University of Milan

About: University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Population & Medicine. The organization has 58413 authors who have published 139784 publications receiving 4636354 citations. The organization is also known as: Università degli Studi di Milano & Statale.

Novel Local CFT and Exact Results on Perturbations of N=4 Super Yang Mills from AdS Dynamics

Abstract: We nd new, local, non-supersymmetric conformal eld theories ob- tained by relevant deformations of the N=4 super Yang Mills theory in the large N limit. We contruct interpolating supergravity solutions that naturally represent theflowfromtheN=4super YangMills UVtheorytothese non-supersymmetric IR xed points. We also study the linearization around the N=4 superconformal point ofN=1supersymmetric, marginaldeformations. WeshowthattheygiverisetoN=1 superconformalxed points, as expected from eld-theoretical arguments.

How to use expert advice

Abstract: We analyze algorithms that predict a binary value by combining the predictions of several prediction strategies, called experts. Our analysis is for worst-case situations, i.e., we make no assumptions about the way the sequence of bits to be predicted is generated. We measure the performance of the algorithm by the difference between the expected number of mistakes it makes on the bit sequence and the expected number of mistakes made by the best expert on this sequence, where the expectation is taken with respect to the randomization in the predictins. We show that the minimum achievable difference is on the order of the square root of the number of mistakes of the best expert, and we give efficient algorithms that achieve this. Our upper and lower bounds have matching leading constants in most cases. We then show how this leads to certain kinds of pattern recognition/learning algorithms with performance bounds that improve on the best results currently know in this context. We also compare our analysis to the case in which log loss is used instead of the expected number of mistakes.

NOTCH Pathway Blockade Depletes CD133‐Positive Glioblastoma Cells and Inhibits Growth of Tumor Neurospheres and Xenografts

Abstract: Cancer stem cells (CSCs) are thought to be critical for the engraftment and long-term growth of many tumors, including glioblastoma (GBM). The cells are at least partially spared by traditional chemotherapies and radiation therapies, and finding new treatments that can target CSCs may be critical for improving patient survival. It has been shown that the NOTCH signaling pathway regulates normal stem cells in the brain, and that GBMs contain stem-like cells with higher NOTCH activity. We therefore used low-passage and established GBM-derived neurosphere cultures to examine the overall requirement for NOTCH activity, and also examined the effects on tumor cells expressing stem cell markers. NOTCH blockade by γ-secretase inhibitors (GSIs) reduced neurosphere growth and clonogenicity in vitro, whereas expression of an active form of NOTCH2 increased tumor growth. The putative CSC markers CD133, NESTIN, BMI1, and OLIG2 were reduced following NOTCH blockade. When equal numbers of viable cells pretreated with either vehicle (dimethyl sulfoxide) or GSI were injected subcutaneously into nude mice, the former always formed tumors, whereas the latter did not. In vivo delivery of GSI by implantation of drug-impregnated polymer beads also effectively blocked tumor growth, and significantly prolonged survival, albeit in a relatively small cohort of animals. We found that NOTCH pathway inhibition appears to deplete stem-like cancer cells through reduced proliferation and increased apoptosis associated with decreased AKT and STAT3 phosphorylation. In summary, we demonstrate that NOTCH pathway blockade depletes stem-like cells in GBMs, suggesting that GSIs may be useful as chemotherapeutic reagents to target CSCs in malignant gliomas.