University of Florence

About: University of Florence is a education organization based out in Florence, Toscana, Italy. It is known for research contribution in the topics: Population & Carbonic anhydrase. The organization has 27292 authors who have published 79599 publications receiving 2341684 citations. The organization is also known as: Università degli studi di Firenze & Universita degli studi di Firenze.

Evidence for the 125 GeV Higgs boson decaying to a pair of τ leptons

Abstract: A search for a standard model Higgs boson decaying into a pair of tau leptons is performed using events recorded by the CMS experiment at the LHC in 2011 and 2012. The dataset corresponds to an integrated luminosity of 4.9 inverse femtobarns at a centre-of-mass energy of 7 TeV and 19.7 inverse femtobarns at 8 TeV. Each tau lepton decays hadronically or leptonically to an electron or a muon, leading to six different final states for the tau-lepton pair, all considered in this analysis. An excess of events is observed over the expected background contributions, with a local significance larger than 3 standard deviations for m[H] values between 115 and 130 GeV. The best fit of the observed H to tau tau signal cross section for m[H] = 125 GeV is 0.78 +- 0.27 times the standard model expectation. These observations constitute evidence for the 125 GeV Higgs boson decaying to a pair of tau leptons.

Pharmacological Strategies for Overcoming Multidrug Resistance

Abstract: Multidrug resistance (MDR) is a major obstacle to the effective treatment of cancer. One of the underlying mechanisms of MDR is cellular overproduction of P-glycoprotein (P-gp) which acts as an efflux pump for various anticancer drugs. P-gp is encoded by the MDR1 gene and its overexpression in cancer cells has become a therapeutic target for circumventing multidrug resistance. A potential strategy is to co-administer efflux pump inhibitors, although such reversal agents might actually increase the side effects of chemotherapy by blocking physiological anticancer drug efflux from normal cells. Although many efforts to overcome MDR have been made using first and second generation reversal agents comprising drugs already in current clinical use for other indications (e.g. verapamil, cyclosporine A, quinidine) or analogues of the first-generation drugs (e.g. dexverapamil, valspodar, cinchonine), few significant advances have been made. Clinical trials with third generation modulators (e.g. biricodar, zosuquidar, and laniquidar) specifically developed for MDR reversal are ongoing. The results however are not encouraging and it may be that the perfect reverser does not exist. Other approaches to multidrug resistance reversal have also been considered: encapsulation of anthracyclines in liposomes or other carriers which deliver these drugs selectively to tumor tissues, the use of P-gp targeted antibodies such as UIC2 or the use of antisense strategies targeting the MDR1 messenger RNA. More recently, the development of transcriptional regulators appears promising. Also anticancer drugs that belong structurally to classes of drugs extruded from cells by P-gp but that are not substrates of this drug transporter may act as potent inhibitors of MDR tumors (e.g. epothilones, second generation taxanes). Taking advantage of MDR has also been studied. Bone marrow suppression, one of the major side effects of cancer chemotherapy, can compromise the potential of curative and palliative chemotherapy. It is conceivable that drug resistance gene transfer into bone marrow stem cells may be able to reduce or abolish chemotherapy-induced myelosuppression and facilitate the use of high dose chemotherapy. Clinical trials of retroviral vectors containing drug resistance genes have established that the approach is safe and are now being designed to address the therapeutically relevant issues.

An Input Output HMM Architecture

Abstract: We introduce a recurrent architecture having a modular structure and we formulate a training procedure based on the EM algorithm. The resulting model has similarities to hidden Markov models, but supports recurrent networks processing style and allows to exploit the supervised learning paradigm while using maximum likelihood estimation.