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Transforming early microbial detection: Investigating innovative biosensors for emerging infectious diseases

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Advances in Biomarker Sciences and Technology 2024年第1期6卷 59-71页

作者： Godfred Yawson Scott Abdullahi Tunde Aborode Ridwan Olamilekan Adesola Emmanuel Ebuka Elebesunu Joseph Agyapong Adamu Muhammad Ibrahim ANGYIBA Serge Andigema Samuel Kwarteng Isreal Ayobami Onifade Adekunle Fatai Adeoye Babatunde Akinola Aluko Taiwo Bakare-Abidola Lateef Olawale Fatai Osasere Jude-Kelly Osayawe Modupe Oladayo Abraham Osinuga Zainab Olapade Anthony Ifeanyi Osu Peter Ofuje Obidi Department of Medical Diagnostics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Research and Development Healthy Africans Platform Ibadan Nigeria Department of Veterinary Medicine Faculty of Veterinary Medicine University of Ibadan Ibadan Nigeria Department of Medical Laboratory Sciences Faculty of Health Sciences and Technology University of Nigeria Enugu Campus Enugu Nigeria College of Health Sciences Faculty of Allied Health Department of Medical Diagnostics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Immunology School of Medical Laboratory Science Usmanu Danfodiyo University Sokoto Nigeria Department of Innovation and Knowledge Dissemination Bisons' Scholars Ghana Department of Molecular Medicine Kwame Nkrumah University of Science and Technology Ghana Department of Biological Sciences University at Albany SUNY USA Department of Mathematics and Statistics Georgia State University Georgia USA Department of Statistics University of Kentucky Lexington KY USA Department: Environmental Science Georgia Southern University Georgia USA Department of Data Science University of Salford United Kingdom Department of Chemistry and Biochemistry Brigham Young University Provo USA Department of Chemistry Central Washington University Washington USA Department of Chemical and Biomolecular Engineering University of Nebraska Lincoln USA Department of Biology Lamar University Texas USA Department of Biology Lamar University USA Department of Mechanical Engineering Missouri University of Science and Technology Rolla USA

The recent global pandemic has highlighted an increase in the prevalence of communicable diseases caused by pathogens. The swift transmission of these diseases within a short timeframe presents a substantial risk to public health worldwide. The inefficiency of traditional diagnostic instruments, which need a time-consuming and complex process in the laboratory, is a significant obstacle to medical care. Currently, there is a high need for the advancement of early detection in order to rapidly diagnose infectious diseases and provide on-site results. This is crucial for prompt and early intervention to improve treatment outcomes. This also provides rapid testing and high-quality microbiological detection, comparable to laboratory standards, in a matter of minutes. Prompt diagnosis and subsequent treatment optimization aid in controlling the spread of infectious diseases. Currently, ongoing techniques and methods are used in the advancements of early detection through biosensors. This review examines the integration of early diagnostics with biosensors, specifically in relation to emerging and re-emerging infectious diseases, challenges, and the future perspective.

关键词： Early detection Revolution Laboratory Innovative Biosensors Infectious diseases

Prominence and dominance in networks 21st

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Prominence and dominance in networks

21st International Conference on knowledge engineering and knowledge Management, EKAW 2018

作者： Schmidt, Andreas Stumme, Gerd Knowledge and Data Engineering Group University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany

ISBN: (纸本)9783030036669

Topographic prominence and dominance were recently developed to quantify the relative importance of mountain peaks. Instead of simply using the height to characterize a mountain, they provide a more meaningful description based on vertical and horizontal distances in the neighborhood. In this paper, we propose structural prominence and dominance for networks, an adaptation of the topographic measures, for the detection of nodes with strong local importance. We create a network "landscape" which is generated by a node’s height and distance to other nodes in the network. We ground our proposed measures on the task of predicting award winners with high and sustainable impact in a co-authorship network. Our experiments show that our measures provide information about a graph, that is not provided by other graph measures. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction

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arXiv 2021年

作者： Ernst, Philipp Chatterjee, Soumick Rose, Georg Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Otto von Guericke University Research Campus STIMULATE Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany

Computed tomography (CT) and magnetic resonance imaging (MRI) are two widely used clinical imaging modalities for noninvasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works. This paper proposes a unified solution for both sparse CT and undersampled radial MRI reconstruction, achieved by applying Fourier transform-based pre-processing on the radial MRI and then finally reconstructing both modalities using sinogram upsampling combined with filtered back-projection. The Primal-Dual network is a deep learning based method for reconstructing sparsely-sampled CT data. This paper introduces Primal-Dual UNet, which improves the Primal-Dual network in terms of accuracy and reconstruction speed. The proposed method resulted in an average SSIM of 0.932±0.021 while performing sparse CT reconstruction for fan-beam geometry with a sparsity level of 16, achieving a statistically significant improvement over the previous model, which resulted in 0.919±0.016. Furthermore, the proposed model resulted in 0.903±0.019 and 0.957±0.023 average SSIM while reconstructing undersampled brain and abdominal MRI data with an acceleration factor of 16, respectively-statistically significant improvements over the original model, which resulted in 0.867±0.025 and 0.949±0.025. Finally, this paper shows that the proposed network not only improves the overall image quality, but also improves the image quality for the regions-of-interest: liver, kidneys, and spleen;as well

关键词： Image reconstruction

Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

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Extremely Short Chinese Text Classification Method Based on ...

作者： Yongzeng Yue Yuhong Zhang Xuegang Hu Peipei Li Key Laboratory of knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology

Short text classification methods have achieved significant progress and wide application on text data such as Twitter and Weibo. However, the extremely short chinese texts like tax invoice data are different with traditional short texts in lackness of contextual semantic information, feature sparseness and extremely short length. The existing short text classification methods are difficult to achieve a satisfactory performance in these texts. To address these problems, this paper proposes a text classification method based on bidirectional semantic extension for extremely short texts like Chinese tax invoice data. More specifically, firstly, the Chinese knowledge graph is introduced for extending bidirectional semantic of texts and label data to expand the extremely short texts and ease the problem of feature sparseness;secondly,the hash vectorization is used to avoid the semantic problem caused by the lackness of contextual information. Experimental results conducted the real tax invoice dataset demonstrate the effectiveness of our proposed method.

关键词： Extremely Short Chinese Text Classification Method Based on Bidirectional Semantic Extension

The minority matters: a diversity-promoting collaborative metric learning algorithm 22

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The minority matters: a diversity-promoting collaborative me...

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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The Influence of Using a Static Diastolic Geometry in ECG Imaging

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The Influence of Using a Static Diastolic Geometry in ECG Im...

Computers in Cardiology (CinC)

作者： Job Stoks Matthijs J.M. Cluitmans Ralf Peeters Paul G.A. Volders Department of Cardiology Maastricht University Medical Centre Maastricht The Netherlands Data Science and Knowledge Engineering Maastricht University Maastricht The Netherlands

ISBN: (数字)9781728169361

ISBN: (纸本)9781728159423

One of the common choices when performing electrocardiographic imaging (ECGI) is that the cardiac geometry is in a static, diastolic state. To test the influence of this approximation, we compared epicardial potential maps and isochrones during systolic and diastolic geometries in four patients. Zero-th order Tikhonov regularization was used to reconstruct ventricular epicardial potentials. A spatiotemporal estimation method was then used to determine the activation and recovery times from the reconstructed epicardial electrograms. Activation times (AT), recovery times (RT) and electrogram correlation coefficients (CC) were compared for both geometries. Furthermore, CC and differences in AT/ RT were correlated against the linear movement and a substitute for rotational movement. Poor correlation was found between linear/rotational movement and reconstruction differences. Overall, agreement between epicardial potential maps and isochrones of both geometries was high when assessed quantitatively, but regional differences might occur for qualitative interpretation. These differences mostly occurred in areas of flat T-waves. This novel, more accurate quantification of the influence of assuming a diastolic geometry in ECGI may further help in interpreting ECGI measurements.

关键词： Geometry Heart Correlation Electric potential Image reconstruction Surface reconstruction Correlation coefficient

XTQA: Span-Level Explanations for Textbook Question Answering

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

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arXiv 2021年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Mushunuri, Raghava Vinaykanth Podishetti, Ranadheer Rao, Rajatha Nagaraja Gopinath, Geetha Doddapaneni Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient’s psychological and physical conditions. Single Image Super-Resolution (SISR), a technique aimed to obtain high-resolution (HR) details from one single low-resolution (LR) input image, achieved with Deep Learning, is the focus of this study. Compared to interpolation techniques or sparse-coding algorithms, deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts. In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI. Images from the IXI dataset have been used as the ground-truth and were artificially downsampled to simulate the low-resolution images. The proposed method has shown statistically significant improvement over the baselines and achieved an SSIM of 0.913 ± 0.045. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Personalizing search results using hierarchical RNN with query-aware attention

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arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines