Bayesian Inference Research Articles

Research on recovering scattering obstacles in inhomogeneous medium based on Bayesian method

ABSTRACT In this paper, the problem of acoustic wave scattering by obstacles in the two-dimensional inhomogeneous medium is considered. When the refractive index is a binary function, a hybrid method for reconstructing multiple impenetrable obstacles is proposed. The obstacle number is predicted based on the measured far-field data by using the neural network method. And then, it is used as the prior information of Bayesian method, the shape and position parameters of multiple obstacles are reconstructed using Markov chain Monte Carlo algorithm. The results of numerical experiments show that this method can effectively reconstruct the number, shape and position of obstacles in the inhomogeneous medium.

New statistical framework for interlaboratory evaluation of anti-doping testing results by WADA

AbstractThe World Anti-doping Agency (WADA) International Standard for Laboratories (ISL), developed as part of the World Anti-Doping Program, requires satisfactory laboratory performance in the WADA External Quality Assessment Scheme (EQAS) in order to obtain and maintain WADA accreditation. Under this mandate, WADA regularly distributes urine and blood test samples to anti-doping laboratories to continuously monitor their proficiency. Over the years, WADA has employed classical, generic statistical methods, in accordance to ISO 13528, to evaluate quantitative EQAS results. Here, we set out the rationale for a modern statistical approach that recognizes and addresses the particular features of the measurement results typically obtained in such tests and present an approach involving Bayesian measurement models and statistical data analysis that is tailored specifically to anti-doping testing.

Bayesian network methodology and machine learning approach: an application on the impact of digital technologies on logistics service quality

PurposeThis paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.Design/methodology/approachUsing a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.FindingsThis study was funded by the European Union—NextGenerationEU, in the framework of the GRINS-Growing Resilient, INclusive and Sustainable project (GRINS PE00000018—CUP B43C22000760006). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.Originality/valueThis research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

A fractional-derivative kernel learning method for indoor position prediction

Precise indoor positioning is increasingly essential for Internet of Things (IoT) deployments. Within the IoT framework, many resource-limited devices require accurate positioning capabilities. Therefore, as a lightweight online learning algorithm, kernel adaptive filter (KAF) has been adopted for indoor positioning. However, the environments and mixed noise often lead to the inaccuracy of indoor positioning. In order to tackle this problem, this paper proposes a novel fractional-derivative kernel adaptive filtering method, which has two significant features: The fractional-derivative assisted weight-update strategy can accurately reflect data distributions and system characteristics, thereby improving the accuracy of prediction. Secondly, the Student’s t-based kernel function can effectively combat non-Gaussian noise in indoor positioning applications, thus the prediction robustness can be well guaranteed. In three representative indoor positioning scenarios, the proposed algorithm is compared with state-of-the-art KAFs and widely used positioning methods (Trilateration, KNN, Naive Bayesian, and transfer learning). Experimental results show that the proposed method can improve positioning accuracy by at least 5.98% with the well-robustness property.

Information content of JWST spectra of WASP-39b

The era of James Webb Space Telescope (JWST) transmission spectroscopy of exoplanetary atmospheres commenced with the study of the Saturn-mass gas giant WASP-39b as part of the Early Release Science (ERS) program. WASP-39b was observed using several different JWST instrument modes (NIRCam, NIRISS, NIRSpec G395H and NIRSpec PRISM) and the spectra were published in a series of papers by the ERS team. The current study examines the information content of these spectra measured using the different instrument modes, focusing on the complexity of the temperature-pressure profiles and number of chemical species warranted by the data. We examine if the molecules H2O CO CO2 K H2S CH4 and SO2 are detected in each of the instrument modes. Two Bayesian inference methods are used to perform atmospheric retrievals: the standard nested sampling method, as well as the supervised machine learning method of the random forest (trained on a model grid). For nested sampling, Bayesian model comparison is used as a guide to identify the set of models with the required complexity to explain the data. Generally, non-isothermal transit chords are needed to fit the transmission spectra of WASP-39b, although the complexity of the temperature-pressure profile required is mode-dependent. The minimal set of chemical species needed to fit a spectrum is mode-dependent as well, and also depends on whether grey or non-grey clouds are assumed. When a non-grey cloud model is used to fit the NIRSpec G395H spectrum, it generates a spectral continuum that compensates for the water opacity. The same compensation is absent when fitting the non-grey cloud model to the NIRSpec PRISM spectrum (which has broader wavelength coverage), suggesting that it is spurious. The interplay between the cloud spectral continuum and the water opacity determines if sulphur dioxide is needed to fit either spectrum. The inferred elemental abundances of carbon and oxygen and the carbon-to-oxygen (C/O) ratios are all mode- and model-dependent, and should be interpreted with caution. Bayesian model comparison does not always offer a clear path forward for favouring specific retrieval models (e.g. grey versus non-grey clouds) and thus for enabling unambiguous interpretations of exoplanet spectra.

Task-Dependent Brain Activity in Generalized Anxiety Disorder Determined By Bayesian Spatio-Temporal Single Case Model

Introduction: Data obtained from functional magnetic resonance imaging (fMRI) have a complex structure. Considering the special features of this type of data in analyses is of particular importance. Previous studies on generalized anxiety disorder (GAD) as a prevalent mental disorder using functional neuroimaging have had conflicting results. In this study, we apply a Bayesian spatiotemporal model to this type of data which considers both spatial and temporal dependence among regions which is one of the most essential features to consider. Methods: In this single-subject study, we analyze data from a patient with GAD and a healthy participant. Both participants are 24-year-old women who are assigned an emotion reactivity task (matching neutral and negative facial expressions) inside a scanner. The spatial Bayesian variable selection method is used to detect blood oxygen level-dependent activation in fMRI data. Results: Activation areas in neutral and negative facial expressions are provided for both participants by posterior probability map. The results of our study show a greater level of activity in the GAD participant in comparison to the healthy participant in responding to the negative matching task. Conclusion: the GAD patient showed more neural activity in response to negative facial expressions than the healthy participant in brain regions related to emotional response in the areas of the frontal Pole, middle frontal gyrus, insular cortex, and frontal orbital cortex. Moreover, the inferior frontal gyrus in the patient with GAD showed more reaction to negative emotional stimuli.

Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities

ObjectiveBayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities.MethodEmploying a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis.ResultThe study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects.ConclusionThe BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

Geographical Disparities in Hypertension Incidence Rate in Iran 2004-2016: Bayesian Spatial Analysis

Introduction: Cardiovascular diseases such as coronary heart disease, heart failure, arrhythmia, and cardiomyopathy all include hypertension as a key risk factor. Research has shown that the early detection and treatment of hypertension and its risk factors, as well as public health policies to reduce behavioral risk factors, have led to a gradual reduction in mortality caused by heart disease and stroke in high-income countries in the past three decades. Trends in hypertension incidence have been monitored at the national level in Iran. The aim of this study examine province-level disparities in Hypertension incidence from 2004 to 2016. Methods: Use the Non-Communicable Diseases Risk-Factors Surveillance in the Islamic Republic of Iran STEPs registry data. to estimate the incidence rate of hypertension for all provinces in 2004, 2006-2009, 2011, and 2016 using a Bayesian spatial model with Markov chain Monte Carlo algorithm in OpenBUGS version 3.2.3 and R version 4.2.2. Results: The estimated Hypertension incidence rate in total increased from 19.87 per 1000 people (95% credible interval 14.28, 25.48) in 2004 to 193.02 (171.92, 220.48) in 2016. According to the estimates of 2016, we found that the provinces of Markazi, Ardabil, and Semnan had the highest rate of hypertension, and the provinces of Hormozgan, and Sistan-Baluchistan had the lowest rate. Our findings show that Khorasan, North, Alborz, and Semnan have the most significant percentage change in incidence rate from 2004-2016. Conclusion: To reduce the prevalence of hypertension in Iranian regions, it is crucial to develop regular hypertension screening programs, especially among the elderly

Apply Bayesian Inference with Normal–Normal Conjugate to Forecast Renewable Energy Generation: A Case Study of Waste-to-Energy in Taiwan

Apply Bayesian Inference with Normal–Normal Conjugate to Forecast Renewable Energy Generation: A Case Study of Waste-to-Energy in Taiwan

Phantom perception as a Bayesian Inference problem - a pilot study.

Tinnitus is the perception of a sound in the absence of an external source. Although the role of the auditory system is well investigates, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see if the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioural and electrophysiological responses to the learning and test phases in (i) healthy young adults and (ii) people with continuous subjective tinnitus and matched controls. We observed that in both parts of the study, the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. In patients with tinnitus, we observe no significant difference in behavioural or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms going together with increased evoked activity during the learning and test phases in the visual paradigm. This impresses the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the current study also presents preliminary data supporting evidence that tinnitus patients maybe building stronger perceptual models.

Retuning of hippocampal representations during sleep.

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation1. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples whileon the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

Cyclic behavior of laminated bio-based connections with slotted-in steel plates: Genetic algorithm, deterministic neural network-based model parameter identification, and uncertainty quantification

To support more sustainable construction, this paper experimentally investigates the cyclic behavior of laminated timber (Laminated Veneer Lumber (LVL)) and glubam (Glue Laminated Bamboo) connections with slotted-in steel plates in terms of experimental test, numerical simulations and parameter identification. Experimental tests included eight different configurations: two materials (LVL and glubam), two bolt diameters (8 and 10 mm), and one or two bolts. Two different cyclic-loading protocols were applied for each type of connection: only tension and tension/compression. The observed behavior is then compared to a finite element model developed in OpenSeesPy, which takes into account factors such as sliding, contact, pinching, cyclic stiffness, and strength degradation. To identify the best set of parameters for the model, three different approaches are considered: genetic algorithm, fast deterministic neural network, and probabilistic Bayesian method. First, the model identification is carried out by means of a genetic algorithm-based optimization. The parameter-identification results are evaluated in terms of elastic stiffness, yielding point, and ductility. Next, a sensitivity analysis is performed to determine the significance of the parameters, and an innovative approach combining neural network and sensitivity analysis is proposed for fast and preliminary parameter identification. Then, probabilistic Bayesian identification is employed to calculate the posterior distribution of the model parameters identified and the confidence bounds of the estimated response. Finally, different model identification parameters are compared and suggestions for algorithm selection are provided.

Exploring the Link Between Sensory Processing and Psychopathology in a Community Sample of Young Adults: Bayesian Network Analyses

AbstractSensory processing is a fundamental aspect of human experience, and varies significantly between individuals, potentially affecting individual functioning, behaviour, and mental health. This study investigated the associations between sensory processing and psychopathology in a sample of the general population. Participants responded to a general psychiatric screening tool (DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure-Adult) and to the Adolescent/Adult Sensory Profile (AASP) questionnaire. The sample comprised 1108 young adults (77.25% females, mean age 22.6). Data were analysed using item response theory and Bayesian network analyses, revealing specific associations between sensory profiles and psychopathological dimensions. Specifically, we detected associations between the low registration sensory profile and the dimensions of psychosis, substance use, and mania. Sensation seeking was associated with mania. Sensory sensitivity was related to depression and mania, while sensation avoiding was inversely correlated with mania. These findings suggest that reduced external sensory awareness may be linked to a predisposition to psychosis, while a multifaceted sensory processing pattern may indicate vulnerability to mood disorders. Assessing sensory processing could yield crucial insights into individual susceptibilities to mental disorders and help identify targeted preventive and therapeutic strategies

First Report of Exserohilum rostratum Causing Brown Spot on Spartina alterniflora in China.

Spartina alterniflora Loisel, a perennial grass, has become an invasive species in China's coastal wetlands (Zhang et al. 2018). In July 2021, brown spot symptoms were observed on S. alterniflora in a coastal wetland (21°45'48″N, 108°44'00″E) in Beihai City, Guangxi Province, China. The disease affected approximately 50% of the plants in the surveyed area (0.2 ha) and was also observed in other regions of Beihai. It caused brown lesions with a gray or whitish center on the leaves and stems of S. alterniflora. As the disease developed, it ultimately led to leaf shedding and plant death. To isolate the causal agent, 18 fragments (~ 5 mm) from six symptomatic plants (3 leaf pieces per plant) were surface sterilized with 1% NaOCl solution for 2 min and rinsed three times with sterilized water. Subsequently, the tissues were placed on potato dextrose agar (PDA) medium supplemented with chloramphenicol (0.1 g/liter) and incubated at 28°C for three days. The hyphal tips were transferred onto fresh PDA to obtain pure cultures. A total of 25 isolates were obtained, 20 of which shared similar morphologies, while the remaining five exhibited distinct morphological characteristics and were non-pathogenic to S. alterniflora. Three isolates (MC16.1.3, MC16.6.2, and MC16.8.3) were randomly selected from the 20 for further investigation. The colonies on PDA were flat with dense aerial mycelia. The colony margins were entire, light brown in the centre, white to grey at the margin; reverse dark brown in the centre, gray at the margin. Conidia were straight to slightly curved, light olive-brown to dark olive-brown, septate, measured 33.5 to 79.1 μm × 10.4 to 18.7 μm (average 52.9 × 14.4 μm, n = 100), with a distinctly protruding hilum swelled from the basal cell. For molecular identification, the genomic DNA was extracted from mycelium on PDA using the CTAB method (Guo et al. 2000). The internal transcribed spacer (ITS), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and translation elongation factor 1 alpha (TEF1-α) genes were amplified and sequenced with the primer pairs ITS1/ITS4 (White et al. 1990), GPD1/GPD2 (Berbee et al. 1999), and EF1-983/EF1-2218 (Rehner et al. 2005), respectively. A BLAST analysis revealed that the ITS (OR516787-9), GAPDH (OR523686-8), and TEF-α (OR523683-5) had 99.1 to 99.7% identity with those of E. rostratum strains BRIP 11417 (LT837836, LT882553, and LT896656) and CBS 128061 (KT265240, LT715900, and LT896658) (Hernández-Restrepo et al. 2018). Based on the concatenated sequences, a phylogenetic tree generated by PhyloSuite software (Zhang et al., 2020) through Bayesian inference (BI) and Maximum Likelihood (ML) methods placed the isolates within E. rostratum. These morphological characteristics and molecular analyses confirmed the pathogen as E. rostratum (Hernández-Restrepo et al. 2018; Kaboré et al. 2022). To confirm pathogenicity, a conidial water suspension (~ 1 × 106 conidia/ml) of each of the three strains was inoculated on nine healthy S. alterniflora plants that had been grown for six months. Control plants were treated with sterile water. All plants were then enclosed in plastic bags and incubated in a greenhouse at 28°C. Six days after inoculation, the plants exhibited symptoms similar to those observed in nature. The control plants developed no symptoms. These experiments were replicated three times with similar results. To fulfill Koch's postulates, E. rostratum was consistently re-isolated from symptomatic tissue and confirmed by morphology and sequencing, whereas no fungus was isolated from the control plants. In recent years, S. alterniflora has posed a serious threat to the indigenous biodiversity of wetland ecosystems (Zhang et al. 2018). To our knowledge, this is the first report of E. rostratum causing brown spot on S. alterniflora worldwide.

Bayesian inference of thermal effects in dense matter within the covariant density functional theory

The high temperatures reached in a proto-neutron star or during the post-merger phase of a binary neutron star coalescence lead to non-negligible thermal effects on the equation of state (EOS) of dense nuclear matter (NM). Here we study these effects within the covariant density functional theory employing the posteriors of a Bayesian inference, which encompasses a large sample of EOS models. Different densities and temperatures are considered. We find that for a number of quantities thermal effects are strongly correlated with the Dirac effective mass (m⁎) of the nucleons and/or its logarithmic derivative as a function of density. These results can be explained within the low temperature approximation though they survive beyond this limit.

Random-effects substitution models for phylogenetics via scalable gradient approximations.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

An optimal exact interval for the risk ratio in the 2×2$$ 2\times 2 $$ table with structural zero

AbstractThe table with a structural zero represents a common scenario in clinical trials and epidemiology, characterized by a specific empty cell. In such cases, the risk ratio serves as a vital parameter for statistical inference. However, existing confidence intervals, such as those constructed through the score test and Bayesian methods, fail to achieve the prescribed nominal level. Our focus is on numerically constructing exact confidence intervals for the risk ratio. We achieve this by optimally combining the modified inferential model method and the ‐function method. The resulting interval is then compared with intervals generated by four existing methods: the score method, the exact score method, the Bayesian tailed‐based method and the inferential model method. This comparison is conducted based on the infimum coverage probability, average interval length and non‐coverage probability criteria. Remarkably, our proposed interval outperforms other exact intervals, being notably shorter. To illustrate the effectiveness of our approach, we discuss two examples in detail.

Video-based sequential Bayesian homography estimation for soccer field registration

A novel Bayesian framework is proposed, which explicitly relates the homography of one video frame to the next through an affine transformation while explicitly modelling keypoint uncertainty. The literature has previously used differential homography between subsequent frames, but not in a Bayesian setting. In cases where Bayesian methods have been applied, camera motion is not adequately modelled, and keypoints are treated as deterministic. The proposed method, Bayesian Homography Inference from Tracked Keypoints (BHITK), employs a two-stage Kalman filter and significantly improves existing methods. Existing keypoint detection methods may be easily augmented with BHITK. It enables less sophisticated and less computationally expensive methods to outperform the state-of-the-art approaches in most homography evaluation metrics. Furthermore, the homography annotations of the WorldCup and TS-WorldCup datasets have been refined using a custom homography annotation tool that has been released for public use. The refined datasets are consolidated and released as the consolidated and refined WorldCup (CARWC) dataset.

Spatial analysis of cholangiocarcinoma in relation to diabetes mellitus and Opisthorchis viverrini infection in Northeast Thailand

Cholangiocarcinoma (CCA) exhibits a heightened incidence in regions with a high prevalence of Opisthorchis viverrini infection, with previous studies suggesting an association with diabetes mellitus (DM). Our study aimed to investigate the spatial distribution of CCA in relation to O. viverrini infection and DM within high-risk populations in Northeast Thailand. Participants from 20 provinces underwent CCA screening through the Cholangiocarcinoma Screening and Care Program between 2013 and 2019. Health questionnaires collected data on O. viverrini infection and DM, while ultrasonography confirmed CCA diagnoses through histopathology. Multiple zero-inflated Poisson regression, accounting for covariates like age and gender, assessed associations of O. viverrini infection and DM with CCA. Bayesian spatial analysis methods explored spatial relationships. Among 263,588 participants, O. viverrini infection, DM, and CCA prevalence were 32.37%, 8.22%, and 0.36%, respectively. The raw standardized morbidity ratios for CCA was notably elevated in the Northeast’s lower and upper regions. Coexistence of O. viverrini infection and DM correlated with CCA, particularly in males and those aged over 60 years, with a distribution along the Chi, Mun, and Songkhram Rivers. Our findings emphasize the association of the spatial distribution of O. viverrini infection and DM with high-risk CCA areas in Northeast Thailand. Thus, prioritizing CCA screening in regions with elevated O. viverrini infection and DM prevalence is recommended.

Human molybdenum exposure risk in industrial regions of China: New critical effect indicators and reference dose

Evidence increasingly suggests molybdenum exposure at environmental levels is still associated with adverse human health, emphasizing the necessity to establish a more protective reference dose (RfD). Herein, we conducted a study measuring 15 urinary metals and 30 clinical health indicators in 2267 participants residing near chemical enterprises across 11 Chinese provinces to investigate their relationships. The kidney and cystatin-C emerged as the most sensitive organ and critical effect indicator of molybdenum exposure, respectively. Odds of cystatin-C-defined chronic kidney disease (CKD) in the highest quantile of molybdenum exposure significantly increased by 133.5% (odds ratio [OR]: 2.34, 95% CI: 1.78, 3.11) and 75.8% (OR: 1.76, 95% CI: 1.24, 2.49) before and after adjusting for urinary 14 metals, respectively. Intriguingly, cystatin-C significantly mediated 15.9–89.5% of molybdenum's impacts on liver and lung function, suggesting nephrotoxicity from molybdenum exposure may trigger hepatotoxicity and pulmonary toxicity. We derived a new RfD for molybdenum exposure (0.87 μg/kg-day) based on cystatin-C-defined estimated glomerular filtration rate by employing Bayesian Benchmark Dose modeling analysis. This RfD is significantly lower than current exposure guidance values (5–30 μg/kg-day). Remarkably, >90% of participants exceeded the new RfD, underscoring the significant health impacts of environmental molybdenum exposure on populations in industrial regions of China.