Ectomycorrhizal fungal communities of native and non-native Pinus and Quercus species in a common garden of 35-year-old trees

Non-native tree species have been widely planted or have become naturalized in most forested landscapes. It is not clear if native trees species collectively differ in ectomycorrhizal fungal (EMF) diversity and communities from that of non-native tree species. Alternatively, EMF species community similarity may be more determined by host plant phylogeny than by whether the plant is native or non-native. We examined these unknowns by comparing two genera, native and non-native Quercus robur and Quercus rubra and native and non-native Pinus sylvestris and Pinus nigra in a 35-year-old common garden in Poland. Using molecular and morphological approaches, we identified EMF species from ectomycorrhizal root tips and sporocarps collected in the monoculture tree plots. A total of 69 EMF species were found, with 38 species collected only as sporocarps, 18 only as ectomycorrhizas, and 13 both as ectomycorrhizas and sporocarps. The EMF species observed were all native and commonly associated with a Holarctic range in distribution. We found that native Q. robur had ca. 120% higher total EMF species richness than the non-native Q. rubra, while native P. sylvestris had ca. 25% lower total EMF species richness than non-native P. nigra. Thus, across genera, there was no evidence that native species have higher EMF species diversity than exotic species. In addition, we found a higher similarity in EMF communities between the two Pinus species than between the two Quercus species. These results support the naturalization of non-native trees by means of mutualistic associations with cosmopolitan and novel fungi

Prognostic Factors in Patients Hospitalized for Heart Failure

Each year, there are over one million hospitalizations for heart failure in the United States, with a similar number in Western Europe. Although these patients respond to initial therapies, they have very high short and intermediate term (2-6 months) mortality and readmission rates, while the healthcare system incurs substantial costs. Several risk prediction models that can accurately identify high-risk patients have been developed using data from clinical trials, large registries or administrative databases. Use of multi-variable risk models at the time of hospital admission or discharge offers better risk stratification and should be encouraged, as it allows for appropriate allocation of existing resources and development of clinical trials testing new treatment strategies for patients admitted with heart failure

Psychometric Properties of the Posttraumatic Stress Checklist among Young African-American Men and Women

Research has demonstrated the validity and reliability of the Posttraumatic Stress Checklist (PCL) in predominantly Caucasian samples. However, there has not been a study that examined the psychometric properties of the PCL specifically for African Americans. The present paper is an examination of the factor structure, internal stability, reliability, and predictive validity of the PCL among a sample of young African American men and women. Confirmatory factor analysis indicated better support for a two-factor model than for a three-factor model reflecting the three diagnostic symptom clusters of posttraumatic stress disorder. High internal consistency and marginal test–retest reliability were observed. The positive predictive power of the PCL in the present study was far lower than that observed in previous studies; several potential explanations for this finding are discussed