Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.

COVID-19 Health Beliefs Regarding Mask-Wearing and Vaccinations on Twitter: A Deep Learning Approach (Preprint)

Background

Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19–related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19.

Objective

The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action.

Methods

A total of 646,885,238 COVID-19–related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets.

Results

In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action.

Conclusions

During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals.

Secondary growth mechanism of SiGe islands deposited on a mixed-phase microcrystalline Si by ion beam co-sputtering

We discuss the SiGe island co-sputtering deposition on a microcrystalline silicon (μc-Si) buffer layer and the secondary island growth based on this pre-SiGe island layer. The growth phenomenon of SiGe islands on crystalline silicon (c-Si) is also investigated for comparison. The pre-SiGe layer grown on μc-Si exhibits a mixed-phase structure, including SiGe islands and amorphous SiGe (a-SiGe) alloy, while the layer deposited on c-Si shows a single-phase island structure. The preferential growth and Ostwald ripening growth are shown to be the secondary growth mechanism of SiGe islands on μc-Si and c-Si, respectively. This difference may result from the effect of amorphous phase Si (AP-Si) in μc-Si on the island growth. In addition, the Si-Ge intermixing behavior of the secondary-grown islands on μc-Si is interpreted by constructing the model of lateral atomic migration, while this behavior on c-Si is ascribed to traditional uphill atomic diffusion. It is found that the aspect ratios of the preferential-grown super islands are higher than those of the Ostwald-ripening ones. The lower lateral growth rate of super islands due to the lower surface energy of AP-Si on the μc-Si buffer layer for the non-wetting of Ge at 700 °C and the stronger Si-Ge intermixing effect at 730 °C may be responsible for this aspect ratio difference.

Evaluation of the genetic diversity of Bupleurum using amplified fragment length polymorphism analysis

Radix bupleuri (Chaihu), the dried root of the Bupleurum plant, is an important component of traditional Chinese medicine. In this study, we examined the genetic diversity of 11 Bupleurum strains, originating from 7 provinces in China, using amplified fragment length polymorphism analysis. A total of 274 polymorphic bands were obtained using 6 primer combinations, indicating a high level of polymorphism across all strains. An estimation of the relative relationships among strains revealed genetic distances ranging from 0.2183 to 0.7372, with an average of 0.4161. The 2 most closely related varieties were Bupleurum chinense DC. strains collected from Lushi, Henan, and Zhangjiakou, Hebei, with a genetic nearness of 0.2183. Hierarchical clustering divided the strains into 3 main groups, with B. falcatum L. from Hebei and Liaoning Provinces forming a cluster that diverged from that of B. smithii Wolff. and B. chinense DC. B. falcatum L. (Sandao chaihu), collected from Heze, Shandong, clustered independently of the other strains, suggesting that this strain may have been introduced from a different location or that it arose as a result of intraspecific variation. B. smithii Wolff. (Hei chaihu) was closely associated with B. scorzonerifolium Willd. (Nan chaihu) and B. chinense DC. (Bei chaihu), suggesting a common genetic origin.

Electronic properties of single Ge/Si quantum dot grown by ion beam sputtering deposition

The dependence of the electronic properties of a single Ge/Si quantum dot (QD) grown by the ion-beam sputtering deposition technique on growth temperature and QD diameter is investigated by conductive atomic force microscopy (CAFM). The Si-Ge intermixing effect is demonstrated to be important for the current distribution of single QDs. The current staircase induced by the Coulomb blockade effect is observed at higher growth temperatures (>700 °C) due to the formation of an additional barrier between dislocated QDs and Si substrate for the resonant tunneling of holes. According to the proposed single-hole-tunneling model, the fact that the intermixing effect is observed to increase as the incoherent QD size decreases may explain the increase in the starting voltage of the current staircase and the decrease in the current step width.