Source Attribution Research Articles

ColiSeq: a multiplex amplicon assay that provides strain level resolution of Escherichia coli directly from clinical specimens.

Escherichia coli is a diverse pathogen, causing a range of disease in humans, from self-limiting diarrhea to urinary tract infections (UTIs). Uropathogenic E. coli (UPEC) is the most frequently observed uropathogen in UTIs, a common disease in high-income countries, incurring billions of dollars yearly in treatment costs. Although E. coli is easily grown and identified in the clinical laboratory, genotyping the pathogen is more complicated, yet critical for reducing the incidence of disease. These goals can be achieved through whole-genome sequencing of E. coli isolates, but this approach is relatively slow and typically requires culturing the pathogen in the laboratory. To genotype E. coli rapidly and inexpensively directly from clinical samples, including but not limited to urine, we developed and validated a multiplex amplicon sequencing assay, called ColiSeq. The assay consists of targets designed for E. coli species confirmation, high resolution genotyping, and mixture deconvolution. To demonstrate its utility, we screened the ColiSeq assay against 230 clinical urine samples collected from a hospital system in Flagstaff, Arizona, USA. A limit of detection analysis demonstrated the ability of ColiSeq to identify E. coli at a concentration of ~2 genomic equivalent (GEs)/mL and to generate high-resolution genotyping at a concentration of 1 × 105 GEs/mL. The results of this study suggest that ColiSeq could be a valuable method to understand the source of UPEC strains and guide infection mitigation efforts. As sequence-based diagnostics become accepted in the clinical laboratory, workflows such as ColiSeq will provide actionable information to improve patient outcomes.IMPORTANCEUrinary tract infections (UTIs), caused primarily by Escherichia coli, create an enormous health care burden in the United States and other high-income countries. The early detection of E. coli from clinical samples, including urine, is important to target therapy and prevent further patient complications. Additionally, understanding the source of E. coli exposure will help with future mitigation efforts. In this study, we developed, tested, and validated an amplicon sequencing assay focused on direct detection of E. coli from urine. The resulting sequence data were demonstrated to provide strain level resolution of the pathogen, not only confirming the presence of E. coli, which can focus treatment efforts, but also providing data needed for source attribution and contact tracing. This assay will generate inexpensive, rapid, and reproducible data that can be deployed by public health agencies to track, diagnose, and potentially mitigate future UTIs caused by E. coli.

Doing Journalism in Times of Conflict: A Cross-National Examination of News Source Attribution and Framing in Allied Countries’ Media

ABSTRACT This research examined the dynamics of news reporting and source attribution approaches employed by prominent media outlets - the New York Times, Guardian, China Daily, and The Times of India - in their coverage of the 2022 Russia-Ukraine crises. Using Quantitative Content Analysis to examine (n = 230) news articles, this study investigated the prevalence of official, unofficial, pro-Russian, and pro-Ukrainian sources, and how these sources impacted the objectivity of news reporting during the peak of the Russia-Ukraine crises. The findings of the study are twofold. Firstly, the study revealed a prevalence of official sources over unofficial sources in the news reports, raising concerns about the representational diversity within the news. Secondly, the study records a scarcity of pro-Russian sources across the media, a trend particularly pronounced in the New York Times and Guardian, where pro-Ukrainian sources held greater prominence, potentially skewing the overall perspective presented. This strategic selection of sources appeared to culminate in a convergence of narratives across the studied media outlets, potentially limiting the availability of varied viewpoints and interpretations for the audience. This study highlights the complex relationship between source selection and news objectivity in reporting international conflicts.

Lost in the Forest: Encoding categorical variables and the absent levels problem

Levels of a predictor variable that are absent when a classification tree is grown can not be subject to an explicit splitting rule. This is an issue if these absent levels are present in a new observation for prediction. To date, there remains no satisfactory solution for absent levels in random forest models. Unlike missing data, absent levels are fully observed and known. Ordinal encoding of predictors allows absent levels to be integrated and used for prediction. Using a case study on source attribution of Campylobacter species using whole genome sequencing (WGS) data as predictors, we examine how target-agnostic versus target-based encoding of predictor variables with absent levels affects the accuracy of random forest models. We show that a target-based encoding approach using class probabilities, with absent levels designated the highest rank, is systematically biased, and that this bias is resolved by encoding absent levels according to the a priori hypothesis of equal class probability. We present a novel method of ordinal encoding predictors via principal coordinates analysis (PCO) which capitalizes on the similarity between pairs of predictor levels. Absent levels are encoded according to their similarity to each of the other levels in the training data. We show that the PCO-encoding method performs at least as well as the target-based approach and is not biased.

Antibiogram of Escherichia coli Isolated from Dairy Cattle and in-Contact Humans in Selected Areas of Central Ethiopia.

Antimicrobial resistance (AMR) is a global threat to public and animal health. Escherichia coli is considered an indicator organism for monitoring AMR among gram-negative Enterobacteriaceae in humans and animals. The current study aims to assess the antibiogram profile of E. coli isolated from dairy cattle and in-contact humans in central Ethiopia and to identify risk factors associated with multidrug resistance (MDR). A cross-sectional study was conducted in which 58 farms were recruited from selected districts of central Ethiopia. E. coli was isolated using standard bacteriological techniques. A total of 200 representative isolates (140 from cattle and 60 from humans in contact) were randomly selected and tested for susceptibility to a panel of 13 antimicrobials using the Kirby-Bauer disc diffusion assay. The highest rate of resistance was observed for sulfamethoxazole+trimethoprim (58.6%, 82/140) and amoxicillin+clavulanic acid (70.0%, 42/60) among E. coli isolates from cattle and hmans, respectively. In contrast, resistance rates in isolates from in contact humans with the cattle were 30%, 33.3%, and 66.7%, respectively. Resistance to tetracycline (p=0.02), streptomycin (p=0.03), and sulfamethoxazole+trimethoprim (p=0.007) was significantly high in E. coli isolated from cattle on commercial dairy farms than in those isolated from cattle on smallholder farms. There was no significant difference (p>0.05) in the rate of resistance between E. coli isolated from in contact humans with smallholder and commercial dairy farms. Antimicrobial use for treatment purpose (p=0.04) and non-compliance with the drug withdrawal period (p=0.03) were significantly associated with the farm-level occurrence of MDR. A high rate of resistance was detected in E. coli isolated from the feces of dairy cattle and in-contact humans. This necessitates an effective intervention through a one-health approach and further molecular studies are required to establish source attribution.

Source attribution of carbonaceous fraction of particulate matter in the urban atmosphere based on chemical and carbon isotope composition

Air quality is of large concern in the city of Krakow, southern Poland. A comprehensive study was launched by us in which two PM fractions (PM1 and PM10) were sampled during 1-year campaign, lasting from April 21, 2018 to March 19, 2019. A suite of modern analytical methods was used to characterize the chemical composition of the collected samples. The contents of 14 sugars, sugar alcohols and anhydrosugars, 16 polycyclic aromatic hydrocarbons, selected metals and non-metals and ions were analyzed, in addition to organic and elemental carbon content. The carbon isotope composition in both analysed PM fractions, combined with an isotope-mass balance method, allowed to distinguish three main components of carbonaceous emissions in the city: (1) emissions related to combustion of hard coal, (2) emissions related to road transport, and (3) biogenic emissions. The heating season emissions from coal combustion had the biggest contribution to the reservoir of carbonaceous aerosols in the PM10 fraction (44%) and, together with the biogenic emission, they were the biggest contributors to the PM1 fraction (41% and 44%, respectively). In the non-heating season, the dominant source of carbon in PM10 and PM1 fraction were the biogenic emissions (48 and 54%, respectively).

Information Source Attributes and Terrorism Risk Preparedness among the Residents of Nairobi City County

Purpose: The main purpose of the study was to establish the association of source attributes and terrorism risk preparedness among the residents of Nairobi City County.
 Materials and Methods: This study followed a mixed-methods design comprising mainly of a questionnaire survey complemented by observation, key informant interviews and document analysis, The design employed the Concurrent Convergent (Triangulation) Parallel strategy. According to the Kenya National Bureau of Statistics, Nairobi City County has a resident population of 4,397,073. A further estimated 2.5 million non-residents visit the city-county daily for business, work, or as tourists and travelers in transit to other counties. Therefore, the target population for this study was approximately 6.5 million. The study population was drawn using stratified purposive random sampling technique where the list of all the sampling locations was categorized into four strata. The sample size was 640 respondents who were proportionately randomly drawn from four different strata. 
 Findings: Regression of coefficients showed that source attributes and terrorism risk preparedness were positively and significantly related (β=0.379, p=0.000). Specifically, 53.6% of the respondents agreed with the statement that receiving information from credible sources on terrorism attacks provides motivation for preparedness in case of a terrorist attack. Similarly, 64.8% agreed with the statement that consistency in terrorism risks communications provides motivation to preparedness behaviour adoption 62.6% of the respondents were in concurrence that competence among that communication terrorism risk preparedness was a motivating factor for preparedness. Further, only 43.1% of the respondents indicated that their organisations were using experts in terrorism risk communication and preparedness. 64.6% were of the opinion that involving non experts in decisions related to terrorism risk communications and preparedness can result in preparedness action taking laxity.
 Implications to Theory, Practice and Policy: The study recommends that professionals charged with emergency risk communication for terrorism preparedness should particularly: demonstrate expertise and competence in the subject matter to instill confidence in their audiences that the information is reliable and useful. This can be achieved by involving qualified professionals, experts, or organizations with relevant experience in emergency risk preparedness and build trust by being transparent, honest, and reliable in communication. Trust can also be enhanced by providing accurate information, acknowledging uncertainties when applicable, and addressing any concerns or doubts promptly.

Size distribution and source-specific risks of atmospheric elements in Dalian, a coastal city in north China

Trace elements in particles play an essential role in particle-related health and environmental effects and were recognized as primary source tracers. An understanding of the size-segregated elemental composition is critical for accurate source attribution and risk evaluation. The present study investigated the characteristics of 28 elements in the size-fractioned particles of a coastal megacity in north China. The size distribution of element concentration posed bimodal peak mode, and the proportion of most trace elements tended to increase with the decreasing particle size, especially toxic metals like Hg, Ga, Tl, As, Pb, Se, and Cd. Source attribution based on the size-segregated concentrations was conducted by positive matrix factorization. Natural sources such as crust and sea spray aerosol dominated the trace element concentration in the large particles, while anthropogenic sources such as traffic, biomass, coal combustion, and industry contributed the majority in the fine particles. The source-specific exposure risk calculation based on the size-segregated dataset showed a noncarcinogenic risk (HQ) of 6.62 for children, mainly contributed by Tl and As from biomass burning, petrochemical refining, and coal combustion. Compared with larger particles, the finer particles generated a higher HQ and carcinogenic risk, especially particles smaller than 0.49 μm. Fine particles primarily emitted from the petrochemical industry and coal combustion brought out the largest HQ, while fine particles from vehicle traffic and the petrochemical industry contributed the largest carcinogenic risk. Transport of sea spray aerosol contributed a considerable amount of the carcinogenic risk in the coastal urban area.

Development and validation of a random forest algorithm for source attribution of animal and human Salmonella Typhimurium and monophasic variants of S. Typhimurium isolates in England and Wales utilising whole genome sequencing data.

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

Mid-to Late Holocene East Antarctic ice-core tephrochronology: Implications for reconstructing volcanic eruptions and assessing their climatic impacts over the last 5,500 years

Ice cores are powerful archives for reconstructing volcanism as they contain both soluble (i.e. aerosols) and insoluble (i.e. tephra) products of volcanic eruptions and for more recent periods have high-precision annually resolved chronologies. The identification and geochemical analysis of cryptotephra in these cores can provide their volcanic source and latitude of injection, complementing records of sulphur injections from volcanic eruptions developed using continuous flow ice-core analysis. Here, we aim to improve the volcanic record for the Southern Hemisphere using a sampling strategy for cryptotephra identification based on coeval deposition of sulphate and microparticles in ice cores from the interior of East Antarctica covering the Mid-to Late Holocene. In total, 15 cryptotephras and one visible horizon were identified and geochemically characterised. Through comparisons to proximal deposits a range of possible sources were isolated for these horizons including the South Sandwich Islands, South Shetland Islands, Victoria Land (Antarctica) and South America. This new tephra framework contributes to the volcanic history of the region by extending the known geographical range of tephra deposition for previously identified events and providing a potential indication of phases of eruptive activity from key sources. Using the tephra-based source attributions and comparison of the timing of the events to a database of sulphur injections from Holocene volcanic eruptions it is possible to refine injection latitudes for some events, which can lead to improved estimates of their radiative forcing potential. The relatively low magnitude of the volcanic stratospheric sulphur injections related to the events in the tephra framework indicates they would have had a limited impact on Southern Hemisphere climate. Further work is required to improve source attributions for some events and/or to determine the magnitude of sulphur injections for individual events during years when coeval eruptions occurred. One limitation of the framework is the dominance of cryptotephra from regional volcanic sources and a lack of tephra from tropical sources, which hampers the refinement of eruption parameters for these large magnitude and often climate-impacting eruptions. This issue could be explored further through increased sampling of these events and/or development of additional analytical techniques for the identification and robust geochemical analysis of glass tephra shards less than 5 μm in diameter. Such investigations could be coupled with model experiments to determine the likelihood that past tropical eruptions deposited glass tephra shards over Antarctica and the potential size range and geographical spread of deposition.

Knowledge fusion method of power grid model based on Seq2seq half pointer and half label method

Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy.

Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology.

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data. We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively. Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network. Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

A perceptual approach to address complex water management issues in lowland permeable catchments

Water quality management is a pressing global concern, and an increasingly complex issue due to climate and land-use change, legacy pollution, and the persistent release of well-known and emerging contaminants from diffuse and point sources. The increasing availability of high-frequency monitoring data is leading existing, often heuristic approaches, to be judged inadequate. Water managers frequently rely on simple qualitative and/or quantitative approaches for decision-making, but a lack of tangible improvement in freshwater quality outcomes is demanding new and innovative approaches that rely more on physical process understanding, rather than precedent. In this study, we drew upon local geological, hydrogeological, and hydrological data to infer a high-level perceptual model of surface/groundwater interactions in a chalk stream in Dorset, UK. We used the perceptual model to interrogate spatial and temporal trends in historical water quality data and to construct reach-scale nutrient mass balances. Through novel representation with Sankey diagrams, the perceptual model highlighted the relative importance of different hydrological features. Surface/groundwater interactions were found to occur predominantly by spring flow. We demonstrate that river flow accretion was dominated by the Chalk aquifer despite only occupying ca. 15 % of the surface bedrock area, and that spring sources, whilst vital to dilute treated sewage inputs in baseflow conditions, were also major sources of legacy nitrate. Nutrient mass balances showed that sewage treatment works contributed ca. 13 % to soluble reactive phosphorus load, with groundwater accounting for ca. 48 %. Thus, a determinand often associated with point-source pollution was shown to be diffuse dominated in this river. The study demonstrated how a multi-disciplinary approach to water management, based on a comprehensive perceptual modelling approach, could identify hitherto unknown sources and relative contributors to freshwater pollution and allow flow and load apportionment studies to provide useful decision-support to manage nutrient pollution. The novel application of perceptual modelling tools, such as the Sankey diagram, allows different source attributions to be presented in an accessible manner, and can be readily transferred to other study areas.

Usability of CEFR Companion Volume scales for the development of an analytic rating scale for academic integrated writing assessment

"Successful academic writing from sources requires a broad range of competencies. When writing from sources, students are expected to mine source texts for relevant ideas, present these ideas with precision and in necessary depth, have efficient paraphrasing skills and the knowledge of proper source attribution. In order to assess the combination of these skills in writing and to provide diagnostic feedback to the learners, there is a need to design a rating scale where the required skills are operationalized in separate criteria (Knoch 2011). However, this endeavour may be challenging due to the complex nature of the academic integrated writing construct. This article describes the process of analytic rating scale development in the context of German higher education (HE). We address the issues of construct complexity and the operationalization of the construct elements in rating scale criteria by a combination of theory-based, descriptor-based, empirical, and intuitive approaches to scale development (e.g., Chan, Chihiro and Taylor 2015; Kuiken and Vedder 2021), with a particular focus on the usability of relevant scales from the CEFR Companion Volume (CEFR/CV; Council of Europe 2020). Besides the CEFR scales, we also explore the usability of existing scales for integrated writing and relevant taxonomies (e.g., Keck 2006; Shi 2004). Finally, we present qualitative insights of intuitive expert judgement from a workshop with four content experts who trialled and refined the first draft of the rating scale. The ensuing validation of the rating scale is, however, beyond the scope of this paper and the mixed-methods validation study will be reported elsewhere. The rating scale development reported here was part of the DFG-funded research project Modelling of academic integrated linguistic competencies, conducted at the University of Bremen and the Leibniz Institute for Research and Information in Education in Frankfurt. The project aim was to evaluate the academic-linguistic preparedness of students taking up English-medium studies in Germany by employing authentic integrated writing tasks and valid assessment procedures. The article offers insights into challenges and critical considerations when developing CEFR-based rating scales for integrated writing, focusing on valid rating criteria, bands, and adapting existing descriptors. "

Oldies but goldies? Comparing the trustworthiness and credibility of ‘new’ and ‘old’ information intermediaries

Abstract People increasingly access news through ‘new’, algorithmic intermediaries such as search engines or aggregators rather than the ‘old’ (i. e., traditional), journalistic intermediaries. As algorithmic intermediaries do not adhere to journalistic standards, their trustworthiness comes into question. With this study, we (1) summarize the differences between journalistic and algorithmic intermediaries as found in previous literature; (2) conduct a cross-media comparison of information credibility and intermediary trustworthiness; and (3) examine how key predictors (such as modality, reputation, source attribution, and prior experience) affect the trustworthiness and credibility assessments. Results from a quasi-experimental online survey (n = 485) confirm that recipients assess journalistic intermediaries more positively than algorithmic intermediaries and audio more positively than text intermediaries. Furthermore, we found reputation and information credibility to be the most important predictors of intermediary trustworthiness, while general media trust is the most important predictor of information credibility. Implications and directions for future research are discussed.

Enhancing betacyanin stability: Comparison of dragon fruit (Hylocereus polyrhizus) pulp and peel powders through encapsulation technology during storage.

Betacyanin can be found in the peel or pulp of dragon fruit. As a natural pigment, betacyanin is unstable, so it requires encapsulation technology to maintain its quality. The stability of encapsulated betacyanin from dragon fruit peel compared to dragon fruit pulp has yet to be discovered. This study aims to compare the stability of encapsulated betacyanin (with maltodextrin and gum Arabic) from dragon fruit peel and pulp dried with vacuum drying. Dragon fruit peel extraction utilized a 50% aqueous ethanol solvent, while pulp juice extraction was performed. The ratio of dragon fruit extract to coating materials was set at 1:3 (solid/solid). Research shows that dragon fruit juice powder had higher stability and phytochemical concentrations than the ethanol extract of dragon fruit peel powder during 30 days of storage. Despite similar color stability (similar range value of ΔE), the color from dragon fruit juice powder more closely resembled the natural fruit, albeit with weaker antioxidant activity than the peel powder. Betacyanin concentration in juice powder was notably higher (82.56-156.82 μg/g) than in the ethanol extract of dragon fruit peel powder (52.51-75.12 μg/g). A combination of maltodextrin and Arabic gum (1:1) as coating materials demonstrated the highest concentrations of total phenolic and total betacyanin (81.15-95.87 mg/g and 121.91-156.82 μg/g, respectively) during the storage period. These findings contribute to our comprehension of betacyanin stability and functionality, facilitating precise applications in industrial processing environments based on their source attributes.

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks.

The study primarily aimed to understand whether individual factors could predict how people perceive and evaluate artworks that are perceived to be produced by AI. Additionally, the study attempted to investigate and confirm the existence of a negative bias toward AI-generated artworks and to reveal possible individual factors predicting such negative bias. A total of 201 participants completed a survey, rating images on liking, perceived positive emotion, and believed human or AI origin. The findings of the study showed that some individual characteristics as creative personal identity and openness to experience personality influence how people perceive the presented artworks in function of their believed source. Participants were unable to consistently distinguish between human and AI-created images. Furthermore, despite generally preferring the AI-generated artworks over human-made ones, the participants displayed a negative bias against AI-generated artworks when subjective perception of source attribution was considered, thus rating as less preferable the artworks perceived more as AI-generated, independently on their true source. Our findings hold potential value for comprehending the acceptability of products generated by AI technology.

The importance of estimating the burden of disease from foodborne transmission of Trypanosoma cruzi.

Chagas disease (ChD), caused by infection with the flagellated protozoan, Trypanosoma cruzi, has a complicated transmission cycle with many infection routes. These include vector-borne (via the triatomine (reduviid bug) vector defecating into a skin abrasion, usually following a blood meal), transplacental transmission, blood transfusion, organ transplant, laboratory accident, and foodborne transmission. Foodborne transmission may occur due to ingestion of meat or blood from infected animals or from ingestion of other foods (often fruit juice) contaminated by infected vectors or secretions from reservoir hosts. Despite the high disease burden associated with ChD, it was omitted from the original World Health Organization estimates of foodborne disease burden that were published in 2015. As these estimates are currently being updated, this review presents arguments for including ChD in new estimates of the global burden of foodborne disease. Preliminary calculations suggest a burden of at least 137,000 Disability Adjusted Life Years, but this does not take into account the greater symptom severity associated with foodborne transmission. Thus, we also provide information regarding the greater health burden in endemic areas associated with foodborne infection compared with vector-borne infection, with higher mortality and more severe symptoms. We therefore suggest that it is insufficient to use source attribution alone to determine the foodborne proportion of current burden estimates, as this may underestimate the higher disability and mortality associated with the foodborne infection route.

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.

The geolocation of features on information surfaces and the use of the open and FAIR data principles in the mountain landscape domain and geoheritage

AbstractThis note suggests that decimal latitude/longitude [dLL] locations should be used to identify features of interest, landforms, sample and investigations sites, in an ‘information landscape’ provided by the geomorphological literature. All the information associated with a labelled, or tagged, geolocation should be available for examination as part of information landscapes that can be explored and represented in books, papers and other publications. This note also outlines the ‘open’ and FAIR data that are findable, accessible, interoperable and reusable and how the principles can be used to better explain landscapes, especially in the mountain landscape domain. Tors and rock glaciers illustrate [dLL] geolocation to identify sites and inform fieldwork and literature searching. Any [dLL]‐specified location is an identifying label, as are names given to landforms and toponyms. Two letters (digraph) are used as landform labels: TO for tors and RG for rock glaciers. Citations, (author–date–title–source) attributions, are also labels. The note shows how these attributions can be linked to [dLL] geolocations specifying locations in time and space and in the literature. The addition of [dLL] will facilitate future literature searches and modelling to explore ‘unknowns’ in the landscape, and this paper suggests ways in which this can be achieved, including geoheritage and geotourism.

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.