Source attribution of salmonellosis by time and geography in New South Wales, Australia

Background Salmonella is a major cause of zoonotic illness around the world, arising from direct or indirect contact with a range of animal reservoirs. In the Australian state of New South Wales (NSW), salmonellosis is believed to be primarily foodborne, but the relative contribution of animal reservoirs is unknown. Methods The analysis included 4543 serotyped isolates from animal reservoirs and 30,073 serotyped isolates from domestically acquired human cases in NSW between January 2008 and August 2019. We used a Bayesian source attribution methodology to estimate the proportion of foodborne Salmonella infections attributable to broiler chickens, layer chickens, ruminants, pigs, and an unknown or unsampled source. Additional analyses included covariates for four time periods and five levels of rurality. Results A single serotype, S. Typhimurium, accounted for 65–75% of included cases during 2008–2014 but < 50% during 2017–2019. Attribution to layer chickens was highest during 2008–2010 (48.7%, 95% CrI 24.2–70.3%) but halved by 2017–2019 (23.1%, 95% CrI 5.7–38.9%) and was lower in the rural and remote populations than in the majority urban population. The proportion of cases attributed to the unsampled source was 11.3% (95% CrI 1.2%–22.1%) overall, but higher in rural and remote populations. The proportion of cases attributed to pork increased from approximately 20% in 2009–2016 to approximately 40% in 2017–2019, coinciding with a rise in cases due to Salmonella ser. 4,5,12:i:-. Conclusion Layer chickens were likely the primary reservoir of domestically acquired Salmonella infections in NSW circa 2010, but attribution to the source declined contemporaneously with increased vaccination of layer flocks and tighter food safety regulations for the handling of eggs.

Resetting Integrity Through Communication on Plagiarism: University Classrooms Weaving Values into the Social Fabric

Academic dishonesty manifested in the proliferating acts of plagiarism can be eradicated by returning to value teaching. In a study involving 37 first-year students in one academic year, a single-group quasi-experimental procedure with mixed qualitative and quantitative analyses of students’ assignments was performed. The procedure involved diagnosing plagiarism by strategic manual detection and classification of occurrences and recording the frequency of occurrence. The objective was to examine the effects of communicating about plagiarism by the designed plagiarism integrity narratives (PIN) intervention on students’ integrity based on their source-attribution practices. In the first semester, an assignment was administered without any word on plagiarism as the baseline data for students’ academic integrity at pre-test. In the second semester, the post-PIN-intervention assignment set with similar cognitive demand as the first was administered. The post-PIN intervention showed 76% of students taking steps to not succumb to plagiarism, far outweighing the 5% not taking heed. Of those who acknowledged information sources, 14% showed excellent referencing skills, capturing the potential first-year role model. In terms of outsourcing and attribution combined, the PIN intervention offered a 95% transformation of moral values, hinting at the possibility of resetting academic integrity via communication and clear directives. Lifting plagiarism rules as a “litmus test” (third assignment) revealed 28% integrity-ready students applying the fundamental attribution rules. Outstanding referencing skills and honesty were portrayed by a self-regulated student who had internalized academic integrity. The findings signal the possibility of curbing plagiarism in university classrooms and nurturing students to start weaving values into the social fabric.

A gridded inventory of Canada's anthropogenic methane emissions

Canada's anthropogenic methane emissions are reported annually to the United Nations Framework Convention on Climate Change (UNFCCC) through Canada's National Inventory Report (NIR). Evaluation of this policy-relevant inventory using observations of atmospheric methane requires prior information on the spatial distribution of emissions but that information is lacking in the NIR. Here we spatially allocate the NIR methane emissions for 2018 on a 0.1º x 0.1º grid (≈ 10 km x 10 km) for individual source sectors and subsectors, with further resolution by source type for the oil/gas sector, using an ensemble of national and provincial geospatial datasets and including facility-level information from Canada's Greenhouse Gas Reporting Program. The highest emissions are from oil/gas production and livestock in western Canada, and landfills in eastern Canada. We find 11 hotspots emitting more than 1 metric ton h-1 on the 0.1º x 0.1º grid. Oil sands mines in northeast Alberta contribute 3 of these hotspots even though oil sands contribute only 4% of national oil/gas emissions. Our gridded inventory shows large spatial differences with the EDGAR v5 inventory commonly used for inversions of atmospheric methane observations, which may reflect EDGAR's reliance on global geospatial datasets. Comparison of our spatially resolved inventory to atmospheric measurements in oil/gas production fields suggests that the NIR underestimates these emissions. We also find strong spatial overlap between oil/gas, livestock, and wetland emissions in western Canada that may complicate source attribution in inversions of atmospheric data.

Salmonella enterica serovar Typhimurium from Wild Birds in the United States Represent Distinct Lineages Defined by Bird Type

Salmonella enterica serovar Typhimurium is typically considered a host generalist, however certain strains are associated with specific hosts and show genetic features of host adaptation. Here, we sequenced 131 S. Typhimurium strains from wild birds collected in 30 U.S. states during 1978-2019. We found that isolates from broad taxonomic host groups including passerine birds, water birds (Aequornithes), and larids (gulls and terns) represented three distinct lineages and certain S. Typhimurium CRISPR types presented in individual lineages. We also showed that lineages formed by wild bird isolates differed from most strains originating from domestic animal sources, and genomes from these lineages substantially improved source attribution of Typhimurium genomes to wild birds by a machine learning classifier. Furthermore, virulence gene signatures that differentiated S. Typhimurium from passerines, water birds, and larids were detected. Passerine isolates tended to lack S. Typhimurium-specific virulence plasmids. Isolates from the passerine, water bird, and larid lineages had close genetic relatedness with human clinical isolates, including those from a 2021 U.S. outbreak linked to passerine birds. These observations indicate that S. Typhimurium from wild birds in the U.S. are likely host-adapted, and the representative genomic dataset examined in this study can improve source prediction and facilitate outbreak investigation.

Phylogenetic Analysis Reveals Source Attribution Patterns for Campylobacter spp. in Tennessee and Pennsylvania

Campylobacteriosis is the most common bacterial foodborne illness in the United States and is frequently associated with foods of animal origin. The goals of this study were to compare clinical and non-clinical Campylobacter populations from Tennessee (TN) and Pennsylvania (PA), use phylogenetic relatedness to assess source attribution patterns, and identify potential outbreak clusters. Campylobacter isolates studied (n = 3080) included TN clinical isolates collected and sequenced for routine surveillance, PA clinical isolates collected from patients at the University of Pennsylvania Health System facilities, and non-clinical isolates from both states for which sequencing reads were available on NCBI. Phylogenetic analyses were conducted to categorize isolates into species groups and determine the population structure of each species. Most isolates were C. jejuni (n = 2132, 69.2%) and C. coli (n = 921, 29.9%), while the remaining were C. lari (0.4%), C. upsaliensis (0.3%), and C. fetus (0.1%). The C. jejuni group consisted of three clades; most non-clinical isolates were of poultry (62.7%) or cattle (35.8%) origin, and 59.7 and 16.5% of clinical isolates were in subclades associated with poultry or cattle, respectively. The C. coli isolates grouped into two clades; most non-clinical isolates were from poultry (61.2%) or swine (29.0%) sources, and 74.5, 9.2, and 6.1% of clinical isolates were in subclades associated with poultry, cattle, or swine, respectively. Based on genomic similarity, we identified 42 C. jejuni and one C. coli potential outbreak clusters. The C. jejuni clusters contained 188 clinical isolates, 19.6% of the total C. jejuni clinical isolates, suggesting that a larger proportion of campylobacteriosis may be associated with outbreaks than previously determined.

An FSKX compliant source attribution model for salmonellosis and a look at its major hidden pitfalls

To reduce the burden of human society that is caused by zoonotic diseases, it is important to attribute sources to human illnesses. One powerful approach in supporting any intervention decision is mathematical modelling. This paper presents a source attribution model which considers five sources (broilers, laying hens, pigs, turkeys) for salmonellosis and uses two datasets from Germany collected over two time periods; one from 2004 to 2007 and one from 2010 to 2011. The model uses a Bayesian modelling approach derived from the so-called Hald model and is based on microbial subtyping. In this case, Salmonella isolates from humans and animals were subtyped with respect to serovar and phage type. Based on that typing, the model estimates how many human salmonellosis cases can be attributed to each of the considered sources. A reference description of the model is available under DOI: 10.1111/zph.12645. Here, we present this model as a ready-to-use resource in the Food Safety Knowledge Exchange (FSKX) format. This open information exchange format allows to re-use, modify, and further develop the model and uses model metadata and controlled vocabulary to harmonise the annotation. In addition to the model, we discuss some technical pitfalls that might occur when running this Bayesian model based on Markov chain Monte Carlo calculations. As source attribution of zoonotic disease is one useful tool for the One Health approach, our work facilitates the exchange, adjustment, and re-usage of this source attribution model by the international and multi-sectoral community.

Comparison of PM10 Sources at Traffic and Urban Background Sites Based on Elemental, Chemical and Isotopic Composition: Case Study from Krakow, Southern Poland

In large urban agglomerations, car traffic is one of the main sources of particulate matter. It consists of particulate matter directly generated in the process of incomplete liquid fuel burning in vehicle engine, secondary aerosols formed from exhaust gaseous pollutants (NOx, SO2) as well as products of tires, brake pads and pavement abrasion. Krakow is one of the cities in Europe with the highest concentrations of particulate matter. The article presents the results of combined elemental, chemical and isotopic analyses of particulate matter PM10 at two contrasting urban environments during winter and summer seasons. Daily PM10 samples were collected during the summer and winter seasons of 2018/2019 at two stations belonging to the network monitoring air quality in the city. Mean PM10 concentrations at traffic-dominated stations were equal to 35 ± 7 µg/m3 and 76 ± 28 µg/m3 in summer and winter, respectively, to be compared with 25.6 ± 5.7 µg/m3 and 51 ± 25 µg/m3 in summer and winter, respectively, recorded at the urban background station. The source attribution of analyzed PM10 samples was carried out using two modeling approaches: (i) The Positive Matrix Factorization (PMF) method for elemental and chemical composition (concentrations of elements, ions, as well as organic and elemental carbon in daily PM10 samples), and (ii) Isotope Mass Balance (IMB) for 13C and 14C carbon isotope composition of carbonaceous fraction of PM10. For PMF application, five sources of particulate matter were identified for each station: fossil fuel combustion, secondary inorganic aerosols, traffic exhaust, soil, and the fifth source which included road dust, industry, construction work. The IMB method allowed the partitioning of the total carbon reservoir of PM10 into carbon originating from coal combustion, from biogenic sources (natural emissions and biomass burning) and from traffic. Both apportionment methods were applied together for the first time in the Krakow agglomeration and they gave consistent results.

Machine learning to predict the source of campylobacteriosis using whole genome data

Campylobacteriosis is among the world’s most common foodborne illnesses, caused predominantly by the bacterium Campylobacter jejuni. Effective interventions require determination of the infection source which is challenging as transmission occurs via multiple sources such as contaminated meat, poultry, and drinking water. Strain variation has allowed source tracking based upon allelic variation in multi-locus sequence typing (MLST) genes allowing isolates from infected individuals to be attributed to specific animal or environmental reservoirs. However, the accuracy of probabilistic attribution models has been limited by the ability to differentiate isolates based upon just 7 MLST genes. Here, we broaden the input data spectrum to include core genome MLST (cgMLST) and whole genome sequences (WGS), and implement multiple machine learning algorithms, allowing more accurate source attribution. We increase attribution accuracy from 64% using the standard iSource population genetic approach to 71% for MLST, 85% for cgMLST and 78% for kmerized WGS data using the classifier we named aiSource. To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). Providing generalizable computationally efficient methods, based upon machine learning and population genetics, we provide a scalable approach to global disease surveillance that can continuously incorporate novel samples for source attribution and identify fine-scale variation in transmission potential.