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Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

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Spectroscopic detection of pigments in tissues: correlation ...

2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

RNNs on Monitoring Physical Activity Energy Expenditure in Older People

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arXiv 2020年

作者： Paraschiakos, Stylianos de Sá, Cláudio Rebelo Okai, Jeremiah Eline Slagboom, P. Beekman, Marian Knobbe, Arno Leiden Institute of Advanced Computer Science Universiteit Leiden Netherlands Molecular Epidemiology Dept. Biomedical Data Science LUMC Leiden Netherlands Data Science research group University of Twente Enschede Netherlands

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a monitoring environment, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the Recurrent Neural Network (RNN). To train the RNN for an elderly population, we used the Growing Old Together Validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. In this paper, we describe our efforts to go beyond the standard facilities of a GRU-based RNN, with the aim of achieving accuracy surpassing the state of the art. These efforts include switching aggregation function from mean to dispersion measures (SD, IQR, ...), combining temporal and static data (person-specific details such as age, weight, BMI) and adding symbolic activity data as predicted by a previously trained ML model. The resulting architecture manages to increase its performance by approximatelly 10% while decreasing training input by a factor of 10. It can thus be employed to investigate associations

关键词： Recurrent neural networks

Crossmodal language grounding in an embodied neurocognitive model

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arXiv 2020年

作者： Heinrich, Stefan Yao, Yuan Hinz, Tobias Liu, Zhiyuan Hummel, Thomas Kerzel, Matthias Weber, Cornelius Wermter, Stefan Knowledge Technology Group Dept. of Informatics Universität Hamburg Germany Natural Language Processing Lab Dept. of Computer Science and Technology Tsinghua University China International Research Center for Neurointelligence University of Tokyo Japan

Human infants are able to acquire natural language seemingly easily at an early age. Their language learning seems to occur simultaneously with learning other cognitive functions as well as with playful interactions with the environment and caregivers. From a neuroscientific perspective, natural language is embodied, grounded in most, if not all, sensory and sensorimotor modalities, and acquired by means of crossmodal integration. However, characterising the underlying mechanisms in the brain is difficult and explaining the grounding of language in crossmodal perception and action remains challenging. In this paper, we present a neurocognitive model for language grounding which reflects bio-inspired mechanisms such as an implicit adaptation of timescales as well as end-to-end multimodal abstraction. It addresses developmental robotic interaction and extends its learning capabilities using larger-scale knowledge-based data. In our scenario, we utilise the humanoid robot NICO in obtaining the EMIL data collection, in which the cognitive robot interacts with objects in a children’s playground environment while receiving linguistic labels from a caregiver. The model analysis shows that crossmodally integrated representations are sufficient for acquiring language merely from sensory input through interaction with objects in an environment. The representations self-organise hierarchically and embed temporal and spatial information through composition and decomposition. This model can also provide the basis for further crossmodal integration of perceptually grounded cognitive representations. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

madmpy: A Python library for creating and validating Data Management Plans

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SoftwareX 2025年 31卷

作者： Alberto Ballesteros-Rodríguez Miguel-Ángel Sicilia Elena García-Barriocanal Biomedical Data Science and Engineering Research Group Ramón y Cajal University Hospital and Ramón y Cajal Health Research Institute (IRYCIS) 28034 Madrid Spain Universidad de Alcalá Computer Science dept. Polytechnic Building Ctra. Barcelona km. 33.6 28871 Alcalá de Henares (Madrid) Spain

Data Management Plans (DMPs) are documents that describe the data used and produced during the course of research projects. Machine-actionable DMPs (maDMPs) are plans written in computer-readable formats. They are designed to support the automation of data-generation processes in scientific research. The madmpy Python package validates maDMPs that follow any version of the RDA DMP Common Standard. These plans can be written in JSON format or built programmatically. It also supports institution- or domain-specific extensions and additional validations that adhere to the standard. The library serves as a building block for research data engineering workflows. It promotes data management and accountability through the use of structured DMPs.

关键词： Data Management Plan Machine-actionable Data validation FAIR

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

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arXiv 2021年

作者： Shan, Hongming de Barros Vimieiro, Rodrigo Borges, Lucas Rodrigues da Costa Vieira, Marcelo Andrade Wang, Ge Dept. of Biomedical Engineering Rensselaer Polytechnic Institute New York United States Dept. of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Dept. of Medical Imaging Hematology and Clinical Oncology Ribeirão Preto School of Medicine University of São Paulo Ribeirão Preto Brazil Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Digital mammography is still the most common imaging tool for breast cancer screening. Although the benefits of using digital mammography for cancer screening outweigh the risks associated with the x-ray exposure, the radiation dose must be kept as low as possible while maintaining the diagnostic utility of the generated images, thus minimizing patient risks. Many studies investigated the feasibility of dose reduction by restoring low-dose images using deep neural networks. In these cases, choosing the appropriate training database and loss function is crucial and impacts the quality of the results. In this work, a modification of the ResNet architecture, with hierarchical skip connections, is proposed to restore low-dose digital mammography. We compared the restored images to the standard full-dose images. Moreover, we evaluated the performance of several loss functions for this task. For training purposes, we extracted 256,000 image patches from a dataset of 400 images of retrospective clinical mammography exams, where different dose levels were simulated to generate low and standard-dose pairs. To validate the network in a real scenario, a physical anthropomorphic breast phantom was used to acquire real low-dose and standard full-dose images in a commercially avaliable mammography system, which were then processed through our trained model. An analytical restoration model for low-dose digital mammography, previously presented, was used as a benchmark in this work. Objective assessment was performed through the signal-to-noise ratio (SNR) and mean normalized squared error (MNSE), decomposed into residual noise and bias. Results showed that the perceptual loss function (PL4) is able to achieve virtually the same noise levels of a full-dose acquisition, while resulting in smaller signal bias compared to other loss functions. The source code for our deep neural network is available at https://***/WANG-AXIS/LdDMDenoising. © 2021, CC BY.

关键词： Image reconstruction

Single-cell regulatory network inference and clustering from high-dimensional sequencing data

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Single-cell regulatory network inference and clustering from...

IEEE International Conference on Big Data

作者： Aristidis G. Vrahatis Georgios N. Dimitrakopoulos Sotiris K. Tasoulis Spiros V. Georgakopoulos Vassilis P. Plagianakos Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece Medical School University of Patras Patras Greece

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.

关键词： Diseases Sequential analysis Gene expression Regulators Tools Big Data computer science

Blockchain-based recommender systems: Applications, challenges and future opportunities

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arXiv 2021年

作者： Himeur, Yassine Sayed, Aya Alsalemi, Abdullah Bensaali, Faycal Amira, Abbes Varlamis, Iraklis Eirinaki, Magdalini Sardianos, Christos Dimitrakopoulos, George Department of Electrical Engineering Qatar University Doha Qatar Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Computer Science University of Sharjah UAE Dept. of Informatics and Telematics Harokopio University of Athens Greece San Jose State University San JoseCA United States

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models’ accuracy and ignore issues related to security and the users’ privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users’ private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. © 2021, CC BY.

关键词： Blockchain

Deep Learning Predicts Prevalent and Incident Parkinson’s Disease From UK Biobank Fundus Imaging

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arXiv 2023年

作者： Tran, Charlie Shen, Kai Liu, Kang Ashok, Akshay Ramirez-Zamora, Adolfo Chen, Jinghua Li, Yulin Fang, Ruogu Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Physics University of Florida GainesvilleFL32611 United States Dept. of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Neurology University of Florida GainesvilleFL32611 United States Department of Ophthalmology University of Florida GainesvilleFL32611 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States J. Crayton Pruitt Family Department. of Biomedical Engineering University of Florida GainesvilleFL32611 United States Center for Cognitive Aging and Memory University of Florida GainesvilleFL32611 United States

Parkinson’s disease is the world’s fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson’s disease and automate diagnostics would greatly improve the treatment of patients with Parkinson’s disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson’s disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson’s disease from UK Biobank fundus imaging. Our results show that Parkinson’s disease individuals can be differentiated from age and gender-matched healthy subjects with an Area Under the Curve (AUC) of 0.77. This accuracy is maintained when predicting either prevalent or incident Parkinson’s disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations. © 2023, CC BY.

关键词： Patient treatment

Testing boolean functions properties

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arXiv 2021年

作者： Zhengwei, Xie Qiu, Daowen Guangya, Cai Gruska, Jozef Mateus, Paulo School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Faculty of Informatics Masaryk University Brno Czech Republic Instituto de Telecomunicações Dept. de Mateḿatica Instituto Superior Técnico Av. Rovisco Pais Lisbon1049-001 Portugal

The goal in the area of functions property testing is to determine whether a given black-box Boolean function has a particular given property or is Ε-far from having that property. We investigate here several types of properties testing for Boolean functions (identity, correlations and balancedness) using the Deutsch-Jozsa algorithm (for the Deutsch-Jozsa (D-J) problem) and also the amplitude amplification technique. At first, we study here a particular testing problem: namely whether a given Boolean function f, of n variables, is identical with a given function g or is Ε-far from g, where Ε is the parameter. We present a one-sided error quantum algorithm to deal with this problem that has the query complexity O(1√Ε). Moreover, we show that our quantum algorithm is optimal. Afterwards we show that the classical randomized query complexity of this problem is Θ(1Ε). Secondly, we consider the D-J problem from the perspective of functional correlations and let C(f, g) denote the correlation of f and g. We propose an exact quantum algorithm for making distinction between |C(f, g)| = Ε and |C(f, g)| = 1 using six queries, while the classical deterministic query complexity for this problem is Θ(2n) queries. Finally, we propose a one-sided error quantum query algorithm for testing whether one Boolean function is balanced versus Ε-far balanced using O(1Ε) queries. We also prove here that our quantum algorithm for balancedness testing is optimal. At the same time, for this balancedness testing problem we present a classical randomized algorithm with query complexity of O(1/Ε2). Also this randomized algorithm is optimal. Besides, we link the problems considered here together and generalize them to the general case. Copyright © 2021, The Authors. All rights reserved.

关键词： Boolean functions