Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

Fast-activating reserve power sources: is lead dead indeed?

The purpose of this research is to improve the performance and reduce the activation time of reserve power sources based on lead-acid systems at lower temperatures, down to –50 °C. Physico-chemical factors affecting the activation speed of reserve power sources based on Pb–HClO4–PbO2 and Zn–HClO4–PbO2 systems are investigated using chronopotentiometry, scanning electron microscopy, and standard contact porosimetry. Two approaches to the improvement of the low-temperature performance of power sources are used. The first one is based on the substitution of lead as anodic material with zinc. This allows the increase in discharge voltage and simultaneous decrease in activation time, but brings about the instability of discharge characteristics and, finally, deteriorates the reliability of power sources. The second approach is based on the use of PbO2 cathode material with enhanced nanoporosity. The chronopotentiometric method in galvanostatic mode is applied to the quality estimation of cathodes. The criterion of applicability of cathodes for reserve power sources consists in the low discharge overvoltage (0.1–0.2 V). Efficient performance of reserve power sources possessing the stable discharge voltage (1.5–1.8 V per cell) and the unprecedentedly short activation time (under 30 ms) even at lower temperatures (down to –50 °C) is achieved. The results are verified by fabrication and testing of pilot batches of miniaturized reserve power sources having microcells’ volume of 0.02 ml. The second approach to the improvement of power sources is transferred into the industrial production

Screening studies of POP levels in bottom sediments from selected lakes in the Paz watercourse

Appendix 5/15 of the publication "State of the environment in the Norwegian, Finnish and Russian border area 2007" (The Finnish Environment 6/2007)

Screening studies of POP levels in fish from selected lakes in the Paz watercourse

Appendix 8/15 of the publication "State of the environment in the Norwegian, Finnish and Russian border area 2007" (The Finnish Environment 6/2007)

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Meteorological information at three hydrological stations in the European part of Russia

Empirical study of the isotopic features of river runoff were carried out at three hydrological posts in 3 different river basins: the Zakza river in the southwest of the Moscow region, the Dubna river in the north of the Moscow region and the Sosna river in the Voronezh region.The amount of precipitation and the precipitation-weighted air temperature corresponding to the sampling intervals are given according to observations at meteorological stations in the basins of the studied rivers

Stable isotope composition (δ18O, δ2H) of river runoff, groundwater and precipitation at three hydrological stations in the European part of Russia

Empirical study of the isotopic features of river runoff were carried out at three hydrological posts in 3 different river basins: the Zakza river in the southwest of the Moscow region, the Dubna river in the north of the Moscow region and the Sosna river in the Voronezh region. Samples of river water, groundwater and precipitation were collected at weekly intervals from September 2019 to October 2021. The analysis was performed by a Picarro L2130-i isotope analyzer. The accuracy was 0.04‰ for δ18О and 0.1‰ for δ2Н. The values are calibrated in the VSMOW-VSLAP scale