Matter-wave interferometer for large molecules

We demonstrate a near-field Talbot-Lau interferometer for C-70 fullerene molecules. Such interferometers are particularly suitable for larger masses. Using three free-standing gold gratings of one micrometer period and a transversally incoherent but velocity-selected molecular beam, we achieve an interference fringe visibility of 40 % with high count rate. Both the high visibility and its velocity dependence are in good agreement with a quantum simulation that takes into account the van der Waals interaction of the molecules with the gratings and are in striking contrast to a classical moire model.Comment: revtex4, 4 pages, 3 figure

Versatile Cold Atom Source for Multi-Species Experiments

We present a dual-species oven and Zeeman slower setup capable of producing slow, high-flux atomic beams for loading magneto-optical traps. Our compact and versatile system is based on electronic switching between different magnetic field profiles and is applicable to a wide range of multi-species experiments. We give details of the vacuum setup, coils and simple electronic circuitry. In addition, we demonstrate the performance of our system by optimized, sequential loading of magneto-optical traps of lithium-6 and cesium-133.Comment: 7 pages, 10 figure

AI for predicting chemical-effect associations at the chemical universe level: DeepFPlearn

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods-even if high throughput-are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data. We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feed-forward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful-experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds. We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn

AI for predicting chemical-effect associations at the chemical universe level – deepFPlearn

Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods – even if high throughput – are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.Supplementary information Supplementary data are available at bioRxiv online.Contact jana.schor{at}ufz.deCompeting Interest StatementThe authors have declared no competing interest

Collisional decoherence observed in matter wave interferometry

We study the loss of spatial coherence in the extended wave function of fullerenes due to collisions with background gases. From the gradual suppression of quantum interference with increasing gas pressure we are able to support quantitatively both the predictions of decoherence theory and our picture of the interaction process. We thus explore the practical limits of matter wave interferometry at finite gas pressures and estimate the required experimental vacuum conditions for interferometry with even larger objects.Comment: 4 pages, 3 figure

The wave nature of biomolecules and fluorofullerenes

We demonstrate quantum interference for tetraphenylporphyrin, the first biomolecule exhibiting wave nature, and for the fluorofullerene C60F48 using a near-field Talbot-Lau interferometer. For the porphyrins, which are distinguished by their low symmetry and their abundant occurence in organic systems, we find the theoretically expected maximal interference contrast and its expected dependence on the de Broglie wavelength. For C60F48 the observed fringe visibility is below the expected value, but the high contrast still provides good evidence for the quantum character of the observed fringe pattern. The fluorofullerenes therefore set the new mark in complexity and mass (1632 amu) for de Broglie wave experiments, exceeding the previous mass record by a factor of two.Comment: 5 pages, 4 figure

Enhanced Raman sideband cooling of caesium atoms in a vapour-loaded magneto-optical trap

We report enhanced three-dimensional degenerated Raman sideband cooling (3D DRSC) of caesium (Cs) atoms in a standard single-cell vapour-loaded magneto-optical trap. Our improved scheme involves using a separate repumping laser and optimized lattice detuning. We load 1.5 × 107 atoms into the Raman lattice with a detuning of −15.5 GHz (to the ground F = 3 state). Enhanced 3D DRSC is used to cool them from 60 µK to 1.7 µK within 12 ms and the number of obtained atoms is about 1.2 × 107. A theoretical model is proposed to simulate the measured number of trapped atoms. The result shows good agreement with the experimental data. The technique paves the way for loading a large number of ultracold Cs atoms into a crossed dipole trap and efficient evaporative cooling in a single-cell system

Computational RNomics of Drosophilids

Recent experimental and computational studies have provided overwhelming evidence for a plethora of diverse transcripts that are unrelated to protein-coding genes. One subclass consists of those RNAs that require distinctive secondary structure motifs to exert their biological function and hence exhibit distinctive patterns of sequence conservation characteristic for positive selection on RNA secondary structure. The deep-sequencing of 12 drosophilid species coordinated by the NHGRI provides an ideal data set of comparative computational approaches to determine those genomic loci that code for evolutionarily conserved RNA motifs. This class of loci includes the majority of the known small ncRNAs as well as structured RNA motifs in mRNAs. We report here on a genome-wide survey using RNAz

Quantum physics meets biology

Quantum physics and biology have long been regarded as unrelated disciplines, describing nature at the inanimate microlevel on the one hand and living species on the other hand. Over the last decades the life sciences have succeeded in providing ever more and refined explanations of macroscopic phenomena that were based on an improved understanding of molecular structures and mechanisms. Simultaneously, quantum physics, originally rooted in a world view of quantum coherences, entanglement and other non-classical effects, has been heading towards systems of increasing complexity. The present perspective article shall serve as a pedestrian guide to the growing interconnections between the two fields. We recapitulate the generic and sometimes unintuitive characteristics of quantum physics and point to a number of applications in the life sciences. We discuss our criteria for a future quantum biology, its current status, recent experimental progress and also the restrictions that nature imposes on bold extrapolations of quantum theory to macroscopic phenomena.Comment: 26 pages, 4 figures, Perspective article for the HFSP Journa