A Multi-Arm Two-Stage (MATS) Design for Proof-of-Concept and Dose Optimization in Early-Phase Oncology Trials

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the $\textit{status quo}$ of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assess the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve proof-of-concept (PoC) and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/

FSD: An Initial Chinese Dataset for Fake Song Detection

Singing voice synthesis and singing voice conversion have significantly advanced, revolutionizing musical experiences. However, the rise of "Deepfake Songs" generated by these technologies raises concerns about authenticity. Unlike Audio DeepFake Detection (ADD), the field of song deepfake detection lacks specialized datasets or methods for song authenticity verification. In this paper, we initially construct a Chinese Fake Song Detection (FSD) dataset to investigate the field of song deepfake detection. The fake songs in the FSD dataset are generated by five state-of-the-art singing voice synthesis and singing voice conversion methods. Our initial experiments on FSD revealed the ineffectiveness of existing speech-trained ADD models for the task of song deepFake detection. Thus, we employ the FSD dataset for the training of ADD models. We subsequently evaluate these models under two scenarios: one with the original songs and another with separated vocal tracks. Experiment results show that song-trained ADD models exhibit a 38.58% reduction in average equal error rate compared to speech-trained ADD models on the FSD test set.Comment: Submitted to ICASSP 202

A dynamic relationship between renewable energy consumption, non-renewable energy consumption, economic growth and CO2 emissions: Evidence from Asian emerging economies

This study aims to explore the relationship between renewable energy consumption, non-renewable energy consumption, carbon dioxide emissions and economic growth in China, India, Bangladesh, Japan, South Korea and Singapore using panel Augmented Mean Group (AMG) estimation techniques over the period 1975–2020. The results of the analysis show that renewable energy consumption, non-renewable energy consumption, employed labor force, and capital formation contribute significantly to long-run economic growth. The study also found that non-renewable energy consumption significantly increased long-term carbon emissions, while renewable energy consumption significantly reduced long-term carbon emissions. GDP and GDP3 have a significant positive impact on environmental degradation, while GDP2 has a significant negative impact on environmental degradation, thereby validating the N-type EKC hypothesis in selected emerging economies. The countrywise AMG strategy records no EKC in India and Bangladesh, an inverted U-shaped EKC in China and Singapore, and an N-shaped EKC in Japan and South Korea. Empirical evidence from the Dumitrescue-Hurlin (2012) panel causality test shows that there is a two-way causality between renewable energy consumption and economic growth, supporting the feedback hypothesis. Strategically, empirical evidence suggests that higher renewable energy is a viable strategy for addressing energy security and reducing carbon emissions to protect the environment and promote future economic growth in selected Asian countries

Attribute-Based Conditional Proxy Re-Encryption in the Standard Model under LWE

Attribute-based conditional proxy re-encryption (AB-CPRE) allows delegators to carry out attribute-based control on the delegation of decryption by setting policies and attribute vectors. The fine-grained control of AB-CPRE makes it suitable for a variety of applications, such as cloud storage and distributed file systems. However, all existing AB-CPRE schemes are constructed under classical number-theoretic assumptions, which are vulnerable to quantum cryptoanalysis. Therefore, we propose the first AB-CPRE scheme based on the learning with errors (LWE) assumption. Constructed from fully key-homomorphic encryption (FKHE) and key-switching techniques, our scheme is unidirectional, single-hop, and enables a polynomial-deep boolean circuit as its policy. Furthermore, we split the ciphertext into two independent parts to avoid two-level or multi-level encryption/decryption mechanisms. Taking advantage of it, we then extend our single-hop AB-CPRE into an efficient and concise multi-hop one. No matter how many transformations are performed, the re-encrypted ciphertext is in constant size, and only one encryption/decryption algorithm is needed. Both of our schemes are proved to be selective secure against chosen-plaintext attacks (CPA) in the standard model

Changes in microstates of first-episode untreated nonsuicidal self-injury adolescents exposed to negative emotional stimuli and after receiving rTMS intervention

BackgroundNonsuicidal self-injury (NSSI) is a common mental health threat in adolescents, peaking in adolescence with a lifetime prevalence of ~17%–60%, making it a high-risk risk factor for suicide. In this study, we compared changes in microstate parameters in depressed adolescents with NSSI, depressed adolescents, and healthy adolescents during exposure to negative emotional stimuli, and further explored the improvement of clinical symptoms and the effect of microstate parameters of repetitive transcranial magnetic stimulation (rTMS) in depressed adolescents with NSSI, and more evidence was provided for potential mechanisms and treatment optimization for the occurrence of NSSI behaviors in adolescents.MethodsSixty-six patients with major depressive disorder (MDD) exhibiting NSSI behavior (MDD + NSSI group), 52 patients with MDD (MDD group), and 20 healthy subjects (HC group) were recruited to perform neutral and negative emotional stimulation task. The age range of all subjects was 12–17 years. All participants completed the Hamilton Depression Scale, the Patient Health Questionnaire-9, the Ottawa Self-Injury Scale and a self-administered questionnaire to collect demographic information. We provided two different treatments to 66 MDD adolescents with NSSI; 31 patients received medication and completed post-treatment scale assessments and EEG acquisitions, and 21 patients received medication combined with rTMS and completed post-treatment scale assessments and EEG acquisitions. Multichannel EEG was recorded continuously from 64 scalp electrodes using the Curry 8 system. EEG signal preprocessing and analysis was performed offline, using the EEGLAB toolbox in MATLAB. Use the Microstate Analysis Toolbox in EEGLAB for segmentation and computation of microstates, and calculate a topographic map of the microstate segmentation of the EEG signal for a single subject in each dataset, and four parameters were obtained for each microstate classification: global explained variance (GEV), mean duration (Duration), average number of occurrences per second (Occurrence), and average percentage of total analysis time occupied (Coverage), which were then statistically analyzed.ResultsOur results indicate that MDD adolescents with NSSI exhibit abnormalities in MS 3, MS 4, and MS 6 parameters when exposed to negative emotional stimuli compared to MDD adolescents and healthy adolescents. The results also showed that medication combined with rTMS treatment improved depressive symptoms and NSSI performance more significantly in MDD adolescents with NSSI compared to medication treatment, and affected MS 1, MS 2, and MS 4 parameters in MDD adolescents with NSSI, providing microstate evidence for the moderating effect of rTMS.ConclusionMDD adolescents with NSSI showed abnormal changes in several microstate parameters when receiving negative emotional stimuli, and compared to those not receiving rTMS treatment, MDD adolescents with NSSI treated with rTMS showed more significant improvements in depressive symptoms and NSSI performance, as well as improvements in EEG microstate abnormalities

Observation of a charged charmoniumlike structure in e+e−→(D∗Dˉ∗)±π∓e^+e^- \to (D^{*} \bar{D}^{*})^{\pm} \pi^\mp at s=4.26\sqrt{s}=4.26GeV

We study the process $e^+e^- \to (D^{*} \bar{D}^{*})^{\pm} \pi^\mp$ at a center-of-mass energy of 4.26GeV using a 827pb$^{-1}$ data sample obtained with the BESIII detector at the Beijing Electron Positron Collider. Based on a partial reconstruction technique, the Born cross section is measured to be $(137\pm9\pm15)$pb. We observe a structure near the $(D^{*} \bar{D}^{*})^{\pm}$ threshold in the $\pi^\mp$ recoil mass spectrum, which we denote as the $Z^{\pm}_c(4025)$. The measured mass and width of the structure are $(4026.3\pm2.6\pm3.7)$MeV/c$^2$ and $(24.8\pm5.6\pm7.7)$MeV, respectively. Its production ratio $\frac{\sigma(e^+e^-\to Z^{\pm}_c(4025)\pi^\mp \to (D^{*} \bar{D}^{*})^{\pm} \pi^\mp)}{\sigma(e^+e^-\to (D^{*} \bar{D}^{*})^{\pm} \pi^\mp)}$ is determined to be $0.65\pm0.09\pm0.06$. The first uncertainties are statistical and the second are systematic.Comment: 7 pages, 4 figures, 1 table; version accepted to be published in PR