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1. Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and Synthesis

Author

Zheng, Jian-Qing, Mo, Yuanhan, Sun, Yang, Li, Jiahua, Wu, Fuping, Wang, Ziyang, Vincent, Tonia, and Papież, Bartłomiej W.

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

In medical imaging, the diffusion models have shown great potential in synthetic image generation tasks. However, these models often struggle with the interpretable connections between the generated and existing images and could create illusions. To address these challenges, our research proposes a novel diffusion-based generative model based on deformation diffusion and recovery. This model, named Deformation-Recovery Diffusion Model (DRDM), diverges from traditional score/intensity and latent feature-based approaches, emphasizing morphological changes through deformation fields rather than direct image synthesis. This is achieved by introducing a topological-preserving deformation field generation method, which randomly samples and integrates a set of multi-scale Deformation Vector Fields (DVF). DRDM is trained to learn to recover unreasonable deformation components, thereby restoring each randomly deformed image to a realistic distribution. These innovations facilitate the generation of diverse and anatomically plausible deformations, enhancing data augmentation and synthesis for further analysis in downstream tasks, such as few-shot learning and image registration. Experimental results in cardiac MRI and pulmonary CT show DRDM is capable of creating diverse, large (over 10\% image size deformation scale), and high-quality (negative rate of the Jacobian matrix's determinant is lower than 1\%) deformation fields. The further experimental results in downstream tasks, 2D image segmentation and 3D image registration, indicate significant improvements resulting from DRDM, showcasing the potential of our model to advance image manipulation and synthesis in medical imaging and beyond. Project page: https://jianqingzheng.github.io/def_diff_rec/

Published
2024

2. Multimodal Deformable Image Registration for Long-COVID Analysis Based on Progressive Alignment and Multi-perspective Loss

Author

Li, Jiahua, Grist, James T., Gleeson, Fergus V., and Papież, Bartłomiej W.

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

Long COVID is characterized by persistent symptoms, particularly pulmonary impairment, which necessitates advanced imaging for accurate diagnosis. Hyperpolarised Xenon-129 MRI (XeMRI) offers a promising avenue by visualising lung ventilation, perfusion, as well as gas transfer. Integrating functional data from XeMRI with structural data from Computed Tomography (CT) is crucial for comprehensive analysis and effective treatment strategies in long COVID, requiring precise data alignment from those complementary imaging modalities. To this end, CT-MRI registration is an essential intermediate step, given the significant challenges posed by the direct alignment of CT and Xe-MRI. Therefore, we proposed an end-to-end multimodal deformable image registration method that achieves superior performance for aligning long-COVID lung CT and proton density MRI (pMRI) data. Moreover, our method incorporates a novel Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning methods for monomodal registration by making them adaptable for multimodal tasks. The registration results achieve a Dice coefficient score of 0.913, indicating a substantial improvement over the state-of-the-art multimodal image registration techniques. Since the XeMRI and pMRI images are acquired in the same sessions and can be roughly aligned, our results facilitate subsequent registration between XeMRI and CT, thereby potentially enhancing clinical decision-making for long COVID management.

Published
2024

3. Collaborative Filtering Recommendation of Online Learning Resources for E-commerce Logistics Talent Training

Author

Li, Jiahua, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gui, Guan, editor, Li, Ying, editor, and Lin, Yun, editor

Published
2024
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4. Research on Online Education Quality Evaluation of E-Commerce Logistics Based on Entropy Weight TOPSIS

Author

Li, Jiahua, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Gui, Guan, editor, Li, Ying, editor, and Lin, Yun, editor

Published
2024
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23. Highly nonclassical phonon emission statistics through two-phonon loss of van der Pol oscillator

Author

Li, Jiahua, Ding, Chunling, and Wu, Ying

Subjects
Physics - Optics, Quantum Physics
Abstract

The ability to produce nonclassical wave in a system is essential for advances in quantum communication and computation. Here, we propose a scheme to generate highly nonclassical phonon emission statistics -- antibunched wave in a quantum van der Pol (vdP) oscillator subject to an external driving, both single- and two-phonon losses. It is found that phonon antibunching depends significantly on the nonlinear two-phonon loss of the vdP oscillator, where the degree of the antibunching increases monotonically with the two-phonon loss and the distinguished parameter regimes with optimal antibunching and single-phonon emission are identified clearly. In addition, we give an in-depth insight into strong antibunching in the emitted phonon statistics by analytical calculations using a three-oscillator-level model, which agree well with the full numerical simulations employing a master-equation approach and a Schrodinger-equation approach at weak driving. In turn, the fluorescence phonon emission spectra of the vdP oscillator, given by the power spectral density, are also evaluated. We further show that high phonon emission amplitudes, simultaneously accompanied by strong phonon antibunching, are attainable in the vdP system, which are beneficial to the correlation measurement in practical experiments. Our approach only requires a single vdP oscillator, without the need for reconfiguring the two coupled nonlinear resonators or the complex nanophotonic structures as compared to the previous unconventional blockade schemes. The present scheme could inspire methods to achieve antibunching in other systems., Comment: 12 pages, 5 figures

Published
2020
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30. An Analysis of the Causes of Evergrande Debt Crisis

Author

Li Jiahua and Qin Yangchunwang

Subjects
Social Sciences
Abstract

Since August 2020, new regulations on real estate financing have been issued, emphasizing “the need to improve the marketization, standardization, and transparency of financing”. Subsequently, the capital liquidity crisis caused by Evergrande’s high debt and high turnover mode has become the focus of public attention in the market and has triggered the credit crisis of the real estate industry to a certain extent. This study is based on the public information such as the financial statements. To analyze and study the causes of the crisis, this paper will analyze the causes of the Evergrande Group crisis by comparing the previous financial statements of the company, using the balance sheet and comparing the profitability, and putting forward suggestions on the future liquidity and long-term development of Evergrande Group. The suggestions give inspiration to the same industry or enterprises in the same model. Warning the existing housing enterprises, Evergrande Group serves as the most negative example in textbooks, playing a warning role to prevent such a serious crisis from happening again.

Published
2024
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35. #1342 Updated results from the RUBY-3 study of povetacicept, an enhanced dual BAFF/APRIL antagonist, in autoantibody-associated glomerulonephritis

Author

Madan, Arvind, primary, Park, Inwhee, additional, Yalavarthy, Rajesh, additional, Mandayam, Sreedhar, additional, Kulkarni, Hemant, additional, Barratt, Jonathan, additional, Davies, Rupert, additional, Enstrom, Amanda, additional, Thomas, Heather, additional, LI, Jiahua, additional, Peng, Stanford L, additional, and Tumlin, James, additional

Published
2024
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37. WCN24-756 POVETACICEPT, AN ENHANCED DUAL BAFF/APRIL ANTAGONIST, IN AUTOANTIBODY-ASSOCIATED GLOMERULONEPHRITIS

Author

Tumlin, James, primary, Mandayam, Sreedhar, additional, Madan, Arvind, additional, Cortazar, Frank, additional, Han, Sang-Woong, additional, Kulkarni, Hemant, additional, Barratt, Jonathan, additional, Rovin, Brad, additional, Zhang, Hong, additional, Davies, Rupert, additional, Enstrom, Amanda, additional, Thomas, Heather, additional, Li, Jiahua, additional, Peng, Stanford L., additional, and Singh, Harmeet, additional

Published
2024
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38. WCN24-762 UPDATED RESULTS FROM THE RUBY-3 STUDY OF POVETACICEPT, AN ENHANCED DUAL BAFF/APRIL ANTAGONIST, IN AUTOANTIBODY-ASSOCIATED GLOMERULONEPHRITIS

Author

Tumlin, James, primary, Mandayam, Sreedhar, additional, Madan, Arvind, additional, Cortazar, Frank, additional, Han, Sang-Woong, additional, Kulkarni, Hemant, additional, Barratt, Jonathan, additional, Rovin, Brad, additional, Zhang, Hong, additional, Davies, Rupert, additional, Enstrom, Amanda, additional, Thomas, Heather, additional, Li, Jiahua, additional, Peng, Stanford L., additional, and Singh, Harmeet, additional

Published
2024
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44. Kidney Recovery and Death in Critically Ill Patients With COVID-19–Associated Acute Kidney Injury Treated With Dialysis: The STOP-COVID Cohort Study

Author

Gupta, Hsu, Faugno, Goyal, Raichoudhury, Tariq, Meyer, Sharma, Leaf, Kshirsagar, Walther, Carl P., Anumudu, Samaya J., Arunthamakun, Justin, Kopecky, Kathleen F., Milligan, Gregory P., McCullough, Peter A., Nguyen, Thuy-Duyen, Shaefi, Shahzad, Krajewski, Megan L., Shankar, Sidharth, Pannu, Ameeka, Valencia, Juan D., Bauer, Kenneth A., Waikar, Sushrut S., Kibbelaar, Zoe A., Athavale, Ambarish M., Hart, Peter, Upadhyay, Shristi, Vohra, Ishaan, Oyintayo, Ajiboye, Green, Adam, Rachoin, Jean-Sebastien, Schorr, Christa A., Shea, Lisa, Edmonston, Daniel L., Mosher, Christopher L., Shehata, Alexandre M., Cohen, Zaza, Allusson, Valerie, Bambrick-Santoyo, Gabriela, Bhatti, Noor ul aain, Mehta, Bijal, WilliamsSamantha K. Brenner, Aquino, Walters, Patricia, Go, Ronaldo C., Rose, Keith M., Hernán, Miguel A., Zhou, Amy M., Kim, Ethan C., Lisk, Rebecca, Chan, Lili, Mathews, Kusum S., Coca, Steven G., Altman, Deena R., Saha, Aparna, Soh, Howard, Wen, Huei Hsun, Bose, Sonali, Leven, Emily A., Wang, Jing G., Mosoyan, Gohar, Pattharanitima, Pattharawin, Gallagher, Emily J., Friedman, Allon N., Guirguis, John, Kapoor, Rajat, Meshberger, Christopher, Kelly, Katherine J., Parikh, Chirag R., Garibaldi, Brian T., Corona-Villalobos, Celia P., Wen, Yumeng, Menez, Steven, Malik, Rubab F., Cervantes, Carmen Elena, Gautam, Samir C., Mallappallil, Mary C., Ouyang, Jie, John, Sabu, Yap, Ernie, Melaku, Yohannes, Mohamed, Ibrahim, Bajracharya, Siddhartha, Puri, Isha, Thaxton, Mariah, Bhattacharya, Jyotsna, Wagner, John, Boudourakis, Leon, Nguyen, H. Bryant, Ahoubim, Afshin, Thomas, Leslie F., Sirganagari, Dheeraj Reddy, Guru, Pramod K., Kashani, Kianoush, Tehranian, Shahrzad, Zhou, Yan, Bergl, Paul A., Rodriguez, Jesus, Shah, Jatan A., Gupta, Mrigank S., Kumar, Princy N., Lazarous, Deepa G., Kassaye, Seble G., Melamed, Michal L., Johns. Ryan Mocerino, Tanya S., Prudhvi, Kalyan, Zhu, Denzel, Levy, Rebecca V., Azzi, Yorg, Fisher, Molly, Yunes, Milagros, Sedaliu, Kaltrina, Golestaneh, Ladan, Brogan, Maureen, Thakkar, Jyotsana, Kumar, Neelja, Ross, Michael J., Chang, Michael, Athreya, Akshay, Farag, Mohamed, Schenck, Edward J., Cho, Soo Jung, Plataki, Maria, Alvarez-Mulett, Sergio L., Gomez-Escobar, Luis G., Pan, Di, Lee, Stefi, Krishnan, Jamuna, Whalen, William, Charytan, David, Macina, Ashley, Srivastava, Anand, Leidner, Alexander S., Martinez, Carlos, Kruser, Jacqueline M., Wunderink, Richard G., Hodakowski, Alexander J., Velez, Juan Carlos Q., Price-Haywood, Eboni G., Matute-Trochez, Luis A., Hasty, Anna E., Mohamed, Muner M.B., Avasare, Rupali S., Zonies, David, Al-Samkari, Hanny, Leaf, Rebecca Karp, Rosovsky, Rachel, Sise, Meghan E., Newman, Erik T., Omar, Samah Abu, Pokharel, Kapil K., Sharma, Shreyak, Singh, Harkarandeep, Correa, Simon, Shaukat, Tanveer, Kamal, Omer, Wang, Wei, Lee, Meghan, Strohbehn, Ian A., Li, Jiahua, Mueller, Ariel L., Redfern, Roberta E., Cairl, Nicholas S., Naimy, Gabriel, Abu-Saif, Abeer, Hall, Danyell, Bickley, Laura, Rowan, Chris, Madhani-Lovely, Farah, Peev, Vasil, Reiser, Jochen, Byun, John J., Vissing, Andrew, Kapania, Esha M., Post, Zoe, Patel, Nilam P., Hermes, Joy-Marie, Sutherland, Anne K., Patrawalla, Amee, Finkel, Diana G., Danek, Barbara A., Arikapudi, Sowminya, Paer, Jeffrey M., Cangialosi, Peter, Liotta, Mark, Radbel, Jared, Puri, Sonika, Sunderram, Jag, Scharf, Matthew T., Ahmed, Ayesha, Berim, Ilya, Vatson, Jayanth S., Karp, George, Anand, Shuchi, Levitt, Joseph E., Garcia, Pablo, Boyle, Suzanne M., Song, Rui, Zhang, Jingjing, Woo, Sang Hoon, Deng, Xiaoying, Katz-Greenberg, Goni, Sharshir, Moh’d A., Rusnak, Vadym V., Bansal, Anip, Podoll, Amber S., Chonchol, Michel, Sharma, Sunita, Burnham, Ellen L., Rashidi, Arash, Hejal, Rana, Judd, Eric, Latta, Laura, Tolwani, Ashita, Albertson, Timothy E., Adams, Jason Y., Chang, Steven Y., Beutler, Rebecca M., Schulze, Carl E., Macedo, Etienne, RheeKa, Harin, thleen D. Liu, Jotwani, Vasantha K., Koyner, Jay L., Shah, Chintan V., Jaikaransingh, Vishal, Toth-Manikowski, Stephanie M., Joo, Min J., Lash, James P., Neyra, Javier A., Chaaban, Nourhan, Elias, Madona, Ahmad, Yahya, Dy, Rajany, Iardino, Alfredo, Au, Elizabeth H., Sosa, Marie Anne, Taldone, Sabrina, Contreras, Gabriel, De La Zerda, David, Gershengorn, Hayley B., Fornoni, Alessia, Hayek, Salim S., Blakely, Pennelope, Berlin, Hanna, Azam, Tariq U., Shadid, Husam, Pan, Michael, O’Hayer, Patrick, Meloche, Chelsea, Feroze, Rafey, Padalia, Kishan J., Leya, Jeff, Donnelly, John P., Admon, Andrew J., Flythe, Jennifer E., Tugman, Matthew J., Chang, Emily H., Brown, Brent R., Leonberg-Yoo, Amanda K., Spiardi, Ryan C., Miano, Todd A., Roche, Meaghan S., Vasquez, Charles R., Bansal, Amar D., Ernecoff, Natalie C., Kapoor, Sanjana, Verma, Siddharth, Chen, Huiwen, Kovesdy, Csaba P., Molnar, Miklos Z., Azhar, Ambreen, Hedayati, S. Susan, Nadamuni, Mridula V., Shastri, Shani, Willett, Duwayne L., Short, Samuel A.P., Renaghan, Amanda D., Enfield, Kyle B., Bhatraju, Pavan K., Malik, A. Bilal, Semler, Matthew W., Vijayan, Anitha, Joy, Christina Mariyam, Li, Tingting, Goldberg, Seth, Kao, Patricia F., Schumaker, Greg L., Christov, Marta, Griffiths, Jennifer, Gupta, Sanjeev, Kapoor, Aromma, Chugh, Savneek, Wilson, Perry, Arora, Tanima, Ugwuowo, Ugochukwu, Hsu, Caroline M., Gupta, Shruti, Tighiouart, Hocine, Goyal, Nitender, Faugno, Anthony J., Tariq, Asma, Raichoudhury, Ritesh, Sharma, Jill H., Meyer, Leah, Kshirsagar, Ravi K., Jose, Aju, Leaf, David E., and Weiner, Daniel E.

Published
2022
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46. The role of age‐associated alpha‐synuclein aggregation in a conditional transgenic mouse model of Parkinson's disease: Implications for Lewy body formation.

Author

Li, Jiahua, Ng, Ka Wai, Sung, Chun Chau, and Chung, Kenny K. K.

Subjects
*PARKINSON'S disease, *TRANSGENIC mice, *LABORATORY mice, *ANIMAL disease models, *ALPHA-synuclein, *DOPAMINERGIC neurons, *DOPAMINE receptors
Abstract

Parkinson's disease (PD) is a common neurodegenerative disorder that is affecting an increasing number of older adults. Although PD is mostly sporadic, genetic mutations have been found in cohorts of families with a history of familial PD (FPD). The first such mutation linked to FPD causes a point mutation (A53T) in α‐synuclein (α‐syn), a major component of Lewy bodies, which are a classical pathological hallmark of PD. These findings suggest that α‐syn is an important contributor to the development of PD. In our previous study, we developed an adenoviral mouse model of PD and showed that the expression of wild‐type (WT) α‐syn or a mutant form with an increased propensity to aggregate, designated as WT‐CL1 α‐syn, could be used to study how α‐syn aggregation contributes to PD. In this study, we established a transgenic mouse model that conditionally expresses WT or WT‐CL1 α‐syn in dopaminergic (DA) neurons and found that the expression of either WT or WT‐CL1 α‐syn was associated with an age‐dependent degeneration of DA neurons and movement dysfunction. Using this model, we were able to monitor the process of α‐syn aggregate formation and found a correlation between age and the number and sizes of α‐syn aggregates formed. These results provide a potential mechanism by which age‐dependent α‐syn aggregation may lead to the formation of Lewy bodies in PD pathogenesis. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Variations of Kelvin Waves around the Tropical Tropopause Inversion Layer from GNSS RO Measurements.

Author

Li, Jiahua, Xu, Xiaohua, and Luo, Jia

Subjects
*OCEAN waves, *GLOBAL Positioning System, *TROPOPAUSE, *ZONAL winds, *ATMOSPHERIC temperature, *WESTERLIES, EL Nino
Abstract

In the present study, the tropical tropopause inversion layer (TIL) Kelvin waves are extracted from the Global Navigation Satellite System (GNSS) radio occultation (RO) temperature data of multiple missions from January 2007 to December 2020. We focus on the variations of TIL Kelvin waves in two longitude regions, the Maritime Continent (MC; 90°–150°E) and the Pacific Ocean (PO; 170°–230°E). The results show that over both regions, ENSO leads to the opposite variations of TIL Kelvin wave temperature amplitude during different ENSO phases. Specifically, during La Niña, the strong (weak) deep convection over MC (PO) leads to strengthened (weakened) static stability. With enhanced easterly (westerly) winds and strengthened (weakened) static stability, the TIL Kelvin wave temperature amplitudes are stronger (weaker) over MC (PO). The opposite phenomenon occurs during El Niño. The zonal-mean zonal winds affect TIL Kelvin wave temperature amplitudes by two mechanisms. First, the prevalence of easterlies (westerlies) in the upper troposphere affects the upward propagation of Kelvin waves, resulting in stronger (weaker) TIL Kelvin wave temperature amplitudes over MC (PO). Second, the TIL Kelvin wave temperature amplitude peaks about 2 months before the zero-wind line of the descending westerly QBO phase occurs, due to dissipation on the critical line. Additionally, the rapid increase of zonal-mean static stability significantly affects the annual variation of TIL Kelvin wave temperature amplitudes. They both reach maxima during DJF and minima during JJA, which should be related to the annual cycles of temperature and ozone mixing ratio in the TIL. Significance Statement: Recent studies indicate that the Kelvin wave temperature amplitudes in the tropical tropopause inversion layer (TIL) exhibit distinct characteristics compared with those in other height levels, while the modulation mechanisms of the TIL Kelvin waves need further investigation. The present study aims to study the differences in the variabilities and the modulation factors of TIL Kelvin waves over two longitude regions. Our findings suggest that the different responses of background conditions during ENSO phases influence the spatiotemporal distribution of the TIL Kelvin waves. Besides, the zonal winds and the static stability significantly affect the temporal variations of TIL Kelvin waves. Our work fills the research gap of TIL Kelvin waves and contributes to understanding the dynamics of tropical tropopause variations. [ABSTRACT FROM AUTHOR]

Published
2024
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