Validation of Gender Specific CAPS Marker in Turkish Fig (Ficus carica L.) Collection and F1 Progenies

In most dioecious plants, distinguishing male and female progenies is not possible until flowering or fruiting stage. The fig (Ficus carica L.) is such a plant where distinguishing male and female plants at the seedling stage can accelerate fig-breeding programs. An orthologue of RAN1 loci was reported to be associated with sex determination in fig (Mori et al., 2017). The objective of this study is to validate this locus on Turkish fig germplasm collection and F1 population obtained from a cross between female genotypes ‘Bursa Siyahi’ and male genotype ‘Ak Ilek’. A total of 144 genotypes from germplasm collection and 115 F1 individuals were tested with CAPS (cleaved amplified polymorphic sequences) marker following the Mori et al. (2017). The loci produced a 315bp amplification product from all genotypes. PciI digestion of PCR products resulted in 100% concordance between phenotypes and molecular tests. On the other hand, HpyCH4IV enzyme digestion of 8 female genotypes resulted in false negatives among the tested materials. Therefore, despite overall results show that the locus is suitable for gender selection of plants at the seedling stage in the breeding programs, care should be taken when HpyCH4IV enzyme is to be employed for CAPS assay

Snow-vegetation-atmosphere interactions in alpine tundra

The interannual variability of snow cover in alpine areas is increasing, which may affect the tightly coupled cycles of carbon and water through snow-vegetation-atmosphere interactions across a range of spatio-temporal scales. To explore the role of snow cover for the land-atmosphere exchange of CO2 and water vapor in alpine tundra ecosystems, we combined three years (2019&ndash;2021) of continuous eddy covariance flux measurements of net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) from the Finse site in alpine Norway (1210 m a.s.l.) with a ground-based ecosystem-type classification and satellite imagery from Sentinel-2, Landsat 8, and MODIS. While the snow conditions in 2019 and 2021 can be described as site-typical, 2020 features an extreme snow accumulation associated with a strong negative phase of the Scandinavian Pattern of the synoptic atmospheric circulation during spring. This extreme snow accumulation caused a one-month delay in melt-out date, which falls on the 92nd-percentile in the distribution of yearly melt-out dates in the period 2001&ndash;2021. The melt-out dates follow a consistent fine-scale spatial relationship with ecosystem types across years. Mountain and lichen heathlands melt out more heterogeneously than fens and flood plains, while late snowbeds melt out up to one month later than the other ecosystem types. While the summertime average Normalized Difference Vegetation Index (NDVI) was reduced considerably during the extreme snow year 2020, it reached the same maximum as in the other years for all but one the ecosystem type (late snowbeds), indicating that the delayed onset of vegetation growth is compensated to the same maximum productivity. Eddy covariance estimates of NEE and ET are gap-filled separately for two wind sectors using a random forest regression model to account for complex and nonlinear ecohydrological interactions. While the two wind sectors differ markedly in vegetation composition and flux magnitudes, their flux response is controlled by the same drivers as estimated by the predictor importance of the random forest model as well as the high correlation of flux magnitudes (correlation coefficient r = 0.92 for NEE and r = 0.89 for ET) between both areas. The one-month delay of the start of the snow-free season in 2020 reduced the total annual ET by 50 % compared to 2019 and 2021, and reduced the growing season carbon assimilation to turn the ecosystem from a moderate annual carbon sink (&minus;31 to &minus;6 gC m&minus;2 yr&minus;1) to a source (34 to 20 gC m&minus;2 yr&minus;1). These results underpin the strong dependence of ecosystem structure and functioning on snow dynamics, whose anomalies can result in important ecological extreme events for alpine ecosystems.</p

Climate–ecosystem modelling made easy: The Land Sites Platform

Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.publishedVersio

Effects of hyperbaric oxygen and iloprost on intestinal ischemia-reperfusion induced acute lung injury

YILMAZ, Yeliz/0000-0003-1811-122XWOS: 000455978400006PubMed: 30603632Purpose: To research the effects of iloprost (IL) and hyperbaric oxygen (HBO) combination treatment on lung injury and on tumor necrosis factor alpha (TNF-alpha), myeloperoxidase (MPO), malondialdehyde (MDA), and soluble intercellular adhesion molecule-1 (sICAM-1) levels after tissue or organ ischemia-reperfusion, and on ischemia-reperfusion induced lung neutrophil sequestration. Methods: Forty white New Zealand rabbits were assigned randomly into 5 groups: HBO, IL, HBO+IL, control, and sham groups, TNF-alpha values were checked before ischemia, in the 1st hour of ischemia and in the 1st and 4th hours of reperfusion, also at the end of reperfusion period, plasma and tissue MPO values, MDA values, and sICAM-1 levels were detected. After sacrifice, the degree of lung injury was determined by histopathological examination. Results: Compared to the control group all therapy groups showed a drastically meaningful reduction in TNF-alpha increase in 1, 2, and 4 hours, Plasma and lung MDA, MPO, and sICAM-1 levels were significantly lower in IL, HBO, HBO+IL, and sham groups compared with the control group. IL and/or HBO suppressed MDA and MPO increase in the lung tissue and in plasma. Additionally, histopathological score was significantly lower in HBO, IL, HBO+IL, and sham groups than that of the control group. Conclusion: Both HBO and IL therapy have a beneficial effect by causing a meaningful reduction in TNF-alpha production, MPO, MDA, sICAM-1 levels and pulmonary neutrophil sequestration; which play a role, especially, in ischemia reperfusion induced lung damage