A categorical foundation for Bayesian probability

Given two measurable spaces $H$ and $D$ with countably generated $\sigma$-algebras, a perfect prior probability measure $P_H$ on $H$ and a sampling distribution $S: H \rightarrow D$, there is a corresponding inference map $I: D \rightarrow H$ which is unique up to a set of measure zero. Thus, given a data measurement $\mu: 1 \rightarrow D$, a posterior probability $\widehat{P_H}= I \circ \mu$ can be computed. This procedure is iterative: with each updated probability $P_H$, we obtain a new joint distribution which in turn yields a new inference map $I$ and the process repeats with each additional measurement. The main result uses an existence theorem for regular conditional probabilities by Faden, which holds in more generality than the setting of Polish spaces. This less stringent setting then allows for non-trivial decision rules (Eilenberg--Moore algebras) on finite (as well as non finite) spaces, and also provides for a common framework for decision theory and Bayesian probability.Comment: 15 pages; revised setting to more clearly explain how to incorporate perfect measures and the Giry monad; to appear in Applied Categorical Structure

The Search for Supersymmetry at the Tevatron Collider

We review the status of searches for Supersymmetry at the Tevatron Collider. After discussing the theoretical aspects relevant to the production and decay of supersymmetric particles at the Tevatron, we present the current results for Runs Ia and Ib as of the summer of 1997. To appear in the book "Perspectives in Supersymmetry", edited by G.L. Kane, World Scientific.Comment: 84 pages with 31 figures imbedded using psfig.tex. Uses sprocl.st

Cancer cells exploit an orphan RNA to drive metastatic progression.

Here we performed a systematic search to identify breast-cancer-specific small noncoding RNAs, which we have collectively termed orphan noncoding RNAs (oncRNAs). We subsequently discovered that one of these oncRNAs, which originates from the 3' end of TERC, acts as a regulator of gene expression and is a robust promoter of breast cancer metastasis. This oncRNA, which we have named T3p, exerts its prometastatic effects by acting as an inhibitor of RISC complex activity and increasing the expression of the prometastatic genes NUPR1 and PANX2. Furthermore, we have shown that oncRNAs are present in cancer-cell-derived extracellular vesicles, raising the possibility that these circulating oncRNAs may also have a role in non-cell autonomous disease pathogenesis. Additionally, these circulating oncRNAs present a novel avenue for cancer fingerprinting using liquid biopsies

An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis; however, its effects on cancer progression remain poorly understood. Here, to address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed dedicated regression-based methods to analyse ribosome profiling and alternative polyadenylation data, and identified heterogeneous nuclear ribonucleoprotein C (HNRNPC) as a translational controller of a specific mRNA regulon. We found that HNRNPC is downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3′ untranslated region lengthening and, subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. In addition, the reduced expression of HNRNPC and its regulon is associated with the worse prognosis in breast cancer patient cohorts

Evidence for the exclusive decay Bc+- to J/psi pi+- and measurement of the mass of the Bc meson

We report first evidence for a fully reconstructed decay mode of the B_c^{\pm} meson in the channel B_c^{\pm} \to J/psi \pi^{\pm}, with J/psi \to mu^+mu^-. The analysis is based on an integrated luminosity of 360 pb$^{-1} in p\bar{p} collisions at 1.96 TeV center of mass energy collected by the Collider Detector at Fermilab. We observe 14.6 \pm 4.6 signal events with a background of 7.1 \pm 0.9 events, and a fit to the J/psi pi^{\pm} mass spectrum yields a B_c^{\pm} mass of 6285.7 \pm 5.3(stat) \pm 1.2(syst) MeV/c^2. The probability of a peak of this magnitude occurring by random fluctuation in the search region is estimated as 0.012%.Comment: 7 pages, 3 figures. Version 3, accepted by PR

Top quark mass measurement using the template method at CDF

We present a measurement of the top quark mass in the lepton+jets and dilepton channels of $t\bar{t}$ decays using the template method. The data sample corresponds to an integrated luminosity of 5.6 fb$^{-1}$ of $p\bar{p}$ collisions at Tevatron with $\sqrt{s}=1.96$ TeV, collected with the CDF II detector. The measurement is performed by constructing templates of three kinematic variables in the lepton+jets and two kinematic variables in the dilepton channel. The variables are two reconstructed top quark masses from different jets-to-quarks combinations and the invariant mass of two jets from the $W$ decay in the lepton+jets channel, and a reconstructed top quark mass and $m_{T2}$, a variable related to the transverse mass in events with two missing particles, in the dilepton channel. The simultaneous fit of the templates from signal and background events in the lepton+jets and dilepton channels to the data yields a measured top quark mass of $M_{top} = 172.1 \pm 1.1(stat) \pm 0.9(syst).$Comment: submitted to Phys. Rev.

Inclusive Search for Anomalous Production of High-pT Like-Sign Lepton Pairs in Proton-Antiproton Collisions at sqrt{s}=1.8 TeV

We report on a search for anomalous production of events with at least two charged, isolated, like-sign leptons with pT > 11 GeV/c using a 107 pb^-1 sample of 1.8 TeV ppbar collisions collected by the CDF detector. We define a signal region containing low background from Standard Model processes. To avoid bias, we fix the final cuts before examining the event yield in the signal region using control regions to test the Monte Carlo predictions. We observe no events in the signal region, consistent with an expectation of 0.63^(+0.84)_(-0.07) events. We present 95% confidence level limits on new physics processes in both a signature-based context as well as within a representative minimal supergravity (tanbeta = 3) model.Comment: 15 pages, 4 figures. Minor textual changes, cosmetic improvements to figures and updated and expanded reference