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1.
BMC Psychol ; 9(1): 133, 2021 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-34479637

RESUMO

BACKGROUND: This study investigated the impact of semantic relevance on the ability to comprehend the appearance and function of a product, as presented in images. METHODS: The images used the constructs of Simile, Metaphor and Analogy to correspond to congruent, related and incongruent semantic structures, and measured the amplitude of Event-Related Potentials (ERPs) to compare these images with Landscape images. Sixteen participants with design-related educational backgrounds were invited to join in the ERP experiment. RESULTS: The results found that the image depicting the Metaphor showed a stronger N600 amplitude in the right anterior region of the brain than the Landscape image and the Analogy image induced a stronger N600 effect in the left anterior and right anterior part of the brain than the Landscape image. However, the Simile image did not trigger the N600. The N600 was triggered when the meaning of the Metaphor and Analogy being presented could not be understood. This indicates that a greater processing effort to comprehend them than was required for Simile. Analogy has a wider N600 distribution than Metaphor in the anterior area, suggesting that Analogy would require higher-level thinking processes and more complex semantic processing mechanisms than Metaphor. CONCLUSIONS: The N600 implicated that an assessment method to detect the semantic relationship between appearance and function of a product would assist in determining whether a symbol was suitable to be associated with a product.


Assuntos
Mapeamento Encefálico , Semântica , Encéfalo/diagnóstico por imagem , Compreensão , Eletroencefalografia , Potenciais Evocados , Humanos , Metáfora
2.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502731

RESUMO

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People's clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.


Assuntos
Processamento de Imagem Assistida por Computador , Pedestres , Algoritmos , Humanos , Pesquisa , Semântica
3.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502738

RESUMO

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.


Assuntos
Pedestres , Semântica , Humanos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação
4.
Sensors (Basel) ; 21(17)2021 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-34502780

RESUMO

When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic cameras in dynamic environments. A real-time instance segmentation network is used to detect potential moving targets in the panoramic image. In order to find the real dynamic targets, potential moving targets are verified according to the sphere's epipolar constraints. Then, when extracting feature points, the dynamic objects in the panoramic image are masked. Only static feature points are used to estimate the pose of the panoramic camera, so as to improve the accuracy of pose estimation. In order to verify the performance of our system, experiments were conducted on public data sets. The experiments showed that in a highly dynamic environment, the accuracy of our system is significantly better than traditional algorithms. By calculating the RMSE of the absolute trajectory error, we found that our system performed up to 96.3% better than traditional SLAM. Our catadioptric panoramic camera semantic SLAM system has higher accuracy and robustness in complex dynamic environments.


Assuntos
Algoritmos , Semântica
5.
Comput Intell Neurosci ; 2021: 3735104, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34471406

RESUMO

How to effectively improve the effectiveness of art teaching has always been one of the hot topics concerned by all sectors of society. Especially, in art teaching, situational interaction helps improve the atmosphere of art class. However, there are few attempts to quantitatively evaluate the aesthetics of ink painting. Ink painting expresses images through ink tone and stroke changes, which is significantly different from photos and paintings in visual characteristics, semantic characteristics, and aesthetic standards. For this reason, this study proposes an adaptive computational aesthetic evaluation framework for ink painting based on situational interaction using deep learning techniques. The framework extracts global and local images as multiple input according to the aesthetic criteria of ink painting and designs a model named MVPD-CNN to extract deep aesthetic features; finally, an adaptive deep aesthetic evaluation model is constructed. The experimental results demonstrate that our model has higher aesthetic evaluation performance compared with baseline, and the extracted deep aesthetic features are significantly better than the traditional manual design features, and its adaptive evaluation results reach a Pearson height of 0.823 compared with the manual aesthetic. In addition, art classroom simulation and interference experiments show that our model is highly resistant to interference and more sensitive to the three painting elements of composition, ink color, and texture in specific compositions.


Assuntos
Pinturas , Simulação por Computador , Estética , Redes Neurais de Computação , Semântica
6.
Comput Intell Neurosci ; 2021: 8387382, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34475949

RESUMO

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model's perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people's requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.


Assuntos
Sistemas de Dados , Redes Neurais de Computação , Humanos , Semântica
7.
Rev Med Liege ; 76(9): 689-696, 2021 Sep.
Artigo em Francês | MEDLINE | ID: mdl-34477341

RESUMO

Since the early 1970s, the concept of quality of life has been the subject of increasing interest in the medical field, although no scientific consensus has emerged on how to define and measure it. The aim of this narrative review of the literature is to decrypt the notion of quality of life in the medical field, in order to enable clinicians-researchers and clinicians who use quality of life measurement instruments in clinical practice to form an informed and nuanced opinion on the issue. To do so, the paper is divided into three parts. Firstly, a brief overview of the origin of the concept in the medical field is given by exposing the main factors explaining its emergence and its rise in importance. Next, the plurality of definitions of quality of life and its derivatives (e.g. health-related quality of life), as well as its measurement instruments in the medical field, are explored. Finally, some benchmarks for the use of health-related quality of life instruments in clinical practice are presented.


Assuntos
Qualidade de Vida , Semântica , Humanos
8.
Handb Clin Neurol ; 183: 283-297, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34389123

RESUMO

Neurological disease can impair emotional communication by several means: damaging the networks important in understanding the meaning of emotional stimuli (emotional semantics); damaging networks important in the perceptual recognition and production of emotional stimuli, and damaging the connections between networks supporting emotional semantics and recognition/production networks. Disorders of emotional expression, comprehension, and emotional semantics may improve with pharmacological or behavioral treatments. Pharmacological treatments can be used to redress naturally occurring or disease-related alterations in the computational properties of target neural systems. No drug treatment can replace a loss of cerebral knowledge related to the pathological loss of neural connectivity. Behavioral treatments that benefit either comprehension or expression of specific emotions may be of value if these emotions are particularly important in enabling human social interaction. However, behavioral treatments that achieve generalization, that is, improve performance with untrained exemplars and in daily life, are much to be preferred, even as they pose the greatest methodological challenges. This chapter will discuss possible mechanisms of generalization and then review what is known about the treatment of expressive and receptive affective aprosodia, deficits in recognition of facial emotions, and pseudobulbar affect. The final section of the chapter is devoted to a discussion of three disorders of emotional semantics, apathy, alexithymia, and impaired empathy.


Assuntos
Compreensão , Semântica , Sintomas Afetivos , Emoções , Empatia , Humanos
9.
Sensors (Basel) ; 21(16)2021 Aug 10.
Artigo em Inglês | MEDLINE | ID: mdl-34450841

RESUMO

Visual simultaneous location and mapping (SLAM) using RGB-D cameras has been a necessary capability for intelligent mobile robots. However, when using point-cloud map representations as most RGB-D SLAM systems do, limitations in onboard compute resources, and especially communication bandwidth can significantly limit the quantity of data processed and shared. This article proposes techniques that help address these challenges by mapping point clouds to parametric models in order to reduce computation and bandwidth load on agents. This contribution is coupled with a convolutional neural network (CNN) that extracts semantic information. Semantics provide guidance in object modeling which can reduce the geometric complexity of the environment. Pairing a parametric model with a semantic label allows agents to share the knowledge of the world with much less complexity, opening a door for multi-agent systems to perform complex tasking, and human-robot cooperation. This article takes the first step towards a generalized parametric model by limiting the geometric primitives to a planar surface and providing semantic labels when appropriate. Two novel compression algorithms for depth data and a method to independently fit planes to RGB-D data are provided, so that plane data can be used for real-time odometry estimation and mapping. Additionally, we extend maps with semantic information predicted from sparse geometries (planes) by a CNN. In experiments, the advantages of our approach in terms of computational and bandwidth resources savings are demonstrated and compared with other state-of-the-art SLAM systems.


Assuntos
Compressão de Dados , Semântica , Algoritmos , Humanos , Redes Neurais de Computação
10.
Sensors (Basel) ; 21(16)2021 Aug 11.
Artigo em Inglês | MEDLINE | ID: mdl-34450870

RESUMO

Recent advances in deep learning models for image interpretation finally made it possible to automate construction site monitoring processes that rely on remote sensing. However, the major drawback of these models is their dependency on large datasets of training images labeled at pixel level, which must be produced manually by skilled personnel. To reduce the need for training data, this study evaluates weakly and semi-supervised semantic segmentation models for construction site imagery to efficiently automate monitoring tasks. As a case study, we compare fully, weakly and semi-supervised methods for the detection of rebar covers, which are useful for quality control. In the experiments, recent models, i.e., IRNet, DeepLabv3+ and the cross-consistency training model are compared for their ability to segment rebar covers from construction site imagery with minimal manual input. The results show that weakly and semi-supervised models can indeed rival with the performance of fully supervised models with the majority of the target objects being properly found. This study provides construction site stakeholders with detailed information on how to leverage deep learning for efficient construction site monitoring and weigh preprocessing, training, and testing efforts against each other in order to decide between fully, weakly and semi-supervised training.


Assuntos
Aprendizado Profundo , Aprendizado de Máquina Supervisionado , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Semântica
11.
Sensors (Basel) ; 21(16)2021 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-34450917

RESUMO

Monocular depth estimation based on unsupervised learning has attracted great attention due to the rising demand for lightweight monocular vision sensors. Inspired by multi-task learning, semantic information has been used to improve the monocular depth estimation models. However, multi-task learning is still limited by multi-type annotations. As far as we know, there are scarcely any large public datasets that provide all the necessary information. Therefore, we propose a novel network architecture Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) to extract multi-resolution depth features and semantic features, which are merged and fed into the decoder, with the goal of predicting depth with the support of semantics. Instead of using loss functions to relate the semantics and depth, the fusion of feature maps for semantics and depth is employed to predict the monocular depth. Therefore, two accessible datasets with similar topics for depth estimation and semantic segmentation can meet the requirements of SFA-MDEN for training sets. We explored the performance of the proposed SFA-MDEN with experiments on different datasets, including KITTI, Make3D, and our own dataset BHDE-v1. The experimental results demonstrate that SFA-MDEN achieves competitive accuracy and generalization capacity compared to state-of-the-art methods.


Assuntos
Semântica
12.
AMIA Annu Symp Proc ; 2021: 345-354, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457149

RESUMO

Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes generalize variably between institutions, but ignored other levels of healthcare organization. We measured SciBERT diagnosis sentiment classifier generalizability between medical specialties using EHR sentences from MIMIC-III. Models trained on one specialty performed better on internal test sets than mixed or external test sets (mean AUCs 0.92, 0.87, and 0.83, respectively; p = 0.016). When models are trained on more specialties, they have better test performances (p < 1e-4). Model performance on new corpora is directly correlated to the similarity between train and test sentence content (p < 1e-4). Future studies should assess additional axes of generalization to ensure deep learning models fulfil their intended purpose across institutions, specialties, and practices.


Assuntos
Aprendizado Profundo , Medicina , Humanos , Idioma , Semântica
13.
AMIA Annu Symp Proc ; 2021: 515-524, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34457167

RESUMO

Natural language is continually changing. Given the prevalence of unstructured, free-text clinical notes in the healthcare domain, understanding the aspects of this change is of critical importance to clinical Natural Language Processing (NLP) systems. In this study, we examine two previously described semantic change laws based on word frequency and polysemy, and analyze how they apply to the clinical domain. We also explore a new facet of change: whether domain-specific clinical terms exhibit different change patterns compared to general-purpose English. Using a corpus spanning eighteen years of clinical notes, we find that the previously described laws of semantic change hold for our data set. We also find that domain-specific biomedical terms change faster compared to general English words.


Assuntos
Processamento de Linguagem Natural , Semântica , Humanos , Idioma , Unified Medical Language System
14.
J Speech Lang Hear Res ; 64(8): 3195-3211, 2021 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-34351812

RESUMO

Purpose Recent behavioral studies have demonstrated the effectiveness of implementing retrieval practice into learning tasks for children. Such approaches have revealed that repeated spaced retrieval (RSR) is particularly effective in promoting children's learning of word form and meaning information. This study further examines how retrieval practice enhances learning of word meaning information at the behavioral and neural levels. Method Twenty typically developing preschool children were taught novel words using an RSR learning schedule for some words and an immediate retrieval (IR) learning schedule for other words. In addition to the label, children were taught two arbitrary semantic features for each item. Following the teaching phase, children's learning was tested using recall tests. In addition, during the 1-week follow-up, children were presented with pictures and an auditory sentence that correctly labeled the item but stated correct or incorrect semantic information. Event-related brain potentials (ERPs) were time locked to the onset of the words noting the semantic feature. Children provided verbal judgments of whether the semantic feature was correctly paired with the item. Results Children recalled more labels and semantic features for items that had been taught in the RSR learning schedule relative to the IR learning schedule. ERPs also differentiated the learning schedules. Mismatching label-meaning pairings elicited an N400 and late positive component (LPC) for both learning conditions; however, mismatching RSR pairs elicited an N400 with an earlier onset and an LPC with a longer duration, relative to IR mismatching label-meaning pairings. These ERP timing differences indicated that the children were more efficient in processing words that were taught in the RSR schedule relative to the IR learning schedule. Conclusions Spaced retrieval practice promotes learning of both word form and meaning information. The findings lay the necessary groundwork for better understanding of processing newly learned semantic information in preschool children. Supplemental Material https://doi.org/10.23641/asha.15063060.


Assuntos
Transtornos do Desenvolvimento da Linguagem , Semântica , Pré-Escolar , Eletroencefalografia , Potenciais Evocados , Feminino , Humanos , Masculino , Aprendizagem Verbal
15.
J Biomed Semantics ; 12(1): 15, 2021 08 09.
Artigo em Inglês | MEDLINE | ID: mdl-34372934

RESUMO

BACKGROUND: The ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This may concern sorting out ontological differences and making choices between conflicting axioms due to limitations in the logic or the subject domain semantics. Examples are dealing with different foundational ontologies in ontology alignment and OWL 2 DL's transitive object property versus a qualified cardinality constraint. Such conflicts have to be resolved somehow. However, only isolated and fragmented guidance for doing so is available, which therefore results in ad hoc decision-making that may not be the best choice or forgotten about later. RESULTS: This work aims to address this by taking steps towards a framework to deal with the various types of modeling conflicts through meaning negotiation and conflict resolution in a systematic way. It proposes an initial library of common conflicts, a conflict set, typical steps toward resolution, and the software availability and requirements needed for it. The approach was evaluated with an actual case of domain knowledge usage in the context of epizootic disease outbreak, being avian influenza, and running examples with COVID-19 ontologies. CONCLUSIONS: The evaluation demonstrated the potential and feasibility of a conflict resolution framework for ontologies.


Assuntos
Ontologias Biológicas/estatística & dados numéricos , Biologia Computacional/estatística & dados numéricos , Armazenamento e Recuperação da Informação/estatística & dados numéricos , Web Semântica , Semântica , Vocabulário Controlado , COVID-19/epidemiologia , COVID-19/prevenção & controle , COVID-19/virologia , Biologia Computacional/métodos , Bases de Dados Factuais/estatística & dados numéricos , Epidemias/prevenção & controle , Humanos , Armazenamento e Recuperação da Informação/métodos , Lógica , SARS-CoV-2/fisiologia
16.
Acta Psychol (Amst) ; 219: 103390, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34390931

RESUMO

While body modifications have increasingly gained acceptance and popularity, how different subpopulations aesthetically appreciate tattoos remains unclear. The present study aimed to investigate the conceptual structure underlying tattoo aesthetics, focusing on the effects of internalized social norms and expertise. Using a timed free-listing task, three groups (≤49 years, ≥50 years, and experts) comprising 497 participants were asked to write down adjectives that could describe tattoo aesthetics. Statistical analyses of frequency, cognitive salience indices, co-occurrence dimensions, semantic dimensions, similarity measures, and valences were applied and, to directly compare the three groups, a generalized Procrustes analysis was applied. The variance and complexity with which individuals verbally expressed their perceived aesthetic appeal of tattoos were highlighted. However, the results do not reveal a unified concept of beauty, nor do they present a clear bipolar dimension of beautiful/ugly for two of the three groups. Nevertheless, the concept of beauty was found to be prominent in tattoo aesthetics, and aesthetic and descriptive-evaluative dimensions were identified, with terms such as beautiful, ugly, multicolored, and interesting being the most notable adjectives, although not with the highest valence. Possible factors explaining the intracultural differences between the three groups are also discussed.


Assuntos
Tatuagem , Beleza , Estética , Humanos , Semântica , Normas Sociais
17.
Acta Psychol (Amst) ; 219: 103391, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34412023

RESUMO

Previous studies have elucidated the neural mechanism of syntactic/semantic processing and pragmatic processing. However, the exact mechanisms by which these two aspects of processing interact during language comprehension remain unknown. In this event-related brain potential study, we examined the interaction between politeness processing and local syntactic/semantic processing of a phrase. We used a full factorial design that crossed politeness consistency with local syntactic/semantic coherence. Politeness violations elicited a P200 effect in the 190-320 ms range, centro-parietally distributed positivity in the 360-866 ms range, and pure local syntactic/semantic violation elicited a broad distributed positivity in the 362-868 ms range. Crucially, we found that event-related potential responses elicited by combined politeness and syntactic/semantic violations resemble those elicited by separate syntactic/semantic violations. These results indicated that local syntactic/semantic processing has a functional primacy over politeness processing. Furthermore, our results support the blocking hypothesis from a politeness processing perspective instead of the independent hypothesis.


Assuntos
Eletroencefalografia , Semântica , Encéfalo , Compreensão , Potenciais Evocados , Humanos
18.
Artif Intell Med ; 118: 102127, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34412844

RESUMO

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG). However, treatments from multiple comorbid CPG often interact adversely (e.g., drug-drug interactions) or introduce operational inefficiencies (e.g., redundant scans). A common solution is the a-priori integration of computerized CPG, which involves integration decisions such as discarding, replacing or delaying clinical tasks (e.g., treatments) to avoid adverse interactions or inefficiencies. We argue this insufficiently deals with execution-time events: as the patient's health profile evolves, acute conditions occur, and real-time delays take place, new CPG integration decisions will often be needed, and prior ones may need to be reverted or undone. Any realistic CPG integration effort needs to further consider temporal aspects of clinical tasks-these are not only restricted by temporal constraints from CPGs (e.g., sequential relations, task durations) but also by CPG integration efforts (e.g., avoid treatment overlap). This poses a complex execution-time challenge and makes it difficult to determine an up-to-date, optimal comorbid care plan. We present a solution for dynamic integration of CPG in response to evolving health profiles and execution-time events. CPG integration policies are formulated by clinical experts for coping with comorbidity at execution-time, with clearly defined integration semantics that build on Description and Transaction Logics. A dynamic planning approach reconciles temporal constraints of CPG tasks at execution-time based on their importance, and continuously updates an optimal task schedule.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Semântica , Comorbidade , Humanos , Tempo
19.
J Biomed Semantics ; 12(1): 18, 2021 08 28.
Artigo em Inglês | MEDLINE | ID: mdl-34454610

RESUMO

BACKGROUND: With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of host-coronavirus interaction (HCI) mechanisms. As of early April 2021, the ImmPort database has stored 7 studies (with 6 having details) that cover topics including molecular immune signatures, epitopes, and sex differences in terms of mortality in COVID-19 patients. The Coronavirus Infectious Disease Ontology (CIDO) represents basic HCI information. We hypothesize that the CIDO can be used as the platform to represent newly recorded information from ImmPort leading the reinforcement of CIDO. METHODS: The CIDO was used as the semantic platform for logically modeling and representing newly identified knowledge reported in the 6 ImmPort studies. A recursive eXtensible Ontology Development (XOD) strategy was established to support the CIDO representation and enhancement. Secondary data analysis was also performed to analyze different aspects of the HCI from these ImmPort studies and other related literature reports. RESULTS: The topics covered by the 6 ImmPort papers were identified to overlap with existing CIDO representation. SARS-CoV-2 viral S protein related HCI knowledge was emphasized for CIDO modeling, including its binding with ACE2, mutations causing different variants, and epitope homology by comparison with other coronavirus S proteins. Different types of cytokine signatures were also identified and added to CIDO. Our secondary analysis of two cohort COVID-19 studies with cytokine panel detection found that a total of 11 cytokines were up-regulated in female patients after infection and 8 cytokines in male patients. These sex-specific gene responses were newly modeled and represented in CIDO. A new DL query was generated to demonstrate the benefits of such integrative ontology representation. Furthermore, IL-10 signaling pathway was found to be statistically significant for both male patients and female patients. CONCLUSION: Using the recursive XOD strategy, six new ImmPort COVID-19 studies were systematically reviewed, the results were modeled and represented in CIDO, leading to the enhancement of CIDO. The enhanced ontology and further seconary analysis supported more comprehensive understanding of the molecular mechanism of host responses to COVID-19 infection.


Assuntos
Ontologias Biológicas , COVID-19 , Interações entre Hospedeiro e Microrganismos , Humanos , Semântica , Glicoproteína da Espícula de Coronavírus/metabolismo
20.
Sensors (Basel) ; 21(15)2021 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-34372398

RESUMO

Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.


Assuntos
Endoscopia , Processamento de Imagem Assistida por Computador , Humanos , Semântica
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