To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure no-reply@cambridge.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The mycosis histoplasmosis is also considered a zoonosis that affects humans and other mammalian species worldwide. Among the wild mammals predisposed to be infected with the etiologic agent of histoplasmosis, bats are relevant because they are reservoir of Histoplasma species, and they play a fundamental role in maintaining and spreading fungal propagules in the environments since the infective mycelial phase of Histoplasma grows in their accumulated guano. In this study, we detected the fungal presence in organ samples of bats randomly captured in urban areas of Araraquara City, São Paulo, Brazil. Fungal detection was performed using a nested polymerase chain reaction to amplify a molecular marker (Hcp100) unique to H. capsulatum, which revealed the pathogen presence in organ samples from 15 out of 37 captured bats, indicating 40.5% of infection. Out of 22 Hcp100-amplicons generated, 41% corresponded to lung and trachea samples and 59% to spleen, liver, and kidney samples. Data from these last three organs suggest that bats develop disseminated infections. Considering that infected bats create environments with a high risk of infection, it is important to register the percentage of infected bats living in urban areas to avoid risks of infection to humans, domestic animals, and wildlife.
Nontuberculous mycobacteria (NTM) is a large group of mycobacteria other than the Mycobacterium tuberculosis complex and Mycobacterium leprae. Epidemiological investigations have found that the incidence of NTM infections is increasing in China, and it is naturally resistant to many antibiotics. Therefore, studies of NTM species in clinical isolates are useful for understanding the epidemiology of NTM infections. The present study aimed to investigate the incidence of NTM infections and types of NTM species. Of the 420 samples collected, 285 were positive for M. tuberculosis, 62 samples were negative, and the remaining 73 samples contained NTM, including 35 (8.3%) only NTM and 38 (9%) mixed (M. tuberculosis and NTM). The most prevalent NTM species were Mycobacterium intracellulare (30.1%), followed by Mycobacterium abscessus (15%) and M. triviale (12%). M. gordonae infection was detected in 9.5% of total NTM-positive cases. Moreover, this study reports the presence of Mycobacterium nonchromogenicum infection and a high prevalence of M. triviale for the first time in Henan. M. intracellulare is the most prevalent, accompanied by some emerging NTM species, including M. nonchromogenicum and a high prevalence of M. triviale in Henan Province. Monitoring NTM transmission and epidemiology could enhance mycobacteriosis management in future.
In September 2023, the UK Health Security Agency identified cases of Salmonella Saintpaul distributed across England, Scotland, and Wales, all with very low genetic diversity. Additional cases were identified in Portugal following an alert raised by the United Kingdom. Ninety-eight cases with a similar genetic sequence were identified, 93 in the United Kingdom and 5 in Portugal, of which 46% were aged under 10 years. Cases formed a phylogenetic cluster with a maximum distance of six single nucleotide polymorphisms (SNPs) and average of less than one SNP between isolates. An outbreak investigation was undertaken, including a case–control study. Among the 25 UK cases included in this study, 13 reported blood in stool and 5 were hospitalized. One hundred controls were recruited via a market research panel using frequency matching for age. Multivariable logistic regression analysis of food exposures in cases and controls identified a strong association with cantaloupe consumption (adjusted odds ratio: 14.22; 95% confidence interval: 2.83–71.43; p-value: 0.001). This outbreak, together with other recent national and international incidents, points to an increase in identifications of large outbreaks of Salmonella linked to melon consumption. We recommend detailed questioning and triangulation of information sources to delineate consumption of specific fruit varieties during Salmonella outbreaks.
Qu, Dassios, and Zhao (2021) suggested an exact simulation method for tempered stable Ornstein–Uhlenbeck processes, but their algorithms contain some errors. This short note aims to correct their algorithms and conduct some numerical experiments.
In a Model Predictive Control (MPC) setting, the precise simulation of the behavior of the system over a finite time window is essential. This application-oriented benchmark study focuses on a robot arm that exhibits various nonlinear behaviors. For this arm, we have a physics-based model with approximate parameter values and an open benchmark dataset for system identification. However, the long-term simulation of this model quickly diverges from the actual arm’s measurements, indicating its inaccuracy. We compare the accuracy of black-box and purely physics-based approaches with several physics-informed approaches. These involve different combinations of a neural network’s output with information from the physics-based model or feeding the physics-based model’s information into the neural network. One of the physics-informed model structures can improve accuracy over a fully black-box model.
This paper considers testing for unit roots in Gaussian panels with cross-sectional dependence generated by common factors. Within our setup, we can analyze restricted versions of the two prevalent approaches in the literature, that of Moon and Perron (2004, Journal of Econometrics 122, 81–126), who specify a factor model for the innovations, and the PANIC setup proposed in Bai and Ng (2004, Econometrica 72, 1127–1177), who test common factors and idiosyncratic deviations separately for unit roots. We show that both frameworks lead to locally asymptotically normal experiments with the same central sequence and Fisher information. Using Le Cam’s theory of statistical experiments, we obtain the local asymptotic power envelope for unit-root tests. We show that the popular Moon and Perron (2004, Journal of Econometrics 122, 81–126) and Bai and Ng (2010, Econometric Theory 26, 1088–1114) tests only attain the power envelope in case there is no heterogeneity in the long-run variance of the idiosyncratic components. We develop a new test which is asymptotically uniformly most powerful irrespective of possible heterogeneity in the long-run variance of the idiosyncratic components. Monte Carlo simulations corroborate our asymptotic results and document significant gains in finite-sample power if the variances of the idiosyncratic shocks differ substantially among the cross-sectional units.
Burden of bacteraemia is rising due to increased average life expectancy in developed countries. This study aimed to compare the epidemiology and outcomes of bacteraemia in two similarly ageing populations with different ethnicities in Singapore and Denmark. Historical cohorts from the second largest acute-care hospital in Singapore and in the hospitals of two Danish regions included patients aged 15 and above who were admitted from 1 January 2006 to 31 December 2016 with at least 1 day of hospital stay and a pathogenic organism identified. Among 13 144 and 39 073 bacteraemia patients from Singapore and Denmark, similar 30-day mortality rates (16.5%; 20.3%), length of hospital stay (median 14 (IQR: 9–28) days; 11 (6–21)), and admission rate to ICU (15.5%; 15.6%) were observed, respectively. Escherichia coli, Klebsiella pneumoniae, and Staphylococcus aureus ranked among the top four in both countries. However, Singaporeans had a higher proportion of patients with diabetes (46.8%) and renal disease (29.5%) than the Danes (28.0% and 13.7%, respectively), whilst the Danes had a higher proportion of patients with chronic pulmonary disease (18.0%) and malignancy (35.3%) than Singaporeans (9.7% and 16.2%, respectively). Our study showed that top four causative organisms and clinical outcomes were similar between the two cohorts despite pre-existing comorbidities differed.
Given a graph $F$, we consider the problem of determining the densest possible pseudorandom graph that contains no copy of $F$. We provide an embedding procedure that improves a general result of Conlon, Fox, and Zhao which gives an upper bound on the density. In particular, our result implies that optimally pseudorandom graphs with density greater than $n^{-1/3}$ must contain a copy of the Peterson graph, while the previous best result gives the bound $n^{-1/4}$. Moreover, we conjecture that the exponent $1/3$ in our bound is tight. We also construct the densest known pseudorandom $K_{2,3}$-free graphs that are also triangle-free. Finally, we give a different proof for the densest known construction of clique-free pseudorandom graphs due to Bishnoi, Ihringer, and Pepe that they have no large clique.
This paper discusses the challenges and opportunities in accessing data to improve workplace relations law enforcement, with reference to minimum employment standards such as wages and working hours regulation. Our paper highlights some innovative examples of government and trade union efforts to collect and use data to improve the detection of noncompliance. These examples reveal the potential of data science as a compliance tool but also suggest the importance of realizing a data ecosystem that is capable of being utilized by machine learning applications. The effectiveness of using data and data science tools to improve workplace law enforcement is impacted by the ability of regulatory actors to access useful data they do not collect or hold themselves. Under “open data” principles, government data is increasingly made available to the public so that it can be combined with nongovernment data to generate value. Through mapping and analysis of the Australian workplace relations data ecosystem, we show that data availability relevant to workplace law compliance falls well short of open data principles. However, we argue that with the right protocols in place, improved data collection and sharing will assist regulatory actors in the effective enforcement of workplace laws.
We investigate here the behaviour of a large typical meandric system, proving a central limit theorem for the number of components of a given shape. Our main tool is a theorem of Gao and Wormald that allows us to deduce a central limit theorem from the asymptotics of large moments of our quantities of interest.
When people are asked to recall their social networks, theoretical and empirical work tells us that they rely on shortcuts, or heuristics. Cognitive social structures (CSSs) are multilayer social networks where each layer corresponds to an individual’s perception of the network. With multiple perceptions of the same network, CSSs contain rich information about how these heuristics manifest, motivating the question, Can we identify people who share the same heuristics? In this work, we propose a method for identifying cognitive structure across multiple network perceptions, analogous to how community detection aims to identify social structure in a network. To simultaneously model the joint latent social and cognitive structure, we study CSSs as three-dimensional tensors, employing low-rank nonnegative Tucker decompositions (NNTuck) to approximate the CSS—a procedure closely related to estimating a multilayer stochastic block model (SBM) from such data. We propose the resulting latent cognitive space as an operationalization of the sociological theory of social cognition by identifying individuals who share relational schema. In addition to modeling cognitively independent, dependent, and redundant networks, we propose a specific model instance and related statistical test for testing when there is social-cognitive agreement in a network: when the social and cognitive structures are equivalent. We use our approach to analyze four different CSSs and give insights into the latent cognitive structures of those networks.
Eaton (1992) considered a general parametric statistical model paired with an improper prior distribution for the parameter and proved that if a certain Markov chain, constructed using the model and the prior, is recurrent, then the improper prior is strongly admissible, which (roughly speaking) means that the generalized Bayes estimators derived from the corresponding posterior distribution are admissible. Hobert and Robert (1999) proved that Eaton’s Markov chain is recurrent if and only if its so-called conjugate Markov chain is recurrent. The focus of this paper is a family of Markov chains that contains all of the conjugate chains that arise in the context of a Poisson model paired with an arbitrary improper prior for the mean parameter. Sufficient conditions for recurrence and transience are developed and these are used to establish new results concerning the strong admissibility of non-conjugate improper priors for the Poisson mean.
The Institute and Faculty of Actuaries UK Asbestos Working Party update 2020 sets out the methodology and assumptions used to estimate the potential cost of asbestos-related claims to the UK Employers’ Liability Insurance Market. The Working Party has estimated the UK EL Insurance Market cost for the following asbestos-related disease types: mesothelioma, lung cancer, asbestosis and pleural thickening, and pleural plaques. For each disease type the Working Party has constructed a range of scenarios to highlight the uncertainty of these estimates. The Working Party reminds practitioners that use the Working Party scenarios that they should always consider the experience and trends that have occurred since the scenarios were published, adjusting the scenarios to take into account new information.