Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete mean-variance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.Comment: 8 pages, 4 figure

AUTOLOGOUS OSTEOCHONDRAL GRAFTING FOR OSTEOCHONDRITIS DESSICANS A CASE REPORT

Osteochondral defects are common injuries among the young population. Athletes are more prone to these injuries given the amount the stress and strain they put on their lower limbs. The viscoelastic nature of cartilage, which allows load bearing, is disrupted. Early recognition and treatment is needed to prevent Early Osteoarthritis. We are presenting one such case, where a 25 year old male patient with Grade 4 OCD was treated with Autologous osteochondral graft taken from the Non weight bearing part of the femur

AUTOLOGOUS OSTEOCHONDRAL GRAFTING FOR OSTEOCHONDRITIS DESSICANS A CASE REPORT

Osteochondral defects are common injuries among the young population. Athletes are more prone to these injuries given the amount the stress and strain they put on their lower limbs. The viscoelastic nature of cartilage, which allows load bearing, is disrupted. Early recognition and treatment is needed to prevent Early Osteoarthritis. We are presenting one such case, where a 25 year old male patient with Grade 4 OCD was treated with Autologous osteochondral graft taken from the Non weight bearing part of the femur

Differential Pre-mRNA Splicing Regulates Nnat Isoforms in the Hypothalamus after Gastric Bypass Surgery in Mice

Background Neuronatin (NNAT) is an endoplasmic reticulum proteolipid implicated in intracellular signalling. Nnat is highly-expressed in the hypothalamus, where it is acutely regulated by nutrients and leptin. Nnat pre-mRNA is differentially spliced to create Nnat-α and -β isoforms. Genetic variation of NNAT is associated with severe obesity. Currently, little is known about the long-term regulation of Nnat. Methods Expression of Nnat isoforms were examined in the hypothalamus of mice in response to acute fast/feed, chronic caloric restriction, diet-induced obesity and modified gastric bypass surgery. Nnat expression was assessed in the central nervous system and gastrointestinal tissues. RTqPCR was used to determine isoform-specific expression of Nnat mRNA. Results Hypothalamic expression of both Nnat isoforms was comparably decreased by overnight and 24-h fasting. Nnat expression was unaltered in diet-induced obesity, or subsequent switch to a calorie restricted diet. Nnat isoforms showed differential expression in the hypothalamus but not brainstem after bypass surgery. Hypothalamic Nnat-β expression was significantly reduced after bypass compared with sham surgery (P = 0.003), and was positively correlated with post-operative weight-loss (R2 = 0.38, P = 0.01). In contrast, Nnat-α expression was not suppressed after bypass surgery (P = 0.19), and expression did not correlate with reduction in weight after surgery (R2 = 0.06, P = 0.34). Hypothalamic expression of Nnat-β correlated weakly with circulating leptin, but neither isoform correlated with fasting gut hormone levels post- surgery. Nnat expression was detected in brainstem, brown-adipose tissue, stomach and small intestine. Conclusions Nnat expression in hypothalamus is regulated by short-term nutrient availability, but unaltered by diet-induced obesity or calorie restriction. While Nnat isoforms in the hypothalamus are co-ordinately regulated by acute nutrient supply, after modified gastric bypass surgery Nnat isoforms show differential expression. These results raise the possibility that in the radically altered nutrient and hormonal milieu created by bypass surgery, resultant differential splicing of Nnat pre-mRNA may contribute to weight-loss

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer. As has been the case with fastMRI, increasing accessibility to raw prostate MRI data will further facilitate research in MR image reconstruction and evaluation with the larger goal of improving the utility of MRI for prostate cancer detection and evaluation. The dataset is available at https://fastmri.med.nyu.edu.Comment: 4 pages, 1 figur

Efficacy of Tezepelumab in Patients with Severe, Uncontrolled Asthma Across Multiple Clinically Relevant Subgroups in the NAVIGATOR Study

INTRODUCTION: Many patients with severe asthma continue to experience symptoms and exacerbations despite treatment with standard-of-care therapy. In the phase 3 NAVIGATOR study, tezepelumab significantly reduced exacerbations over 52 weeks compared with placebo in patients with severe, uncontrolled asthma. This analysis assessed the efficacy of tezepelumab in reducing asthma exacerbations in various clinically relevant subgroups of patients in NAVIGATOR. METHODS: NAVIGATOR was a phase 3, multicentre, randomized, double-blind, placebo-controlled study. Participants (12-80 years old) with severe, uncontrolled asthma were randomized 1:1 to receive tezepelumab 210 mg or placebo subcutaneously every 4 weeks for 52 weeks. Pre-specified and post hoc analyses were performed to evaluate the annualized asthma exacerbation rate (AAER) over 52 weeks in clinically relevant subgroups of patients defined by baseline patient characteristics, medical history, exacerbation triggers, medication eligibility and medication use before and during the study. RESULTS: Tezepelumab reduced the AAER over 52 weeks compared with placebo across a wide range of patient subgroups assessed. Reductions in exacerbations were similar across subgroups defined by baseline patient characteristics, ranging from 48% (95% confidence interval [CI]: 21, 65) to 60% (95% CI: 44, 71) in subgroups analysed by sex, smoking history and body mass index. Among the asthma-related comorbidity subgroups investigated, patients with aspirin or NSAID sensitivity had the greatest reductions in AAER with tezepelumab compared with placebo (83%; 95% CI: 66, 91). In patients eligible to receive dupilumab, tezepelumab reduced exacerbations compared with placebo by 64% (95% CI: 54, 71). Reductions in the AAER with tezepelumab compared with placebo were also observed irrespective of exacerbation trigger category and the number of asthma controller medications patients were receiving at baseline. CONCLUSION: These findings further support the benefits of tezepelumab in patients with severe, uncontrolled asthma and can help to inform healthcare providers\u27 treatment decisions

Digitized-counterdiabatic quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) has proved to be an e ective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitizedcounterdiabatic QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms standard QAOA in all cases we study