Nonreciprocal photon blockade in a spinning optomechanical system with nonreciprocal coupling.

A scheme is presented to achieve quantum nonreciprocity by manipulating the statistical properties of the photons in a composite device consisting of a double-cavity optomechanical system with a spinning resonator and nonreciprocal coupling. It can be found that the photon blockade can emerge when the spinning device is driven from one side but not from the other side with the same driving amplitude. Under the weak driving limit, to achieve the perfect nonreciprocal photon blockade, two sets of optimal nonreciprocal coupling strengths are analytically obtained under different optical detunings based on the destructive quantum interference between different paths, which are in good agreement with the results obtained from numerical simulations. Moreover, the photon blockade exhibits thoroughly different behaviors as the nonreciprocal coupling is altered, and the perfect nonreciprocal photon blockade can be achieved even with weak nonlinear and linear couplings, which breaks the orthodox perception.

The Use of Readily Available Longitudinal Data to Predict the Likelihood of Surgery in Crohn Disease.

BACKGROUND: Although imaging, endoscopy, and inflammatory biomarkers are associated with future Crohn disease (CD) outcomes, common laboratory studies may also provide prognostic opportunities. We evaluated machine learning models incorporating routinely collected laboratory studies to predict surgical outcomes in U.S. Veterans with CD. METHODS: Adults with CD from a Veterans Health Administration, Veterans Integrated Service Networks (VISN) 10 cohort examined between 2001 and 2015 were used for analysis. Patient demographics, medication use, and longitudinal laboratory values were used to model future surgical outcomes within 1 year. Specifically, data at the time of prediction combined with historical laboratory data characteristics, described as slope, distribution statistics, fluctuation, and linear trend of laboratory values, were considered and principal component analysis transformations were performed to reduce the dimensionality. Lasso regularized logistic regression was used to select features and construct prediction models, with performance assessed by area under the receiver operating characteristic using 10-fold cross-validation. RESULTS: We included 4950 observations from 2809 unique patients, among whom 256 had surgery, for modeling. Our optimized model achieved a mean area under the receiver operating characteristic of 0.78 (SD, 0.002). Anti-tumor necrosis factor use was associated with a lower probability of surgery within 1 year and was the most influential predictor in the model, and corticosteroid use was associated with a higher probability of surgery. Among the laboratory variables, high platelet counts, high mean cell hemoglobin concentrations, low albumin levels, and low blood urea nitrogen values were identified as having an elevated influence and association with future surgery. CONCLUSIONS: Using machine learning methods that incorporate current and historical data can predict the future risk of CD surgery.

Assessing Clinical Disease Recurrence Using Laboratory Data in Surgically Resected Patients From the TOPPIC Trial.

Background: Machine learning methodologies play an important role in predicting progression of disease or responses to medical therapy. We previously derived and validated a machine learning algorithm to predict response to thiopurines in an inflammatory bowel disease population. We aimed to apply a modified algorithm to predict postsurgical treatment response using clinical trial data. Methods: TOPPIC was a multicenter randomized double-blinded placebo-controlled trial of 240 patients, evaluating the effectiveness of 6-mercaptopurine in preventing or delaying postsurgical Crohn disease recurrence. We adapted a well-established machine learning algorithm to predict clinical recurrence postresection using age and multiple laboratory-specific covariates, and compared this to the thiopurine metabolite, 6-thioguanine. Results: The random forest machine learning algorithm demonstrates a mean under the receiver operator curve (AuROC) of 0.62 [95% confidence interval (CI) 0.47, 0.78]. Similar results were evident when adding thiopurine metabolite (6-thioguanine) results. Alanine aminotransferase/mean corpuscular volume (ALT/MCV) and potassium × alkaline phosphatase (POT × ALK) predicted endoscopic and biologic recurrence, respectively, with AuROCs of 0.714 (95% CI 0.601, 0.827) and 0.730 (95% CI 0.618, 0.841). Conclusions: A machine learning algorithm with laboratory data from within the first 3 months postsurgically does not discriminate clinical recurrence well. Alternative noninvasive measures should be considered and further evaluated.

Temperature-resistant generation of robust entanglement with blue-detuning driving and mechanical gain.

We present a proposal to generate robust optomechanical entanglement induced by the blue-detuning laser and the mechanical gain in a double-cavity optomechanical system. We show that the stability of the system can be obtained by introducing a cavity mode driven by the red-detuning laser in the blue-detuning regime. In contrast to the red-detuning regime, we find that the entanglement in the blue-detuning regime is extremely robust to temperature. The cavity mode driven by the blue-detuning laser can control indirectly the optomechanical entanglement between mechanical resonator and cavity mode driven by the red-detuning laser. Moreover, the entanglement between two cavity modes without direct coupling can also be achieved in our system. Although the entanglement is weak, it is robust to temperature, and meanwhile, the optomechanical entanglement is hardly affected by the temperature when the damping rate of the mechanical oscillator is close to zero. Furthermore, the entanglement amplification at high temperature can be achieved by adjusting the mechanical gain appropriately. Our proposal provides an efficient way to achieve robust optomechanical entanglement in the blue-detuning regime and entanglement amplification in optomechanical system with mechanical gain.

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

Importance: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy. Identification of likely responders could reduce costs and delays in remission. Objective: To identify patients with Crohn disease likely to be durable responders to ustekinumab before committing to long-term treatment. Design, Setting, and Participants: This cohort study analyzed data from 3 phase 3 randomized clinical trials (UNITI-1, UNITI-2, and IM-UNITI) conducted from 2011 to 2015. Participants (n = 401) were individuals with active (C-reactive protein [CRP] measurement of ≥5 mg/L at enrollment) Crohn disease who received ustekinumab therapy. Data analysis was performed from November 1, 2017, to June 1, 2018. Exposures: All included patients were exposed to 1 or more dose of ustekinumab for 8 weeks or more. Main Outcomes and Measures: Random forest methods were used in building 2 models for predicting Crohn disease remission, with a CRP level lower than 5 mg/dL as a proxy for biological remission, beyond week 42 of ustekinumab treatment. The first model used only baseline data, and the second used data through week 8. Results: In total, 401 participants, with a mean (SD) age of 36.3 (12.6) years and 170 male (42.4%), were included. The week-8 model had a mean area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI, 0.69-0.87). In the testing data set, 27 of 55 participants (49.1%) classified as likely to have treatment success achieved success with a CRP level lower than 5 mg/L after week 42, and 7 of 65 participants (10.8%) classified as likely to have treatment failure achieved this outcome. In the full cohort, 87 patients (21.7%) attained remission after week 42. A prediction model using the week-6 albumin to CRP ratio had an AUROC of 0.76 (95% CI, 0.71-0.82). Baseline ustekinumab serum levels did not improve the model's prediction performance. Conclusions and Relevance: In patients with active Crohn disease, demographic and laboratory data before week 8 of treatment appeared to allow the prompt identification of likely nonresponders to ustekinumab without the need for costly drug-level monitoring.

Ground-state cooling of rotating mirror in double-Laguerre-Gaussian-cavity with atomic ensemble.

A scheme is proposed to cool a rotating mirror close to its ground state in a double-Laguerre-Gaussian-cavity optomechanical system, where an auxiliary cavity and a two-level atomic ensemble simultaneously couple to the original optomechanical cavity. By choosing parameters reasonably, we find that the cooling process of the rotating mirror can be strengthened greatly while the heating process can be suppressed effectively. We show that the proposed ground-state cooling scheme can work well no matter whether in the weak or strong coupling regime for the atomic ensemble and original cavity. Compared with previous related schemes, our scheme works in the unresolved sideband regime with fewer strict limitations for the auxiliary systems.