Purpose Phosphatidylinositol 3-kinase (PI3K) signaling is critical for the proliferation and survival of malignant B cells. Copanlisib, a pan-class I PI3K inhibitor with predominant activity against PI3K-α and -δ isoforms, has demonstrated efficacy and a manageable safety profile in patients with indolent lymphoma. Patients and Methods In this phase II study, 142 patients with relapsed or refractory indolent lymphoma after two or more lines of therapy were enrolled to receive copanlisib 60 mg intravenously on days 1, 8, and 15 of a 28-day cycle. The primary end point was objective response rate; secondary end points included duration of response, progression-free survival, and overall survival. In addition, safety and gene expression were evaluated. Results Median age was 63 years (range, 25 to 82 years), and patients had received a median of three (range, two to nine) prior regimens. The objective response rate was 59% (84 of 142 patients); 12% of patients achieved a complete response. Median time to response was 53 days. Median duration of response was 22.6 months, median progression-free survival was 11.2 months, and median overall survival had not yet been reached. The most frequent treatment-emergent adverse events were transient hyperglycemia (all grades, 50%; grade 3 or 4, 41%) and transient hypertension (all grades, 30%; grade 3, 24%). Other grade ≥3 events included decreased neutrophil count (24%) and lung infection (15%). High response rates to copanlisib were associated with high expression of PI3K/B-cell receptor signaling pathway genes. Conclusion PI3K-α and -δ inhibition by copanlisib demonstrated significant efficacy and a manageable safety profile in heavily pretreated patients with relapsed or refractory indolent lymphoma.

We calculate chiral susceptibilities in (2 þ 1)-flavor QCD for different masses of the light quarks using the functional renormalization group (fRG) approach to first principles QCD. We follow the evolution of the chiral susceptibilities with decreasing masses as obtained from both the light-quark and the reduced quark condensate. The latter compares very well with recent results from the HotQCD Collaboration for pion masses m π ≳ 100 MeV. For smaller pion masses, fRG and lattice results are still consistent. In particular, the estimates for the chiral critical temperature are in very good agreement. We close by discussing different extrapolations to the chiral limit.

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In biomedical studies, it is often of interest to classify/predict a subject’s disease status based on a variety of biomarker measurements. A commonly used classification criterion is based on AUC - Area under the Receiver Operating Characteristic Curve. Many methods have been proposed to optimize approximated empirical AUC criteria, but there are two limitations to the existing methods. First, most methods are only designed to find the best linear combination of biomarkers, which may not perform well when there is strong nonlinearity in the data. Second, many existing linear combination methods use gradient-based algorithms to find the best marker combination, which often result in sub-optimal local solutions. In this paper, we address these two problems by proposing a new kernel-based AUC optimization method called Ramp AUC (RAUC). This method approximates the empirical AUC loss function with a ramp function, and finds the best combination by a difference of convex functions algorithm. We show that as a linear combination method, RAUC leads to a consistent and asymptotically normal estimator of the linear marker combination when the data is generated from a semiparametric generalized linear model, just as the Smoothed AUC method (SAUC). Through simulation studies and real data examples, we demonstrate that RAUC out-performs SAUC in finding the best linear marker combinations, and can successfully capture nonlinear pattern in the data to achieve better classification performance. We illustrate our method with a dataset from a recent HIV vaccine trial.

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Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). However, there is still a problem with this algorithm associated with the selection of relevant modes. To solve this problem, this paper proposes a novel signal-filtering method that combines VMD with Hausdorff distance (HD) in the VMD-HD method. A noisy signal is first decomposed into a given number K of band-limited intrinsic mode functions by VMD. The probability density function is then estimated for each mode. The aim of this method is to reconstruct the signal using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the input signal and that of each mode. Various similarity measures are investigated and compared, and the HD is shown to offer the best performance. The results of filtering of simulation signals illustrate the validity of the proposed method when compared with EMD-based methods under comprehensive quantitative evaluation criteria. As a specific example, the proposed method is successfully used for filtering the pipeline leakage signal as evaluated by the de-trended fluctuation analysis algorithm.

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