World Neuroscientists Awards

The researcher is a senior analytical chemist specializing in veterinary drug residues, food safety, and regulatory laboratory science, with a career spanning leadership roles in governmental laboratories and extensive technical expertise in mass spectrometry–based residue analysis. Their work focuses on ensuring compliance with EU and international food safety standards through the development, validation, and application of advanced analytical methods for contaminants, pesticides, and pharmacologically active substances in the food chain. With long-term experience directing a national veterinary drug residues laboratory, the researcher has contributed to method harmonization, laboratory accreditation processes, and coordinated risk-assessment activities linked to public health protection. They have served as an invited expert for European Commission missions on dietary risk assessment and have undergone specialized training at leading European institutions in residue analysis, risk communication, and food safety evaluation. Their contributions include participation in expert networks, study visits to European Union institutes, and collaborations that support national monitoring programs and regulatory decision-making.
Yuxin Yin is a Presidential Chair Professor at the School of Medicine, The Chinese University of Hong Kong, Shenzhen, recognized for pioneering contributions to molecular oncology, genome stability, and translational systems biomedicine. Trained at UNC-Chapel Hill and Princeton and previously a tenured professor at Columbia University, he later led basic medical sciences at Peking University before joining CUHK-Shenzhen. His research dissects tumor-suppressor signaling networks, including p53 and PTEN pathways, DNA-replication stress responses, genome-stability mechanisms, and the immunological dynamics that shape cancer progression. He has also advanced AI-driven metabolomics for early cancer screening, contributing to cross-disciplinary integration between cancer biology, data science, and neuroscience-inspired approaches. With more than 189 publications and an extensive research portfolio of approximately 190 completed and ongoing projects, his work has been cited over 7,264 times, with 6,352 citing articles and an average of 38.43 citations per item. He holds an H-index of 45, reflecting sustained global influence in cancer biology and biomedical innovation. Beyond academia, Yin contributes to translational and industry-facing initiatives, including AI-metabolomics screening programs and multidisciplinary collaborations that accelerate precision medicine. His leadership and scientific achievements continue to shape emerging directions in cancer research, systems biomedicine, and next-generation diagnostic technologies.
Lu, Y., Ding, D., Chen, H., Jiang, P., Luo, J., Shan, H., Wang, G., Luo, J., and Yin, Y. (2025). Structural determination of the human taurine transporter TauT reveals the mechanism of substrate and inhibitor recognition. Cell Reports, 116591.
Shen, Z., Zhong, A., Zhang, C., Tang, X., Zhao, X., Hou, Z., Liang, H., and Yin, Y. (2025). LncPTEN1, a long non-coding RNA generated from PTEN, suppresses lung cancer metastasis through the regulation of EMT progress. Non-coding RNA Research.
Tang, X., Zhang, Q., Shen, Z., Xiao, J., Li, M., Meng, X., Wang, C., Zhang, G., Liu, A., and Yin, Y. (2025). Single-cell multi-omics analysis reveals cancer regulatory elements of transcriptional programs and clinical implications. Cell Death & Disease.
Liu, A., Xiao, J., Wang, C., Meng, X., He, C., Li, M., Zhang, G., Tang, X., and Yin, Y. (2025). Single-cell multi-omics analysis reveals the plasticity of isthmus stem cells in gastric carcinogenesis. Computers in Biology and Medicine, 110662.
Li, Y., Zhou, J., Zhang, Z., Jiang, S., Yin, Q., Xiao, J., Li, X., Yin, Y., Ye, L., Peng, S., et al. (2025). RPA1 protects DNA damage induced PANoptosis in limb development. Science Advances.
Yu, S., Ding, J., Wang, J., Wang, W., Zuo, P., Yang, A., Dai, Z., Yin, Y., Sun, J., and Liang, L. (2025). Structural insights into cholesterol sensing by the LYCHOS-mTORC1 pathway. Nature Communications.
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., … (2016). The dynamics of functional brain networks: Integrated network states during cognitive task performance. Neuron, 92(2), 544–554.
Hansen, J. Y., Shafiei, G., Markello, R. D., Smart, K., Cox, S. M. L., Nørgaard, M., … (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience, 25(11), 1569–1581.
Lurie, D. J., Kessler, D., Bassett, D. S., Betzel, R. F., Breakspear, M., Kheilholz, S., … (2020). Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Network Neuroscience, 4(1), 30–69.
Shine, J. M., Breakspear, M., Bell, P. T., Ehgoetz Martens, K. A., Shine, R., … (2019). Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nature Neuroscience, 22(2), 289–296.
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M. Y., … (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6, 8885.
Shine, J. M., Matar, E., Ward, P. B., Frank, M. J., Moustafa, A. A., Pearson, M., … (2013). Freezing of gait in Parkinson’s disease is associated with functional decoupling between the cognitive control network and the basal ganglia. Brain, 136(12), 3671–3681.
Lavanya, C., Venkataswamy, M. M., Sibin, M. K., Srinivas Bharath, M. M., Manoj, M. J., & others. (2018). Down regulation of human telomerase reverse transcriptase (hTERT) expression by BIBR1532 in human glioblastoma LN18 cells. Cytotechnology, 70(4), 1143–1154.
Sibin, M. K., Bhat, D. I., Narasingarao, K. V. L., Lavanya, C., & Chetan, G. K. (2015). CDKN2A (p16) mRNA decreased expression is a marker of poor prognosis in malignant high-grade glioma. Tumor Biology, 36(10), 7607–7614.
Li, J., Fang, K., Choppavarapu, L., Yang, K., Yini, X., & others. (2021). Hi-C profiling of cancer spheroids identifies 3D-growth-specific chromatin interactions in breast cancer endocrine resistance. Clinical Epigenetics, 13, Article 175.
Yang, Y., Choppavarapu, L., Fang, K., Naeini, A. S., Nosirov, B., Li, J., Yang, K., & others. (2020). The 3D genomic landscape of differential response to EGFR/HER2 inhibition in endocrine-resistant breast cancer cells. Biochimica et Biophysica Acta (BBA) – Gene Regulatory Mechanisms, 1863(11), 194631.
Lavanya, C., Sibin, M. K., Srinivas Bharath, M. M., Manoj, M. J., & others. (2016). RNA interference mediated downregulation of human telomerase reverse transcriptase (hTERT) in LN18 cells. Cytotechnology, 68(6), 2311–2321.
Sibin, M. K., Bhat, D. I., Lavanya, C., Manoj, M. J., Aakershita, S., & Chetan, G. K. (2014). CDKN2A exon-wise deletion status and novel somatic mutations in Indian glioma patients. Tumor Biology, 35(2), 1467–1472.
Professor Hui Gan is a distinguished researcher at Chongqing Medical University, serving as a master’s supervisor, associate research fellow, and Assistant to the Dean of the School of Basic Medical Sciences, where she also leads the Department of Pathophysiology in an acting capacity. Her research primarily focuses on neuroinflammation, microglial regulation, and inflammasome-mediated injury following cerebral hemorrhage, with significant contributions to understanding TRIM21-mediated mechanisms and microglial transcriptional pathways such as c-MAF–NLRP3 signaling. She has secured multiple competitive national and regional research grants supporting investigations into inflammasome activation, microcirculatory dysfunction, and post-hemorrhagic inflammatory injury. Professor Gan has received notable recognitions, including awards for early-career excellence and contributions to microcirculation research. She plays an active role in professional societies as a committee member of the Microcirculation Committee of the Chinese Society of Pathophysiology and a young committee member of the Translational Medicine Committee of the Chinese Society of Microcirculation, contributing to academic development in basic and translational medical sciences. Her scholarly output includes 18 documents, with 196 citations across 175 citing documents, reflecting a solid research impact supported by an h-index of 8. Through her scientific leadership and innovative research, she continues to advance understanding of inflammatory mechanisms in neurological injury.
Gan, H., Gan, H.-Y., Zhang, M., Duan, Y., Palahati, A., He, Q., Tan, J., Li, Y., Zhai, X., & Zhao, J. (2025). Microglial NFAT5 aggravates neuroinflammation via mediating NLRP6 inflammasome in experimental ischemic stroke. Genes and Diseases, 12(6),
Jie Xia is a researcher in electronic science and technology whose work focuses on brain imaging, biomedical signal processing, and micro–nano sensing systems. She is pursuing advanced training at Zhejiang University, where her studies integrate artificial intelligence–guided learning with next-generation sensing and detection technologies. Her academic background in automation and electronic engineering provides a strong foundation in circuits, digital logic, and signal analysis. Xia’s research experience includes serving as an algorithm engineer at the Institute of Optoelectronics at Westlake University, where she contributed to the development of a non-invasive photoelectric multimodal brain imaging system. In this role, she designed and optimized algorithms for near-infrared functional brain imaging and developed computational methods to quantify changes in oxygenated and deoxygenated hemoglobin, supporting real-time monitoring of cerebral activity. Her research contributions span neuroimaging technology, optical sensing, and computational modeling, with growing impact in the field. She has produced multiple scholarly outputs, reflected in 67 citations, 65 documents, an h-index of 5, and an i10-index of 3, demonstrating consistent influence and early-career productivity. Xia continues to develop interdisciplinary methods that bridge engineering and neuroscience, aiming to advance non-invasive brain monitoring technologies and their translational applications.
Zhang, F., Zhang, L., Xia, J., Zhao, W., Dong, S., Ye, Z., Pan, G., Luo, J., & Zhang, S. (2023). Multimodal electrocorticogram active electrode array based on zinc oxide‐thin film transistors. Advanced Science, 10(2), 2204467.
Zhou, P., Bai, C., Xia, J., & Chen, S. (2020). CMRDF: A real-time food alerting system based on multimodal data. IEEE Internet of Things Journal, 9(9), 6335–6349.
Li, B., Li, M., Xia, J., Jin, H., Dong, S., & Luo, J. (2024). Hybrid integrated wearable patch for brain EEG-fNIRS monitoring. Sensors, 24(15), 4847.
Wang, S., Yang, L., Jiang, H., Xia, J., Li, W., Zhang, Z., Zhang, S., Jin, H., Luo, J., … (2023). Multifunctional evaluation technology for diagnosing malfunctions of regional pelvic floor muscles based on stretchable electrode array probe. Diagnostics, 13(6), 1158.
Zhou, P., Bai, C., & Xia, J. (2019). ZJUTCVR Team at ImageCLEFlifelog2019 Lifelog Moment Retrieval Task. CLEF (Working Notes), 11.
Xia, J., Zhang, F., Zhang, L., Cao, Z., Dong, S., Zhang, S., Luo, J., & Zhou, G. (2024). Magnetically compatible brain electrode arrays based on single-walled carbon nanotubes for long-term implantation. Nanomaterials, 14(3), 240.
Zhou, P., Xia, J., Peng, X., Zhao, W., Ye, Z., Li, Z., Yang, S., Pan, J., Chen, Y., … (2025). Neural-driven image editing. NeurIPS 2025.
Pan, J., Xia, J., Zhang, F., Zhang, L., Zhang, S., Pan, G., & Dong, S. (2023). 7T magnetic compatible multimodality electrophysiological signal recording system. Electronics, 12(17), 3648.
Dr. A. Jayanthiladevi is a distinguished academic and researcher in Computer Science and Engineering, currently serving as a Professor at the Department of Computer Science and Engineering, BGSIT, Adichunchanagiri University, Karnataka, India. She has completed two Post-Doctoral Research Fellowships, one at the Center of Artificial Intelligence, Amity University Dubai, and another at Srinivas University under the mentorship of leading experts in artificial intelligence and enterprise systems. She holds a Ph.D. in Computer Applications from Anna University, where her doctoral work proposed an efficient spectrum utilization and retransmission rerouting mechanism in Mobile IP. Her academic background further includes advanced qualifications in intellectual property law and computer applications. Dr. Jayanthiladevi has extensive professional experience as Professor, Assistant Professor, Director of Post-Doctoral Fellowship Programmes, and collaborator for industry–academia research initiatives across various reputed institutions. Her research contributions span artificial intelligence, wireless networks, spectrum mobility, and cybersecurity. She has authored numerous publications with a citation count of 639 and 569 across platforms, an h-index of 13 and 12, and an i10-index of 15 and 14, reflecting her strong scholarly impact. With strong research leadership and collaborative expertise, she continues to contribute significantly to high-quality publications, patents, and international research advancements.
Priya, S. S., Sivaram, M., Yuvaraj, D., & Jayanthiladevi, A. (2020). Machine learning based DDoS detection. In 2020 International Conference on Emerging Smart Computing and Informatics.
Balamurugan, E., Flaih, L. R., Yuvaraj, D., Sangeetha, K., & Jayanthiladevi, A. (2019). Use case of artificial intelligence in machine learning manufacturing 4.0. In 2019 International Conference on Computational Intelligence and Knowledge.
Nagarajan, G., Minu, R. I., & Jayanthila Devi, A. (2020). Optimal nonparametric Bayesian model-based multimodal BoVW creation using multilayer pLSA. Circuits, Systems, and Signal Processing, 39(2), 1123–1132.
Nagarajan, G., Minu, R. I., & Jayanthiladevi, A. (2019). Brain computer interface for smart hardware device. International Journal of RF Technologies, 10(3–4), 131–139.
Jayanthiladevi, A., Raj, A. G., Narmadha, R., Chandran, S., Shaju, S., & Prasad, K. K. (2020). AI in video analysis, production and streaming delivery. Journal of Physics: Conference Series, 1712(1), 012014.
Sakthivel, K., Jayanthiladevi, A., & Kavitha, C. (2016). Automatic detection of lung cancer nodules by employing intelligent fuzzy c-means and support vector machine. Biomedical Research–India, 27, S123–S127.
Ranjeeth, S., Latchoumi, T. P., Sivaram, M., Jayanthiladevi, A., & Kumar, T. S. (2019). Predicting student performance with ANNQ3H: A case study in secondary education. In 2019 International Conference on Computational Intelligence and Knowledge.
Minu, R., Nagarajan, G., Suresh, A., & Devi, J. A. (2016). Cognitive computational semantic for high resolution image interpretation using artificial neural network. Biomedical Research–India, 27, S306–S309.
Mishra, P., Jimmy, L., Ogunmola, G. A., Phu, T. V., Jayanthiladevi, A., et al. (2020). Hydroponics cultivation using real time IoT measurement system. Journal of Physics: Conference Series, 1712(1), 012040.
Latchoumi, T. P., Vasanth, A. V., Bhavya, B., Viswanadapalli, A., & Jayanthiladevi, A. (2020). QoS parameters for comparison and performance evaluation of reactive protocols. In 2020 International Conference on Computational Intelligence for Smart Power.
Arunkarthikeyan, K., Balamurugan, K., Nithya, M., & Jayanthiladevi, A. (2019). Study on deep cryogenic treated-tempered WC–CO insert in turning of AISI 1040 steel. In 2019 International Conference on Computational Intelligence and Knowledge.
Neroladaki, A., Botsikas, D., Boudabbous, S., Becker, C. D., & Montet, X. (2013). Computed tomography of the chest with model-based iterative reconstruction using a radiation exposure similar to chest X-ray examination: Preliminary observations. European Radiology, 23(2), 360–366.
Mundada, P., Kohler, R., Boudabbous, S., Toutous Trellu, L., Platon, A., & Becker, C. D., et al. (2017). Injectable facial fillers: Imaging features, complications, and diagnostic pitfalls at MRI and PET CT. Insights into Imaging, 8(6), 557–572.
Boudabbous, S., Arditi, D., Paulin, E., Syrogiannopoulou, A., Becker, C., & Montet, X. (2015). Model-based iterative reconstruction (MBIR) for the reduction of metal artifacts on CT. American Journal of Roentgenology, 205(2), 380–385.
Botsikas, D., Triponez, F., Boudabbous, S., Hansen, C., Becker, C. D., & Montet, X. (2014). Incidental adrenal lesions detected on enhanced abdominal dual-energy CT: Can the diagnostic workup be shortened by the implementation of virtual unenhanced images? European Journal of Radiology, 83(10), 1746–1751.
Botsikas, D., Bagetakos, I., Picarra, M., Da Cunha Afonso Barisits, A. C., Boudabbous, S., & Montet, X., et al. (2019). What is the diagnostic performance of 18-FDG-PET/MR compared to PET/CT for the N- and M-staging of breast cancer? European Radiology, 29(4), 1787–1798.
Mundada, P., Becker, M., Lenoir, V., Stefanelli, S., Rougemont, A. L., Beaulieu, J. Y., … & Boudabbous, S. (2019). High resolution MRI of nail tumors and tumor-like conditions. European Journal of Radiology, 112, 93–105.
Delattre, B. M. A., Boudabbous, S., Hansen, C., Neroladaki, A., Hachulla, A. L., Becker, C. D., & Montet, X. (2020). Compressed sensing MRI of different organs: Ready for clinical daily practice? European Radiology, 30(1), 308–319.
Malinauskaite, I., Hofmeister, J., Burgermeister, S., Neroladaki, A., Hamard, M., Boudabbous, S., … & Montet, X. (2020). Radiomics and machine learning differentiate soft‐tissue lipoma and liposarcoma better than musculoskeletal radiologists. Sarcoma, 2020(1), Article 7163453.