37 research outputs found
Occurrence, dispersion and habitat preferences of Amur sleeper (Perccottus glenii) in oxbow lakes of a large river and its tributary
The influence of Ceratophyllum demersum L. and Stratiotes aloides L. on richness and diversity of aquatic vegetation in the lakes of mid-eastern Poland
Ocena stanu jeziora Krzywego [Ukiel] w Olsztynie metoda makrofitoindykacji [MFI]
Ze względu na inny charakter morfologiczny oraz inne zagospodarowanie zlewni poszczególnych części jeziora Krzywego (Ukiel) obserwuje się inną strukturę i przestrzenne ukształtowanie roślinności jeziora. W najlepszym stanie pozostaje Część Gutkowska, którą zaklasyfikowano do grupy „jezior młodych” pod względem zaawansowania rozwojowego w procesie sukcesji oraz do grupy „jezior naturalnych” pod względem natężenia antropopresji. W tej części wyróżniono najwięcej fitocenoz (20), a co za tym idzie uzyskano największą wartość zróżnicowania fitocenotycznego (H), roślinność wnikała najgłębiej (4,5-5,0 m). Tylko w tej części zaobserwowano trzy zbiorowiska z klasy Charetea. Natomiast najgorszy stan wykazano Części Olsztyńskiej („stare” oraz „synantropijne”), gdzie uzyskano najmniejszą wartość wskaźnika zasiedlania (Z), ponieważ roślinność wnikała tylko do 2 m głębokości. W tej części znajduje się główna plaża miasta Olsztyna, liczne przystanie oraz restauracje na wodzie. We wszystkich częściach jeziora w fitolitoralu dominowały lub współdominowaly fitocenozy Ceratophylletum demersi, co wskazuje na daleko posuniętą eutrofizację roślinności całego zbiornika.Particular parts of Krzywe (Ukiel) Lake differ in structure and spatial arrangement of the vegetation due to their specific morphology and various methods of catchment management. The part referred to as Gutkowska Part is in best condition. As regards its development in the process of succession and anthropopressure intensity, it was classified to the group of „young lakes”, and „natural lakes” respectively. The most phytocenoses (20) were found in this part, what resulted in the highest value of phytocenotic diversity index (H); the plants penetrated here to the highest depth (4.5-5.0 m). Three Charetea communities were observed in this part only. The part referred to as Olsztyńska Part was in the poorest condition - it was classified to the group of „old lakes” and „synanthropic lakes”. The colonization index (Z) was here the lowest, as the plants penetrated to a depth of 2 m only. This is probably connected with the fact that the main city beach, numerous boat houses and restaurants are located in this part. Ceratophylletum demersi phytocenoses dominated in the phytolittoral of all parts of Krzywe Lake, what indicates its advanced eutrophication
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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Evaluation of polycyclic aromatic hydrocarbon contents and risk assessment for infant formula in Korea
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