Use of the Functioning Assessment Short Test (FAST) in defining functional recovery in bipolar I disorder. Post-hoc analyses of long-term studies of aripiprazole once monthly as maintenance treatment

Purpose: There is growing agreement that definitions of "recovery" in bipolar-I disorder (BP-I) should include functional outcomes beyond sustained symptomatic remission. In this post-hoc analysis, we assessed functional recovery rates according to the validated Functioning Assessment Short Test (FAST) in participants with BP-I after 52 weeks of maintenance treatment with aripiprazole once monthly (AOM). Patients and methods: Rates offunctional recovery with AOM 400 were investigated in two 52-week studies. NCT01567527 was a placebo-controlled, double-blind, randomized-withdrawal study and NCT01710709 was an open-label study. Functional recovery, assessed at the end of the respectivemaintenancephases,wasdefinedasatotal FASTscoreof ≤11for8consecutive weeks. Results: Post-hoc analyses included 229 patients from the randomized-withdrawal study (AOM 400 n=116; placebo n=113). The open-label study included 402 patients (including 321 de novo patients and 81 rollover patients who had completed the randomized-withdrawal study). In the randomized-withdrawal study, functional recovery was achieved by 30.2% (n=35) of the AOM 400 group compared with 24.8% (n=28) in the placebo group. The difference was not statistically significant (p=0.39). In the open-label study, 36% (n=116) of de novo patients and 43% (n=35) of rollover patients had functionally recovered after 52 weeks of AOM 400 treatment. Conclusion: These data highlight the utility of a sustained FAST total score of ≤11 as a definition of recovery and emphasize the possibility of achieving this ambitious treatment goal with effective long-term treatment

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.

Deep Neural Network Ensembles Using Class-vs-Class Weighting

Ensembling is a popular and powerful technique to utilize predictions from several different machine learning models. The fundamental precondition of a well-working ensemble model is a diverse set of combined constituents. Rapid development in the deep learning field provides an ever-increasing palette of diverse model architectures. This rich variety of models provides an ideal situation to improve classification accuracy by ensembling. In this regard, we propose a novel weighted ensembling classification approach with unique weights for each combined classifier and each pair of classes. The novel weighting scheme allows us to account for the different abilities of individual classifiers to distinguish between pairs of classes. First, we analyze a theoretical scenario, in which our approach yields optimal classification. Second, we test its practical applicability on computer vision benchmark datasets. We evaluate the effectiveness of our proposed method and averaging ensemble baseline on an image classification task using the CIFAR-100 and ImageNet1k benchmarks. We use deep convolutional neural networks, vision transformers, and an MLP-Mixer as ensemble constituents. Statistical tests show that our proposed method provides higher accuracy gains than a popular baseline ensemble on both datasets. On the CIFAR-100 dataset, the proposed method attains accuracy improvements ranging from 2% to 5% compared to the best ensemble constituent. On the Imagenet dataset, these improvements range from 1% to 3% in most cases. Additionally, we show that when constituent classifiers are well -calibrated and have similar performance, the simple averaging ensemble yields good results