A one health approach to tackling AMR and why gender matters: findings from pastoralist communities in Tanzania.

Introduction: Inappropriate use of antimicrobials is a major driver of AMR in low-resource settings, where the regulation of supply for pharmaceuticals is limited. In pastoralist settings in Tanzania, men and women face varying degrees of exposure to antibiotics due to gender relations that shape access and use of antimicrobials. For example, critical limitations in healthcare systems in these settings, including inadequate coverage of health services put people at risk of AMR, as families routinely administer self-treatment at home with antimicrobials. However, approaches to understanding AMR drivers and risk distribution, including the One Health approach, have paid little attention to these gender considerations. Understanding differences in access and use of antimicrobials can inform interventions to reduce AMR risk in community settings. This paper focuses on the gendered risk of AMR through a study of gender and social determinants of access to and use of antimicrobials in low-resource pastoralist settings in Tanzania. Methods: A mixed methods approach involving household surveys, interviews and ethnographic participant observation in homes and sites of healthcare provision was used, to investigate access and administration of antibiotics in 379 adults in Naiti, Monduli district in northern Tanzania. A purposive sampling technique was used to recruit study participants and all data was disaggregated by sex, age and gender. Results: Gender and age are significantly associated with the use of antibiotics without a prescription in the study population. Young people aged 18-24 are more likely to use unprescribed antibiotics than older people and may be at a higher risk of AMR. Meanwhile, although more men purchase unprescribed antibiotics than women, the administration of these drugs is more common among women. This is because men control how women use drugs at the household level. Discussion: AMR interventions must consider the critical importance of adopting and implementing a gender-sensitive One Health approach, as gender interacts with other social determinants of health to shape AMR risk through access to and use of antimicrobials, particularly in resource-limited pastoralist settings.

Cumulative adversity and survival in the wild.

Protecting populations contending with co-occurring stressors requires a better understanding of how multiple early-life stressors affect the fitness of natural systems. However, the complexity of such research has limited its advancement and prevented us from answering new questions. In human studies, cumulative risk models predict adult health risk based on early adversity exposure. We apply a similar framework in wild yellow-bellied marmots (Marmota flaviventer). We tested cumulative adversity indices (CAIs) across different adversity types and time windows. All CAIs were associated with decreased pup survival and were well supported. Moderate and acute, but not standardized CAIs were associated with decreased lifespan, supporting the cumulative stress hypothesis and the endurance of early adversity. Multivariate models showed that differences in lifespan were driven by weaning date, precipitation, and maternal loss, but they performed poorly compared with CAI models. We highlight the development, utility, and insights of CAI approaches for ecology and conservation.

Predictors for diabetes and hypertension among bus drivers and conductors in South India.

Bus drivers and conductors are facing various health hazards due to stressful working conditions. They are exposed to various occupational hazards which lead to deterioration of their health over a period of time. Therefore, it is of interest to evaluate the prevalence of diabetes and hypertension among bus drivers and conductors and to determine the factors associated with diabetes and hypertension. This cross-sectional study was done among 293 bus drivers and 157 conductors during March 2018 to December 2018 and the data was collected using a semi structured questionnaire after obtaining informed consent. Each individual was investigated for Blood sugar and Blood Pressure. Out of 450 study participants, about 6.9% were diabetic and 50.2% were hypertensive. Transport workers with single marital status, those who belong to rural areas and drivers were significant predictors for diabetes. Overweight was significantly associated with the Diabetes in negative direction. Marital status, years of experience and anxiety were significantly associated with hypertension. Hemoglobin level, total cholesterol level and blood urea level also emerged as predictors for Hypertension. Non-communicable diseases like diabetes and hypertension have surpassed the communicable diseases in affecting the health of people with distinct occupations like bus drivers and conductors.

Unraveling Spatially Diverse and Interactive Regulatory Mechanisms of Wetland Methane Fluxes to Improve Emission Estimation.

Methane fluxes (FCH4) vary significantly across wetland ecosystems due to complex mechanisms, challenging accurate estimations. The interactions among environmental drivers, while crucial in regulating FCH4, have not been well understood. Here, the interactive effects of six environmental drivers on FCH4 were first analyzed using 396,322 half-hourly measurements from 22 sites across various wetland types and climate zones. Results reveal that soil temperature, latent heat turbulent flux, and ecosystem respiration primarily exerted direct effects on FCH4, while air temperature and gross primary productivity mainly exerted indirect effects by interacting with other drivers. Significant spatial variability in FCH4 regulatory mechanisms was highlighted, with different drivers demonstrated varying direct, indirect, and total effects among sites. This spatial variability was then linked to site-specific annual-average air temperature (17.7%) and water table (9.0%) conditions, allowing the categorization of CH4 sources into four groups with identified critical drivers. An improved estimation approach using a random forest model with three critical drivers was consequently proposed, offering accurate FCH4 predictions with fewer input requirements. By explicitly accounting for environmental interactions and interpreting spatial variability, this study enhances our understanding of the mechanisms regulating CH4 emissions, contributing to more efficient modeling and estimation of wetland FCH4.

Understanding fire regimes: A biogeographical perspective.

Fire regimes are often considered to be either driven by climate, fuel load or human activities. A significant proportion of fires across various ecosystems occur via large fire events. Recently, suggestions have been made that fires are becoming more severe and frequent as a consequence of current climate change. Although there are many factors influencing fire events, scientists have not found a suitable framework that can provide for understanding fires at the macroscale level. This review article proposes a new conceptual framework to better understand fire regimes. The proposed framework relies on a biogeographical perspective of fire regimes that include characteristics that have been underestimated in previous frameworks and to mitigate time as well as spatial scale issues at the macrolevel.

Anthropogenic vs. climatic drivers: Dissecting Lake desiccation on the Iranian plateau.

Lakes are indispensable to ecological balance, supporting biodiversity and human welfare. Despite their importance, lakes around the globe are diminishing at an alarming rate, presenting substantial environmental challenges exacerbated by climate variability and human-induced pressures, such as unsustainable land management and excessive water withdrawals. This study offers an extensive analysis of the desiccation phenomena affecting major lakes on the Iranian plateau, dissecting the complex interplay of contributing factors. We examine meteorological, hydrological, and agricultural drought conditions using indices like SPI, SDI, and SPEI to gain nuanced insights into drought dynamics. This study sheds light on the factors that contribute to the drying up of lakes and quantifies the impact of anthropogenic and climatic drivers on this phenomenon. The findings indicate that the area of all lakes has declined dramatically after the change point, ranging from 51% in the Urmia Lake to an impressive rate (i.e. 100%) in Southern Hamun. Despite the stable rate of meteorological droughts (increasing in temperature by about 1 °C since the 90s and stable precipitation patterns across most basins), the research highlights the ongoing struggle against severe hydrological and agricultural drought conditions. The study delineates the predominant role of anthropogenic activities in the diminishing lake inflows and the consequential drying of the lake, accounting for approximately 100 % in five of the seven lakes.

Identifying the drivers of overall rating of cancer care: Insights from the second wave of the Swiss Cancer Patient Experiences study.

BACKGROUND: Patient experience surveys gather information on various aspects of care via numerous survey items. Identifying the most critical areas of patient experience to prioritize for quality care improvement can be challenging. The objective of this study was to determine which care experience items are the drivers influencing patients' overall rating of cancer care. METHODS: Data from 2750 adult patients with cancer from the second wave of the Swiss Cancer Patient Experiences study were analyzed. This cross-sectional survey was conducted in eight Swiss hospitals from September 2021 to February 2022. Stepwise logistic regression examined the relationship between overall care rating and 29 patient experience items covering different patient-centered care dimensions while adjusting for sociodemographic and health variables. RESULTS: Overall, patients rated their cancer care experience at 8.9 out of 10. Stepwise regression identified seven drivers contributing to overall care rating. The strongest drivers were "professionals worked well together" (odds ratio [OR], 4.81) and "tests were not repeated" (OR, 2.09) from the coordination and integration dimension, "offered support for symptoms during treatment" (OR, 2.11) from the physical comfort dimension, followed by "hospital staff ensured available home support" (OR, 1.99), "offered to see health professional for concerns" (OR, 1.91), "treatment options were explained" (OR, 1.75), and "involved in treatment decisions as desired" (OR, 1.68). CONCLUSIONS: This study evaluated the care experiences of patients with cancer with a comprehensive tool that identified seven key factors independently associated with overall care rating. By concentrating on these areas, hospitals can not only improve the patient care experience but also efficiently allocate resources to quality improvement initiatives.

Understanding users of online energy efficiency counseling: comparison to representative samples in Norway.

Introduction: To achieve substantial energy efficiency improvements in the privately owned building stock, it is important to communicate with potential renovators at the right point in time and provide them with targeted information to strengthen their renovation ambitions. The European Union recommends using one-stop-shops (OSSs), which provide information and support throughout the whole process, from planning to acquisition of funding, implementation, and evaluation as a measure to remove unnecessary barriers. Methods: For this paper, we invited visitors of two Norwegian websites with OSS characteristics to answer an online survey about their renovation plans and energy efficiency ambitions. The participants visited the websites out of their own interest; no recruitment for the websites was conducted as part of the study (N = 437). They also rated a range of psychological drivers, facilitators, and barriers to including energy upgrades in a renovation project. Their answers were then compared to existing data from representative samples of Norwegian households regarding home renovation in 2014, 2018, and 2023, as well as data from a sample of people who were engaged in renovation projects in 2014, which was collected by the research team with a similar online survey. Furthermore, 78 visitors completed a brief follow-up online survey one year later to report the implemented measures. Results: We found that visitors of the websites are involved in more comprehensive renovation projects and have substantially higher ambitions for the upgrade of energy efficiency compared to the representative samples. They also perceive stronger personal and social norms, as well as have a different profile of facilitators and barriers. Discussion: The findings suggest to policymakers that OSSs should be marketed especially to people motivated to upgrade energy efficiency but lack information and are unable to implement their plans alone. Also, the construction industry might refer interested people to such low-threshold online solutions to assist informed and more ambitious decisions.

Examining outpatient occupational therapy utilization among children and youth with special health care needs in South Carolina: A structural equation model of individual- and community-level factors.

BACKGROUND: Children and youth with special health care needs (CYSHCN) comprise 23 % of the pediatric population in South Carolina (SC), compared to state prevalences of 13.6 %-24 % nationwide. While occupational therapy (OT) is critical to maximizing CYSHCN's participation in daily activities, there are significant disparities in rehabilitation access in the Southeast region. OBJECTIVE: This study examines associations between patient-and community-level factors and outpatient OT utilization. METHODS: This study analyzes OT utilization data among CYSHCN (N = 1913) in a health system in SC from 07/01/2022-06/31/2023, merged with Child Opportunity Index 2.0 zip-code level social drivers of health (SDOH) data. SDOH are non-medical factors that affect individual's long-term health, with estimates ranging from "very low" to "very high" opportunity. Structural equation modeling was conducted to understand complex associations between observed and latent variables in a real-world context. RESULTS: CYSHCN were diagnosed with congenital (40.77 %), developmental (37.87 %), and neurological/neuromuscular conditions (21.36 %). CYSHCN who were non-Hispanic Black or Hispanic and were hospitalized were associated with living in lower opportunity areas. CYSHCN with private or military/other insurance/self-pay were associated with living in higher opportunity areas than Medicaid-insured CYSHCN. CYSHCN who were female and ≥12 years demonstrated lower OT utilization. CYSHCN who participated in speech and/or physical therapy demonstrated higher OT utilization. OT utilization increased with each increase in SDOH. CONCLUSION: With these findings, healthcare providers may consider accessibility barriers, including transportation, when referring CYSHCN to OT services. Further research is needed to examine the impact of household-level SDOH on OT access across SC.

Chinese adaptation of the reckless driving behaviour scale: Testing its psychometric properties and links with safe driving climate among family and peers.

Purpose: The present study aimed to revise the Reckless Driving Behaviour Scale (RDBS) and examined its reliability and validity among young Chinese drivers. Methods: The RDBS, the Safe Driving Climate among Friends Scale (SDCaF), the Family Climate for Road Safety Scale (FCRSS) and a social desirability scale were administrated to 560 young drivers. Exploratory factor analysis (EFA, n = 250) and confirmatory factor analysis (CFA, n = 250) were conducted to examine the factorial structure of the RDBS. Results: The Chinese version of the RDBS has 18 items that are divided into 4 factors: distraction, substance use, extreme behaviour and positioning. Both the results of EFA and CFA confirmed its factorial structure. The reliability of the RDBS was acceptable and the concurrent validity of the scale was supported by its significant associations with the SDCaF and FCRSS factors. Finally, drivers who had violation involvement scored higher on all four factors than their peers who did not have violation involvement, providing evidence for its known-group validity. Conclusion: The revised RDBS has similar structure with the original version and its reliability and validity were satisfactory. It is an effective tool to measure the reckless driving behaviour of young drivers in China and interventions that incorporated joint efforts of family and peers should be developed.

A comprehensive, large-scale analysis of "terroir" cheese and milk microbiota reveals profiles strongly shaped by both geographical and human factors.

An exhaustive analysis was performed on more than 2000 microbiotas from French Protected Designation of Origin (PDO) cheeses, covering most cheese families produced throughout the world. Thanks to a complete and accurate set of associated metadata, we have carried out a deep analysis of the ecological drivers of microbial communities in milk and "terroir" cheeses. We show that bacterial and fungal microbiota from milk differed significantly across dairy species while sharing a core microbiome consisting of four microbial species. By contrast, no microbial species were detected in all ripened cheese samples. Our network analysis suggested that the cheese microbiota was organized into independent network modules. These network modules comprised mainly species with an overall relative abundance lower than 1%, showing that the most abundant species were not those with the most interactions. Species assemblages differed depending on human drivers, dairy species, and geographical area, thus demonstrating the contribution of regional know-how to shaping the cheese microbiota. Finally, an extensive analysis at the milk-to-cheese batch level showed that a high proportion of cheese taxa were derived from milk under the influence of the dairy species and protected designation of origin.

Dispersion Influence of Electroacoustic Transducer Parameters in the Design Process of Miniature Loudspeaker Arrays and Omnidirectional Sound Sources.

Electroacoustic transducers represent one of the crucial materials used in the construction of loudspeaker arrays. The dispersion in their parameters may influence the performance of a speaker set. Parametric loudspeaker arrays and omnidirectional sound sources have been used for years. However, the possible influence of transducer manufacturing tolerances on the arrays' performance has not been investigated. In previous research, the sources of possible dispersion in acoustic measurements carried out with omnidirectional sources were studied, pointing out that the problems with sound sources may be a significant reason behind the small measurement repeatability in standards. This paper investigated the measurement of several common types of miniature speakers, using 10 pieces of each type and investigating the influence of their parameter dispersion in electric and acoustic ways. Numerical simulations of omnidirectional sound sources were performed to investigate the drivers' dispersion influence sensitivity. The results provided proof of the small-signal parameter dispersion reaching 20% of the variation. The acoustic measurements show that the loudspeakers may differ in sensitivity parameters by up to 4 dB in 10 transducer tests. The analysis of an example multitransducer array indicated that a dispersion of a sensitivity higher than 1 dB might lead to significant misperformance in constructed arrays and measurement deviations with this type of array.

Using machine learning to reveal drivers of soil microplastics and assess their stock: A national-scale study.

The issue of microplastic (MP) contamination in soil is a significant concern. However, due to limited large-scale studies and stock assessments, our understanding of the drivers of their distribution and fate remains incomplete. To address this, we conducted a comprehensive study in China, collected MP data from 621 sites, and utilized machine learning techniques for analysis. Our findings revealed 9 key factors influencing the distribution of soil MPs, highlighting their nonlinear influence processes. Among these factors, atmospheric deposition emerged as the most dominant driver, while wind and precipitation could lead to the transformation of soil from a sink to a source of MPs. MP concentrations in Chinese soils vary from 1.4 to 4333.1 particles/kg, with human activities significantly affecting their distribution, resulting in higher concentrations in the east and lower concentrations in the west. The estimated MP stock in Chinese soils is 1.92 × 1018 particles, equivalent to a mass of 2.11-8.64 million tonnes. This stock alone surpasses that found in global oceans, making global soil the largest reservoir of MPs. Overall, this study enhances our understanding of the environmental behavior of MPs and provides valuable data and theoretical support for the prevention, control, and management of this contamination.

Near miss road traffic accidents and associated factors among truck drivers in Gamo zone, southern Ethiopia by using a contributory factors interaction model.

Background: Road traffic accidents (RTAs) are among the leading causes of injuries, fatalities, and the resulting increase in financial burdens worldwide. Every year, RTAs cause numerous serious injuries and fatalities in Ethiopia. it is important to understand how prevalent near-miss crash accidents are, and which by definition could have injured the victim but did not result in an actual accident. The determinants of these near-misses are essential in road crash accident reduction strategies. In spite of the fact that near-miss accidents are much more common than actual losses or injuries, very little research has been conducted on them. Thus, this study was intended to assess the near-miss accidents and associated factors among truckers in Gamo zone, southern Ethiopia. Methodology: The community-based cross-sectional study was employed from May 12 to July 10,2022, using a structured interviewer-administered questionnaire. A simple random sampling technique was used to select participants. The data were analyzed using the statistical package for social sciences. A binary and multivariate logistic regression model was used to identify the determinants of near-miss accidents. A statistical significance level was set at p < 0.05. Results: About 72.5% of truckers had experienced near-miss road traffic accidents. The majority of the near-miss accidents were caused by speeding, followed by driving on the wrong side of the road and skidding, 65 (22.6%), 39 (13.5%), and 38 (13.2%), respectively. Driving frequency per week, location of accidents, condition of the road, sleeping status, and weather conditions were significantly associated with near-miss accidents. Conclusion: The prevalence of near-miss accidents is high in the Gamo zone. Being a younger and less educated driver, high driving frequency per week, driving on major roads and junctions, foggy weather, and inadequate sleep all contribute to the occurrence of accidents. Road safety measures that could address these identified factors are required to mitigate potential RTAs.

The relationship between clinical impairment and blood drug concentration: Comparison between the most prevalent traffic relevant drug groups.

AIM: The aim of the present study was to investigate the relationship between blood concentrations of four different drug classes; ethanol, benzodiazepines, amphetamines and tetrahydrocannabinol (THC) and driver impairment as assessed by a clinical test of impairment (CTI). METHODS: Data was retrieved from a national database on CTI assessments and accompanying blood drug concentrations from apprehended drivers. All drug concentrations in blood were quantified using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS), and compared to the results of the CTI which were categorized as either "not impaired", "mildly impaired", "moderately impaired", or "considerably impaired". RESULTS: A total number of 15 514 individual mono drug-cases collected over 9 years was included. 89 % were men and the median age was 34 years. In addition, 3 684 individual cases with similar age and gender distribution where no drugs were detected, were included as a reference group. For ethanol and benzodiazepines the percentage of clinically impaired cases increased markedly from lower to higher concentration windows, from 60 % to 97 % for ethanol and from 38 % to 76 % for benzodiazepines. The corresponding increase for amphetamines and THC was modest, from 43 % to 58 % for amphetamines and from 41 % to 55 % for THC. The correlation between drug concentration and degree of impairment was high for ethanol (Spearman´s rho=0.548, p<0.001) and relatively high for benzodiazepines (Spearman´s rho=0.377, p<0.001), but low for amphetamines (Spearman´s rho=0.078, p<0.001) and THC (Spearman´s rho=0.100, p<0.001). CONCLUSION: The percentage of impaired drivers increased with increasing blood drug concentration for all four drug classes, most pronounced for ethanol and benzodiazepines and much less for amphetamines and THC. The median blood drug concentration increased with increasing magnitude of impairment for ethanol and benzodiazepines, while this was much less pronounced for amphetamines and THC. The ranges of drug concentrations, however, were wide for all four drug classes in all impairment categories as assessed by individual clinical examination.

A comprehensive conceptual framework for signaling in-lake CO2 through dissolved organic matter.

Organic carbon (C) and CO2 pools are closely interactive in aquatic environments. While there are strong indications linking freshwater CO2 to dissolved organic matter (DOM), the specific mechanisms underlying their common pathways remain unclear. Here, we present an extensive investigation from 20 subtropical lakes in China, establishing a comprehensive conceptual framework for identifying CO2 drivers and retrieving CO2 magnitude through co-trajectories of DOM evolution. Based on this framework, we show that lake CO2 during wet period is constrained by a combination of biogeochemical processes, while photo-mineralization of activated aromatic compounds fuels CO2 during dry period. We clearly determine that biological degradation of DOM governs temporal variations in CO2 rather than terrestrial C inputs within the subtropical lakes. Specifically, our results identify a shared route for the uptake of atmospheric polycyclic aromatic compounds and CO2 by lakes. Using machine learning, in-lake CO2 levels are well modelled through DOM signaling regardless of varying CO2 mechanisms. This study unravels the mechanistic underpinnings of causal links between lake CO2 and DOM, with important implications for understanding obscure aquatic CO2 drivers amidst the ongoing impacts of global climate change.

Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework.

The causes of traffic violations by elderly drivers are different from those of other age groups. To reduce serious traffic violations that are more likely to cause serious traffic crashes, this study divided the severity of traffic violations into three levels (i.e., slight, ordinary, severe) based on point deduction, and explore the patterns of serious traffic violations (i.e., ordinary, severe) using multi-source data. This paper designed an interpretable machine learning framework, in which four popular machine learning models were enhanced and compared. Specifically, adaptive synthetic sampling method was applied to overcome the effects of imbalanced data and improve the prediction accuracy of minority classes (i.e., ordinary, severe); multi-objective feature selection based on NSGA-II was used to remove the redundant factors to increase the computational efficiency and make the patterns discovered by the explainer more effective; Bayesian hyperparameter optimization aimed to obtain more effective hyperparameters combination with fewer iterations and boost the model adaptability. Results show that the proposed interpretable machine learning framework can significantly improve and distinguish the performance of four popular machine learning models and two post-hoc interpretation methods. It is found that six of the top ten important factors belong to multi-scale built environment attributes. By comparing the results of feature contribution and interaction effects, some findings can be summarized: ordinary and severe traffic violations have some identical influencing factors and interactive effects; have the same influencing factors or the same combinations of influencing factors, but the values of the factors are different; have some unique influencing factors and unique combinations of influencing factors.

Principal Components Analysis of Driving Simulator Variables in Novice Drivers.

Objective: Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments. Method: One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments. Results: The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment. Conclusion: These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen.

Understanding Nanoscale Interactions between Minerals and Microbes: Opportunities for Green Remediation of Contaminated Sites.

In situ contaminant degradation and detoxification mediated by microbes and minerals is an important element of green remediation. Improved understanding of microbe-mineral interactions on the nanoscale offers promising opportunities to further minimize the environmental and energy footprints of site remediation. In this Perspective, we describe new methodologies that take advantage of an array of multidisciplinary tools─including multiomics-based analysis, bioinformatics, machine learning, gene editing, real-time spectroscopic and microscopic analysis, and computational simulations─to identify the key microbial drivers in the real environments, and to characterize in situ the dynamic interplay between minerals and microbes with high spatiotemporal resolutions. We then reflect on how the knowledge gained can be exploited to modulate the binding, electron transfer, and metabolic activities at the microbe-mineral interfaces, to develop new in situ contaminant degradation and detoxication technologies with combined merits of high efficacy, material longevity, and low environmental impacts. Two main strategies are proposed to maximize the synergy between minerals and microbes, including using mineral nanoparticles to enhance the versatility of microorganisms (e.g., tolerance to environmental stresses, growth and metabolism, directed migration, selectivity, and electron transfer), and using microbes to synthesize and regenerate highly dispersed nanostructures with desired structural/surface properties and reactivity.