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Genetic mutation patterns among glioblastoma patients in the Taiwanese population – insights from a single institution retrospective study

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Cancer Gene Therapy 2024年第6期31卷 894-903页

作者： Huang, Yu-Fen Chiao, Ming-Tsang Hsiao, Tzu-Hung Zhan, Yong-Xiang Chen, Tse-Yu Lee, Chung-Hsin Liu, Szu-Yuan Liao, Chih-Hsiang Cheng, Wen-Yu Yen, Chun-Ming Lai, Chih-Ming Chen, Jun-Peng Shen, Chiung-Chyi Yang, Meng-Yin Department of Neurosurgery Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung 40705 Taiwan Precision Medicine Center Taichung Veterans General Hospital Taichung 40705 Taiwan Doctoral Program in Translational Medicine National Chung Hsing University Taichung Taiwan Rong Hsing Translational Medicine Research Center National Chung Hsing University Taichung Taiwan Institute of Medicine Chung Shan Medical University Taichung 402 Taiwan Graduate Institute of Life Science Department of Life Science College of Life Science National Chung Hsing University Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine Taipei Medical University Taipei Taiwan Department of Minimally Invasive Skull Base Neurosurgery Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Physical Therapy Hung Kuang University Taichung Taiwan Institute of Biomedical Sciences National Chung Hsing University Taichung Taiwan School of Medicine National Defense Medical Center Taipei Taiwan Department of Critical Care Medicine Taichung Veterans General Hospital Taichung Taiwan Functional Neurosurgery Division Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Institute of Molecular Biology College of Life Science National Chung Hsing University Taichung Taiwan Biostatistics Task Force Taichung Veterans General Hospital Taichung Taiwan Basic Medical Education Center Central Taiwan University of Science and Technology Taichung Taiwan

This study utilized Next-Generation Sequencing (NGS) to explore genetic determinants of survival duration in Glioblastoma Multiforme (GBM) patients. We categorized 30 primary GBM patients into two groups based on their survival periods: extended survival (over two years, N = 17) and abbreviated survival (under two years, N = 13). For identifying pathogenic or likely pathogenic variants, we leveraged the ClinVar database. The cohort, aged 23 to 66 (median: 53), included 17 patients in Group A (survival >2 years, 10 males, 7 females), and 13 patients in Group B (survival <2 years, 8 males, 5 females), with a 60% to 40% male-to-female ratio. Identified mutations included CHEK2 (c.1477 G > A, p.E493K), IDH1 (c.395 G > A, p.R132H), and TP53 mutations. Non-coding regions exhibited variants in the TERT promoter (c.-146C > T, c.-124C > T) and TP53 RNA splicing site (c.376-2 A > C, c.376-2 A > G). While Group A had more mutations, statistical significance wasn’t reached, likely due to sample size. Notably, TP53, and ATR displayed a trend toward significance. Surprisingly, TP53 mutations were more prevalent in Group A, contradicting Western findings on poorer GBM prognosis. In Taiwanese GBM patients, bevacizumab usage is linked to improved survival rates, affirming its safety and effectiveness. EGFR mutations are infrequent, suggesting potential distinctions in carcinogenic pathways. Further research on EGFR mutations and amplifications is essential for refining therapeutic approaches. TP53 mutations are associated with enhanced survival, but their functional implications necessitate detailed exploration. This study pioneers genetic analysis in Taiwanese GBM patients using NGS, advancing our understanding of their genetic landscape. © The Author(s) 2024.

关键词： Cancer genetics CNS cancer

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Visual Interpretability of Deep Learning Models in Glaucoma Detection Using Color Fundus Images

Visual Interpretability of Deep Learning Models in Glaucoma ...

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2022 IET International Conference on Engineering Technologies and Applications, IET-ICETA 2022

作者： Lu, Da-Wen Hsu, Wei-Wen Huang, Yu-Chuan Lee, Lung-Chi Guo, Jing-Ming Hsiao, Yu-Ting Lin, I-Hung Chang, Yao-Chung Ko, Mei-Lan National Defense Medical Center Tri-Service General Hospital Dept. of Ophthalmology Taipei Taiwan National Taitung University Dept. of Computer Science and Information Engineering Taitung Taiwan School of Pharmacy National Defense Medical Center Taipei Taiwan National Taiwan University of Science and Technology Dept. of Electrical Engineering Taipei Taiwan Taipei Medical University Hospital Dept. of Ophthalmology Taipei Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan National Taiwan University Hospital Dept. of Ophthalmology Hsinchu Taiwan National Tsing Hua University Dept. of Biomedical Engineering and Environmental Science Hsinchu Taiwan

ISBN: (数字)9781665491389

ISBN: (纸本)9781665491389

With the advancement of deep learning in many computer vision applications, the computer-aided diagnosis (CAD) systems were developed using the deep learning approaches to assist physicians in clinical diagnosis as a second opinion. Many studies have reported plausible performance of the deep learning models on glaucoma detection simply based on fundus images. However, the mechanism of the deep learning models in detecting glaucoma using color fundus is still not clear. Therefore, the deep features extracted by the deep learning models were discussed in this study to provide the visual interpretability for the frameworks of deep convolutional neural network (DCNN) in glaucoma assessment. In our experiments, 986 fundus photographs were collected from National Taiwan University Hospital Hsin-Chu Branch, categorized into two groups: the experimental group of 512 glaucomatous cases with ganglion cell complex (GCC) impairment and the control group of 474 non-glaucomatous cases with normal GCC thickness. The experimental results show the deep learning models mainly focus on the areas of optic nerve head (ONH) for the diagnosis of glaucoma, which is accordant to the clinical rules in glaucoma assessment. Surprisingly, the DCNN models can still achieved high prediction accuracy in detecting the glaucomatous cases with the cropped images of macular areas only. Several cases show the model's focus areas correspond to the region with GCC impairment. The results imply the deep learning models can detect the morphologically detailed alterations in fundus photographs, which may be beyond experts' visualization. © 2022 IEEE.

关键词： Eye protection

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Fault Diagnosis of Wind Energy Conversion Systems Using Gaussian Process Regression-based Multi-Class Random Forest

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IFAC-PapersOnLine 2022年第6期55卷 127-132页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Science and Technology of Kasserine University of Kairouan PO Box 471 1200 Kasserine Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

This work proposes a new fault diagnosis approach for a wind energy conversion (WEC) system. The proposed technique merges the benefits of feature extraction based on Gaussian Process Regression (GPR) and Multi-Class Random Forest (MCRF)-based fault classification where instances are classified into one or more classes. In the developed GPR-MCRF approach, the nonlinear statistical features including the mean vector M GPR and the variance matrix C GPR are computed using the GPR model with aim of extracting the most relevant features from the WEC system. Then, these features are introduced to the RF classifier for classification and diagnosis purposes. Therefore, the application of the GPR-MCRF technique for WEC systems aims to enhance the use of the classical raw data-based MCRF and diagnosis accuracy. Three kinds of faults (wear-out, open-circuit, and short-circuit faults) are considered in this work. Different case studies are investigated in order to illustrate the effectiveness and robustness of the developed technique compared to the state-of-the-art methods. The obtained results show that the the developed GPR-MCRF technique is an effective feature extraction and fault diagnosis technique for WEC systems.

关键词： Random Forest (RF) Multi-Class Gaussian Process Regression (GPR) Fault Diagnosis Wind Energy Conversion Systems

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Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics

arXiv

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arXiv 2025年

作者： Mei, Jie Rodriguez-Garcia, Alejandro Takeuchi, Daigo Wainstein, Gabriel Hubig, Nina Mohsenzadeh, Yalda Ramaswamy, Srikanth IT:U Interdisciplinary Transformation University Austria Linz Austria International Research Center for Neurointelligence The University of Tokyo Tokyo Japan Department of Anatomy University of Quebec in Trois-Rivieres Trois-RivieresQC Canada Neural Circuits Laboratory Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle upon Tyne United Kingdom Graduate School of Medicine The University of Tokyo Tokyo Japan Brain and Mind Centre The University of Sydney SydneyNSW Australia University of Helsinki Helsinki Finland CharlestonSC United States Department of Computer Science Western University LondonON Canada Vector Institute for Artificial Intelligence TorontoON Canada Okinawa Institute of Science and Technology Graduate University Okinawa Japan

Continuous, adaptive learning—the ability to adapt to the environment and improve performance—is a hallmark of both natural and artificial intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to dynamic environments, making them a rich source of inspiration for artificial neural networks (ANNs). This study explores how neuromodulation, a fundamental feature of biological learning systems, can help address challenges such as catastrophic forgetting and enhance the robustness of ANNs in continuous learning scenarios. Driven by neuromodulators including dopamine (DA), acetylcholine (ACh), serotonin (5-HT) and noradrenaline (NA), neuromodulatory processes in the brain operate at multiple scales, facilitating dynamic responses to environmental changes through mechanisms ranging from local synaptic plasticity to global network-wide adaptability. Importantly, the relationship between neuromodulators, and their interplay in the modulation of sensory and cognitive processes are more complex than expected, demonstrating a "many-to-one" neuromodulator-to-task mapping. To inspire the design of novel neuromodulation-aware learning rules, we highlight (i) how multi-neuromodulatory interactions enrich single-neuromodulator-driven learning, (ii) the impact of neuromodulators across multiple spatial and temporal scales, and correspondingly, (iii) strategies for approximating and integrating neuromodulated learning processes in ANNs. To illustrate these principles, we present a case study to demonstrate how neuromodulation-inspired mechanisms, such as DA-driven reward processing and NA-based cognitive flexibility, can enhance ANN performance in a Go/No-Go task. By integrating multi-scale neuromodulation, we aim to bridge the gap between biological learning and artificial systems, paving the way for ANNs with greater flexibility, robustness, and adaptability. © 2025, CC BY-NC-SA.

关键词： Brain mapping

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Socially acceptable route planning and trajectory behavior analysis of personal mobility device for mobility management with improved sensing

arXiv

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arXiv 2021年

作者： Mishra, Sumit Rajendran, Praveen Kumar Har, Dongsoo The Robotics Program Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Division of Future Vehicle Department of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The CCS Graduate School of Green Transportation Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

In urban cities, with increasing acceptability of shared spaces used by pedestrians and personal mobility devices (PMDs), there is need for pragmatic socially acceptable path planning and navigation management policies. Hence, we propose a socially acceptable global route planner and assess the legibility of the resulting global route. Our approach proposed for choosing global route avoids streets penetrating shared spaces and main routes with high probability of dense usage. The experimental study shows that socially acceptable routes can be effectively found with an average of 10 % increment of route length with optimal hyperparameters. This helps PMDs to reach the goal while taking a socially acceptable and safe route with minimal interaction of different PMDs and pedestrians. When PMDs interact with pedestrians and other types of PMDs in shared spaces, micro-mobility simulations are of prime usage for acceptable and safe navigation policy. Social force models being state of the art for pedestrian simulation are calibrated for capturing random movements of pedestrian behavior. Social force model with calibration can imitate the required behavior of PMDs in a pedestrian mix navigation scheme. Based on calibrated models, simulations on shared space links and gate structures are performed to assist policies related to deciding waiting and stopping time. Also, based on simulated PMDs interaction with pedestrians, location data with finer resolution can be obtained if the resolution of GPS sensor is 0.2 m or less. This will help in formalizing better modelling and hence better micro-mobility policies. Copyright © 2021, The Authors. All rights reserved.

关键词： Navigation

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Experimental Analyses of the Combustion Instability Parameters in an Ammonia/Methane Fuelled Low Eddy Premix Burner for Gas Turbine Power Generation

SSRN

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SSRN 2022年

作者： Ilbas, Mustafa Kumuk, Osman Alabas, Bugrahan Department of Energy Systems Engineering Faculty of Technology Gazi University Teknikokullar Ankara Turkey Graduate School of Natural and Applied Science Gazi University Teknikokullar Ankara Turkey Unmanned Aerial Vehicle Technology and Operatorship Program Electronic and Automation Department Iskenderun Vocational School of Higher Education Iskenderun Technical University Hatay Turkey Department of Aeronautical Engineering Faculty of Aeronautics and Astronautics Erciyes University Kayseri Turkey

The main factor that directs scientists to work on alternative energy sources is the environmental damage caused by the waste gases generated by the use of fossil fuels. Since ammonia is a carbonless substance and contains hydrogen atoms per unit volume, it can be used as an alternative fuel. In this study, experimental analyzes were carried out to examine the combustion instability properties of methane-ammonia mixed fuels for a low eddy and premix burner. The aim of this study is to determine the effect of ammonia addition on combustion instability with different compelling acoustic frequencies, dynamic pressure amplitude and light emission intensity. In addition, the effects of ammonia addition to methane fuel gas on temperature and emissions were also investigated. Pure methane and three different methane-ammonia mixtures (by volume 100% CH4, 90% CH 4 - 10 NH3, 80% CH4 - 20% NH3 and 70% CH4-30 NH3) as fuel at 0.7 equivalents and with three different acoustics have been used for analyzes. The burner power was determined as 5 kW and the excitation frequencies of the combustion chamber were determined as 95, 160 and 300 Hz. In this study, as the ammonia addition increased, there was an increase in the flame instability values up to 30% NH3, especially at the 95 Hz frequency, where the highest distortion effect occurred. It was observed that there was no significant change in the combustion performance with the addition of ammonia to methane fuel. The predicted NOX emission levels were found to increase significantly due to nitrogen bound to ammonia. However, it is seen that the NOx emission levels in the exhaust measurements are not very high (Maximum 560 ppm). Therefore, it can be concluded that ammonia supplemented methane fuels have significant potential as an alternative fuel. © 2022, The Authors. All rights reserved.

关键词： Ammonia

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Procedure to Reveal the Mechanism of Pattern Formation Process by Topological Data Analysis

arXiv

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arXiv 2022年

作者： Mototake, Yoh-Ichi Mizumaki, Masaichiro Kudo, Kazue Fukumizu, Kenji Graduate School of Social Data Science Hitotsubashi University 2-1 Naka Kunitachi Tokyo186-8601 Japan Graduate School of Science and Technology Kumamoto University 2-39-1 Kurokami Chuo-ku Kumamoto Kumamoto860-8555 Japan Department of Computer Science Ochanomizu University 2-1-1 Otsuka Bunkyo-ku Tokyo112-8610 Japan Department of Computer and Mathematical Sciences Tohoku University 6-3-09 Aoba Aramaki-aza Aoba-ku Sendai Miyagi980-8579 Japan The Institute of Statistical Mathematics 10-3 Midori-cho Tachikawa Tokyo190-8562 Japan

Topological data analysis (TDA) is a versatile tool that can be used to extract scientific knowledge from complex pattern formation processes. However, the physics correspondence between the features obtained from TDA and pattern dynamics does not agree one-to-one, and the physical interpretation of the TDA features needs to be set appropriately according to the phenomenon to be analyzed. In this study, we propose an analytical procedure to physically interpret pattern dynamics through TDA and machine learning techniques. The proposed procedure was applied to the process of magnetic domain pattern formation to quantify non-trivial domain pattern classifications and reveal the nature of the underlying dynamics. On the basis of these findings, we also propose a candidate reduction model to understand the nature of magnetic domain formation. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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THE TEMPORAL STRUCTURE OF LANGUAGE PROCESSING IN THE HUMAN BRAIN CORRESPONDS TO THE LAYERED HIERARCHY OF DEEP LANGUAGE MODELS

arXiv

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arXiv 2023年

作者： Goldstein, Ariel Ham, Eric Schain, Mariano Nastase, Samuel A. Zada, Zaid Grinstein-Dabus, Avigail Aubrey, Bobbi Gazula, Harshvardhan Feder, Amir Doyle, Werner Devore, Sasha Dugan, Patricia Friedman, Daniel Reichart, Roi Brenner, Michael Hassidim, Avinatan Devinsky, Orrin Flinker, Adeen Levy, Omer Hasson, Uri Department of Psychology The Neuroscience Institute Princeton University PrincetonNJ United States Google Research United States New York University Grossman School of Medicine New YorkNY United States School of Engineering and Applied Science Harvard University CambridgeMA United States New York University Tandon School of Engineering BrooklynNY United States Blavatnik School of Computer Science Tel-Aviv University Israel Department of Cognitive and Brain Sciences Business School Hebrew University of Jerusalem Israel Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Technion - Israel Institute of Technology Haifa Israel

Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholinguistic models, DLMs use layered sequences of continuous numerical vectors to represent words and context, allowing a plethora of emerging applications such as human-like text generation. In this paper we show evidence that the layered hierarchy of DLMs may be used to model the temporal dynamics of language comprehension in the brain by demonstrating a strong correlation between DLM layer depth and the time at which layers are most predictive of the human brain. Our ability to temporally resolve individual layers benefits from our use of electrocorticography (ECoG) data, which has a much higher temporal resolution than noninvasive methods like fMRI. Using ECoG, we record neural activity from participants listening to a 30-minute narrative while also feeding the same narrative to a high-performing DLM (GPT2-XL). We then extract contextual embeddings from the different layers of the DLM and use linear encoding models to predict neural activity. We first focus on the Inferior Frontal Gyrus (IFG, or Broca’s area) and then extend our model to track the increasing temporal receptive window along the linguistic processing hierarchy from auditory to syntactic and semantic areas. Our results reveal a connection between human language processing and DLMs, with the DLM’s layer-by-layer accumulation of contextual information mirroring the timing of neural activity in high-order language areas. © 2023, CC BY-NC-ND.

关键词： Brain

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Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data

Vehicle Classification in Intelligent Transportation Systems...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Sherief Hashima Mohamed H. Saad Kohei Hatano Hamada Rizk Computational Learning Theory Team RIKEN-AIP Fukuoka Japan Engineering Dept Egyptian Atomic Energy Authority Cairo Egypt Radiation Engineering Dept NCRRT Egyptian Atomic Energy Authority Cairo Egypt Department of Informatics Kyushu University Fukuoka Japan Graduate School of Information Science and Technology Osaka University Suita Japan Computer and Control Engineering Department Tanta University Tanta Egypt

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques.

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PnP-DETR: Towards Efficient Visual Analysis with Transformers

arXiv

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arXiv 2021年

作者： Wang, Tao Yuan, Li Chen, Yunpeng Feng, Jiashi Yan, Shuicheng Institute of Data Science National University of Singapore Singapore Yitu Technology Integrative Science and Engineering Programme NUS Graduate School National University of Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

Recently, DETR [3] pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translating the full feature map can be costly due to redundant computation on some area like the background. In this work, we encapsulate the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which we build an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result. Moreover, the PnP-augmented model can instantly achieve various desired trade-offs between performance and computation with a single model by varying the sampled feature length, without requiring to train multiple models as existing methods. Thus it offers greater flexibility for deployment in diverse scenarios with varying computation constraint. We further validate the generalizability of the PnP module on panoptic segmentation and the recent transformer-based image recognition model ViT [7] and show consistent efficiency gain. We believe our method makes a step for efficient visual analysis with transformers, wherein spatial redundancy is commonly observed. Code will be available at https://***/twangnh/pnp-detr. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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