Clostridium botulinum causes infant botulism by colonising the intestines and producing botulinum neurotoxin in situ. Previous reports have linked infant botulism cases to C. botulinum spores in household dust, yet the baseline incidence of C. botulinum spores in residential households is currently unknown. Vacuum cleaner dust from 963 households in 13 major Canadian cities was tested for C. botulinum using a novel real-time PCR assay directed against all known subtypes of the botulinum neurotoxin gene. None of the samples tested positive for C. botulinum. Analysis of a random subset of samples by MALDI Biotyper revealed that the most common anaerobic bacterial isolates were of the genus Clostridium and the most common species recovered overall was Clostridium perfringens. Dust that was spiked with C. botulinum spores of each toxin type successfully produced positive real-time PCR reactions. These control experiments indicate that this is a viable method for the detection of C. botulinum spores in household dust. We make several recommendations for future work that may help discover a common environmental source of C. botulinum spores that could lead to effective preventative measures for this rare but deadly childhood disease.

Longitudinal studies on the variations of phenotypic and genotypic characteristics of K. pneumoniae across two decades are rare. We aimed to determine the antimicrobial susceptibility and virulence factors for K. pneumoniae isolated from patients with bacteraemia or urinary tract infection (UTI) from 1999 to 2022. A total of 699 and 1,267 K. pneumoniae isolates were isolated from bacteraemia and UTI patients, respectively, and their susceptibility to twenty antibiotics was determined; PCR was used to identify capsular serotypes and virulence-associated genes. K64 and K1 serotypes were most frequently observed in UTI and bacteraemia, respectively, with an increasing frequency of K20, K47, and K64 observed in recent years. entB and wabG predominated across all isolates and serotypes; the least frequent virulence gene was htrA. Most isolates were susceptible to carbapenems, amikacin, tigecycline, and colistin, with the exception of K20, K47, and K64 where resistance was widespread. The highest average number of virulence genes was observed in K1, followed by K2, K20, and K5 isolates, which suggest their contribution to the high virulence of K1. In conclusion, we found that the distribution of antimicrobial susceptibility, virulence gene profiles, and capsular types of K. pneumoniae over two decades were associated with their clinical source.

Rodents and shrews are major reservoirs of various pathogens that are related to zoonotic infectious diseases. The purpose of this study was to investigate co-infections of zoonotic pathogens in rodents and shrews trapped in four provinces of China. We sampled different rodent and shrew communities within and around human settlements in four provinces of China and characterised several important zoonotic viral, bacterial, and parasitic pathogens by PCR methods and phylogenetic analysis. A total of 864 rodents and shrews belonging to 24 and 13 species from RODENTIA and EULIPOTYPHLA orders were captured, respectively. For viral pathogens, two species of hantavirus (Hantaan orthohantavirus and Caobang orthohantavirus) were identified in 3.47% of rodents and shrews. The overall prevalence of Bartonella spp., Anaplasmataceae, Babesia spp., Leptospira spp., Spotted fever group Rickettsiae, Borrelia spp., and Coxiella burnetii were 31.25%, 8.91%, 4.17%, 3.94%, 3.59%, 3.47%, and 0.58%, respectively. Furthermore, the highest co-infection status of three pathogens was observed among Bartonella spp., Leptospira spp., and Anaplasmataceae with a co-infection rate of 0.46%. Our results suggested that species distribution and co-infections of zoonotic pathogens were prevalent in rodents and shrews, highlighting the necessity of active surveillance for zoonotic pathogens in wild mammals in wider regions.

This article presents a system architecture and a set of interfaces that can build scalable information systems capable of large city modeling based on dynamic geospatial knowledge graphs to avoid pitfalls of Web 2.0 applications while blending artificial and human intelligence during the knowledge enhancement processes. We designed and developed a GeoSpatial Processor, an SQL2SPARQL Transformer, and a geospatial tiles ordering tasks and integrated them into a City Export Agent to visualize and interact with city models on an augmented 3D web client. We designed a Thematic Surface Discovery Agent to automatically upgrade the model’s level of detail to interact with thematic parts of city objects by other agents. We developed a City Information Agent to help retrieve contextual information, provide data concerning city regulations, and work with a City Energy Analyst Agent that automatically estimates the energy demands for city model members. We designed a Distance Agent to track the interactions with the model members on the web, calculate distances between objects of interest, and add new knowledge to the Cities Knowledge Graph. The logical foundations and CityGML-based conceptual schema used to describe cities in terms of the OntoCityGML ontology, together with the system of intelligent autonomous agents based on the J-Park Simulator Agent Framework, make such systems capable of assessing and maintaining ground truths with certainty. This new era of GeoWeb 2.5 systems lowers the risk of deliberate misinformation within geography web systems used for modeling critical infrastructures.

We study generalised quasirandom graphs whose vertex set consists of $q$ parts (of not necessarily the same sizes) with edges within each part and between each pair of parts distributed quasirandomly; such graphs correspond to the stochastic block model studied in statistics and network science. Lovász and Sós showed that the structure of such graphs is forced by homomorphism densities of graphs with at most $(10q)^q+q$ vertices; subsequently, Lovász refined the argument to show that graphs with $4(2q+3)^8$ vertices suffice. Our results imply that the structure of generalised quasirandom graphs with $q\ge 2$ parts is forced by homomorphism densities of graphs with at most $4q^2-q$ vertices, and, if vertices in distinct parts have distinct degrees, then $2q+1$ vertices suffice. The latter improves the bound of $8q-4$ due to Spencer.

Turkeys are important sources of antimicrobial-resistant Campylobacter. A total of 1063 isolates were obtained from 293 turkey flocks across Canada between 2016 and 2021 to evaluate their antimicrobial resistance (AMR) prevalence, patterns, distribution, and association with antimicrobial use (AMU). A high proportion of C. jejuni and C. coli isolates were resistant to tetracyclines and fluoroquinolones, despite the very low use of these drugs. C. jejuni isolates had a higher probability of being resistant to tetracyclines than C. coli isolates. The chance of C. jejuni isolates being resistant to fluoroquinolones, macrolides, and lincosamides was lower compared to C. coli. Isolates from the western region had a higher probability of being resistant to fluoroquinolones than isolates from Ontario. Isolates from Ontario had higher odds of being resistant to tetracyclines than isolates from Quebec. No associations were noted between the resistance and use of the same antimicrobial, but the use of certain antimicrobial classes may have played a role in the maintenance of resistance in Campylobacter (fluoroquinolone resistance – bacitracin and streptogramin use, tetracycline resistance – flavophospholipids and streptogramins use, macrolide resistance – flavophospholipid use). Low-level multidrug-resistant Campylobacter was observed indicating a stable AMR in turkeys. This study provided insights aiding future AMU and AMR surveillance.

Climate risks are systemic risks and may be clustered according to so-called volatilities, uncertainties, complexities, and ambiguities (VUCA) criteria. We analyze climate risk in the VUCA concept and provide a framework that allows to interpret systemic risks as model risk. As climate risks are characterized by deep uncertainties (unknown unknowns), we argue that precautionary and resilient principles should be applied instead of capital-based risk measures (reasonable for known unknows). A prominent example of the proposed principles is the precommitment approach (PCA). Within the PCA, subjective probabilities allow to discriminate between tolerable risks and acceptable ones. The amount of determined solvency capital for acceptable risks and estimations of model risk may be aggregated by means of a multiplier approach. This framework is in line with the three-pillar approach of Solvency II, especially with the recovery and resolution plan. Furthermore, it fits smoothly to a hybrid approach of micro- and macroprudential supervision.