Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

OphthalMimic: A new alternative apparatus without animal tissue for the evaluation of topical ophthalmic drug products.

The necessity of animal-free performance tests for novel ophthalmic formulation screening is challenging. For this, we developed and validated a new device to simulate the dynamics and physical-chemical barriers of the eye for in vitro performance tests of topic ophthalmic formulations. The OphthalMimic is a 3D-printed device with an artificial lacrimal flow, a cul-de-sac area, a support base, and a simulated cornea comprised of a polymeric membrane containing poly-vinyl alcohol 10 % (w/v), gelatin 2.5 % (w/v), and different proportions of mucin and poloxamer, i.e., 1:1 (M1), 1:2 (M2), and 2:1 (M3) w/v, respectively. The support base is designed to move between 0° and 50° to replicate the movement of an eyelid. We challenged the model by testing the residence performance of poloxamer®407 16 % and poloxamer®407 16 % + chitosan 1 % (PLX16CS10) gels containing fluconazole. The test was conducted with a simulated tear flow of 1.0 mL.min-1 for 5 min. The OphthalMimic successfully distinguished PLX16 and PLX16C10 formulations based on their fluconazole drainage (M1: 65 ± 14 % and 27 ± 10 %; M2: 58 ± 6 % and 38 ± 9 %; M3: 56 ± 5 % and 38 ± 18 %). In conclusion, the OphthalMimic is a promising tool for comparing the animal-free performance of ophthalmic formulations.

Hydrogel-based hybrid membrane enhances in vitro ophthalmic drug evaluation in the OphthalMimic device.

Envisaging to improve the evaluation of ophthalmic drug products while minimizing the need for animal testing, our group developed the OphthalMimic device, a 3D-printed device that incorporates an artificial lacrimal flow, a cul-de-sac area, a moving eyelid, and a surface that interacts effectively with ophthalmic formulations, thereby providing a close representation of human ocular conditions. An important application of such a device would be its use as a platform for dissolution/release tests that closely mimic in vivo conditions. However, the surface that artificially simulates the cornea should have a higher resistance (10 min) than the previously described polymeric films (5 min). For this key assay upgrade, we describe the process of obtaining and thoroughly characterizing a hydrogel-based hybrid membrane to be used as a platform base to simulate the cornea artificially. Also, the OphthalMimic device suffered design improvements to fit the new membrane and incorporate the moving eyelid. The results confirmed the successful synthesis of the hydrogel components. The membrane's water content (86.25 ± 0.35 %) closely mirrored the human cornea (72 to 85 %). Furthermore, morphological analysis supported the membrane's comparability to the natural cornea. Finally, the performance of different formulations was analysed, demonstrating that the device could differentiate their drainage profile through the viscosity of PLX 14 (79 ± 5 %), PLX 16 (72 ± 4 %), and PLX 20 (57 ± 14 %), and mucoadhesion of PLXCS0.5 (69 ± 1 %), PLX16CS1.0 (65 ± 3 %), PLX16CS1.25 (67 ± 3 %), and the solution (97 ± 8 %). In conclusion, using the hydrogel-based hybrid membrane in the OphthalMimic device represents a significant advancement in the field of ophthalmic drug evaluation, providing a valuable platform for dissolution/release tests. Such a platform aligns with the ethical mandate to reduce animal testing and promises to accelerate the development of safer and more effective ophthalmic drugs.

Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links.

In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.

Toward an Ultimate Solution for Peptide Retention Time Prediction: The Effect of Column Temperature on Separation Selectivity.

We studied the effect of the column temperature on the selectivity of reversed-phase peptide separation in bottom-up proteomics. The number of peptide identifications from 2 h liquid chromatography with tandem mass spectrometry (LC-MS/MS) acquisitions reaches a plateau at 45-55 °C, driven simultaneously by improved separation efficiency, a gradual decrease in peptide retention, and possible on-column degradation of peptides at elevated temperatures. Performing 2D LC-MS/MS acquisitions at 25, 35, 45, and 55 °C resulted in the identification of ∼100,000 and ∼120,000 unique peptides for nonmodified and tandem mass tags (TMT)-labeled samples, respectively. These peptide collections were used to investigate the temperature-driven retention features. The latter is governed by the specific temperature response of individual residues, peptide hydrophobicity and length, and amphipathic helicity. On average, peptide retention decreased by 0.56 and 0.5% acetonitrile for each 10 °C increase for label-free and TMT-labeled peptides, respectively. This generally linear response of retention shifts allowed the extrapolation of predictive models beyond the studied temperature range. Thus, (trap) column cooling from room temperature to 0 °C will allow the retention of an additional 3% of detectable tryptic peptides. Meanwhile, the application of 90 °C would result in the loss of ∼20% of tryptic peptides that were amenable to MS/MS-based identification.

Gap State-Modulated Van Der Waals Short-Term Memory with Broad Band Negative Photoconductance.

Floating gate memory (FGM), composed of van der Waals (vdW) junctions with an atomically thin floating layer for charge storage, is widely employed to develop logic-in memories and in-sensor computing devices. Most research efforts of FGM are spent on achieving long-term charge storage and fast reading/writing for flash and random-access memory. However, dynamic modulation of memory time via a tunneling barrier and vdW interfaces, which is critical for synaptic computing and machine vision, is still lacking. Here, a van der Waals short-term memory with tunable memory windows and retention times from milliseconds to thousands of seconds is reported, which is approximately exponentially proportional to the thickness h-BN (hexagonal boron nitride) barrier. The specific h-BN barrier with fruitful gap states provides charge release channels for trapped charges, making the vdW device switchable between positive photoconductance and negative photoconductance with a broadband light from IR to UV range. The dynamic short-term memory with tunable photo response highlights the design strategy of novel vdW memory vis interface engineering for further intelligent information storage and optoelectronic detection.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target-decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

Hydrophobic Sorption Properties of an Extended Series of Anionic Per- and Polyfluoroalkyl Substances Characterized by C18 Chromatographic Retention Measurement.

Partitioning from water to nonaqueous phases is an important process that controls the behavior of contaminants in the environment and biota. However, for ionic chemicals including many perfluoroalkyl and polyfluoroalkyl substances (PFAS), environmentally relevant partition coefficients cannot be predicted using the octanol/water partition coefficient, which is commonly used as a hydrophobicity indicator for neutral compounds. As an alternative, this study measured C18 liquid chromatography retention times of 39 anionic PFAS and 20 nonfluorinated surfactants using isocratic methanol/water eluent systems. By measuring a series of PFAS with different perfluoroalkyl chain lengths, retention factors at 100% water (k0) were successfully extrapolated even for long-chain PFAS. Molecular size was the most important factor determining the k0 of PFAS and non-PFAS, suggesting that the cavity formation process is the key driver for retention. Log k0 showed a high correlation with the log of partition coefficients from water to the phospholipid membrane, air/water interface, and soil organic carbon. The results indicate the potential of C18 retention factors as predictive descriptors for anionic PFAS partition coefficients and the possibility of developing a more comprehensive multiparameter model for the partitioning of anionic substances in general.

Combined Phototrophic Simultaneous Nitrification-Endogenous Denitrification with Phosphorus Removal (P-SNDPR) System Treating Low Carbon to Nitrogen Ratio Wastewater for Potential Carbon Neutrality.

Conventional biological nutrient removal processes rely on external aeration and produce significant carbon dioxide (CO2) emissions. This study constructed a phototrophic simultaneous nitrification-denitrification phosphorus removal (P-SNDPR) system to treat low carbon to nitrogen (C/N) ratios wastewater and investigated the impact of sludge retention time (SRT) on nutrient removal performance, nitrogen conversion pathway, and microbial structure. Results showed that the P-SNDPR system at SRT of 15 days had the highest nutrient removal capacity, achieving over 85% and 98% removal of nitrogen and phosphorus, respectively, meanwhile maintaining minimal CO2 emissions. Nitrogen removal was mainly through assimilation at SRTs of 5 and 10 days, and nitrification-denitrification at SRTs of 15 and 20 days. Stable partial nitrification was facilitated by photoinhibition and low DO levels. Flow cytometry sorting technique results revealed SRT drove community structural changes in translational activity (BONCAT+) microbes, where BONCAT+ microbes were mainly simultaneous nitrogen and phosphorus removal bacteria (Candidatus Accumulibacter), denitrifying bacteria (Candidatus Competibacter and Plasticicumulans), ammonia-oxidizing bacteria (Nitrosomonas), and microalgae (Chlorella and Dictyosphaerium). The P-SNDPR system represents a novel, carbon-neutral process for efficient nutrient removal from low C/N ratio wastewater without aeration and external carbon source additions.

Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning.

Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.

DRD2 TaqIA polymorphism-related functional connectivity between anterior insula and dorsolateral prefrontal cortex predicts the retention time in heroin-dependent individuals under methadone maintenance treatment.

BACKGROUND: Dopamine receptor D2 (DRD2) TaqIA polymorphism has an influence on addiction treatment response and prognosis by mediating brain dopaminergic system efficacy. Insula is crucial for conscious urges to take drugs and maintain drug use. However, it remains unclear about the contribution of DRD2 TaqIA polymorphism to the regulation of insular on addiction behavioral and its relation with the therapeutic effect of methadone maintenance treatment (MMT). METHODS: 57 male former heroin dependents receiving stable MMT and 49 matched male healthy controls (HC) were enrolled. Salivary genotyping for DRD2 TaqA1 and A2 alleles, brain resting-state functional MRI scan and a 24-month follow-up for collecting illegal-drug-use information was conducted and followed by clustering of functional connectivity (FC) patterns of HC insula, insula subregion parcellation of MMT patients, comparing the whole brain FC maps between the A1 carriers and non-carriers and analyzing the correlation between the genotype-related FC of insula sub-regions with the retention time in MMT patients by Cox regression. RESULTS: Two insula subregions were identified: the anterior insula (AI) and the posterior insula (PI) subregion. The A1 carriers had a reduced FC between the left AI and the right dorsolateral prefrontal cortex (dlPFC) relative to no carriers. And this reduced FC was a poor prognostic factor for the retention time in MMT patients. CONCLUSION: DRD2 TaqIA polymorphism affects the retention time in heroin-dependent individuals under MMT by mediating the functional connectivity strength between left AI and right dlPFC, and the two brain regions are promising therapeutic targets for individualized treatment.

New insights into syntrophic ethanol oxidation: Effects of operational modes and solids retention times.

Anaerobic ethanol oxidation relies on syntrophic interactions among functional microorganisms to become thermodynamically feasible. Different operational modes (sequencing batch reactors, SBRs, and continuous flow reactors, CFRs) and solids retention times (SRT, 25 days and 10 days) were employed in four ethanol-fed reactors, named as SBR25d, SBR10d, CFR25d, and CFR10d, respectively. System performance, syntrophic relationships, microbial communities, and metabolic pathways were examined. During the long-term operation, 2002.7 ± 56.0 mg COD/L acetate was accumulated in CFR10d due to the washout of acetotrophic methanogens. Microorganisms with high half-saturation constants were enriched in reactors of 25-day SRT. Moreover, ethanol oxidizing bacteria and acetotrophic methanogens with high half-saturation constants could be acclimated in SBRs. In SBRs, Syner-01 and Methanothrix dominated, and the low SRT of 10 days increased the relative abundance of Geobacter to 38.0%. In CFRs, the low SRT of 10 days resulted in an increase of Desulfovibrio among syntrophic bacteria, and CFR10d could be employed in enriching hydrogenotrophic methanogens like Methanobrevibacter.

Mechanistic understanding of acclimation and energy metabolism of acetoclastic methanogens under different substrate to microorganism ratios.

Mechanistic understanding of acetoclastic methanogenesis is pivotal for optimizing anaerobic digestion for efficient methane production. In this study, two different operational modes, continuous flow reactor (CFR) and sequencing batch reactor (SBR), accompanied with solids retention times (SRT) of 10 days (SBR10d and CFR10d) and 25 days (SBR25d and CFR25d) were implemented to elucidate their impacts on microbial communities and energy metabolism of methanogens in acetate-fed systems. Microbial community analysis revealed that the relative abundance of Methanosarcina (16.0%-46.0%) surpassed Methanothrix (3.7%-22.9%) in each reactor. SBRs had the potential to enrich both Methanothrix and Methanosarcina. Compared to SBRs, CFRs had lower total relative abundance of methanogens. Methanosarcina exhibited a superior enrichment in reactors with 10-day SRT, while Methanothrix preferred to be acclimated in reactors with 25-day SRT. The operational mode and SRT were also observed to affect the distribution of acetate-utilizing bacteria, including Pseudomonas, Desulfocurvus, Mesotoga, and Thauera. Regarding enzymes involved in energy metabolism, Ech and Vho/Vht demonstrated higher relative abundances at 10-day SRT compared to 25-day SRT, whereas Fpo and MtrA-H showed higher relative abundances in SBRs than those in CFRs. The relative abundance of genes encoding ATPase harbored by Methanothrix was higher than Methanosarcina at 25-day SRT. Additionally, the relative abundance of V/A-type ATPase (typically for methanogens) was observed higher in SBRs compared to CFRs, while the F-type ATPase (typically for bacteria) exhibited higher relative abundance in CFRs than that in SBRs.

Evaluation and comparison of liquid chromatography/high-resolution mass spectrometry platforms for the separation and characterization of ginsenosides from the leaves of Panax ginseng.

The measurement of data repeatability in small-molecule metabolites acquired within and among different liquid chromatography-mass spectrometry (LC-MS) platforms is crucial for data sharing or data transfer in natural products research. This work was designed to investigate and evaluate the separation and detection performance of three commercial high-resolution LC-MS platforms (e.g., Agilent 6550 QTOF, Waters Vion IM-QTOF, and Thermo Scientific Orbitrap Exploris 120) using 68 ginsenoside references and the extract of Panax ginseng leaf. The retention time (tR), measured on these three platforms (under the same chromatography condition), showed good stability in different concentration tests, and within/among different instruments for both intra-day and inter-day precision examinations. Correlation in tR of ginsenosides was also highly determined on these three platforms. In spite of the different mass analyzers involved, these three platforms gave the accurate mass determination ability, especially enhanced resolution gained because of the ion mobility (IM) separation facilitated by IM-quadrupole time-of-flight. The current study has systematically evaluated the separation and MS detection performance enabled by three high-resolution LC-MS platforms taking ginsenosides as the template, and the reported findings can benefit the researchers for the selection of analytical platforms and the purpose of data sharing or data transfer.

Effects of two fillers and process conditions on the water treatment efficiency of a continuous packed bed biofilm reactor.

This study evaluated the treatment efficiency of two selected fillers and their combination for improving the water quality of aquaculture wastewater using a packed bed biofilm reactor (PBBR) under various process conditions. The fillers used were nanosheet (NS), activated carbon (AC), and a combination of both. The results indicated that the use of combined fillers and the hydraulic retention time (HRT) of 4 h significantly enhanced water quality in the PBBR. The removal rates of chemical oxygen demand, NO2-─N, total suspended solids（TSS）, and chlorophyll a were 63.55%, 74.25%, 62.75%, and 92.85%, respectively. The microbiota analysis revealed that the presence of NS increased the abundance of microbial phyla associated with nitrogen removal, such as Nitrospirae and Proteobacteria. The difference between the M1 and M2 communities was minimal. Additionally, the microbiota in different PBBR samples displayed similar preferences for carbon sources, and carbohydrates and amino acids were the most commonly utilized carbon sources by microbiota. These results indicated that the combination of NS and AC fillers in a PBBR effectively enhanced the treatment efficiency of aquaculture wastewater when operated at an HRT of 4 h. The findings provide valuable insights into optimizing the design of aquaculture wastewater treatment systems.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India.

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.

ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction.

In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT's exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries.

Damming exacerbates the discontinuities of phytoplankton in a subtropical river in China.

Phytoplankton is sensitive to changes in river ecosystems. Increasing dams disrupt the continuity of river ecosystems. However, the spatial impacts of dams on phytoplankton have not been well documented. In this study, using multiple statistical analyses, the relationships between environmental drivers and phytoplankton community structures in natural background reaches, reservoirs, and corresponding post-dam reaches were explored in the Jiulong River with multiple cascaded dams, which encountered eutrophication and algal blooms in the past 15 years. Results illustrated that damming exacerbated longitudinal discontinuities of phytoplankton communities. The relative abundance of phytoplankton varied in three types of river sections. The average phytoplankton abundance in the reservoirs (1.62 × 105 cell·L-1) was higher than those in the natural background reaches (5.15 × 104 cell·L-1) and the corresponding downstream reaches (4.55 × 104 cell·L-1). The total ß diversity ranged from 0.38 to 0.89 with an average of 0.64 and dominated by species replacement and least by species richness. The water environmental factors and hydraulic parameters rather than nutrients were more attributable to phytoplankton community variability in three river sections. These findings facilitate the management of rivers with multiple cascade dams by releasing environmental flows, jointly operating cascade hydropower stations, and developing nutrient reduction schemes to mitigate the negative impacts of damming in the river.

Strategies to attenuate ciprofloxacin inhibition on enhanced biological phosphorus removal from wastewater and its recoverability.

The inhibiting effects of ciprofloxacin (CIP) on enhanced biological phosphorus removal (EBPR) were investigated with no change in reactor operation and with increased aeration rate and sludge retention time (SRT) to explore inhibition-alleviating solutions. Additionally, performance recoverability was evaluated. The results showed that the phosphorus removal efficiency in the presence of 0.002-0.092 mg/L CIP for 7 days was only 12.5%. Increasing the aeration rate relieved inhibition (33.5% phosphorus removal efficiency on Day 7), and increasing SRT slowed EBPR performance deterioration. The EBPR performance recovered from CIP inhibition and increases in the aeration rate and SRT resulted in different recovery phenomena. The maximum PO43--P release rate continued to decrease in the first 2 days of the recovery stage and then gradually increased. However, the maximum PO43--P uptake rate immediately increased at different rates among reactors, which might be attributed to variations in the microbial community structure, decreased poly-P content, and enhanced abundances of ABC transporters and quorum sensing. It was found that some microorganisms associated with phosphorus removal were more tolerant to CIP than glycogen accumulating organisms. Moreover, the increased relative abundance of the qepA gene indicated that the microorganisms in the EBPR system had strong antibiotic resistance capacity. The bacterial community structure was significantly affected by CIP and could not recover to the initial structure. The results help to provide technical support for the operation of the EBPR process in the presence of CIP and to increase the understanding of system recoverability.

Performance of a high-rate membrane bioreactor for energy-efficient treatment of textile wastewater.

High-rate membrane bioreactors (MBR), where the wastewater undergoes partial oxidation due to the applied short sludge retention time (SRT) and hydraulic retention time (HRT) values, retain the majority of the organic substances in the sludge through growth and biological flocculation. Thus, a raw material source with a high biomethane production potential is created for the widespread use of circular economy or energy-neutral plants in wastewater treatment. While high-rate MBRs have been successfully employed for energy-efficient treatment of domestic wastewater, there is a lack of research specifically focused on textile wastewater. This study aimed to investigate the textile wastewater treatment and organic matter recovery performances of an aerobic MBR system containing a hollow fiber ultrafiltration membrane with a 0.04 µm pore diameter. The system was initially operated at short SRTs (5 and 3 d) and different SRT/HRT ratios (5, 10, and 20) and subsequently at high-rate conditions (SRT of 0.5-2 d and HRT of 1.2-9.6 h) which are believed to be the most limiting conditions tested for treatment of real textile wastewater. The results showed that chemical oxygen demand (COD) removal averaged 77% even at SRT of 0.5 d and HRT of 1.2 h. Slowly biodegradable substrates and soluble microbial products (SMP) accumulated within the MBR at SRT of 0.5 and 1 d, which resulted in decreased sludge filterability. The observed sludge yield (Yobs) exhibited a considerable increase when SRT was reduced from 5 to 1 d. On the other hand, the SRT/HRT ratio displayed a decisive effect on the energy requirement for aeration.