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Articles by Xiaodong Luo
The clinical value of ultrasound parameters combined with clinical indicators in predicting embryo implantation rates in polycystic ovary syndrome patients at different body mass index levels
Abstract Objective: To evaluate the predictive value of endometrial and uterine artery blood flow parameters, combined with clinical indicators, for embryo implantation outcomes in PCOS patients undergoing IVF-ET across different BMI categories. Methods: We retrospectively analyzed 129 PCOS patients who underwent IVF-ET at our hospital from June 2022 to June 2025. Participants were stratified into four BMI groups. Transvaginal ultrasound parameters and clinical data were statistically analyzed.
SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD
Abstract Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-scale LLM training systems are built around GPU-based clusters, this report presents an end-to-end optimization practice on the Ascend NPU SuperPOD.
A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG
Abstract Depression disorder (DD) is a common mental disorder and a leading contributor to the global burden of disease. However, accurate diagnosis remains challenging due to the reliance of conventional approaches on subjective assessments. To address this limitation, this study proposes a Multiscale Spatio-temporal Convolutional Attention Network (MSTANet) for EEG-based depression detection.
From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework
Abstract The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study aimed to develop a deep learning model based on resting-state electroencephalography (EEG) for automated and quantitative prediction of depression severity. Resting-state EEG data were collected from 70 patients with depressive disorder (DD) and 30 healthy controls (HC). A novel deep learning framework—PLI_GE_gMLP—was proposed.
The impact of daily steps on polycystic ovary syndrome patients
Abstract Objectives: As a reproductive-metabolic disorder, lifestyle interventions play a crucial role in managing Polycystic Ovary Syndrome (PCOS) patients. However, maintaining physical activity in PCOS patients is often challenging. Daily steps are now widely used to provide relatively simple physical activity goals for the general public. This study examines the impact of daily steps on patients with PCOS, aiming to develop simple and effective intervention strategies for them.
PARP inhibitors as radiosensitizers: a comprehensive review of preclinical evidence and clinical applications
1 Introduction Radiotherapy is a commonly used cancer treatment that employs high-energy ionizing radiation (such as X-rays and γ-rays) to directly or indirectly induce DNA damage (1). Direct effects involve radiation disrupting DNA structure, leading to single-strand breaks (SSBs) and double-strand breaks (DSBs), among other lesions (2, 3).
CT ventriculographic identification and neuroendoscopic treatment of acquired hydrocephalus due to cerebral aqueduct lesions: a two-case report
Introduction Hydrocephalus is a neurological disorder characterized by the pathological accumulation of cerebrospinal fluid (CSF) within the ventricular system, resulting in ventricular dilation and elevated intracranial pressure (1–3). Although congenital hydrocephalus is well documented, acquired hydrocephalus due to intraventricular space-occupying lesions of the cerebral aqueduct is rare and presents significant diagnostic challenges (4, 5).
Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach
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Phase characteristics of vocal tract filter can distinguish speakers
Masahiro Okada1* Hiroshi Ito2 1National Research Institute of Police Science, Kashiwa, Japan 2Faculty of Design, Kyushu University, Fukuoka, Japan Introduction: Speaker recognition has been performed by considering individual variations in the power spectrograms of speech, which reflect the resonance phenomena in the speaker's vocal tract filter. In recent years, phase-based features have been used for speaker recognition.
Seismic-2022
The past couple of years has been a rollercoaster for energy professionals, but it did not stop the incredible achievements in machine-learning (ML) techniques, particularly neural networks to improve seismic imaging and multiple attribute extraction to decipher reservoir rock properties and fluid contact movements in areas with data paucity and increasing difficulty in finding new or remaining hydrocarbon accumulations.
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