Bayesian nonparametric inference for heterogeneously mixing infectious disease models.

SignificanceMathematical models of infectious disease transmission continue to play a vital role in understanding, mitigating, and preventing outbreaks. The vast majority of epidemic models in the literature are parametric, meaning that they contain inherent assumptions about how transmission occurs in a population. However, such assumptions can be lacking in appropriate biological or epidemiological justification and in consequence lead to erroneous scientific conclusions and misleading predictions. We propose a flexible Bayesian nonparametric framework that avoids the need to make strict model assumptions about the infection process and enables a far more data-driven modeling approach for inferring the mechanisms governing transmission. We use our methods to enhance our understanding of the transmission mechanisms of the 2001 UK foot and mouth disease outbreak.

Model-based evaluation of admission screening strategies for the detection and control of carbapenemase-producing Enterobacterales in the English hospital setting.

BACKGROUND: Globally, detections of carbapenemase-producing Enterobacterales (CPE) colonisations and infections are increasing. The spread of these highly resistant bacteria poses a serious threat to public health. However, understanding of CPE transmission and evidence on effectiveness of control measures is severely lacking. This paper provides evidence to inform effective admission screening protocols, which could be important in controlling nosocomial CPE transmission. METHODS: CPE transmission within an English hospital setting was simulated with a data-driven individual-based mathematical model. This model was used to evaluate the ability of the 2016 England CPE screening recommendations, and of potential alternative protocols, to identify patients with CPE-colonisation on admission (including those colonised during previous stays or from elsewhere). The model included nosocomial transmission from colonised and infected patients, as well as environmental contamination. Model parameters were estimated using primary data where possible, including estimation of transmission using detailed epidemiological data within a Bayesian framework. Separate models were parameterised to represent hospitals in English areas with low and high CPE risk (based on prevalence). RESULTS: The proportion of truly colonised admissions which met the 2016 screening criteria was 43% in low-prevalence and 54% in high-prevalence areas respectively. Selection of CPE carriers for screening was improved in low-prevalence areas by adding readmission as a screening criterion, which doubled how many colonised admissions were selected. A minority of CPE carriers were confirmed as CPE positive during their hospital stay (10 and 14% in low- and high-prevalence areas); switching to a faster screening test pathway with a single-swab test (rather than three swab regimen) increased the overall positive predictive value with negligible reduction in negative predictive value. CONCLUSIONS: Using a novel within-hospital CPE transmission model, this study assesses CPE admission screening protocols, across the range of CPE prevalence observed in England. It identifies protocol changes-adding readmissions to screening criteria and a single-swab test pathway-which could detect similar numbers of CPE carriers (or twice as many in low CPE prevalence areas), but faster, and hence with lower demand on pre-emptive infection-control resources. Study findings can inform interventions to control this emerging threat, although further work is required to understand within-hospital transmission sources.

A multistate modeling approach to investigate long-term effects of claw horn disruption lesions and early lesion development in dairy cows.

Claw horn disruption lesions (CHDL) are a leading cause of lameness in dairy cattle, and the development, effect, and pathology of these lesions remains an open area of interest within dairy cattle health. Current literature typically attempts to measure the effect of risk factors on the development of CHDL over a relatively short time period. Further understanding of the interaction of CHDL and the long-term effect of early CHDL in a cow's life remains an important area of research which is so far mostly unexplored. In this study 57,974 cows from 1,332 herds were selected and their regular claw trimming records containing important claw health information were used to model the long-term effect of lesions in a cow's lifetime in a 6-state multistate model. A multistate model predicts the time before transition from any one state to another and the probability of transition to a future state. The 6 lesion states that were modeled were as follows: never had a lesion, first recorded lesion event, no recorded lesion after first lesion event, second or subsequent recorded lesion event, no recorded lesion after second or subsequent lesion event, and culled. The effect of various cow level covariates on the transition probabilities between various states was tested. For the first time, this study shows the importance and effect of the first lesion and other cow level factors on long-term claw health. Model results showed that the timing and severity of the first recorded lesion event significantly influenced the likelihood of a future lesion being present. Cows with CHDL present within the 180 d of first calving had a short-term increased risk and long-term decreased risk of a future lesion, compared with cows that present with CHDL later than 180 d of first lactation. Moreover, presence of a severe first lesion increased a cow's risk of a future lesion being present. The model was used to evaluate the relative difference between high-risk cows (age of first calving ≥793 d, breeding values in the lowest quartile) and low-risk cows (age of first calving ≤718 d, breeding values in the highest quartile). Our results indicated that these low-risk cows present with a lesion on an average 3 mo later than high-risk cows. Furthermore, results from the model evaluation of a simulated herd with cows with breeding values in the higher quartile indicated that cows present with a CHDL on an average 7.5 mo later compared with a herd where cows have breeding values distributed in a lower quartile.

Pair-based likelihood approximations for stochastic epidemic models.

Fitting stochastic epidemic models to data is a non-standard problem because data on the infection processes defined in such models are rarely observed directly. This in turn means that the likelihood of the observed data is intractable in the sense that it is very computationally expensive to obtain. Although data-augmented Markov chain Monte Carlo (MCMC) methods provide a solution to this problem, employing a tractable augmented likelihood, such methods typically deteriorate in large populations due to poor mixing and increased computation time. Here, we describe a new approach that seeks to approximate the likelihood by exploiting the underlying structure of the epidemic model. Simulation study results show that this approach can be a serious competitor to data-augmented MCMC methods. Our approach can be applied to a wide variety of disease transmission models, and we provide examples with applications to the common cold, Ebola, and foot-and-mouth disease.

Modelling, Bayesian inference, and model assessment for nosocomial pathogens using whole-genome-sequence data.

Whole-genome sequencing of pathogens in outbreaks of infectious disease provides the potential to reconstruct transmission pathways and enhance the information contained in conventional epidemiological data. In recent years, there have been numerous new methods and models developed to exploit such high-resolution genetic data. However, corresponding methods for model assessment have been largely overlooked. In this article, we develop both new modelling methods and new model assessment methods, specifically by building on the work of Worby et al. Although the methods are generic in nature, we focus specifically on nosocomial pathogens and analyze a dataset collected during an outbreak of MRSA in a hospital setting.

A Statistical View on Calcium Oscillations.

Transient rises and falls of the intracellular calcium concentration have been observed in numerous cell types and under a plethora of conditions. There is now a growing body of evidence that these whole-cell calcium oscillations are stochastic, which poses a significant challenge for modelling. In this review, we take a closer look at recently developed statistical approaches to calcium oscillations. These models describe the timing of whole-cell calcium spikes, yet their parametrisations reflect subcellular processes. We show how non-stationary calcium spike sequences, which e.g. occur during slow depletion of intracellular calcium stores or in the presence of time-dependent stimulation, can be analysed with the help of so-called intensity functions. By utilising Bayesian concepts, we demonstrate how values of key parameters of the statistical model can be inferred from single cell calcium spike sequences and illustrate what information whole-cell statistical models can provide about the subcellular mechanistic processes that drive calcium oscillations. In particular, we find that the interspike interval distribution of HEK293 cells under constant stimulation is captured by a Gamma distribution.

Reconstructing promoter activity from Lux bioluminescent reporters.

The bacterial Lux system is used as a gene expression reporter. It is fast, sensitive and non-destructive, enabling high frequency measurements. Originally developed for bacterial cells, it has also been adapted for eukaryotic cells, and can be used for whole cell biosensors, or in real time with live animals without the need for euthanasia. However, correct interpretation of bioluminescent data is limited: the bioluminescence is different from gene expression because of nonlinear molecular and enzyme dynamics of the Lux system. We have developed a computational approach that, for the first time, allows users of Lux assays to infer gene transcription levels from the light output. This approach is based upon a new mathematical model for Lux activity, that includes the actions of LuxAB, LuxEC and Fre, with improved mechanisms for all reactions, as well as synthesis and turn-over of Lux proteins. The model is calibrated with new experimental data for the LuxAB and Fre reactions from Photorhabdus luminescens-the source of modern Lux reporters-while literature data has been used for LuxEC. Importantly, the data show clear evidence for previously unreported product inhibition for the LuxAB reaction. Model simulations show that predicted bioluminescent profiles can be very different from changes in gene expression, with transient peaks of light output, very similar to light output seen in some experimental data sets. By incorporating the calibrated model into a Bayesian inference scheme, we can reverse engineer promoter activity from the bioluminescence. We show examples where a decrease in bioluminescence would be better interpreted as a switching off of the promoter, or where an increase in bioluminescence would be better interpreted as a longer period of gene expression. This approach could benefit all users of Lux technology.

Neuroimaging Biomarkers Predict Brain Structural Connectivity Change in a Mouse Model of Vascular Cognitive Impairment.

BACKGROUND AND PURPOSE: Chronic hypoperfusion in the mouse brain has been suggested to mimic aspects of vascular cognitive impairment, such as white matter damage. Although this model has attracted attention, our group has struggled to generate a reliable cognitive and pathological phenotype. This study aimed to identify neuroimaging biomarkers of brain pathology in aged, more severely hypoperfused mice. METHODS: We used magnetic resonance imaging to characterize brain degeneration in mice hypoperfused by refining the surgical procedure to use the smallest reported diameter microcoils (160 µm). RESULTS: Acute cerebral blood flow decreases were observed in the hypoperfused group that recovered over 1 month and coincided with arterial remodeling. Increasing hypoperfusion resulted in a reduction in spatial learning abilities in the water maze that has not been previously reported. We were unable to observe severe white matter damage with histology, but a novel approach to analyze diffusion tensor imaging data, graph theory, revealed substantial reorganization of the hypoperfused brain network. A logistic regression model from the data revealed that 3 network parameters were particularly efficient at predicting group membership (global and local efficiency and degrees), and clustering coefficient was correlated with performance in the water maze. CONCLUSIONS: Overall, these findings suggest that, despite the autoregulatory abilities of the mouse brain to compensate for a sudden decrease in blood flow, there is evidence of change in the brain networks that can be used as neuroimaging biomarkers to predict outcome.

Bayesian non-parametric inference for stochastic epidemic models using Gaussian Processes.

This paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.

Evidence for Community Transmission of Community-Associated but Not Health-Care-Associated Methicillin-Resistant Staphylococcus Aureus Strains Linked to Social and Material Deprivation: Spatial Analysis of Cross-sectional Data.

BACKGROUND: Identifying and tackling the social determinants of infectious diseases has become a public health priority following the recognition that individuals with lower socioeconomic status are disproportionately affected by infectious diseases. In many parts of the world, epidemiologically and genotypically defined community-associated (CA) methicillin-resistant Staphylococcus aureus (MRSA) strains have emerged to become frequent causes of hospital infection. The aim of this study was to use spatial models with adjustment for area-level hospital attendance to determine the transmission niche of genotypically defined CA- and health-care-associated (HA)-MRSA strains across a diverse region of South East London and to explore a potential link between MRSA carriage and markers of social and material deprivation. METHODS AND FINDINGS: This study involved spatial analysis of cross-sectional data linked with all MRSA isolates identified by three National Health Service (NHS) microbiology laboratories between 1 November 2011 and 29 February 2012. The cohort of hospital-based NHS microbiology diagnostic services serves 867,254 usual residents in the Lambeth, Southwark, and Lewisham boroughs in South East London, United Kingdom (UK). Isolates were classified as HA- or CA-MRSA based on whole genome sequencing. All MRSA cases identified over 4 mo within the three-borough catchment area (n = 471) were mapped to small geographies and linked to area-level aggregated socioeconomic and demographic data. Disease mapping and ecological regression models were used to infer the most likely transmission niches for each MRSA genetic classification and to describe the spatial epidemiology of MRSA in relation to social determinants. Specifically, we aimed to identify demographic and socioeconomic population traits that explain cross-area extra variation in HA- and CA-MRSA relative risks following adjustment for hospital attendance data. We explored the potential for associations with the English Indices of Deprivation 2010 (including the Index of Multiple Deprivation and several deprivation domains and subdomains) and the 2011 England and Wales census demographic and socioeconomic indicators (including numbers of households by deprivation dimension) and indicators of population health. Both CA-and HA-MRSA were associated with household deprivation (CA-MRSA relative risk [RR]: 1.72 [1.03-2.94]; HA-MRSA RR: 1.57 [1.06-2.33]), which was correlated with hospital attendance (Pearson correlation coefficient [PCC] = 0.76). HA-MRSA was also associated with poor health (RR: 1.10 [1.01-1.19]) and residence in communal care homes (RR: 1.24 [1.12-1.37]), whereas CA-MRSA was linked with household overcrowding (RR: 1.58 [1.04-2.41]) and wider barriers, which represent a combined score for household overcrowding, low income, and homelessness (RR: 1.76 [1.16-2.70]). CA-MRSA was also associated with recent immigration to the UK (RR: 1.77 [1.19-2.66]). For the area-level variation in RR for CA-MRSA, 28.67% was attributable to the spatial arrangement of target geographies, compared with only 0.09% for HA-MRSA. An advantage to our study is that it provided a representative sample of usual residents receiving care in the catchment areas. A limitation is that relationships apparent in aggregated data analyses cannot be assumed to operate at the individual level. CONCLUSIONS: There was no evidence of community transmission of HA-MRSA strains, implying that HA-MRSA cases identified in the community originate from the hospital reservoir and are maintained by frequent attendance at health care facilities. In contrast, there was a high risk of CA-MRSA in deprived areas linked with overcrowding, homelessness, low income, and recent immigration to the UK, which was not explainable by health care exposure. Furthermore, areas adjacent to these deprived areas were themselves at greater risk of CA-MRSA, indicating community transmission of CA-MRSA. This ongoing community transmission could lead to CA-MRSA becoming the dominant strain types carried by patients admitted to hospital, particularly if successful hospital-based MRSA infection control programmes are maintained. These results suggest that community infection control programmes targeting transmission of CA-MRSA will be required to control MRSA in both the community and hospital. These epidemiological changes will also have implications for effectiveness of risk-factor-based hospital admission MRSA screening programmes.

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms.

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.

Clustering of antimicrobial resistance outbreaks across bacterial species in the intensive care unit.

BACKGROUND: There are frequent reports of intensive care unit (ICU) outbreaks due to transmission of particular antibiotic-resistant bacteria. Less is known about the burden of outbreaks of resistance due to horizontal transfer of mobile genetic elements between species. Moreover, the potential of existing statistical software as a preliminary means for detecting such events has never been assessed. This study uses a software package to determine the burden of species and resistance outbreaks in 2 adjacent ICUs and to look for evidence of clustering of resistance outbreaks consistent with interspecies transmission of resistance elements. METHODS: A retrospective analysis of data from 2 adjacent 15-bed adult ICUs between 2002 and 2009 was undertaken. Detection of bacterial species-groups and resistance outbreaks was conducted using SaTScan and WHONet-SaTScan software. Resampling and permutation methods were applied to investigate temporal clustering of outbreaks. RESULTS: Outbreaks occurred for 69% of bacterial species-groups (18/26), and resistance outbreaks were detected against 63% of antibiotics (10/16). Resistance outbreaks against 7 of 10 antibiotics were observed in multiple species-groups simultaneously and there was evidence of inter-species-group dependence for 4 of 7 antibiotics; background temporal changes in resistance did not explain the temporal aggregation of outbreaks in 3 of 7 antibiotics. CONCLUSIONS: Species outbreaks occurred for the majority of bacteria commonly identified in the ICU. There was evidence for frequent temporal clustering of resistance outbreaks consistent with interspecies transmission of resistance elements. Wider application of outbreak detection software combined with targeted sequencing of bacterial genomes is needed to understand the contribution of interspecies gene transfer to resistance emergence.

Estimating the effectiveness of isolation and decolonization measures in reducing transmission of methicillin-resistant Staphylococcus aureus in hospital general wards.

Infection control for hospital pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) often takes the form of a package of interventions, including the use of patient isolation and decolonization treatment. Such interventions, though widely used, have generated controversy because of their significant resource implications and the lack of robust evidence with regard to their effectiveness at reducing transmission. The aim of this study was to estimate the effectiveness of isolation and decolonization measures in reducing MRSA transmission in hospital general wards. Prospectively collected MRSA surveillance data from 10 general wards at Guy's and St. Thomas' hospitals, London, United Kingdom, in 2006-2007 were used, comprising 14,035 patient episodes. Data were analyzed with a Markov chain Monte Carlo algorithm to model transmission dynamics. The combined effect of isolation and decolonization was estimated to reduce transmission by 64% (95% confidence interval: 37, 79). Undetected MRSA-positive patients were estimated to be the source of 75% (95% confidence interval: 67, 86) of total transmission events. Isolation measures combined with decolonization treatment were strongly associated with a reduction in MRSA transmission in hospital general wards. These findings provide support for active methods of MRSA control, but further research is needed to determine the relative importance of isolation and decolonization in preventing transmission.

Quantifying type-specific reproduction numbers for nosocomial pathogens: evidence for heightened transmission of an Asian sequence type 239 MRSA clone.

An important determinant of a pathogen's success is the rate at which it is transmitted from infected to susceptible hosts. Although there are anecdotal reports that methicillin-resistant Staphylococcus aureus (MRSA) clones vary in their transmissibility in hospital settings, attempts to quantify such variation are lacking for common subtypes, as are methods for addressing this question using routinely-collected MRSA screening data in endemic settings. Here we present a method to quantify the time-varying transmissibility of different subtypes of common bacterial nosocomial pathogens using routine surveillance data. The method adapts approaches for estimating reproduction numbers based on the probabilistic reconstruction of epidemic trees, but uses relative hazards rather than serial intervals to assign probabilities to different sources for observed transmission events. The method is applied to data collected as part of a retrospective observational study of a concurrent MRSA outbreak in the United Kingdom with dominant endemic MRSA clones (ST22 and ST36) and an Asian ST239 MRSA strain (ST239-TW) in two linked adult intensive care units, and compared with an approach based on a fully parametric transmission model. The results provide support for the hypothesis that the clones responded differently to an infection control measure based on the use of topical antiseptics, which was more effective at reducing transmission of endemic clones. They also suggest that in one of the two ICUs patients colonized or infected with the ST239-TW MRSA clone had consistently higher risks of transmitting MRSA to patients free of MRSA. These findings represent some of the first quantitative evidence of enhanced transmissibility of a pandemic MRSA lineage, and highlight the potential value of tailoring hospital infection control measures to specific pathogen subtypes.

A linear mixed model approach to compare the evolution of multiple biological rhythms.

The assessment and comparison of multiple biological rhythms represent an important challenge in chronobiology. They allow the investigation of whether a well-defined time-qualified relationship between biorhythms of the same frequency range is maintained in the presence of functional alterations, which may lead to chronodisruption or internal desynchronization. We propose a multivariate linear mixed model approach where functions of several biorhythms are jointly modeled in a multivariate longitudinal fashion, handling both the correlation between biorhythms of multiple outcomes and the correlation between measurements collected over time within the same biological entity. Furthermore, between-subject heterogeneity is also taken into account with the inclusion of random effects. Pairwise comparisons between biorhythms are performed by means of proper contrasts. As an example, we define contrasts which allow us testing whether or not two biorhythms are identical or opposing, providing additional support in clinical practice. Moreover, we illustrate the proposed method using both simulated and biological real data, concerning the comparison of three specific lymphocytes profiles which modulate the function of immune system between healthy subjects and non-small lung cancer patients. Finally, the corresponding SAS syntax is provided.

An association between bacterial genotype combined with a high-vancomycin minimum inhibitory concentration and risk of endocarditis in methicillin-resistant Staphylococcus aureus bloodstream infection.

INTRODUCTION: Antimicrobial resistance and bacterial virulence factors may increase the risk of hematogenous complications during methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infection (BSI). This study reports on the impact of increasing vancomycin minimum inhibitory concentrations (V-MICs) and MRSA clone type on risk of hematogenous complications from MRSA BSI during implementation of an effective MRSA control program. METHODS: In sum, spa typing, staphylococcal cassette chromosome mec allotyping, and vancomycin and teicoplanin MICs were performed on 821 consecutive MRSA bloodstream isolates from 1999 to 2009. Prospectively collected data, including focus of infection, were available for 695 clinically significant cases. Logistic and multinomial logistic regression was used to determine the association between clone type, vancomycin MIC (V-MIC), and focus of infection. RESULTS: MRSA BSIs decreased by ∼90% during the 11 years. Typing placed isolates into 3 clonal complex (CC) groups that had different population median V-MICs (CC30, 0.5 µg/mL [n = 349]; CC22, 0.75 µg/mL [n = 272]; non-CC22/30, 1.5 µg/mL [n = 199]). There was a progressive increase in the proportion of isolates with a V-MIC above baseline median in each clonal group and a disproportionate fall in the clone group with lowest median V-MIC (CC30). In contrast, there were no increases in teicoplanin MICs. High V-MIC CC22 isolates (1.5-2 µg/mL) were strongly associated with endocarditis (odds ratio, 12; 95% confidence interval, 3.72-38.9) and with a septic metastasis after catheter-related BSI (odds ratio, 106; 95% confidence interval, 12.6-883) compared with other clone type/V-MIC combinations. CONCLUSIONS: An interaction between clone type and V-MIC can influence the risk of endocarditis associated with MRSA BSI, implying involvement of both therapeutic and host-pathogen factors.

Antimicrobial resistance in dairy slurry tanks: A critical point for measurement and control.

Waste from dairy production is one of the largest sources of contamination from antimicrobial resistant bacteria (ARB) and genes (ARGs) in many parts of the world. However, studies to date do not provide necessary evidence to inform antimicrobial resistance (AMR) countermeasures. We undertook a detailed, interdisciplinary, longitudinal analysis of dairy slurry waste. The slurry contained a population of ARB and ARGs, with resistances to current, historical and never-used on-farm antibiotics; resistances were associated with Gram-negative and Gram-positive bacteria and mobile elements (ISEcp1, Tn916, Tn21-family transposons). Modelling and experimental work suggested that these populations are in dynamic equilibrium, with microbial death balanced by fresh input. Consequently, storing slurry without further waste input for at least 60 days was predicted to reduce ARB spread onto land, with > 99 % reduction in cephalosporin resistant Escherichia coli. The model also indicated that for farms with low antibiotic use, further reductions are unlikely to reduce AMR further. We conclude that the slurry tank is a critical point for measurement and control of AMR, and that actions to limit the spread of AMR from dairy waste should combine responsible antibiotic use, including low total quantity, avoidance of human critical antibiotics, and choosing antibiotics with shorter half-lives, coupled with appropriate slurry storage.

Predator size affects the intensity of mutual interference in a predatory mirid.

Interference competition occurs when access to an available resource is negatively affected by interactions with other individuals, where mutual interference involves individuals of the same species. The interactive phenomena among individuals may be size-dependent, since body size is a major factor that may alter prey consumption rates and ultimately the dynamics and structure of food webs.A study was initiated in order to evaluate the effect of mutual interference in the prey-specific attack rates and handling times of same size class predators, incorporating variation in consumer size. For this purpose, laboratory functional response experiments were conducted using same age predators, that is, newly hatched (first instar) or mature (fifth instar) nymphs of the polyphagous mirid predator Macrolophus pygmaeus preying on Ephestia kuehniella (Lepidoptera: Pyralidae) eggs.The experiments involved four predator density treatments, that is, one, two, three, or four predators of same age, that is, either first- or fifth-instar nymphs, which were exposed to several prey densities. The Crowley-Martin model, which allows for interference competition between foraging predators, was used to fit the data.The results showed that mutual interference between predator's nymphs may occur that affect their foraging efficiency. The values of the attack rate coefficient were dependent on the predator density and for the first-instar nymphs were significantly lower at the highest predator density than the lower predator densities, whereas for the fifth-instar nymphs in all density treatments were significantly lower to that of the individual foragers' ones.These results indicate that mutual interference is more intense for larger predators and is more obvious at low prey densities where the competition level is higher. The wider use of predator-dependent functional response models will help toward a mechanistic understanding of intraspecific interactions and its consequences on the stability and structure of food webs.

The spatial percept of tinnitus is associated with hearing asymmetry: Subgroup comparisons.

The spatial percept of tinnitus is hypothesized as an important variable for tinnitus subtyping. Hearing asymmetry often associates with tinnitus laterality, but not always. One of the methodological limitations for cross-study comparisons is how the variables for hearing asymmetry and tinnitus spatial perception are defined. In this study, data from two independent datasets were combined (n=833 adults, age ranging from 20 to 91 years, 404 males, 429 females) to investigate characteristics of subgroups with different tinnitus spatial perception focusing on hearing asymmetry. Three principle findings emerged. First, a hearing asymmetry variable emphasizing the maximum interaural difference most strongly discriminated unilateral from bilateral tinnitus. Merging lateralized bilateral tinnitus (perceived in both ears but worse in one side) with unilateral tinnitus weakened this relationship. Second, there was an association between unilateral tinnitus and ipsilateral asymmetric hearing. Third, unilateral and bilateral tinnitus were phenotypically distinct, with unilateral tinnitus being characterized by older age, asymmetric hearing, more often wearing one hearing aid, older age at tinnitus onset, shorter tinnitus duration, and higher percentage of time being annoyed by tinnitus. We recommend that careful consideration is given to the definitions of hearing asymmetry and tinnitus spatial perception in order to improve the comparability of findings across studies.

Assessing the role of undetected colonization and isolation precautions in reducing methicillin-resistant Staphylococcus aureus transmission in intensive care units.

BACKGROUND: Screening and isolation are central components of hospital methicillin-resistant Staphylococcus aureus (MRSA) control policies. Their prevention of patient-to-patient spread depends on minimizing undetected and unisolated MRSA-positive patient days. Estimating these MRSA-positive patient days and the reduction in transmission due to isolation presents a major methodological challenge, but is essential for assessing both the value of existing control policies and the potential benefit of new rapid MRSA detection technologies. Recent methodological developments have made it possible to estimate these quantities using routine surveillance data. METHODS: Colonization data from admission and weekly nares cultures were collected from eight single-bed adult intensive care units (ICUs) over 17 months. Detected MRSA-positive patients were isolated using single rooms and barrier precautions. Data were analyzed using stochastic transmission models and model fitting was performed within a Bayesian framework using a Markov chain Monte Carlo algorithm, imputing unobserved MRSA carriage events. RESULTS: Models estimated the mean percent of colonized-patient-days attributed to undetected carriers as 14.1% (95% CI (11.7, 16.5)) averaged across ICUs. The percent of colonized-patient-days attributed to patients awaiting results averaged 7.8% (6.2, 9.2). Overall, the ratio of estimated transmission rates from unisolated MRSA-positive patients and those under barrier precautions was 1.34 (0.45, 3.97), but varied widely across ICUs. CONCLUSIONS: Screening consistently detected >80% of colonized-patient-days. Estimates of the effectiveness of barrier precautions showed considerable uncertainty, but in all units except burns/general surgery and one cardiac surgery ICU, the best estimates were consistent with reductions in transmission associated with barrier precautions.