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1. Romanization Encoding For Multilingual ASR

Author

Ding, Wen, Jia, Fei, Xu, Hainan, Xi, Yu, Lai, Junjie, and Ginsburg, Boris

Subjects
Computer Science - Computation and Language, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

We introduce romanization encoding for script-heavy languages to optimize multilingual and code-switching Automatic Speech Recognition (ASR) systems. By adopting romanization encoding alongside a balanced concatenated tokenizer within a FastConformer-RNNT framework equipped with a Roman2Char module, we significantly reduce vocabulary and output dimensions, enabling larger training batches and reduced memory consumption. Our method decouples acoustic modeling and language modeling, enhancing the flexibility and adaptability of the system. In our study, applying this method to Mandarin-English ASR resulted in a remarkable 63.51% vocabulary reduction and notable performance gains of 13.72% and 15.03% on SEAME code-switching benchmarks. Ablation studies on Mandarin-Korean and Mandarin-Japanese highlight our method's strong capability to address the complexities of other script-heavy languages, paving the way for more versatile and effective multilingual ASR systems.

Published
2024

2. Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter

Author

Xi, Yu, Ding, Wen, Yu, Kai, and Lai, Junjie

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Code-switching (CS) phenomenon occurs when words or phrases from different languages are alternated in a single sentence. Due to data scarcity, building an effective CS Automatic Speech Recognition (ASR) system remains challenging. In this paper, we propose to enhance CS-ASR systems by utilizing rich unsupervised monolingual speech data within a semi-supervised learning framework, particularly when access to CS data is limited. To achieve this, we establish a general paradigm for applying noisy student training (NST) to the CS-ASR task. Specifically, we introduce the LLM-Filter, which leverages well-designed prompt templates to activate the correction capability of large language models (LLMs) for monolingual data selection and pseudo-labels refinement during NST. Our experiments on the supervised ASRU-CS and unsupervised AISHELL-2 and LibriSpeech datasets show that our method not only achieves significant improvements over supervised and semi-supervised learning baselines for the CS task, but also attains better performance compared with the fully-supervised oracle upper-bound on the CS English part. Additionally, we further investigate the influence of accent on AESRC dataset and demonstrate that our method can get achieve additional benefits when the monolingual data contains relevant linguistic characteristic., Comment: Accepted by SLT2024

Published
2024

3. Chiral quantum heating and cooling with an optically controlled ion

Author

Bu, Jin-Tao, Zhang, Jian-Qi, Ding, Ge-Yi, Li, Jia-Chong, Zhang, Jia-Wei, Wang, Bin, Ding, Wen-Qiang, Yuan, Wen-Fei, Chen, Liang, Zhong, Qi, Keçebaş, Ali, Özdemir, Şahin K., Zhou, Fei, Jing, Hui, and Feng, Mang

Subjects
Quantum Physics
Abstract

Quantum heat engines and refrigerators are open quantum systems, whose dynamics can be well understood using a non-Hermitian formalism. A prominent feature of non-Hermiticity is the existence of exceptional points (EPs), which has no counterpart in closed quantum systems. It has been shown in classical systems that dynamical encirclement in the vicinity of an EP, whether the loop includes the EP or not, could lead to chiral mode conversion. Here, we show that this is valid also for quantum systems when dynamical encircling is performed in the vicinity of their Liouvillian EPs (LEPs) which include the effects of quantum jumps and associated noise - an important quantum feature not present in previous works. We demonstrate, using a Paul-trapped ultracold ion, the first chiral quantum heating and refrigeration by dynamically encircling a closed loop in the vicinity of an LEP. We witness the cycling direction to be associated with the chirality and heat release (absorption) of the quantum heat engine (quantum refrigerator). Our experiments have revealed that not only the adiabaticity-breakdown but also the Landau-Zener-St\"uckelberg process play an essential role during dynamic encircling, resulting in chiral thermodynamic cycles. Our observations contributes to further understanding of chiral and topological features in non-Hermitian systems and pave a way to exploring the relation between chirality and quantum thermodynamics., Comment: Accepted by Light: Science & Applications

Published
2024
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4. Gecko: Versatile Text Embeddings Distilled from Large Language Models

Author

Lee, Jinhyuk, Dai, Zhuyun, Ren, Xiaoqi, Chen, Blair, Cer, Daniel, Cole, Jeremy R., Hui, Kai, Boratko, Michael, Kapadia, Rajvi, Ding, Wen, Luan, Yi, Duddu, Sai Meher Karthik, Abrego, Gustavo Hernandez, Shi, Weiqiang, Gupta, Nithi, Kusupati, Aditya, Jain, Prateek, Jonnalagadda, Siddhartha Reddy, Chang, Ming-Wei, and Naim, Iftekhar

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

We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language models (LLMs) into a retriever. Our two-step distillation process begins with generating diverse, synthetic paired data using an LLM. Next, we further refine the data quality by retrieving a set of candidate passages for each query, and relabeling the positive and hard negative passages using the same LLM. The effectiveness of our approach is demonstrated by the compactness of the Gecko. On the Massive Text Embedding Benchmark (MTEB), Gecko with 256 embedding dimensions outperforms all existing entries with 768 embedding size. Gecko with 768 embedding dimensions achieves an average score of 66.31, competing with 7x larger models and 5x higher dimensional embeddings., Comment: 18 pages

Published
2024

5. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. 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M. 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Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol

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

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

12. Second-order Approximation of Exponential Random Graph Models

Author

Ding, Wen-Yi and Fang, Xiao

Subjects
Mathematics - Probability
Abstract

Exponential random graph models (ERGMs) are flexible probability models allowing edge dependency. However, it is known that, to a first-order approximation, many ERGMs behave like Erd\"os-R\'enyi random graphs, where edges are independent. In this paper, to distinguish ERGMs from Erd\"os-R\'enyi random graphs, we consider second-order approximations of ERGMs using two-stars and triangles. We prove that the second-order approximation indeed achieves second-order accuracy in the triangle-free case. The new approximation is formally obtained by Hoeffding decomposition and rigorously justified using Stein's method., Comment: 15 pages

Published
2024

15. Wespeaker baselines for VoxSRC2023

Author

Wang, Shuai, Liang, Chengdong, Xiang, Xu, Han, Bing, Chen, Zhengyang, Wang, Hongji, and Ding, Wen

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This report showcases the results achieved using the wespeaker toolkit for the VoxSRC2023 Challenge. Our aim is to provide participants, especially those with limited experience, with clear and straightforward guidelines to develop their initial systems. Via well-structured recipes and strong results, we hope to offer an accessible and good enough start point for all interested individuals. In this report, we describe the results achieved on the VoxSRC2023 dev set using the pretrained models, you can check the CodaLab evaluation server for the results on the evaluation set.

Published
2023

23. First sodium laser guide star asterism launching platform in China on 1.8m telescope at Gaomeigu Observatory

Author

Wang, Rui-Tao, Li, Hong-Yang, Feng, Lu, Li, Min, Bian, Qi, Zuo, Jun-Wei, Jin, Kai, Wang, Chen, Liang, Yue, Wang, Ming, Dou, Jun-Feng, Zhang, Ding-Wen, Wei, Kai, Guo, You-Ming, Bo, Yong, and Xue, Sui-Jian

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The application of sodium laser guide star is the key difference between modern adaptive optics system and traditional adaptive optics system. Especially in system like multi-conjugate adaptive optics, sodium laser guide star asterism which is formed by several laser guide stars in certain pattern is required to probe more atmospheric turbulence in different directions. To achieve this, a sodium laser guide star asterism launching platform is required. In this paper, we will introduce the sodium laser guide star asterism launching platform built and tested on the 1.8m telescope of the Gaomeigu Observatory. The platform has two functions: one is to compare the performance of sodium laser guide stars generated by different lasers at the same place; the other is to generate sodium laser guide star asterism with adjustable shape. The field test results at the beginning of 2021 verify the important role of the platform, which is also the first time to realize sodium laser guide star asterism in China.

Published
2023
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24. Research status and prospects of UWB radar life information recognition for mine rescue

Author

ZHENG Xuezhao, MA Yang, HUANG Yuan, CAI Guobin, and DING Wen

Subjects
mine rescue, life information recognition, uwb radar, clutter filtering, life information extraction, object recognition, life quantification, Mining engineering. Metallurgy, TN1-997
Abstract

Ultra-wide band (UWB) radar can penetrate non-magnetic media such as coal and rock to detect life information of personnel after collapse. Due to the complex mining environment, UWB radar loaded with vital sign signals is prone to interference from environmental noise and clutter signals. It is difficult to recognize human subject information. This paper introduces the principle of UWB radar life detection system and its application in mine rescue. This paper summarizes the current research status of UWB radar life information recognition from three aspects: UWB radar life information extraction, dynamic and static human object recognition, and life quantification. This paper points out the current issues with the application of UWB radar life detection technology in the field of mine rescue. ① There is limited research on filtering methods for non-stationary signals and environmental noise in underground collapse environments. ② The extraction and representation methods for posture, behavior, life status, and other information of moving (or micro moving) objects need to be improved. The human life information recognition model is not yet perfect and the feature correlation between models is low. ③ There is a lack of solutions to the 'overlapping' problem caused by multiple objects. This paper proposes the prospects for the research direction of UWB radar life information recognition for mine rescue. ① It is suggested to continuously optimize noise and clutter adaptive filtering methods for multiple types of mine disaster environments. ② It is suggested to construct a human life information recognition model suitable for the field of mine rescue. ③ It is suggested to further improve the quantification capability of multi-object after mine shelter. ④ It is suggested to conduct depth exploration of the method for determining the optimal detection frequency band for UWB radar.

Published
2024
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25. Successful treatment of retinopathy of prematurity in oculocutaneous albinism with OCA2 variants: a case report and review of literature

Author

Xiao-Yu Zheng, Ding-Wen Wu, Lan Yu, and Zheng-Yan Zhao

Subjects
Retinopathy of prematurity, Oculocutaneous albinism, OCA2, Laser photocoagulation, Anti-vascular endothelial growth factor, Pediatrics, RJ1-570
Abstract

Abstract Background Oculocutaneous albinism (OCA) is a group of autosomal recessive hereditary disorders that affect melanin biosynthesis, resulting in abnormalities in hair, skin, and eyes. Retinopathy of prematurity (ROP) is a proliferative retinopathy mainly observed in premature infants with low birth weight and early gestational age, but it can also affect full-term infants or children with normal weight, particularly in developing countries. The coexistence of ROP and OCA is rare. There is limited documentation regarding treatment approaches, with few studies reporting positive outcomes with laser treatment due to the absence of melanin pigment. This study discusses the treatment challenges in a female infant diagnosed with ROP and OCA, and underscores the importance of genetic analysis in guiding therapeutic decisions for this rare comorbid condition. Case presentation The study presents a case of ROP occurring concurrently with OCA. Genetic testing revealed two variants, c.727C > T (p.R243C) and c.1832 T > C (p.L611P), in the OCA2 gene, inherited from the patient's mother and father, respectively. The identified mutations were consistent with a diagnosis of OCA2, classified as a subtype of OCA. The patient initially received intravitreal anti-vascular endothelial growth factor (anti-VEGF) injection, followed by laser photocoagulation therapy for a recurrent event. A favorable outcome was observed during the 2-month follow-up period. Conclusions The co-occurrence of ROP and OCA is a rare phenomenon, and this is the first recorded case in the Chinese population. The current case supports the use of laser as the primary treatment modality for ROP in OCA2 patients with partial pigmentation impairment. Furthermore, genetic analysis can aid in predicting the effectiveness of laser photocoagulation in this patient population.

Published
2024
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26. Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition

Author

Chen, Yu, Ding, Wen, and Lai, Junjie

Subjects
Computer Science - Sound, Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition(ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performance of NST on non-target domain data in ASR tasks. Hypotheses with and without a Language Model are generated and the CER differences between them are utilized as a filter threshold. Results reveal that significant improvements of 10.4% compared with no data filtering baselines. We can achieve 3.31% CER in AISHELL-1 test set, which is best result from our knowledge without any other supervised data. We also perform evaluations on the supervised 1000 hour AISHELL-2 dataset and competitive results of 4.73% CER can be achieved., Comment: This paper is accepted by the ICASSP 2023 conference

Published
2022

30. Data-Driven Performance-Based Generative Design and Digital Fabrication for Industry 4.0: Precedent Work, Current Progress, and Future Prospects

Author

Bao, Ding Wen, Yan, Xin, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Barberio, Maurizio, editor, Colella, Micaela, editor, Figliola, Angelo, editor, and Battisti, Alessandra, editor

Published
2024
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31. Where is the field of autophagy research heading?

Author

Abeliovich, Hagai, Debnath, Jayanta, Ding, Wen-Xing, Jackson, William, Kim, Do-Hyung, Klionsky, Daniel, Ktistakis, Nicholas, Münz, Christian, Petersen, Morten, Sadoshima, Junichi, Vergne, Isabelle, and Margeta, Marta

Subjects
Ideas for your grant proposal, suggestions, the big picture, the grand scheme, thoughts, yada yada yada, Autophagy, Biomedical Research
Abstract

In this editors corner, the section editors were asked to indicate where they see the autophagy field heading and to suggest what they consider to be key unanswered questions in their specialty area.

Published
2023

36. Glutamine synthetase limits b-catenin-mutated liver cancer growth by maintaining nitrogen homeostasis and suppressing mTORC1

Author

Dai, Weiwei, Shen, Jianliang, Yan, Junrong, Bott, Alex J, Maimouni, Sara, Daguplo, Heineken Q, Wang, Yujue, Khayati, Khoosheh, Guo, Jessie Yanxiang, Zhang, Lanjing, Wang, Yongbo, Valvezan, Alexander, Ding, Wen-Xing, Chen, Xin, Su, Xiaoyang, Gao, Shenglan, and Zong, Wei-Xing

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, Cancer, Digestive Diseases, Liver Disease, Rare Diseases, Animals, Mice, Glutamate-Ammonia Ligase, beta Catenin, Mechanistic Target of Rapamycin Complex 1, Ammonia, Nitrogen, Liver Neoplasms, Liver, Glutamine, Homeostasis, Urea, Hepatology, Liver cancer, Metabolism, Medical and Health Sciences, Immunology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Glutamine synthetase (GS) catalyzes de novo synthesis of glutamine that facilitates cancer cell growth. In the liver, GS functions next to the urea cycle to remove ammonia waste. As a dysregulated urea cycle is implicated in cancer development, the impact of GS's ammonia clearance function has not been explored in cancer. Here, we show that oncogenic activation of β-catenin (encoded by CTNNB1) led to a decreased urea cycle and elevated ammonia waste burden. While β-catenin induced the expression of GS, which is thought to be cancer promoting, surprisingly, genetic ablation of hepatic GS accelerated the onset of liver tumors in several mouse models that involved β-catenin activation. Mechanistically, GS ablation exacerbated hyperammonemia and facilitated the production of glutamate-derived nonessential amino acids, which subsequently stimulated mechanistic target of rapamycin complex 1 (mTORC1). Pharmacological and genetic inhibition of mTORC1 and glutamic transaminases suppressed tumorigenesis facilitated by GS ablation. While patients with hepatocellular carcinoma, especially those with CTNNB1 mutations, have an overall defective urea cycle and increased expression of GS, there exists a subset of patients with low GS expression that is associated with mTORC1 hyperactivation. Therefore, GS-mediated ammonia clearance serves as a tumor-suppressing mechanism in livers that harbor β-catenin activation mutations and a compromised urea cycle.

Published
2022

37. Maintaining ecological connectivity in highly urbanized areas: Optimal strategy, key patches and protection policy

Author

Xiuming Wang, Ding Wen, Piao Liu, Chentao Wei, Peng Zhao, and Naizhong Xu

Subjects
Stepping-stones, Complex network, Node centrality, Network robustness, Policy protection rate, Ecology, QH540-549.5
Abstract

Maintaining and restoring ecological connectivity (EC) are important measures of mitigating habitat isolation caused by urbanization. Considering the prominent contradiction of land use in highly urbanized areas, the EC protection measures proposed in the previous studies, such as expanding the ecological sources and widening the ecological corridors, are unrealistic. By using the stepping-stone and complex network theories, this paper simulated the ecological network (EN), evaluated the centrality of each ecological source and stepping-stone, and analysed the network robustness under different damage scenarios, then identified the optimal strategy and key patches for EC maintaining. Meanwhile, this paper evaluated whether the key patches are protected by China’s new policies, which are, Ecological Protection Red Line (EPRL) and the Three Lines One Permit (TLOP). The results were as follows: (1) The EN is composed of 53 ecological sources and 291 stepping-stones, with 591 links between them. (2) The network stability can be optimally maintained by prioritizing the protection of stepping-stones with high centrality. (3) The 53 ecological sources and 51 stepping-stones with the highest centrality can ensure the stable operation of the EN, that is, they are key patches for EC maintaining. (4) Compared with EPRL, the protection rate for ecological sources and stepping-stones are significantly improved by TLOP; however, stepping-stones with high centrality require stronger protection. This study enriched the research cases of complex network theory applied in landscape ecology, and provided references for the improvement of policies in the future.

Published
2024
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38. Coupling Deep Learning with Full Waveform Inversion

Author

Ding, Wen, Ren, Kui, and Zhang, Lu

Subjects
Mathematics - Numerical Analysis, 35R30, 49N45, 65M32, 74J25, 78A46, 86A22
Abstract

Full waveform inversion (FWI) aims at reconstructing unknown physical coefficients in wave equations using the wave field data generated from multiple incoming sources. In this work, we propose an offline-online computational strategy for coupling classical least-squares based computational inversion with modern deep learning based approaches for FWI to achieve advantages that can not be achieved with only one of the components. In a nutshell, we develop an offline learning strategy to construct a robust approximation to the inverse operator and utilize it to design a new objective function for the online inversion with new datasets. We demonstrate through numerical simulations that our coupling strategy improves the computational efficiency of FWI with reliable offline training on moderate computational resources (in terms of both the size of the training dataset and the computational cost needed).

Published
2022

39. TFEB regulates sulfur amino acid and coenzyme A metabolism to support hepatic metabolic adaptation and redox homeostasis

Author

Matye, David, Gunewardena, Sumedha, Chen, Jianglei, Wang, Huaiwen, Wang, Yifeng, Hasan, Mohammad Nazmul, Gu, Lijie, Clayton, Yung Dai, Du, Yanhong, Chen, Cheng, Friedman, Jacob E, Lu, Shelly C, Ding, Wen-Xing, and Li, Tiangang

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Digestive Diseases, Liver Disease, Chronic Liver Disease and Cirrhosis, Complementary and Integrative Health, Genetics, Nutrition, Oral and gastrointestinal, Metabolic and endocrine, Zero Hunger, Amino Acids, Sulfur, Animals, Antioxidants, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Coenzyme A, Cysteine, Cystine, Dietary Proteins, Fatty Liver, Glutathione, Homeostasis, Lipids, Liver, Methionine, Methionine Adenosyltransferase, Mice, Oxidation-Reduction
Abstract

Fatty liver is a highly heterogenous condition driven by various pathogenic factors in addition to the severity of steatosis. Protein insufficiency has been causally linked to fatty liver with incompletely defined mechanisms. Here we report that fatty liver is a sulfur amino acid insufficient state that promotes metabolic inflexibility via limiting coenzyme A availability. We demonstrate that the nutrient-sensing transcriptional factor EB synergistically stimulates lysosome proteolysis and methionine adenosyltransferase to increase cysteine pool that drives the production of coenzyme A and glutathione, which support metabolic adaptation and antioxidant defense during increased lipid influx. Intriguingly, mice consuming an isocaloric protein-deficient Western diet exhibit selective hepatic cysteine, coenzyme A and glutathione deficiency and acylcarnitine accumulation, which are reversed by cystine supplementation without normalizing dietary protein intake. These findings support a pathogenic link of dysregulated sulfur amino acid metabolism to metabolic inflexibility that underlies both overnutrition and protein malnutrition-associated fatty liver development.

Published
2022

47. The relationship between adolescent risk perception and emotions during the COVID-19: a short-term longitudinal study

Author

Xin, Tong-tong, Li, Xiu-jun, Lei-Shen, Ding, Wen-yu, Wei-Liu, and Ning-Chen

Subjects
Epidemics -- Social aspects -- Psychological aspects -- China, Risk perception -- Health aspects, Teenagers -- Psychological aspects -- Health aspects, Youth -- Psychological aspects -- Health aspects, Psychology and mental health
Abstract

This study explores the relationship between adolescents' perceptions of epidemic risk and their emotions through three follow-up surveys during the early stages of the COVID-19 pandemic on February 11th (T1), 18th (T2), and 25th (T3), 2020. Three hundred and four adolescents in different academic stages (junior high middle school, senior high middle school, and university) participated in the online survey, and cross-lag analysis was used to examine the causal relationship between epidemic risk perceptions and positive and negative emotions. The results found that the individual's positive emotions were significantly higher than the negative emotions in T1, T2 and T3. Cross-lag analysis found that for positive emotions, T2 positive emotions could negatively predict T3 epidemic risk perceptions, and T2 epidemic risk perceptions could negatively predict the individual's T3 positive emotions. For negative emotions, risk perceptions at T1 could positively predict negative emotions at T2, and at the same time, negative emotions at T1 could also positively predict epidemic risk perceptions at T2. This indicates that during the early stages of the COVID-19 pandemic, there was a causal relationship between the perceptions of epidemic risk and the emotions of adolescents, and this relationship had high stability among groups of different genders and academic stages., Author(s): Tong-tong Xin [sup.1] , Xiu-jun Li [sup.1] Lei-Shen [sup.1] , Wen-yu Ding [sup.1] Wei-Liu [sup.1] Ning-Chen [sup.1] Author Affiliations: (1) grid.412531.0, 0000 0001 0701 1077, Shanghai Normal University, , [...]

Published
2023
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