g factor of lithiumlike silicon 28Si11+

The g factor of lithiumlike 28Si11+ has been measured in a triple-Penning trap with a relative uncertainty of 1.1x10^{-9} to be g_exp=2.0008898899(21). The theoretical prediction for this value was calculated to be g_th=2.000889909(51) improving the accuracy to 2.5x10^{-8} due to the first rigorous evaluation of the two-photon exchange correction. The measured value is in excellent agreement with the state-of-the-art theoretical prediction and yields the most stringent test of bound-state QED for the g factor of the 1s^22s state and the relativistic many-electron calculations in a magnetic field

High-precision measurement of the atomic mass of the electron

The quest for the value of the electron's atomic mass has been subject of continuing efforts over the last decades. Among the seemingly fundamental constants which parameterize the Standard Model (SM) of physics and which are thus responsible for its predictive power, the electron mass me plays a prominent role, as it is responsible for the structure and properties of atoms and molecules. This manifests in the close link with other fundamental constants, such as the Rydberg constant and the fine-structure constant {\alpha}. However, the low mass of the electron considerably complicates its precise determination. In this work we present a substantial improvement by combining a very accurate measurement of the magnetic moment of a single electron bound to a carbon nucleus with a state-of-the-art calculation in the framework of bound-state Quantum Electrodynamics. The achieved precision of the atomic mass of the electron surpasses the current CODATA value by a factor of 13. Accordingly, the result presented in this letter lays the foundation for future fundamental physics experiments and precision tests of the SM

Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Fulvene — Isomere benzoider Verbindungen

In ihrem Bindungszustand und ihrer Reaktivität nehmen die Fulvene eine Mittelstellung zwischen den ihnen isomeren benzoiden Verbindungen und den Olefinen ein. In Abhängigkeit von den Substituenten am exocyclischen C-Atom bestimmen der Diencharakter des gekreuzt konjugierten Systems oder die cyclische Konjugation im fünfgliedrigen Ring das chemische und physikalische Verhalten der Fulvene. Neben einigen neuen Substitutionsreaktionen werden besonders Synthesen und Reaktionen von den mit Anilinen und Phenolen isomeren 6-Amino- und 6-Hydroxy-fulvenen beschrieben. Derivate dieser Verbindungen ermöglichen die Darstellung neuartiger nichtbenzoider, cyclisch konjugierter Systeme wie carbo- und hetero-cyclischer Azulene, Pseudoazulene, Thiepine, Dihydropyridazine und des s-Indacens

Inhibition of HERG1 K+ channel protein expression decreases cell proliferation of human small cell lung cancer cells

HERG (human ether-à-go-go-related gene) K+ currents fulfill important ionic functions in cardiac and other excitable cells. In addition, HERG channels influence cell growth and migration in various types of tumor cells. The mechanisms underlying these functions are still not resolved. Here, we investigated the role of HERG channels for cell growth in a cell line (SW2) derived from small cell lung cancer (SCLC), a malignant variant of lung cancer. The two HERG1 isoforms (HERG1a, HERG1b) as well as HERG2 and HERG3 are expressed in SW2 cells. Inhibition of HERG currents by acute or sustained application of E-4031, a specific ERG channel blocker, depolarized SW2 cells by 10–15 mV. This result indicated that HERG K+ conductance contributes considerably to the maintenance of the resting potential of about −45 mV. Blockage of HERG channels by E-4031 for up to 72 h did not affect cell proliferation. In contrast, siRNA-induced inhibition of HERG1 protein expression decreased cell proliferation by about 50%. Reduction of HERG1 protein expression was confirmed by Western blots. HERG current was almost absent in SW2 cells transfected with siRNA against HERG1. Qualitatively similar results were obtained in three other SCLC cell lines (OH1, OH3, H82), suggesting that the HERG1 channel protein is involved in SCLC cell growth, whereas the ion-conducting function of HERG1 seems not to be important for cell growth