High Campylobacter diversity in retail chicken: epidemiologically important strains may be missed with current sampling methods.

Campylobacter spp. are leading bacterial gastroenteritis pathogens. Infections are largely underreported, and the burden of outbreaks may be underestimated. Current strategies of testing as few as one isolate per sample can affect attribution of cases to epidemiologically important sources with high Campylobacter diversity, such as chicken meat. Multiple culture method combinations were utilized to recover and sequence Campylobacter from 45 retail chicken samples purchased across Norwich, UK, selecting up to 48 isolates per sample. Simulations based on resampling were used to assess the impact of Campylobacter sequence type (ST) diversity on outbreak detection. Campylobacter was recovered from 39 samples (87%), although only one sample was positive through all broth, temperature, and plate combinations. Three species were identified (Campylobacter jejuni, Campylobacter coli, and Campylobacter lari), and 33% of samples contained two species. Positive samples contained 1-8 STs. Simulation revealed that up to 87 isolates per sample would be required to detect 95% of the observed ST diversity, and 26 isolates would be required for the average probability of detecting a random theoretical outbreak ST to reach 95%. An optimized culture approach and selecting multiple isolates per sample are essential for more complete Campylobacter recovery to support outbreak investigation and source attribution.

Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050.

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Historical co-enrichment, source attribution, and risk assessment of critical nutrients and heavy metal/metalloids in lake sediments: insights from Chaohu Lake, China.

In Chinese freshwater lakes, eutrophication often coincides with heavy metal/metalloids (HM/Ms) pollution, yet the coevolution of critical nutrients (P, S, Se) and HM/Ms (Cd, Hg, etc.) remains understudied. To address this gap, we conducted a sedimentary chemistry analysis on a 30 cm-deep core, dating back approximately 200 years, retrieved from Chaohu Lake, China. The age-depth model revealed a gradual increase in deposition rates over time. Notably, the concentrations and enrichment factors (EFs) of most target elements surged in the uppermost ~ 15 cm layer, covering the period from 1953 to 2013, while both the concentrations and EFs in deeper layers remained relatively stable, except for Hg. This trend indicates a significant co-enrichment and near-synchronous increase in the levels and EFs of both nutrients and HM/Ms in the upper sediment layers since the mid-twentieth century. Anthropogenic factors were identified as the primary drivers of the enrichment of P, Se, Cd, Hg, Zn, and Te in the upper core, with their contributions also showing a coupled evolutionary trend over time. Conversely, geological activities governed the enrichment of elements in the lower half of the core. The gradual accumulation of anthropogenic Hg between the - 30 to - 15 cm layers might be attributed to global Hg deposition resulting from the industrial revolution. The ecological risk index (RI) associated with HM/Ms loading has escalated rapidly over the past 50 years, with Cd and Hg posing the greatest threats. Furthermore, the PMF model was applied to specifically quantify source contributions of these elements in the core, with anthropogenic and geogenic factors accounting for ~ 60 and ~ 40%, respectively. A good correlation (r2 = 0.87, p < 0.01) between the PMF and Ti-normalized method was observed, indicating their feasibility and cross-validation in source apportionment. Finally, we highlighted environment impact and health implications of the co-enrichment of nutrients and HM/Ms. This knowledge is crucial for developing strategies to protect freshwater ecosystems from the combined impacts of eutrophication and HM/Ms pollution, thereby promoting water environment and human health.

Sources of Human Immunodeficiency Virus Infections Among Men Who Have Sex With Men With a Migration Background: A Viral Phylogenetic Case Study in Amsterdam, The Netherlands.

BACKGROUND: Men and women with a migration background comprise an increasing proportion of incident human immunodeficiency virus (HIV) cases across Western Europe. METHODS: To characterize sources of transmission in local transmission chains, we used partial HIV consensus sequences with linked demographic and clinical data from the opt-out AIDS Therapy Evaluation in the Netherlands (ATHENA) cohort of people with HIV in the Netherlands and identified phylogenetically and epidemiologically possible HIV transmission pairs in Amsterdam. We interpreted these in the context of estimated infection dates, and quantified population-level sources of transmission to foreign-born and Dutch-born Amsterdam men who have sex with men (MSM) within Amsterdam transmission chains. RESULTS: We estimate that Dutch-born MSM were the predominant sources of infections among all Amsterdam MSM who acquired their infection locally in 2010-2021, and among almost all foreign-born Amsterdam MSM subpopulations. Stratifying by 2-year intervals indicated time trends in transmission dynamics, with a majority of infections originating from foreign-born MSM since 2016, although uncertainty ranges remained wide. CONCLUSIONS: Native-born MSM have predominantly driven HIV transmissions in Amsterdam in 2010-2021. However, in the context of rapidly declining incidence in Amsterdam, the contribution from foreign-born MSM living in Amsterdam is increasing, with some evidence that most local transmissions have been from foreign-born Amsterdam MSM since 2016.

The impact of evolving synoptic weather patterns on multi-scale transport and sources of persistent high-concentration ozone pollution event in the Yangtze River Delta, China.

High-concentration ozone pollution pose threats to ecosystems and human health. However, there is limited research on the impact of the alternating evolution of synoptic weather patterns (SWPs) on the multi-scale transport processes and sources of ozone. From June 14 to 18, 2018, a rare consecutive ozone pollution plagued in Hefei and broader Yangtze River Delta region (YRD). This study investigates the meteorological factors and sources using in-situ observational data and WRF-Chem model simulations. Analysis reveals a northeastern low-pressure system moving from north to south generated a cold front. This moving cold front facilitated the vertical transport of warm air masses carrying high-concentration ozone originating from North China. Subsequently, Ozone-rich air masses (ORMs) were transported over the YRD, influenced by the eastward movement of the Mongolian high-pressure system. Based on WRF-Chem model with NOx tagging mechanisms and WRF-FLEXPART backward simulations, it is confirmed that a notable atmospheric transport originated from North China region (NCR) to Hefei, especially on June 15. As the Mongolian high-pressure weakens and shifts east-southward, it carried ORMs generated by NOx emissions from the YRD, accumulating over the sea within the range of 120°E to 126°E and 25°N to 30°N. Both WRF-chem model results and TRopospheric Ozone and Precursors from Earth System Sounding (TROPESS) Chemistry Reanalysis dataset Version 2 (TCR-2) revealed the existence of ORMs in this geographic range. Subsequently, the ORMs carried out to sea by the weakened high-pressure system were reintroduced inland, influenced by southeast winds brought about by the peripheral circulation of typhoon "Gaemi". In summary, the alternating evolution of SWPs significantly influences multi-scale ozone transport from both the NCR and the YRD regions, making substantial contributions to this prolonged episode. These findings offer valuable insights for improving regional ozone pollution prevention and control mechanisms.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking.

Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6-7100 g) CH4/h over durations (0.4-10.2 h) under a wind speed range of (0.7-9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9-13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1-1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Integrating water quality data with a Bayesian network model to improve spatial and temporal phosphorus attribution: Application to the Maumee River Basin.

Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrient sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies of nutrient source attribution have focused on large watersheds or counties at annual time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a generalizable Bayesian network model for phosphorus source attribution at the subwatershed scale (12-digit Hydrologic Unit Code). Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow information simulated by the SWAT model, and in-stream water quality measurements using Approximate Bayesian Computation to derive a posterior that attributes phosphorus contributions to subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach currently adopted by environmental agencies. Fertilizer contributes more soluble reactive phosphorus than manure, while manure contributes most of the unreactive phosphorus. While developed for the specific context of Maumee River Basin, our lightweight and generalizable model framework could be adapted to other regions and pollutants and could help inform targeted environmental regulation and enforcement.

Sensitivities of ozone to its precursors during heavy ozone pollution events in the Yangtze River Delta using the adjoint method.

Although the concentrations of five basic ambient air pollutants in the Yangtze River Delta (YRD) have been reduced since the implementation of the "Air Pollution Prevention and Control Action Plan" in 2013, the ozone concentrations still increase. In order to explore the causes of ozone pollution in YRD, we use the GEOS-Chem and its adjoint model to study the sensitivities of ozone to its precursor emissions from different source regions and emission sectors during heavy ozone pollution events under typical circulation patterns. The Multi-resolution Emission Inventory for China (MEIC) of Tsinghua University and 0.25° × 0.3125° nested grids are adopted in the model. By using the T-mode principal component analysis (T-PCA), the circulation patterns of heavy ozone pollution days (observed MDA8 O3 concentrations ≥160 µg m-3) in Nanjing located in the center area of YRD from 2013 to 2019 are divided into four types, with the main features of Siberian Low, Lake Balkhash High, Northeast China Low, Yellow Sea High, and southeast wind at the surface. The adjoint results show that the contributions of emissions emitted from Jiangsu and Zhejiang are the largest to heavy ozone pollution in Nanjing. The 10 % reduction of anthropogenic NOx and NMVOCs emissions in Jiangsu, Zhejiang and Shanghai could reduce the ozone concentrations in Nanjing by up to 3.40 µg m-3 and 0.96 µg m-3, respectively. However, the reduction of local NMVOCs emissions has little effect on ozone concentrations in Nanjing, and the reduction of local NOx emissions would even increase ozone pollution. For different emissions sectors, industry emissions account for 31 %-74 % of ozone pollution in Nanjing, followed by transportation emissions (18 %-49 %). This study could provide the scientific basis for forecasting ozone pollution events and formulating accurate strategies of emission reduction.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification.

Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach.

Aquaculture located in urban river estuaries, where other anthropogenic activities may occur, has an impact on and may be affected by the environment where they are inserted, namely by the exchange of antimicrobial resistance genes. The latter may ultimately, through the food chain, represent a source of resistance genes to the human resistome. In an exploratory study of the presence of resistance genes in aquaculture sediments located in urban river estuaries, two machine learning models were applied to predict the source of 34 resistome observations in the aquaculture sediments of oysters and gilt-head sea bream, located in the estuaries of the Sado and Lima Rivers and in the Aveiro Lagoon, as well as in the sediments of the Tejo River estuary, where Japanese clams and mussels are collected. The first model included all 34 resistomes, amounting to 53 different antimicrobial resistance genes used as source predictors. The most important antimicrobial genes for source attribution were tetracycline resistance genes tet(51) and tet(L); aminoglycoside resistance gene aadA6; beta-lactam resistance gene blaBRO-2; and amphenicol resistance gene cmx_1. The second model included only oyster sediment resistomes, amounting to 30 antimicrobial resistance genes as predictors. The most important antimicrobial genes for source attribution were the aminoglycoside resistance gene aadA6, followed by the tetracycline genes tet(L) and tet(33). This exploratory study provides the first information about antimicrobial resistance genes in intensive and semi-intensive aquaculture in Portugal, helping to recognize the importance of environmental control to maintain the integrity and the sustainability of aquaculture farms.

Campylobacteriosis in Finland: Passive Surveillance in 2004-2021 and a Pilot Case-Control Study with Whole-Genome Sequencing in Summer 2022.

Campylobacteriosis causes a significant disease burden in humans worldwide and is the most common type of zoonotic gastroenteritis in Finland. To identify infection sources for domestic Campylobacter infections, we analyzed Campylobacter case data from the Finnish Infectious Disease Register (FIDR) in 2004-2021 and outbreak data from the National Food- and Waterborne Outbreak Register (FWO Register) in 2010-2021, and conducted a pilot case-control study (256 cases and 756 controls) with source attribution and patient sample analysis using whole-genome sequencing (WGS) in July-August 2022. In the FIDR, 41% of the cases lacked information on travel history. Based on the case-control study, we estimated that of all cases, 39% were of domestic origin. Using WGS, 22 clusters of two or more cases were observed among 185 domestic cases, none of which were reported to the FWO register. Based on this case-control study and source attribution, poultry is an important source of campylobacteriosis in Finland. More extensive sampling and comparison of patient, food, animal, and environmental isolates is needed to estimate the significance of other sources. In Finland, campylobacteriosis is more often of domestic origin than FIDR notifications indicate. To identify the domestic cases, travel information should be included in the FIDR notification, and to improve outbreak detection, all domestic patient isolates should be sequenced.

A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing.

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

Chemical characteristics, distribution patterns, and source apportionment of particulate elements and inorganic ions in snowpack in Harbin, China.

Snowpack, which serves as a natural archive of atmospheric deposition of multiple pollutants, is a practical environmental media that can be used for assessing atmospheric records and input of the pollutants to the surface environments and ecosystems. A total of 29 snowpack samples were collected at 20 sampling sites covering three different functional areas of a major city (Harbin) in Northeast China. Two samples at the "snow layer" and one or two samples at the "particulate layer" were collected at each sampling site in the industrial areas characterized by multi-layer snowpack, and only one sample at the "snow layer" was collected at each sampling site in the cultural and recreational as well as agricultural areas. The snow contents of 31 elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Y, Cd, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Pb) and six major water-soluble inorganic ions (WSIIs, NH4+, K+, Ca2+, NO2-, NO3-, and SO42-) were analyzed. The total mass of the measured elements is dominated (95.8%-99.2%) by crustal elements. Heavy metals only account for 0.77%-4.07% of the total mass of the elements, but are occasionally close to or even above the standard limit in the "Environmental Quality Standards for Surface Water" of China (GB3838-2002). SO42- and Ca2+ are the main anion and cation, accounting for 34.9%-81.1% and 1.43%-29.9%, respectively, of the measured total ions. Total atmospheric deposition of crustal elements and heavy metals is dominated by wet deposition in areas near the petrochemical plant and by dry deposition in areas near the cement plant. Coal combustion, industrial emissions, and traffic-related activities lead to the enrichment of heavy metals in the snowpacks of urban and suburban areas, while coal combustion and biomass burning contribute to pollution in rural areas. The cities and regions situated in the western, northwestern, northern, and northeastern directions from Harbin are potential source regions of these pollutant species.

Elucidating drivers of severe wintertime fine particulate matter pollution episodes in the Yangtze River Delta region of eastern China.

Understanding the causes and sources responsible for severe fine particulate matter (PM2.5) pollution episodes that occur under conducive synoptic weather patterns (SWPs) is essential for regional air quality management. The Yangtze River Delta (YRD) region in eastern China has experienced recurrent severe PM2.5 episodes during the winters from 2013 to 2017. In this study, we employed an objective classification approach, the self-organizing map, to investigate the underlying impact of predominant SWPs on PM2.5 pollution in the YRD. We further conducted a series of source apportionment simulations using the Particulate Source Apportionment Technology (PSAT) tool integrated within the Comprehensive Air Quality Model with Extensions (CAMx) to quantify the source contributions to PM2.5 pollution under different SWPs. Here we identified six predominant SWPs over the YRD that are robustly connected to the evolution of the Siberian High. Considering the regional average PM2.5 anomalies, our results show that polluted SWPs favourable for the occurrence of regional PM2.5 pollution account for 61-78 %. The most conducive SWP, associated with the highest regional exceedance (46 %) of PM2.5 levels, is characterized by noticeable cyclonic anomalies at 850 hPa and stagnant surface weather conditions. Our source apportionment analysis emphasizes the pivotal role of local emissions and intra-regional transport within the YRD in shaping PM2.5 pollution in representative cities. Local emissions have the most significant impact on PM2.5 levels in Shanghai (32-48 %), while PM2.5 pollution in Nanjing, Hangzhou, and Hefei is more influenced by intra-regional transport (33-61 %). Industrial and residential emissions are the dominant sources, contributing 32-41 % and 24-38 % to PM2.5, respectively. Under specific SWPs associated with a stronger influence of inter-regional transport from northern China, there is a synchronously remarkable enhancement in the contribution of residential emissions. Our study pinpoints the opportunities for future air quality planning that would benefit from quantitative source attribution linked to prevailing SWPs.

[Source Analysis of Ozone and Its Precursors in Zibo Based on 3-D Air Quality Model].

Based on the ISAM module in the WRF-CMAQ model, this study analyzed the source contribution(both regional and sectoral) of O3 and its precursors(NO2 and VOCs) in Zibo in June 2021. Days with a maximum daily 8-h average(MDA8) O3 higher(lower) than 160 µg·m-3 were defined as polluted(clean) days. Differences in the source contribution between clean days and polluted days were compared, and a typical pollution period was selected for further process analysis. The results showed that NO2 in Zibo mainly came from local emissions in summer, with a relative contribution of 45.1%. Vehicle emissions(33.8%) and natural sources(20.7%) were the primary NO2 sources. VOC contributions from natural sources, solvent usage, and the petrochemical industry were significant, with a total contribution of 78.5%. The MDA8 contribution from local sources was 21.4%, whereas the impact of regional transport(32%) and surrounding cities(26.8%) was also substantial. Among local emission sources, vehicle emissions, the power industry, and the building materials industry contributed 10.9%-18.8% to local MDA8. On O3 pollution days, the MDA8 contribution from local emissions and surrounding cities increased. However, the relative contributions from local sources were similar under different pollution conditions.

Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success.

People's ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people's ability to regulate emotion. Using ecological momentary assessments (N = 396; 7 days; 5,466 observations), we measured adults' degree of knowledge about the source of their negative emotions. We used language processing to show that higher reported knowledge led to more concrete written descriptions of the source. We found that higher knowledge of the source predicted more emotion-regulation attempts; increased the use of emotion-regulation strategies that target the source (cognitive reappraisal, situation modification) versus strategies that do not (distraction, emotional eating); predicted greater perceived success in regulating emotions; and greater well-being. These patterns were evident both within and between persons. Our findings suggest that pinpointing the source of emotions might play an important role in emotion regulation.

Analysis of reservoir sources of Campylobacter isolates to free-range broilers in Denmark.

Campylobacter is a common cause of food poisoning in many countries, with broilers being the main source. Organic and free-range broilers are more frequently Campylobacter-positive than conventionally raised broilers and may constitute a higher risk for human infections. Organic and free-range broilers may get exposed to Campylobacter from environmental reservoirs and livestock farms, but the relative importance of these sources is unknown. The aim of the study was to describe similarities and differences between the genetic diversity of the Campylobacter isolates collected from free-range/organic broilers with those isolated from conventional broilers and other animal hosts (cattle, pigs, and dogs) in Denmark to make inferences about the reservoir sources of Campylobacter to free-range broilers. The applied aggregated surveillance data consisted of sequenced Campylobacter isolates sampled in 2015 to 2017 and 2018 to 2021. The data included 1,102 isolates from free-range (n = 209), conventional broilers (n = 577), cattle (n = 261), pigs (n = 30), and dogs (n = 25). The isolates were cultivated from either fecal material (n = 434), food matrices (n = 569), or of nondisclosed origin (n = 99). Campylobacter jejuni (94.5%) dominated and subtyping analysis found 170 different sequence types (STs) grouped into 75 clonal complexes (CCs). The results suggest that CC-21 and CC-45 are the most frequent CCs found in broilers. The relationship between the CCs in the investigated sources showed that the different CCs were shared by most of the animals, but not pigs. The ST-profiles of free-range broilers were most similar to that of conventional broilers, dogs and cattle, in that order. The similarity was stronger between conventional broilers and cattle than between conventional and free-range broilers. The results suggest that cattle may be a plausible reservoir of C. jejuni for conventional and free-range broilers, and that conventional broilers are a possible source for free-range broilers or reflect a dominance of isolates adapted to the same host environment. Aggregated data provided valuable insight into the epidemiology of Campylobacter sources for free-range broilers, but time-limited sampling of isolates from different sources within a targeted area would hold a higher predictive value.

Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.

BACKGROUND: Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method. METHODS: A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time. RESULTS: The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers. CONCLUSIONS: In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

Animal sources of antimicrobial-resistant bacterial infections in humans: a systematic review.

Bacterial antimicrobial resistance (AMR) is among the leading global health challenges of the century. Animals and their products are known contributors to the human AMR burden, but the extent of this contribution is not clear. This systematic literature review aimed to identify studies investigating the direct impact of animal sources, defined as livestock, aquaculture, pets, and animal-based food, on human AMR. We searched four scientific databases and identified 31 relevant publications, including 12 risk assessments, 16 source attribution studies, and three other studies. Most studies were published between 2012 and 2022, and most came from Europe and North America, but we also identified five articles from South and South-East Asia. The studies differed in their methodologies, conceptual approaches (bottom-up, top-down, and complex), definitions of the AMR hazard and outcome, the number and type of sources they addressed, and the outcome measures they reported. The most frequently addressed animal source was chicken, followed by cattle and pigs. Most studies investigated bacteria-resistance combinations. Overall, studies on the direct contribution of animal sources of AMR are rare but increasing. More recent publications tailor their methodologies increasingly towards the AMR hazard as a whole, providing grounds for future research to build on.