Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming

We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. In particular, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our new approach yields the optimal tradeoff? between power to detect an effect in the overall population versus power to detect effects in subpopulations. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV

Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees

We revisit the domain of off-policy policy optimization in RL from the perspective of coordinate ascent. One commonly-used approach is to leverage the off-policy policy gradient to optimize a surrogate objective -- the total discounted in expectation return of the target policy with respect to the state distribution of the behavior policy. However, this approach has been shown to suffer from the distribution mismatch issue, and therefore significant efforts are needed for correcting this mismatch either via state distribution correction or a counterfactual method. In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient. This design obviates the need for distribution correction or importance sampling in the policy improvement step of off-policy policy gradient. We establish the global convergence of CAPO with general coordinate selection and then further quantify the convergence rates of several instances of CAPO with popular coordinate selection rules, including the cyclic and the randomized variants of CAPO. We then extend CAPO to neural policies for a more practical implementation. Through experiments, we demonstrate that CAPO provides a competitive approach to RL in practice.Comment: 47 pages, 4 figure

Spatially Uniform and Quantitative Surface-Enhanced Raman Scattering under Modal Ultrastrong Coupling Beyond Nanostructure Homogeneity Limits

We developed a substrate that enables highly sensitive and spatially uniform surface-enhanced Raman scattering (SERS). This substrate comprises densely packed gold nanoparticles (d-AuNPs)/titanium dioxide/Au film (d-ATA). The d-ATA substrate demonstrates modal ultrastrong coupling between localized surface plasmon resonances (LSPRs) of AuNPs and Fabry–Pérot nanocavities. d-ATA exhibits a significant enhancement of the near-field intensity, resulting in a 78-fold increase in the SERS signal for crystal violet (CV) compared to that of d-AuNP/TiO2 substrates. Importantly, high sensitivity and a spatially uniform signal intensity can be obtained without precise control of the shape and arrangement of the nanoscale AuNPs, enabling quantitative SERS measurements. Additionally, SERS measurements of rhodamine 6G (R6G) on this substrate under ultralow adsorption conditions (0.6 R6G molecules/AuNP) show a spatial variation in the signal intensity within 3%. These findings suggest that the SERS signal under modal ultrastrong coupling originates from multiple plasmonic particles with quantum coherence

Targeting PML-RARα and Oncogenic Signaling Pathways by Chinese Herbal Mixture Tien-Hsien Liquid in Acute Promyelocytic Leukemia NB4 Cells

Tien-Hsien Liquid (THL) is a Chinese herbal mixture that has been used worldwide as complementary treatment for cancer patients in the past decade. Recently, THL has been shown to induce apoptosis in various types of solid tumor cells in vitro. However, the underlying molecular mechanisms have not yet been well elucidated. In this study, we explored the effects of THL on acute promyelocytic leukemia (APL) NB4 cells, which could be effectively treated by some traditional Chinese remedies containing arsenic trioxide. The results showed THL could induce G2/M arrest and apoptosis in NB4 cells. Accordingly, the decrease of cyclin A and B1 were observed in THL-treated cells. The THL-induced apoptosis was accompanied with caspase-3 activation and decrease of PML-RARα fusion protein. Moreover, DNA methyltransferase 1 and oncogenic signaling pathways such as Akt/mTOR, Stat3 and ERK were also down-regulated by THL. By using ethyl acetate extraction and silica gel chromatography, an active fraction of THL named as EAS5 was isolated. At about 0.5–1% of the dose of THL, EAS5 appeared to have most of THL-induced multiple molecular targeting effects in NB4 cells. Based on the findings of these multi-targeting effects, THL might be regarding as a complementary and alternative therapeutic agent for refractory APL

A Collaborative Model for Calculus Reform-A Preliminary Report

Abstract For the past two decades, both pros and cons of calculus reform have been discussed. A question often asked is, "Has the calculus reform project improved students' understanding of mathematics?" The advocates of the reform movement claim that reform-based calculus may help students gain an intuitive understanding of mathematical propositions and have a better grasp of the real-world applications. Nonetheless, many still question its effect and argue that calculus reform purges calculus of its mathematical rigor and poorly prepares students for advanced mathematical training. East Asian students often rank in the top 10 of TIMSS and PISA. However, out-performing others in an international comparison may not guarantee their success in the learning of calculus. Taiwanese college students usually have a high failure rate in calculus. The National Science Council of Taiwan therefore initiated several projects in 2008 for improving students' learning in calculus. This paper provides a preliminary report on one of the projects, PLEASE, and discusses how it was planned to respond to the tenets of calculus reform movement

Comparison of coplanar and noncoplanar intensity-modulated radiation therapy and helical tomotherapy for hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>To compare the differences in dose-volume data among coplanar intensity modulated radiotherapy (IMRT), noncoplanar IMRT, and helical tomotherapy (HT) among patients with hepatocellular carcinoma (HCC) and portal vein thrombosis (PVT).</p> <p>Methods</p> <p>Nine patients with unresectable HCC and PVT underwent step and shoot coplanar IMRT with intent to deliver 46 - 54 Gy to the tumor and portal vein. The volume of liver received 30Gy was set to keep less than 30% of whole normal liver (V30 < 30%). The mean dose to at least one side of kidney was kept below 23 Gy, and 50 Gy as for stomach. The maximum dose was kept below 47 Gy for spinal cord. Several parameters including mean hepatic dose, percent volume of normal liver with radiation dose at X Gy (Vx), uniformity index, conformal index, and doses to organs at risk were evaluated from the dose-volume histogram.</p> <p>Results</p> <p>HT provided better uniformity for the planning-target volume dose coverage than both IMRT techniques. The noncoplanar IMRT technique reduces the V10 to normal liver with a statistically significant level as compared to HT. The constraints for the liver in the V30 for coplanar IMRT vs. noncoplanar IMRT vs. HT could be reconsidered as 21% vs. 17% vs. 17%, respectively. When delivering 50 Gy and 60-66 Gy to the tumor bed, the constraints of mean dose to the normal liver could be less than 20 Gy and 25 Gy, respectively.</p> <p>Conclusion</p> <p>Noncoplanar IMRT and HT are potential techniques of radiation therapy for HCC patients with PVT. Constraints for the liver in IMRT and HT could be stricter than for 3DCRT.</p

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model