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Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model

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SSRN 2025年

作者： Xiong, Yida Li, Kun Chen, Jiameng Zhang, Hongzhi Lin, Di Che, Yan Hu, Wenbin School of Computer Science Wuhan University Wuhan China Engineering Research Center for Big Data Application in Private Health Medicine of Fujian Universities Putian University Putian China Wuhan University Shenzhen Research Institute Shenzhen China

Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular samples in the vast chemical space is unrealistic for predictors. As a result, errors and noise are inevitably introduced during property prediction due to the nature of approximation. This leads to discrepancy accumulation, generalization reduction and suboptimal molecular candidates. In this paper, we propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM). TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions, thereby mitigating error propagation during diffusion process. By fusing physically and chemically detailed textual semantics with specialized molecular representations, TransDLM effectively integrates diverse information sources to guide precise optimization, which enhances the model's ability to balance structural retention and property enhancement. Additionally, the success of a case study further demonstrates TransDLM's ability to solve practical problems. Experimentally, our approach surpasses state-of-the-art methods in maintaining molecular structural similarity and enhancing chemical properties on the benchmark dataset. © 2025, The Authors. All rights reserved.

关键词： Cyclization

Ultrasonic backscatter communication for implantable medical devices

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arXiv 2022年

作者： Wang, Qianqian Guan, Quansheng Cheng, Julian Tang, Yuankun School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China School of Engineering The University of British Columbia KelownaBCV1V 1V7 Canada Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Southampton Wireless Group School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This paper proposes an ultrasonic backscatter communication (UsBC) system for passive implantable medical devices (IMDs) that can operate without batteries, enabling versatile revolutionary applications for future healthcare. The proposed UsBC system consists of a reader and a tag. The reader sends interrogation pulses to the tag. The tag backscatters the pulses based on the piezoelectric effect of a piezo transducer. We present several basic modulation schemes for UsBC by impedance matching of the piezo transducer. To mitigate the interference of other scatters in the human body, the tag transmits information bits by codeword mapping, and the reader performs codeword matching before energy detection in the reader. We further derive the theoretical bit-error rate (BER) expression. Monte Carlo simulations verify the theoretical analysis and show that passive UsBC can achieve low BER and low complexity, which is desirable for size-and energy-constrained IMDs. Copyright © 2022, The Authors. All rights reserved.

关键词： Piezoelectricity

Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

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arXiv 2021年

作者： Liao, Zehui Hu, Shishuai Xie, Yutong Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Australian Institute for Machine Learning The University of Adelaide AdelaideSA5000 Australia

Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators’ preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution Learning (PADL) framework to address it from the perspective of disentangling an annotator’s preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator’s segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

applications of Raman Spectroscopy in Clinical Medicine

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arXiv 2023年

作者： Qi, Yaping Chen, Esther Xinyi Hu, Dan Yang, Ying Wu, Zhenping Zheng, Ming Sadi, Mohammad A. Jiang, Yucheng Zhang, Kang Chen, Zi Chen, Yong P. Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Av. Wai Long China Tohoku University Sendai980-8577 Japan Faculty of Medicine Macau University of Science and Technology Av. Wai Long China State Key Laboratory of Information Photonics and Optical Communications School of Science Beijing University of Posts and Telecommunications Beijing100876 China School of Materials Science and Physics China University of Mining and Technology Xuzhou221116 China Department of Physics and Astronomy Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Jiangsu Suzhou215009 China Division of Thoracic Surgery Brigham and Women's Hospital Harvard Medical School 75 Francis Street BostonMA02115 United States Institute of Physics and Astronomy Villum Center for Hybrid Quantum Materials and Devices Aarhus University AarhusC 8000 Denmark

Raman spectroscopy provides spectral information related to the specific molecular structures of substances and has been well established as a powerful tool for studying biological tissues and diagnosing diseases. This article reviews recent advances in Raman spectroscopy and its applications in diagnosing various critical diseases, including cancers, infections, and neurodegenerative diseases, and in predicting surgical outcomes. These advances are explored through discussion of state-of-the-art forms of Raman spectroscopy, such as surface-enhanced Raman spectroscopy, resonance Raman spectroscopy, and tip-enhanced Raman spectroscopy employed in biomedical sciences. We discuss biomedical applications, including various aspects and methods of ex vivo and in vivo medical diagnosis, sample collection, data processing, and achievements in realizing the correlation between Raman spectra and biochemical information in certain diseases. Finally, we present the limitations of the current study and provide perspectives for future research. © 2023, CC BY.

关键词： COVID-19

Retraction Note: Explainable AI based wearable electronic optical data analysis with quantum photonics and quadrature amplitude neural computing

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Optical and Quantum Electronics 2024年第11期56卷 1-1页

作者： Kumar, Prashant Sharma, Neeraj Kumar, T. Ganesh Kalia, Puneet Sharma, Meenakshi Singh, Rajiv Ranjan Department of Electronics and Communication Engineering Teerthanker Mahaveer University Moradabad India Department of Computer Science and Application IIMT University Meerut India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Electronics and Communication Engineering School of Engineering and Technology Jaipur National University Jagatpura India Department of Education Sanskriti University Mathura India Department of Electronics and Communication Engineering Presidency University Bengaluru India

Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation for Better Domain Generalization

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SSRN

SSRN 2024年

作者： Wang, Hao Lin, Jiatai Li, Danyi Wang, Jing Zhao, Bingchao Shi, Zhenwei Liang, Changhong Han, Guoqiang Liang, Li Han, Chu Liu, Zaiyi The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China The Department of Pathology Nanfang Hospital Basic Medical College Southern Medical University Guangdong Guangzhou China Guangdong Province Key Laboratory of Molecular Tumor Pathology Guangdong Guangzhou China

Mitosis detection is one of the fundamental tasks in computational pathology, which is extremely challenging due to the heterogeneity of mitotic cell. Most of the current studies solve the heterogeneity in the technical aspect by increasing the model complexity. However, lacking consideration of the biological knowledge and the complex model design may lead to the overfitting problem while limited the generalizability of the detection model. In this paper, we systematically study the morphological appearances in different mitotic phases as well as the ambiguous non-mitotic cells and identify that balancing the data and feature diversity can achieve better generalizability. Based on this observation, we propose a novel generalizable framework (MitDet) for mitosis detection. The data diversity is considered by the proposed diversity-guided sample balancing (DGSB). And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP). Stain enhancement (SE) module is introduced to enhance the domain-relevant diversity of both data and features simultaneously. Extensive experiments have demonstrated that our proposed model outperforms all the state-of-the-art (SOTA) approaches in several popular mitosis detection datasets in both internal and unseen test sets using point annotations only. Comprehensive ablation studies have also proven the effectiveness of the rethinking of data and feature diversity balancing. By analyzing the results quantitatively and qualitatively, we believe that our proposed model not only achieves SOTA performance but also might inspire the future studies in new perspectives. © 2024, The Authors. All rights reserved.

关键词： Pathology

PSGR: Pixel-wise sparse graph reasoning for COVID-19 pneumonia segmentation in CT images

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arXiv 2021年

作者： Jia, Haozhe Tang, Haoteng Ma, Guixiang Cai, Weidong Huang, Heng Zhan, Liang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Intel Labs 2111 NE 25th Ave HillsboroOR97124 United States School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation Mountain View CaliforniaCA94043 United States

Automated and accurate segmentation of the infected regions in computed tomography (CT) images is critical for the prediction of the pathological stage and treatment response of COVID-19. Several deep convolutional neural networks (DCNNs) have been designed for this task, whose performance, however, tends to be suppressed by their limited local receptive fields and insufficient global reasoning ability. In this paper, we propose a pixel-wise sparse graph reasoning (PSGR) module and insert it into a segmentation network to enhance the modeling of long-range dependencies for COVID-19 infected region segmentation in CT images. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel. The long-range information reasoning is performed on the sparsely-connected graph to generate enhanced features. The advantages of this module are two-fold: (1) the pixel-wise mapping strategy not only avoids imprecise pixel-to-node projections but also preserves the inherent information of each pixel for global reasoning;and (2) the sparsely-connected graph construction results in effective information retrieval and reduction of the noise propagation. The proposed solution has been evaluated against four widely-used segmentation models on three public datasets. The results show that the segmentation model equipped with our PSGR module can effectively segment COVID-19 infected regions in CT images, outperforming all other competing models. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

Face Mask Integrated with Flexible and Wearable Manganite Oxide Respiration Sensor

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SSRN

SSRN 2023年

作者： Ye, Lianxu Wu, Fan Xu, Ruixing Zhang, Di Lu, Juanjuan Wang, Chuanlong Dong, Anjiang Xu, Sichen Xue, Lejun Fan, Zixin Xu, Longjie Li, Kaifeng Li, Dong Kursumovic, Ahmed Zhao, Run Tang, Rujun Qiu, Lei Wang, Haiyan MacManus-Driscoll, Judith L. Jing, Qingshen Li, Weiwei Yang, Hao College of Physics MIIT Key Laboratory of Aerospace Information Materials and Physics Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Microelectronics & Data Science Anhui University of Technology Maanshan243002 China School of Materials Engineering School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Jiangsu Key Laboratory of Thin Films School of Physical Science and Technology Soochow University Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China Department of Materials Science & Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8LT United Kingdom State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing211106 China

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices. © 2023, The Authors. All rights reserved.

关键词： Mica

Microplastics and nanoplastics in environment: Sampling, characterization and analytical methods

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Groundwater for Sustainable Development 2024年 26卷

作者： Mahapatra, Sarbanee Maity, Jyoti Prakash Singha, Shuvendu Mishra, Tanisha Dey, Gobinda Chandra Samal, Alok Banerjee, Pritam Biswas, Chhanda Chattopadhyay, Sourasis Patra, Rasmi Ranjan Patnaik, Sneha Bhattacharya, Prosun Environmental Science laboratory Department of Chemistry School of Applied Sciences KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Agricultural Chemistry National Taiwan University Taipei106319 Taiwan Department of Environmental Science University of Kalyani Nadia West Bengal 741235 India Department of Environmental Science Policy and Management University of California University Avenue and Oxford St BerkeleyCA94720 United States Research & Development KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science & Application Odisha University of Agriculture & Technology Odisha Bhubaneswar India School of Public Health KIMS Medical College KIIT University Bhubaneswar751024 India Department of Healthcare Administration Asia University ROC Taiwan KTH-International Groundwater Arsenic Research Group Department of Sustainable Development Environmental Science and Engineering KTH Royal Institute of Technology Teknikringen 10B StockholmSE-100 44 Sweden

Microplastics (MPs) and nanoplastics (NPs) have gained increasing attention in environmental research due to their ubiquitousness and potential impacts on natural environments and human health as per the UN Sustainable Development Goals (SDGs), particularly SDG-14 to address global threats where at least 12 SDGs, directly/indirectly impacts. Present review is undertaken to highlight the process of breakdown of diverse groups of plastic products in soil, surface water, and groundwater under the influence of different factors (UV, light, heat, microbe, etc.), which are mobilized as MPs/NPs to the surface water, groundwater, air, soil, and living organisms by different natural and anthropogenic processes. Review also highlights a comprehensive overview of the methodology for sampling, characterization, and analysis for these minuscule plastic particles (PPs) in various environmental samples, encompassing surface/subsurface water, sediments, soils, and biological organisms. The collection, extraction, and characterization of MPs/NPs, typically employ filtration processes, wherein a known volume of water is passed through a fine mesh to capture MPs/NPs from water samples. Sediment/soil samples require sieving and density separation techniques to isolate PPs from the surrounding matrix. Biological samples require digestion steps to remove organic matter, leaving behind plastics for analysis. Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and scanning electron microscopy (SEM) are commonly utilized to determine the polymer composition, size, shape, and surface characteristics of PPs. Quantification involves several approaches, visual counting, image analysis, and spectroscopic techniques. Abundance of MPs/NPs in the respective environmental samples (water, air, soil, etc.) can be determined by comparing the richness (i.e., number/mass) of plastics to the volume or weight of the original sample. Such comprehensive analytical methodologies contribute to

关键词： Sustainable development