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Morphological and metabolic asymmetries of the thalamic subregions in temporal lobe epilepsy predict cognitive functions

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Scientific Reports 2023年第1期13卷 1-9页

作者： Tung, Hsin Tsai, Shih-Chuan Huang, Pu-Rong Hsieh, Peiyuan F. Lin, Yi-Ching Peng, Syu-Jyun Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Center of Faculty Development Taichung Veterans General Hospital Taichung Taiwan Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Nuclear Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Technology Institute of Radiological Science Central Taiwan University of Science and Technology Taichung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Both morphological and metabolic imaging were used to determine how asymmetrical changes of thalamic subregions are involved in cognition in temporal lobe epilepsy (TLE). We retrospectively recruited 24 left-TLE and 15 right-TLE patients. Six thalamic subnuclei were segmented by magnetic resonance imaging, and then co-registered onto Positron emission tomography images. We calculated the asymmetrical indexes of the volumes and normalized standard uptake value ratio (SUVR) of the entire and individual thalamic subnuclei. The SUVR of ipsilateral subnuclei were extensively and prominently decreased compared with the volume loss. The posterior and medial subnuclei had persistently lower SUVR in both TLE cases. Processing speed is the cognitive function most related to the metabolic asymmetry. It negatively correlated with the metabolic asymmetrical indexes of subregions in left-TLE, while positively correlated with the subnuclei volume asymmetrical indexes in right-TLE. Epilepsy duration negatively correlated with the volume asymmetry of most thalamic subregions in left-TLE and the SUVR asymmetry of ventral and intralaminar subnuclei in right-TLE. Preserved metabolic activity of contralateral thalamic subregions is the key to maintain the processing speed in both TLEs. R-TLE had relatively preserved volume of the ipsilateral thalamic volume, while L-TLE had relatively decline of volume and metabolism in posterior subnucleus. © 2023, The Author(s).

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A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark datasets

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arXiv 2020年

作者： Zeng, Chengchang Li, Shaobo Li, Qin Hu, Jie Hu, Jianjun Chengdu Institute of Computer Application University of Chinese Academy of Sciences School of Mechanical Engineering Guizhou University College of Big Data and Statistics GuiZhou University of Finance and Economics Department of Computer Science and Engineering University of South Carolina

Machine Reading Comprehension (MRC) is a challenging NLP research field with wide real world applications. The great progress of this field in recent years is mainly due to the emergence of large-scale datasets and deep learning. At present, a lot of MRC models have already surpassed the human performance on many datasets despite the obvious giant gap between existing MRC models and genuine human-level reading comprehension. This shows the need of improving existing datasets, evaluation metrics and models to move the MRC models toward’real’ understanding. To address this lack of comprehensive survey of existing MRC tasks, evaluation metrics and datasets, herein, (1) we analyzed 57 MRC tasks and datasets;proposed a more precise classification method of MRC tasks with 4 different attributes (2) we summarized 9 evaluation metrics of MRC tasks and (3) 7 attributes and 10 characteristics of MRC datasets;(4) We also discussed some open issues in MRC research and highlight some future research directions. In addition, to help the community, we have collected, organized, and published our data on a companion website(https://***/) where MRC researchers could directly access each MRC dataset, papers, baseline projects and browse the leaderboard. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

On the computational Bayesian survival spatial dengue hemorrhagic fever (DHF) modeling with double-exponential CAR frailty

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Journal of Physics: Conference Series 2021年第1期1722卷

作者： D Rantini M N Abdullah N Iriawan Irhamah M Rusli Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Internal Medicine Faculty of Medicine Universitas Airlangga Surabaya Indonesia

In statistics, there are many types of data. Some data carry information about the location where observations occur, so that they can have a spatial effect. Dengue hemorrhagic fever (DHF) data which is easily transmitted, will be consequently has a spatial effect on its patient survival. In this study, we included DHF patient recovery time as a response variable, and several other variables as covariates considered to influence the patient's recovery time. Our aim in this study is to model how these variables affect the recovery rate for DHF patients with the accompanying patient residence as the spatial effects. Survival analysis is the best method for modeling the recovery rate for DHF patients. A conditional autoregressive (CAR) model is given to explain the relationship between adjacent locations, which is not explained in the general survival analysis. Several researchers have used the Cox model coupled with the Normal CAR. In this study, we used the Cox model using Normal CAR and compared it with the Double-Exponential (DE) CAR. To estimate the regression parameters of the Cox model, we used the Stan software. The advantage of Stan compared to the other Bayesian software such as BUGS and JAGS is the creativity of the researcher in writing the distribution as user-defined, as well as writing the CAR model in the Stan. Based on the WAIC value, modeling the DHF data using the Cox model coupled with the DE CAR is better than coupled with the Normal CAR. Based on the best model, variables that affect the recovery rate of DHF patients are age, the high schools in last education, unemployed in the type of occupations, the stadium II in severity level, pulse, temperature, and leukocytes.

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SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data

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arXiv 2024年

作者： Karchev, Konstantin Grayling, Matthew Boyd, Benjamin M. Trotta, Roberto Mandel, Kaisey S. Weniger, Christoph Via Bonomea 265 TriesteI-34136 Italy Institute of Astronomy Kavli Institute for Cosmology Madingley Road CambridgeCB3 0HA United Kingdom Astrophysics Group Physics Department Blackett Lab Imperial College London Prince Consort Road LondonSW7 2AZ United Kingdom INFN - National Institute for Nuclear Physics Via Valerio 2 TriesteI-34127 Italy Italian Research Center on High-Performance Computing Big Data and Quantum Computing Via Magnanelli 2 BO Casalecchio di RenoI-40033 Italy Statistical Laboratory DPMMS University of Cambridge Wilberforce Road CambridgeCB3 0WB United Kingdom University of Amsterdam Science Park 904 AmsterdamNL-1098 XH Netherlands

We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovæ (SNæ Ia). Our hardware-accelerated forward model, released in the Python package slicsim, includes stochastic variations of each SN's spectral flux distribution (based on the pre-trained BayeSN model), extinction from dust in the host and in the Milky Way, redshift, and realistic instrumental noise. By utilizing truncated marginal neural ratio estimation (TMNRE), a neural network-enabled simulation-based inference technique, we implicitly marginalize over 4000 latent variables (for a set of ≈100 SNæ Ia) to efficiently infer SN Ia absolute magnitudes and host-galaxy dust properties at the population level while also constraining the parameters of individual objects. Amortization of the inference procedure allows us to obtain coverage guarantees for our results through Bayesian validation and frequentist calibration. Furthermore, we show a detailed comparison to full likelihood-based inference, implemented through Hamiltonian Monte Carlo, on simulated data and then apply TMNRE to the light curves of 86 SNæ Ia from the Carnegie Supernova Project, deriving marginal posteriors in excellent agreement with previous work. Given its ability to accommodate arbitrarily complex extensions to the forward model, e.g. different populations based on host properties, redshift evolution, complicated photometric redshift estimates, selection effects, and non-Ia contamination, without significant modifications to the inference procedure, TMNRE has the potential to become the tool of choice for cosmological parameter inference from future, large SN Ia samples. © 2024, CC BY.

关键词： Supernovae

BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models

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arXiv 2024年

作者： Liu, Mianxin Ding, Jinru Xu, Jie Hu, Weiguo Li, Xiaoyang Zhu, Lifeng Bai, Zhian Shi, Xiaoming Wang, Benyou Song, Haitao Liu, Pengfei Zhang, Xiaofan Wang, Shanshan Li, Kang Wang, Haofen Ruan, Tong Huang, Xuanjing Sun, Xin Zhang, Shaoting Shanghai Artificial Intelligence Laboratory Shanghai200232 China Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200025 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China The Shanghai Artificial Intelligence Research Institute Shanghai Jiao Tong University Shanghai200240 China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Sichuan University West China Hospital Chengdu610041 China The College of Design and Innovation Tongji University Shanghai200092 China The Department of Computer Science and Technology East China University of Science and Technology Shanghai200237 China The School of Computer Science Fudan University Shanghai200433 China Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai200092 China

Ensuring the general efficacy and goodness for human beings from medical large language models (LLM) before real-world deployment is crucial. However, a widely accepted and accessible evaluation process for medical LLM, especially in the Chinese context, remains to be established. In this work, we introduce "MedBench", a comprehensive, standardized, and reliable benchmarking system for Chinese medical LLM. First, MedBench assembles the currently largest evaluation dataset (300,901 questions) to cover 43 clinical specialties and performs multi-facet evaluation on medical LLM. Second, MedBench provides a standardized and fully automatic cloud-based evaluation infrastructure, with physical separations for question and ground truth. Third, MedBench implements dynamic evaluation mechanisms to prevent shortcut learning and answer remembering. Applying MedBench to popular general and medical LLMs, we observe unbiased, reproducible evaluation results largely aligning with medical professionals' perspectives. This study establishes a significant foundation for preparing the practical applications of Chinese medical LLMs. MedBench is publicly accessible at https://***. © 2024, CC BY.

关键词： Benchmarking

BettaNet: A Deep Learning Architecture for Classification of Wild Siamese Betta Species

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IOP Conference Series: Materials science and Engineering 2021年第1期1055卷

作者： Voravarun Pattana-anake Pimsiri Danphitsanuparn Ferdin Joe John Joseph Data Science and Analytics Laboratory Faculty of Information Technology Thai – Nichi Institute of Technology Bangkok Thailand Department of Biology and Environmental Sciences Kamnoetvidya Science Academy Rayong Thailand

Fish classification is a mix of animal sciences and artificial intelligence. With the advent of machine learning in artificial intelligence, classification has been done using computer vision algorithms and now deep learning is gaining prominence. Betta fish classification is not much explored. The wild species of Betta Splendens which are native to the Kingdom of Thailand are taken in the research reported in this paper. BettaNet architecture, a modified version of ResNet 152 is used to classify 6 species of wild species of betta. The experimental results show that the proposed BettaNet architecture holds better in performance in terms of accuracy and F1-scores. Two different datasets were used and the performance obtained by the proposed architecture reduced the cross-entropy loss over different experimental configurations.

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Visible light-driven removal of methylene blue through photocatalytic reaction using CuFe₂O₄/N-TiO₂

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Environmental Challenges 2025年 20卷

作者： Nurhaqi, Abda Ali, Badrut Tamam Ibnu Subagyo, Riki Rasyid, Herlina Maharani, Rani Kartikowati, Christina Wahyu Umaningrum, Dewi Fatmawati, Sri Ediati, Ratna Gunawan, Triyanda Asih, Retno Akhlus, Syafsir Kusumawati, Yuly Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Keputih-Sukolilo East Java Surabaya 60111 Indonesia Research Center for Process and Manufacturing Industry Technology National Research and Innovation Agency 625 Building Technology Energy Cluster PUSPIPTEK South Tangerang Banten 15314 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Hasanuddin University South Sulawesi Makassar 90245 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Jatinangor West Java Sumedang 45363 Indonesia Department of Chemical Engineering Brawijaya University East Java Malang 65145 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Lambung Mangkurat Jl. Ahmad Yani Km. 35 8 Banjarbaru East Kalimantan 70714 Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia

CuFe2O4/N-TiO2 photocatalyst was used for the removal of methylene blue (MB) dye under visible light. Various characterisation techniques were performed, including X-ray diffraction (XRD), UV Diffuse Reflectance Spect... 详细信息

CuFe₂O₄/N-TiO₂ photocatalyst was used for the removal of methylene blue (MB) dye under visible light. Various characterisation techniques were performed, including X-ray diffraction (XRD), UV Diffuse Reflectance Spectroscopy (UV-DRS), scanning electron microscopy (SEM) and vibrating-sample magnetometry (VSM). UV-DRS results showed a reduced TiO₂ bandgap after nitrogen doping and addition of CuFe₂O₄. The photocatalytic removal of MB was achieved 98.7 % after 180 min irradiation for nitrogen doped TiO₂ (N-TiO₂). The addition of CuFe₂O₄ improved the magnetic properties. The optimal composition of 5 % CuFe₂O₄/N-TiO₂, exhibited good magnetic properties and 97.56 % of MB removal efficiency. The 5 % CuFe₂O₄/N-TiO₂ also exhibit maintaining over 90 % of MB removal efficiency across three cycles process. The proposed degradation pathway was presented based on Liquid Chromatography Mass Spectrophotometer (LCMS). Based on the obtained results, 5 % CuFe₂O₄/N-TiO₂ can be proposed as photocatalyst for MB removal and its magnetic properties is beneficial for quick separation from the solution after photocatalytic process. © 2025 The Authors

关键词： CuFe2O4/N-TiO2 Magnetic photocatalytic Methylene blue removal Visible Light driven Wastewater treatment

Binary neural networks: A survey

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys