A new cheiracanthid acanthodian species, Cheiracanthus flabellicostatus sp. nov., is described based on scales found in the beds of the Aruküla Regional Stage of Siverskiy and Zaitsevo localities of the Leningrad Region. It is distinguished from other members of the genus in the presence of a V-shaped eminence in the medial part of the scale crown and 8-10 large ribs branching into smaller ribs in the anterior part of the crown; large lacunas are observed at crossings of circular and ascending canals in the middle part of the scale neck.
We present the results of the depression Genome-wide association studies study performed on a cohort of Russian-descent individuals, which identified a novel association at chromosome 7q21 locus. Gene prioritization analysis based on already known depression risk genes indicated MAGI2 (S-SCAM) as the most probable gene from the locus and potential susceptibility gene for the disease. Brain and gut expression patterns were the main features highlighting functional relatedness of MAGI2 to the previously known depression risk genes. Local genetic covariance analysis, analysis of gene expression, provided initial suggestive evidence of hospital anxiety and depression scale and diagnostic and statistical manual of mental disorders scales having a different relationship with gut-brain axis disturbance. It should be noted, that while several independent methods successfully in silico validate the role of MAGI2, we were unable to replicate genetic association for the leading variant in the MAGI2 locus, therefore the role of rs521851 in depression should be interpreted with caution.
Buried Late Holocene paleosols of the Nienshants historical monument at the junction of the Neva and Okhta rivers (St. Petersburg) have been studied. These soils developed from estuary deposits of the Littorina basin with abundant artifacts of the Neolithic and Early Iron ages (7-2 ka BP). The soil cover of the area consists of the mature dark humus profile gleyed soils on elevated elements of the mesotopography (3.0-3.5 a.s.l.) and dark humus gley soils in the local depressions (2.0-2.6 m a.s.l.). The soils are character ized by the low to moderate content of humus of the fulvate-humate type. The beginning of humus formation in the dark humus gley soil on the slope facing the Neva River is estimated at about 2600 yrs ago; for the dark humus profile gleyed soils of the studied paleocatena, at about 2000 and 1780 yrs ago; and for the dark humus gley soil, at about 1440 years ago. Judging from the spore-pollen spectra, the development of these soils took place in the Subatlantic period under birch and pine-birch forests with the admixture of spruce and alder trees. The gleyed horizons of the buried soil at the depth of 1.6-1.2 m on the Neva facing slope date back to the Late Subboreal period (2500-2600 yrs ago), when pine-birch-spruce forests were widespread in the area. The new data contribute to our knowledge of the environmental conditions during the Neolithic and Iron ages.
This review article is dedicated to a relatively young, actively developing approach to biodiversity assessment analysis of environmental DNA (or eDNA). Current views on the nature of eDNA, a brief overview of the history of this approach and methods of eDNA analysis are presented. Major research directions, utilizing eDNA techniques, and perspectives of their application to the study of biodiversity are described. Key issues in development of eDNA approach, its advantages and drawbacks are outlined.
We introduce a user-friendly machine learning tool for risk gene, cell type, and drug ranking for complex traits - GCDPipe. It uses gene-level GWAS-derived data and publicly available expression data to train a model for prediction of disease risk genes and relevant cell types. Gene-ranking information is then coupled with known drug targets data to prioritize drugs based on their estimated functional effects associated with identified risk genes. The pipeline was tested in two case studies: inflammatory bowel disease (IBD) and schizophrenia, then it was applied to Alzheimer's disease to investigate potential options for drug repurposing. The results show that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets.
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