Landscape genetics aims to investigate functional connectivity among wild populations by evaluating the impact of landscape features on gene flow. Genetic distances among populations or individuals ...are generally better explained by least-cost path (LCP) distances derived from resistance surfaces than by simple Euclidean distances. Resistance surfaces reflect the cost for an organism to move through particular landscape elements. However, determining the effects of landscape types on movements is challenging. Because of a general lack of empirical data on movements, resistance surfaces mostly rely on expert knowledge. Habitat-suitability models potentially provide a more objective method to estimate resistance surfaces than expert opinions, but they have rarely been applied in landscape genetics so far. We compared LCP distances based on expert knowledge with LCP distances derived from habitat-suitability models to evaluate their performance in landscape genetics. We related all LCP distances to genetic distances in linear mixed effect models on an empirical data set of wolves (Canis lupus) from Italy. All LCP distances showed highly significant (P ≤ 0.0001) standardized β coefficients and R² values, but LCPs from habitat-suitability models generally showed higher values than those resulting from expert knowledge. Moreover, all LCP distances better explained genetic distances than Euclidean distances, irrespective of the approaches used. Considering our results, we encourage researchers in landscape genetics to use resistance surfaces based on habitat suitability which performed better than expert-based LCPs in explaining patterns of gene flow and functional connectivity.
Abstract The return of the wolf in its historical range is raising social conflicts with local communities for the perceived potential threat to people safety. In this study we applied molecular ...methods to solve an unusual case of wolf attack towards a man in the Northern Italian Apennines. We analysed seven biological samples, collected from the clothes of the injured man, using mtDNA sequences, the Amelogenin gene, 39 unlinked autosomal and four Y-linked microsatellites. Results indicated that the aggression was conducted by a male dog and not by a wolf nor a wolf x dog hybrid. Our findings were later confirmed by the victim, who confessed he had been attacked by the guard dog of a neighbour. The genetic profile of the owned dog perfectly matched with that identified from the samples previously collected. Our results prove once again that the wolf does not currently represent a risk for human safety in developed countries, whereas most animal aggressions are carried out by its domestic relative, the dog.
Non-invasive genetic sampling has been used to reconstruct spatial patterns of carnivore distributions, identify regions where conflicts with human activities could threaten the survival of a ...species, and assess the effectiveness of conservation strategies. In this study, we used detailed information on wolf (Canis lupus) and livestock distributions to infer depredation risks in a wide area of the Italian Apennines. We carried out a General Niche Environment System Factor Analysis (GNESFA) to define the potential distribution of wolves genotyped from 8565 samples collected during 12 years of non-invasive genetic monitoring in 3622 locations. Habitat suitability models indicated that the proportion of meadows, altitude, slope, roughness, and distance from human settlements were the main factors positively related to the potential wolf distribution, in contrast with the extension of cultivated fields and human settlements. Results of GNESFA were used to infer the local depredation risk, which was high in 46.9 % of the pastures, and to rank the areas where prevention tools should be used with priority. In this way, the use of often-limited financial resources for prevention could be promoted in pastures with the highest depredation risk and conflicts between husbandry and wolf presence might be mitigated.
Background and Purpose. The accurate prediction of prognosis and pattern of failure is crucial for optimizing treatment strategies for patients with cancer, and early evidence suggests that image ...texture analysis has great potential in predicting outcome both in terms of local control and treatment toxicity. The aim of this study was to assess the value of pretreatment 18F-FDG PET texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma (HNSCC) treated with concurrent chemoradiation therapy. Methods. We performed a retrospective analysis of 90 patients diagnosed with primary HNSCC treated between January 2010 and June 2017 with concurrent chemo-radiotherapy. All patients underwent 18F-FDG PET/CT before treatment. 18F-FDG PET/CT texture features of the whole primary tumor were measured using an open-source texture analysis package. Least absolute shrinkage and selection operator (LASSO) was employed to select the features that are associated the most with clinical outcome, as progression-free survival and overall survival. We performed a univariate and multivariate analysis between all the relevant texture parameters and local failure, adjusting for age, sex, smoking, primary tumor site, and primary tumor stage. Harrell c-index was employed to score the predictive power of the multivariate cox regression models. Results. Twenty patients (22.2%) developed local failure, whereas the remaining 70 (77.8%) achieved durable local control. Multivariate analysis revealed that one feature, defined as low-intensity long-run emphasis (LILRE), was a significant predictor of outcome regardless of clinical variables (hazard ratio < 0.001, P=0.001).The multivariate model based on imaging biomarkers resulted superior in predicting local failure with a c-index of 0.76 against 0.65 of the model based on clinical variables alone. Conclusion. LILRE, evaluated on pretreatment 18F-FDG PET/CT, is associated with higher local failure in patients with HNSCC treated with chemoradiotherapy. Using texture analysis in addition to clinical variables may be useful in predicting local control.
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