Diversity of lactase persistence in African milk drinkers

The genetic trait of lactase persistence is attributable to allelic variants in an enhancer region upstream of the lactase gene, LCT. To date, five different functional alleles, -13910*T, -13907*G, -13915*G, -14009*G and -14010*C, have been identified. The co-occurrence of several of these alleles in Ethiopian lactose digesters leads to a pattern of sequence diversity characteristic of a 'soft selective sweep'. Here we hypothesise that throughout Africa, where multiple functional alleles co-exist, the enhancer diversity will be greater in groups who are traditional milk drinkers than in non-milk drinkers, as the result of this sort of parallel selection. Samples from 23 distinct groups from 10 different countries were examined. Each group was classified 'Yes 'or 'No' for milk-drinking, and ethnicity, language spoken and geographic location were recorded. Predicted lactase persistence frequency and enhancer diversity were, as hypothesised, higher in the milk drinkers than the non-milk-drinkers, but this was almost entirely accounted for by the Afro-Asiatic language speaking peoples of east Africa. The other groups, including the 'Nilo-Saharan language speaking' milk-drinkers, show lower frequencies of LP and lower diversity, and there was a north-east to south-west decline in overall diversity. Amongst the Afro-Asiatic (Cushitic) language speaking Oromo, however, the geographic cline was not evident and the southern pastoralist Borana showed much higher LP frequency and enhancer diversity than the other groups. Together these results reflect the effects of parallel selection, the stochastic processes of the occurrence and spread of the mutations, and time depth of milk drinking tradition

Assesment of norway spruce health using multispectral images acquired from unmanned aerial vehicle with non-metric cameras

W pracy oceniono przydatność zdjęć w zakresie bliskiej podczerwieni. Zdjęcia wykonano zmodyfikowanymi aparatami cyfrowymi (Sigma DP2) zamontowanymi na bezzałogowym statku latającym AVI-1. Dla wybranego obiektu badawczego o pow. 284 ha, wykonano 930 zdjęć o rozdzielczości terenowej 0,15 m. Zdjęcia zostały przetworzone do postaci ortomozaik w barwach naturalnych oraz zbliżonych do spektrostrefowych. W terenie wykonano obserwacje koron świerków rosnących w ok. 40-letnim, jednogatunkowym drzewostanie. Określono gęstość igliwia, jego kolor oraz inne symptomy świadczące o kondycji drzew. Te same drzewa zidentyfikowano na ortomozaikach i poddano klasyfikacji, której wyniki porównano z danymi uzyskanymi w terenie. Ocenę stanu zdrowotnego świerka (Picea bies) na ortomozaikach wykonano analizując udział barwy czerwonej związanej z różnymi objawami chorobowymi i uszkodzeń koron. Obserwacje wykonało niezależnie od siebie dwóch obserwatorów, każdy w dwóch seriach. Ocenę jednolitości rozpoznawania barw przez obserwatorów oparto na porównaniu uzyskanych wartości. Stwierdzono, że jest możliwe podzielenie obserwowanych drzew na kilka klas zdrowotności.Several tests were carried out of unmanned aerial vehicles (UAV) equipped with customer cameras to take pictures of forest areas. Based on this a special own UAV was built which can carry a set of non-metric digital cameras recording images in RGB and near-IR channels. Approximately 1000 aerial images with a ground resolution of 0.15 m were taken for the selected research facility with an area of approximately 250 ha. These images were used to produce an orthomosaic in natural and multispectral bands. It was decided to find out whether it is possible to evaluate the health condition of spruce (Picea abies). The field observations were made of the crowns of spruces growing in about 40-year-old single-species stand. Information on the density of pine needles, its color, and other symptoms of the tree condition were recorded. The same trees were identified on orthomosaic and classified. Results of classification from the field and orthomosaic were compared. It was concluded, that it is possible to divide the observed number of trees into several health condition classes