Synergistic disinfection effects and reduction of disinfection by-products in water treatment using magnetic quaternized cyclodextrin polymer combined with chorine disinfection process.

Disinfection is vital in ensuring water safety. However, the traditional chlorine disinfection process is prone to producing toxic and harmful disinfection by-products (DBPs). The combination of quaternary ammonium polymer and the chlorine disinfection process can solve this shortcoming. Currently, research on the control of DBPs through the combined process is not systematic and the control effect between reducing the dosage of disinfectants and DBPs remains to be studied. Quaternized cyclodextrin polymers have attracted increasing attention due to their excellent adsorption and antibacterial properties, but their synergistic effect with chlorine disinfection is still unclear. In this study, a magnetic quaternized cyclodextrin polymer (MQCDP) is synthesized in an ionic liquid green system, and a combined process of MQCDP treatment and chlorine disinfection is established. The disinfection performance of the combined process on the actual water body along with its reducing effect on the amount of chlorine disinfectant as well as the trihalomethanes (THMs) and haloacetic acids (HAAs) DBPs are explored. MQCDP has a porous structure with a specific surface area of 825 m2 g-1 and is easily magnetically separated. MQCDP can remove most of the natural organic matter (UV254 absorbance decreased by 97 %) in the water at the dosage of 1 g L-1 and kill bacteria with a sterilization rate of 85 %. Compared with disinfection using chlorine alone, the combined process has higher disinfection efficiency and significantly reduces the amount of disinfectant used. A concentration of 5 mg/L of NaClO was needed to meet the standard by chlorine disinfectant alone, while only 2 mg/L of NaClO can meet the standard for the combined process, indicating 60 % of the chlorine demand was reduced. More importantly, the combined process can significantly reduce the generation potential of DBPs. When 10 mg/L of NaClO is added, the THMs and HAAs generated by the combined process decreased by 65 % and 34 %, respectively, compared with the levels produced by single chlorine disinfection. The combined process can reduce the dosage of chlorine disinfectant and MQCDP can adsorb humic acid DBP precursors in raw water, thus lowering the generation of DBPs during disinfection. In summary, MQCDP has excellent separation and antibacterial ability, and its synergistic effects combined with the chlorine disinfection process are of great significance for controlling the amount of disinfectant and the formation potential of DBPs, which has potential applications in actual water treatment.

ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVD.

Recently, deep learning (DL) has enabled rapid advancements in electrocardiogram (ECG)-based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead systems based on the potential differences between electrodes placed on the limbs and the chest. When applying DL models, ECG signals are usually treated as synchronized signals arranged in Euclidean space, which is the abstraction and generalization of real space. However, conventional DL models typically merely focus on temporal features when analyzing Euclidean data. These approaches ignore the spatial relationships of different leads, which are physiologically significant and useful for CVD diagnosis because different leads represent activities of specific heart regions. These relationships derived from spatial distributions of electrodes can be conveniently created in non-Euclidean data, making multi-lead ECGs better conform to their nature. Considering graph convolutional network (GCN) adept at analyzing non-Euclidean data, a novel spatial-temporal residual GCN for CVD diagnosis is proposed in this work. ECG signals are firstly divided into single-channel patches and transferred into nodes, which will be connected by spatial-temporal connections. The proposed model employs residual GCN blocks and feed-forward networks to alleviate over-smoothing and over-fitting. Moreover, residual connections and patch dividing enable the capture of global and detailed spatial-temporal features. Experimental results reveal that the proposed model achieves at least a 5.85% and 6.80% increase in F1 over other state-of-the-art algorithms with similar parameters and computations in both PTB-XL and Chapman databases. It indicates that the proposed model provides a promising avenue for intelligent diagnosis with limited computing resources.

Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics.

Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People's Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20-99.76%) and 97.62% (95% confidence interval: 96.80-98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.

Two-year online measurements of volatile organic compounds (VOCs) at four sites in a Chinese city: Significant impact of petrochemical industry.

Volatile organic compounds (VOCs) management has been recently given a high priority in China to mitigate ozone (O3) air pollution. However, there is a relatively poor understanding of VOCs due to their complexity and fewer observations. To better understand the pollution characteristics of VOCs and their impact on O3 pollution, two-year continuous measurements were conducted at four representative sites in Ji'nan, eastern China. These four sites cover urban, background, and industrial areas (within a petroleum refinery). Ambient VOCs showed higher concentrations at industrial site than at urban and background sites, owing to intensive emissions from petrochemical industry. The VOCs compositions present spatial heterogeneity with alkenes dominated in total reactivity at urban and background sites, while alkenes and aromatics together dominated at industrial site. The VOCs emission profile from petrochemical industry was calculated based on observational data, which revealed a huge impact on light alkanes (C2-C5), light alkenes (ethene), and aromatics (toluene and m/p-xylene). The positive matrix factorization (PMF) model analysis further refined the impact of different petrochemical industrial processes. Alkanes and alkenes dominated the emissions during refining process, while aromatics dominated during solvent usage process. Analysis by an observation-based model indicated stronger in-situ O3 production and higher sensitivity to nitrogen oxides at industrial site compared to urban and background sites. The reduction of VOCs emissions from petrochemical industry would significantly reduce the O3 concentrations. The analyses underline the significant impact of petrochemical industry on VOCs and O3 pollution, and provide important reference for the formulation of refined and effective control strategies.

A joint cross-dimensional contrastive learning framework for 12-lead ECGs and its heterogeneous deployment on SoC.

The utilization of unlabeled electrocardiogram (ECG) data is always a critical topic in artificial intelligence healthcare, as the manual annotation for ECG data is a time-consuming task that requires much medical expertise. The recent development of self-supervised learning, especially contrastive learning, has provided helpful inspirations to solve this problem. In this paper, a joint cross-dimensional contrastive learning algorithm for unlabeled 12-lead ECGs is proposed. Unlike existing studies about ECG contrastive learning, our algorithm can simultaneously exploit unlabeled 1-dimensional ECG signals and 2-dimensional ECG images. A cross-dimensional contrastive learning method enhances the interaction between 1-dimensional and 2-dimensional ECG data, resulting in a more effective self-supervised feature learning. Combining this cross-dimensional contrastive learning, a 1-dimensional contrastive learning with ECG-specific transformations is employed to constitute a joint model. To pre-train this joint model, a new hybrid contrastive loss balances the 2 algorithms and uniformly describes the pre-training target. In the downstream classification task, the features learned by our algorithm shows impressive advantages. Compared with other representative methods, it achieves a at least 5.99% increase in accuracy. For real-world applications, an efficient heterogenous deployment on a "system-on-a-chip" (SoC) is designed. According to our experiments, the model can process 12-lead ECGs in real-time on the SoC. Furthermore, this heterogenous deployment can achieve a 14 × faster inference than the pure software deployment on the same SoC. In summary, our algorithm is a good choice for unlabeled 12-lead ECG utilization, the proposed heterogenous deployment makes it more practical in real-world applications.

Spatial dynamics of bacterial community in chlorinated drinking water distribution systems supplied with two treatment plants: An integral study of free-living and particle-associated bacteria.

With the expansion of cities, the deterioration of drinking water quality undergoing complex and long-distance distribution is gaining increasing attention. However, spatial variations between free-living bacteria (FLB) and particle-associated bacteria (PAB) in chlorinated drinking water distribution systems (DWDSs) have not been fully explored, especially in complex water supply areas with multiple interconnected DWDSs. To fill this gap, this study utilized 16S rRNA approaches to characterize the spatial patterns of FLB and PAB in DWDSs with intersection regions. Based on distance-decay analysis, transportation distance is a potential driver of bacterial variation for both FLB (Pearson's r = -0.476, p < 0.01) and PAB. (Pearson's r = -0.352, p < 0.01). Moreover, the influence of transportation distance was further confirmed by a 1.20-99.45% decline in microbial contribution to the source of FLB and PAB communities in pipe water along the transportation pipelines. Meanwhile, significant difference (PERMANOVA, R2 = 0.14, p < 0.01) was found between FLB and PAB in DWDSs. Average proportions of Pseudomonas spp. were 59.84% and 45.59% for the PAB and intersection regions based on the 16S rRNA results, respectively, suggesting that PAB are potential reservoirs for high-risk bacteria, and a greater microbial risk may exist in intersection regions. In summary, transportation distance and pipeline intersection exerted significant impacts on the FLB and PAB in DWDSs. Therefore, precautionary strategies for controlling microbial risks that consider different microbial components and intersection regions in long-distance and multi-plant DWDSs should be implemented.

Structure-dependent antimicrobial mechanism of quaternary ammonium resins and a novel synthesis of highly efficient antimicrobial resin.

The demand for powerful and multifunctional water-treatment materials and reagents is increasing, because we are facing worse raw water quality, various tolerant bacteria, and risky disinfection by-products (DBPs) in drinking water. Quaternary ammonium resins (QARs) are promising candidates for water disinfection and purification, but their limited bactericidal capacities are difficult to improve because of the lack of guidelines for enhancing antibacterial efficiency. Therefore, we first systematically studied the structure-dependent antimicrobial mechanism of QARs and found that the best resin skeleton is acrylic-type, the optimal bactericidal alkyl is hexyl or octyl, the most applicable sizes are 80-100 meshes, the best counter anion is iodide ion, and the optimum quaternization reagent is iodoalkane. Moreover, the antibacterial capacity was demonstrated to depend on surficial N+ groups, correlating with surficial N+ charge density (R2 of 0.98) but not with exchange capacity (R2 of 0.26), physical adsorption of resin skeleton, or electrostatic adsorption of N+ groups. Based on these principles, we synthesized a new resin, Ac-81, with a surficial antibacterial design, which simultaneously exhibited better antimicrobial efficiency (two orders of magnitude) as well as higher contaminant removal potential (61.92%) compared to the traditional Ac-8C antibacterial resin. Furthermore, the new resin showed remarkable broad-spectrum antibacterial effects against Gram-negative E. coli and P. aeruginosa and Gram-positive B. subtilis and S. aureus in simulated water and actual water. Simultaneously, water quality was significantly improved, with HCO3-, SO42-, TN, TP, and TOC reduced by 79-90%, >99%, 66-85%, >99%, and 22-26%, respectively. Ac-81 is characterized by facile reusability, high treatment capacity of 1500 bed volume, and good adaptability for treating actual water, providing a promising alternative for drinking-water disinfection and purification.

Surficial N+ charge density indicating antibacterial capacity of quaternary ammonium resins in water environment.

The antibacterial effects of quaternary ammonium resins (QARs) have been reported for decades, but there are few practical applications because of limited improvements in bactericidal capacity and the absence of an efficient antibacterial-indicating parameter. An in-situ determination method of surficial N+ groups for QARs, defined as surficial N+ charge density, was first established to merely quantify the exposed surficial quaternary ammonium groups (QAs). The mechanism of the new method depends on the tetraphenylboron sodium standard solution (TS), which is a colloidal solution with high steric hindrance, making it difficult to permeate into QARs and further react with the inner QAs. The results showed that the antibacterial efficacy of QARs correlates with the surficial N+ charge density with R2 > 0.95 (R2 of 0.97 for Escherichia coli, R2 of 0.96 for Staphylococcus aureus) but not with the strong-base group exchange capacity or zeta potential. Furthermore, the surficial N+ charge density was demonstrated efficient to indicate the antibacterial capacities against both gram-negative and gram-positive bacteria for commercial QARs, including acrylic, styrene and pyridine resin skeletons, especially for the QARs with similar skeletons and similar QAs. Based on the finding that the bactericidal groups merely involve the surficial QAs of QARs, this study proposes a new direction for improving the antibacterial capacity by enriching the surficial QAs and enhancing the bactericidal property of these surficial QAs, and provides a practicable synthesis with two-step quaternization.

Multifunctional ß-Cyclodextrin Polymer for Simultaneous Removal of Natural Organic Matter and Organic Micropollutants and Detrimental Microorganisms from Water.

Natural organic matter (NOM), organic micropollutants (OMPs), and detrimental microorganisms are three major pollutants that affect water quality. To remove these pollutants, a quaternary ammonium-functionalized ß-cyclodextrin polymer (ß-CDP) is successfully synthesized in the aqueous phase. The N2 and CO2 adsorption/desorption analysis showed that the polymer mainly contains ultra-micropores (<1 nm), with a Langmuir surface area of 89 m2 g-1. Two kinds of NOM, humic acid and fulvic acid, and five OMPs, 2-naphthol (2-NO), 3-phenylphenol (3-PH), 2,4,6-trichlorophenol (2,4,6-TCP), bisphenol A (BPA), and bisphenol S (BPS), were selected as model pollutants to study the performance of ß-CDP and three kinds of commercial adsorbents, including granular activated carbon, DARCO-AC, and two resins, XAD-4 and D-201, were used for comparison. The polymer shows ultrarapid adsorption kinetics for the removal of these pollutants, with pseudo-second-order rate constants two to three orders of magnitude higher than that of the commercial activated carbon and resins. Due to the different adsorption sites of NOM and OMPs, ß-CDP can simultaneously remove these pollutants without competitive adsorption. The maximum adsorption capacity of ß-CDP for HA, FA, 2-NO, 3-PH, 2,4,6-TCP, BPA, and BPS based on the Langmuir model is 40, 166, 74, 101, 108, 103, and 117 mg g-1, respectively. After use, the polymer can be easily regenerated at room temperature. In addition, ß-CDP also showed excellent bactericidal properties due to the quaternary ammonium groups. At a concentration of 15 g L-1, ß-CDP can remove 98% of the tested Escherichia coli. Moreover, the synthesis of ß-CDP is simple, green, and easy to industrialize. All of these findings indicate that ß-CDP, as an ideal multifunctional material, presents potential for practical applications for water treatment and disinfection.

Antimicrobial resins with quaternary ammonium salts as a supplement to combat the antibiotic resistome in drinking water treatment plants.

The increasing finding of pathogens and antibiotic resistance genes (ARGs) in drinking water has become one of the most challenging global health threats worldwide. However, conventional disinfection strategies in drinking water treatment plants (DWTPs) require further optimization in combating the antibiotic resistome. Here, we show that antimicrobial resins with quaternary ammonium salts (AMRs-QAS) exhibit great potentials in diminishing specific potential pathogens that relatively resist chlorine or UV disinfection in DWTPs, and comprehensive analyses using microscopy and fluorescence techniques revealed that the antimicrobial capacity of AMRs-QAS mainly proceed via the bacterial adsorption and cell membrane dissociation. Moreover, a total of 15 among 30 selected ARGs, as well as 4 selected potential pathogens including Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli and Staphylococcus aureus were all detected in the source water. Coupling the AMRs-QAS with 0.2 mg/L chlorine resulted in higher removal efficiencies than chlorination (2 mg/L) or UV disinfection (400 mJ cm-2) for all the detected pathogens and ARGs in drinking water and significantly decreased the relative abundances of Pseudomonas aeruginosa, Bacillus subtilis, Escherichia coli, as well as all the detected ARGs (p < 0.05). Co-occurrences of pathogens and ARGs were revealed by a correlation network and possibly accounts for the ARGs removal. This coupled disinfection strategy overcomes the limitations of individual disinfection methods, i.e. the enrichment of specific pathogens and ARGs among bacterial populations, and provides an alternative for minimizing health risks induced by the antibiotic resistome in DWTPs.

Antibiotic Resistome Alteration by Different Disinfection Strategies in a Full-Scale Drinking Water Treatment Plant Deciphered by Metagenomic Assembly.

Disinfection regimes are considered the most solid strategy to reduce microbial risks in drinking water, but their roles in shaping the antibiotic resistome are poorly understood. This study revealed the alteration of antibiotic resistance genes (ARGs) profiles, their co-occurrence with mobile genetic elements (MGEs), and potential hosts during drinking water disinfection based on metagenomic assembly. We found the ozone/chlorine (O3/Cl2) coupled disinfection significantly increased the relative abundance of ARGs and MGE-carrying antibiotic resistance contigs (ARCs) through the enrichment of ARGs within the resistance-nodulation-cell division and ATP-binding cassette antibiotic efflux families that are primarily carried by Pseudomonas, Acinetobacter, Mycobacterium, and Methylocystis, whereas the antimicrobial resin/chlorine coupled disinfection posed unremarkable changes to the ARG and MGE abundances. Moreover, the co-occurrence patterns of antibiotic efflux and beta-lactam ARGs and MGEs were widely identified, and ARCs carrying the recR and mexH genes were detected in all the samples, with the highest abundance of 2.25 × 10-2 copies per cell after O3/Cl2 disinfection. Sequence-independent binning analysis successfully retrieved two draft ARG-carrying genomes of Acidovorax sp. MR-S7 and Hydrogenophaga sp. IBVHS2, further revealing the host-ARG relationship during O3/Cl2 disinfection. Overall, this study provides novel insights into the antibiotic resistome alteration during drinking water disinfection.

Identification and engineering of cholesterol oxidases involved in the initial step of sterols catabolism in Mycobacterium neoaurum.

Mycobacteria have been modified to transform sterols to produce valuable steroids. Here, we demonstrated that the oxidation of sterols to sterones is a rate-limiting step in the catabolic pathway of sterols in Mycobacterium neoaurum. Two cholesterol oxidases ChoM1 and ChoM2 involved in the step were identified in M. neoaurum and the ChoM2 shared up to 45% identity with other cholesterol oxidases. We demonstrated that the combination of ChoM1 and ChoM2 plays a significant role in this step. Accordingly, we developed a strategy to overcome this rate-limiting step by augmenting the activity of cholesterol oxidases in M. neoaurum strains to enhance their transformation productivity of sterols to valuable steroids. Our results indicated that the augmentation of ChoM2 achieved 5.57g/l androst-1,4-diene-3,17-dione in M. neoaurum NwIB-01MS and 6.85g/l androst-4-ene-3,17-dione in M. neoaurum NwIB-R10, greatly higher than the original yield, 3.87g/l androst-1,4-diene-3,17-dione and 4.53g/l androst-4-ene-3,17-dione, respectively.

Liquid chromatography/tandem mass spectrometry method for the quantification of deserpidine in human plasma: Application to a pharmacokinetic study.

A sensitive and rapid liquid chromatography/tandem mass spectrometric (LC/MS/MS) method was developed and validated for the determination of deserpidine in human plasma. The plasma samples were prepared using liquid-liquid extraction (LLE) with ethyl ether-dichloromethane (3:2, v/v). Chromatographic separation was accomplished on an Ultimate XB-C18 column. The mobile phase consisted of methanol-5mM ammonium acetate-formic acid (72:28:0.036, v/v/v). Detection of deserpidine and the internal standard tropisetron was achieved by tandem mass spectrometry with an electrospray ionization interface in positive ion mode. The lower limit of quantification was 4.0pg/ml. The linear range of the method was from 4.0 to 2000pg/ml. The intra- and inter-day precisions were lower than 14.7% in terms of relative standard deviation (RSD), and the accuracy was within +/-8.7% in terms of relative error (RE). This validated method was successfully applied for the evaluation of pharmacokinetics of deserpidine after a single oral administration dose of 0.25mg deserpidine to 22 healthy volunteers.