T-YOLO: a lightweight and efficient detection model for nutrient buds in complex tea-plantation environments.

BACKGROUND: Quick and accurate detection of nutrient buds is essential for yield prediction and field management in tea plantations. However, the complexity of tea plantation environments and the similarity in color between nutrient buds and older leaves make the location of tea nutrient buds challenging. RESULTS: This research presents a lightweight and efficient detection model, T-YOLO, for the accurate detection of tea nutrient buds in unstructured environments. First, a lightweight module, C2fG2, and an efficient feature extraction module, DBS, are introduced into the backbone and neck of the YOLOv5 baseline model. Second, the head network of the model is pruned to achieve further lightweighting. Finally, the dynamic detection head is integrated to mitigate the feature loss caused by lightweighting. The experimental data show that T-YOLO achieves a mean average precision (mAP) of 84.1%, the total number of parameters for model training (Params) is 11.26 million (M), and the number of floating-point operations (FLOPs) is 17.2 Giga (G). Compared with the baseline YOLOv5 model, T-YOLO reduces Params by 47% and lowers FLOPs by 65%. T-YOLO also outperforms the existing optimal detection YOLOv8 model by 7.5% in terms of mAP. CONCLUSION: The T-YOLO model proposed in this study performs well in detecting small tea nutrient buds. It provides a decision-making basis for tea farmers to manage smart tea gardens. The T-YOLO model outperforms mainstream detection models on the public dataset, Global Wheat Head Detection (GWHD), which offers a reference for the construction of lightweight and efficient detection models for other small target crops. © 2024 Society of Chemical Industry.

Antimicrobial research of carbohydrate polymer- and protein-based hydrogels as reservoirs for the generation of reactive oxygen species: A review.

Reactive oxygen species (ROS) play an important role in biological milieu. Recently, the rapid growth in our understanding of ROS and their promise in antibacterial applications has generated tremendous interest in the combination of ROS generators with bulk hydrogels. Hydrogels represent promising supporters for ROS generators and can locally confine the nanoscale distribution of ROS generators whilst also promoting cellular integration via biomaterial-cell interactions. This review highlights recent efforts and progress in developing hydrogels derived from biological macromolecules with embedded ROS generators with a focus on antimicrobial applications. Initially, an overview of passive and active antibacterial hydrogels is provided to show the significance of proper hydrogel selection and design. These are followed by an in-depth discussion of the various approaches for ROS generation in hydrogels. The structural engineering and fabrication of ROS-laden hydrogels are given with a focus on their biomedical applications in therapeutics and diagnosis. Additionally, we discuss how a compromise needs to be sought between ROS generation and removal for maximizing the efficacy of therapeutic treatment. Finally, the current challenges and potential routes toward commercialization in this rapidly evolving field are discussed, focusing on the potential translation of laboratory research outcomes to real-world clinical outcomes.

Prompt-enhanced hierarchical transformer elevating cardiopulmonary resuscitation instruction via temporal action segmentation.

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a specialized CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, promoting an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which experimentally facilitates the model excavation on the CPR dataset. In general, we probe into a feasible pipeline that elevates the CPR instruction qualification via action segmentation equipped with novel deep learning techniques. Associated experiments on the CPR dataset advocate our resolution with surpassing 91.0% on Accuracy, Edit score, and F1 score.

Development of an indirect ELISA for detection of the adaptive immune response of black carp (Mylopharyngodon piceus).

Black carp (Mylopharyngodon piceus) is an important fishery resource and the main breeding target in China. Due to the lack of an assay of immunoglobulin M (IgM) antibodies in black carp, there is no effective method to evaluate adaptive immune response, which limits immunological studies and vaccine development. The present study used mAbs (monoclonal antibodies) against serum IgM of grass carp as capture antibodies. The results of Western blot analysis indicated that these antibodies had strong affinity and specificity to IgM heavy chain in black carp serum and were used to detect the antibody titer, optimize the conditions, perform a sensitivity test, and develop an indirect ELISA (enzyme-linked immunosorbent assay) to detect specific IgM antibodies in the serum. This detection method has good specificity and is effective only for grass carp (Ctenopharyngodon idella) and black carp and not for crucian carp (Carassius aumtus), silver carp (Hypophthalmichthys molitrix), bighead carp (Hypophthalmichthys nobilis), mandarin fish (Siniperca chuatsi), black bream (Megalobrama skolkovii), or yellow catfish (Pseudobagrus fulvidraco). The lowest antigen detection level was 0.05 µg/ml. The error of experimental repetition in the same sample was 1.61-4.61%. The levels of specific IgM in black carp serum were steadily increased after immunization, peaked on day 28, and then slowly decreased. Indirect ELISA can be applied to detect the changes in specific antibodies in black carp serum. Moreover, indirect ELISA provides a convenient and reliable serological detection method for immunological research and evaluation of immune effects of a vaccine in black carp.

400nm ultra-broadband gratings for near-single-cycle 100 Petawatt lasers.

Compressing high-energy laser pulses to a single-cycle and realizing the "λ3 laser concept", where λ is the wavelength of the laser, will break the current limitation of super-scale projects and contribute to the future 100-petawatt and even Exawatt lasers. Here, we have realized ultra-broadband gold gratings, core optics in the chirped pulse amplification, in the 750-1150 nm spectral range with a > 90% -1 order diffraction efficiency for near single-cycle pulse stretching and compression. The grating is also compatible with azimuthal angles from -15° to 15°, making it possible to design a three-dimensional compressor. In developing and manufacturing processes, a crucial grating profile with large base width and sharp ridge is carefully optimized and controlled to dramatically broaden the high diffraction efficiency bandwidth from the current 100-200 nm to over 400 nm. This work has removed a key obstacle to achieving the near single-cycle 100-PW lasers in the future.

A Real-Time Detection Method of Hg2+ in Drinking Water via Portable Biosensor: Using a Smartphone as a Low-Cost Micro-Spectrometer to Read the Colorimetric Signals.

This paper reported a real-time detection strategy for Hg2+ inspired by the visible spectrophotometer that used a smartphone as a low-cost micro-spectrometer. In combination with the smartphone's camera and optical accessories, the phone's built-in software can process the received light band image and then read out the spectral data in real time. The sensor was also used to detect gold nanoparticles with an LOD of 0.14 µM, which are widely used in colorimetric biosensors. Ultimately, a gold nanoparticles-glutathione (AuNPs-GSH) conjugate was used as a probe to detect Hg2+ in water with an LOD of 1.2 nM and was applied successfully to natural mineral water, pure water, tap water, and river water samples.

RCMNet: A deep learning model assists CAR-T therapy for leukemia.

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

Cropland encroachment detection via dual attention and multi-loss based building extraction in remote sensing images.

The United Nations predicts that by 2050, the world's total population will increase to 9.15 billion, but the per capita cropland will drop to 0.151°hm2. The acceleration of urbanization often comes at the expense of the encroachment of cropland, the unplanned expansion of urban area has adversely affected cultivation. Therefore, the automatic extraction of buildings, which are the main carriers of urban population activities, in remote sensing images has become a more meaningful cropland observation task. To solve the shortcomings of traditional building extraction methods such as insufficient utilization of image information, relying on manual characterization, etc. A U-Net based deep learning building extraction model is proposed and named AttsegGAN. This study proposes an adversarial loss based on the Generative Adversarial Network in terms of training strategy, and the additionally trained learnable discriminator is used as a distance measurer for the two probability distributions of ground truth Pdata and prediction P g . In addition, for the sharpness of the building edge, the Sobel edge loss based on the Sobel operator is weighted and jointly participated in the training. In WHU building dataset, this study applies the components and strategies step by step, and verifies their effectiveness. Furthermore, the addition of the attention module is also subjected to ablation experiments and the final framework is determined. Compared with the original, AttsegGAN improved by 0.0062, 0.0027, and 0.0055 on Acc, F1, and IoU respectively after adopting all improvements. In the comparative experiment. AttsegGAN is compared with state-of-the-arts including U-Net, DeeplabV3+, PSPNet, and DANet on both WHU and Massachusetts building dataset. In WHU dataset, AttsegGAN achieved 0.9875, 0.9435, and 0.8907 on Acc, F1, and IoU, surpassed U-Net by 0.0260, 0.1183, and 0.1883, respectively, demonstrated the effectiveness of the proposed components in a similar hourglass structure. In Massachusetts dataset, AttsegGAN also surpassed state-of-the-arts, achieved 0.9395, 0.8328, and 0.7130 on Acc, F1, and IoU, respectively, it improved IoU by 0.0412 over the second-ranked PSPNet, and it was 0.0025 and 0.0101 higher than the second place in Acc and F1.

Enhanced anaerobic degradation of quinoline and indole with the coupling of sodium citrate and polyurethane.

A coupling system of sodium citrate and biofilm based on polyurethane was prepared to analyse the coupling enhancement degradation on quinoline and indole. Four reactors (R1: sludge, R2: sludge + sodium citrate, R3: biofilm on polyurethane, and R4: biofilm + sodium citrate) were operated 120 days to compare the degradation efficiency. During whole running phases, R4 showed high degradation efficiency on quinoline (≥98.55%) and indole (≥95.44%). Analysis of bacterial colony showed anaerobic sludge reactors benefited the enrichment of Aminicenantes, Levilinea, and Longilinea, while anaerobic biofilm reactors benefited the enrichment of Giesbergeria and Comamonas. Furthermore, analysis of archaea colony showed acetic acid metabolism to produce methane was the main mode in anaerobic sludge reactors, while acetic acid and hydrogen metabolism to produce methane were both the main modes in biofilm reactors. This study can provide some references for the treatment of nitrogen heterocyclic wastewater.

A Low-Cost 3D Phenotype Measurement Method of Leafy Vegetables Using Video Recordings from Smartphones.

Leafy vegetables are an essential source of the various nutrients that people need in their daily lives. The quantification of vegetable phenotypes and yield estimation are prerequisites for the selection of genetic varieties and for the improvement of planting methods. The traditional method is manual measurement, which is time-consuming and cumbersome. Therefore, there is a need for efficient and convenient in situ vegetable phenotype identification methods to provide data support for breeding research and for crop yield monitoring, thereby increasing vegetable yield. In this paper, a novel approach was developed for the in-situ determination of the three-dimensional (3D) phenotype of vegetables by recording video clips using smartphones. First, a smartphone was used to record the vegetable from different angles, and then the key frame containing the crop area in the video was obtained using an algorithm based on the vegetation index and scale-invariant feature transform algorithm (SIFT) matching. After obtaining the key frame, a dense point cloud of the vegetables was reconstructed using the Structure from Motion (SfM) method, and then the segmented point cloud and a point cloud skeleton were obtained using the clustering algorithm. Finally, the plant height, leaf number, leaf length, leaf angle, and other phenotypic parameters were obtained through the point cloud and point cloud skeleton. Comparing the obtained phenotypic parameters to the manual measurement results, the root-mean-square error (RMSE) of the plant height, leaf number, leaf length, and leaf angle were 1.82, 1.57, 2.43, and 4.7, respectively. The measurement accuracy of each indicators is greater than 80%. The results show that the proposed method provides a convenient, fast, and low-cost 3D phenotype measurement pipeline. Compared to other methods based on photogrammetry, this method does not need a labor-intensive image-capturing process and can reconstruct a high-quality point cloud model by directly recording videos of crops.

Field evaluation of spray drift and environmental impact using an agricultural unmanned aerial vehicle (UAV) sprayer.

Unmanned Aerial Vehicle (UAV) applications at low-volume using fine and very fine size droplets have been adopted in several commercial spray scenarios allowing water-saving and high-efficiency operation in delivery of pesticides. However, spray drift associated with UAV applications, especially for fine droplets generated from spinning disk nozzles, has not been fully understood, raising environmental and regulatory concerns. The objectives of this study were to compare the drift potential of three different volume median diameter (VMD, or Dv0.5) of 100, 150 and 200 µm from a commercial quadcopter equipped with centrifugal nozzles exposed to different wind speeds under field conditions. Prior to field test, the droplet size of the centrifugal nozzle was measured by a laser-diffraction particle-size analyzer. The results showed that the relationship between rotation speed and Dv0.5 agrees with the negative power function. Field tests found that the deposition at 12 m downwind direction decreased by an order of magnitude compared with the average deposition within the in-swath zone. The deposition of almost all the treatments at 50 m downwind is lower than the detection limits of 0.0002 µL/cm2. Based on the results from this study, the drift distance of this specific very popular UAV model is much less than that of manned aerial applicators. Based on the predicted equation (R2 = 0.83), the detected drift amount increased with increasing wind speed and decreasing Dv0.5. This work provides basic information to quantify the effect of wind speeds and droplet sizes on UAV spray drift potential which supports on-going regulatory guideline development for spray buffer zone and drift risk assessment protocols.

Insights into electroactive biofilms for enhanced phenolic degradation of coal pyrolysis wastewater (CPW) by magnetic activated coke (MAC): Metagenomic analysis in attached biofilm and suspended sludge.

To investigate the role of electroactive biofilms for enhanced phenolic degradation, lignite activated coke (LAC) and MAC were used as carriers in moving-bed biofilm reactor (MBBR) for CPW treatment. In contrast to activated sludge (AS) reactor, the carriers improved degradation performance of MBBR. Although two MBBRs exerted similar degradation capacity with over 92% of COD and 93% phenols removal under the highest phenolics concentration (500 mg/L), the effluent of MAC-based MBBR remained higher biodegradability (BOD5/COD = 0.34 vs 0.18) than that of LAC-based MBBR. Metagenomic analysis revealed that electroactive biofilms determined phenolic degradation of MAC-based MBBR. Primarily, Geobacter (17.33%) started Fe redox cycle on biofilms and developed syntrophy with Syntrophorhabdus (6.47%), which fermented phenols into easily biodegradable substrates. Subsequently, Ignavibacterium (3.38% to 2.52%) and Acidovorax (0.46% to 8.83%) conducted biological electricity from electroactive biofilms to suspended sludge. They synergized with dominated genus in suspended sludge, Alicycliphilus (19.56%) that accounted for phenolic oxidation and nitrate reduction. Consequently, the significantly advantage of Geobater and Syntrophorhabdus was the keystone reason for superior biodegradability maintenance of MAC-based MBBR.

Enhanced anaerobic degradation of nitrogen heterocyclic compounds with methanol, sodium citrate, chlorella, spirulina, and carboxymethylcellulose as co-metabolic substances.

The aim of the study was to explore the feasibility of methanol, sodium citrate, chlorella, spirulina, and carboxymethylcellulose (CMC) as co-metabolic substances in strengthening the anaerobic degradation of selected nitrogen heterocyclic compounds (NHCs). Chlorella, spirulina, and CMC as co-metabolic substances were first introduced into the enhanced anaerobic treatment of refractory compounds. With the addition of 300 µg/L sodium citrate, chlorella, spirulina, and CMC, reactor 3, reactor 4, reactor 5, and reactor 6 had higher degradation ratios than reactor 2 with methanol as co-metabolic substance. The addition of sodium citrate, chlorella, spirulina, and CMC increased the number of bacterial sequences, promoted the richness and diversity of the bacterial community structure, and enriched the functional genera (Levilinea and Longilinea) responsible for the degradation of quinoline and indole.

Enhanced anaerobic degradation of quinoline, pyriding, and indole with polyurethane (PU), Fe3O4@PU, powdered activated carbon (PAC), Fe(OH)3@PAC, biochar, and Fe(OH)3@biochar and analysis of microbial succession in different reactors.

The study was to explore the feasibility of polyurethane (PU), Fe3O4@PU, powdered activated carbon (PAC), Fe(OH)3@PAC, biochar, and Fe(OH)3@biochar as biological carriers in strengthening anaerobic degradation of quinoline, pyridine, and indole. When the concentrations of pollutants were 25 mg/L and 50 mg/L, reactors based on PAC and Fe(OH)3@PAC had higher degradation ratios than the other reactors. However, when the concentrations of pollutants were 75 mg/L and 100 mg/L, with the addition of PU and Fe3O4@PU, reactors began to show their superiority in the degradation of the selected NHCs. Among these, the reactor based on Fe3O4@PU had the optimal degradation ratio on quinoline, pyridine, and indole. PU, PAC, Fe(OH)3@PAC, biochar, and Fe(OH)3@biochar benefited the enrichment of Acinetobacter, Comamonas, Levilinea, Longilinea, and Desulfomicrobium. The reactor with the carrier of Fe3O4@PU had some specificity, which benefited the enrichment of Zoogloea, Thiobacillus, Anaeromyxobacter, Sphingobium, Terrimonas, Parcubacteria genera incertae sedis, Bdellovibrio, Rhizobium, and Acidovorax.

Enhanced biodegradation of coal gasification wastewater with anaerobic biofilm on polyurethane (PU), powdered activated carbon (PAC), and biochar.

The primary objective was to explore the feasibility of anaerobic biofilm on polyurethane (PU), powdered activated carbon (PAC), and biochar in strengthening anaerobic degradation of phenolic compounds and selected nitrogen heterocyclic compounds (NHCs) in coal gasification wastewater (CGW). When total phenols (TPh) was less than 300 mg/L, PAC-based biofilm was more efficient. Whereas, when the TPh concentration was more than 450 mg/L, PU-based biofilm performed the optimal degradation efficiency. Furthermore, microbial community structure analysis showed that PAC and biochar had little effect on the microbial community structure after 120 days of operation, while the addition of PU could lead to the enrichment of Giesbergeria, Caldisericum, Thauera, Methanolinea, and Methanoregula.

Enhanced anaerobic degradation of selected nitrogen heterocyclic compounds with the assistance of carboxymethyl cellulose.

Carboxymethyl cellulose (CMC) is a modified cellulose compound that is dispersible in water. Microbial anaerobic degradation of nitrogen heterocyclic compounds (NHCs) in wastewater treatment may be enhanced by CMC addition, but this remains uncertain due to a lack of experimental evidence. In this study, It was demonstrated that CMC is a suitable co-metabolic matrix in an enhanced anaerobic degradation of quinoline and indole in coal gasification wastewater. When the dosage of CMC was 0.5 mg/L, a reactor exhibited a high degradation efficiency on quinoline and indole, with ratios of 95.23 ±â€¯1.99% and 94.33 ±â€¯3.45%. The addition of CMC increased the concentration of extracellular polymeric substances in anaerobic sludge and increased the particle size of the sludge, which improved the microbial stability and sedimentation of anaerobic granular sludge. Analysis of high-throughput sequencing indicated that the addition of CMC improved the richness and diversity of bacterial and archaea communities. Acetic acid metabolism was the primary mechanism to produce methane during anaerobic degradation of NHCs wastewater.

Comparative investigation on carbon-based moving bed biofilm reactor (MBBR) for synchronous removal of phenols and ammonia in treating coal pyrolysis wastewater at pilot-scale.

By regulating the extraction solvent and alkali in pretreatment, two carbon-based MBBRs were compared in pilot-scale to synchronously remove phenols and ammonia of coal pyrolysis wastewater (CPW) under fluctuant phenols-ammonia loadings. It revealed that lignite activated coke (LAC)-based MBBR performed more stable with phenols increasing (250-550 mg/L), and reached higher tolerance limit to ammonia (>320 mg/L) than activated carbon (AC)-based MBBR under fluctuant ammonia loadings. During the phenols-ammonia synchronous removal process, the LAC provided the firm basis for shock resistance due to superior resilient adsorption capacity, enhanced sludge property and microbial cooperation. Furthermore, microbial analysis revealed that the strengthened collaboration between archaea and facultative bacteria played the primary role in phenols-ammonia synchronous degradation. Specifically, the heterotrophic bacteria consumed phenols-ammonia by partial nitrification process and ammonia assimilation, following by denitrifying process to further eliminate phenols. The multifunctional Comamonas was the critical genus participating in all procedures.

Enhanced nitrogen removal of coal pyrolysis wastewater with low COD to nitrogen ratio by partial nitrification-denitrification bioprocess assisted with polycaprolactone.

The purpose of this study is to investigate the enhancement of polycaprolactone (PCL) on total nitrogen (TN) removal of coal pyrolysis wastewater (CPW) with low COD to nitrogen ratio by partial nitrification-denitrification bioprocess (PNDB) in one single reactor. With the innovative combination of PCL and PNDB, the TN removal efficiency in the experimental reactor (signed as R1) was 10.21% higher than control reactor (R2). Nitrite accumulation percentage (NAP) in R1 was 82.02%, which was 17.49% higher than R2 at the dissolved oxygen (DO) concentration of 0.9-1.5 mg/L, for the reason that the extra DO was consumed by PCL biodegradation at the aerobic period. Gel permeation chromatography (GPC) results demonstrated that organics with the molecular weight of 185 Da, which could serve as additional carbon sources for denitrifiers, were generated during the PCL hydrolysis process at the anoxic period. PCL was hydrolyzed by extracellular enzymes with the break of the ester bond which was confirmed by FT-IR spectrometer. Microbial community analysis revealed that Ferruginibacter was the dominant hydrolysis bacteria in R1. Nitrosomonas were the main ammonium-oxidizing bacteria (AOB) and Hyphomicrobium were the denitrifiers in this study.

Effect of low-intensity direct current electric field on microbial nitrate removal in coal pyrolysis wastewater with low COD to nitrogen ratio.

The coupling of bioelectrochemical system with the biological denitrification process (R1) was constructed for nitrate removal in coal pyrolysis wastewater (CPW) and the effect of low-intensity direct current electric field was investigated. Compared with control reactor (R2), the total nitrogen (TN) removal efficiency in R1 at the optimized voltage of 0.9 V was 94.20 ±â€¯2.14%, which was 14.42% higher than that in R2. The biofilm on the cathode of R1 enhanced the nitrate reducing, however, nitrite was only reduced by bacteria in suspended activated sludge, which was confirmed by cyclic voltammetry measurement (CV). Microbial community network analysis revealed that exoelectrogenic bacteria of Pseudomonas was enriched on the anode of R1, and the "small world", including Zoogloea, Pseudomonas and Arenimonas, was established under the stimulation of voltage. Therefore, Pseudomonas transferred electron to anode, and Arenimonas could utilize electron from anode to reduce nitrate, which enhanced TN removal in R1.

Synergistic degradation on phenolic compounds of coal pyrolysis wastewater (CPW) by lignite activated coke-active sludge (LAC-AS) process: Insights into succession of microbial community under selective pressure.

This study illustrated synergistic degradation of phenolic compounds by LAC-AS process via the insight into succession of microbial community under selective pressure. The results demonstrated that high phenols exhibited toxicity pressure to single AS process by eliminating non-tolerate bacteria, inducing vicious circulation by intermediates (catechol, nitrate, etc.) accumulation. However, LAC exerted another selective pressure and facilitated positive bio-community succession of moving biological bed reactor (MBBR). Firstly, it created rich microenvironments for diverse bacteria and promoted resilient adsorption for phenols with the assistance of biodegradation. Secondly, LAC enriched facultative bacteria, which developed multiple degradation paths on phenols and nitrogen based on multifunctional genes, counteracting the toxicity pressure. Specifically, phenols were degraded by the combination of anaerobic hydrolysis and oxidation, while conventional and shortcut nitrification-denitrification (SND) and nitrogen fixation all participated in nitrogen removal, achieving high removal of COD (93.49%), Tph (93.74%), TN (92.20%) and NH4+-N (93.20%) under the highest phenols.