Fluctuation scaling relationships have been observed in a wide range of processes ranging from internet router traffic to measles cases. Taylor’s law is one such scaling relationship and has been widely applied in ecology to understand communities including trees, birds, human populations, and insects. We show that monthly crime reports in the UK show complex fluctuation scaling which can be approximated by Taylor’s law relationships corresponding to local policing neighborhoods and larger regional and countrywide scales. Regression models applied to local scale data from Derbyshire and Nottinghamshire found that different categories of crime exhibited different scaling exponents with no significant difference between the two regions. On this scale, violence reports were close to a Poisson distribution (α = 1.057±0.026) while burglary exhibited a greater exponent (α = 1.292±0.029) indicative of temporal clustering. These two regions exhibited significantly different pre-exponential factors for the categories of anti-social behavior and burglary indicating that local variations in crime reports can be assessed using fluctuation scaling methods. At regional and countrywide scales, all categories exhibited scaling behavior indicative of temporal clustering evidenced by Taylor’s law exponents from 1.43±0.12 (Drugs) to 2.094±0081 (Other Crimes). Investigating crime behavior via fluctuation scaling gives insight beyond that of raw numbers and is unique in reporting on all processes contributing to the observed variance and is either robust to or exhibits signs of many types of data manipulation.
Analyte quantitation by mass spectrometry underpins a diverse range of scientific endeavors. The fast-growing field of mass spectrometer development has resulted in several targeted and untargeted acquisition modes suitable for these applications. By characterizing the acquisition methods available on an ion mobility (IM)-enabled orthogonal acceleration time-of-flight (oa-ToF) instrument, the optimum modes for analyte semi-quantitation can be deduced.Methods: Serial dilutions of commercial metabolite, peptide, or cross-linked peptide analytes were prepared in matrices of human urine or Escherichia coli digest. Each analyte dilution was introduced into an IM separation-enabled oa-ToF mass spectrometer by reversed-phase liquid chromatography and electrospray ionization.Data were acquired for each sample in duplicate using nine different acquisition modes, including four IM-enabled acquisitions modes, available on the mass spectrometer.Results: Five (metabolite) or seven (peptide/cross-linked peptide) point calibration curves were prepared for analytes across each of the acquisition modes. A nonlinear response was observed at high concentrations for some modes, attributed to saturation effects. Two correction methods, one MS1 isotope-correction and one MS2 ion intensity-correction, were applied to address this observation, resulting in an up to twofold increase in dynamic range. By averaging the semi-quantitative results across analyte classes, two parameters, linear dynamic range (LDR) and lower limit of quantification (LLOQ), were determined to evaluate each mode.Hannah M. Britt and Tristan Cragnolini contributed equally to this manuscript.
Shiga toxin-producing Escherichia coli O157: H7 (STEC) is a zoonotic pathogen that is globally dispersed, causing severe gastroenteritis when transmitted from ruminants to humans through direct or indirect contact with animals, their environment or contaminated food. Symptoms are varied in severity; from mild to bloody diarrhoea with more serious sequalae including hemolytic uremic syndrome (HUS) which can be fatal. Although there is compelling evidence that the Shiga toxin sub-type is a key predictor of disease severity, differences in virulence potential of strains with the same Shiga toxin profile are often observed. In this study, we employ machine learning algorithms to explore the relationship between the STEC genome with clinical outcome. Kmer-counts of variable length (9-100 base pair) from 1148 isolates of STEC O157:H7, representing two years of routine surveillance in England, were matched to their respective clinical outcome data. A Random Forest classifier was developed and validated with the objective of inferring the clinical symptoms associated with a given STEC genome. Clinical outcomes were categorised into asymptomatic, diarrhoea, bloody diarrhoea and HUS. The model correctly classified 160 out of 190 cases of bloody diarrhoea, 81 out of 128 cases of diarrhoea and 7 out of 12 cases of HUS, with average AUC ROC score of 90%. Kmers deemed important for distinct classification were characterised and matches related to Shiga toxin 2a phage integration and excision genes and adhesion and transporter proteins were identified. This is consistent with reported virulence factors in the literature, supporting this approach of de novo pathogen characterisation.
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