Extended two-stage adaptive designswith three target responses forphase II clinical trials

We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization tofind extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial.Our proposed designs include the celebrated Simon’s two-stage design and its extension that allows two target responserates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperformsgreedy algorithms, which are currently used to find such designs when there are only a few parameters; it is also capableof solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of ourproposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one ofthe three target response rates of the drug, subject to various user-specified testing error rates. Our designs aretherefore more flexible and interestingly, do not necessarily require larger expected sample size requirements thantwo-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires onehalf fewer subjects than the implemented design in the study

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model

SC VALL-E: Style-Controllable Zero-Shot Text to Speech Synthesizer

Expressive speech synthesis models are trained by adding corpora with diverse speakers, various emotions, and different speaking styles to the dataset, in order to control various characteristics of speech and generate the desired voice. In this paper, we propose a style control (SC) VALL-E model based on the neural codec language model (called VALL-E), which follows the structure of the generative pretrained transformer 3 (GPT-3). The proposed SC VALL-E takes input from text sentences and prompt audio and is designed to generate controllable speech by not simply mimicking the characteristics of the prompt audio but by controlling the attributes to produce diverse voices. We identify tokens in the style embedding matrix of the newly designed style network that represent attributes such as emotion, speaking rate, pitch, and voice intensity, and design a model that can control these attributes. To evaluate the performance of SC VALL-E, we conduct comparative experiments with three representative expressive speech synthesis models: global style token (GST) Tacotron2, variational autoencoder (VAE) Tacotron2, and original VALL-E. We measure word error rate (WER), F0 voiced error (FVE), and F0 gross pitch error (F0GPE) as evaluation metrics to assess the accuracy of generated sentences. For comparing the quality of synthesized speech, we measure comparative mean option score (CMOS) and similarity mean option score (SMOS). To evaluate the style control ability of the generated speech, we observe the changes in F0 and mel-spectrogram by modifying the trained tokens. When using prompt audio that is not present in the training data, SC VALL-E generates a variety of expressive sounds and demonstrates competitive performance compared to the existing models. Our implementation, pretrained models, and audio samples are located on GitHub

Compound Identification Using Penalized Linear Regression on Metabolomics

Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized linear regressions such as ridge regression and the lasso are used instead of the ordinary least squares regression. Furthermore, two-step approaches using the dot product and Pearson’s correlation along with the penalized linear regression are proposed in this study

Comparison of characteristics among Korean American male smokers between survey and cessation studies

This study compared characteristics of Korean American men in two studies: a telephone survey with a random sample of Korean American men who reported daily smoking versus a smoking cessation clinical trial with a convenience sample of Korean American men who reported smoking at least 10 cigarettes a day. Guided by the Theory of Planned Behavior (TPB), both studies attempted to explain how much its theoretical variables (attitudes, perceived social norms, and self-efficacy) would explain quit intentions in Korean American men. Participants in the cessation study were less likely to have health insurance coverage (χ2 [2, 271] = 138.31, p = 0.001) than those in the survey study. The cessation group was more likely to smoke in indoor offices (χ2 [1, 231] = 18.09, p = 0.003) and had higher nicotine dependence than the survey group (t269 = 3.32, p = 0.001) but these differences became insignificant when only those who smoked 10 or more cigarettes were compared. Participants in the cessation study had more positive attitudes towards quitting (t267 = 4.99, p \u3c 0.001), stronger perceived social norms favoring quitting (t269 = 5.63, p \u3c 0.001) and greater quit intentions (t268 = 9.86, p \u3c 0.001) at baseline than those in the survey study. Korean American men are more likely to have a quit intention and make a quit attempt when they have more positive and fewer negative attitudes towards quitting and perceive stronger social norms favoring quitting. To motivate Korean American men to quit smoking, clinicians should underscore the immediate health benefits of quitting, promote quitting with cessation aids to reduce perceived risks of quitting in anticipation of withdrawal symptoms, and encourage family members to relate firm anti-smoking messages