In this work we analyse bucket increasing tree families. We introduce two simple stochastic growth processes, generating random bucket increasing trees of size n, complementing the earlier result of Mahmoud and Smythe (1995, Theoret. Comput. Sci. 144 221–249.) for bucket recursive trees. On the combinatorial side, we define multilabelled generalisations of the tree families d-ary increasing trees and generalised plane-oriented recursive trees. Additionally, we introduce a clustering process for ordinary increasing trees and relate it to bucket increasing trees. We discuss in detail the bucket size two and present a bijection between such bucket increasing tree families and certain families of graphs called increasing diamonds, providing an explanation for phenomena observed by Bodini et al. (2016, Lect. Notes Comput. Sci. 9644 207–219.). Concerning structural properties of bucket increasing trees, we analyse the tree parameter $K_n$. It counts the initial bucket size of the node containing label n in a tree of size n and is closely related to the distribution of node types. Additionally, we analyse the parameters descendants of label j and degree of the bucket containing label j, providing distributional decompositions, complementing and extending earlier results (Kuba and Panholzer (2010), Theoret. Comput. Sci. 411(34–36) 3255–3273.).

This study was performed to investigate the occurrence of livestock-associated methicillin-resistant Staphylococcus aureus (LA-MRSA) in batches of pigs at slaughter and at different stages along the slaughter line. Nasal and ear skin swabs were collected from 105 batches of 10 pigs at six abattoirs. Cultures (pooled or individual) were performed for MRSA using selective media; presumptive MRSA were confirmed by mecA and nuc gene detection and a selection was spa-typed. MRSA was detected in 46 batches. All spa-types detected were those associated with LA-MRSA clonal complex 398. The proportion of positive batches varied among abattoirs (0–100%). Two abattoirs were subsequently further investigated, with samples taken at post-stunning, chiller and either at lairage or post-singe. Results suggested cross-contamination occurred between the lairage and point of post-stunning, but the slaughter processes appeared effective at reducing contamination before carcases entered the chiller. One abattoir provided only negative samples in the initial study and in the subsequent study along the slaughter line (26 batches in total), suggesting differences possibly in the MRSA status of pigs on arrival from supply farms or in its abattoir practices affecting the MRSA status of pigs at the sampling points. This study highlights that in the investigated abattoirs, MRSA was detected in 43.8% of batches of pigs at slaughter using sensitive selective culture methods.

We explore the possibility of combining a knowledge-based reduced order model (ROM) with a reservoir computing approach to learn and predict the dynamics of chaotic systems. The ROM is based on proper orthogonal decomposition (POD) with Galerkin projection to capture the essential dynamics of the chaotic system while the reservoir computing approach used is based on echo state networks (ESNs). Two different hybrid approaches are explored: one where the ESN corrects the modal coefficients of the ROM (hybrid-ESN-A) and one where the ESN uses and corrects the ROM prediction in full state space (hybrid-ESN-B). These approaches are applied on two chaotic systems: the Charney–DeVore system and the Kuramoto–Sivashinsky equation and are compared to the ROM obtained using POD/Galerkin projection and to the data-only approach based uniquely on the ESN. The hybrid-ESN-B approach is seen to provide the best prediction accuracy, outperforming the other hybrid approach, the POD/Galerkin projection ROM, and the data-only ESN, especially when using ESNs with a small number of neurons. In addition, the influence of the accuracy of the ROM on the overall prediction accuracy of the hybrid-ESN-B is assessed rigorously by considering ROMs composed of different numbers of POD modes. Further analysis on how hybrid-ESN-B blends the prediction from the ROM and the ESN to predict the evolution of the system is also provided.

Nearly 1 year into the coronavirus disease 2019 pandemic, the first severe acute respiratory syndrome coronavirus 2 vaccines received emergency use authorisation and vaccination campaigns began. A number of factors can reduce the averted burden of cases and deaths due to vaccination. Here, we use a dynamic model, parametrised with Bayesian inference methods, to assess the effects of non-pharmaceutical interventions (NPIs) (such as social distancing, mask mandates, school and workplace closure), and vaccine administration and uptake rates on infections and deaths averted in the United States. We show that scenarios depicting higher compliance with NPIs avert more than 60% of infections and 70% of deaths during the period of vaccine administration, and that increasing the vaccination rate from 5 to 11 million people per week could increase the averted burden by more than one-third. These findings underscore the importance of maintaining NPIs and increasing vaccine administration rates.

This study aimed to investigate differences in the antimicrobial susceptibility of members of the Mycobacterium abscessus complex (MABC): subsp. massiliense and subsp. abscessus, and to identify associations between strain genotypes and antimicrobial resistance phenotypes. A total of 383 clinical MABC isolates (subsp. abscessus: n = 218, 56.9%; subsp. massiliense: n = 163, 42.6%; subsp. bolletii: n = 2, 0.5%) were characterised using multilocus variable number tandem repeat (VNTR) typing and drug susceptibility testing. Most isolates exhibited susceptibility to amikacin, clarithromycin and azithromycin but resistance to cefoxitin and minocycline was statistically more associated with isolates unclustered by VNTR type. The Simpson's diversity indexes of VNTR typing for M. abscessus and M. massiliense isolates were 0.999 and 0.997, respectively. Genotyping of M. abscessus and M. massiliense isolates by VNTR may provide valuable information for predicting resistance phenotype.

Our study was conducted to assess the sepsis-associated hospitalisations and antimicrobials prescribed for sepsis inpatients in Hong Kong. Demographic, diagnostic and antimicrobial prescription data were analysed for patients admitted to public hospitals with a diagnosis of septicaemia from 2000 to 2015. A total of 223 250 sepsis hospitalisations were recorded in Hong Kong from 2000 to 2015 during which the hospitalisation rate increased by 85.6%. The majority of the sepsis hospitalisations occurred in adults ≥65 years and children aged 0–4 years. Adults with a secondary diagnosis of sepsis were often admitted with a primary diagnosis of urological conditions or pneumonia; whereas diabetes mellitus was the most common secondary diagnosis among those with primary sepsis. Paediatric sepsis patients aged 0–4 years were often diagnosed with disorders relating to short gestation and low birthweight. Antimicrobial prescriptions increased by 51.1% and 34.4% for primary and secondary sepsis patients, respectively. β-Lactam and β-lactamase inhibitor combinations were the most used antibiotics whereas the usage of carbapenems increased more than 10 times over the study period. A substantial burden of hospitalisations was attributable to sepsis in Hong Kong, particularly in the extremes of age. Broad-spectrum and last-resort antibiotics had been increasingly dispensed for sepsis inpatients.

The Internet of Things (IoT) is currently developing fast and its potential as driver of innovative solutions is increasing, pushed by technologies, networks, communication, and computing power, and has the potential to drive the development of technological ecosystems, such as innovation clusters. Innovation clusters are agglomeration of enterprises and research organizations, which cooperate, interact and compete, generating innovation and driving the growth of ecosystems. The narrative around innovation clusters has been developing since many years and policy-makers seek to use such clusters as a policy instrument to support the growth of technology on the one hand and regional and sectoral development on the other hand. This policy paper expands an empirical study on IoT innovation clusters in Europe and places it within the current debate around clusters and innovation clusters to provide evidence-based advice to policy-makers on what may and may not work as public policy measures. The paper highlights the findings of the interaction with several hundred European IoT innovation clusters and points out their points of view on their own creation factors, operational characteristics, and success stories, as well as their expectations in respect to policy interventions for IoT and for clusters. Suggestions for IoT policy-making are provided. The paper has also undertaken an extensive review of up-to date research on innovation cluster creation and performance, thoroughly analyzing the real possibility to define causal relationships between clusters, productivity and economic growth, and business performance, and providing suggestions for policy-makers on the approach to cluster policy.

Effectiveness of corona virus disease-19 (COVID-19) vaccines used in India is unexplored and need to be substantiated. The present case-control study was planned to elicit the effectiveness of COVID-19 vaccines in preventing infection and disease severity in the general population of Bihar, India. This case-control study was conducted among people aged ≥45 years during April to June 2021. The cases were the COVID-19 patients admitted or visited All India Institute of Medical Sciences (AIIMS), Patna, Bihar, India, and were contacted directly. The controls were the individuals tested negative for severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) at the Virology laboratory, AIIMS-Patna and contacted telephonically for collection of relevant information. The vaccine effectiveness (VE) was calculated by using the formula (VE = 1 – odds ratio). The adjusted VE for partial and full vaccination were estimated to be 52.0% (95% confidence interval (CI) 39.0–63.0%) and 83.0% (95% CI 73.0–89.0%) respectively for preventing SARS CoV-2 infection. The sub-group analyses of the cases have shown that the length of hospital stays (LOS) (partially vaccinated: 9 days vs. unvaccinated: 12 days; P = 0.028) and the severity of the disease (fully vaccinated: 30.3% vs. partially vaccinated: 51.3% and unvaccinated: 54.1%; P = 0.035) were significantly low among vaccinated compared to unvaccinated individuals. To conclude, four out of every five fully vaccinated individuals are estimated to be protected from contracting SARS CoV-2 infection. Vaccination lowered LOS and chances of development of severe disease.

The use of antimicrobials in food-producing animals can lead to increased bacterial resistance. Important information to address this problem can be provided by monitoring antimicrobial resistance (AMR) in foodborne pathogens. As part of preliminary activities for the implementation of AMR surveillance in Brazil, a nationwide survey on AMR in Salmonella enterica isolates from poultry meat was conducted. The survey evaluated 146 Salmonella isolates from poultry meat in 2014, and 163 isolates obtained in 2017. Minimal inhibitory concentrations of 13 antimicrobials were determined by broth microdilution, and isolates were assigned to serotypes by automated ribotyping. High resistance rates were found in 2014 and 2017, in particular to nalidixic acid (84/146, 57.5% and 141/163, 86.5%, respectively), ampicillin (82/146, 56.2% and 125/163, 76.7%), cefotaxime (76/146, 52.1% and 124/163, 76.1%), ceftazidime (73/146, 50.0% and 124/163, 76.1%), ciprofloxacin (83/146, 56.9% and 145/163, 89.0%) and tetracycline (88/146, 60.3% and 135/163, 82.8%). There was a significant increase in resistance to these antibiotics in the second survey period. Salmonella ser. Heidelberg and Salmonella ser. Minnesota were the main serotypes expressing resistance to these antimicrobials. Multidrug resistance was found in 50.7% (74/146) of the isolates from 2014, and in 77.3% (126/163) of isolates from 2017 (P < 0.05). None of the isolates was resistant to azithromycin or meropenem. These findings indicate high and increasing rates of resistance among Salmonella from poultry meat in Brazil, mainly associated with Salmonella ser. Heidelberg and Salmonella ser. Minnesota, stressing the importance of continuous monitoring of AMR in the poultry chain.

We consider the component structure of the random digraph D(n,p) inside the critical window $p = n^{-1} + \lambda n^{-4/3}$. We show that the largest component $\mathcal{C}_1$ has size of order $n^{1/3}$ in this range. In particular we give explicit bounds on the tail probabilities of $|\mathcal{C}_1|n^{-1/3}$.

By now there is a solid theory for Polya urns. Finding the covariances is somewhat laborious. While these papers are “structural,” our purpose here is “computational.” We propose a practicable method for building the asymptotic covariance matrix in tenable balanced urn schemes, whereupon the asymptotic covariance matrix is obtained by solving a linear system of equations. We demonstrate the use of the method in growing tenable balanced irreducible schemes with a small index and in critical urns. In the critical case, the solution to the linear system of equations is explicit in terms of an eigenvector of the scheme.

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.

We describe here a notion of diffusion similarity, a method for defining similarity between vertices in a given graph using the properties of random walks on the graph to model the relationships between vertices. Using the approach of graph vertex embedding, we characterize a vertex vi by considering two types of diffusion patterns: the ways in which random walks emanate from the vertex vi to the remaining graph and how they converge to the vertex vi from the graph. We define the similarity of two vertices vi and vj as the average of the cosine similarity of the vectors characterizing vi and vj. We obtain these vectors by modifying the solution to a differential equation describing a type of continuous time random walk.

This method can be applied to any dataset that can be assigned a graph structure that is weighted or unweighted, directed or undirected. It can be used to represent similarity of vertices within community structures of a network while at the same time representing similarity of vertices within layered substructures (e.g., bipartite subgraphs) of the network. To validate the performance of our method, we apply it to synthetic data as well as the neural connectome of the C. elegans worm and a connectome of neurons in the mouse retina. A tool developed to characterize the accuracy of the similarity values in detecting community structures, the uncertainty index, is introduced in this paper as a measure of the quality of similarity methods.