High Variability in Outcomes of Two-Stage Exchange to Treat Periprosthetic Joint Infection

Introduction: Periprosthetic joint infection (PJI) is a challenging condition to manage with sobering morbidity and mortality.1,2 Treatment options range from simple irrigation and debridement with prosthetic retention to explantation and placement of a temporary cement spacer. Indictations for each option are unclear and non-uniform despite signi­cant efforts to understand the management outcomes. Until recently, a uniform de­nition of success was unavailable, thus clouding the discussion of treatment options. Two-stage exchange is currently considered the “gold-standard” in North America, yet an appropriate understanding of the actual success and ancillary effects of treatment is needed. With the advantage of an expert opinion de­ning success, this study was designed to understand the status of the current literature and the guidance it provides regarding two-stage exchange arthroplasty

Demographic trade-offs predict tropical forest dynamics

Understanding tropical forest dynamics and planning for their sustainable management require efficient, yet accurate, predictions of the joint dynamics of hundreds of tree species. With increasing information on tropical tree life histories, our predictive understanding is no longer limited by species data but by the ability of existing models to make use of it. Using a demographic forest model, we show that the basal area and compositional changes during forest succession in a neotropical forest can be accurately predicted by representing tropical tree diversity (hundreds of species) with only five functional groups spanning two essential trade-offs—the growth-survival and stature-recruitment trade-offs. This data-driven modeling framework substantially improves our ability to predict consequences of anthropogenic impacts on tropical forests

Modeling species' distributions to improve conservation in semiurban landscapes: Koala case study

Models of species' distributions are commonly used to inform landscape and conservation planning. In urban and semiurban landscapes, the distributions of species are determined by a combination of natural habitat and anthropogenic impacts. Understanding the spatial influence of these two processes is crucial for making spatially explicit decisions about conservation actions. We present a logistic regression model for the distribution of koalas (Phascolarctos cinereus) in a semiurban landscape in eastern Australia that explicitly separates the effect of natural habitat quality and anthropogenic impacts on koala distributions. We achieved this by comparing the predicted distributions from the model with what the predicted distributions would have been if anthropogenic variables were at their mean values. Similar approaches have relied on making predictions assuming anthropogenic variables are zero, which will be unreliable if the training data set does not include anthropogenic variables close to zero. Our approach is novel because it can be applied to landscapes where anthropogenic variables are never close to zero. Our model showed that, averaged across the study area, natural habitat was the main determinant of koala presence. At a local scale, however, anthropogenic impacts could be more important, with consequent implications for conservation planning. We demonstrated that this modeling approach, combined with the visual presentation of predictions as a map, provides important information for making decisions on how different conservation actions should be spatially allocated. This method is particularly useful for areas where wildlife and human populations exist in close proximity

Spatiotemporal Structure of Molecular Evolution of H5N1 Highly Pathogenic Avian Influenza Viruses in Vietnam

BackgroundVietnam is one of the countries most affected by outbreaks of H5N1 highly pathogenic avian influenza viruses. First identified in Vietnam in poultry in 2001 and in humans in 2004, the virus has since caused 111 cases and 56 deaths in humans. In 2003/2004 H5N1 outbreaks, nearly the entire poultry population of Vietnam was culled. Our earlier study (Wan et al., 2008, PLoS ONE, 3(10): e3462) demonstrated that there have been at least six independent H5N1 introductions into Vietnam and there were nine newly emerged reassortants from 2001 to 2007 in Vietnam. H5N1 viruses in Vietnam cluster distinctly around Hanoi and Ho Chi Minh City. However, the nature of the relationship between genetic divergence and geographic patterns is still unclear.Methodology/Principal FindingsIn this study, we hypothesized that genetic distances between H5N1 viruses in Vietnam are correlated with geographic distances, as the result of distinct population and environment patterns along Vietnam's long north to south longitudinal extent. Based on this hypothesis, we combined spatial statistical methods with genetic analytic techniques and explicitly used geographic space to explore genetic evolution of H5N1 highly pathogenic avian influenza viruses at the sub-national scale in Vietnam. Our dataset consisted of 125 influenza viruses (with whole genome sets) isolated in Vietnam from 2003 to 2007. Our results document the significant effect of space and time on genetic evolution and the rise of two regional centers of genetic mixing by 2007. These findings give insight into processes underlying viral evolution and suggest that genetic differentiation is associated with the distance between concentrations of human and poultry populations around Hanoi and Ho Chi Minh City.Conclusions/SignificanceThe results show that genetic evolution of H5N1 viruses in Vietnamese domestic poultry is highly correlated with the location and spread of those viruses in geographic space. This correlation varies by scale, time, and gene, though a classic isolation by distance pattern is observed. This study is the first to characterize the geographic structure of influenza viral evolution at the sub-national scale in Vietnam and can shed light on how H5N1 HPAIVs evolve in certain geographic settings

Plantation vs. natural forest: Matrix quality determines pollinator abundance in crop fields

In terrestrial ecosystems, ecological processes and patterns within focal patches frequently depend on their matrix. Crop fields (focal patches) are often surrounded by a mosaic of other land-use types (matrix), which may act as habitats for organisms and differ in terms of the immigration activities of organisms to the fields. We examined whether matrix quality affects wild pollinator abundance in crop fields, given that the species (Apis cerana) generally nest in the cavities of natural trees. We examined fields of a pollination-dependent crop surrounded by plantations and natural forests, which comprised the matrix. Our analysis revealed a clear positive effect of the natural forest on the pollinator abundance, but the plantation forest had little effects. These indicate that agricultural patches are influenced by their matrix quality and the resulting crop pollinator abundance, suggesting the importance of matrix management initiatives such as forest restoration surrounding agricultural fields to improve crop production

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Chronicity of sleep problems in children with chronic illness: a longitudinal population-based study

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to examine the chronicity of sleep problems in children with chronic illness, and potential predictors of sleep problems.</p> <p>Methods</p> <p>Using data from a longitudinal total population study in Norway, The Bergen Child Study, data on sleep problems, chronic illness and potential confounders were assessed at ages 79 and 1113.</p> <p>Results</p> <p>295 of 4025 (7.3%) children had a chronic illness, and the prevalence of chronic sleep problems was significantly higher in this group compared to children without chronic illness (6.8% versus 3.6%). Sleep problems at the first wave increased the risk of sleep problems at the second wave, also when adjusting for potential confounders (odds-ratio = 5.41). Hyperactivity and emotional problems were also independent risk factors for later sleep problems.</p> <p>Conclusion</p> <p>These findings call for increased awareness and development of treatment strategies of sleep problems in children with chronic illness.</p

Arousal frequency is associated with increased fatigue in obstructive sleep apnea

Fatigue is an important and often underemphasized symptom in patients with obstructive sleep apnea (OSA). Sleep fragmentation, i.e., arousals and disruptions in sleep architecture, is common in patients with OSA and may potentially contribute to their fatigue. We hypothesized that arousal frequency and changes in sleep architecture contribute to the fatigue experienced by patients with OSA. Seventy-three patients with diagnosed but untreated OSA (AHI ≥ 15) were enrolled in this study. A baseline polysomnogram was obtained, and fatigue was measured with the Multidimensional Fatigue Symptom Inventory-short form (MFSI-sf). We evaluated the association between fatigue and arousals and various polysomongraphic variables, including sleep stages and sleep efficiency. Significant correlations between MFSI-sf subscale scores and various arousal indices were noted. Emotional fatigue scores were associated with total arousal index (r = 0.416, p = .021), respiratory movement arousal index (r = 0.346, p = .025), and spontaneous movement arousal index (r = 0.378, p = .025). Physical fatigue scores were associated with total arousal index (r = 0.360, p = .033) and respiratory movement arousal index (r = 0.304, p = .040). Percent of stage 1 sleep and REM sleep were also associated with physical and emotional fatigue scores. Hierarchal linear regression analysis demonstrated that emotional fatigue scores were independently associated with spontaneous movement arousals after controlling for age, body mass index, depression, and sleep apnea severity. These findings suggest that arousals may contribute to the fatigue seen in patients with OSA

Distribution and Molecular Evolution of Bacillus anthracis Genotypes in Namibia

The recent development of genetic markers for Bacillus anthracis has made it possible to monitor the spread and distribution of this pathogen during and between anthrax outbreaks. In Namibia, anthrax outbreaks occur annually in the Etosha National Park (ENP) and on private game and livestock farms. We genotyped 384 B. anthracis isolates collected between 1983–2010 to identify the possible epidemiological correlations of anthrax outbreaks within and outside the ENP and to analyze genetic relationships between isolates from domestic and wild animals. The isolates came from 20 animal species and from the environment and were genotyped using a 31-marker multi-locus-VNTR-analysis (MLVA) and, in part, by twelve single nucleotide polymorphism (SNP) markers and four single nucleotide repeat (SNR) markers. A total of 37 genotypes (GT) were identified by MLVA, belonging to four SNP-groups. All GTs belonged to the A-branch in the cluster- and SNP-analyses. Thirteen GTs were found only outside the ENP, 18 only within the ENP and 6 both inside and outside. Genetic distances between isolates increased with increasing time between isolations. However, genetic distance between isolates at the beginning and end of the study period was relatively small, indicating that while the majority of GTs were only found sporadically, three genetically close GTs, accounting for more than four fifths of all the ENP isolates, appeared dominant throughout the study period. Genetic distances among isolates were significantly greater for isolates from different host species, but this effect was small, suggesting that while species-specific ecological factors may affect exposure processes, transmission cycles in different host species are still highly interrelated. The MLVA data were further used to establish a model of the probable evolution of GTs within the endemic region of the ENP. SNR-analysis was helpful in correlating an isolate with its source but did not elucidate epidemiological relationships