The local properties of bold signal fluctuations at rest monitor inhibitory control training in adolescents
Mesenchymal Stem Cells (MSC), also termed Mesenchymal Stromal Cells, are multipotent cells that can differentiate into a variety of cell types and have the capacity for self renewal. MSC have been shown to differentiate in vitro or in vivo into adipocytes, chondrocytes, osteoblasts, myocytes, neurons, hepatocytes, and pancreatic islet cells. Optimized PromoCell media are available to support both the growth of MSC and their differentiation into several different lineages. Recent experiments suggest that differentiation capabilities into diverse cell types vary between MSC of different origin.
PromoCell hMSC are harvested from normal human adipose tissue, bone marrow, and umbilical cord matrix (Wharton's jelly) of individual donors.
The cells are tested for their ability to differentiate in vitro into adipocytes, chondrocytes, and osteoblasts. Our hMSC show a verified marker expression profile that complies with ISCT* recommendations, providing well characterized cells.
Journal of Information Security and Applications
Volume 54, October 2020, 102527
Anomaly detection in substation networks
Abstract
Fundamental components of the distribution systems of electric energy are primary and secondary substation networks. Considering the incorporation of legacy communication infrastructure in these systems, they often have in- herent cybersecurity vulnerabilities. Moreover, traditional intrusion defence strategies for IT systems are often not applicable. With the aim to improve cybersecurity in substation networks, in this paper we present two methods for monitoring SCADA system: the first one exploiting neural networks, while the second one is based on formal methods. To evaluate the effective- ness of the proposed methods, we conducted experiments on a real test bed representing the substation domain as close to real-world as possible. From this test bed we collect data during normal operation and during situations where the system is under attack. To this end several different types of attack are conducted. The data collected is used to test two versions of the mon- itoring system: one based on machine learning with a neural network and one using a model-checking approach. Moreover, the two proposed models are tested with new data to evaluate their performance. The experiments demonstrate that both methods obtain an accuracy greater than 90%. In particular, the methodology based on formal methods achieves better per- formance if compared to the one based on neural networks.
Results
We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set.
Conclusion
This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort.
Physica C: Superconductivity
Volumes 153–155, Part 3, June 1988, Pages 1301-1302
Ginzburg Landau theory of resonating valence bonds and its U(1) phase dynamics
Author links open overlay panelAtsushiNakamuraTetsuoMatsui
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Abstract
We formulate a Ginzburg Landau theory for the resonating valence bond model of antiferromagnetism and high Tc superconductivity, the order parameter of which are complex link variables. We investigate the dynamics of their phase part by Monte Carlo mothod and obtain an indication of a transition to a superconducting phase. When the doping parameter is zero, the theory becomes equivalent to a U(1) lattice gauge theory in two dimension.