Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accurate diagnosis of ASD is pivotal for successful intervention. The limited availability of large datasets for neuroimaging investigations, however, poses a significant challenge to the timely and precise identification of ASD. To address this problem, we propose a breakthrough approach, GARL, for ASD diagnosis using neuroimaging data. GARL innovatively integrates the power of GANs and Deep Q-Learning to augment limited datasets and enhance diagnostic precision. We utilized the Autistic Brain Imaging Data Exchange (ABIDE) I and II datasets and employed a GAN to expand these datasets, creating a more robust and diversified dataset for analysis. This approach not only captures the underlying sample distribution within ABIDE I and II but also employs deep reinforcement learning for continuous self-improvement, significantly enhancing the capability of the model to generalize and adapt. Our experimental results confirmed that GAN-based data augmentation effectively improved the performance of all prediction models on both datasets, with the combination of InfoGAN and DQN's GARL yielding the most notable improvement.

Study on the quality difference between raw and ginger juice processed Magnoliae officinalis cortex by UPLC-Q-TOF-MS/MS and GC-MS coupled with color measurement.

INTRODUCTION: Magnoliae officinalis cortex (MOC) has been used for thousands of years as a traditional Chinese herb. In Chinese Pharmacopoeia (2020 edition), it has two types of decoction pieces, raw Magnoliae officinalis cortex (RMOC) and ginger juice processed Magnoliae officinalis cortex (GMOC). The quality difference between RMOC and GMOC has not been explored systemically. OBJECTIVE: This study aimed to discover the quality difference between RMOC and GMOC, and clarify the effect of ginger juice during processing comprehensively. METHODS: Ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS/MS) and gas chromatography-mass spectrometry (GC-MS) were applied to study the non-volatile and volatile components of RMOC and GMOC; electronic eye was applied for color measurement. Meanwhile, water processed Magnoliae officinalis cortex (WMOC) was studied as the blank sample. RESULTS: There were 155 non-volatile and 72 volatile substances identified. Between RMOC and GMOC, 29 distinctive non-volatile and 34 distinctive volatile compounds were detected, among which 23 new compounds appeared and five compounds disappeared due to the addition of ginger juice during processing. The intensities of 12 common non-volatile compounds and the relative percentage contents of four common volatile compounds showed significant differences between RMOC and GMOC. In color measurement of RMOC, GMOC, and WMOC, 14 common compounds with significant differences were discovered related to their color values, and their mathematical prediction functions were built. CONCLUSION: There were significant differences between RMOC and GMOC; the processing mechanism of GMOC would be carried out based on the differential compounds in further investigation.

A novel deep domain adaptation method for automated detection of sleep apnea/hypopnea events.

Objective.Sleep apnea-hypopnea syndrome (SAHS) is a common sleep-related respiratory disorder that is generally assessed for severity using polysomnography (PSG); however, the diversity of sampling devices and patients makes this not only costly but may also degrade the performance of the algorithms.Approach.This paper proposes a novel deep domain adaptation module which uses a long short-term memory-convolutional neural network embedded with the channel attention mechanism to achieve autonomous extraction of high-quality features. Meanwhile, a domain adaptation module was built to achieve domain-invariant feature extraction for reducing the differences in data distribution caused by different devices and other factors. In addition, during the training process, the algorithm used the last second label as the label of the PSG segment, so that second-by-second evaluation of respiratory events could be achieved.Main results.The algorithm applied the two datasets provided by PhysioNet as the source and target domains. The accuracy, sensitivity and specificity of the algorithm on the source domain were 86.46%, 86.11% and 93.17%, respectively, and on the target domain were 83.63%, 82.52%, 91.62%, respectively. The proposed algorithm showed strong generalization ability and the classification results were comparable to the current advanced methods. Besides, the apnea-hypopnea index values estimated by the proposed algorithm showed a high correlation with the manual scoring values on both domains.Significance.The proposed algorithm can effectively perform SAHS detection and evaluation with certain generalization.