While irradiation can effectively treat brain tumors, this therapy also causes cognitive impairments, some of which may stem from the disruption of hippocampal neurogenesis. To study how radiation affects neurogenesis, we combine phenotyping of subpopulations of hippocampal neural stem and progenitor cells with double- and triple S-phase labeling paradigms. Using this approach, we reveal new features of division, survival, and differentiation of neural stem and progenitor cells after exposure to gamma radiation. We show that dividing neural stem cells, while susceptible to damage induced by gamma rays, are less vulnerable than their rapidly amplifying progeny. We also show that dividing stem and progenitor cells that survive irradiation are suppressed in their ability to replicate 0.5–1 day after the radiation exposure. Suppression of division is also observed for cells that entered the cell cycle after irradiation or were not in the S phase at the time of exposure. Determining the longer term effects of irradiation, we found that 2 months after exposure, radiation-induced suppression of division is partially relieved for both stem and progenitor cells, without evidence for compensatory symmetric divisions as a means to restore the normal level of neurogenesis. By that time, most mature young neurons, born 2–4 weeks after the irradiation, still bear the consequences of radiation exposure, unlike younger neurons undergoing early stages of differentiation without overt signs of deficient maturation. Later, 6 months after an exposure to 5 Gy, cell proliferation and neurogenesis are further impaired, though neural stem cells are still available in the niche, and their pool is preserved. Our results indicate that various subpopulations of stem and progenitor cells in the adult hippocampus have different susceptibility to gamma radiation, and that neurogenesis, even after a temporary restoration, is impaired in the long term after exposure to gamma rays. Our study provides a framework for investigating critical issues of neural stem cell maintenance, aging, interaction with their microenvironment, and post-irradiation therapy.

Airborne laser scanning (ALS) based stand level forest inventory has been used in Finland and other Nordic countries for several years. In the Russian Federation, ALS is not extensively used for forest inventory purposes, despite a long history of research into the use of lasers for forest measurement that dates back to the 1970s. Furthermore, there is also no generally accepted ALS-based methodology that meets the official inventory requirements of the Russian Federation. In this paper, a method developed for Finnish forest conditions is applied to ALS-based forest inventory in the Perm region of Russia. Sparse Bayesian regression is used with ALS data, SPOT satellite images and field reference data to estimate five forest parameters for three species groups (pine, spruce, deciduous): total mean volume, basal area, mean tree diameter, mean tree height, and number of stems per hectare. Parameter estimates are validated at both the plot level and stand level, and the validation results are compared to results published for three Finnish test areas. Overall, relative root mean square errors (RMSE) were higher for forest parameters in the Perm region than for the Finnish sites at both the plot and stand level. At the stand level, relative RMSE generally decreased with increasing stand size and was lower when considered overall than for individual species groups.

No abstract

Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.