scholarly journals Retinal Blood Vessel Segmentation on Style-augmented Images

2021 ◽  
Vol 66 (1) ◽  
pp. 74
Author(s):  
M.D. Toth ◽  
A. Kiss
Keyword(s):  
Current Trend ◽  
The Elderly ◽  
Data Set ◽  
Human Lifespan ◽  
Small Variety ◽  
Major Organ ◽  
Tremendous Amount ◽  
Supervised Methods ◽  
Natural Solution ◽  
Technological Improvements

The average human lifespan increased dramatically in the second half of 20th century. It was mainly due to technological improvements, which were driven by the continuous war preparations, and while humans have got another 20 years to live, unfortunately there are some sad side effects added to the elderly life. Various diseases can attack the eye, our major organ responsible for receiving information, therefore many researches were devoted to examine these diseases, their early signs, and how could they be stopped. From the start of 21th century, methods aided by computer were more and more involved in these processes, up to the current trend of using Convolutional Neural Networks (CNNs). While supervised methods, CNNs do achieve accuracy which can be compared to a skilled ophtalmologist, they require a tremendous amount of labeled data which is sparse in medical fields because the amount of time and resources needed to create them. One natural solution is to augment the data present, that is, copying the distribution while adding a small variety, like coloring an image differently. That is, what our paper aims to explore, whether a texturing algorithm, the Neural Style Transfery can be used to make a data set richer, and therefore helping a classifier CNN to achieve better results.

scholarly journals Determinants of prepaid systems of healthcare financing: a worldwide country-level perspective

2021 ◽  
Author(s):  
Andrea M. Leiter ◽  
Engelbert Theurl
Keyword(s):  
Health Care ◽  
Panel Data ◽  
Health Care Financing ◽  
Value Added ◽  
The Elderly ◽  
Dynamic Panel Data ◽  
Individual Characteristics ◽  
Minor Importance ◽  
Data Set ◽  
Country Level

AbstractIn this paper we examine determinants of prepaid modes of health care financing in a worldwide cross-country perspective. We use three different indicators to capture the role of prepaid modes in health care financing: (i) the share of total prepaid financing as percent of total current health expenditures, (ii) the share of voluntary prepaid financing as percent of total prepaid financing, and (iii) the share of compulsory health insurance as percent of total compulsory prepaid financing. In the econometric analysis, we refer to a panel data set comprising 154 countries and covering the time period 2000–2015. We apply a static as well as a dynamic panel data model. We find that the current structure of prepaid financing is significantly determined by its different forms in the past. The significant influence of GDP per capita, governmental revenues, the agricultural value added, development assistance for health, degree of urbanization and regulatory quality varies depending on the financing structure we look at. The share of the elderly and the education level are only of minor importance for explaining the variation in a country’s share of prepaid health care financing. The importance of the mentioned variables as determinants for prepaid health care financing also varies depending on the countries’ socio-economic development. From our analysis we conclude that more detailed information on indicators which reflect the distribution of individual characteristics (such as income, family size and structure and health risks) within a country’s population would be needed to gain deeper insight into the decisive determinants for prepaid health care financing.

AN AUTOMATED METHOD TO DETECT AGE-RELATED MACULAR DEGENERATION FROM OPTICAL COHERENCE TOMOGRAPHIC IMAGES

2021 ◽  
pp. 2150036
Author(s):  
Anju Thomas ◽  
P. M. Harikrishnan ◽  
Varun P. Gopi ◽  
P. Palanisamy
Keyword(s):  
Early Detection ◽  
Macular Degeneration ◽  
The Elderly ◽  
Age Related Macular Degeneration ◽  
Data Sets ◽  
Sigmoid Function ◽  
Data Set ◽  
Automated Method ◽  
Age Related ◽  
Sd Oct

Age-related macular degeneration (AMD) is an eye disease that affects the elderly. AMD’s prevalence is increasing as society’s population ages; thus, early detection is critical to prevent vision loss in the elderly. Arrangement of a comprehensive examination of the eye for AMD detection is a challenging task. This paper suggests a new poly scale and dual path (PSDP) convolutional neural network (CNN) architecture for early-stage AMD diagnosis automatically. The proposed PSDP architecture has nine convolutional layers to classify the input image as AMD or normal. A PSDP architecture is used to enhance classification efficiency due to the high variation in size and shape of perforation present in OCT images. The poly scale approach employs filters of various sizes to extract features from local regions more effectively. Simultaneously, the dual path architecture incorporates features extracted from different CNN layers to boost features in the global regions. The sigmoid function is used to classify images into binary categories. The Mendeley data set is used to train the proposed network and tested on Mendeley, Duke, SD-OCT Noor, and OCTID data sets. The testing accuracy of the network in Mendeley, Duke, SD-OCT Noor, and OCT-ID is 99.73%,96.66%,94.89%,99.61%, respectively. The comparison with alternative approaches showed that the proposed algorithm is efficient in detecting AMD. Despite having been trained on the Mendeley data set, the proposed model exhibited good detection accuracy when tested on other data sets. This shows that the suggested model can distinguish AMD/Normal images from various data sets. As compared to other methods, the findings show that the proposed algorithm is efficient at detecting AMD. Rapid eye scanning for early detection of AMD could be possible with the proposed architecture. The proposed CNN can be applied in real-time due to its lower complexity and less learnable parameters.

Effect of Rotating Speed on Performance of Electrical Submersible Pump

2019 ◽  
Author(s):  
Yang Yang ◽  
Ling Zhou ◽  
Weidong Shi ◽  
Chuan Wang ◽  
Wei Li ◽  
...  
Keyword(s):  
Flow Field ◽  
Current Trend ◽  
Performance Testing ◽  
Flow Fields ◽  
Computational Domain ◽  
Submersible Pump ◽  
Data Set ◽  
Rotating Speed ◽  
Compact Size ◽  
Electrical Submersible Pump

Abstract High speed rotating pump is the current trend in pump’s development and application, which has the advantages of compact size and energy-saving features. The electrical submersible pump, typically called an ESP, is an efficient and reliable artificial-lift method for lifting moderate to high volumes of fluids from wellbores, which have been wildly used for oil or groundwater extraction. To verify the similarity of pump performance under different rotating speeds, a typical ESP is selected as the model pump. By employing the numerical simulation and performance testing methods, the external performance characteristics and internal flow fields under different rotating speeds of the pump are studied. The entire computational domain is established by two stages ESP, and then meshed with the high-quality structured grid based on the Q-type and Y-type block topology. Grid sensitivity analysis is carried out to determine the appropriate mesh density for mesh independent solution. SST k-ω turbulence model with standard wall function in conjunction with Reynolds-Averaged Navier-Stokes (RANS) equations is used to solve the steady flow field. The results show that the increase in the rotating speed could increase the ESP’s head significantly. ESP’s external characteristics under different speeds meet the similar conversion rule quite well. In addition, the flow field distributions in the main flow components of the pump have great similarity at different rotating speeds. The experimental test results for a prototype show good agreement with the simulation results, including the pump’s head, efficiency and axial force. This paper provides a data set for further understanding of the effects of rotating speeds on ESP’s performance and inner flow fields.

scholarly journals PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

Algorithms ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 126 ◽  
Author(s):  
Feiyang Chen ◽  
Ying Jiang ◽  
Xiangrui Zeng ◽  
Jing Zhang ◽  
Xin Gao ◽  
...  
Keyword(s):  
Training Data ◽  
Data Sets ◽  
Local Similarity ◽  
Biomedical Data ◽  
Data Set ◽  
Biomedical Image ◽  
Feature Representations ◽  
3D Data ◽  
Supervised Methods ◽  
Data Driven Decisions

Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings, and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method.

scholarly journals ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 852
Author(s):  
Jaime A. Rincon ◽  
Angelo Costa ◽  
Paulo Novais ◽  
Vicente Julian ◽  
Carlos Carrascosa
Keyword(s):  
Decision Making ◽  
Elderly Population ◽  
Current Trend ◽  
The Elderly ◽  
Decision Making Process ◽  
The European Union ◽  
Emotion Detection ◽  
Physical Exercises ◽  
Personal Assistant ◽  
New Policies

Recent studies show that the elderly population has increased considerably in European society in recent years. This fact has led the European Union and many countries to propose new policies for caring services directed to this group. The current trend is to promote the care of the elderly in their own homes, thus avoiding inverting resources on residences. With this in mind, there are now new solutions in this direction, which try to make use of the continuous advances in computer science. This paper tries to advance in this area by proposing the use of a personal assistant to help older people at home while carrying out their daily activities. The proposed personal assistant is called ME3CA, and can be described as a cognitive assistant that offers users a personalised exercise plan for their rehabilitation. The system consists of a sensorisation platform along with decision-making algorithms paired with emotion detection models. ME3CA detects the users’ emotions, which are used in the decision-making process allowing for more precise suggestions and an accurate (and unbiased) knowledge about the users’ opinion towards each exercise.

scholarly journals An integrative review of drug utilization by the elderly in primary health care

2016 ◽  
Vol 50 (1) ◽  
pp. 163-174 ◽  
Author(s):  
Luciane Paula Batista Araújo de Oliveira ◽  
Sílvia Maria Azevedo dos Santos
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Drug Utilization ◽  
Current Trend ◽  
The Elderly ◽  
The United States ◽  
Integrative Review ◽  
Primary Health ◽  
Health Care Context ◽  
Health Complications

ABSTRACT OBJECTIVE To identify knowledge produced about drug utilization by the elderly in the primary health care context from 2006 to 2014. METHOD An integrative review of the PubMed, LILACS, BDENF, and SCOPUS databases, including qualitative research papers in Portuguese, English, and Spanish. It excluded papers with insufficient information regarding the methodological description. RESULTS Search found 633 papers that, after being subjected to the inclusion and exclusion criteria, made up a corpusof 76 publications, mostly in English and produced in the United States, England, and Brazil. Results were pooled in eight thematic categories showing the current trend of drug use in the elderly, notably the use of psychotropics, polypharmacy, the prevention of adverse events, and adoption of technologies to facilitate drug management by the elderly. Studies point out the risks posed to the elderly as a consequence of changes in metabolism and simultaneous use of several drugs. CONCLUSION There is strong concern about improving communications between professionals and the elderly in order to promote an exchange of information about therapy, and in this way prevent major health complications in this population.

Electroconvulsive therapy versus transcranial magnetic stimulation for major depression: a review with recommendations for future research

2008 ◽  
Vol 20 (6) ◽  
pp. 291-294 ◽  
Author(s):  
Keith G. Rasmussen
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Major Depression ◽  
Electroconvulsive Therapy ◽  
Magnetic Stimulation ◽  
Large Scale ◽  
The Elderly ◽  
Future Research ◽  
Sample Sizes ◽  
Data Set ◽  
Differential Outcomes

Objective:To review the literature comparing electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) for major depression.Methods:Data from the six randomised, prospective studies were agglutinated into one data set. Special attention was given to the methods of both TMS and ECT as well as data pertaining to differential outcomes in subgroups such as psychotic depressives and the elderly.Results:There is a highly significant advantage for ECT in the prospective, randomised trials. The two non-randomised, retrospective comparative trials found the treatments to be equal in one study and superior for ECT in another. However, sample sizes are small in these studies, and both TMS and ECT may have been used suboptimally. Furthermore, the possibilities of differential efficacy of ECT or TMS for psychotic depressives or as a function of age have yet to be fully explored.Conclusions:The data to date do not support the contention that TMS is equivalent in efficacy to ECT. It is recommended that a large-scale trial be undertaken using aggressive forms of both TMS and ECT with sample sizes sufficiently large to detect effects of moderating variables such as age and psychosis status.

scholarly journals Calabi-Yau Spaces in the String Landscape

2020 ◽  
Author(s):  
Yang-Hui He
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Data Science ◽  
Theoretical Physics ◽  
Superstring Theory ◽  
Data Set ◽  
Pure Mathematics ◽  
String Landscape ◽  
Natural Solution ◽  
Vacuum Solutions

Calabi-Yau spaces, or Kähler spaces admitting zero Ricci curvature, have played a pivotal role in theoretical physics and pure mathematics for the last half century. In physics, they constituted the first and natural solution to compactification of superstring theory to our 4-dimensional universe, primarily due to one of their equivalent definitions being the admittance of covariantly constant spinors. Since the mid-1980s, physicists and mathematicians have joined forces in creating explicit examples of Calabi-Yau spaces, compiling databases of formidable size, including the complete intersecion (CICY) data set, the weighted hypersurfaces data set, the elliptic-fibration data set, the Kreuzer-Skarke toric hypersurface data set, generalized CICYs, etc., totaling at least on the order of 1010 manifolds. These all contribute to the vast string landscape, the multitude of possible vacuum solutions to string compactification. More recently, this collaboration has been enriched by computer science and data science, the former in bench-marking the complexity of the algorithms in computing geometric quantities, and the latter in applying techniques such as machine learning in extracting unexpected information. These endeavours, inspired by the physics of the string landscape, have rendered the investigation of Calabi-Yau spaces one of the most exciting and interdisciplinary fields.

scholarly journals Driving and Alzheimer's disease: A neuropsychological screening battery for the elderly

2019 ◽  
Vol 13 (3) ◽  
pp. 312-320
Author(s):  
Lucía Crivelli ◽  
María Julieta Russo ◽  
Mauricio Franco Farez ◽  
Mariana Bonetto ◽  
Cecilia Prado ◽  
...  
Keyword(s):  
Cognitive Assessment ◽  
Driving Simulator ◽  
The Elderly ◽  
Driving Performance ◽  
Driving Ability ◽  
Elderly Subjects ◽  
Cognitive Tests ◽  
Data Set ◽  
Mild Dementia ◽  
Healthy Elderly

ABSTRACT As life expectancy increases, there is a marked increase in the elderly population eager to continue driving. A large proportion of these elderly drive safely, however, patients with mild dementia are high-risk drivers. Objective: to identify the cognitive tests that best predict driving ability in subjects with mild dementia. Methods: 28 drivers with mild dementia and 28 healthy elderly subjects underwent an extensive cognitive assessment (NACC Uniform Data Set Neuropsychological Battery), completed an adapted On Road Driving Test (ORDT) and a Driving Simulator assessment. Results: drivers with mild dementia made more mistakes on the ORDT and had slower responses in the simulator tasks. Cognitive tests correlated strongly with on road and simulator driving performance. Age, the Digit Symbol Modalities Test and Boston Naming Test scores were the variables that best predicted performance on the ORDT and were included in a logistic regression model. Conclusion: the strong correlation between driving performance and performance on specific cognitive tests supports the importance of cognitive assessment as a useful tool for deciding whether patients with mild dementia can drive safely. The algorithm including these three variables could be used as a screening tool for the detection of unsafe driving in elderly subjects with cognitive decline.

scholarly journals Metabolic Syndrome and Sarcopenia

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3519
Author(s):  
Hiroki Nishikawa ◽  
Akira Asai ◽  
Shinya Fukunishi ◽  
Shuhei Nishiguchi ◽  
Kazuhide Higuchi
Keyword(s):  
Skeletal Muscle ◽  
Metabolic Syndrome ◽  
Muscle Mass ◽  
The Elderly ◽  
Muscle Quality ◽  
Health Events ◽  
Persistent Inflammation ◽  
Major Organ ◽  
Skeletal Muscle Loss ◽  
The Relationship

Skeletal muscle is a major organ of insulin-induced glucose metabolism. In addition, loss of muscle mass is closely linked to insulin resistance (IR) and metabolic syndrome (Met-S). Skeletal muscle loss and accumulation of intramuscular fat are associated with a variety of pathologies through a combination of factors, including oxidative stress, inflammatory cytokines, mitochondrial dysfunction, IR, and inactivity. Sarcopenia, defined by a loss of muscle mass and a decline in muscle quality and muscle function, is common in the elderly and is also often seen in patients with acute or chronic muscle-wasting diseases. The relationship between Met-S and sarcopenia has been attracting a great deal of attention these days. Persistent inflammation, fat deposition, and IR are thought to play a complex role in the association between Met-S and sarcopenia. Met-S and sarcopenia adversely affect QOL and contribute to increased frailty, weakness, dependence, and morbidity and mortality. Patients with Met-S and sarcopenia at the same time have a higher risk of several adverse health events than those with either Met-S or sarcopenia. Met-S can also be associated with sarcopenic obesity. In this review, the relationship between Met-S and sarcopenia will be outlined from the viewpoints of molecular mechanism and clinical impact.

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