OligoWalk: an online siRNA design tool utilizing hybridization thermodynamics

Given an mRNA sequence as input, the OligoWalk web server generates a list of small interfering RNA (siRNA) candidate sequences, ranked by the probability of being efficient siRNA (silencing efficacy greater than 70%). To accomplish this, the server predicts the free energy changes of the hybridization of an siRNA to a target mRNA, considering both siRNA and mRNA self-structure. The free energy changes of the structures are rigorously calculated using a partition function calculation. By changing advanced options, the free energy changes can also be calculated using less rigorous lowest free energy structure or suboptimal structure prediction methods for the purpose of comparison. Considering the predicted free energy changes and local siRNA sequence features, the server selects efficient siRNA with high accuracy using a support vector machine. On average, the fraction of efficient siRNAs selected by the server that will be efficient at silencing is 78.6%. The OligoWalk web server is freely accessible through internet at http://rna.urmc.rochester.edu/servers/oligowalk

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

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

Fundamental differences in the equilibrium considerations for siRNA and antisense oligodeoxynucleotide design

Both siRNA and antisense oligodeoxynucleotides (ODNs) inhibit the expression of a complementary gene. In this study, fundamental differences in the considerations for RNA interference and antisense ODNs are reported. In siRNA and antisense ODN databases, positive correlations are observed between the cost to open the mRNA target self-structure and the stability of the duplex to be formed, meaning the sites along the mRNA target with highest potential to form strong duplexes with antisense strands also have the greatest tendency to be involved in pre-existing structure. Efficient siRNA have less stable siRNA–target duplex stability than inefficient siRNA, but the opposite is true for antisense ODNs. It is, therefore, more difficult to avoid target self-structure in antisense ODN design. Self-structure stabilities of oligonucleotide and target correlate to the silencing efficacy of siRNA. Oligonucleotide self-structure correlations to efficacy of antisense ODNs, conversely, are insignificant. Furthermore, self-structure in the target appears to correlate with antisense ODN efficacy, but such that more effective antisense ODNs appear to target mRNA regions with greater self-structure. Therefore, different criteria are suggested for the design of efficient siRNA and antisense ODNs and the design of antisense ODNs is more challenging

Measurement of proton electromagnetic form factors in e+e−→ppˉe^+e^- \to p\bar{p} in the energy region 2.00-3.08 GeV

The process of $e^+e^- \rightarrow p\bar{p}$ is studied at 22 center-of-mass energy points ($\sqrt{s}$) from 2.00 to 3.08 GeV, exploiting 688.5~pb$^{-1}$ of data collected with the BESIII detector operating at the BEPCII collider. The Born cross section~($\sigma_{p\bar{p}}$) of $e^+e^- \rightarrow p\bar{p}$ 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 ($|G_{E}/G_{M}|$) and the value of the effective ($|G_{\rm{eff}}|$), electric ($|G_E|$) and magnetic ($|G_M|$) form factors are measured by studying the helicity angle of the proton at 16 center-of-mass energy points. $|G_{E}/G_{M}|$ and $|G_M|$ are determined with high accuracy, providing uncertainties comparable to data in the space-like region, and $|G_E|$ 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

Search for the decay J/ψ→γ+invisibleJ/\psi\to\gamma + \rm {invisible}

We search for $J/\psi$ radiative decays into a weakly interacting neutral particle, namely an invisible particle, using the $J/\psi$ produced through the process $\psi(3686)\to\pi^+\pi^-J/\psi$ in a data sample of $(448.1\pm2.9)\times 10^6$ $\psi(3686)$ 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 $\mathrm{\ Ge\kern -0.1em V}/c^2$. The upper limit corresponding to an invisible particle with zero mass is 7.0$\times 10^{-7}$ at the 90\% confidence level

First observations of hc→h_c \to hadrons

Based on $(4.48 \pm 0.03) \times 10^{8}$ $\psi(3686)$ events collected with the BESIII detector, five $h_c$ hadronic decays are searched for via process $\psi(3686) \to \pi^0 h_c$. Three of them, $h_c \to p \bar{p} \pi^+ \pi^-$, $\pi^+ \pi^- \pi^0$, and $2(\pi^+ \pi^-) \pi^0$ are observed for the first time, with statistical significances of 7.4$\sigma$, $4.9\sigma$, and 9.1$\sigma$, and branching fractions of $(2.89\pm0.32\pm0.55)\times10^{-3}$, $(1.60\pm0.40\pm0.32)\times10^{-3}$, and $(7.44\pm0.94\pm1.56)\times10^{-3}$, 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 $B(h_c \to 3(\pi^+ \pi^-) \pi^0)<8.7\times10^{-3}$ and $B(h_c \to K^+ K^- \pi^+ \pi^-)<5.8\times10^{-4}$ at 90% confidence level.Comment: 17 pages, 16 figure

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

Precise Measurements of Branching Fractions for Ds+D_s^+ Meson Decays to Two Pseudoscalar Mesons

We measure the branching fractions for seven $D_{s}^{+}$ two-body decays to pseudo-scalar mesons, by analyzing data collected at $\sqrt{s}=4.178\sim4.226$ GeV with the BESIII detector at the BEPCII collider. The branching fractions are determined to be $\mathcal{B}(D_s^+\to K^+\eta^{\prime})=(2.68\pm0.17\pm0.17\pm0.08)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta^{\prime}\pi^+)=(37.8\pm0.4\pm2.1\pm1.2)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\eta)=(1.62\pm0.10\pm0.03\pm0.05)\times10^{-3}$, $\mathcal{B}(D_s^+\to\eta\pi^+)=(17.41\pm0.18\pm0.27\pm0.54)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+K_S^0)=(15.02\pm0.10\pm0.27\pm0.47)\times10^{-3}$, $\mathcal{B}(D_s^+\to K_S^0\pi^+)=(1.109\pm0.034\pm0.023\pm0.035)\times10^{-3}$, $\mathcal{B}(D_s^+\to K^+\pi^0)=(0.748\pm0.049\pm0.018\pm0.023)\times10^{-3}$, where the first uncertainties are statistical, the second are systematic, and the third are from external input branching fraction of the normalization mode $D_s^+\to K^+K^-\pi^+$. Precision of our measurements is significantly improved compared with that of the current world average values

Measurements of Weak Decay Asymmetries of Λc+→pKS0\Lambda_c^+\to pK_S^0, Λπ+\Lambda\pi^+, Σ+π0\Sigma^+\pi^0, and Σ0π+\Sigma^0\pi^+

Using $e^+e^-\to\Lambda_c^+\bar\Lambda_c^-$ production from a 567 pb$^{-1}$ data sample collected by BESIII at 4.6 GeV, a full angular analysis is carried out simultaneously on the four decay modes of $\Lambda_c^+\to pK_S^0$, $\Lambda \pi^+$, $\Sigma^+\pi^0$, and $\Sigma^0\pi^+$. For the first time, the $\Lambda_c^+$ transverse polarization is studied in unpolarized $e^+e^-$ collisions, where a non-zero effect is observed with a statistical significance of 2.1$\sigma$. The decay asymmetry parameters of the $\Lambda_c^+$ weak hadronic decays into $pK_S^0$, $\Lambda\pi^+$, $\Sigma^+\pi^0$ and $\Sigma^0\pi^+$ are measured to be $0.18\pm0.43(\rm{stat})\pm0.14(\rm{syst})$, $-0.80\pm0.11(\rm{stat})\pm0.02(\rm{syst})$, $-0.57\pm0.10(\rm{stat})\pm0.07(\rm{syst})$, and $-0.73\pm0.17(\rm{stat})\pm0.07(\rm{syst})$, respectively. In comparison with previous results, the measurements for the $\Lambda\pi^+$ and $\Sigma^+\pi^0$ modes are consistent but with improved precision, while the parameters for the $pK_S^0$ and $\Sigma^0\pi^+$ modes are measured for the first time