Predicting Rate Constants of Reactive Chlorine Species toward Organic Compounds by Combining Machine Learning and Quantum Chemical Calculation

Reactive chlorine species (RCS), such as chlorine (HOCl/OCl–), chlorine dioxide (ClO2), chlorine atom (Cl•), and dichlorine radical (Cl2•–), play a crucial role in oxidation and disinfection worldwide. In this study, we developed machine learning (ML)-based quantitative structure–activity relationship (QSAR) models to predict the rate constants of RCS toward organic compounds by using quantum chemical descriptors (QDs) and Morgan fingerprints (MFs) as input features along with three tree-based ML algorithms. The ML-based models (RMSEtest = 0.528–1.131) outperform multiple linear regression-based models (RMSEtest = 0.772–4.837). Moreover, the QSAR models developed by combining QDs and MFs as input features (RMSEtest = 0.528–0.948) show better prediction performance than that by QDs (RMSEtest = 0.616–1.875) or MFs alone (RMSEtest = 0.636–1.439) for all four RCS. The SHapely Additive exPlanation (SHAP) analysis reveals that the energy of the highest occupied molecular orbital (EHOMO), charge, and −O––NH2 and −CO are the most important descriptors affecting the rate constants of RCS. This study demonstrates that the combination of QDs and MFs as input features achieves much better model prediction performance for RCS, which can be extrapolated to other oxidants in water treatment

Predicting Rate Constants of Reactive Chlorine Species toward Organic Compounds by Combining Machine Learning and Quantum Chemical Calculation

Reactive chlorine species (RCS), such as chlorine (HOCl/OCl–), chlorine dioxide (ClO2), chlorine atom (Cl•), and dichlorine radical (Cl2•–), play a crucial role in oxidation and disinfection worldwide. In this study, we developed machine learning (ML)-based quantitative structure–activity relationship (QSAR) models to predict the rate constants of RCS toward organic compounds by using quantum chemical descriptors (QDs) and Morgan fingerprints (MFs) as input features along with three tree-based ML algorithms. The ML-based models (RMSEtest = 0.528–1.131) outperform multiple linear regression-based models (RMSEtest = 0.772–4.837). Moreover, the QSAR models developed by combining QDs and MFs as input features (RMSEtest = 0.528–0.948) show better prediction performance than that by QDs (RMSEtest = 0.616–1.875) or MFs alone (RMSEtest = 0.636–1.439) for all four RCS. The SHapely Additive exPlanation (SHAP) analysis reveals that the energy of the highest occupied molecular orbital (EHOMO), charge, and −O––NH2 and −CO are the most important descriptors affecting the rate constants of RCS. This study demonstrates that the combination of QDs and MFs as input features achieves much better model prediction performance for RCS, which can be extrapolated to other oxidants in water treatment

Supplementary document for DeepSCI: Scalable speckle correlation imaging using physics-enhanced deep learning - 6334775.pdf

This document provides supplementary information to "DeepSCI: Scalable speckle correlation imaging using physics enhanced deep learning"

Data_Sheet_1_Collectivism, face concern and Chinese-style lurking among university students: the moderating role of trait mindfulness.CSV

IntroductionThis study focuses on understanding the unique causes and mechanisms of “Chinese-style lurking” on WeChat among university students, within a cultural context that emphasizes collectivism and face concern. The research also looks into the moderating role of trait mindfulness.MethodsFor the confirmation of these phenomena and to validate the theories, a structural equation model was constructed using the Stress-Strain-Outcome (SSO) theory and mindfulness buffering theory. The model was then tested and validated with data from 1,453 valid online surveys. These data were analyzed using the SmartPLS 4.0 software.ResultsThe results indicate that collectivism increases face concern, which in turn escalates online social anxiety. Face concern completely mediates between collectivism and online social anxiety, creating a serial mediation effect between face concern, online social anxiety, and lurking behavior. Additionally, trait mindfulness was found to negatively modulate the pathways from collectivism to face concern and from online social anxiety to lurking.DiscussionThe findings underscore the influence of traditional Chinese culture on contemporary students' online behavior and provide a new perspective for understanding social media lurking in an Eastern context. The results suggest that a mindfulness-based approach could be used to mitigate the associated silence and anxiety.</p

Investigation of Supercritical Methane Adsorption of Overmature Shale in Wufeng-Longmaxi Formation, Southern Sichuan Basin, China

Accurately determining the gas sorption capacity of a specific shale reservoir is critical for further assessment of shale gas reserves. A series of high-pressure methane adsorption measurements were conducted at 60 °C with a pressure of up to 30 MPa for Wufeng-Longmaxi shales from the southern Sichuan Basin, which is considered as the most promising shale-gas target in China, to evaluate the fitting quality of different excess adsorption models and to determine the effect of organic matter content, maturity, mineralogy, and pore structure on the gas adsorption capacity. Both the Langmuir- and supercritical Dubinin–Radushkevich (SDR)-based adsorption models are closely fitted with the measured excess adsorption amount. However, the freely fitted SDR model is considered to be the most reasonable model, in which the adsorbed-phase density is always lower than the liquid methane density at the boiling point (0.424 g/cm3) and the average relative error (ARE) is relatively small. Adsorbed-phase density is a key parameter for calculating absolute adsorption isotherms. For a specific shale, a lower constant adsorbed-phase density applied in the adsorption model would result in higher absolute adsorption capacity. For the Langmuir-based model, the actual absolute adsorption capacity would be underestimated when adsorption experiments were only conducted at the low-pressure range (0–15 MPa). The methane adsorption capacities show a great positive correlation with the total organic carbon (TOC) content. The TOC-normalized adsorption capacities have a negative relationship with maturity at an overmature stage. The clay content shows a positive correlation with the TOC-normalized adsorption capacities, indicating that clays also make some contribution to methane sorption on these organic-rich shales. Furthermore, methane adsorption in overmature shales is mainly controlled by the structure of the pore <20 nm in size, revealing that the adsorbed methane is occupied not only in micropores but also in fine mesopores

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

Legumain/pH dual-responsive lytic peptide–paclitaxel conjugate for synergistic cancer therapy

After molecule targeted drug, monoclonal antibody and antibody–drug conjugates (ADCs), peptide–drug conjugates (PDCs) have become the next generation targeted anti-tumor drugs due to its properties of low molecule weight, efficient cell penetration, low immunogenicity, good pharmacokinetic and large-scale synthesis by solid phase synthesis. Herein, we present a lytic peptide PTP7-drug paclitaxel conjugate assembling nanoparticles (named PPP) that can sequentially respond to dual stimuli in the tumor microenvironment, which was designed for passive tumor-targeted delivery and on-demand release of a tumor lytic peptide (PTP-7) as well as a chemotherapeutic agent of paclitaxel (PTX). To achieve this, tumor lytic peptide PTP-7 was connected with polyethylene glycol by a peptide substrate of legumain to serve as hydrophobic segments of nanoparticles to protect the peptide from enzymatic degradation. After that, PTX was connected to the amino group of the polypeptide side chain through an acid-responsive chemical bond (2-propionic-3-methylmaleic anhydride, CDM). Therefore, the nanoparticle (PPP) collapsed when it encountered the weakly acidic tumor microenvironment where PTX molecules fell off, and further triggered the cleavage of the peptide substrate by legumain that is highly expressed in tumor stroma and tumor cell surface. Moreover, PPP presents improved stability, improved drug solubility, prolonged blood circulation and significant inhibition ability on tumor growth, which gives a reasonable strategy to accurately deliver small molecule drugs and active peptides simultaneously to tumor sites.</p

An Integrated Mass Spectroscopy Data Processing Strategy for Fast Identification, In-Depth, and Reproducible Quantification of Protein <i>O</i>‑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

An Integrated Mass Spectroscopy Data Processing Strategy for Fast Identification, In-Depth, and Reproducible Quantification of Protein <i>O</i>‑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