Changes in macroelement content in Nuphar lutea [L.] Sibith. and Sm. during the growing season

This study presents the results of monitoring studies carried out to determine the chemical composition of Nuphar lutea in two phytocoenoses of Nupharo-Nymphaeetum albae Tomasz. 1977 occurring in two lakes of different trophic types (eutrophic Lake Łaśmiady and oligo-humotrophic Lake Pływające Wyspy). The leaves (collected starting in May), rhizomes and roots of Nuphar lutea as well as water and sediment samples were collected from March to November in the above phytocoenoses (for 3 years in Lake Pływające Wyspy and for 4 years in Lake Łaśmiady). The samples were analysed for several parameters including: phosphate, nitrate, total nitrogen, potassium, sodium, calcium, total iron, sulphate and silica dissolved. In addition the manganese, cadmium, zinc and lead contents were determined in the leaves, rhizomes and roots of the plants collected in July (at the height of the growing season). It was found that the differences in the chemical composition of water and sediments between the lakes studied were more pronounced than in the case of leaves, rhizomes and roots of Nuphar lutea

ESR of an alkylalkoxyamino radical trapped in X‑irradiated decanal oxime O‑methyl ether‑urea inclusion crystals

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Tectonic infarct analysis: A computational tool for automated whole-brain infarct analysis from TTC-stained tissue

Background: Infarct volume measured from 2,3,5-triphenyltetrazolium chloride (TTC)-stained brain slices is critical to in vivo stroke models. In this study, we developed an interactive, tunable, software that automatically computes whole-brain infarct metrics from serial TTC-stained brain sections. Methods: Three rat ischemic stroke cohorts were used in this study (Total n = 91 rats; Cohort 1 n = 21, Cohort 2 n = 40, Cohort 3 n = 30). For each, brains were serially-sliced, stained with TTC and scanned on both anterior and posterior sides. Ground truth annotation and infarct morphometric analysis (e.g., brain-Vbrain, infarct-Vinfarct, and non-infarct-Vnon-infarct volumes) were completed by domain experts. We used Cohort 1 for brain and infarct segmentation model development (n = 3 training cases with 36 slices [18 anterior and posterior faces], n = 18 testing cases with 218 slices [109 anterior and posterior faces]), as well as infarct morphometrics automation. The infarct quantification pipeline and pre-trained model were packaged as a standalone software and applied to Cohort 2, an internal validation dataset. Finally, software and model trainability were tested as a use-case with Cohort 3, a dataset from a separate institute. Results: Both high segmentation and statistically significant quantification performance (correlation between manual and software) were observed across all datasets. Segmentation performance: Cohort 1 brain accuracy = 0.95/f1-score = 0.90, infarct accuracy = 0.96/f1-score = 0.89; Cohort 2 brain accuracy = 0.97/f1-score = 0.90, infarct accuracy = 0.97/f1-score = 0.80; Cohort 3 brain accuracy = 0.96/f1-score = 0.92, infarct accuracy = 0.95/f1-score = 0.82. Infarct quantification (cohort average): Vbrain (ρ = 0.87, p < 0.001), Vinfarct (0.92, p < 0.001), Vnon-infarct (0.80, p < 0.001), %infarct (0.87, p = 0.001), and infarct:non-infact ratio (ρ = 0.92, p < 0.001). Conclusion: Tectonic Infarct Analysis software offers a robust and adaptable approach for rapid TTC-based stroke assessment