Influence of shower fluctuations and primary composition on studies of the shower longitudinal development

We study the influence of shower fluctuations, and the possible presence of different nuclear species in the primary cosmic ray spectrum, on the experimental determination of both shower energy and the proton air inelastic cross section from studies of the longitudinal development of atmospheric showers in fluorescence experiments. We investigate the potential of track length integral and shower size at maximum as estimators of shower energy. We find that at very high energy (~10^19-10^20 eV) the error of the total energy assignment is dominated by the dependence on the hadronic interaction model, and is of the order of 5%. At lower energy (~10^17-10^18 eV), the uncertainty of the energy determination due to the limited knowledge of the primary cosmic ray composition is more important. The distribution of depth of shower maximum is discussed as a measure of the proton-air cross section. Uncertainties in a possible experimental measurement of this cross section introduced by intrinsic shower fluctuations, the model of hadronic interactions, and the unknown mixture of primary nuclei in the cosmic radiation are numerically evaluated.Comment: 12 pages, 11 figures, 4 table

Constraints on the Ultra High Energy Photon flux using inclined showers from the Haverah Park array

We describe a method to analyse inclined air showers produced by ultra high energy cosmic rays using an analytical description of the muon densities. We report the results obtained using data from inclined events (60^{\circ}<\theta<80^{\circ}) recorded by the Haverah Park shower detector for energies above 10^19 eV. Using mass independent knowledge of the UHECR spectrum obtained from vertical air shower measurements and comparing the expected horizontal shower rate to the reported measurements we show that above 10^19 eV less than 48 % of the primary cosmic rays can be photons at the 95 % confidence level and above 4 X 10^19 eV less than 50 % of the cosmic rays can be photonic at the same confidence level. These limits place important constraints on some models of the origin of ultra high-energy cosmic rays.Comment: 45 pages, 25 figure

New hadrons as ultra-high energy cosmic rays

Ultra-high energy cosmic ray (UHECR) protons produced by uniformly distributed astrophysical sources contradict the energy spectrum measured by both the AGASA and HiRes experiments, assuming the small scale clustering of UHECR observed by AGASA is caused by point-like sources. In that case, the small number of sources leads to a sharp exponential cutoff at the energy E<10^{20} eV in the UHECR spectrum. New hadrons with mass 1.5-3 GeV can solve this cutoff problem. For the first time we discuss the production of such hadrons in proton collisions with infrared/optical photons in astrophysical sources. This production mechanism, in contrast to proton-proton collisions, requires the acceleration of protons only to energies E<10^{21} eV. The diffuse gamma-ray and neutrino fluxes in this model obey all existing experimental limits. We predict large UHE neutrino fluxes well above the sensitivity of the next generation of high-energy neutrino experiments. As an example we study hadrons containing a light bottom squark. These models can be tested by accelerator experiments, UHECR observatories and neutrino telescopes.Comment: 17 pages, revtex style; v2: shortened, as to appear in PR

A large-scale RNAi screen in human cells identifies new components of the p53 pathway

RNA interference (RNAi) is a powerful new tool with which to perform loss-of-function genetic screens in lower organisms and can greatly facilitate the identification of components of cellular signalling pathways. In mammalian cells, such screens have been hampered by a lack of suitable tools that can be used on a large scale. We and others have recently developed expression vectors to direct the synthesis of short hairpin RNAs (shRNAs) that act as short interfering RNA (siRNA)-like molecules to stably suppress gene expression. Here we report the construction of a set of retroviral vectors encoding 23,742 distinct shRNAs, which target 7,914 different human genes for suppression. We use this RNAi library in human cells to identify one known and five new modulators of p53-dependent proliferation arrest. Suppression of these genes confers resistance to both p53-dependent and p19ARF-dependent proliferation arrest, and abolishes a DNAdamage- induced G1 cell-cycle arrest. Furthermore, we describe siRNA bar-code screens to rapidly identify individual siRNA vectors associated with a specific phenotype. These new tools will greatly facilitate large-scale loss-of-function genetic screens in mammalian cells

Gene expression profiling predicts clinical outcome of breast cancer

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70 — 80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCAI carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy