Study of the reactione + e −→K + K − in the energy range1350 \leqq \sqrt s \leqq 2400 MeV

Thee ⁺ e ^?→K ⁺ K ^? cross section has been measured from about 750 events in the energy interval \(1350 \leqq \sqrt s \leqq 2400 MeV\) with the DM2 detector at DCI. TheK ^± form factor |F _F ^±| cannot be explained by the ρ, ω, ? and ρ′(1600). An additional resonant amplitude at 1650 MeV has to be added as suggested by a previous experiment.     

Composition of Lie transforms with rigorous estimates and applications to Hamiltonian perturbation theory

In this paper we exhibit a rigorous perturbation theory for nearly integrable Hamiltonian systems, based on the composition of Lie Transforms. Precisely, we first study the algorithm for the composition of Lie transforms, and provide rigorous estimates for the convergence radius and the truncation errors of the series; then we use our estimates for a particular model-example, namely a system of weakly coupled harmonic oscillators having Diophantine frequencies, and work out Nekhoroshev-like exponential estimates for the stability times.     

Radiative decay ofJ/ψ into γ π+ π−

The radiative decayJ/ψ → γ π⁺ π^? has been studied using the 8.6 millionJ/ψ produced in the DM2 experiment at the DCIe ⁺e^? storage rings at Orsay. The π⁺ π^? mass spectrum shows a cleanf ₂ (1270) signal, and the possible presence of two other states at thef ₂ (1720) andf ₄ (2030) masses. For thef ₂ (1270), the branching ratio BR(J/ψ →γf)xBR(f→π⁺ π^?) is measured to be (7.50±0.30±1.12)×10^?4, and the spin analysis prefers theJ=2 assignment, with helicity parametersx=0.83±0.06 andy=0.01±0.06. The existence of higher mass states is discussed.     

Chemical-genetic screen identifies riluzole as an enhancer of Wnt/β-catenin signaling in melanoma

To identify new protein and pharmacological regulators of Wnt/β-catenin signaling, we used a cell-based reporter assay to screen a collection of 1857 human-experienced compounds for their ability to enhance activation of the β-catenin reporter by a low concentration of WNT3A. This identified 44 unique compounds, including the FDA-approved drug riluzole, which is presently in clinical trials for treating melanoma. We found that treating melanoma cells with riluzole in?vitro enhances the ability of WNT3A to regulate gene expression, to promote pigmentation, and to decrease cell proliferation. Furthermore riluzole, like WNT3A, decreases metastases in a mouse melanoma model. Interestingly, siRNAs targeting the metabotropic glutamate receptor, GRM1, a reported indirect target of riluzole, enhance β-catenin signaling. The unexpected regulation of β-catenin signaling by both riluzole and GRM1 has implications for the future uses of this drug.     

Mikrochemie

Ohne Zusammenfassung     

Non-planar smoulder propagation governed by diffusion

The steady propagation of a thin smouldering front in a half-spacehas been considered. A suitable coordinate transformation hasallowed the region near the leading edge of the front to beexamined for both a maintained planar surface and with surfacecollapse due to material shrinkage. The change in the oxidizerconcentration for a small increment in the propagation speedfor large time and surface collapse has been determined. Theinfluence of two types of nonlinear diffusion on the shape ofthe smouldering front has been found; other cases can be dealtwith in a similar manner.     

Alpha decay of neutron-deficient isotopes with 78 ⪕ Z ⩽ 83 including the new isotopes 183, 184Pb and 188Bi

Using on-line mass separation of evaporation residues from heavy-ion-induced fusion reactions, three new isotopes were identified by their alpha decay, E_α given in keV: ¹⁸³Pb, 6715(20), ¹⁸⁴Pb, 6632(10) and ¹⁸⁸Bi, 6820(20). Half-lives were determined for ¹⁸⁴Pb, 0.55(6)s, ¹⁸⁵Pb, 4.1(3)s and ¹⁸³Tl, 0.060(15) s. The alpha branching ratios were re-measured for ^182–184Hg and ^{178, 179}Pt, the results are discussed within the framework of alpha width systematics.     

Fitting quantum machine learning potentials to experimental free energy data: predicting tautomer ratios in solution

The computation of tautomer ratios of druglike molecules is enormously important in computer-aided drug discovery, as over a quarter of all approved drugs can populate multiple tautomeric species in solution. Unfortunately, accurate calculations of aqueous tautomer ratios—the degree to which these species must be penalized in order to correctly account for tautomers in modeling binding for computer-aided drug discovery—is surprisingly difficult. While quantum chemical approaches to computing aqueous tautomer ratios using continuum solvent models and rigid-rotor harmonic-oscillator thermochemistry are currently state of the art, these methods are still surprisingly inaccurate despite their enormous computational expense. Here, we show that a major source of this inaccuracy lies in the breakdown of the standard approach to accounting for quantum chemical thermochemistry using rigid rotor harmonic oscillator (RRHO) approximations, which are frustrated by the complex conformational landscape introduced by the migration of double bonds, creation of stereocenters, and introduction of multiple conformations separated by low energetic barriers induced by migration of a single proton. Using quantum machine learning (QML) methods that allow us to compute potential energies with quantum chemical accuracy at a fraction of the cost, we show how rigorous relative alchemical free energy calculations can be used to compute tautomer ratios in vacuum free from the limitations introduced by RRHO approximations. Furthermore, since the parameters of QML methods are tunable, we show how we can train these models to correct limitations in the underlying learned quantum chemical potential energy surface using free energies, enabling these methods to learn to generalize tautomer free energies across a broader range of predictions.

We show how alchemical free energies can be calculated with QML potentials to identify deficiencies in RRHO approximations for computing tautomeric free energies, and how these potentials can be learned from experiment to improve prediction accuracy.