescence microscopy. In addition, Illumina high-throughput global transcriptome analysis was performed to explore the transcriptome profiles of zebrafish embryos after exposure to AgNP. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to elucidate the top 3000 differentially expressed genes (DEGs) between AgNP-exposed and control groups. Results: We systematically investigated the neural and vascular developmental toxicities of AgNP exposure in zebrafish. The results demonstrated that AgNP exposure could cause neurodevelopmental anomalies, including a small-eye phenotype, neuronal morphology defects, and inhibition of athletic abilities. In addition, we found that AgNP exposure induces angiogenesis malformation in zebrafish embryos. Further RNA-seq revealed that DEGs were mainly enriched in the neuroactive ligand-receptor interaction and vascular endothelial growth factor (Vegf) signaling pathways in AgNP-treated zebrafish embryos. Specifically, the mRNA levels of the neuroactive ligand-receptor interaction pathway and Vegf signaling pathway-related genes, including si:ch73-55i23.1, nfatc2a, prkcg, si:ch211-132p1.2, lepa, mchr1b, pla2g4aa, rac1b, p2ry6, adrb2, chrnb1, and chrm1b, were significantly regulated in AgNP-treated zebrafish embryos. Conclusion: Our findings indicate that AgNP exposure transcriptionally induces developmental toxicity in neural and vascular development by disturbing neuroactive ligand-receptor interactions and the Vegf signaling pathway in zebrafish embryos.

Nuclear translocation of cGAS orchestrates VEGF-A-mediated angiogenesis.

Cyclic GMP-AMP synthase (cGAS) senses cytosolic incoming DNA and consequently activates stimulator of interferon response cGAMP interactor 1 (STING) to mount immune response. Here, we show nuclear cGAS could regulate VEGF-A-mediated angiogenesis in an immune-independent manner. We found VEGF-A stimulation induces cGAS nuclear translocation via importin-ß pathway. Moreover, nuclear cGAS subsequently regulates miR-212-5p-ARPC3 cascade to modulate VEGF-A-mediated angiogenesis through affecting cytoskeletal dynamics and VEGFR2 trafficking from trans-Golgi network (TGN) to plasma membrane via a regulatory feedback loop. In contrast, cGAS deficiency remarkably impairs VEGF-A-mediated angiogenesis in vivo and in vitro. Furthermore, we found strong association between the expression of nuclear cGAS and VEGF-A, and the malignancy and prognosis in malignant glioma, suggesting that nuclear cGAS might play important roles in human pathology. Collectively, our findings illustrated the function of cGAS in angiogenesis other than immune surveillance, which might be a potential therapeutic target for pathological angiogenesis-related diseases.

Ca-La layered double hydroxide (LDH) for selective and efficient removal of phosphate from wastewater.

Adsorption showed advantages in removing phosphorus (P) at low concentrations. Desirable adsorbents should have sufficiently high adsorption capacity and selectivity. In this study, a Ca-La layered double hydroxide (LDH) was synthesized for the first time by using a simple hydrothermal coprecipitation method for phosphate removal from wastewater. A maximum adsorption capacity of 194.04 mgP/g was achieved, ranking on the top of known LDHs. Adsorption kinetic experiments showed that 0.02 g/L Ca-La LDH could effectively reduce PO43-P from 1.0 to <0.02 mg/L within 30 min. With the copresence of bicarbonate and sulfate at concentrations 17.1 and 35.7 times of that of PO43-P, the Ca-La LDH showed promising selectivity towards phosphate (with a reduction in the adsorption capacity of <13.6%). In addition, four other (Mg-La, Co-La, Ni-La, and Cu-La) LDHs containing different divalent metal ions were synthesized by using the same coprecipitation method. Results showed much higher P adsorption performance of the Ca-La LDH than those LDHs. Field Emission Electron Microscopy (FE-SEM)-Energy Dispersive Spectroscopy (EDS), X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Fourier Transform Infrared Spectroscopy (FTIR), and mesoporous analysis were performed to characterize and compare the adsorption mechanisms of different LDHs. The high adsorption capacity and selectivity of the Ca-La LDH were mainly explained by selective chemical adsorption, ion exchange, and inner sphere complexation.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models. RESULTS: The models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935-0.940), 0.919 (95%CI 0.916-0.922), and 0.959 (95%CI 0.956-0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800-0.807), 0.852 (95%CI 0.846-0.857), and 0.863 (95%CI 0.857-0.887) in the validation cohorts, respectively. CONCLUSION: We developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.

Improving sludge dewaterability by free nitrous acid and lysozyme pretreatment: Performances and mechanisms.

Reducing the water content of waste activated sludge (WAS) is critical for sludge treatment and disposal in wastewater treatment plants (WWTPs). In this study, a new combined conditioning processes by using lysozyme (LZM) and free nitrous acid (FNA) were proposed and demonstrated to enhance the dewaterability of WAS. The water content of sludge cake dropped from 82.82 % to 68.42 % (1 h FNA treatment + 1 h LZM treatment) and 69.52 % (6 h FNA treatment + 1 h LZM treatment) with the combined FNA and LZM treatment; and the corresponding capillary suction time (CST) reduction efficiency increased 49.29 % (1 h FNA treatment + 1 h LZM treatment) and 52.98 % (6 h FNA treatment + 1 h LZM treatment). A comprehensive investigation conducted in this study revealed the underlying mechanism of dewaterability improvement lies in the transformations of extracellular polymeric substances (EPS). The combined conditioning led to enhanced hydrophobicity in the sludge, as suggested by FTIR protein secondary structure and interfacial free energy. The reduced zeta potential and the potential barrier indicated the reduction of the repulsive force of sludge particles and the bound water content in the conditioned floc. The hydrophobicity, flow permeability and flocculability were enhanced after combined treatment, leading to the release of bound water.

ResNet Model Automatically Extracts and Identifies FT-NIR Features for Geographical Traceability of Polygonatum kingianum.

Medicinal plants have incredibly high economic value, and a practical evaluation of their quality is the key to promoting industry development. The deep learning model based on residual convolutional neural network (ResNet) has the advantage of automatic extraction and the recognition of Fourier transform near-infrared spectroscopy (FT-NIR) features. Models are difficult to understand and interpret because of unknown working mechanisms and decision-making processes. Therefore, in this study, artificial feature extraction methods combine traditional partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) models to understand and compare deep learning models. The results show that the ResNet model has significant advantages over traditional models in feature extraction and recognition. Secondly, preprocessing has a great impact on the feature extraction and feature extraction, and is beneficial for improving model performance. Competitive adaptive reweighted sampling (CARS) and variable importance in projection (VIP) methods screen out more feature variables after preprocessing, but the number of potential variables (LVs) and successive projections algorithm (SPA) methods obtained is fewer. The SPA method only extracts two variables after preprocessing, causing vital information to be lost. The VIP feature of traditional modelling yields the best results among the four methods. After spectral preprocessing, the recognition rates of the PLS-DA and SVM models are up to 90.16% and 88.52%. For the ResNet model, preprocessing is beneficial for extracting and identifying spectral image features. The ResNet model based on synchronous two-dimensional correlation spectra has a recognition accuracy of 100%. This research is beneficial to the application development of the ResNet model in foods, spices, and medicinal plants.

The complete chloroplast genome sequences of three lilies: genome structure, comparative genomic and phylogenetic analyses.

We sequenced and analyzed the complete chloroplast genomes of Lilium amoenum, Lilium souliei, and Nomocharis forrestii in detail, including the first sequence and structural comparison of Nomocharis forrestii. We found that the lengths and nucleotide composition of the three chloroplast genes showed little variation. The chloroplast genomes of the three Lilium species contain 87 protein coding genes (PCGs), 38 tRNAs, and 8 rRNA genes. The only difference is that Nomocharis forrestii had an additional infA pseudogene. In the sequence analysis of the Lilium chloroplast genomes, 216 SSRs, 143 pairs of long repeats, 571 SNPs, and 202 indels were detected. In addition, we identified seven hypervariable regions that can be used as potential molecular markers and DNA barcodes of Lilium through complete sequence alignment. The phylogenetic tree was constructed from the three chloroplast genome sequences of Lilium obtained here and 40 chloroplast genome sequences from the NCBI database (including 35 Lilium species, 4 Fritillaria species, and one species of Smilax). The analysis showed that the species clustering of the genus Lilium essentially conformed to the classical morphological classification system of Comber, but differences in the classification of individual species remained. In our report, we support the reclassification of Lilium henryi and Lilium rosthorniiy in the genus Lilium. In general, this study not only provides genome data for three Lilium species, but also provides a comparative analysis of the Lilium chloroplast genomes. These advances will help to identify Lilium species, clarify the phylogenetic analysis of the Lilium genus, and help to solve and improve the disputes and deficiencies in the traditional morphological classification.

A local tumor microenvironment acquired super-enhancer induces an oncogenic driver in colorectal carcinoma.

Tumors exhibit enhancer reprogramming compared to normal tissue. The etiology is largely attributed to cell-intrinsic genomic alterations. Here, using freshly resected primary CRC tumors and patient-matched adjacent normal colon, we find divergent epigenetic landscapes between CRC tumors and cell lines. Intriguingly, this phenomenon extends to highly recurrent aberrant super-enhancers gained in CRC over normal. We find one such super-enhancer activated in epithelial cancer cells due to surrounding inflammation in the tumor microenvironment. We restore this super-enhancer and its expressed gene, PDZK1IP1, following treatment with cytokines or xenotransplantation into nude mice, thus demonstrating cell-extrinsic etiology. We demonstrate mechanistically that PDZK1IP1 enhances the reductive capacity CRC cancer cells via the pentose phosphate pathway. We show this activation enables efficient growth under oxidative conditions, challenging the previous notion that PDZK1IP1 acts as a tumor suppressor in CRC. Collectively, these observations highlight the significance of epigenomic profiling on primary specimens.

Silver nanoparticles induce developmental toxicity via oxidative stress and mitochondrial dysfunction in zebrafish (Danio rerio).

Sliver nanoparticles (AgNPs) are widely used in industry, agriculture, and medicine, potentially resulting in adverse effects on human health and aquatic environments. Here, we investigated the developmental toxicity of zebrafish embryos with acute exposure to AgNPs. Our results demonstrated developmental defects in 4 hpf zebrafish embryos after exposure to different concentrations of AgNPs for 72 h. In addition, RNA-seq profiling of zebrafish embryos after AgNPs treatment. Further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in DNA replication initiation, oxidoreductase activity, DNA replication, cellular senescence, and oxidative phosphorylation signaling pathways in the AgNPs-treated group. Notably, we also found that AgNPs exposure could result in the accumulation of reactive oxygen species (ROS) and malondialdehyde (MDA), the inhibition of superoxide dismutase (SOD), catalase (CAT), and mitochondrial complex I-V activities, and the downregulated expression of SOD, CAT, and mitochondrial complex I-IV chain-related genes. Moreover, the expression of mitochondrion-mediated apoptosis signaling pathway-related genes, such as bax, bcl2, caspase-3, and caspase-9, was significantly regulated after AgNPs exposure in zebrafish. Therefore, these findings demonstrated that AgNPs exposure could cause oxidative stress, induce mitochondrial dysfunction, and ultimately lead to developmental toxicity.

Impact of divalent cations on lysozyme-induced solubilisation of waste-activated sludge: Perspectives of extracellular polymeric substances and surface electronegativity.

Lysozyme hydrolysis can accelerate waste-activated sludge (WAS) solubilisation, which can significantly shorten the process and promote the efficiency of anaerobic digestion. This study investigated the impact of divalent cations on lysozyme-induced solubilisation of WAS. The performance of lysozyme pretreatment was dramatically inhibited by Mg2+ and Ca2+. Compared to the control group, the amount of net SCOD, protein, and polysaccharides released to the supernatant were reduced by 36.6%, 44.7%, and 35.8%, respectively, in the presence of divalent cations. The extracellular polymeric substance (EPS) matrix became tightly bound, resulting in fewer proteins and polysaccharides being extracted from loosely-bound EPS (LB-EPS) with divalent cations, which was detrimental to the solubilisation of WAS. Divalent cations decreased the surface electronegativity of sludge particles and prolonged the adsorption of lysozymes by sludge flocs. More than 16.6% of total lysozymes remained in the liquid phase of WAS after 240 min Mg2+ and Ca2+ strengthened the binding among proteins and polysaccharides and promoted the intermolecular cross-linking of polysaccharides. The EPS matrix formed a dense spatial reticular structure that blocked the transfer of lysozymes from the EPS matrix to the pellet. As a result, the lysozymes accumulated in LB-EPS rather than hydrolysing the microorganism's cell wall. This study provides a new perspective on the restriction of WAS pretreatment with lysozymes and optimises the method of lysozyme-induced solubilisation of WAS.