In Situ Test and Numerical Analysis of the Subway-Induced Vibration Influence in Historical and Cultural Reserves.

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle-track-tunnel-stratum-structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures.

Fast and accurate tracking control of robotic manipulators subject to state constraints and input saturation by effectively integrating planning strategies.

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments.

Effects of grazing exclusion on soil microbial diversity and its functionality in grasslands: a meta-analysis.

Grazing exclusion (GE) is considered an effective strategy for restoring the degradation of overgrazed grasslands on the global scale. Soil microbial diversity plays a crucial role in supporting multiple ecosystem functions (multifunctionality) in grassland ecosystems. However, the impact of grazing exclusion on soil microbial diversity remains uncertain. Here, we conducted a meta-analysis using a dataset comprising 246 paired observations from 46 peer-reviewed papers to estimate how GE affects microbial diversity and how these effects vary with climatic regions, grassland types, and GE duration ranging from 1 to 64 years. Meanwhile, we explored the relationship between microbial diversity and its functionality under grazing exclusion. Overall, grazing exclusion significantly increased microbial Shannon (1.9%) and microbial richness (4.9%) compared to grazing group. For microbial groups, GE significantly increased fungal richness (8.6%) and bacterial richness (5.3%), but decreased specific microbial richness (-11.9%). The responses of microbial Shannon to GE varied among climatic regions, grassland types, and GE duration. Specifically, GE increased microbial diversity in in arid, semi-arid, and dry sub-humid regions, but decreased it in humid regions. Moreover, GE significantly increased microbial Shannon in semidesert grasslands (5.9%) and alpine grasslands (3.0%), but not in temperate grasslands. Long-term (>20 year) GE had greater effects on microbial diversity (8.0% for Shannon and 6.7% for richness) compared to short-term (<10 year) GE (-0.8% and 2.4%). Furthermore, grazing exclusion significantly increased multifunctionality, and both microbial and plant Shannon positively correlated with multifunctionality. Overall, our findings emphasize the importance of considering climate, GE duration, and grassland type for biodiversity conservation and sustainable grassland ecosystem functions.

MultiWD: Multi-label wellness dimensions in social media posts.

BACKGROUND: Halbert L. Dunn's concept of wellness is a multi-dimensional aspect encompassing social and mental well-being. Neglecting these dimensions over time can have a negative impact on an individual's mental health. The manual efforts employed in in-person therapy sessions reveal that underlying factors of mental disturbance if triggered, may lead to severe mental health disorders. OBJECTIVE: In our research, we introduce a fine-grained approach focused on identifying indicators of wellness dimensions and mark their presence in self-narrated human-writings on Reddit social media platform. DESIGN AND METHOD: We present the MultiWD dataset, a curated collection comprising 3281 instances, as a specifically designed and annotated dataset that facilitates the identification of multiple wellness dimensions in Reddit posts. In our study, we introduce the task of identifying wellness dimensions and utilize state-of-the-art classifiers to solve this multi-label classification task. RESULTS: Our findings highlights the best and comparative performance of fine-tuned large language models with fine-tuned BERT model. As such, we set BERT as a baseline model to tag wellness dimensions in a user-penned text with F1 score of 76.69. CONCLUSION: Our findings underscore the need of trustworthy and domain-specific knowledge infusion to develop more comprehensive and contextually-aware AI models for tagging and extracting wellness dimensions.

Reservoir Computing With Dynamic Reservoir using Cascaded DNA Memristors.

This article proposes molecular and DNA memristors where the state is defined by a single output variable. In past molecular and DNA memristors, the state of the memristor was defined based on two output variables. These memristors cannot be cascaded because their input and output sizes are different. We introduce a different definition of state for the molecular and DNA memristors. This change allows cascading of memristors. The proposed memristors are used to build reservoir computing (RC) models that can process temporal inputs. An RC system consists of two parts: reservoir and readout layer. The first part projects the information from the input space into a high-dimensional feature space. We also study the input-state characteristics of the cascaded memristors and show that the cascaded memristors retain the memristive behavior. The cascade connections in a reservoir can change dynamically; this allows the synthesis of a dynamic reservoir as opposed to a static one in the prior work. This reduces the number of memristors significantly compared to a static reservoir. The inputs to the readout layer correspond to one molecule per state instead of two; this significantly reduces the number of molecular and DSD reactions for the readout layer. A DNA RC system consisting of DNA memristors and a DNA readout layer is used to detect seizures from intra-cranial electroencephalogram (iEEG). We also demonstrate that a DNA RC system consisting of three cascaded DNA memristors and a DNA readout layer can be used to solve the time-series prediction task. The proposed approach can reduce the number of DNA strand displacement (DSD) reactions by three to five times compared to prior approaches.

Different types of milk consumption and the risk of dementia: Analysis from a large-scale cohort study.

BACKGROUND & AIMS: Previous studies have investigated whether milk consumption has a role in preventing the development of cognitive impairment, but the results were inconsistent. Importantly, most of them have disregarded the role of different types of milk. This study aimed to examine the associations between different types of milk consumption and the risk of dementia. METHODS: In this large-scale cohort study, participants without cognitive impairment at baseline were included from the UK Biobank. The type of milk mainly used was self-reported at baseline, including full-cream milk, skimmed-milk, soy milk, other milk, and no milk. The primary outcome was all-cause dementia. Secondary outcomes included Alzheimer's disease and vascular dementia. RESULTS: Of the 307,271 participants included in the study (mean age 56.3 [SD 8.1] years), 3789 (1.2%) incident all-cause dementia cases were observed over a median follow-up of 12.3 years. After adjustment for potential confounders, only soy milk consumers had a statistically significantly lower risk of all-cause dementia compared with no milk consumers (hazard ratio [HR], 0.69; 95% confidence interval [CI], 0.54 to 0.90). When compared with soy milk non-consumers consisting of full-cream milk, skimmed-milk, and other milk consumers, soy milk consumers still showed a lower risk of all-cause dementia (HR, 0.76; 95% CI, 0.63 to 0.92), and there was no significant interaction with genetic risk for dementia (P for interaction = 0.15). Soy milk consumers showed a lower risk of Alzheimer's disease (HR, 0.70; 95% CI, 0.51 to 0.94; P = 0.02), while the association was not significant for vascular dementia (HR, 0.72; 95% CI, 0.47 to 1.12; P = 0.14). CONCLUSIONS: The main consumption of soy milk was associated with a lower risk of dementia, particularly non-vascular dementia. Additional studies are needed to investigate how this association varies with the dose or frequency of the consumption of soy milk and to examine the generalizability of these findings in different populations.

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis.

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey.

BACKGROUND: Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. METHODS: An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module's function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. RESULTS: A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. CONCLUSIONS: Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a "pluripotent module", which could be potential marker for GC progression.

Navigating Sex-Specific Disease Dynamics in Incident Dementia.

Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.

CD163 as a Potential Biomarker in Colorectal Cancer for Tumor Microenvironment and Cancer Prognosis: A Swedish Study from Tissue Microarrays to Big Data Analyses.

(1) Background: CD163, a specific macrophage receptor, affects the progression of malignant tumors. Unfortunately, the regulation and expression of CD163 are poorly understood. In this study, we determined the expressions of CD163 in TMA samples from CRC patients and combined them with patient data from several Swedish hospitals. (2) Methods: The expressions of CD163 in tissue samples from CRC patients were examined. After combining 472 CRC patients' gene expression and 438 CRC patients' clinical data with the TCGA database, 964 cases from the GEO database, and experimental expression data from 1247 Swedish CRC patients, we selected four genes (PCNA, LOX, BCL2, and CD163) and analyzed the tumor-infiltrating immune cells (TICs) and CRC prognosis. (3) Results: Based on histopathological TMA analysis, CD163 was strongly expressed in the stroma of both normal and cancer tissues, and the expressions in normal and cancer cells varied from negative to strong. The results from public databases show decreased expression of CD163 in cancer tissue compared to normal mucosa (|log FC| > 1 and FDR < 0.01), and it is a negative prognostic factor for CRC patients (p-value < 0.05). Through tumor microenvironment (TME) analysis, we found a potential influence of CD163 on immune cell infiltration. Furthermore, the enrichment analysis indicated the possible interaction with other proteins and biological pathways. (4) Conclusions: CD163 is expressed differently in CRC tissue and is a negative prognostic factor. Its expression is associated with the TME and tumor purity of CRC. Considering all results, CD163 has the potential to be a predictive biomarker in the investigation of CRC.

Hepatic arterial infusion chemotherapy and sequential ablation treatment in large hepatocellular carcinoma.

AIM: To investigate the individualized survival benefit of hepatic arterial infusion chemotherapy (HAIC) and sequential ablation treatment in large hepatocellular carcinoma (HCC) patients. METHODS: Between February 2016 and December 2020, a total of 228 HCC patients (diameter > 5 cm) who underwent HAIC alone (HAIC group, n = 135) or HAIC and sequential ablation (HAIC-ablation group, n = 93) treatment were reviewed. We applied the inverse probability of treatment weighting (IPTW) to adjust for potential bias of two treatment groups. The overall survival (OS) and progression-free survival (PFS) were compared with Kaplan-Meier curves. The Cox regression model was used to identify independent prognostic factors. And a prediction nomogram based on these independent prognostic factors was built, aiming to make probabilistic survival predictions and estimate personalized ablation benefits. RESULTS: After a median follow-up of 17.9 months, HCC patients in the HAIC-ablation group have longer significantly OS and PFS than those in the HAIC alone group (median OS: 22.2 months vs. 14.5 months; median PFS: 8.5 months vs. 4.6 months; both, p < 0.001). The IPTW-adjusted analysis revealed similar findings (both, p < 0.001). Tumor size, tumor number, and treatment modality were identified as independent prognostic factors for OS. The nomogram based on these factors showed favorable discrimination and well calibration. CONCLUSIONS: HAIC and sequential ablation provided significant survival benefits in patients with large HCC. The nomogram could help predict individual survival probabilities and estimate personalized sequential ablation benefits.

Calibration and analysis of discrete element simulation parameters of Chinese cabbage seeds.

OBJECTIVE: To improve the accuracy of parameters used in discrete element simulation test of Chinese cabbage seeds harvesting process. METHODS: Firstly, the key physical parameters of Chinese cabbage seeds were measured. According to the results, the discrete element simulation model was established and the value range of simulation test parameters was determined. Then, the actual repose angle of Chinese cabbage seeds was obtained by physical accumulation test using bottomless conical cylinder lifting method. Plackett-Burman test, steepest climb test, Box-Behnken test and parameter optimization test were carried out in sequence with the actual angle of repose as the response value. Finally, the obtained parameters are verified. RESULTS: 1. The Plackett-Burman test showed that the seed-seed rolling friction coefficient, the seed-steel rolling friction coefficient, the seed-seed static friction coefficient, and the seed-steel static friction coefficient had significant effects on the repose angle of Chinese cabbage seeds (P<0.05). 2. The optimization test showed that the seed-seed rolling friction coefficient was 0.08, the seed-steel rolling friction coefficient was 0.109, the seed-seed static friction coefficient was 0.496, and the seed-steel static friction coefficient was 0.415. 3. The validation test showed that the repose angle of Chinese cabbage seeds under such parameter was 23.62°, and the error with the repose angle of the physical test was 0.73%. CONCLUSION: The study show that the discrete element simulation parameters of Chinese cabbage seeds model and calibration are reliable, which can provide reference for the discrete element simulation of Chinese cabbage seeds.

DNA Memristors and Their Application to Reservoir Computing.

This paper introduces memristors realized by molecular and DNA reactions. Molecular memristors process one input molecule, generate two output molecules, and are realized using two molecular reactions with two different rate constants. The DNA memristors are realized using five DNA strand displacement (DSD) reactions with two effective rate constants. The hysteresis behavior is preserved in the proposed memristors, and this behavior can be altered by changing the ratios of the rate constants. The state of the memristor can be computed from the concentrations of the two output molecules using bipolar fractional coding. We describe how the proposed memristors can be used to learn the spatial and temporal properties of data via the reservoir computing (RC) model. An RC system can be divided into two parts: reservoir and readout layer. The first part transfers the information from the input space to a high-dimensional spatiotemporal feature space represented by the state of reservoirs. The connectivity structure of the reservoir will remain fixed through the dynamical evaluations. The readout layer effectively maps the projected features to the target output. A dynamical memristor array is used to implement an RC system that exploits the internal dynamical processes of the memristors. The readout layer implements a matrix-vector multiplication using molecular reactions, also based on bipolar fractional coding. All molecular reactions are mapped to DSD reactions. The RC system based on the DNA reservoir and the DNA readout layer is used to solve a handwritten digit recognition task and a second-order time series prediction task. The performance of the DNA RC system is comparable to that of an electronic memristor RC system for both tasks.

Long non-coding RNA H19X promotes tumorigenesis and metastasis of colorectal cancer through regulating the miR-503-5p/KANK1 axis.

BACKGROUND: It has been well established that the long non-coding RNAs (lncRNAs) plays a critical role in tumor progression. However, the function of these transcripts and mechanisms responsible for their deregulation in colorectal cancer (CRC) remain to be investigated. OBJECTIVE: To explore the potential effect and regulation mechanism of lncRNA H19X in colorectal cancer. METHODS: We predicted and validated long non-coding RNA H19X from microarray data of colorectal cancer tissues. In addition, the biological behaviors of H19X and miR-503-5p on CRC were examined in vitro and in vivo, including MTT, colony formation assay, Hoechst33342 and transwell assay. The mRNA and protein levels of KN Motif and Ankyrin Repeat Domains 1 (KANK1) were analyzed by Quantitative real-time PCR (qRT-PCR), western blotting (WB) assay. Moreover, bioinformatics tools and dual-luciferase reporter assay were applied to demonstrate the relationship between KANK1 and miR-503-5p. RESULTS: H19X was remarkably up-regulated in CRC tissues. Its expression related to tumor size (p = 0.041), lymph node metastasis (p = 0.037), distal metastasis (p = 0.028), advanced TNM stage (p = 0.034) and poor survival in CRC. H19X acted as an oncogenic lncRNA that induced CRC cell proliferation, invasion and metastasis. Through a number of functional studies, we found that H19X silencing inhibited the malignance phenotype of cancer cells through loss of miR-503-5p. Further studies demonstrated that miR-503-5p was involved in the progression of CRC by directly regulating the downstream target KANK1. CONCLUSION: Collectively, the findings of the present study indicate H19X/miR-503-5p/KANK1 axis has critical role in the progression of colorectal cancer, providing an effective prognostic indicator and promising target in treatment of colorectal cancer.

Reservoir Computing Using DNA Oscillators.

This paper presents novel implementations for reservoir computing (RC) using DNA oscillators. An RC system consists of two parts: reservoir and readout layer. The reservoir projects input signals into a high-dimensional feature space which is formed by the state of the reservoir. The internal connectivity structure of the reservoir remains unchanged throughout computation. After training, the readout layer maps the projected features into the desired output. It has been shown in prior work that coupled deoxyribozyme oscillators can be used as the reservoir. In this paper, we utilize the n-phase molecular oscillator (n ≥ 3) presented in our prior work. The readout layer implements a matrix-vector multiplication using molecular reactions based on molecular analog multiplication. All molecular reactions are mapped to DNA strand displacement (DSD) reactions. We also introduce a novel encoding method that can significantly reduce the reaction time. The feasibility of the proposed RC systems based on the DNA oscillator is demonstrated for the handwritten digit recognition task and a second-order nonlinear prediction task.

Electroluminescent Fabric Woven by Ultrastretchable Fibers for Arbitrarily Controllable Pattern Display.

Flexible textile displays can be revolutionary for information transmission at any place and any time. Typically, textile displays are fabricated by traditional rigid electronics that sacrifice mechanical flexibility of devices or by flexible electronics that do not have an appropriate choice to arbitrarily control single pixels. This work reports on an electroluminescent fabric woven by ultrastretchable fibers (electroluminescent fibers up to 400% stretch, electrode fibers up to 250% stretch), which can exhibit the pixel-based arbitrarily controllable pattern display by a mobile phone application. To realize ultrastretchability, we made these fibers by encapsulating liquid metals on a polyurethane core (high elasticity). To realize arbitrary control, the design shows a plain-woven structure comprising ZnS-based electroluminescent fibers and perpendicular electrode fibers. The cross-points between the electroluminescent fiber and the electrode fiber form pixels that can be switched on or off independently and can further form the pixel-based arbitrarily controllable pattern display. By doping with different elements, ZnS-based electroluminescent fibers can emit green, blue, or yellow lights. Meanwhile, the fabrication of these fibers employs dip-coating, a scalable manufacturing method without high temperature or vacuum atmosphere. These fabrics show great potential in a wide range of applications such as wearable electronic devices, healthcare, and fashion design.

Life cycle water footprint comparison of biomass-to-hydrogen and coal-to-hydrogen processes.

Water is essential for the industrial production of hydrogen. This study investigates the production of hydrogen from biomass and coal. To date, there are few studies focusing on the water footprint of biomass-to-hydrogen and coal-to-hydrogen processes. This research conducted a life cycle water use analysis on wheat straw biomass and coal to hydrogen via pyrolysis gasification processes. The results show that the water consumption of the entire biomass-to-hydrogen process was 76.77 L/MJ, of which biomass cultivation was the dominant contributor (99%). Conversely, the water consumption of the coal-to-hydrogen process was only 1.06 L/MJ, wherein the coal production stage accounted for only 4.15% for the total water consumption, which is far lower than that of the biomass-to-hydrogen process. The hydrogen production stage of biomass hydrogen production accounted for 76% of the total water consumption when excluding the water consumption of straw growth, whereas that of the coal hydrogen production stage was 96%. This research provides the associated water consumption, within a specified boundary, of both hydrogen production processes, and the influence of major factors on total water consumption was demonstrated using sensitivity analysis.

Strong Self-Trapped Exciton Emissions in Two-Dimensional Na-In Halide Perovskites Triggered by Antimony Doping.

Soft lattice and strong exciton-phonon coupling have been demonstrated in layered double perovskites (LDPs) recently; therefore, LDPs represents a promising class of compounds as excellent self-trapped exciton (STE) emitters for applications in solid-state lighting. However, few LDPs with outstanding STE emissions have been discovered, and their optoelectronic properties are still unclear. Based on the three-dimensional (3D) Cs2 NaInCl6 , we synthesized two 2D derivatives (PEA)4 NaInCl8 :Sb (PEA=phenethylamine) and (PEA)2 CsNaInCl7 :Sb with monolayer and bilayer inorganic sheets by a combination of dimensional reduction and Sb-doping. Bright broadband emissions were obtained for the first time under ambient temperature and pressure, with photoluminescence quantum efficiency (PLQE) of 48.7 % (monolayer) and 29.3 % (bilayer), superior to current known LDPs. Spectroscopic characterizations and first-principles calculations of excited state indicate the broadband emissions originate from STEs trapped at the introduced [SbCl6 ]3- octahedron.

In situ phytoremediation of polycyclic aromatic hydrocarbon-contaminated agricultural greenhouse soil using celery.

Although celery has been established as an effective plant in the remediation of organic pollutant-contaminated soil, few studies have investigated the associated biological processes in rhizosphere and the effect of celery on agricultural field remediation in situ. In this study, a polycyclic aromatic hydrocarbon (PAH)-contaminated agricultural greenhouse was used as the experimental site, and three celery species (Apium graveolens L., Oenanthe javanica (Blume) DC., Libanotis seseloides (Fisch. & C.A. Mey. ex Turcz.) Turcz.) were applied for in situ remediation. After 90 days, the PAH dissipation rate of the L. seseloides treatment was highest (50.21%), and most of the PAHs were limited to its roots (translocation factor 0.516). This suggested that L. seseloides is a potential species for phytoremediation coupled with agro-production. The culturable microbial population and invertase activity results strongly supported that O. javanica is suitable for the establishment of exogenous bacteria-celery co-remediation techniques. Pearson's correlation analysis showed that the polyphenol oxidase (PPO) activity was highly significantly positively correlated with the PAH dissipation rate (r = 0.984, P < 0.01), and we suggest that PPO can be used as a microecological index during PAH remediation.