Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.

A Bayesian multistage spatio-temporally dependent model for spatial clustering and variable selection.

In spatio-temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time-varying effects on the health outcome. Due to the heterogeneity of spatio-temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this article proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio-temporally varying disease risks. A multistage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007 to 2018 and investigates the time-varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors.

A Simple Schiff Base as Fluorescent Probe for Detection of Al3+ in Aqueous Media and its Application in Cells Imaging.

A novel fluorescence probe for the detection of Al3+ was developed based on methionine protected gold nanoclusters (Met-AuNCs). A fluorescent Schiff base (an aldimine) is formed between the aldehyde group of salicylaldehyde (SA) and the amino groups of Met on the AuNCs, and developed for selective detection of Al3+ in aqueous solution. Al3+ can strongly bind with the Schiff base ligands, accompanied by the blue-shift and an obvious fluorescence emission enhancement at 455 nm. The limits of detection (LODs) of the probe are 2 pmol L-1 for Al3+. Moreover, the probe can successfully be used in fluorescence imaging of Al3+ in living cells (SHSY5Y cells), suggesting that the simple fluorescent probe has great potential use in biological imaging.

Performance, comparison and utilization of reduced sulfur (-2) compounds (S2-, FeS and SCN-) in autotrophic denitrification process by thiosulfate-driven autotrophic denitrifier.

The coexistence of reduced sulfur (-2) compounds (S2-, FeS and SCN-) are found in some industrial wastewaters due to pre-treatment of Fe(II) salts. These compounds as electron donors have attracted increasing interest in autotrophic denitrification process. However, the difference of their functions still remain unknown, which limit efficient utilization in autotrophic denitrification process. The study aimed to investigate and compare utilization behavior of these reduced sulfur (-2) compounds in autotrophic denitrification process activated by thiosulfate-driven autotrophic denitrifiers (TAD). Results showed that the best denitrification performance was observed in SCN-; while the reduction of nitrate was significantly inhibited in S2- system and the efficient accumulation of nitrite was observed in FeS system with cycle experiments continuing. Additionally, intermediates containing sulfur were produced rarely in SCN- system. However, the utilization of SCN- was limited obviously in comparison with S2- in coexistence systems. Moreover, the presence of S2- increased the accumulation peak of nitrite in coexistence systems. The biological results indicated that the TAD utilized rapidly these sulfur (-2) compounds, in which genus of Thiobacillus, Magnetospirillum and Azoarcus might play main roles. Moreover, Cupriavidus might also participate in sulfur oxidation in SCN- system. In conclusion, these might be attributed to the characteristics of sulfur (-2) compounds including the toxicity, solubility and reaction process. These findings provide theoretical basis for regulation and utilization of these reduced sulfur (-2) compounds in autotrophic denitrification process.

Multistate modeling and structure selection for multitype recurrent events and terminal event data.

In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time-varying coefficients, time-independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study.

Mitochondrial aggregation caused by cytochalasin B compromises the efficiency and safety of three-parent embryo.

It is widely accepted that cytochalasin B (CB) is required in enucleation of the oocyte in order to stabilize the cytoplasm. However, CB treatment results in the uneven distribution of mitochondria, with aggregation towards the nucleus, which might compromise the efficiency and safety of a three-parent embryo. Here, we demonstrated that CB treatment affected mitochondrial dynamics, spindle morphology and mitochondrial DNA carryover in a concentration-dependent manner. Our results showed that mouse oocytes treated with over 1 µg/ml CB exhibited a more aggregated pattern of mitochondria and diminished filamentous actin expression. Abnormal fission of mitochondria together with changes in spindle morphology increased as CB concentration escalated. Based on the results of mouse experiments, we further revealed the practical value of these findings in human oocytes. Chip-based digital PCR and pyrosequencing revealed that the mitochondrial carryover in reconstituted human embryos was significantly reduced by modifying the concentration of CB from the standard 5 µg/ml to 1 µg/ml before spindle transfer and pronuclear transfer. In conclusion, our findings provide an optimal manipulation for improving the efficiency and safety of mitochondrial replacement therapy.

Penalized joint generalized estimating equations for longitudinal binary data.

In statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally cannot be expressed in analytically closed form, and standard variable selection methods cannot be directly applied. As an alternative, the penalized generalized estimating equation (PGEE) is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Fréchet-Hoeffding upper bound. In this paper, we proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under different scenarios are made to assess the performance of the proposed method. Compared to existing PGEE methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real data set on Clinical Global Impression is analyzed for illustration.

Ultrasensitive colorimetric strategy for Hg2+ detection based on T-Hg2+-T configuration and target recycling amplification.

A novelty aptasensor for ultrasensitive detection of Hg2+ is developed, exploiting the combination of plasmonic properties of gold nanoparticles (AuNPs) and exonuclease III (Exo III)-assisted target recycling for signal amplification. In the presence of Hg2+, a DNA duplex can be formed due to the strong coordination of Hg2+ and T bases of single-stranded DNA (ssDNA) probe. Exo III digests the DNA duplex from the 3' to 5' direction, resulting in the releasing of Hg2+. Then, the released Hg2+ binds with another ssDNA probe through T-Hg2+-T coordination. After Exo III-assisted Hg2+ cycles, numerous ssDNA probes are exhausted, which promotes poly(diallyldimethylammonium chloride) (PDDA)-induced AuNP aggregation, leading to an obvious color change and aggregation-induced plasmon red shift of AuNPs (from 520 to 610 nm). Therefore, this biosensor is ultrasensitive, which is applicable to the detection of trace level of Hg2+ with a linear range from 5 pM to 0.6 nM and an ultralow detection limit of 0.2 pM. Furthermore, it enables visual detection of Hg2+ as low as 50 pM by the naked eye. More importantly, the assay can be applied to the reliable determination of spiked Hg2+ in sea water samples with good recovery.

Preimplantation genetic diagnosis for a carrier with m.3697G > A mitochondrial DNA mutation.

OBJECTIVE: To explore inheritance of the m.3697G > A mitochondrial DNA (mtDNA) mutation and the effectiveness of preimplantation genetic diagnosis (PGD) for the carrier. METHODS: The study encompassed a pedigree of m.3697G > A mtDNA mutation, including one asymptomatic patient who pursued for PGD treatment. Twelve cumulus oocyte complexes (COCs) were collected in the first PGD cycle and 11 COCs in the second cycle. The efficiency of cumulus cells, polar bodies, and trophectoderm (TE) in predicting the m.3697G > A heteroplasmy of embryos was analyzed. RESULTS: From 23 COCs, 20 oocytes were fertilized successfully. On day 5 and 6 post-fertilization, 15 blastocysts were biopsied. The m.3697G > A mutation load of TE biopsies ranged from 15.2 to 100%. In the first cycle, a blastocyst with mutation load of 31.7% and chromosomal mosaicism was transferred, but failed to yield a clinical pregnancy. In the second cycle, a euploid blastocyst with mutation load of 53.9% was transferred, which gave rise to a clinical pregnancy. However, the pregnancy was terminated due to fetal cleft lip and palate. The mutation loads of different tissues (47.7 ± 1.8%) from the induced fetus were comparable to that of the biopsied TE and amniotic fluid cell (49.7%). The mutation load of neither cumulus cells (R2 = 0.02, p = 0.58) nor polar bodies (R2 = 0.33, p = 0.13) correlated with TE mutation load which was regarded as a gold standard. CONCLUSIONS: The m.3697G > A mutation showed a random pattern of inheritance. PGD could be used to reduce the risk of inheritance of a high mutation load. Cumulus cells are not a suitable predictor of blastocyst mutation load.

D-optimal designs of mean-covariance models for longitudinal data.

Longitudinal data analysis has been very common in various fields. It is important in longitudinal studies to choose appropriate numbers of subjects and repeated measurements and allocation of time points as well. Therefore, existing studies proposed many criteria to select the optimal designs. However, most of them focused on the precision of the mean estimation based on some specific models and certain structures of the covariance matrix. In this paper, we focus on both the mean and the marginal covariance matrix. Based on the mean-covariance models, it is shown that the trick of symmetrization can generate better designs under a Bayesian D-optimality criterion over a given prior parameter space. Then, we propose a novel criterion to select the optimal designs. The goal of the proposed criterion is to make the estimates of both the mean vector and the covariance matrix more accurate, and the total cost is as low as possible. Further, we develop an algorithm to solve the corresponding optimization problem. Based on the algorithm, the criterion is illustrated by an application to a real dataset and some simulation studies. We show the superiority of the symmetric optimal design and the symmetrized optimal design in terms of the relative efficiency and parameter estimation. Moreover, we also demonstrate that the proposed criterion is more effective than the previous criteria, and it is suitable for both maximum likelihood estimation and restricted maximum likelihood estimation procedures.

Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data.

BACKGROUND: Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. RESULTS: We proposed an algorithm called sssHD to achieve stable sparse feature selection applied it to complicated class-imbalanced data. sssHD is based on the Hellinger distance (HD) coupled with sparse regularization techniques. We stated that Hellinger distance is not only class-insensitive but also translation-invariant. Simulation result indicates that HD-based selection algorithm is effective in recognizing key features and control false discoveries for class-imbalance learning. Five gene expression datasets are also employed to test the performance of the sssHD algorithm, and a comparison with several existing selection procedures is performed. The result shows that sssHD is highly competitive in terms of five assessment metrics. In addition, sssHD presents limited differences between performing and not performing re-balance preprocessing. CONCLUSIONS: sssHD is a practical feature selection method for high-dimensional class-imbalanced data, which is simple and can be an alternative for performing feature selection in class-imbalanced data. sssHD can be easily extended by connecting it with different re-balance preprocessing, different sparse regularization structures as well as different classifiers. As such, the algorithm is extremely general and has a wide range of applicability.

Immobilization of Phosphatidylserine by Ethanol and Lysozyme on the Cell Surface for Evaluation of Apoptosis-Like Decay in Activated-Sludge Bacteria.

Accurate determination of microbial viability can be crucial in microbe-dominated biosystems. However, the identification of metabolic decay in bacterial cells can be elaborate and difficult. We sought to identify apoptosis-like bacterial processes by using annexin V-fluorescein isothiocyanate (FITC) (AVF), a probe typically used to stain phosphatidylserine (PS) on exposed cell membranes. The bacterial cell wall provides a barrier that is responsible for low efficiency of direct PS staining of decayed bacterial cells. This can be overcome by pretreatment of the bacteria with 70% ethanol, which fixates the bacteria and preserves the PS status, combined with lysozyme treatment to hydrolyze the cell wall. That treatment improved the efficiency of AVF staining considerably, as shown for pure strains of an Ochrobactrum sp. and a Micrococcus sp. Using this method, decayed bacterial cells (induced by starvation) were more strongly stained, indicating externalization of PS to a greater extent than seen for cells harvested at logarithmic growth. A multispecies microbial sludge was artificially decayed by heat treatment or alternating anoxic-oxic treatment, which also induced increased AVF staining, again presumably via decay-related PS externalization. The method developed proved to be efficient for identification of bacterial decay and has potential for the evaluation of multispecies bacterial samples from sources like soil matrix, bioaerosol, and activated sludge.IMPORTANCE Since the externalization of phosphatidylserine (PS) is considered a crucial characteristic of apoptosis, we sought to identify apoptosis-like decay in bacterial cells by PS staining using AVF. We show that this is possible, provided the bacteria are pretreated with ethanol plus lysozyme to remove a physical staining barrier and preserve the original, decay-related externalization of PS. Our work suggests that PS externalization occurs in starved bacteria and this can be quantified with AVF staining, providing a measure of bacterial decay. Since PS is the common component of the lipid bilayer in bacterial cell membranes, this approach also has potential for evaluation of cell decay of other bacterial species.

Preventive use of non-invasive ventilation is associated with reduced risk of extubation failure in patients on mechanical ventilation for more than 7 days: a propensity-matched cohort study.

BACKGROUND: Extubation failure (EF) is high in patients on mechanical ventilation for more than 7 days. However, strategies to prevent EF in this population are lacking. AIMS: To evaluate the efficacy of preventive use of noninvasive ventilation in patients on mechanical ventilation for more than 7 days. METHODS: We performed a retrospective study in an intensive care unit of a teaching hospital. We enrolled patients who received mechanical ventilation for more than 7 days and successfully completed a weaning trial. After extubation, patients who immediately received non-invasive ventilation (NIV) were classified as the NIV group, and those who received conventional oxygenation therapy only were classified as the usual care group. RESULTS: We enrolled 95 patients in the NIV group and 61 patients in the usual care group. NIV is associated with reduced risk of EF compared to usual care both 72 h following extubation (11.6% vs 32.8%, P < 0.01, for the overall cohort; 8.6% vs 42.9%, P < 0.01, for the propensity-matched cohort) and 7 days following extubation (25.3% vs 45.9%, P < 0.01, for the overall cohort; 28.6% vs 51.4%, P = 0.09, for the propensity-matched cohort). Within 7 days of extubation, the NIV group had a lower proportion of EF than the controls (log rank test: P < 0.01 and P = 0.02 for the overall and propensity-matched cohorts, respectively). CONCLUSIONS: In patients on mechanical ventilation for more than 7 days, preventive use of NIV is associated with a reduction in EF.

Colorimetric determination of DNA using an aptamer and plasmonic nanoplatform.

A facile plasmonic nanoplatform was developed for rapid and sensitive determination of nucleic acid. Hg2+-regulated molecular beacon (MB, hairpin) containing rich thymine (T) bases at both ends is used as the probe. A hairpin structure can be formed in the MB probe due to the strong binding of Hg2+ to T. However, in the presence of target DNA, the hairpin structure is opened owing to target DNA-specific hybridization with the aptamer. Simultaneously, the opened MB interacts with poly(diallyldimethylammonium chloride) (PDDA) and hinders PDDA-induced aggregation of AuNPs, accompanied by a color change from blue to red and a decrease in absorption ratio (A620/A520). Hence, a good linear relationship was observed between the decreased absorption ratio (A620/A520) and DNA concentration ranging from 0.02 to 2 nmol/L with a low detection limit of 4.42 pmol/L. Moreover, this nanoplatform has been successfully utilized to discriminate between perfect target and mismatch sequences. More importantly, the bioassay is simple, versatile, rapid, and cost-effective compared with other common methods, which holds great promise for clinical diagnosis and biomedical application. Graphical abstract.

A hairpin-type DNA probe for direct colorimetric detection of endonuclease activity and inhibition based on the deaggregation of gold nanoparticles.

A method is described for the determination of the activity of endonuclease. It based on the deaggregation of gold nanoparticles (AuNPs) aggregated by the action of poly(diallyldimethylammonium chloride) (PDDA). A single-stranded DNA (ssDNA) is released after enzymatic cleavage catalyzed by endonuclease. The released fragments bind electrostatically to PDDA and inhibit the PDDA-induced aggregation of AuNPs. This is accompanied by a color change from blue to red and a decrease in the absorption ratio (A630/A520). Under the optimal conditions, this ratio increases linearly in the 0.001 to 1 U·µL-1 EcoRI endonuclease activity range. The detection limit is of 2 × 10-4 U·µL-1 which is much better or at least comparable to previous reports. The method is deemed to have wide scope in that it may be used to study other endonuclease activity (such as BamHI) by simply changing the specific recognition site of the hairpin-like DNA probe. The assay may also be employed to screening for inhibitors of EcoRI endonuclease. Graphical abstract Schematic presentation of the colorimetric assay based on the deaggregation of AuNPs for the detection of endonuclease activity. A single-stranded sequence (ssDNA) is released by the EcoRI cleavage, which electrostatically binds to PDDA and inhibits the PDDA-induced aggregation of AuNPs accompanying with a color change from blue to red.

Tuning model parameters in class-imbalanced learning with precision-recall curve.

An issue for class-imbalanced learning is what assessment metric should be employed. So far, precision-recall curve (PRC) as a metric is rarely used in practice as compared with its alternative of receiver operating characteristic (ROC). This study investigates the performance of PRC as the evaluating criterion to address the class-imbalanced data and focuses on the comparison of PRC with ROC. The advantages of PRC over ROC on assessing class-imbalanced data are also investigated and tested on our proposed algorithm by tuning the whole model parameters in simulation studies and real data examples. The result shows that PRC is competitive with ROC as performance measurement for handling class-imbalanced data in tuning the model parameters. PRC can be considered as an alternative but effective assessment for preprocessing (such as variable selection) skewed data and building a classifier in class-imbalanced learning.

An aptamer-based colorimetric Pt(II) assay based on the use of gold nanoparticles and a cationic polymer.

A colorimetric method is described for the determination of Pt(II). It is based on the use of gold nanoparticles (AuNPs) which are known to aggregate in the presence of a cationic polymer such as poly(diallyldimethylammonium chloride) (PDDA). If, however, a mismatched aptamer (AA) electrostatically binds to PDDA, aggregation is prevented. Upon the addition of Pt(II), it will bind to the aptamer and induce the formation of a hairpin structure. Hence, interaction between aptamer and PDDA is suppressed and PDDA will induce the aggregation of the AuNPs. This is accompanied by a color change from red to blue. The effect can be observed with bare eyes and quantified by colorimetry via measurement of the ratio of absorbances at 610 nm and 520 nm. Response is linear in the 0.24-2 µM Pt(II) concentration range, and the detection limit is 58 nM. The assay is completed within 15 min and selective for Pt(II) even in the presence of other metal ions. It was successfully applied to the rapid determination of Pt(II) in spiked soil samples. Graphical abstract Schematic representation of the method for detection of Pt(II) based on the use of a cationic polymer and gold nanoparticles. In the presence of Pt(II), aptamer interacts with the Pt(II) and prevents the interaction between aptamer and cationic polymer. Hence, cationic polymer induce the aggregation of the AuNPs and lead to the color change from red to blue.

Joint longitudinal and survival-cure models in tumour xenograft experiments.

In tumour xenograft experiments, treatment regimens are administered, and the tumour volume of each individual is measured repeatedly over time. Survival data are recorded because of the death of some individuals during the observation period. Also, cure data are observed because of a portion of individuals who are completely cured in the experiments. When modelling these data, certain constraints have to be imposed on the parameters in the models to account for the intrinsic growth of the tumour in the absence of treatment. Also, the likely inherent association of longitudinal and survival-cure data has to be taken into account in order to obtain unbiased estimators of parameters. In this paper, we propose such models for the joint modelling of longitudinal and survival-cure data arising in xenograft experiments. Estimators of parameters in the joint models are obtained using a Markov chain Monte Carlo approach. Real data analysis of a xenograft experiment is carried out, and simulation studies are also conducted, showing that the proposed joint modelling approach outperforms the separate modelling methods in the sense of mean squared errors.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox.

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (XAN/XTSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.

A feasibility study of metal sulfide (FeS and MnS) on simultaneous denitrification and chromate reduction.

Metal sulfide-based biological process is considered as a promising biotechnology for next-generation wastewater treatment. However, it is not clear if simultaneous bio-reduction of nitrate and chromate was achievable in this process. This study aimed to evaluate the feasibility of metal sulfides (FeS and MnS) on simultaneous denitrification and chromate reduction in autotrophic denitrifying column bioreactors. Results showed that simultaneous reduction of nitrate and chromate was achieved using metal sulfides (FeS and MnS) as electron donors, in which sulfate was the sole soluble end-product. Apart from the sulfur element in the metal sulfides, Fe(II) and Mn(II) were also involved in nitrate and chromate reduction as indicative by the formation of their oxidative states compounds. In microbial communities, SHD-231 and Thiobacillus were the most predominant bacteria, which might have played important roles in simultaneous denitrification and chromate reduction. Compared to FeS, MnS showed a higher performance on nitrate and chromate removal, which could also reduce the toxic inhibition of chromate on nitrate reduction. According to results of XRD and XPS, as well as a lower sulfate production in the FeS system, FeS might have been covered easily to hydroxides due to its bio-oxidation, which limited mass transfer efficiency and bio-availability of FeS. The findings in this study offered insights in the development of promising approaches for the treatment of toxic and hazardous compounds using metal sulfide.