Extracting the Single-Particle Gap in Carbon Nanotubes with Lattice Quantum Monte Carlo

We show how lattice Quantum Monte Carlo simulations can be used to calculate electronic properties of carbon nanotubes in the presence of strong electron-electron correlations. We employ the path integral formalism and use methods developed within the lattice QCD community for our numerical work and compare our results to empirical data of the Anti-Ferromagnetic Mott Insulating gap in large diameter tubes.Comment: 8 pages, 5 figures, Lat2017 proceedin

Virtual Round Table on Innovation for Smart and Sustainable Cities

A Dialogue between Paola Clerici Maestosi and Giovanni Vetritto (IT), Olga Kordas (SE), Johhanes Brezet (NL/DK) and Jonas Bylund (SE

ATHENA: A Phase 3, Open-Label Study Of The Safety And Effectiveness Of Oliceridine (TRV130), A G-Protein Selective Agonist At The µ-Opioid Receptor, In Patients With Moderate To Severe Acute Pain Requiring Parenteral Opioid Therapy.

Background:Pain management with conventional opioids can be challenging due to dose-limiting adverse events (AEs), some of which may be related to the simultaneous activation of β-arrestin (a signaling pathway associated with opioid-related AEs) and G-protein pathways. The investigational analgesic oliceridine is a G-protein-selective agonist at the µ-opioid receptor with less recruitment of β-arrestin. The objective of this phase 3, open-label, multi-center study was to evaluate the safety and tolerability, of IV oliceridine for moderate to severe acute pain in a broad, real-world patient population, including postoperative surgical patients and non-surgical patients with painful medical conditions. Methods:Adult patients with a score ≥4 on 11-point NRS for pain intensity received IV oliceridine either by bolus or PCA; multimodal analgesia was permitted. Safety was assessed using AE reports, study discontinuations, clinical laboratory and vital sign measures. Results:A total of 768 patients received oliceridine. The mean age (SD) was 54.1 (16.1) years, with 32% ≥65 years of age. Most patients were female (65%) and Caucasian (78%). Surgical patients comprised the majority of the study population (94%), most common being orthopedic (30%), colorectal (15%) or gynecologic (15%) procedures. Multimodal analgesia was administered to 84% of patients. Oliceridine provided a rapid reduction in NRS pain score by 2.2 ± 2.3 at 30 mins from a score of 6.3 ± 2.1 (at baseline) which was maintained to the end of treatment. No deaths or significant cardiorespiratory events were reported. The incidence of AEs leading to early discontinuation and serious AEs were 2% and 3%, respectively. Nausea (31%), constipation (11%), and vomiting (10%) were the most common AEs. AEs were mostly of mild (37%) or moderate (25%) severity and considered possibly or probably related to oliceridine in 33% of patients. Conclusion:Oliceridine IV for the management of moderate to severe acute pain was generally safe and well tolerated in the patients studied. ClinicalTrialsgov identifier:NCT02656875

Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment

We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at https://github.com/RayRuizhiLiao/joint_chestxray.Comment: The two first authors contributed equally. To be published in the proceedings of MICCAI 202