Effective Risk Assessment Research Articles

Enhancing Risk Management: Leveraging the Likelihood/Severity Matrix for Effective Risk Assessment and Mitigation in the Electrical and Electronic Sector

This paper presents the basic concept of risk and reviews commonly applied tools and techniques for risk assessment. Generally, risk assessment is a completely experiential decision-making process based on experience and knowledge of risk assistants. This paper emphasises one quantitative/qualitative technique, namely the likelihood/severity matrix approach, which aims to direct the organisation’s attention towards risks that have the highest potential to have a negative effect. This paper’s main contribution lies in introducing a proposed model that utilises the likelihood/severity matrix approach to categorise risks into ‘regions’ and subsequently rank them. This process supports risk managers in making informed decisions to reduce risks effectively. A likelihood/severity matrix was examined through a case study belonging to the electrical and electronic sector to find the critical risks that hinder the assembly line of personal computers. Results showed that the ‘Breaking parts during assembly’, ‘Shocking the components from static electricity discharge’ and ‘Using wrong compatible parts’ risks had the maximum risk score, with values of 10–15 as the most critical risks. These results can influence decision makers in developing actions to mitigate these highlighted risks.

Degradation of the novel herbicide tiafenacil in aqueous solution: Kinetics, various influencing factors, hydrolysis products identification, and toxicity assessment

As new pesticides are continually introduced into agricultural systems, understanding their environmental behavior and potential toxicity effects is crucial for effective risk assessment. This study utilized QuEChERS and UPLC-QTOF-MS/MS techniques to analyze Tiafenacil (TFA) and its six hydrolysis products (HP1 to HP6) in water, marking the first comprehensive report on these degradation products. Calibration curves demonstrated strong linearity (R2 ≥ 0.9903) across concentrations ranging from 0.02 to 3.50 mg L−1. TFA's hydrolysis followed single first-order kinetic (SFOK) model, with rapid degradation observed under alkaline and high-temperature conditions, resulting in half-lives ranging from 0.22 to 84.82 days. The ECOSAR model predicts that TFA's hydrolysis products exhibit acute and chronic toxicity to fish, Daphnia, and green algae. Additionally, hydrolysis products HP1, HP5, and HP6 were detected in irrigation water from citrus orchards, posing higher predicted toxicity risks to fish and green algae. This highlights the necessity for further risk assessments considering transformation products. Overall, this study enhances our understanding of TFA's environmental fate and supports its safe agricultural application and monitoring practices.

Optimizing child protection systems: A systematic review of the literature on risk assessment practices

Child maltreatment is a global phenomenon that has affected millions of children, with far-reaching consequences for their development. The assessment of risk has emerged as a fundamental and indispensable component within child protection services. Professionals should use safe and effective risk assessment practices, always prioritizing the best interests of the child. This systematic review aims to elucidate the prevailing risk assessment practices employed by professionals within the child protection systems. The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Thirteen empirical studies were selected, featuring participants from diverse fields such as social services, health, psychology, and law enforcement. The professionals relied on a multifaceted approach, incorporating clinical judgment, professional expertise, actuarial risk assessment tools, individual interviews with children, adolescents, and family members, as well as the observation of familial dynamics. Moreover, intra- and interinstitutional collaboration played a pivotal role in the comprehensive evaluation of risk. Consequently, the discerned efficient practice appears to be a synergistic effort undertaken by a transdisciplinary team. This collaborative action encompasses the utilization of clinical judgment, augmented by the judicious administration of actuarial risk assessment instruments.

Disaster loss index development and comprehensive assessment: A case study of Shanghai

Accurate measurement of socio-economic loss and the impact of disasters is crucial for effective risk assessment and management. This study introduces a Disaster Severity Index (DSI) and a Disaster Impact Index (DII), along with a composite severity matrix, to evaluate comprehensive disaster losses. By utilizing seven loss indicators across six types of natural disasters and socioeconomic indicators, we thoroughly assessed Shanghai’s historical disaster losses from 1978 to 2020. Our analysis reveals that Shanghai’s most severe annual disaster losses occurred in 1991 and 2005; however, the overall impact on the local socio-economy has been declining. According to the DSI, Chongming (0.88), Songjiang (0.64), and Qingpu (0.51) Districts exhibit the highest severity. In terms of the DII, Songjiang (0.87), Chongming (0.71), and Qingpu (0.64) Districts are most affected. The comprehensive disaster severity grading indicates that Chongming and Songjiang Districts experience the most severe impacts, while Huangpu and Xuhui Districts are less affected. The spatial distribution of disaster losses is concentrated in suburban areas, with less impact in the central city. Our methodology provides a framework for analyzing and comparing historical disaster losses in terms of time (inter-annual), space (administrative divisions), and types of disasters. It can also be used for statistical analysis and regular monitoring of disaster losses. This study not only elucidates the spatiotemporal distribution of historical disaster losses in Shanghai but also validates the efficacy of our comprehensive index models.

Association of the rs1042522 SNP with prostate cancer risk: a study of cancer tissues, primary tumor cultures, and serum samples from a Spanish Caucasian population.

Prostate cancer (PCa) is a leading cause of cancer-related deaths in European men, emphasizing the urgent need for effective risk assessment strategies. The TP53 gene, a tumor suppressor gene frequently mutated in cancer, commonly harbors the rs1042522 single nucleotide polymorphism (SNP), known as the P72R SNP, which may influence PCa susceptibility. This study investigated the prevalence of the P72R SNP in European Caucasian PCa samples and its association with PCa risk. Genotyping was conducted on 12 hormone-naïve aggressive PCa cultures (hnPCs) from untreated patients (Gleason ≥8), 11 radical prostatectomies (RP), and 94 serum samples using DNA Sanger sequencing and melting curve analysis. Comparative analysis utilized data from the GnomAD database's European Caucasian non-cancer population. Our results demonstrate a significantly higher frequency of the P72R SNP in PCa samples and serums compared to the general European non-cancer population. A robust and statistically significant association (p < 0.0001) between the SNP and prostate cancer risk was identified, with an odds ratio of 7.937 (95% CI 5.37-11.00). Notably, the G allele (R72) showed a pronounced prevalence in high Gleason score (≥8) patients, although statistical significance was not reached. These results highlight a potential association with undifferentiated and malignant PCa lesions. The compelling association between the P72R SNP and prostate cancer risk underscores the potential utility of this marker for the early identification of patients at risk of aggressive metastatic prostate cancer. This insight could empower further research to intervene at an early stage by offering enhanced opportunities for timely and targeted interventions.

Groundwater LNAPL Contamination Source Identification Based on Stacking Ensemble Surrogate Model

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R2 of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%.

Analysis of Meteorological Drought for Selected Districts of Saurashtra Region of Gujarat

Drought, in particular, is a complex and recurring phenomenon triggered by insufficient precipitation or changes in its distribution, often resulting in decreased water availability. It evolves slowly, affecting large populations' access to food and water, disrupting the entire hydrological cycle, and causing long-term economic losses. The present study attempts a comprehensive analysis of meteorological drought characteristics for selected districts (Junagadh, Amreli, Jamnagar, and Rajkot) in the semi-arid Saurashtra region of Gujarat. The analysis of meteorological drought was performed using rainfall (RF), maximum temperature (Tmax), and minimum temperature (Tmin) data from four stations (each district has one station) for the 43 years duration (1981–2023). The Standardized Precipitation Evapotranspiration Index (SPEI) is utilized across multiple timescales to assess the number of drought affected years, occurrence of drought and drought parameters (total drought duration, magnitude, and intensity). The meteorological drought incidence varied significantly across Junagadh, Amreli, Jamnagar and Rajkot, with Junagadh experiencing droughts in 29% of years, Amreli in 32%, and higher rates in Jamnagar (37%) and Rajkot (36%). The most severe drought years in the four districts were 1987 and 2002, with additional major droughts occurred in 1985, 1986, 1987, 1993, 2002 and 2012. These districts also faced consecutive droughts from 1985 to 1987 and again from 1999 to 2002.Analysis of drought parameters in Junagadh, Amreli, Jamnagar and Rajkot districts showed that drought duration (DD) ranged from 29.46% (Junagadh) to 35.85% (Jamnagar) of months, drought magnitude (DM) varied between -37.58% (Jamnagar) and -34.10% (Junagadh), and drought intensity (DI) spanned from -1.17 (Rajkot) to -1.04 (Junagadh). The analysis of drought occurrence in all districts shows that mild and moderate droughts have been most frequent while severe and extreme droughts have been less common. The study's findings enhance water managers' understanding of how climate change influences drought patterns in the region, thereby assisting policymakers in conducting effective drought risk assessments.

Prediction of Adverse Maternal Outcomes in Preeclampsia Using the FullPIERS (Preeclampsia Integrated Estimate of Risk) Model in a Tertiary Care Hospital of Eastern India.

Preeclampsia, characterized by hypertensive disorders and systemic inflammatory response, remains a leading cause of maternal morbidity and mortality globally. Effective risk assessment tools are crucial for predicting adverse maternal outcomes. This study evaluates the performance of the fullPIERS (Preeclampsia Integrated Estimate of Risk) model in predicting adverse maternal outcomes within 24 hours of admission for preeclampsia. A cross-sectional study was conducted over one year, involving 100 preeclamptic patients admitted to Nil Ratan Sircar Medical College & Hospital (NRSMCH). Predictor variables were collected within 24 hours of admission and analyzed using the fullPIERS model. The fullPIERS model effectively stratified maternal risk. Adverse outcomes were significantly associated with systolic blood pressure (BP) ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, oxygen saturation ≤ 95%, frontal headache, visual disturbances, chest pain/dyspnea, and abnormal random blood sugar, albumin, alanine aminotransferase, platelet count, and creatinine levels. A fullPIERS score ≥ 30 was strongly predictive of adverse maternal outcomes. The fullPIERS model is a valuable tool for predicting adverse maternal outcomes in preeclampsia, aiding in timely and effective clinical decision-making.

Exploring the Role of Destination Resilience Plans in Promoting Tourism: A Literature Review

The study investigates the critical importance of destination resilience in enhancing tourism, particularly in the wake of challenges such as the COVID-19 pandemic. The primary aim of the research is to analyze how effective governance, stakeholder engagement, resource management, and risk assessment contribute to the resilience of tourist destinations and micro, small, and medium enterprises (MSMEs). To achieve this, the study employs a comprehensive literature review methodology, synthesizing existing research on destination resilience strategies and their implications for tourism promotion. The findings reveal that destination resilience plans significantly impact tourism performance, stakeholder participation, and sustainable development. Additionally, the study highlights the necessity of integrating innovative practices and technology to bolster the resilience of tourism sectors. In conclusion, this research provides valuable insights for destination management organizations, policymakers, and industry researchers, emphasizing the need for strategic planning and collaboration to foster long-term sustainability and resilience in tourism. The study serves as a foundation for future investigations and policy-making in the realm of sustainable tourism, addressing existing gaps in the literature and offering practical recommendations for enhancing destination resilience.

Morphology and location of thrombus and sludge in patients with non-valvular atrial fibrillation

BackgroundStroke and thromboembolism in nonvalvular atrial fibrillation (NVAF) primarily arise from thrombi or sludge in the left atrial appendage (LAA). Comprehensive insight into the characteristics of these formations is essential for effective risk assessment and management.MethodsWe conducted a single-center retrospective observational of 176 consecutive NVAF patients with confirmed atrial/appendage thrombus or sludge determined by a pre-ablation transesophageal echocardiogram (TEE) from December 2017 to April 2019. We obtained clinical and echocardiographic characteristics, including left atrial appendage emptying velocity (LAAeV) and filling velocity (LAAfV). Data analysis focused on identifying the morphology and location of thrombus or sludge. Patients were divided into the solid thrombus and sludge groups, and the correlation between clinical and echocardiographic variables and thrombotic status was analyzed.ResultsMorphological classification: In total, thrombi were identified in 78 patients, including 71 (40.3%) mass and 7 (4.0%) lamellar, while sludge was noted in 98 (55.7%). Location classification: 92.3% (72/78) of patients had thrombus confined to the LAA; 3.8% (3/78) had both LA and LAA involvement; 2.7% (2/78) had LA, LAA and RAA extended into the RA, the remained 1.2%(1/78) was isolated to RAA. 98.0% (96/98) of patients had sludge confined to the LAA; the remaining 2.0% (2/98) were present in the atrial septal aneurysm, which protrusion of interatrial septum into the RA. The thrombus and sludge groups showed low LAAeV (19.43 ± 9.59 cm/s) or LAAfV (17.40 ± 10.09 cm/s). Only LA dimension ≥ 40 mm was independently associated with the thrombus state in the multivariable model.ConclusionThis cohort study identified rare thrombus morphology and systematically summarized the classification of thrombus morphology. The distribution of thrombus and sludge outside limited to LAA was updated, including bilateral atrial and appendage involvement and rare atrial septal aneurysm sludge. LAAeV and LAAfV were of limited value in distinguishing solid thrombus from sludge.Clinical Trial NumberChiCTR-OCH-13,003,729.

Concurrent Analysis of Tiafenacil and Its Transformation Products in Soil by Using Newly Developed UHPLC-QTOF-MS/MS-Based Approaches.

As new pesticides continue to emerge in agricultural systems, understanding their environmental behavior is crucial for effective risk assessment. Tiafenacil (TFA), a promising novel pyrimidinedione herbicide, was the focus of this study. We developed an efficient QuEChERS-UHPLC-QTOF-MS/MS method to measure TFA and its transformation products (TP1, TP2, TP3, TP4, and TP5) in soil. Our calibration curves exhibited strong linearity (R2 ≥ 0.9949) ranging from 0.015 to 2.0 mg/kg within a low limit of quantification (LOQ) of 2.0 µg/kg. Inter-day and intra-day recoveries (0.10 to 2.0 mg/kg, 80.59% to 110.05%, RSD from 0.28% to 12.93%) demonstrated high sensitivity and accuracy. Additionally, TFA dissipation under aerobic conditions followed first-order kinetics, mainly yielding TP1 and TP4. In contrast, TP1 and TP2 were mainly found under sterilized and anaerobic conditions, and TFA dissipation followed second-order kinetics. Moreover, we predicted the transformation pathways of TFA using density functional theory (DFT) and assessed the toxicity levels of TFA and its TPs to aquatic organisms using ECOSAR. Collectively, these findings hold significant implications for a better understanding of TFA fate in diversified soil, benefiting its risk assessment and rational utilization.

Interpretable feature-temporal transformer for short-term wind power forecasting with multivariate time series

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making.

Building reliability of risk assessment of domino effects in chemical tank farm through an improved uncertainty analysis method

The severity and complexity of fire-induced domino effects have received widespread attention. The spatial and temporal evolution process modeling of domino accidents is still stuck in single crisp value, in which the uncertainty will affect the reliability of the results. In this paper, we propose a computational method for uncertainty propagation of the evolution process modeling of the domino effect called Uncertainty Propagation of Domino Evolution Model (UPDEM), aiming to increase the reliability of risk assessment results. The method is capable of obtaining ranges and corresponding probability envelopes for tiame to failure and escalation probabilities, and quantifying uncertainty for all computational models that require input parameters. The uncertainty is quantified based on the evidence theory and a parameter cropping method based on the stress-strength interference model is proposed to optimize the evidence theory. The proposed method is applied to a case study and compared with previous studies. The comparison confirms that the results are more reliable after considering uncertainty and can produce the worst possible scenario consequence. Conducting the sensitivity analysis to prioritize the treatment of parameters and identify effective measures in risk control. The methodology can provide an important reference for decision-makers to prevent and control domino effects.

A paradigm for the contextual safety assessment of agricultural microbes: a closer look at Klebsiella variicola

Adopting a risk assessment paradigm that is contextualized and strain-specific will be critical to enabling the continued development and safe use of microbes, particularly bacteria, in numerous industries and applications. An overly simplistic approach of labeling bacterial species as either harmful or beneficial is ill-suited for the complexities of their interactions with hosts and other microbes, where the lines between friend, foe, and innocent bystander are often unclear. Many such nuanced relationships have been described in human microbiome studies, illustrating the inherent challenges of defining bacterial safety. Any effective risk assessment framework must take into account bacterial niche and environment, fitness, host health, route and extent of exposure, and strain characterization. Klebsiella variicola, a diazotrophic soil bacterium isolated around the world, has been the subject of increasing interest on both environmental and clinical fronts, and has been used commercially as a biofertilizer on millions of farm acres. Here we review its population structure, relevance in clinical and environmental settings, and use as a biofertilizer in light of the risk assessment framework described.

Risk Assessment And Patient Safety In Physiotherapy Practice: A Comprehensive Analysis Of Factors Contributing To Patient Falls

Risk assessment is a systematic procedure employed to detect potential dangers and evaluate the possible consequences of disasters or calamities, ensuring comprehensive hazard identification in the work environment. Integrating risk assessment into management and organizational processes is crucial, especially in healthcare settings like physiotherapy, where patient safety is paramount. This comprehensive review systematically compiled and analyzed relevant studies from scholarly journals, bibliographies, and related articles to evaluate the effectiveness of risk assessment procedures in identifying and mitigating potential hazards in physiotherapy practice. The review specifically focused on the use of the STEADI tool in conjunction with electronic health records (EHR) for joint risk assessments. The risk assessment process involves three key stages: identification, calculation, and implementation of control measures. Various methodologies were explored, including models like CATCH fall administration, PISTI management, multidisciplinary collaboration, and Fall TIPS. Falls, a major global health issue, are the 13th leading cause of death worldwide, with preventive strategies shown to reduce fall-related deaths by up to 92%. Effective risk assessment is essential for ensuring patient safety in physiotherapy. By identifying and mitigating potential risks, particularly those related to falls, healthcare providers can significantly improve patient outcomes and safety in clinical practice.

Workers’ Exposure to Chemical Risk in Small and Medium-Sized Enterprises: Assessment Methodology and Field Study

This study aimed to develop a state-of-the-art method for assessing chemical risks in small and medium-sized enterprises (SMEs). The method combined desk research findings and the authors’ practical knowledge to identify major shortcomings in performing risk assessments at the workplace. Data were collected from nine SMEs across three sectors: agriculture, laboratory research, and conservation and restoration laboratories. On-site observations, focus group discussions, and textual content analysis of occupational safety and health documentation were used to gather information on chemical risks and workers’ perceptions. The developed method uses numerical factors to account for the level of conformity with regulations and the duration of exposure to support an estimation of the probability of an incident. The method was designed to enhance risk assessment practices and tackle common issues encountered by SMEs, including the lack of data for estimating probability. Feedback from SMEs involved in the study indicated that the method’s performance was satisfactory, with its well-organized information flow and supportive components being particularly valued. The study highlights the importance of effective risk assessment in creating a safer and healthier work environment for workers.

Extending X-reality technologies to digital twin in cultural heritage risk management: a comparative evaluation from the perspective of situation awareness

DT systems, characterized by real-time capabilities, high precision, and high integration, have become essential in various domains. In the context of cultural heritage, a DT system encompasses comprehensive information about heritage sites, contextual data, and expert knowledge, forming a complex dynamic system. The substantial volume of information and diverse sources significantly increases the cognitive load for management personnel in understanding on-site situations. This study, from the perspective of situational awareness, introduces X-reality technologies (VR and AR) into DT systems for cultural heritage risk management. The aim is to evaluate the effectiveness of different X-reality technologies in cultural heritage risk perception and their impact mechanisms. A total of 184 participants were divided into two groups and experienced three different applications (2D desktop, VR, and AR). Using situational awareness rating techniques, participants' responses were measured across three dimensions: attention resource demand, attention resource supply, and understanding of the situation. SEM was employed to estimate the stability of the scale data. The results indicate that, compared to traditional 2D desktops, both VR and AR demonstrate advantages in enhancing heritage risk situational awareness. However, in AR mode, no significant advantages were found in the dimensions of attention resource demand and attention resource supply compared to traditional 2D desktop applications. Furthermore, a significant difference in immersion between VR and AR was found to affect the attention resource demand dimension. Although the results suggest differences in the interactivity of VR and AR in affecting the attention resource demand dimension, no significant differences were found. By comprehensively understanding the functional mechanisms of X-reality technologies in influencing cultural heritage risk situational awareness, this study provides design references for constructing DT systems for cultural heritage risk management. Additionally, it offers insights for heritage site managers, experts, and stakeholders to enhance risk perception efficiency, promoting more effective risk assessment, analysis, and strategic decision-making, thereby reducing damage to cultural heritage.

Classification and regression machine learning models for predicting the combined toxicity and interactions of antibiotics and fungicides mixtures

Antibiotics and triazole fungicides coexist in varying concentrations in natural aquatic environments, resulting in complex mixtures. These mixtures can potentially affect aquatic ecosystems. Accurately distinguishing synergistic and antagonistic mixtures and predicting mixture toxicity are crucial for effective mixture risk assessment. We tested the toxicities of 75 binary mixtures of antibiotics and fungicides against Auxenochlorella pyrenoidosa. Both regression and classification models for these mixtures were developed using machine learning models: random forest (RF), k-nearest neighbors (KNN), and kernel k-nearest neighbors (KKNN). The KKNN model emerged as the best regression model with high values of determination coefficient (R2 = 0.977), explained variance in prediction leave-one-out (Q2LOO = 0.894), and explained variance in external prediction (Q2F1 = 0.929, Q2F2 = 0.929, and Q2F3 = 0.923). The RF model, the leading classifier, exhibited high accuracy (accuracy = 1 for the training set and 0.905 for the test set) in distinguishing the synergistic and antagonistic mixtures. These results provide crucial value for the risk assessment of mixtures.

High-risk ARGs (HRA) Chip: A high-throughput qPCR-based array for assessment of high-risk ARGs from the environment

The global surge in antibiotic resistance genes (ARGs) presents a serious public health challenge. While methods like metagenomic analysis and qPCR arrays have been instrumental in investigating ARG distributions and dynamics, the vast diversity of ARGs often complicates effective monitoring and risk assessment. Here, we developed a High-Risk ARGs (HRA) chip based on high-capacity quantitative PCR array targeting previously identified high-risk ARGs. These ARGs are known to be prevalent in human-related environments, exhibit gene mobility, and are present in ESKAPE pathogens. The HRA chip include 101 primer sets and the 16S rRNA gene as a reference. These primer sets consist of 34 obtained from previous studies, and 67 newly designed primer sets which were validated in silico and experimentally. Absolute quantification of targeted ARGs is accomplished by generating standard curves for all ARGs with serially ten-fold diluted mixed plasmids containing targeted ARG sequences. The amplification efficiencies of all ARGs exceed 99% via plasmid template dilution tests, suggesting high reliability in quantification. The performance of HRA chip is further evaluated by practical applications in environmental samples from wastewater treatment plants (WWTPs) and soils with various land use types and fertilization regimes. The results indicate the dynamics of high-risk ARGs during wastewater treatment process, and reveal the profiles of soil high-risk ARGs which is distinct from those derived via metagenomic approaches. These findings highlight the potentials of HRA Chip in the evaluation of anthropogenic impacts on the environmental resistome with a more focused spectrum of high-risk ARGs. Overall, the HRA Chip emerges as a powerful and efficient high-throughput tool for rapid detection and quantification of high-risk ARGs, facilitating comprehensive profiling of high-risk resistomes in environmental samples which is essential for human health risk assessment of ARGs.

Novel Mito-Nuclear Combinations Facilitate the Global Invasion of a Major Agricultural Crop Pest.

A fundamental understanding of the underlying mechanisms involved in biological invasions is crucial to developing effective risk assessment and control measures against invasive species. The fall armyworm (FAW), Spodoptera frugiperda, is a highly invasive pest that has rapidly spread from its native Americas into much of the Eastern Hemisphere, with a highly homogeneous nuclear genetic background. However, the exact mechanism behind its rapid introduction and propagation remains unclear. Here, a systematic investigation is conducted into the population dynamics of FAW in China from 2019 to 2021 and found that FAW individuals carrying "rice" mitochondria (FAW-mR) are more prevalent (>98%) than that with "corn" mitochondria (FAW-mC) at the initial stage of the invasion and in newly-occupied non-overwintering areas. Further fitness experiments show that the two hybrid-strains of FAW exhibit different adaptions in the new environment in China, and this may have been facilitated by amino acid changes in mitochondrial-encoded proteins. FAW-mR used increases energy metabolism, faster wing-beat frequencies, and lower wing loadings to drive greater flight performance and subsequent rapid colonization of new habitats. In contrast, FAW-mC individuals adapt with more relaxed mitochondria and shuttle energetics into maternal investment, observed as faster development rate and higher fecundity. The presence of two different mitochondria types within FAW has the potential to significantly expand the range of damage and enhance competitive advantage. Overall, the study describes a novel invasion mechanism displayed by the FAW population that facilitates its expansion and establishment in new environments.