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1. Progress of photonuclear cross sections for medical radioisotope production at the SLEGS energy domain

Author

Pang, Xuan, Sun, Bao-Hua, Zhu, Li-Hua, Lu, Guang-Hong, Zhou, Hong-Bo, and Yang, Dong

Subjects
Nuclear Experiment
Abstract

Photonuclear reactions using a laser Compton scattering (LCS) gamma source provide a new method for producing radioisotopes for medical applications. Compared with the conventional method, this method has the advantages of a high specific activity and less heat. Initiated by the Shanghai Laser Electron Gamma Source (SLEGS), we conducted a survey of potential photonuclear reactions, $(\upgamma,n)$, $(\upgamma,p)$, and $(\upgamma,\upgamma')$ whose cross-sections can be measured at SLEGS by summarizing the experimental progress. In general, the data are rare and occasionally inconsistent. Therefore, theoretical calculations are often used to evaluate the production of medical radioisotopes. Subsequently, we verified the model uncertainties of the widely used reaction code TALYS-1.96, using the experimental data of the \ce{^100Mo}$(\upgamma,n)$\ce{^99Mo}, \ce{^65Cu}$(\upgamma,n)$\ce{^64Cu}, and \ce{^68Zn}$(\upgamma,p)$\ce{^67Cu} reactions., Comment: 16 pages, 19 figures

Published
2024
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2. Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup

Author

Huggins, Ashley A, Baird, C Lexi, Briggs, Melvin, Laskowitz, Sarah, Hussain, Ahmed, Fouda, Samar, Haswell, Courtney, Sun, Delin, Salminen, Lauren E, Jahanshad, Neda, Thomopoulos, Sophia I, Veltman, Dick J, Frijling, Jessie L, Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia BJ, Nawjin, Laura, Wang, Li, Zhu, Ye, Li, Gen, Stein, Dan J, Ipser, Jonathan, Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L, Suarez-Jimenez, Benjamin, Zhu, Xi, Kim, Yoojean, He, Xiaofu, Zilcha-Mano, Sigal, Lazarov, Amit, Neria, Yuval, Stevens, Jennifer S, Ressler, Kerry J, Jovanovic, Tanja, van Rooij, Sanne JH, Fani, Negar, Hudson, Anna R, Mueller, Sven C, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K, Schmahl, Christian, Herzog, Julia I, Říha, Pavel, Rektor, Ivan, Lebois, Lauren AM, Kaufman, Milissa L, Olson, Elizabeth A, Baker, Justin T, Rosso, Isabelle M, King, Anthony P, Liberzon, Isreal, Angstadt, Mike, Davenport, Nicholas D, Sponheim, Scott R, Disner, Seth G, Straube, Thomas, Hofmann, David, Qi, Rongfeng, Lu, Guang Ming, Baugh, Lee A, Forster, Gina L, Simons, Raluca M, Simons, Jeffrey S, Magnotta, Vincent A, Fercho, Kelene A, Maron-Katz, Adi, Etkin, Amit, Cotton, Andrew S, O’Leary, Erin N, Xie, Hong, Wang, Xin, Quidé, Yann, El-Hage, Wissam, Lissek, Shmuel, Berg, Hannah, Bruce, Steven, Cisler, Josh, Ross, Marisa, Herringa, Ryan J, Grupe, Daniel W, Nitschke, Jack B, Davidson, Richard J, Larson, Christine L, deRoon-Cassini, Terri A, Tomas, Carissa W, Fitzgerald, Jacklynn M, Blackford, Jennifer Urbano, Olatunji, Bunmi O, Kremen, William S, Lyons, Michael J, Franz, Carol E, Gordon, Evan M, May, Geoffrey, Nelson, Steven M, Abdallah, Chadi G, Levy, Ifat, and Harpaz-Rotem, Ilan

Subjects
Clinical and Health Psychology, Psychology, Post-Traumatic Stress Disorder (PTSD), Mental Illness, Behavioral and Social Science, Anxiety Disorders, Mental Health, Brain Disorders, Mind and Body, Neurosciences, Mental health, Humans, Stress Disorders, Post-Traumatic, Cerebellum, Female, Male, Adult, Magnetic Resonance Imaging, Middle Aged, White Matter, Gray Matter, Organ Size, Deep Learning, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR

Published
2024

3. A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning

Author

Rahman, Fathima Abdul and Lu, Guang

Subjects
Computer Science - Computation and Language, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, I.2.10
Abstract

Owing to the recent developments in Generative Artificial Intelligence (GenAI) and Large Language Models (LLM), conversational agents are becoming increasingly popular and accepted. They provide a human touch by interacting in ways familiar to us and by providing support as virtual companions. Therefore, it is important to understand the user's emotions in order to respond considerately. Compared to the standard problem of emotion recognition, conversational agents face an additional constraint in that recognition must be real-time. Studies on model architectures using audio, visual, and textual modalities have mainly focused on emotion classification using full video sequences that do not provide online features. In this work, we present a novel paradigm for contextualized Emotion Recognition using Graph Convolutional Network with Reinforcement Learning (conER-GRL). Conversations are partitioned into smaller groups of utterances for effective extraction of contextual information. The system uses Gated Recurrent Units (GRU) to extract multimodal features from these groups of utterances. More importantly, Graph Convolutional Networks (GCN) and Reinforcement Learning (RL) agents are cascade trained to capture the complex dependencies of emotion features in interactive scenarios. Comparing the results of the conER-GRL model with other state-of-the-art models on the benchmark dataset IEMOCAP demonstrates the advantageous capabilities of the conER-GRL architecture in recognizing emotions in real-time from multimodal conversational signals., Comment: 5 pages (4 main + 1 reference), 2 figures. Submitted to IEEE FG2024

Published
2023

5. Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

Author

Zhu, Xi, Kim, Yoojean, Ravid, Orren, He, Xiaofu, Suarez-Jimenez, Benjamin, Zilcha-Mano, Sigal, Lazarov, Amit, Lee, Seonjoo, Abdallah, Chadi, Angstadt, Michael, Averill, Christopher, Baird, C, Baugh, Lee, Blackford, Jennifer, Bomyea, Jessica, Bruce, Steven, Bryant, Richard, Cao, Zhihong, Choi, Kyle, Cisler, Josh, Cotton, Andrew, Daniels, Judith, Davenport, Nicholas, Davidson, Richard, DeBellis, Michael, Dennis, Emily, Densmore, Maria, deRoon-Cassini, Terri, Disner, Seth, Hage, Wissam, Etkin, Amit, Fani, Negar, Fercho, Kelene, Fitzgerald, Jacklynn, Forster, Gina, Frijling, Jessie, Geuze, Elbert, Gonenc, Atilla, Gordon, Evan, Gruber, Staci, Grupe, Daniel, Guenette, Jeffrey, Haswell, Courtney, Herringa, Ryan, Herzog, Julia, Hofmann, David, Hosseini, Bobak, Hudson, Anna, Huggins, Ashley, Ipser, Jonathan, Jahanshad, Neda, Jia-Richards, Meilin, Jovanovic, Tanja, Kaufman, Milissa, Kennis, Mitzy, King, Anthony, Kinzel, Philipp, Koch, Saskia, Koerte, Inga, Koopowitz, Sheri, Korgaonkar, Mayuresh, Krystal, John, Lanius, Ruth, Larson, Christine, Lebois, Lauren, Li, Gen, Liberzon, Israel, Lu, Guang, Luo, Yifeng, Magnotta, Vincent, Manthey, Antje, Maron-Katz, Adi, May, Geoffery, McLaughlin, Katie, Mueller, Sven, Nawijn, Laura, Nelson, Steven, Neufeld, Richard, Nitschke, Jack, OLeary, Erin, Olatunji, Bunmi, Olff, Miranda, Peverill, Matthew, Phan, K, Qi, Rongfeng, Quidé, Yann, Rektor, Ivan, Ressler, Kerry, Riha, Pavel, Ross, Marisa, Rosso, Isabelle, Salminen, Lauren, Sambrook, Kelly, Schmahl, Christian, Shenton, Martha, Sheridan, Margaret, Shih, Chiahao, Sicorello, Maurizio, Sierk, Anika, and van Rooij, Sanne

Subjects
Classification, Deep learning, Machine learning, Multimodal MRI, Posttraumatic stress disorder, Humans, Stress Disorders, Post-Traumatic, Reproducibility of Results, Big Data, Neuroimaging, Magnetic Resonance Imaging, Brain
Abstract

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Published
2023

6. Research on flow mechanism and development law of Jiyang shale oil

Author

ZHANG Shiming, SUN Hongxia, LIU Zupeng, LIU Yan, LU Guang, CHEN Liyang, CHEN Jianxun, ZHU Yurui, LIU Wei, and ZHANG Diandong

Subjects
shale oil, flow mechanism, multi-scale pore and fracture, stress sensitivity, development law, Chemical technology, TP1-1185, Petroleum refining. Petroleum products, TP690-692.5, Geology, QE1-996.5
Abstract

Jiyang shale oil resources have enormous potential. However， there is a lack of systematic understanding of seepage mechanism and development laws in the multi-scale spaces after fracturing due to the influence of complex geological conditions， making it difficult to achieve large-scale beneficial development. In order to clarify the formation mechanism of the development law of Jiyang shale oil， the multi-scale fracture characteristics of Jiyang shale oil were analyzed. The formation mechanism and combination mode of fracture networks after fracturing were studied using physical experiments and numerical simulations. In addition， seepage mechanisms in multi-medium spaces were researched， including the stress sensitivity of multi-scale fractures， nonlinear flow in multi-scale pores and fractures， and imbibition displacement of pore-fracture systems. According to the differences in the distribution of multi-scale fracture networks and their flow laws after fracturing， a three-zone flow pattern of “easy flow-slow flow-difficult flow” for shale oil was established. Finally， the flow characteristics and production laws of Jiyang shale oil at different flow stages were clarified. The research results indicated that the developed bedding fractures could improve the physical properties of reservoirs， enhance imbibition displacement efficiency， and reduce start-up pressure gradient. During the elastic development stage， the flow channels reduce， and the fracture network conductivity decreases as the formation pressure declines， resulting in significant stress sensitivity. In the development process of Jiyang shale oil， the initial flow state is mainly dominated by linear flow and elliptical flow of fractures supplied by the easy flow zone. In the middle stage of development， the slow flow zone supplies energy to the easy flow zone， and the flow state changes to elliptical flow in the formation. Moreover， the shale oil in the difficult flow zone is gradually produced， and the flow state changes to boundary-controlled flow in the later stage of development. Due to the influence of geological conditions and production systems， there are significant differences in the energy change， water cut decrease， and production change law of different horizontal wells. Reasonably optimizing the shut-in time and production system can ensure imbibition displacement efficiency， efficiently utilize formation energy， and improve shale oil production capacity.

Published
2024
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7. Hybrid Long Document Summarization using C2F-FAR and ChatGPT: A Practical Study

Author

Lu, Guang, Larcher, Sylvia B., and Tran, Tu

Subjects
Computer Science - Computation and Language
Abstract

Text summarization is a downstream natural language processing (NLP) task that challenges the understanding and generation capabilities of language models. Considerable progress has been made in automatically summarizing short texts, such as news articles, often leading to satisfactory results. However, summarizing long documents remains a major challenge. This is due to the complex contextual information in the text and the lack of open-source benchmarking datasets and evaluation frameworks that can be used to develop and test model performance. In this work, we use ChatGPT, the latest breakthrough in the field of large language models (LLMs), together with the extractive summarization model C2F-FAR (Coarse-to-Fine Facet-Aware Ranking) to propose a hybrid extraction and summarization pipeline for long documents such as business articles and books. We work with the world-renowned company getAbstract AG and leverage their expertise and experience in professional book summarization. A practical study has shown that machine-generated summaries can perform at least as well as human-written summaries when evaluated using current automated evaluation metrics. However, a closer examination of the texts generated by ChatGPT through human evaluations has shown that there are still critical issues in terms of text coherence, faithfulness, and style. Overall, our results show that the use of ChatGPT is a very promising but not yet mature approach for summarizing long documents and can at best serve as an inspiration for human editors. We anticipate that our work will inform NLP researchers about the extent to which ChatGPT's capabilities for summarizing long documents overlap with practitioners' needs. Further work is needed to test the proposed hybrid summarization pipeline, in particular involving GPT-4, and to propose a new evaluation framework tailored to the task of summarizing long documents.

Published
2023

14. Natural Language Processing for Corporate Culture Assessment: Lessons Learned for Building a Strong Employee Value Proposition with GPTW Switzerland AG

Author

Lu, Guang, Heroth, Timo, Lüthi, Cédric, Mollet, Patrick, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, R. Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
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15. Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods

Author

Yang, Jin, Huggins, Ashley A., Sun, Delin, Baird, C. Lexi, Haswell, Courtney C., Frijling, Jessie L., Olff, Miranda, van Zuiden, Mirjam, Koch, Saskia B. J., Nawijn, Laura, Veltman, Dick J., Suarez-Jimenez, Benjamin, Zhu, Xi, Neria, Yuval, Hudson, Anna R., Mueller, Sven C., Baker, Justin T., Lebois, Lauren A. M., Kaufman, Milissa L., Qi, Rongfeng, Lu, Guang Ming, Říha, Pavel, Rektor, Ivan, Dennis, Emily L., Ching, Christopher R. K., Thomopoulos, Sophia I., Salminen, Lauren E., Jahanshad, Neda, Thompson, Paul M., Stein, Dan J., Koopowitz, Sheri M., Ipser, Jonathan C., Seedat, Soraya, du Plessis, Stefan, van den Heuvel, Leigh L., Wang, Li, Zhu, Ye, Li, Gen, Sierk, Anika, Manthey, Antje, Walter, Henrik, Daniels, Judith K., Schmahl, Christian, Herzog, Julia I., Liberzon, Israel, King, Anthony, Angstadt, Mike, Davenport, Nicholas D., Sponheim, Scott R., Disner, Seth G., Straube, Thomas, Hofmann, David, Grupe, Daniel W., Nitschke, Jack B., Davidson, Richard J., Larson, Christine L., deRoon-Cassini, Terri A., Blackford, Jennifer U., Olatunji, Bunmi O., Gordon, Evan M., May, Geoffrey, Nelson, Steven M., Abdallah, Chadi G., Levy, Ifat, Harpaz-Rotem, Ilan, Krystal, John H., Morey, Rajendra A., and Sotiras, Aristeidis

Published
2024
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17. Correlation and risk factors of peripheral and cervicocephalic arterial atherosclerosis in patients with ischemic cerebrovascular disease

Author

Lu-guang Li, Xin Ma, Xiaoxi Zhao, Xiangying Du, and Chen Ling

Subjects
Medicine, Science
Abstract

Abstract Patients with ischemic cerebrovascular disease (ICVD) frequently develop concomitant peripheral artery disease (PAD) or renal artery stenosis (RAS), and multiterritorial atherosclerotic patients usually have a worse prognosis. We aimed to evaluate the status of peripheral atherosclerosis (AS) and cervicocephalic AS (CAS) in ICVD patients with AS, their correlation, and related risk factors contributing to coexisting cervicocephalic-peripheral AS (CPAS). Based on the severity and extent of AS evaluated by computed tomography angiography and ultrasound, the degree of AS was triple categorized to assess the correlation between CAS and PAD/RAS. CAS and PAD/RAS were defined as the most severe stenosis being ≥ 50% luminal diameter in cervicocephalic or lower limb arteries, and a peak systolic velocity at the turbulent site being ≥ 180 cm/s in the renal artery. Among 403 patients with symptom onset within 30 days, CAS, PAD, and RAS occurrence rates were 68.7%, 25.3%, and 9.9%, respectively. PAD was independently associated with the degree of extracranial and intracranial CAS (p = 0.042, OR = 1.428, 95% CI 1.014–2.012; p = 0.002, OR = 1.680, 95% CI 1.206–2.339), while RAS was independently associated with the degree of extracranial CAS (p = 0.001, OR = 2.880, 95% CI 1.556–5.329). Independent CPAS risk factors included an ischemic stroke history (p = 0.033), increased age (p

Published
2024
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42. CAMK2D serves as a molecular scaffold for RNF8-MAD2 complex to induce mitotic checkpoint in glioma

Author

Chuah, You Heng, Tay, Emmy Xue Yun, Grinchuk, Oleg V., Yoon, Jeehyun, Feng, Jia, Kannan, Srinivasaraghavan, Robert, Matius, Jakhar, Rekha, Liang, Yajing, Lee, Bernice Woon Li, Wang, Loo Chien, Lim, Yan Ting, Zhao, Tianyun, Sobota, Radoslaw M., Lu, Guang, Low, Boon Chuan, Crasta, Karen Carmelina, Verma, Chandra Shekhar, Lin, Zhewang, and Ong, Derrick Sek Tong

Published
2023
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44. A deep-learning model for intracranial aneurysm detection on CT angiography images in China: a stepwise, multicentre, early-stage clinical validation study

Author

Hu, Bin, Shi, Zhao, Lu, Li, Miao, Zhongchang, Wang, Hao, Zhou, Zhen, Zhang, Fandong, Wang, Rongpin, Luo, Xiao, Xu, Feng, Li, Sheng, Fang, Xiangming, Wang, Xiaodong, Yan, Ge, Lv, Fajin, Zhang, Meng, Sun, Qiu, Cui, Guangbin, Liu, Yubao, Zhang, Shu, Pan, Chengwei, Hou, Zhibo, Liang, Huiying, Pan, Yuning, Chen, Xiaoxia, Li, Xiaorong, Zhou, Fei, Tan, Bin, Liu, Feidi, Chen, Feng, Gu, Hongmei, Hou, Mingli, Xu, Rui, Zuo, Rui, Tao, Shumin, Chen, Weiwei, Chai, Xue, Wang, Wulin, Dai, Yongjian, Chen, Yueqin, Zhou, Changsheng, Lu, Guang Ming, Schoepf, U.Joseph, Moore, W.Garrison, Varga-Szemes, Akos, Yu, Yizhou, Hu, Chunfeng, Zhang, Longjiang, Schoepf, U Joseph, Garrison Moore, W, and Zhang, Long Jiang

Published
2024
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