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761 results on '"Lu, Yifei"'

1. Augmenting the Veracity and Explanations of Complex Fact Checking via Iterative Self-Revision with LLMs

Author

Zhang, Xiaocheng, Wang, Xi, Lu, Yifei, Ye, Zhuangzhuang, Wang, Jianing, Bao, Mengjiao, Yan, Peng, and Su, Xiaohong

Subjects
Computer Science - Computation and Language
Abstract

Explanation generation plays a more pivotal role than fact verification in producing interpretable results and facilitating comprehensive fact-checking, which has recently garnered considerable attention. However, previous studies on explanation generation has shown several limitations, such as being confined to English scenarios, involving overly complex inference processes, and not fully unleashing the potential of the mutual feedback between veracity labels and explanation texts. To address these issues, we construct two complex fact-checking datasets in the Chinese scenarios: CHEF-EG and TrendFact. These datasets involve complex facts in areas such as health, politics, and society, presenting significant challenges for fact verification methods. In response to these challenges, we propose a unified framework called FactISR (Augmenting Fact-Checking via Iterative Self-Revision) to perform mutual feedback between veracity and explanations by leveraging the capabilities of large language models(LLMs). FactISR uses a single model to address tasks such as fact verification and explanation generation. Its self-revision mechanism can further revision the consistency between veracity labels, explanation texts, and evidence, as well as eliminate irrelevant noise. We conducted extensive experiments with baselines and FactISR on the proposed datasets. The experimental results demonstrate the effectiveness of our method.

Published
2024

2. Demystifying and Assessing Code Understandability in Java Decompilation

Author

Qin, Ruixin, Xiong, Yifan, Lu, Yifei, and Pan, Minxue

Subjects
Computer Science - Software Engineering
Abstract

Decompilation, the process of converting machine-level code into readable source code, plays a critical role in reverse engineering. Given that the main purpose of decompilation is to facilitate code comprehension in scenarios where the source code is unavailable, the understandability of decompiled code is of great importance. In this paper, we propose the first empirical study on the understandability of Java decompiled code and obtained the following findings: (1) Understandability of Java decompilation is considered as important as its correctness, and decompilation understandability issues are even more commonly encountered than decompilation failures. (2) A notable percentage of code snippets decompiled by Java decompilers exhibit significantly lower or higher levels of understandability in comparison to their original source code. (3) Unfortunately, Cognitive Complexity demonstrates relatively acceptable precision while low recall in recognizing these code snippets exhibiting diverse understandability during decompilation. (4) Even worse, perplexity demonstrates lower levels of precision and recall in recognizing such code snippets. Inspired by the four findings, we further proposed six code patterns and the first metric for the assessment of decompiled code understandability. This metric was extended from Cognitive Complexity, with six more rules harvested from an exhaustive manual analysis into 1287 pairs of source code snippets and corresponding decompiled code. This metric was also validated using the original and updated dataset, yielding an impressive macro F1-score of 0.88 on the original dataset, and 0.86 on the test set., Comment: 18 pages, 16 figures

Published
2024

3. Proteomics identifies potential immunological drivers of postinfection brain atrophy and cognitive decline

Author

Duggan, Michael R., Peng, Zhongsheng, Sipilä, Pyry N., Lindbohm, Joni V., Chen, Jingsha, Lu, Yifei, Davatzikos, Christos, Erus, Guray, Hohman, Timothy J., Andrews, Shea J., Candia, Julián, Tanaka, Toshiko, Joynes, Cassandra M., Alvarado, Chelsea X., Nalls, Mike A., Cordon, Jenifer, Daya, Gulzar N., An, Yang, Lewis, Alexandria, Moghekar, Abhay, Palta, Priya, Coresh, Josef, Ferrucci, Luigi, Kivimäki, Mika, and Walker, Keenan A.

Published
2024
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4. TechGPT-2.0: A large language model project to solve the task of knowledge graph construction

Author

Wang, Jiaqi, Chang, Yuying, Li, Zhong, An, Ning, Ma, Qi, Hei, Lei, Luo, Haibo, Lu, Yifei, and Ren, Feiliang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models have exhibited robust performance across diverse natural language processing tasks. This report introduces TechGPT-2.0, a project designed to enhance the capabilities of large language models specifically in knowledge graph construction tasks, including named entity recognition (NER) and relationship triple extraction (RTE) tasks in NLP applications. Additionally, it serves as a LLM accessible for research within the Chinese open-source model community. We offer two 7B large language model weights and a QLoRA weight specialized for processing lengthy texts.Notably, TechGPT-2.0 is trained on Huawei's Ascend server. Inheriting all functionalities from TechGPT-1.0, it exhibits robust text processing capabilities, particularly in the domains of medicine and law. Furthermore, we introduce new capabilities to the model, enabling it to process texts in various domains such as geographical areas, transportation, organizations, literary works, biology, natural sciences, astronomical objects, and architecture. These enhancements also fortified the model's adeptness in handling hallucinations, unanswerable queries, and lengthy texts. This report provides a comprehensive and detailed introduction to the full fine-tuning process on Huawei's Ascend servers, encompassing experiences in Ascend server debugging, instruction fine-tuning data processing, and model training. Our code is available at https://github.com/neukg/TechGPT-2.0

Published
2024

8. Synthetic cationic helical polypeptides for the stimulation of antitumour innate immune pathways in antigen-presenting cells

Author

Lee, DaeYong, Huntoon, Kristin, Wang, Yifan, Kang, Minjeong, Lu, Yifei, Jeong, Seong Dong, Link, Todd M., Gallup, Thomas D., Qie, Yaqing, Li, Xuefeng, Dong, Shiyan, Schrank, Benjamin R., Grippin, Adam J., Antony, Abin, Ha, JongHoon, Chang, Mengyu, An, Yi, Wang, Liang, Jiang, Dadi, Li, Jing, Koong, Albert C., Tainer, John A., Jiang, Wen, and Kim, Betty Y. S.

Published
2024
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9. Venture Capitalist: The Factors of the Successful Investment

Author

Li, Wangshu, Lu, Yifei, Wan, Yueying, Appolloni, Andrea, Series Editor, Caracciolo, Francesco, Series Editor, Ding, Zhuoqi, Series Editor, Gogas, Periklis, Series Editor, Huang, Gordon, Series Editor, Nartea, Gilbert, Series Editor, Ngo, Thanh, Series Editor, Striełkowski, Wadim, Series Editor, Balli, Faruk, editor, Au Yong, Hui Nee, editor, Ali Qalati, Sikandar, editor, and Zeng, Ziqiang, editor

Published
2024
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10. Age-associated disparity in phagocytic clearance affects the efficacy of cancer nanotherapeutics

Author

Wang, Yifan, Deng, Weiye, Lee, DaeYong, Yan, Long, Lu, Yifei, Dong, Shiyan, Huntoon, Kristin, Antony, Abin, Li, Xuefeng, Ye, Rui, Zhao, Yan, Zhao, Feiyan, Schrank, Benjamin R., Ha, JongHoon, Kang, Minjeong, Yang, Mingming, Gong, Ping, Lorenzi, Philip L., Tan, Lin, Gallup, Thomas D., Tang, Sarah K., Yang, Zhaogang, Li, Jing, Sanford, Nina N., Wang, Hongmei, Kim, Betty Y. S., and Jiang, Wen

Published
2024
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11. Species abundance distribution characteristics of forest communities and its relationship with elevation in Longwangshan, Zhejiang

Author

LIU Jinliang, LIU Weiyong, JIN Shanshan, YANG Zhongjie, LU Yifei, ZHANG Aiying, and YU Mingjian

Subjects
subtropical forests, community structure, species abundance distribution, models, vegetation types, longwangshan, Botany, QK1-989
Abstract

Species abundance distribution (SAD) combines species richness with species abundance in a community and is an important indicator of community structure. Although many studies have applied mathematical models to fit the SADs of plant communities, there are still few studies on the changes in the shape of SAD (i.e., the skewness of the SAD curves) along the environmental gradient. Especially for forest communities, it is still unclear whether the model fitting and shape change of SADs are consistent in different vegetation types. Here, 28 plots of 20 m × 20 m were set up in Hynobius amjiensis National Nature Reserve according to the main forest vegetation types. The species composition and abundance of each species were surveyed in each plot. The logseries model and the lognormal model were fitted to the SAD in each plot, and then the best-fit model was selected based on the corrected Akaike's Information Criteria (AICc). The α value in Gambin model and the η value in Weibull model were calculated to reflect the shape of SAD in each plot. The λ value in Weibull model was calculated to reflect the scale of SAD (i.e., the range observed in abundance). The relationship between altitude and the shape and scale of SAD was analyzed. The results were as follows: (1) The logseries model fitted SAD better than the lognormal model. (2) When all plots were included, there was no significant correlation between SAD shape (α and η) and altitude, but there was a significant positive correlation between the λ value and altitude. (3) There was a negative correlation between altitude and α and η values in the mixed evergreen and deciduous broad-leaved forests, and there was a significant positive correlation between the α value and altitude in the deciduous broad-leaved forests, while there was no significant correlations between both α value and η value and altitude. The results indicate that the model fitting and shape change of SADs along the environmental gradient are related to the vegetation types of the forest community. Therefore, it is necessary to consider the vegetation types when analyzing the relationship between SAD and impact factors in plant communities.

Published
2024
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15. Deep Learning-Enabled Image Classification for the Determination of Aluminum Ions

Author

Wang, Ce, Wang, Zhaoliang, Lu, Yifei, Hao, Tingting, Hu, Yufang, Wang, Sui, and Guo, Zhiyong

Subjects
Image processing -- Technology application, Ions -- Identification and classification -- Chemical properties, Machine learning -- Usage, Aluminum -- Identification and classification -- Chemical properties, Technology application, Chemistry
Abstract

In this work, an image classification based on deep learning for quantitative field determination of aluminum ions (Al.sup.3+) was developed. Carbon quantum dots with yellow fluorescence were synthesized by a one-pot hydrothermal method which could specifically recognize Al.sup.3+ and produce enhanced green fluorescence. Using the convolutional neural network model in deep learning, an image classification was constructed to classify Al.sup.3+ samples at different concentrations. Then, a fitting method for classification information was proposed for the first time which could convert discontinuous, semi-quantitative concentration classification information into continuous, quantitative, and accurate concentration information. Recoveries of 92.0-110.3% in the concentration range of 0.3-320 [mu]M were obtained with a lower limit of detection of 0.3 [mu]M, exhibiting excellent accuracy and sensitivity. It could be completed in 2 min simply without requiring large equipment. Thus, the deep learning-enabled image classification paves a new way for the determination of metal ions., Author(s): Ce Wang [sup.1] , Zhaoliang Wang [sup.2] , Yifei Lu [sup.1] , Tingting Hao [sup.1] , Yufang Hu [sup.1] , Sui Wang [sup.1] , Zhiyong Guo [sup.1] Author Affiliations: [...]

Published
2023
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28. Implementation of Global Hearts Hypertension Control Programs in 32 Low- and Middle-Income Countries: JACC International

Author

Pathni, Anupam, Osi, Kufor, Dessie, Girma, Zhang, Xiulei, Cazabon, Danielle, Banigbe, Bolanle, Farrell, Margaret, Erojikwe, Okeoma, Swahoo, Swagata, Krishna, Ashish, Yusuf, Abdikaid Ahmed, Kebede, Henok Gebreyohannes, Berhanu, Molla Gedefaw, Bezabih, Tilahun, Tegegn, Teshome Ayalew, Demissie, Workneh, Burka, Daniel, Tasmin, Misu, Ogbeide, Bolatito, Cheadle, Timothy, Odu, Joseph, Beyene, Meron Hailu, Borkhataria, Cecile, Elliott, Kate, Jaffe, Marc, Garg, Renu, Frieden, Thomas R., Khan, Taskeen, Slama, Slim, Mikkelson, Bente, Lai, Truong Duc, Santos, Rauell John, Aquino, Tyrone, Calla, Dofel Joseph, Kunwar, Abhishek, Durgad, Kiran, Bunluesin, Sushera, Dewan, Mary, Obagha, C.E., Msyamboza, Kelias, Das, Bidisha, Kishore, Navneet, Puthussery, Yannick Poulose, Sambandam, Gopinath Thirugnana, Immanuel, A Branch, Chandrakant Joshi, Chakshu, Bhardawaj, Rupali, Wankhede, Amol, Khedkar, Suhas Namdevrao, Bhardawaj, Vishwajit Washudeorao, Kumar Reddy, Thokala Sravan, Chavan, Tejpalsinh Anandrao, Chittimella, Raghavendra Patel, Bhandari, Subhasis, Shah, Urvin Kumar, Chandram, Deenadayalan, Wassey, Mohammed Abdul, Kadia, Abhinav, Sarode, Lalit Dinkar, Chauhan, Lalita, Ponna, Satyendra Nath, Babu, Suresh, Gupta, Piyush, Reddy, Thej Kiran, Sekhon, Hanspreet, Jeelani, Asif, Nath, Gunjan, Wankhede, Virendra, Jayaprasad, Niniya, Mohanty, Trishna, Kumar, Praveen, Khanna, Abhishek, Singh, Shweta, Bhat, Ashish, Thakkar, Jatin, Anitha Sreekumar, Aiswarya Lakshmi, Lakshmanasamy, Ravivarman, Rafique, Mohamed Essa, Dar, Sunil Kumar, Chintala, Sreedhar, Jagadeesh, Bharath, Patruni, Manoj, Raimule, Madhur, Prabhu Valawalkar, Shahanara S., Ordunez, Pedro, Giraldo, Gloria, Martinez, Ramon, Rosende, Andres, Rodriguez, Yenny, Lombardi, Cintia, Gamarra, Angelo, Rodriguez, Libardo, Parra, Natalia, Rodriguez, Gonzalo, Villalta, Noelia, Escobar, Maria C., Villacres, Nilda, Sorto, Carlos, Baca, Edith, Batista, Vhania, Perez, Vivian, Victoria, Jorge, Abaakouk, Zohra, Malcolm, Taraleen, Kostova, Deliana, Richter, Patricia, Whelton, Paul K., Lackland, Daniel, Weber, Michael, Pineiro, Daniel, Wood, David, Pinto, Fausto, Mwangi, Jeremiah, Perel, Pablo, Codato, Elisa, Eiselé, Jean-Luc, Kaur, Prabhdeep, Parashuraman, Ganeshkumar, Sharma, Meenakshi, Shivashankar, Roopa, Bangar, Sampada Dipak, Chakma, Tapas, Pragya, Pragati, Malik, Abdul, Awal, Khondker Abdul, Malik, Fazilatunnesa, Choudhury, Sohel Reza, Bhuiyan, Mahfuzur Rahman, Jubayer, Shamim, Al Mamun, Mohammad Abdullah, Ishraquzzaman, Mir, Abrar, Ahmed Khairul, Akhter, Jubaida, Sultana, Sarmin, Parvej, Aslam, Sayem Khandoker Ehsanul Amin, Noor Nabi, Islam, Md Shahinul, Dey Parag, Deb Dulal, Amin, Ruhul, Luna, Mahfuja, Ibn Karim, Md Abbas, Mahajabin, Progga, Khan, Iqbal Ahmed, Hossain, Sazzad, Ullah, Riasat Razi, Nahid, Muhtamim Fuwad, Zhang, Xinhua, Huang, Hongxia, Wang, Hao, Guan, Tingrui, Zhao, Haiying, Ojji, Dike, Shedul, Gabriel Lamkur, Shedul, Grace, Orji, Anthony, Asuku, Regina, Alo-Joseph, Confidence, Akor, Charity, Egenti, Nonye, Nwankwo, Ada, Omitiran, Kasarachi, Okoli, Rosemary, Ripiye, Nanna, Akor, Blessing, Cai, Jun, Wang, Lu, Wang, Wenjie, Fan, Luyun, Xu, Jianrong (Jenny), Tang, Shanshan, Wu, Guoli, Smith, Barbara, Kenyon, Thomas, Mandal, Sudarsan, Swarnakar, Sunny, Wickramasinghe, S.C., Herath, Amila Bandarra, Ratnayake, I.U., Samarasinghe, Kosala, Kumarasiri, K.H.N., Vensentini, Natalia, Mendieta, Daniel Palacios, Rivera, Marcela, Gonzalez, Yamile Valdez, Cevallos, Lucia, Villatoro, Matías Humberto, Gloria Hernandez, Laura Elena, Arenas, Elsa, Fernández, Daniel, De Oleo, Daniel Mola, Cyr, Shana, Mitchell, Roxanne, Hossain, AHM Enayet, Amin, Mohammad Robed, Alim, Abdul, Milon, Sarowar Uddin, Alex-Okoh, M.O., Odoh-Bako, D.O., Malau, M.T., Oshundele, B.R., Akinkoye, O.K., Abraham, E.A., Eze, S.V., Elemuwa, Chris, Lamorde, Suleiman, Oguntayo, E.A., Rajab, Ashiru, Ibrahim, Auwalu, Soyinka, Festus, Caluag, Maria Elizabeth, Uy, Maria Rosario, Adriano Subaan, Regional Director, Gayomali, Aimee Marie, Lapascua, John Richard, Taffese, Hiwot Solomon, Bac, Truong Dinh, Bao, Tran Quoc, Guo, Xiaolei, Xu, Chunxiao, Zhang, Bingyin, Thi Tho, Nguyen Thi, Van, Ngo Hai, Danh, Phan Cong, Tinh, Ho Huu, Hau, Tran Phuc, Appel, Lawrence J., Matsushita, Kunihiro, Andersen, Katye, Ayensu-Asiedu, Christabelle, Byiringiro, Samuel, Brady, Tammy, Charleston, Jeanne, Chen, Mengkun, Commodore–Mensah, Yvonne, Dalcin, Arlene, Dennison-Himmelfarb, Cheryl, Ding, Ning, Doyle, Christopher K., Edward, Anbrasi, de Souza Elias, Sabrina, Foti, Kathryn, Greer, Raquel, Henry, Megan, Hu, Xiao, Ishigami, Junichi, Kou, Minghao, Lord, Katherine, Lu, Yifei, Marklund, Matti, Amezcua, Pablo Martinez, Mattingly, Joey, McArthur, Kristin, Miller, Edgar R., III, Neupane, Dinesh, Ogungbe, Bunmi, Pariyo, George, Pathak, Priya, Pilla, Scott, Rao, Krishna, Resnick, Carol, Riekert, Kristin, Sakulsaengprapha, Tangkwa, Scheiber, Simona, Shilkofski, Nicole, Tamplin, Steve, Tang, Olive, Zhao, Di, Campbell, Norman, Ogedegbe, Olugbenga, Di Pette, Donald, Brettler, Jeffrey, Connell, Kenneth, Kishore, Sandeep P., Huffman, Mark, Hutchinson, Brian, Abdalla, Marwa, Moran, Andrew E., and Gupta, Reena

Published
2023
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35. Research on an intelligent routing of optical cable path based on Dueling DQN and KSP

Author

LU Yifei, GU Junjia, SHEN Haocong, WU Ying, PAN Junyao, and XING Xuliang

Subjects
power communication optical cable, optical path routing, dueling dqn, shortest paths, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Currently， the traditional method for optical cable path routing is manually decided and inefficient， leading to the slow recovery of the electric power communication business. Therefore， an intelligent routing of optical cable path based on the combination of KSP （K-shortest paths） and Dueling DQN （deep Q-networks） is proposed. The network topology of the power communication optical cable and data such as the optical cable， the information of optical cables in the same channel， optical cable length， and the number of optical cable cores are used to search the K-shortest path routing under the KSP algorithm. Afterward， the Dueling DQN is used to evaluate the risk value of each routing path and complete the routing selection. The effect of the method above is compared with those of the traditional KSP algorithm and Nature DQN algorithm. The results show that the intelligent routing accuracy of the optical cable path is up to 99.5%， and the decision time is reduced to seconds.

Published
2022
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36. Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Author

Momtazmanesh, Sara, Moghaddam, Sahar Saeedi, Ghamari, Seyyed-Hadi, Rad, Elaheh Malakan, Rezaei, Negar, Shobeiri, Parnian, Aali, Amirali, Abbasi-Kangevari, Mohsen, Abbasi-Kangevari, Zeinab, Abdelmasseh, Michael, Abdoun, Meriem, Abdulah, Deldar Morad, Md Abdullah, Abu Yousuf, Abedi, Aidin, Abolhassani, Hassan, Abrehdari-Tafreshi, Zahra, Achappa, Basavaprabhu, Adane Adane, Denberu Eshetie, Adane, Tigist Demssew, Addo, Isaac Yeboah, Adnan, Mohammad, Sakilah Adnani, Qorinah Estiningtyas, Ahmad, Sajjad, Ahmadi, Ali, Ahmadi, Keivan, Ahmed, Ali, Ahmed, Ayman, Rashid, Tarik Ahmed, Al Hamad, Hanadi, Alahdab, Fares, Alemayehu, Astawus, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Almustanyir, Sami, Altirkawi, Khalid A., Alvis-Guzman, Nelson, Dehkordi, Javad Aminian, Amir-Behghadami, Mehrdad, Ancuceanu, Robert, Andrei, Catalina Liliana, Andrei, Tudorel, Antony, Catherine M., Anyasodor, Anayochukwu Edward, Arabloo, Jalal, Arulappan, Judie, Ashraf, Tahira, Athari, Seyyed Shamsadin, Attia, Engi F., Ayele, Meshesha Tsegazeab, Azadnajafabad, Sina, Babu, Abraham Samuel, Bagherieh, Sara, Baltatu, Ovidiu Constantin, Banach, Maciej, Bardhan, Mainak, Barone-Adesi, Francesco, Barrow, Amadou, Basu, Saurav, Bayileyegn, Nebiyou Simegnew, Bensenor, Isabela M., Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhat, Ajay Nagesh, Bhattacharyya, Krittika, Bouaoud, Souad, Braithwaite, Dejana, Brauer, Michael, Butt, Muhammad Hammad, Butt, Zahid A., Calina, Daniela, Cámera, Luis Alberto, Chanie, Gashaw Sisay, Charalampous, Periklis, Chattu, Vijay Kumar, Chimed-Ochir, Odgerel, Chu, Dinh-Toi, Cohen, Aaron J., Cruz-Martins, Natália, Dadras, Omid, Darwesh, Aso Mohammad, Das, Saswati, Debela, Sisay Abebe, Delgado-Ortiz, Laura, Dereje, Diriba, Dianatinasab, Mostafa, Diao, Nancy, Diaz, Daniel, Digesa, Lankamo Ena, Dirirsa, Gebisa, Doku, Paul Narh, Dongarwar, Deepa, Douiri, Abdel, Dsouza, Haneil Larson, Eini, Ebrahim, Ekholuenetale, Michael, Ekundayo, Temitope Cyrus, Mustafa Elagali, Ahmed Elabbas, Elhadi, Muhammed, Enyew, Daniel Berhanie, Erkhembayar, Ryenchindorj, Etaee, Farshid, Fagbamigbe, Adeniyi Francis, Faro, Andre, Fatehizadeh, Ali, Fekadu, Ginenus, Filip, Irina, Fischer, Florian, Foroutan, Masoud, Franklin, Richard Charles, Gaal, Peter Andras, Gaihre, Santosh, Gaipov, Abduzhappar, Gebrehiwot, Mesfin, Gerema, Urge, Getachew, Motuma Erena, Getachew, Tamiru, Ghafourifard, Mansour, Ghanbari, Reza, Ghashghaee, Ahmad, Gholami, Ali, Gil, Artyom Urievich, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Guadie, Habtamu Alganeh, Gupta, Bhawna, Gupta, Sapna, Gupta, Veer Bala, Gupta, Vivek Kumar, Hadei, Mostafa, Halwani, Rabih, Hanif, Asif, Hargono, Arief, Harorani, Mehdi, Hartono, Risky Kusuma, Hasani, Hamidreza, Hashi, Abdiwahab, Hay, Simon I., Heidari, Mohammad, Hellemons, Merel E., Herteliu, Claudiu, Holla, Ramesh, Horita, Nobuyuki, Hoseini, Mohammad, Hosseinzadeh, Mehdi, Huang, Junjie, Hussain, Salman, Hwang, Bing-Fang, Iavicoli, Ivo, Ibitoye, Segun Emmanuel, Ibrahim, Sufyan, Ilesanmi, Olayinka Stephen, Ilic, Irena M., Ilic, Milena D., Immurana, Mustapha, Ismail, Nahlah Elkudssiah, Merin J, Linda, Jakovljevic, Mihajlo, Jamshidi, Elham, Janodia, Manthan Dilipkumar, Javaheri, Tahereh, Jayapal, Sathish Kumar, Jayaram, Shubha, Jha, Ravi Prakash, Johnson, Olatunji, Joo, Tamas, Joseph, Nitin, Jozwiak, Jacek Jerzy, K, Vaishali, Kaambwa, Billingsley, Kabir, Zubair, Kalankesh, Laleh R., Kalhor, Rohollah, Kandel, Himal, Karanth, Shama D., Karaye, Ibraheem M., Kassa, Bekalu Getnet, Kassie, Gizat M., Keikavoosi-Arani, Leila, Keykhaei, Mohammad, Khajuria, Himanshu, Khan, Imteyaz A., Khan, Moien A.B., Khan, Yusra H., Khreis, Haneen, Kim, Min Seo, Kisa, Adnan, Kisa, Sezer, Knibbs, Luke D., Kolkhir, Pavel, Komaki, Somayeh, Kompani, Farzad, Koohestani, Hamid Reza, Koolivand, Ali, Korzh, Oleksii, Koyanagi, Ai, Krishan, Kewal, Krohn, Kris J., Kumar, Naveen, Kumar, Nithin, Kurmi, Om P., Kuttikkattu, Ambily, La Vecchia, Carlo, Lám, Judit, Lan, Qing, Lasrado, Savita, Latief, Kamaluddin, Lauriola, Paolo, Lee, Sang-woong, Lee, Yo Han, Legesse, Samson Mideksa, Lenzi, Jacopo, Li, Ming-Chieh, Lin, Ro-Ting, Liu, Gang, Liu, Wei, Lo, Chun-Han, Lorenzovici, László, Lu, Yifei, Mahalingam, Soundarya, Mahmoudi, Elham, Mahotra, Narayan B., Malekpour, Mohammad-Reza, Malik, Ahmad Azam, Mallhi, Tauqeer Hussain, Malta, Deborah Carvalho, Mansouri, Borhan, Mathews, Elezebeth, Maulud, Sazan Qadir, Mechili, Enkeleint A., Nasab, Entezar Mehrabi, Menezes, Ritesh G., Mengistu, Dechasa Adare, Mentis, Alexios-Fotios, Meshkat, Mahboobeh, Mestrovic, Tomislav, Micheletti Gomide Nogueira de Sá, Ana Carolina, Mirrakhimov, Erkin M., Misganaw, Awoke, Mithra, Prasanna, Moghadasi, Javad, Mohammadi, Esmaeil, Mohammadi, Mokhtar, Mohammadshahi, Marita, Mohammed, Shafiu, Mohan, Syam, Moka, Nagabhishek, Monasta, Lorenzo, Moni, Mohammad Ali, Moniruzzaman, Md, Montazeri, Fateme, Moradi, Maryam, Mostafavi, Ebrahim, Mpundu-Kaambwa, Christine, Murillo-Zamora, Efrén, Murray, Christopher J.L., Nair, Tapas Sadasivan, Nangia, Vinay, Swamy, Sreenivas Narasimha, Narayana, Aparna Ichalangod, Natto, Zuhair S., Nayak, Biswa Prakash, Negash, Wogene Wogene, Nena, Evangelia, Kandel, Sandhya Neupane, Niazi, Robina Khan, Nogueira de Sá, Antonio Tolentino, Nowroozi, Ali, Nzoputam, Chimezie Igwegbe, Nzoputam, Ogochukwu Janet, Oancea, Bogdan, Obaidur, Rahman Md, Odukoya, Oluwakemi Ololade, Okati-Aliabad, Hassan, Okekunle, Akinkunmi Paul, Okonji, Osaretin Christabel, Olagunju, Andrew T., Bali, Ahmed Omar, Ostojic, Sergej M., A, Mahesh P., Padron-Monedero, Alicia, Padubidri, Jagadish Rao, Pahlevan Fallahy, Mohammad Taha, Palicz, Tamás, Pana, Adrian, Park, Eun-Kee, Patel, Jay, Paudel, Rajan, Paudel, Uttam, Pedersini, Paolo, Pereira, Marcos, Pereira, Renato B., Petcu, Ionela-Roxana, Pirestani, Majid, Postma, Maarten J., Prashant, Akila, Rabiee, Mohammad, Radfar, Amir, Rafiei, Sima, Rahim, Fakher, Ur Rahman, Mohammad Hifz, Rahman, Mosiur, Rahman, Muhammad Aziz, Rahmani, Amir Masoud, Rahmani, Shayan, Rahmanian, Vahid, Rajput, Prashant, Rana, Juwel, Rao, Chythra R., Rao, Sowmya J., Rashedi, Sina, Rashidi, Mohammad-Mahdi, Ratan, Zubair Ahmed, Rawaf, David Laith, Rawaf, Salman, Rawal, Lal, Rawassizadeh, Reza, Razeghinia, Mohammad Sadegh, Mohamed Redwan, Elrashdy Moustafa, Rezaei, Maryam, Rezaei, Nazila, Rezaei, Nima, Rezaeian, Mohsen, Rodrigues, Mónica, Buendia Rodriguez, Jefferson Antonio, Roever, Leonardo, Rojas-Rueda, David, Rudd, Kristina E., Saad, Aly M.A., Sabour, Siamak, Saddik, Basema, Sadeghi, Erfan, Sadeghi, Masoumeh, Saeed, Umar, Sahebazzamani, Maryam, Sahebkar, Amirhossein, Sahoo, Harihar, Sajid, Mirza Rizwan, Sakhamuri, Sateesh, Salehi, Sana, Samy, Abdallah M., Santric-Milicevic, Milena M., Sao Jose, Bruno Piassi, Sathian, Brijesh, Satpathy, Maheswar, Saya, Ganesh Kumar, Senthilkumaran, Subramanian, Seylani, Allen, Shahabi, Saeed, Shaikh, Masood Ali, Shanawaz, Mohd, Shannawaz, Mohammed, Sheikhi, Rahim Ali, Shekhar, Shashank, Sibhat, Migbar Mekonnen, Simpson, Colin R., Singh, Jasvinder A., Singh, Paramdeep, Singh, Surjit, Skryabin, Valentin Yurievich, Skryabina, Anna Aleksandrovna, Soltani-Zangbar, Mohammad Sadegh, Song, Suhang, Soyiri, Ireneous N., Steiropoulos, Paschalis, Stockfelt, Leo, Sun, Jing, Takahashi, Ken, Talaat, Iman M., Tan, Ker-Kan, Tat, Nathan Y., Tat, Vivian Y., Taye, Birhan Tsegaw, Thangaraju, Pugazhenthan, Thapar, Rekha, Thienemann, Friedrich, Tiyuri, Amir, Ngoc Tran, Mai Thi, Tripathy, Jaya Prasad, Car, Lorainne Tudor, Tusa, Biruk Shalmeno, Ullah, Irfan, Ullah, Sana, Vacante, Marco, Valdez, Pascual R., Valizadeh, Rohollah, van Boven, Job F.M., Vasankari, Tommi Juhani, Vaziri, Siavash, Violante, Francesco S., Vo, Bay, Wang, Ning, Wei, Melissa Y., Westerman, Ronny, Wickramasinghe, Nuwan Darshana, Xu, Suowen, Xu, Xiaoyue, Yadav, Lalit, Yismaw, Yazachew, Yon, Dong Keon, Yonemoto, Naohiro, Yu, Chuanhua, Yu, Yong, Yunusa, Ismaeel, Zahir, Mazyar, Zangiabadian, Moein, Zareshahrabadi, Zahra, Zarrintan, Armin, Zastrozhin, Mikhail Sergeevich, Zegeye, Zelalem Banjaw, Zhang, Yunquan, Naghavi, Mohsen, Larijani, Bagher, and Farzadfar, Farshad

Published
2023
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48. Changes in Alzheimer Disease Blood Biomarkers and Associations With Incident All-Cause Dementia.

Author

Lu, Yifei, Pike, James Russell, Chen, Jinyu, Walker, Keenan A., Sullivan, Kevin J., Thyagarajan, Bharat, Mielke, Michelle M., Lutsey, Pamela L., Knopman, David, Gottesman, Rebecca F., Sharrett, A. Richey, Coresh, Josef, Mosley, Thomas H., and Palta, Priya

Subjects
*ALZHEIMER'S disease, *BLOOD diseases, *GLIAL fibrillary acidic protein, *DEMENTIA, *BIOMARKERS, *LIFE course approach
Abstract

Key Points: Question: What is the association of plasma biomarker (amyloid-β 42 to amyloid-β 40 [Aβ42:Aβ40] ratio, phosphorylated tau at threonine [p-tau181], neurofilament light [NfL], glial fibrillary acid protein [GFAP]) changes from midlife to late life with all-cause dementia? Findings: In this retrospective analysis of prospectively collected plasma biomarkers from 1525 adults from the Atherosclerosis Risk in Communities study, only Alzheimer disease (AD)–specific (Aβ42:Aβ40, p-Tau181) biomarkers in midlife demonstrated significant long-term associations with late-life dementia. In late life, each of the biomarkers and their change from midlife were significantly associated with incident all-cause dementia. Meaning: AD-specific biomarkers' association with dementia starts in midlife whereas late-life measures of AD, neuronal injury, and astrogliosis biomarkers are all associated with dementia. Importance: Plasma biomarkers show promise for identifying Alzheimer disease (AD) neuropathology and neurodegeneration, but additional examination among diverse populations and throughout the life course is needed. Objective: To assess temporal plasma biomarker changes and their association with all-cause dementia, overall and among subgroups of community-dwelling adults. Design, Setting, and Participants: In 1525 participants from the US-based Atherosclerosis Risk in Communities (ARIC) study, plasma biomarkers were measured using stored specimens collected in midlife (1993-1995, mean age 58.3 years) and late life (2011-2013, mean age 76.0 years; followed up to 2016-2019, mean age 80.7 years). Midlife risk factors (hypertension, diabetes, lipids, coronary heart disease, cigarette use, and physical activity) were assessed for their associations with change in plasma biomarkers over time. The associations of biomarkers with incident all-cause dementia were evaluated in a subpopulation (n = 1339) who were dementia-free in 2011-2013 and had biomarker measurements in 1993-1995 and 2011-2013. Exposure: Plasma biomarkers of amyloid-β 42 to amyloid-β 40 (Aβ42:Aβ40) ratio, phosphorylated tau at threonine 181 (p-tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) were measured using the Quanterix Simoa platform. Main Outcomes and Measures: Incident all-cause dementia was ascertained from January 1, 2012, through December 31, 2019, from neuropsychological assessments, semiannual participant or informant contact, and medical record surveillance. Results: Among 1525 participants (mean age, 58.3 [SD, 5.1] years), 914 participants (59.9%) were women, and 394 participants (25.8%) were Black. A total of 252 participants (16.5%) developed dementia. Decreasing Aβ42:Aβ40 ratio and increasing p-tau181, NfL, and GFAP were observed from midlife to late life, with more rapid biomarker changes among participants carrying the apolipoprotein E epsilon 4 (APOEε4) allele. Midlife hypertension was associated with a 0.15-SD faster NfL increase and a 0.08-SD faster GFAP increase per decade; estimates for midlife diabetes were a 0.11-SD faster for NfL and 0.15-SD faster for GFAP. Only AD-specific biomarkers in midlife demonstrated long-term associations with late-life dementia (hazard ratio per SD lower Aβ42:Aβ40 ratio, 1.11; 95% CI, 1.02-1.21; per SD higher p-tau181, 1.15; 95% CI, 1.06-1.25). All plasma biomarkers in late life had statistically significant associations with late-life dementia, with NfL demonstrating the largest association (1.92; 95% CI, 1.72-2.14). Conclusions and Relevance: Plasma biomarkers of AD neuropathology, neuronal injury, and astrogliosis increase with age and are associated with known dementia risk factors. AD-specific biomarkers' association with dementia starts in midlife whereas late-life measures of AD, neuronal injury, and astrogliosis biomarkers are all associated with dementia. This cohort study characterizes temporal changes in plasma biomarkers, identifies factors associated with changes in plasma biomarkers over time, and evaluates the prospective associations of plasma biomarkers with late-life all-cause dementia. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Boosting the charge injection of polymer electrets for light-stimulated artificial synaptic transistors.

Author

Li, Dongfan, Hu, Runyi, Zhu, Yufeng, Lu, Yifei, Hou, Kunzhi, Liu, Jiamei, Lu, Guanghao, and Bu, Laju

Abstract

Semiconductor/polymer electret constructions are extensively utilized in organic field-effect transistors, memories, and artificial synapses. The challenge of exciton separation efficiency and charge injection from semiconductors into insulating polymers persist due to their disparate energy levels. In this work, we propose an effective method to enhance charge injection efficiency, thereby improving the optical response, by incorporating N,N′-dioctyl-3,4,9,10-perylenetetracarboxylic diimide (C8-PTCDI) between the p-type semiconductor and the electret layer. For example, the 40 nm 2,7-diocty[1]benzothieno[3,2-b]benzothiophene (C8-BTBT)/2 nm C8-PTCDI/polymer electret-based synaptic transistors show a superior photosensitivity of 5.6 × 106, compared with other transistors. C8-PTCDI not only acts as an electron acceptor to improve the exciton dissociation and increase the photo-induced charge concentration, but also provides an electron transport layer in the channel for electron injection into PS. To verify its universality, 40 nm C8-BTBT/2 nm [6,6]-phenyl-C71-butyric acid methyl ester (PC71BM)/polymer electret synaptic transistors were also prepared with a photosensitivity of up to 1.4 × 106. Moreover, utilizing a configuration of C8-BTBT/C8-PTCDI/polymer electrets, light-stimulated artificial synaptic transistors were fabricated and employed in "pup" picture learning, neural network computing and image recognition tasks (achieving a recognition rate of up to 98.3%). [ABSTRACT FROM AUTHOR]

Published
2024
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