Superconvergence of Strang splitting for NLS in TdT^d

In this paper we investigate the convergence properties of semi-discretized approximations by Strang splitting method applied to fast-oscillating nonlinear Schr¨odinger equations. In a first step and for further use, we briefly adapt a known convergence result for Strang method in the context of NLS on $T^d$ for a large class of nonlinearities. In a second step, we examine how errors depend on the length of the period $\varepsilon$, the solutions being considered on intervals of fixed length (independent of the period). Our main contribution is to show that Strang splitting with constant step-sizes is unexpectedly more accurate by a factor $\varepsilon$ as compared to established results when the step-size is chosen as an integer fraction of the period, owing to an averaging effect

Improved error estimates for splitting methods applied to highly-oscillatory nonlinear Schrödinger equations

International audienceIn this work, the error behavior of operator splitting methods is analyzed for highly-oscillatory differential equations. The scope of applications includes time-dependent nonlinear Schrödinger equations, where the evolution operator associated with the principal linear part is highly-oscillatory and periodic in time. In a first step, a known convergence result for the second-order Strang splitting method applied to the cubic Schrödinger equation is adapted to a wider class of nonlinearities. In a second step, the dependence of the global error on the decisive parameter 0 < ε < < 1, defining the length of the period, is examined. The main result states that, compared to established error estimates, the Strang splitting method is more accurate by a factor ε, provided that the time stepsize is chosen as an integer fraction of the period. This improved error behavior over a time interval of fixed length, which is independent of the period, is due to an averaging effect. The extension of the convergence result to higher-order splitting methods and numerical illustrations complement the investigations

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

GaN heterostructures as innovative x-ray imaging sensors — change of paradigm

Direct conversion of X-ray irradiation using a semiconductor material is an emerging technology in medical and material sciences. Existing technologies face problems, such as sensitivity or resilience. Here, we describe a novel class of X-ray sensors based on GaN thin film and GaN/AlGaN high-electron-mobility transistors (HEMTs), a promising enabling technology in the modern world of GaN devices for high power, high temperature, high frequency, optoelectronic, and military/space applications. The GaN/AlGaN HEMT-based X-ray sensors offer superior performance, as evidenced by higher sensitivity due to intensification of electrons in the two-dimensional electron gas (2DEG), by ionizing radiation. This increase in detector sensitivity, by a factor of 104 compared to GaN thin film, now offers the opportunity to reduce health risks associated with the steady increase in CT scans in today’s medicine, and the associated increase in exposure to harmful ionizing radiation, by introducing GaN/AlGaN sensors into X-ray imaging devices, for the benefit of the patient

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

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

673. Study of Prescribing patterns and Effectiveness of Ceftolozane/Tazobactam Real-world Analysis (SPECTRA): Results from a multi-national, multicenter observational study

Abstract Background Ceftolozane/tazobactam (C/T) has demonstrated efficacy to treat complicated intra-abdominal infections (cIAI), complicated urinary tract infections (cUTI) and hospital acquired bacterial and ventilator-associated bacterial pneumonia. However, physicians, providers, and other stakeholders including payers want broader real-world evidence to inform clinical decisions and optimize healthcare resource use. Methods SPECTRA is a multi-national, multicenter, retrospective, inpatient, observational study of patients treated with C/T in Australia, Austria, Germany, Italy, Mexico, Spain and The United Kingdom. Adult inpatients treated with ≥48 hours of C/T were included. Demographics, clinical characteristics, treatment management patterns, and outcomes were analyzed. Results There were 687 patients from 38 participating hospitals in 7 countries. The average age was 57.6 years (±17.3 [SD]) and most were male 456 (66.4%). The majority had at least one comorbidity 563 (82.0%), with the most common being heart disease 208 (30.3%), immunocompromised state 207 (30.1%) and chronic pulmonary disease 195 (28.4%). The most common indications were pneumonia 204 (29.7%), sepsis 147 (21.4%), and cIAI 106 (15.4%); 162 (23.6%) had multiple sites of infection and 245 (35.7%) were polymicrobial infections. Median C/T treatment was 12.0 days (11.0 [IQR]). Half of the patients were admitted to the ICU 343 (49.9%), 43.4% of which was related to the infection. Clinical success was 66.1%. All-cause in-hospital mortality was 22.0% with 8.7% being infection related. 30-day all-cause readmission was 9.8% and 4.7% were infection related. Conclusion C/T was used to treat infections among critically ill patients and for multi-source, polymicrobial infections. Despite the complexity of the patients in this real-world analysis, most C/T patients had beneficial outcomes that are similar to results of controlled clinical trials. Disclosures Alex Soriano, MD, MSD, Pfizer, Shionogi, Angelini, Menarini, Gilead: Honoraria Laura A. Puzniak, MPH, PhD, Merck & Co., Inc.: former employee and stockholder David Paterson, MBBS, Accelerate: Honoraria|bioMerieux: Honoraria|Entasis: Advisor/Consultant|Janssen-Cilag: Grant/Research Support|MSD: Advisor/Consultant|MSD: Grant/Research Support|MSD: Honoraria|Pfizer: Grant/Research Support|Pfizer: Honoraria|PPD: Grant/Research Support|Shionogi: Grant/Research Support|VenatoRx: Advisor/Consultant Stefan Kluge, MD, Astrazeneca: Lecture fees|Biotest: Lecture fees|Cytosorbents: Grant/Research Support|Cytosorbents: Lecture fees|Daiichi Sankyo: Grant/Research Support|Daiichi Sankyo: Lecture fees|Fresenius Medical Care: Advisor/Consultant|Fresenius Medical Care: Lecture fees|Gilead: Advisor/Consultant|Gilead: Lecture fees|Mitsubishi Tanabe Pharma: Lecture fees|MSD: Advisor/Consultant|MSD: Lecture fees|Pfizer: Advisor/Consultant|Pfizer: Lecture fees|Phillips: Lecture fees|Zoll: Lecture fees Alexandre H. Watanabe, PharmD, Merck & Co., Inc.: Employee Engels N. Obi, PhD, Merck & Co., Inc.: Employee|Merck & Co., Inc.: Stocks/Bonds Sunny Kaul, BSc, MBChB, PHD, FRCP, FFICM, Chiesi: Speaker fees|Gilead: Speaker fees|GlaxoSmithKline: Speaker fees|MSD: Grant/Research Support|MSD: Speaker fees|Shionogi: Speaker fees|Vifor Pharma: Grant/Research Support

Expert Opinion on Dose Regimen and Therapeutic Drug Monitoring for Long-Term Use of Dalbavancin: Expert Review Panel

Background: Dalbavancin is a lipoglycopeptide with a long elimination half-life, currently licensed for the treatment of acute bacterial skin and skin structure infections (ABSSSI) in adults. Dalbavancin's potential in treating off-label complex gram-positive infections is promising and real-world experience in treating such infections is growing. However, clear guidance on extended dosing regimens is lacking. Objectives: We aim to provide clear expert opinion based on recent pharmacokinetic literature and expert and real-world experience in infection areas that require &gt;2 weeks of treatment. Methods: A single face-to-face meeting was held in September 2022 to collate expert opinion and present safety data of dalbavancin use in these clinical indications. A survey was completed by all authors on their individual experience with dalbavancin which highlighted the heterogeneity in the regimens used. Results: After review of the survey data and recent literature, we present expert panel proposals which accommodate different healthcare settings and resource availability, and centre around the length of treatment duration including up to, or exceeding, 6 weeks. To achieve adequate dalbavancin concentrations for up to 6 weeks, 3,000mg of dalbavancin should be given over 4 weeks for the agreed complex infections requiring &gt;2 weeks treatment. Therapeutic drug monitoring (TDM) is advised for longer treatment durations and in case of renal failure. Specific dosing recommendations for other special populations require further investigation. Conclusions: These proposals based on expert opinion have been defined to encourage best practice with dalbavancin to optimise its administration beyond the current approved licenced dose across different healthcare settings

A Preoperative Clinical Risk Score Including C-Reactive Protein Predicts Histological Tumor Characteristics and Patient Survival after Surgery for Sporadic Non-Functional Pancreatic Neuroendocrine Neoplasms:An International Multicenter Cohort Study

Background: Oncological survival after resection of pancreatic neuroendocrine neoplasms (panNEN) is highly variable depending on various factors. Risk stratification with preoperatively available parameters could guide decision-making in multidisciplinary treatment concepts. C-reactive Protein (CRP) is linked to inferior survival in several malignancies. This study assesses CRP within a novel risk score predicting histology and outcome after surgery for sporadic non-functional panNENs. Methods: A retrospective multicenter study with national exploration and international validation. CRP and other factors associated with overall survival (OS) were evaluated by multivariable cox-regression to create a clinical risk score (CRS). Predictive values regarding OS, disease-specific survival (DSS), and recurrence-free survival (RFS) were assessed by time-dependent receiver-operating characteristics. Results: Overall, 364 patients were included. Median CRP was significantly higher in patients >60 years, G3, and large tumors. In multivariable analysis, CRP was the strongest preoperative factor for OS in both cohorts. In the combined cohort, CRP (cut-off >= 0.2 mg/dL; hazard-ratio (HR):3.87), metastases (HR:2.80), and primary tumor size >= 3.0 cm (HR:1.83) showed a significant association with OS. A CRS incorporating these variables was associated with postoperative histological grading, T category, nodal positivity, and 90-day morbidity/mortality. Time-dependent area-under-the-curve at 60 months for OS, DSS, and RFS was 69%, 77%, and 67%, respectively (all p <0.001), and the inclusion of grading further improved the predictive potential (75%, 84%, and 78%, respectively). Conclusions: CRP is a significant marker of unfavorable oncological characteristics in panNENs. The proposed internationally validated CRS predicts histological features and patient survival