Inducing an optical Feshbach resonance via stimulated Raman coupling

We demonstrate a novel method of inducing an optical Feshbach resonance based on a coherent free-bound stimulated Raman transition. In our experiment atoms in a Rb87 Bose-Einstein condensate are exposed to two phase-locked Raman laser beams which couple pairs of colliding atoms to a molecular ground state. By controlling the power and relative detuning of the two laser beams, we can change the atomic scattering length considerably. The dependence of scattering length on these parameters is studied experimentally and modelled theoretically.Comment: 8 pages, 8 figures, submitted to PR

Long Distance Transport of Ultracold Atoms using a 1D optical lattice

We study the horizontal transport of ultracold atoms over macroscopic distances of up to 20 cm with a moving 1D optical lattice. By using an optical Bessel beam to form the optical lattice, we can achieve nearly homogeneous trapping conditions over the full transport length, which is crucial in order to hold the atoms against gravity for such a wide range. Fast transport velocities of up to 6 m/s (corresponding to about 1100 photon recoils) and accelerations of up to 2600 m/s2 are reached. Even at high velocities the momentum of the atoms is precisely defined with an uncertainty of less than one photon recoil. This allows for construction of an atom catapult with high kinetic energy resolution, which might have applications in novel collision experiments.Comment: 15 pages, 8 figure

Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction

Purpose: Sparse-view computed tomography (CT) is an effective way to reduce dose by lowering the total number of views acquired, albeit at the expense of image quality, which, in turn, can impact the ability to detect diseases. We explore deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Methods: We trained a U-Net for artefact reduction on simulated sparse-view cranial CT scans from 3000 patients obtained from a public dataset and reconstructed with varying levels of sub-sampling. Additionally, we trained a convolutional neural network on fully sampled CT data from 17,545 patients for automated hemorrhage detection. We evaluated the classification performance using the area under the receiver operator characteristic curves (AUC-ROCs) with corresponding 95% confidence intervals (CIs) and the DeLong test, along with confusion matrices. The performance of the U-Net was compared to an analytical approach based on total variation (TV). Results: The U-Net performed superior compared to unprocessed and TV-processed images with respect to image quality and automated hemorrhage diagnosis. With U-Net post-processing, the number of views can be reduced from 4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973; 0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256 views (0.967; 0.964-0.969) with a slight performance decrease (P<.001). Conclusion: The results suggest that U-Net based artifact reduction substantially enhances automated hemorrhage detection in sparse-view cranial CTs. Our findings highlight that appropriate post-processing is crucial for optimal image quality and diagnostic accuracy while minimizing radiation dose.Comment: 11 pages, 6 figures, 1 tabl

Dark state experiments with ultracold, deeply-bound triplet molecules

We examine dark quantum superposition states of weakly bound Rb2 Feshbach molecules and tightly bound triplet Rb2 molecules in the rovibrational ground state, created by subjecting a pure sample of Feshbach molecules in an optical lattice to a bichromatic Raman laser field. We analyze both experimentally and theoretically the creation and dynamics of these dark states. Coherent wavepacket oscillations of deeply bound molecules in lattice sites, as observed in one of our previous experiments, are suppressed due to laser-induced phase locking of molecular levels. This can be understood as the appearance of a novel multilevel dark state. In addition, the experimental methods developed help to determine important properties of our coupled atom / laser system.Comment: 20 pages, 9 figure

Improving Image Quality of Sparse-view Lung Cancer CT Images with a Convolutional Neural Network

Purpose: To improve the image quality of sparse-view computed tomography (CT) images with a U-Net for lung cancer detection and to determine the best trade-off between number of views, image quality, and diagnostic confidence. Methods: CT images from 41 subjects (34 with lung cancer, seven healthy) were retrospectively selected (01.2016-12.2018) and forward projected onto 2048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views, respectively. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, seven healthy) for a single-blinded reader study. The selected slices, for all levels of subsampling, with and without post-processing by the U-Net model, were presented to three readers. Image quality and diagnostic confidence were ranked using pre-defined scales. Subjective nodule segmentation was evaluated utilizing sensitivity (Se) and Dice Similarity Coefficient (DSC) with 95% confidence intervals (CI). Results: The 64-projection sparse-view images resulted in Se = 0.89 and DSC = 0.81 [0.75,0.86] while their counterparts, post-processed with the U-Net, had improved metrics (Se = 0.94, DSC = 0.85 [0.82,0.87]). Fewer views lead to insufficient quality for diagnostic purposes. For increased views, no substantial discrepancies were noted between the sparse-view and post-processed images. Conclusion: Projection views can be reduced from 2048 to 64 while maintaining image quality and the confidence of the radiologists on a satisfactory level

Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Purpose: To optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO) on computed tomography (CT) images. Methods: 7,194 CT images (3,597 with COPD; 3,597 healthy controls) from 78 subjects (43 with COPD; 35 healthy controls) were selected retrospectively (10.2018-12.2019) and preprocessed. For each image, intensity values were manually clipped to the emphysema window setting and a baseline 'full-range' window setting. Class-balanced train, validation, and test sets contained 3,392, 1,114, and 2,688 images. The network backbone was optimized by comparing various CNN architectures. Furthermore, automated WSO was implemented by adding a customized layer to the model. The image-level area under the Receiver Operating Characteristics curve (AUC) [lower, upper limit 95% confidence] and P-values calculated from one-sided Mann-Whitney U-test were utilized to compare model variations. Results: Repeated inference (n=7) on the test set showed that the DenseNet was the most efficient backbone and achieved a mean AUC of 0.80 [0.76, 0.85] without WSO. Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0.86 [0.82, 0.89] (P=0.03). By adding a customized WSO layer to the DenseNet, an optimal window in the proximity of the emphysema window setting was learned automatically, and a mean AUC of 0.82 [0.78, 0.86] was achieved. Conclusion: Detection of COPD with DenseNet models was improved by WSO of CT data to the emphysema window setting range

Human AlkB Homologue 5 Is a Nuclear 2-Oxoglutarate Dependent Oxygenase and a Direct Target of Hypoxia-Inducible Factor 1α (HIF-1α)

Human 2-oxoglutarate oxygenases catalyse a range of biological oxidations including the demethylation of histone and nucleic acid substrates and the hydroxylation of proteins and small molecules. Some of these processes are centrally involved in regulation of cellular responses to hypoxia. The ALKBH proteins are a sub-family of 2OG oxygenases that are defined by homology to the Escherichia coli DNA-methylation repair enzyme AlkB. Here we report evidence that ALKBH5 is probably unique amongst the ALKBH genes in being a direct transcriptional target of hypoxia inducible factor-1 (HIF-1) and is induced by hypoxia in a range of cell types. We show that purified recombinant ALKBH5 is a bona fide 2OG oxygenase that catalyses the decarboxylation of 2OG but appears to have different prime substrate requirements from those so far defined for other ALKBH family members. Our findings define a new class of HIF-transcriptional target gene and suggest that ALKBH5 may have a role in the regulation of cellular responses to hypoxia

mRNA trans-splicing dual AAV vectors for (epi)genome editing and gene therapy

Large genes including several CRISPR-Cas modules like gene activators (CRISPRa) require dual adeno-associated viral (AAV) vectors for an efficient in vivo delivery and expression. Current dual AAV vector approaches have important limitations, e.g., low reconstitution efficiency, production of alien proteins, or low flexibility in split site selection. Here, we present a dual AAV vector technology based on reconstitution via mRNA trans-splicing (REVeRT). REVeRT is flexible in split site selection and can efficiently reconstitute different split genes in numerous in vitro models, in human organoids, and in vivo. Furthermore, REVeRT can functionally reconstitute a CRISPRa module targeting genes in various mouse tissues and organs in single or multiplexed approaches upon different routes of administration. Finally, REVeRT enabled the reconstitution of full-length ABCA4 after intravitreal injection in a mouse model of Stargardt disease. Due to its flexibility and efficiency REVeRT harbors great potential for basic research and clinical applications