In Latvia, livestock depredation by wolves has increased during the last two decades. Most attacks had occurred in summer and autumn within wolf hunting season. Cumulative numbers of wolf attacks and number of affected sheep per year at regional forest management units were analyzed in relation to estimated wolf density, extent of culling and proportion of juveniles, as well as sheep density and estimated number of wild prey. The response variables (cumulative number of attacks and cumulative number of affected sheep) were modelled by a negative binomial regression, testing effects of every covariate separately and building models from the significant covariates. Depredation level was related to sheep density and estimated wolf population size. No reducing effect was found for culling, and even greater depredation rate was expected at higher proportions of culled wolves. Estimated number of wild prey or proportion of juvenile wolves had an insignificant effect. However, greater numbers of affected sheep were expected at higher red deer density, suggesting increased opportunistic livestock depredation when the red deer may locally outcompete the preferred wolf prey – roe deer.

Background: Detection of small proportion of serious bacterial infections (SBI) with potentially life threating course in a large group of children with fever admitted to emergency department (ED) is still complicated. Measurement of immature granulocytes (IG) percentage may be used as a marker of bacterial infections. The aim of the study was to evaluate whether the IG percentage is a useful additional predictive marker of SBI. Methods: This study included 258 children with febrile infections admitted to the ED. Clinical follow-up, microbiological and radiological tests were used as reference standards for the definition of SBI. Study population was categorized into two groups: (i) infected patients with no suspicion of SBI (n = 75); (ii) patients with suspicion of SBI (n = 183). IG percentage, white blood cell count (WBC) and C-reactive protein (CRP) levels were analyzed from the first routine blood samples at hospital admission. Results: A statistically significant difference in IG percentage levels was observed in children with SBI and those without - the mean IG percentage was 1.2% for the SBI group, 0.3% for those without SBI. The cutoff level of IG percentage to predict SBI was 0.45 (84% specificity, 66% sensitivity, 90% positive predictive value). We combine variables and evaluate their additive values. The sensitivity of WBC to detected SBI improved from 74% to 85% when IG percentage was added to the prediction models. When CRP, WBC and IG percentage were combined, the sensitivity to predict SBI increased to 93%, the specificity to 86%. (95% CI 77–93%). Receiver operator characteristic analysis to predict SBI showed an area under the curve (AUC) of 0.80 for IG percentage. Conclusion: Addition of IG percentage to traditionally used markers of SBI as WBC and CRP may help to identify children with serious bacterial infections. Furthermore IG percentage can be rapidly obtained from the traditional full blood count without any extra sampling and costs.

The aim of this study is to investigate “resource portfolios” and total capital, as well as the degree of those resources capitalization, which representatives of different social classes in the modern Latvia have at their disposal. The amount and structure of “resource portfolio” and total capital of different social classes studied using the resource-asset-capital approach. The article presents results of the sociological survey of social stratification in modern Latvia on the example of its one region – Latgale (2019, n = 798, representative sample of the adult population), identifying social classes based on two objective (income and education) and one subjective (self-identification of respondents) criteria. Based on the example of the lower working class and the middle class, the authors proved that representatives of these polar social classes have a total capital of different amount, which is determined by two main reasons: 1) the lower working class has statistically significantly smaller “resource portfolio” than the middle class; 2) the lower working class is not so successful as the middle class in activating the resources at their disposal, turning them into their capital. These statistically significant two-level differences have to be considered when pursuing social policies on reducing differences between social classes.