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1. Abnormally enhanced Hall Lorenz number in the magnetic Weyl semimetal NdAlSi

Author

Zhang, Nan, Tu, Daifeng, Li, Ding, Tang, Kaixin, Nie, Linpeng, Li, Houpu, Li, Hongyu, Qi, Tao, Wu, Tao, Zhou, Jianhui, Xiang, Ziji, and Chen, Xianhui

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

In Landau's celebrated Fermi liquid theory, electrons in a metal obey the Wiedemann--Franz law at the lowest temperatures. This law states that electron heat and charge transport are linked by a constant $L_0$, i.e., the Sommerfeld value of the Lorenz number ($L$). Such relation can be violated at elevated temperatures where the abundant inelastic scattering leads to a reduction of the Lorenz number ($L < L_0$). Here, we report a rare case of remarkably enhanced Lorenz number ($L > L_0$) discovered in the magnetic topological semimetal NdAlSi. Measurements of the transverse electrical and thermal transport coefficients reveal that the Hall Lorenz number $L_{xy}$ in NdAlSi starts to deviate from the canonical value far above its magnetic ordering temperature. Moreover, $L_{xy}$ displays strong nonmonotonic temperature and field dependence, reaching its maximum value close to 2$L_0$ in an intermediate parameter range. Further analysis excludes charge-neutral excitations as the origin of enhanced $L_{xy}$. Alternatively, we attribute it to the Kondo-type elastic scattering off localized 4$f$ electrons, which creates a peculiar energy distribution of the quasiparticle relaxation time. Our results provide insights into the perplexing transport phenomena caused by the interplay between charge and spin degrees of freedom., Comment: 23 pages, 5 figures

Published
2024
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2. Education and Literacy in the Development and Transmission of Chinese Yao Nationality Folk Songs

Author

Hongyu Chen and Peerapong Sensai

Abstract

Chinese Yao Nationality Folk Songs are a distinctive cultural treasure thriving within the Guangxi Zhuang Autonomous Region, intimately intertwined with the lives and traditions of the Yao people. The objective of this study is to investigate the role of education and literacy in the development and transmission of Chinese Yao Nationality folk songs in the Guangxi Zhuang Autonomous Region. The research site, Jinxiu Yao Autonomous County, serves as a culturally significant backdrop, chosen for its accessibility and cultural relevance to the research objectives. To gain comprehensive insights into this complex topic, three key informants with specialized knowledge and cultural insights were meticulously selected. An array of research tools, including observation forms, interview forms, and questionnaires, were employed to gather data and analyze various aspects of Yao Nationality folk songs. The results reveal the multifaceted phases of development these songs have undergone, from their sacred origins to their role in communal celebrations and emotional expression. The transmission of these songs is showcased through a blend of oral traditions and written records, with a contemporary integration of media and technology. This research suggests the need for a balanced approach to preserving cultural heritage while embracing modernity.

Published
2024

3. Stressful Life Events among College Student: Direct and Indirect Relationships with Insomnia, Depression, Self-Esteem, and Suicide Ideation

Author

Hongyu Zou, Xiaohua Kong, Ziyao Chen, and Wei Zhang

Abstract

Suicide is a serious public health problem and is the fourth leading cause of death among adolescents. Suicide ideation is the most significant predictor of suicide. However, few empirical studies have examined the relationship between stressful life events, insomnia, depression symptoms, self-esteem, and suicide ideation among college students. This study builds on O'Connor's The integrated motivational-volitional model of suicidal behavior theory will verify the relationship between these five factors through a large data study among college students, and further reveal the mechanisms and interventions of suicide ideation among college students. A total of 2717 (mean age = 19.81 years, 22.49% male) college students from South China participated in this behavioral and health survey. Using self-reported questionnaires which were Adolescent Self Rating Life Events Check-list, MINI-C, Insomnia Severity Index, PHQ-9, and Self-Esteem Scale to assess the subjects' demographic information, suicide ideation, stressful life events, insomnia, depressive symptoms, and self-esteem levels as comprehensively as possible. A moderated chain mediation model was used to validate their relationship. The results showed that stressful life events significant positively predicted insomnia symptoms, depressive symptoms, and suicide ideation. The mediating effect of depression between stressful life events and suicide ideation was significant. The chain mediating effect of insomnia and depression between stressful life events and suicide ideation was significant. However, the mediating effect of insomnia between stressful life events and suicide ideation was not significant. Also, the positive prediction of stressful life events and depression on suicide ideation was weaker when the individual's self-esteem level was higher, and the moderated chain mediating effect was significant. This study developed a moderated chain mediation model, and proposes a new integrated stress coping-self-perception model of suicide ideation to discover the important role of self-esteem levels in adolescent suicide prevention.

Published
2024
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4. STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data

Author

Zhang, Wei, Zeng, Weiming, Chen, Hongyu, Liu, Jie, Yan, Hongjie, Zhang, Kaile, Tao, Ran, Siok, Wai Ting, and Wang, Nizhuan

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Accurate diagnosis of depression is crucial for timely implementation of optimal treatments, preventing complications and reducing the risk of suicide. Traditional methods rely on self-report questionnaires and clinical assessment, lacking objective biomarkers. Combining fMRI with artificial intelligence can enhance depression diagnosis by integrating neuroimaging indicators. However, the specificity of fMRI acquisition for depression often results in unbalanced and small datasets, challenging the sensitivity and accuracy of classification models. In this study, we propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating CNN and RNN to capture both temporal and spatial features of brain activity. STANet comprises the following steps:(1) Aggregate spatio-temporal information via ICA. (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the SMOTE to generate new samples for minority classes. (4) Employ the AFGRU classifier, which combines Fourier transformation with GRU, to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. The experimental results demonstrate that STANet achieves superior depression diagnostic performance with 82.38% accuracy and a 90.72% AUC. The STFA module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and stacked GRU, attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. STANet outperforms traditional or deep learning classifiers, and functional connectivity-based classifiers, as demonstrated by ten-fold cross-validation., Comment: This paper is published on Tomography

Published
2024
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5. Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas

Author

Feng, Yu, Zeng, Weiming, Xie, Yifan, Chen, Hongyu, Wang, Lei, Wang, Yingying, Yan, Hongjie, Zhang, Kaile, Tao, Ran, Siok, Wai Ting, and Wang, Nizhuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Background: Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information, making it particularly suitable for the neural dynamics of depression research. Methods: To address this gap, our study firstly leveraged fMRI to delineate activated regions associated with positive and negative emotions in healthy individuals, resulting in the creation of positive emotion atlas (PEA) and negative emotion atlas (NEA). Subsequently, we examined neuroimaging changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning. Results: Our findings demonstrate that the classification accuracy of depressed patients based on PEA and NEA exceeded 0.70, a notable improvement compared to the whole-brain atlases. Furthermore, ALFF analysis unveiled significant differences between depressed patients and healthy controls in eight functional clusters during the NEA, focusing on the left cuneus, cingulate gyrus, and superior parietal lobule. In contrast, the PEA revealed more pronounced differences across fifteen clusters, involving the right fusiform gyrus, parahippocampal gyrus, and inferior parietal lobule. Limitations: Due to the limited sample size and subtypes of depressed patients, the efficacy may need further validation in future. Conclusions: These findings emphasize the complex interplay between emotion modulation and depression, showcasing significant alterations in both PEA and NEA among depression patients. This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation., Comment: 24 pages

Published
2024
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6. Van-Hove annihilation and nematic instability on a Kagome lattice

Author

Jiang, Yu-Xiao, Shao, Sen, Xia, Wei, Denner, M. Michael, Ingham, Julian, Hossain, Md Shafayat, Qiu, Qingzheng, Zheng, Xiquan, Chen, Hongyu, Cheng, Zi-Jia, Yang, Xian P., Kim, Byunghoon, Yin, Jia-Xin, Zhang, Songbo, Litskevich, Maksim, Zhang, Qi, Cochran, Tyler A., Peng, Yingying, Chang, Guoqing, Guo, Yanfeng, Thomale, Ronny, Neupert, Titus, and Hasan, M. Zahid

Subjects
Condensed Matter - Strongly Correlated Electrons, Condensed Matter - Materials Science
Abstract

Novel states of matter arise in quantum materials due to strong interactions among electrons. A nematic phase breaks the point group symmetry of the crystal lattice and is known to emerge in correlated materials. Here we report the observation of an intra-unit-cell nematic order and signatures of Pomeranchuk instability in the Kagome metal ScV6Sn6. Using scanning tunneling microscopy and spectroscopy, we reveal a stripe-like nematic order breaking the crystal rotational symmetry within the Kagome lattice itself. Moreover, we identify a set of van Hove singularities adhering to the Kagome layer electrons, which appear along one direction of the Brillouin zone while being annihilated along other high-symmetry directions, revealing a rotational symmetry breaking. Via detailed spectroscopic maps, we further observe an elliptical deformation of Fermi surface, which provides direct evidence for an electronically mediated nematic order. Our work not only bridges the gap between electronic nematicity and Kagome physics, but also sheds light on the potential mechanism for realizing symmetry-broken phases in correlated electron systems., Comment: 19 pages, 5 figures

Published
2024
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27. Refining breast cancer genetic risk and biology through multi-ancestry fine-mapping analyses of 192 risk regions

Author

Jia, Guochong, Chen, Zhishan, Ping, Jie, Cai, Qiuyin, Tao, Ran, Li, Chao, Bauer, Joshua A., Xie, Yuhan, Ambs, Stefan, Barnard, Mollie E., Chen, Yu, Choi, Ji-Yeob, Gao, Yu-Tang, Garcia-Closas, Montserrat, Gu, Jian, Hu, Jennifer J., Iwasaki, Motoki, John, Esther M., Kweon, Sun-Seog, Li, Christopher I., Matsuda, Koichi, Matsuo, Keitaro, Nathanson, Katherine L., Nemesure, Barbara, Olopade, Olufunmilayo I., Pal, Tuya, Park, Sue K., Park, Boyoung, Press, Michael F., Sanderson, Maureen, Sandler, Dale P., Shen, Chen-Yang, Troester, Melissa A., Yao, Song, Zheng, Ying, Ahearn, Thomas, Brewster, Abenaa M., Falusi, Adeyinka, Hennis, Anselm J. M., Ito, Hidemi, Kubo, Michiaki, Lee, Eun-Sook, Makumbi, Timothy, Ndom, Paul, Noh, Dong-Young, O’Brien, Katie M., Ojengbede, Oladosu, Olshan, Andrew F., Park, Min-Ho, Reid, Sonya, Yamaji, Taiki, Zirpoli, Gary, Butler, Ebonee N., Huang, Maosheng, Low, Siew-Kee, Obafunwa, John, Weinberg, Clarice R., Zhang, Haoyu, Zhao, Hongyu, Cote, Michelle L., Ambrosone, Christine B., Huo, Dezheng, Li, Bingshan, Kang, Daehee, Palmer, Julie R., Shu, Xiao-Ou, Haiman, Christopher A., Guo, Xingyi, Long, Jirong, and Zheng, Wei

Published
2025
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50. Population-wide DNA methylation polymorphisms at single-nucleotide resolution in 207 cotton accessions reveal epigenomic contributions to complex traits

Author

Zhao, Ting, Guan, Xueying, Hu, Yan, Zhang, Ziqian, Yang, Han, Shi, Xiaowen, Han, Jin, Mei, Huan, Wang, Luyao, Shao, Lei, Wu, Hongyu, Chen, Qianqian, Zhao, Yongyan, Pan, Jiaying, Hao, Yupeng, Dong, Zeyu, Long, Xuan, Deng, Qian, Zhao, Shengjun, Zhang, Mengke, Zhu, Yumeng, Ma, Xiaowei, Chen, Zequan, Deng, Yayuan, Si, Zhanfeng, Li, Xin, Zhang, Tianzhen, Gu, Fei, Gu, Xiaofeng, and Fang, Lei

Published
2024
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