Scholarly interest in workaholism has increased dramatically in recent years. This research has underscored the detrimental effects of workaholism for employees, their families, and the organizations ...that employ them. Despite drastic improvements in the quality of studies examining workaholism over the past several decades, researchers continue to almost exclusively rely on older measures of workaholism or new measures derived from these original measures. In the present study, we outline why a new measure is needed and propose a multidimensional conceptualization of workaholism that encompasses motivational, cognitive, emotional, and behavioral dimensions. We then develop and validate a new multidimensional measure of workaholism: the Multidimensional Workaholism Scale (MWS). Evidence from 5 samples representing individuals working in a wide variety of occupations and industries throughout the United States (total N = 1,252) provides support that (a) our proposed 4-factor structure replicates and fits better than alternative models; (b) the measure demonstrates high reliability and content validity; (c) the measure demonstrates evidence for convergent and discriminant validity with constructs in workaholism's nomological network; (d) the measure demonstrates incremental validity in the prediction of important outcomes over and above prior measures of workaholism; and (e) the different dimensions demonstrate incremental validity in the prediction of specific outcomes over and above other dimensions of the MWS. Overall, results from the present study suggest that the MWS is a reliable and valid measure that can advance a more nuanced approach to research and practice relating to workaholism.
Host Specificity in Variable Environments Wells, Konstans; Clark, Nicholas J.
Trends in parasitology,
June 2019, 2019-06-00, 20190601, Volume:
35, Issue:
6
Journal Article
Peer reviewed
Open access
Host specificity encompasses the range and diversity of host species that a parasite is capable of infecting and is considered a crucial measure of a parasite’s potential to shift hosts and trigger ...disease emergence. Yet empirical studies rarely consider that regional observations only reflect a parasite’s ‘realized’ host range under particular conditions: the true ‘fundamental’ range of host specificity is typically not approached. We provide an overview of challenges and directions in modelling host specificity under variable environmental conditions. Combining tractable modelling frameworks with multiple data sources that account for the strong interplay between a parasite's evolutionary history, transmission mode, and environmental filters that shape host–parasite interactions will improve efforts to quantify emerging disease risk in times of global change.
Increasing interest in emerging infectious diseases and parasite spillover coincides with a rise in studies reporting and comparing host specificity for a multihost parasite. Intuitively, higher host specificity means less spillover risk but, to date, a systematic consensus on such a relationship is lacking.
Host specificity can vary in space and time due to changing compositions of potential host species and constraints in environmental conditions. Ecoevolutionary dynamics and contemporary conditions synergistically determine ‘realized’ host specificity across regional scales.
Modelling advances to capture spatiotemporal variation in the distributions and biotic interactions of species provide the basis to quantify variation in realized host specificity and progress towards determining how this relates to spillover risk.
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•Global diversity patterns for avian haemosporidians is determined.•A methodical review of molecular avian haemosporidian research was conducted.•Lineage richness for avian ...haemosporidians increases in tropical ‘hotspots’.•Biogeographic patterns differ for the two parasite genera.•Methodological biases may hinder estimations of parasite diversity.
Biogeographic patterns of parasite diversity are useful for determining how host–parasite interactions can influence speciation. However, variation in methodologies and sampling effort can skew diversity estimates. Avian haemosporidians are vector-transmitted blood parasites represented by over 1300 unique genetic lineages spread across over 40 countries. We used a global database of lineage distributions for two avian haemosporidian genera, Plasmodium and Haemoproteus, to test for congruence of diversity among haemosporidians and their avian hosts across 13 geographic regions. We demonstrated that avian haemosporidians exhibit similar diversity patterns to their avian hosts; however, specific patterns differ between genera. Haemoproteus spp. diversity estimates were significantly higher than those of Plasmodium spp. in all areas where the genera co-occurred, apart from the Plasmodium spp.-rich region of South America. The geographic distributions of parasite genera also differed, with Haemoproteus spp. absent from the majority of oceanic regions while Plasmodium spp. were cosmopolitan. These findings suggest fundamental differences in the way avian haemosporidians diverge and colonise new communities. Nevertheless, a review of the literature suggests that accurate estimates of avian haemosporidian diversity patterns are limited by (i) a concentration of sampling towards passerines from Europe and North America, (ii) a frequent failure to include microscopic techniques together with molecular screening and (iii) a paucity of studies investigating distributions across vector hosts.
Tick paralysis resulting from bites from Ixodes holocyclus and I. cornuatus is one of the leading causes of emergency veterinary admissions for companion animals in Australia, often resulting in ...death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviors that reduce exposures to ticks and improve awareness of owners for the need of lifesaving preventative ectoparasite treatment. Improved awareness of clinicians and pet owners about temporal changes in tick paralysis risk can be assisted by ecological forecasting frameworks that integrate environmental information into statistical time series models. Using an 11-year time series of tick paralysis cases from veterinary clinics in one of Australia's hotspots for the paralysis tick Ixodes holocyclus, we asked whether an ensemble model could accurately forecast clinical caseloads over near-term horizons. We fit a series of statistical time series (ARIMA, GARCH) and generative models (Prophet, Generalised Additive Model) using environmental variables as predictors, and then combined forecasts into a weighted ensemble to minimise prediction interval error. Our results indicate that variables related to temperature anomalies, levels of vegetation moisture and the Southern Oscillation Index can be useful for predicting tick paralysis admissions. Our model forecasted tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability, outperforming a field-leading benchmark Exponential Smoothing model by reducing both point and prediction interval errors. Using online particle filtering to assimilate new observations and adjust forecast distributions when new data became available, our model adapted to changing temporal conditions and provided further reduced forecast errors. We expect our model pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.
The management of the COVID-19 pandemic is hampered by long delays associated with centralised laboratory PCR testing. In hospitals, these delays lead to poor patient flow and nosocomial ...transmission. Rapid, accurate tests are therefore urgently needed in preparation for the next wave of the pandemic.
We did a prospective, interventional, non-randomised, controlled study of molecular point-of-care testing in patients aged 18 years or older presenting with suspected COVID-19 to the emergency department or other acute areas of Southampton General Hospital during the first wave of the pandemic in the UK. Nose and throat swab samples taken at admission from patients in the point-of-care testing group were tested with the QIAstat-Dx Respiratory SARS-CoV-2 Panel. Samples taken from patients in a contemporaneous control group were tested by laboratory PCR. The primary outcome was time to results in the full cohort. This study is registered with ISRCTN (ISRCTN14966673) and is completed.
Between March 20 and April 29, 2020, 517 patients were assessed for eligibility, of whom 499 were recruited to the point-of-care testing group and tested by the QIAstat-Dx Respiratory SARS-CoV-2 Panel. 555 contemporaneously identified patients were included in the control group and tested by laboratory PCR. The two groups were similar with regard to the distribution of sex, age, and ethnicity. 197 (39%) patients in the point-of-care testing group and 155 (28%) in the control group tested positive for COVID-19 (difference 11·5% 95% CI 5·8-17·2, p=0·0001). Median time to results was 1·7 h (IQR 1·6-1·9) in the point-of-care testing group and 21·3 h (16·0-27·9) in the control group (difference 19·6 h 19·0-20·3, p<0·0001). A Cox proportional hazards regression model controlling for age, sex, time of presentation, and severity of illness also showed that time to results was significantly shorter in the point-of-care testing group than in the control group (hazard ratio 4023 95% CI 545-29 696, p<0·0001).
Point-of-care testing is associated with large reductions in time to results and could lead to improvements in infection control measures and patient flow compared with centralised laboratory PCR testing.
University Hospitals Southampton NHS Foundation Trust.
Inferring interactions between co-occurring species is key to identify processes governing community assembly. Incorporating interspecific interactions in predictive models is common in ecology, yet ...most methods do not adequately account for indirect interactions (where an interaction between two species is masked by their shared interactions with a third) and assume interactions do not vary along environmental gradients. Markov random fields (MRF) overcome these limitations by estimating interspecific interactions, while controlling for indirect interactions, from multispecies occurrence data. We illustrate the utility of MRFs for ecologists interested in interspecific interactions, and demonstrate how covariates can be included (a set of models known as Conditional Random Fields, CRF) to infer how interactions vary along environmental gradients. We apply CRFs to two data sets of presence–absence data. The first illustrates how blood parasite (Haemoproteus, Plasmodium, and nematode microfilaria spp.) co-infection probabilities covary with relative abundance of their avian hosts. The second shows that co-occurrences between mosquito larvae and predatory insects vary along water temperature gradients. Other applications are discussed, including the potential to identify replacement or shifting impacts of highly connected species along climate or land-use gradients. We provide tools for building CRFs and plotting/interpreting results as an R package.
Aim: Characterizing macroecological patterns in biodiversity is key to improve our understanding of community assembly. Global biodiversity for many taxa follows a latitudinal gradient, with ...increased diversity in tropical latitudes. Less is known about global parasite biodiversity, inhibiting our ability to predict how global change will impact parasitic disease emergence. Using distribution and phylogenetic data for 2,386 avian haemosporidian blood parasites (genera Plasmodium, Haemoproteus and Leucocytozoon), I assessed how contemporary and historical drivers influence the composition of parasite communities worldwide. Location: Global. Time period: Current. Major taxa studied: Haemosporidian blood parasites. Methods: Parasite distribution and cytochrome b sequence data were accessed from an opensource database. Bayesian phylogenetic tree distributions were constructed for each parasite genus using two substitution models to capture uncertainty. Hierarchical regressions assessed effects of environmental variation, latitude and phylogenetic β-diversity (βdiv; a proxy for phylogenetic uniqueness) on the diversity and asymmetry of parasite communities around the globe. Results: I uncovered biodiversity hotspots and identified broad variation in global diversity patterns among parasite genera. Community diversity increased with increasing phylogenetic uniqueness for all three parasite genera; however, these diverse and unique regions did not consistently occur in the tropics. I found no evidence of a latitudinal diversity gradient, and support for a latitudinal gradient in community phylogenetic asymmetry was weak. Main conclusions: Global variation in avian haemosporidian phylogenetic diversity does not reflect a latitudinal gradient. Instead, parasite biogeography may reflect fundamental differences in hostswitching tendencies or the timing of avian evolutionary radiations. Examining the interplay between shared evolutionary history and phylogenetic diversity can provide important insights into the drivers of parasite biodiversity at global scales.
Spectral matching of MS
fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS
fragmentation ...properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .
Q fever is a zoonotic disease caused by Coxiella burnetii. This bacterium survives harsh conditions and attaches to dust, suggesting environmental dispersal is a risk factor for outbreaks. Spatial ...epidemiology studies collating evidence on Q fever geographical contamination gradients are needed, as human cases without occupational exposure are increasing worldwide.
We used a systematic literature search to assess the role of distance from ruminant holdings as a risk factor for human Q fever outbreaks. We also collated evidence for other putative drivers of C. burnetii geographical dispersal.
In all documented outbreaks, infective sheep or goats, not cattle, was the likely source. Evidence suggests a prominent role of airborne dispersal; Coxiella burnetii travels up to 18 km on gale force winds. In rural areas, highest infection risk occurs within 5 km of sources. Urban outbreaks generally occur over smaller distances, though evidence on attack rate gradients is limited. Wind speed / direction, spreading of animal products, and stocking density may all contribute to C. burnetii environmental gradients.
Q fever environmental gradients depend on urbanization level, ruminant species, stocking density and wind speed. While more research is needed, evidence suggests that residential exclusion zones around holdings may be inadequate to contain this zoonotic disease, and should be species-specific.
Generalised additive models (GAMs) are increasingly popular tools for estimating smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology ...for representing hierarchical functions for discrete responses that encompass complex features including zero inflation, truncation and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data.
We introduce dynamic generalised additive models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are especially useful for analysing multiple series, as they can estimate hierarchical smooth functions while learning complex temporal associations via dimension‐reduced latent factor processes. We implement our models in the mvgam R package, which estimates unobserved parameters for smoothing splines and latent temporal processes in a probabilistic framework.
Using simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real‐world case study to highlight some of mvgam's key features, which include functions for calculating correlations among series' latent trends, performing model selection using rolling window forecasts and posterior predictive checks, online data augmentation via a recursive particle filter and visualising probabilistic uncertainties for smooth functions and predictions.
Dynamic GAMs (DGAMs) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology.
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