258 research outputs found
Search for eta-mesic 4He in the dd->3He n pi0 and dd->3He p pi- reactions with the WASA-at-COSY facility
The search for 4He-eta bound states was performed with the WASA-at-COSY
facility via the measurement of the excitation function for the dd->3He n pi0
and dd->3He p pi- processes. The beam momentum was varied continuously between
2.127 GeV/c and 2.422 GeV/c, corresponding to the excess energy for the dd->4He
eta reaction ranging from Q=-70 MeV to Q=30 MeV. The luminosity was determined
based on the dd->3He n reaction and quasi-free proton-proton scattering via
dd->pp n_spectator n_spectator reactions. The excitation functions determined
independently for the measured reactions do not reveal a structure which could
be interpreted as a narrow mesic nucleus. Therefore, the upper limits of the
total cross sections for the bound state production and decay in
dd->(4He-eta)_bound->3He n pi0 and dd->(4He-eta)_bound->3He p pi- processes
were determined taking into account the isospin relation between both the
channels considered. The results of the analysis depend on the assumptions of
the N* momentum distribution in the anticipated mesic-4He. Assuming as in the
previous works, that this is identical with the distribution of nucleons bound
with 20 MeV in 4He, we determined that (for the mesic bound state width in the
range from 5 MeV to 50 MeV) the upper limits at 90% confidence level are about
3 nb and about 6 nb for npi0 and ppi- channels, respectively. However, based on
the recent theoretical findings of the N*(1535) momentum distribution in the
N*-3He nucleus bound by 3.6 MeV, we find that the WASA-at-COSY detector
acceptance decreases and hence the corresponding upper limits are 5 nb and 10
nb for npi0 and ppi- channels respectively.Comment: This article will be submitted to JHE
Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs
Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques
Approximate Bayesian feature selection on a large meta-dataset offers novel insights on factors that effect siRNA potency
Motivation: Short interfering RNA (siRNA)-induced RNA interference is an endogenous pathway in sequence-specific gene silencing. The potency of different siRNAs to inhibit a common target varies greatly and features affecting inhibition are of high current interest. The limited success in predicting siRNA potency being reported so far could originate in the small number and the heterogeneity of available datasets in addition to the knowledge-driven, empirical basis on which features thought to be affecting siRNA potency are often chosen. We attempt to overcome these problems by first constructing a meta-dataset of 6483 publicly available siRNAs (targeting mammalian mRNA), the largest to date, and then applying a Bayesian analysis which accommodates feature set uncertainty. A stochastic logistic regression-based algorithm is designed to explore a vast model space of 497 compositional, structural and thermodynamic features, identifying associations with siRNA potency
Search for the decay
We search for radiative decays into a weakly interacting neutral
particle, namely an invisible particle, using the produced through the
process in a data sample of
decays collected by the BESIII detector
at BEPCII. No significant signal is observed. Using a modified frequentist
method, upper limits on the branching fractions are set under different
assumptions of invisible particle masses up to 1.2 . The upper limit corresponding to an invisible particle with zero mass
is 7.0 at the 90\% confidence level
Measurement of proton electromagnetic form factors in in the energy region 2.00-3.08 GeV
The process of is studied at 22 center-of-mass
energy points () from 2.00 to 3.08 GeV, exploiting 688.5~pb of
data collected with the BESIII detector operating at the BEPCII collider. The
Born cross section~() of is
measured with the energy-scan technique and it is found to be consistent with
previously published data, but with much improved accuracy. In addition, the
electromagnetic form-factor ratio () and the value of the
effective (), electric () and magnetic () form
factors are measured by studying the helicity angle of the proton at 16
center-of-mass energy points. and are determined with
high accuracy, providing uncertainties comparable to data in the space-like
region, and is measured for the first time. We reach unprecedented
accuracy, and precision results in the time-like region provide information to
improve our understanding of the proton inner structure and to test theoretical
models which depend on non-perturbative Quantum Chromodynamics
Precise Measurements of Branching Fractions for Meson Decays to Two Pseudoscalar Mesons
We measure the branching fractions for seven two-body decays to
pseudo-scalar mesons, by analyzing data collected at
GeV with the BESIII detector at the BEPCII collider. The branching fractions
are determined to be ,
,
,
,
,
,
,
where the first uncertainties are statistical, the second are systematic, and
the third are from external input branching fraction of the normalization mode
. Precision of our measurements is significantly improved
compared with that of the current world average values
First observations of hadrons
Based on events collected with
the BESIII detector, five hadronic decays are searched for via process
. Three of them, ,
, and are observed for the first
time, with statistical significances of 7.4, , and
9.1, and branching fractions of ,
, and ,
respectively, where the first uncertainties are statistical and the second
systematic. No significant signal is observed for the other two decay modes,
and the corresponding upper limits of the branching fractions are determined to
be and at 90% confidence level.Comment: 17 pages, 16 figure
Measurements of Weak Decay Asymmetries of , , , and
Using production from a 567 pb
data sample collected by BESIII at 4.6 GeV, a full angular analysis is carried
out simultaneously on the four decay modes of , , , and . For the first time, the
transverse polarization is studied in unpolarized
collisions, where a non-zero effect is observed with a statistical significance
of 2.1. The decay asymmetry parameters of the weak
hadronic decays into , , and
are measured to be ,
,
, and
, respectively. In comparison with
previous results, the measurements for the and
modes are consistent but with improved precision, while the parameters for the
and modes are measured for the first time
Functional Identification of Tumor Suppressor Genes Through an in vivo RNA Interference Screen in a Mouse Lymphoma Model
2010 April 6Short hairpin RNAs (shRNAs) capable of stably suppressing gene function by RNA interference (RNAi) can mimic tumor-suppressor-gene loss in mice. By selecting for shRNAs capable of accelerating lymphomagenesis in a well-characterized mouse lymphoma model, we identified over ten candidate tumor suppressors, including Sfrp1, Numb, Mek1, and Angiopoietin 2. Several components of the DNA damage response machinery were also identified, including Rad17, which acts as a haploinsufficient tumor suppressor that responds to oncogenic stress and whose loss is associated with poor prognosis in human patients. Our results emphasize the utility of in vivo RNAi screens, identify and validate a diverse set of tumor suppressors, and have therapeutic implications
Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective
RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html
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