Freeze stress of deciduous trees induces attacks by opportunistic ambrosia beetles

A broad host range and the utilization of living but weakened trees contribute, in part, to the invasion success of ambrosia beetles (Curculionidae: Scolytinae). The present study assessed the capability of freeze stress to induce attacks by ambrosia beetles. Freeze stress predisposed Cercis canadensis L., Cornus florida L., Malus pumila Mill. and Styrax japonicus Sieb. to attack under field conditions, although no attacks occurred on untreated trees. More attacks occurred on freeze-stressed versus flood-stressed M. pumila in Virginia but not for S. japonicus in Ohio. Attacks on flooded trees were skewed towards the base of the trunk, whereas attacks on freeze-stressed trees mainly occurred around the upper regions of the trunk and into the branches. The predominant species recovered were Anisandrus maiche Stark and Xylosandrus germanus (Blandford) in Ohio, and Xylosandrus crassiusculus (Motschulsky) in Virginia. Ethanol emissions from trunks of S. japonicus were detected by solid phase microextraction-gas chromatography-mass spectrometry at 1 day after imposing freeze stress, peaking 4 days after injury. Trees with an intolerance of freeze stress are predicted to be vulnerable to attack, especially when subjected to mild winter temperatures followed by late-spring freezes. Freeze stress could thereby facilitate the destructiveness of exotic ambrosia beetles.Floriculture and Nursery Research Initiative (USDA-FNRI); Horticultural Research Institute (HRI); USDA-ARS National Program 304-Crop Protection and Quarantine [3607-22 000-012-00D]This research was supported by the Floriculture and Nursery Research Initiative (USDA-FNRI), Horticultural Research Institute (HRI) and USDA-ARS National Program 304-Crop Protection and Quarantine (Project 3607-22 000-012-00D). We thank Jenny Barnett (USDA-ARS), Hannah Knapic (USDA-ARS) and Shelby Reutter (USDA-ARS) for technical assistance. We thank the anonymous reviewers for useful comments. Mention of proprietary products or companies does not imply any endorsement or preferential treatment by the USDA-Agricultural Research Service.Public domain – authored by a U.S. government employe

Development of a five-year mortality model in systemic sclerosis patients by different analytical approaches.

Contains fulltext : 88505.pdf (publisher's version ) (Closed access)OBJECTIVES: Systemic sclerosis (SSc) is a multiorgan disease with high mortality rates. Several clinical features have been associated with poor survival in different populations of SSc patients, but no clear and reproducible prognostic model to assess individual survival prediction in scleroderma patients has ever been developed. METHODS: We used Cox regression and three data mining-based classifiers (Naive Bayes Classifier [NBC], Random Forests [RND-F] and logistic regression [Log-Reg]) to develop a robust and reproducible 5-year prognostic model. All the models were built and internally validated by means of 5-fold cross-validation on a population of 558 Italian SSc patients. Their predictive ability and capability of generalisation was then tested on an independent population of 356 patients recruited from 5 external centres and finally compared to the predictions made by two SSc domain experts on the same population. RESULTS: The NBC outperformed the Cox-based classifier and the other data mining algorithms after internal cross-validation (area under receiving operator characteristic curve, AUROC: NBC=0.759; RND-F=0.736; Log-Reg=0.754 and Cox= 0.724). The NBC had also a remarkable and better trade-off between sensitivity and specificity (e.g. Balanced accuracy, BA) than the Cox-based classifier, when tested on an independent population of SSc patients (BA: NBC=0.769, Cox=0.622). The NBC was also superior to domain experts in predicting 5-year survival in this population (AUROC=0.829 vs. AUROC=0.788 and BA=0.769 vs. BA=0.67). CONCLUSIONS: We provide a model to make consistent 5-year prognostic predictions in SSc patients. Its internal validity, as well as capability of generalisation and reduced uncertainty compared to human experts support its use at bedside. Available at: http://www.nd.edu/~nchawla/survival.xls