ChromLoops: a comprehensive database for specific protein-mediated chromatin loops in diverse organisms.

Chromatin loops (or chromatin interactions) are important elements of chromatin structures. Disruption of chromatin loops is associated with many diseases, such as cancer and polydactyly. A few methods, including ChIA-PET, HiChIP and PLAC-Seq, have been proposed to detect high-resolution, specific protein-mediated chromatin loops. With rapid progress in 3D genomic research, ChIA-PET, HiChIP and PLAC-Seq datasets continue to accumulate, and effective collection and processing for these datasets are urgently needed. Here, we developed a comprehensive, multispecies and specific protein-mediated chromatin loop database (ChromLoops, https://3dgenomics.hzau.edu.cn/chromloops), which integrated 1030 ChIA-PET, HiChIP and PLAC-Seq datasets from 13 species, and documented 1 491 416 813 high-quality chromatin loops. We annotated genes and regions overlapping with chromatin loop anchors with rich functional annotations, such as regulatory elements (enhancers, super-enhancers and silencers), variations (common SNPs, somatic SNPs and eQTLs), and transcription factor binding sites. Moreover, we identified genes with high-frequency chromatin interactions in the collected species. In particular, we identified genes with high-frequency interactions in cancer samples. We hope that ChromLoops will provide a new platform for studying chromatin interaction regulation in relation to biological processes and disease.

PLA2G4A and ACHE modulate lipid profiles via glycerophospholipid metabolism in platinum-resistant gastric cancer.

BACKGROUND: Bioactive lipids involved in the progression of various diseases. Nevertheless, there is still a lack of biomarkers and relative regulatory targets. The lipidomic analysis of the samples from platinum-resistant in gastric cancer patients is expected to help us further improve our understanding of it. METHODS: We employed LC-MS based untargeted lipidomic analysis to search for potential candidate biomarkers for platinum resistance in GC patients. Partial least squares discriminant analysis (PLS-DA) and variable importance in projection (VIP) analysis were used to identify differential lipids. The possible molecular mechanisms and targets were obtained by metabolite set enrichment analysis and potential gene network screened. Finally, verified them by immunohistochemical of a tissue microarray. RESULTS: There were 71 differential lipid metabolites identified in GC samples between the chemotherapy-sensitivity group and the chemotherapy resistance group. According to Foldchange (FC) value, VIP value, P values (FC > 2, VIP > 1.5, p < 0.05), a total of 15 potential biomarkers were obtained, including MGDG(43:11)-H, Cer(d18:1/24:0) + HCOO, PI(18:0/18:1)-H, PE(16:1/18:1)-H, PE(36:2) + H, PE(34:2p)-H, Cer(d18:1 + hO/24:0) + HCOO, Cer(d18:1/23:0) + HCOO, PC(34:2e) + H, SM(d34:0) + H, LPC(18:2) + HCOO, PI(18:1/22:5)-H, PG(18:1/18:1)-H, Cer(d18:1/24:0) + H and PC(35:2) + H. Furthermore, we obtained five potential key targets (PLA2G4A, PLA2G3, DGKA, ACHE, and CHKA), and a metabolite-reaction-enzyme-gene interaction network was built to reveal the biological process of how they could disorder the endogenous lipid profile of platinum resistance in GC patients through the glycerophospholipid metabolism pathway. Finally, we further identified PLA2G4A and ACHE as core targets of the process by correlation analysis and tissue microarray immunohistochemical verification. CONCLUSION: PLA2G4A and ACHE regulated endogenous lipid profile in the platinum resistance in GC patients through the glycerophospholipid metabolism pathway. The screening of lipid biomarkers will facilitate earlier precision medicine interventions for chemotherapy-resistant gastric cancer. The development of therapies targeting PLA2G4A and ACHE could enhance platinum chemotherapy effectiveness.

Cell division cycle 42 attenuates high glucose-treated renal tubular epithelial cell apoptosis, fibrosis, and inflammation, but activates the PAK1/AKT pathway.

BACKGROUND: Cell division cycle 42 (CDC42) modulates metabolism, inflammation, and fibrosis to engage in the pathology of diabetic complications. This study intended to further investigate the influence of CDC42 on viability, apoptosis, inflammation, epithelial-mesenchymal transition, and fibrosis in high glucose (HG)-treated renal tubular epithelial cells. METHODS: HK-2 cells were exposed to HG medium (30 mM) to establish the diabetic nephropathy (DN) cellular model, then the cells were transfected with scramble overexpression control (oeNC) or CDC42 overexpression (oeCDC42) vectors. RESULTS: Both the level of CDC42 mRNA and protein were decreased in HG-treated HK-2 cells in a dose- and time-dependent manner. Then HG-treated HK-2 cells were proposed for the following experiments. It was found that CDC42 increased CCK-8 detected viability and EdU positive cells. On the contrary, CDC42 reduced cell apoptosis, which was reflected by decreased TUNEL positive rate, increased BCL2, and reduced BAX. Interestingly, CDC42 inhibited fibrosis, which was reflected by increased E-Cadherin, as well as decreased Vimentin, TGF-ß1, Collagen1, and α-SMA. Apart from these, CDC42 also attenuated proinflammatory cytokine production, including TNF-α, IL-1ß, and IL-6. Moreover, CDC42 activated the PAK1/AKT pathway, which was reflected by increased p-PAK1 and p-AKT. However, CDC42 did not affect p-ERK. CONCLUSION: CDC42 may retard DN progression via its regulation of renal tubular epithelial cell functions, which may be due to its stimulation of the PAK1/AKT pathway.

DeepSCI: scalable speckle correlation imaging using physics-enhanced deep learning.

In this Letter we present a physics-enhanced deep learning approach for speckle correlation imaging (SCI), i.e., DeepSCI. DeepSCI incorporates the theoretical model of SCI into both the training and test stages of a neural network to achieve interpretable data preprocessing and model-driven fine-tuning, allowing the full use of data and physics priors. It can accurately reconstruct the image from the speckle pattern and is highly scalable to both medium perturbations and domain shifts. Our experimental results demonstrate the suitability and effectiveness of DeepSCI for solving the problem of limited generalization generally encountered in data-driven approaches.

The emerging role of glycolysis and immune evasion in gastric cancer.

Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of cancer-related deaths worldwide. Similar to other types of tumors, GC cells undergo metabolic reprogramming and switch to a "predominantly glycolytic" metabolic pattern to promote its survival and metastasis, also known as "the Warburg effect", which is characterized by enhanced glucose uptake and lactate production. A large number of studies have shown that targeting cancer cells to enhanced glycolysis is a promising strategy, that can make cancer cells more susceptible to other conventional treatment methods of treatment, including chemotherapy, radiotherapy and immunotherapy, and so on. Therefore, this review summarizes the metabolic characteristics of glycolysis in GC cells and focuses on how abnormal lactate concentration can lead to immunosuppression through its effects on the differentiation, metabolism, and function of infiltrating immune cells, and how targeting this phenomenon may be a potential strategy to improve the therapeutic efficacy of GC.

Characterization of site-specific N-glycosylation signatures of isolated uromodulin from human urine.

Uromodulin (Umod, Tamm-Horsfall protein) is the most abundant urinary N-glycoprotein produced exclusively by the kidney. It can form filaments to antagonize the adhesion of uropathogens. However, the site-specific N-glycosylation signatures of Umod in healthy individuals and patients with IgA nephropathy (IgAN) remain poorly understood due to the lack of suitable isolation and analytical methods. In this study, we first presented a simple and fast method based on diatomaceous earth adsorption to isolate Umod. These isolated glycoproteins were digested by trypsin and/or Glu-C. Intact N-glycopeptides with or without HILIC enrichment were analyzed using our developed EThcD-sceHCD-MS/MS. Based on the optimized workflow, we identified a total of 780 unique intact N-glycopeptides (7 N-glycosites and 152 N-glycan compositions) from healthy individuals. As anticipated, these glycosites exhibited glycoform heterogeneity. Almost all N-glycosites were modified completely by the complex type, except for one N-glycosite (N275), which was nearly entirely occupied by the high-mannose type for mediating Umod's antiadhesive activity. Then, we compared the N-glycosylation of Umod between healthy controls (n = 9) and IgAN patients (n = 9). The N-glycosylation of Umod in IgAN patients will drastically decrease and be lost. Finally, we profiled the most comprehensive site-specific N-glycosylation map of Umod and revealed its alterations in IgAN patients. Our method provides a high-throughput workflow for characterizing the N-glycosylation of Umod, which can aid in understanding its roles in physiology and pathology, as well as serving as a potential diagnostic tool for evolution of renal tubular function.

Application of machine learning and deep learning methods for hydrated electron rate constant prediction.

Accurately determining the second-order rate constant with eaq- (keaq-) for organic compounds (OCs) is crucial in the eaq- induced advanced reduction processes (ARPs). In this study, we collected 867 keaq- values at different pHs from peer-reviewed publications and applied machine learning (ML) algorithm-XGBoost and deep learning (DL) algorithm-convolutional neural network (CNN) to predict keaq-. Our results demonstrated that the CNN model with transfer learning and data augmentation (CNN-TL&DA) greatly improved the prediction results and overcame over-fitting. Furthermore, we compared the ML/DL modeling methods and found that the CNN-TL&DA, which combined molecular images (MI), achieved the best overall performance (R2test = 0.896, RMSEtest = 0.362, MAEtest = 0.261) when compared to the XGBoost algorithm combined with Mordred descriptors (MD) (0.692, RMSEtest = 0.622, MAEtest = 0.399) and Morgan fingerprint (MF) (R2test = 0.512, RMSEtest = 0.783, MAEtest = 0.520). Moreover, the interpretation of the MD-XGBoost and MF-XGBoost models using the SHAP method revealed the significance of MDs (e.g., molecular size, branching, electron distribution, polarizability, and bond types), MFs (e.g, aromatic carbon, carbonyl oxygen, nitrogen, and halogen) and environmental conditions (e.g., pH) that effectively influence the keaq- prediction. The interpretation of the 2D molecular image-CNN (MI-CNN) models using the Grad-CAM method showed that they correctly identified key functional groups such as -CN, -NO2, and -X functional groups that can increase the keaq- values. Additionally, almost all electron-withdrawing groups and a small part of electron-donating groups for the MI-CNN model can be highlighted for estimating keaq-. Overall, our results suggest that the CNN approach has smaller errors when compared to ML algorithms, making it a promising candidate for predicting other rate constants.

Machine learning approaches to predict the apparent rate constants for aqueous organic compounds by ferrate.

The apparent second-order rate constant with hexavalent ferrate (Fe(VI)) (kFe(VI)) is a key indicator to evaluate the removal efficiency of a molecule by Fe(VI) oxidation. kFe(VI) is often determined by experiment, but such measurements can hardly catch up with the rapid growth of organic compounds (OCs). To address this issue, in this study, a total of 437 experimental second-order kFe(VI) rate constants at a range of conditions (pH and temperature) were used to train four machine learning (ML) algorithms (lasso regression (LR), ridge regression (RR), extreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM)). Using the Morgan fingerprint (MF)) of a range of organic compounds (OCs) as the input, the performance of the four algorithms was comprehensively compared with respect to the coefficient of determination (R2) and root-mean-square error (RMSE). It is shown that the RR, XGBoost, and LightGBM models displayed generally acceptable performance kFe(VI) (R2test > 0.7). In addition, the shapely additive explanation (SHAP) and feature importance methods were employed to interpret the XGBoost/LightGBM and RR models, respectively. The results showed that the XGBoost/LightGBM and RR models suggestd pH as the most important predictor and the tree-based models elucidate how electron-donating and electron-withdrawing groups influence the reactivity of the Fe(VI) species. In addition, the RR model share eight common features, including pH, with the two tree-based models. This work provides a fast and acceptable method for predicting kFe(VI) values and can help researchers better understand the degradation behavior of OCs by Fe(VI) oxidation from the perspective of molecular structure.

Concurrent inhibition of CDK2 adds to the anti-tumour activity of CDK4/6 inhibition in GIST.

BACKGROUND: Advanced gastrointestinal stromal tumour (GIST) is characterised by genomic perturbations of key cell cycle regulators. Oncogenic activation of CDK4/6 results in RB1 inactivation and cell cycle progression. Given that single-agent CDK4/6 inhibitor therapy failed to show clinical activity in advanced GIST, we evaluated strategies for maximising response to therapeutic CDK4/6 inhibition. METHODS: Targeted next-generation sequencing and multiplexed protein imaging were used to detect cell cycle regulator aberrations in GIST clinical samples. The impact of inhibitors of CDK2, CDK4 and CDK2/4/6 was determined through cell proliferation and protein detection assays. CDK-inhibitor resistance mechanisms were characterised in GIST cell lines after long-term exposure. RESULTS: We identify recurrent genomic aberrations in cell cycle regulators causing co-activation of the CDK2 and CDK4/6 pathways in clinical GIST samples. Therapeutic co-targeting of CDK2 and CDK4/6 is synergistic in GIST cell lines with intact RB1, through inhibition of RB1 hyperphosphorylation and cell proliferation. Moreover, RB1 inactivation and a novel oncogenic cyclin D1 resulting from an intragenic rearrangement (CCND1::chr11.g:70025223) are mechanisms of acquired CDK-inhibitor resistance in GIST. CONCLUSIONS: These studies establish the biological rationale for CDK2 and CDK4/6 co-inhibition as a therapeutic strategy in patients with advanced GIST, including metastatic GIST progressing on tyrosine kinase inhibitors.

Shedding Light on Luminescent Janus Nanoparticles: From Synthesis to Photoluminescence and Applications.

Luminescent Janus nanoparticles refer to a special category of Janus-based nanomaterials that not only exhibit dual-asymmetric surface nature but also attractive optical properties. The introduction of luminescence has endowed conventional Janus nanoparticles with many alluring light-responsive functionalities and broadens their applications in imaging, sensing, nanomotors, photo-based therapy, etc. The past few decades have witnessed significant achievements in this field. This review first summarizes well-established strategies to design and prepare luminescent Janus nanoparticles and then discusses optical properties of luminescent Janus nanoparticles based on downconversion and upconversion photoluminescence mechanisms. Various emerging applications of luminescent Janus nanoparticles are also introduced. Finally, opportunities and future challenges are highlighted with respect to the development of next-generation luminescent Janus nanoparticles with diverse applications.

Solar-induced chlorophyll fluorescence imaging spectrometer: design, manufacture, and evaluation.

A scientific imaging spectrometer has been presented for the observation of solar-induced chlorophyll fluorescence of vegetation in NIR waveband, which may provide a new method to scale SIF application from leaf to canopy for the research of terrestrial vegetation photosynthesis. The SIF imaging spectrometer accommodates a telescope with a medium spatial resolution (1 mrad) over a field of view of 20°, a high spectral resolution (0.3nm) to measure the fluorescence spectrum within two oxygen absorption bands (O2A and O2B), and a high numerical aperture (0.25) for high SNR. Both of transmission optical systems, with high etendue and dispersive prism-VPH grating (P-G) with high diffraction efficiency, have been utilized for the optical design of imaging spectrometer. The design and prototype present excellent optical performances as demonstrated by the latest simulation and calibration. The in-situ observation proves that the advanced SIF imaging spectrometer could provide precise fluorescence data. The instrument will highlight SIF signal retrieval strategies, techniques for field and airborne and satellite sensing, and applications of these capabilities in evaluation of photosynthesis and stress effects for fluorescence science.

Exploring Pathways and Mechanisms for Dichloroacetonitrile Formation from Typical Amino Compounds during UV/Chlorine Treatment.

The formation of disinfection byproducts (DBPs) during UV/chlorine treatment, especially nitrogenous DBPs, is not well understood. This study investigated the formation mechanisms for dichloroacetonitrile (DCAN) from typical amino compounds during UV/chlorine treatment. Compared to chlorination, the yields of DCAN increase by 88-240% during UV/chlorine treatment from real waters, while the yields of DCAN from amino compounds increase by 3.3-5724 times. Amino compounds with electron-withdrawing side chains show much higher DCAN formation than those with electron-donating side chains. Phenylethylamine, l- phenylalanine, and l-phenylalanyl-l-phenylalanine were selected to represent amines, amino acids, and peptides, respectively, to investigate the formation pathways for DCAN during UV/chlorine treatment. First, chlorination of amines, amino acids, and peptides rapidly forms N-chloramines via chlorine substitution. Then, UV photolysis but not radicals promotes the transformation from N-chloramines to N-chloroaldimines and then to phenylacetonitrile, with yields of 5.4, 51.0, and 19.8% from chlorinated phenylethylamine, l-phenylalanine, and l-phenylalanyl-l-phenylalanine to phenylacetonitrile, respectively. Finally, phenylacetonitrile is transformed to DCAN with conversion ratios of 14.2-25.6%, which is attributed to radical oxidation, as indicated by scavenging experiments and density functional theory calculations. This study elucidates the pathways and mechanisms for DCAN formation from typical amino compounds during UV/chlorine treatment.

Near-infrared mediated orthogonal bioimaging and intracellular tracking of upconversion nanophotosensitizers.

Although upconversion photodynamic therapy (PDT) has gained extensive interests in disease treatment, the intracellular migration pathway of upconversion photosensitizers and underlying cell-particle interaction mechanism is still largely unexplored. In this work photoswitchable upconversion nanoparticles (UCNPs) are reported  that can release orthogonal emissions excited by two near-infrared lights, i.e., red color of 980-nm and green color of 808-nm light excitation. Taking advantage of the dual-emissive property, a methodology based on Pearson's correlation analysis is proposed to verify the accuracy of upconversion luminescence signals under different excitation lights, which has been previously neglected. Meanwhile, we have designed a near-infrared mediated bioimaging nanoplatform that can generate reactive oxygen species (ROS) using one light and simultaneously track the location of upconversion photosensitizers using another excitation light. Our study not only depicts the migration pathway of upconversion photosensitizers, but also demonstrates the organelle escape of these upconversion nanoparticles via PCI (photochemical internalization) process. It is believed that our results inspire more efficient synergistic therapy by combining PDT with other modalities in a programmable manner.

Non-line-of-sight imaging under white-light illumination: a two-step deep learning approach.

Non-line-of-sight (NLOS) imaging has received considerable attentions for its ability to recover occluded objects from an indirect view. Various NLOS imaging techniques have been demonstrated recently. Here, we propose a white-light NLOS imaging method that is equipped only with an ordinary camera, and not necessary to operate under active coherent illumination as in other existing NLOS systems. The central idea is to incorporate speckle correlation-based model into a deep neural network (DNN), and form a two-step DNN strategy that endeavors to learn the optimization of the scattered pattern autocorrelation and object image reconstruction, respectively. Optical experiments are carried out to demonstrate the proposed method.

Systems analysis of plasma IgG intact N-glycopeptides from patients with chronic kidney diseases via EThcD-sceHCD-MS/MS.

Immunoglobulin G (IgG) molecules modulate an immune response. However, site-specific N-glycosylation signatures of plasma IgG in patients with chronic kidney disease (CKD) remain unclear. This study aimed to propose a novel method to explore the N-glycosylation pattern of IgG and to compare it with reported methods. We separated human plasma IgG from 58 healthy controls (HC) and 111 patients with CKD. Purified IgG molecules were digested by trypsin. Tryptic peptides without enrichment of intact N-glycopeptides were analyzed using a combination of electron-transfer/higher-energy collisional dissociation (EThcD) and stepped collision energy/higher-energy collisional dissociation (sceHCD) mass spectrometry (EThcD-sceHCD-MS/MS). This resulted in higher spectral quality, more informative fragment ions, higher Byonic score, and nearly twice the depth of intact N-glycopeptide identification than sceHCD or EThcD alone. Site-specific N-glycosylation mapping revealed that intact N-glycopeptides were differentially expressed in HC and CKD patients; thus, it can be a diagnostic tool. This study provides a method for the determination of glycosylation patterns in CKD and a framework for understanding the role of IgG in the pathophysiology of CKD. Data are available via ProteomeXchange with identifier PXD027174.

Mechanochemical preparation of chrysomycin A self-micelle solid dispersion with improved solubility and enhanced oral bioavailability.

BACKGROUND: Chrysomycin A (CA) has been reported as numerous excellent biological activities, such as antineoplastic and antibacterial. Though, poor solubility of CA limited its application in medical field. Due to good amphiphilicity and potential anticancer effect of disodium glycyrrhizin (Na2GA) as an excipient, an amorphous solid dispersion (Na2GA/CA-BM) consisting of CA and Na2GA was prepared in the present study by mechanochemical technology (roll mill ML-007, zirconium balls, 30 rpm, 2.5 h) to improve the solubility and oral bioavailability of CA. Then, Na2GA/CA-BM was self-assembled to micelles in water. The interaction of CA and Na2GA in solid state were investigated by X-ray diffraction studies, polarized light microscopy, and scanning electron microscope. Meanwhile, the properties of the sample solution were analyzed by dynamic light scattering and transmission electron. Furthermore, the oral bioavailability and antitumor ability of Na2GA/CA-BM in vivo were tested, providing a theoretical basis for future application of CA on cancer therapy. RESULTS: CA encapsulated by Na2GA was self-assembled to nano-micelles in water. The average diameter of nano-micelle was 131.6 nm, and zeta potential was - 11.7 mV. Three physicochemical detections showed that CA was transformed from crystal into amorphous form after treated with ball milling and the solubility increased by 50 times. Na2GA/CA-BM showed a significant increase of the bioavailability about two time that of free CA. Compared with free CA, the in-vivo antitumor studies also exhibited that Na2GA/CA-BM had an excellent inhibition of tumor growth. CONCLUSIONS: Na2GA/CA-BM nanoparticles (131.6 nm, - 11.7 mV) prepared by simple and low-cost mechanochemical technology can improve oral bioavailability and antitumor efficacy of CA in vivo, suggesting a potential formulation for efficient anticancer treatment.

Bioengineered Ferritin Nanocarriers for Cancer Therapy.

Ferritin naturally exists in most organisms and can specifically recognize the transferrin 1 receptor (TfR1), which is generally highly expressed on various types of tumor cells. The pH dependent reversible assembling and disassembling property of ferritin renders it as a suitable candidate for encapsulating a variety of anticancer drugs and imaging probes. Ferritins external surface is chemically and genetically modifiable which can serve as attachment site for tumor specific targeting peptides or moieties. Moreover, the biological origin of these protein cages makes it a biocompatible nanocarrier that stabilizes and protects the enclosed particles from the external environment without provoking any toxic or immunogenic responses. Recent studies, further establish ferritin as a multifunctional nanocarrier for targeted cancer chemotherapy and phototherapy. In this review, we introduce the favorable characteristics of ferritin drug carriers, the specific targeted surface modification and a multifunctional nanocarriers combined chemotherapy with phototherapy for tumor treatment. Taken together, ferritin is a potential ideal base of engineered nanoparticles for tumor therapy and still needs to explore more on its way.

An Integrated Mass Spectroscopy Data Processing Strategy for Fast Identification, In-Depth, and Reproducible Quantification of Protein O-Glycosylation in a Large Cohort of Human Urine Samples.

Protein O-glycosylation has long been recognized to be closely associated with many diseases, particularly with tumor proliferation, invasion, and metastasis. The ability to efficiently profile the variation of O-glycosylation in large-scale clinical samples provides an important approach for the development of biomarkers for cancer diagnosis and for therapeutic response evaluation. Therefore, mass spectrometry (MS)-based techniques for high throughput, in-depth and reliable elucidation of protein O-glycosylation in large clinical cohorts are in high demand. However, the wide existence of serine and threonine residues in the proteome and the tens of mammalian O-glycan types lead to extremely large searching space composed of millions of theoretical combinations of peptides and O-glycans for intact O-glycopeptide database searching. As a result, an exceptionally long time is required for database searching, which is a major obstacle in O-glycoproteome studies of large clinical cohorts. More importantly, because of the low abundance and poor ionization of intact O-glycopeptides and the stochastic nature of data-dependent MS2 acquisition, substantially elevated missing data levels are inevitable as the sample number increases, which undermines the quantitative comparison across samples. Therefore, we report a new MS data processing strategy that integrates glycoform-specific database searching, reference library-based MS1 feature matching and MS2 identification propagation for fast identification, in-depth, and reproducible label-free quantification of O-glycosylation of human urinary proteins. This strategy increases the database searching speeds by up to 20-fold and leads to a 30%-40% enhanced intact O-glycopeptide quantification in individual samples with an obviously improved reproducibility. In total, we identified 1300 intact O-glycopeptides in 36 healthy human urine samples with a 30%-40% reduction in the amount of missing data. This is currently the largest dataset of urinary O-glycoproteome and demonstrates the application potential of this new strategy in large-scale clinical investigations.

Comparative Study for Interactions of Sulfate Radical and Hydroxyl Radical with Phenol in the Presence of Nitrite.

Sulfate radical (SO4•-)- and hydroxyl radical (HO•)-based advanced oxidation processes (AOPs) are effective for the removal of organic pollutants in water treatment. This study compared the interactions of SO4•- and HO• for the transformation of phenol in UV/peroxydisulfate (PDS) and UV/H2O2 with the presence of NO2-, which is widely present in aquatic environments and transforms SO4•- and HO• to •NO2. By using laser flash photolysis, the products of phenol reacting with SO4•- and HO• were demonstrated to be phenoxy radical and phenol-HO-adduct radical, respectively. This result, along with density functional theory (DFT) calculations, indicate that the predominant reaction mechanisms of phenol with SO4•- and HO• with phenol are electron transfer and addition, respectively. The different mechanisms induced the much higher formation of nitrophenols by SO4•- than HO• in the presence of NO2- through the fast combination of phenoxy radicals and •NO2. The conversion yields of phenol to nitrophenols (including 2-nitrophenol and 4-nitrophenol), were 47.5% by SO4•- versus 5.3% by HO• at the experimental conditions. Increasing PDS/H2O2 dosages from 0.2 to 1 mM resulted in a 61.9% increase of nitrophenol conversion yield in UV/PDS/NO2- but a 35.4% decrease of that in UV/H2O2/NO2-. In addition, the significant formation of phenoxy radicals by SO4•- also induced many nitrated polymers in UV/PDS/NO2-, while those induced in UV/H2O2/NO2- were negligible. The significant formation of nitrophenols and nitrated polymers increased the mutagenicity by 860.5% when the removal rate of phenol was 98% by UV/PDS/NO2-. This is the first study to demonstrate the different mechanisms of phenol transformation by SO4•- and HO• in the presence of NO2-.

Roles of Bromine Radicals and Hydroxyl Radicals in the Degradation of Micropollutants by the UV/Bromine Process.

The inevitable occurrence of Br- in natural water affects the degradation kinetics of micropollutants in the UV/chlorine process, the radical chemistry of which, however, is largely unclear. As Br- in the UV/chlorine process first forms free bromine (HOBr/OBr-), this study investigated the radical chemistry of the UV/bromine process for the degradation of selected micropollutants resistant to bromine, i.e., ibuprofen and benzoate, to focus on the roles of radicals. The actual quantum yields of HOBr and OBr- by UV photolysis at 254 nm are 0.43 (±0.025) and 0.26 (±0.025) mol Einstein-1, respectively. Br• and HO• are generated first, and then, Br2•- is formed, with the signal detectable at 360 nm by laser flash photolysis. Compared with Cl• in the UV/chlorine system, Br• exists at higher concentrations (∼10-12 M) in the UV/bromine system while HO• exists at similar concentrations. In the UV/bromine process, reactive bromine species (RBS) dominates the degradation of ibuprofen, while HO• dominates the degradation of benzoate. Br• and Br2•- are reactive toward ibuprofen which second-order rate constants (k) were determined to be 2.2 × 109 and 5.3 × 107 M-1 s-1, respectively, by laser flash photolysis. Br• was the major RBS for ibuprofen degradation by the UV/bromine treatment, whereas Br2•- increasingly contributed to ibuprofen degradation with increasing free bromine or Br- concentrations. Br• could be scavenged by HCO3- and natural organic matter (NOM), and the k with NOM was determined to be 2.6 × 104 (mg/L)-1 s-1. Both Br• and Br2•- prefer to react with ibuprofen via electron transfer with activation energy barriers (Δ‡G0SET) of 1.35 and 7.78 kcal mol-1, respectively. RBS promoted the formation of hydroxylated products. Then free bromine, rather than RBS, was responsible for the formation of brominated products, increasing the total organic bromine (TOBr) and tribromomethane yields in the UV/bromine system. This study demonstrates for the first time the roles of RBS and HO• in micropollutant degradation in the UV/bromine process.