BackgroundAlthough subacute and chronic gastrointestinal symptoms are very common in primary care, epidemiological date are sparse. The aim of the study was to examine and quantify the prevalence of subacute and chronic gastrointestinal symptoms and their associations with somatic and mental disorders in the general population.MethodsData were collected prospectively between 1981 (age m = 22, f = 23) and 2008 (age 49/50) from the Zurich Cohort Study (n = 292 men, 299 women), a representative general population survey. The participants were assessed using a semi-structured interview, the “Structured Psychopathological Interview and Rating of the Social Consequences of Psychological Disturbances for Epidemiology” (SPIKE). Prevalence rates were computed to be representative of the general population aged 22–50. Associations were quantified by odds ratios (ORs) and their 99% confidence intervals (CI).ResultsThe prevalences of intestinal and of gastric symptoms were significantly higher among women in all categories examined. For example, any gastric symptoms: f. 26.4% vs m.15.2%; any intestinal symptoms: 27.6% vs 14.6%; nausea/vomitus: 19.1% vs 4.5%; constipation: 15.8% vs 6.5% (all p < 0.001). Strong associations (all p < 0.0001) were found between fatigue (1 month) and chronic stomach (OR = 9.96, 99%-CI: 5.53–17.94) and chronic intestinal symptoms (OR = 9.02, 99%-CI: 4.92–16.54). Panic attacks were associated with subacute intestinal symptoms (OR = 4.00, 99%-CI: 2.43–6.59). Anxiety was more strongly associated with subacute intestinal symptoms (OR = 3.37, 99%-CI: 2.23–5.08) than with subacute stomach symptoms (OR = 1.85, 1.20–2.86). Bipolar disorders were associated with subacute stomach symptoms (OR = 1.83, 1.18–2.17) and unipolar depression with subacute intestinal symptoms (OR = 2.05, 1.34–3.15).ConclusionsRemarkably high prevalence rates of gastric and intestinal complaints were observed in women (over 1/4; men 1/7). Fatigue/neurasthenia was the strongest co-factor in both conditions. Various syndromes related to anxiety, phobia, and panic disorders showed further significant associations. The integration of psychiatric and/or psychological treatment could help address the functional part of gastric and intestinal syndromes.

& Key message Discovery of the first case of allotriploid juniper in a wild population in the French Alps where the parental species occurs in sympatry. & Context Interspecific hybridization and polyploidy are important evolutionary phenomena in vascular plants. Natural hybridization between species living in sympatry can sometimes occur. Less frequent are successful hybridizations between species having different ploidy levels. At Saint Crépin location (French Alps), where sympatry between the tetraploid Juniperus thurifera and the diploid Juniperus sabina occurs, three individuals with an atypical morphology have been observed. & Aims Prospect interspecific hybrids and interspecific genetic introgression occurrence. & Methods Flow cytometry was employed to screen ploidy levels. Four chloroplast markers, nrDNA (ITS), and AFLP markers were used to unravel hybridization and potential introgression events. Variability of pollen size and morphology was assessed to have a first insight on the regularity of microsporogenesis in hybrids. & Results The three putative hybrids were shown to be triploids. Molecular data demonstrated that these individuals were hybrids originated from a cross between J. sabina and J. thurifera and suggested that a backcross at least with J. thurifera is possible. Male triploid hybrids produced heterogeneous pollen and displayed evidence of irregularity in the microsporogenesis. & Conclusion This study sheds light on a rare case of hybridization in a natural sympatric population of two Juniperus species with different ploidy levels. This mechanism might have been an important driver for the evolution and diversification of this coniferous genus.

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Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories with existing experimental data from particle colliders. Using machine learning, the technique obtained comparable results to previous methods (>90% precision and recall) with only a fraction of their computing resources (<10%). This makes it possible to test models that were impossible to probe before, and allows for large-scale testing of new physics theories.