The terrestrial environment of the East European tundra consists of a mosaic of habitat types. In addition to the natural habitat diversity, various human-influenced types may occur. In the town of Vorkuta, Komi Republic, Russia the manure-enriched soils near hydrogen sulfide springs were observed. This site represents an unusually nutrient-rich location with considerable development of organic soils, in contrast to the naturally forming soils in East European tundra which are typically thin and nutrient poor. In these organic soils, two species of Lumbricidae and two species of Collembola previously not recorded from the natural ecosystems in the study area of research territory were found. One earthworm species, Dendrodrilus
rubidus
tenuis, is likely to have been introduced. The presence of the three other species (Eiseniella
tetraedra, Folsomia
fimetaria, and Proisotoma
minuta) is quite natural in East European tundra and such anthropogenic soils with high organic content may be a good habitat for them.
Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molecular diagnostics for nervous system neoplasms can revolutionize the efficacy and safety of medical care and radically reduce healthcare costs. Our pilot study aimed to evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I–IV) based on preoperative magnetic resonance imaging (MRI) from Burdenko Neurosurgery Center’s database. A total of 707 MRI studies was included. A “3D classification” approach predicting tumor type for the entire patient’s MRI data showed the best result (accuracy = 83%, ROC AUC = 0.95), consistent with that of other authors who used different methodologies. Our preliminary results proved the separability of MR T1 axial images with contrast enhancement by WHO grade using DL.
The European North-East of Russia is the territory which includes the Nenets Autonomous District, represented by the East European tundra (from Kanin Peninsula to Vaigach Island), Komi Republic with its taiga ecosystems and the Urals (Northern, SubPolar and Polar). Over 20 years of systematic studies of soil fauna in the studied region has resulted in a huge amount of data being accumulated that can be analysed from different positions. Considering that the representation of Russian soil biota data, especially from European North-East of Russia in the GBIF database is not large, our data are of great interest to the scientific world community. The accumulation of such data will solve questions on national and global scales using large arrays.
This study produced a dataset containing information on occurrences on soil invertebrates (Lumbricidae, Chilopoda, Diplopda, Collembola, Elateridae and Staphylinidae) in the European North-East of Russia. The dataset summarises occurrences noted in natural and disturbed forests, tundra and mountain ecosystems.
Data from 196 geo-referenced localities of European North-East of Russia (tundra, taiga and mountains ecosystems) have been collated. A total of 5412 occurrences are included in the resource. The current project surveys 13 species of earthworms, 20 species of millipedes, 246 species of springtails, 446 species of rove beetles and 60 species of click beetles. The diversity of soil invertebrates in the European North-East of Russia has not been fully explored and further exploration will lead to more taxa.
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