Mathematical Methods and Data Analysis in Health and Biomedical Sciences

A special issue of Life (ISSN 2075-1729).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 30423

Special Issue Editors

Faculty of Health Sciences, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; computational statistics; mathematical modeling in medicine, physics, astronomy; computational pharmacology
Special Issues, Collections and Topics in MDPI journals
Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; biophysics; mathematical modeling in biomedical and social sciences; health and biomedical informatics
Special Issues, Collections and Topics in MDPI journals
Department of Immunobiology and Environmental Microbiology, Medical University of Gdańsk, 80-309 Gdańsk, Poland
Interests: medicine biochemistry; genetics and molecular biology; immunology and microbiology; pharmacology; toxicology and pharmaceutics environmental science; neuroscience; social sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing importance of the mathematical modeling of applications of computational methods and of data analysis in medical sciences (and vice versa) has attracted the attention of both medical scientists and mathematicians. Though the worlds of mathematical and health sciences are not separated anymore, a full synergy effect is far from being reached. Frequently, it is difficult to find a proper balance between the expectations of the medical community and limitations of the computational methods that are currently available. In many cases, computer scientists and experts on mathematical modeling are not aware of the medical problems that might be solved using mathematical methods. Additionally, medical scientists do not always look for aid in finding answers to their questions in mathematics. There exists a large and hardly explored area of possible extensions of the applicability of different computational techniques in medical sciences. The most natural extensions include the development of higher accuracy methods and the implementation of new approaches that reveal some unknown (also unexpected) aspects of the considered subjects.

The present Special Issue of Life is aimed at the extension of areas where mathematics and medical sciences overlap and support each other. The submitted original and review articles may contain descriptions of mathematical models, of computational methods, and of new algorithms or may discuss the significance of some specific mathematics-based approaches in medical sciences. Papers dealing with medical data analysis, in particular with different aspects of graphical and numerical representations of the data and discussing various applications of the computational methods in different branches of medicine, in quality of life research, public health, bioinformatics, etc., are welcome.

Review articles should be comprehensive. The main text of review papers should be a minimum of 4000 words and include at least two figures or tables. Research articles: should have a main text that is 3000 words minimum and should have more than 30 references. Life has no restrictions on the maximum length of research manuscripts, provided that the text is concise and comprehensive.

  1. Manuscripts should present an important novelty of the content and high potential impact in the relevant field of research;
  2. They should have a high standard of English (expression, grammar, and spelling);
  3. The experiment design should be sound and thorough, and the methodology should be described in detail to guarantee the reproducibility of the study;
  4. For all Western blot figures, the densitometry readings/intensity ratio of each band should be included. In addition, the whole blot (uncropped blots) showing all the bands with all molecular weight markers on the Western blot should be included in the Supplemental Materials;
  5. Manuscripts should include the reference of approval by the ethical committee for experimental studies.

Detailed instructions for formatting/preparing the manuscript can be found at: https://0-www-mdpi-com.brum.beds.ac.uk/journal/life/instructions.

Dr. Piotr Wąż
Dr. Dorota Bielińska-Wąż
Dr. Katarzyna Zorena
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical modeling
  • graphical representations of data and of research outcomes
  • computational statistics
  • health and biomedical informatics
  • bioinformatics methods
  • techniques in data analysis
  • data interpretation
  • applications of computational methods in health and medical sciences

Published Papers (14 papers)

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Research

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20 pages, 1479 KiB  
Article
Deep Learning and Procrustes Analysis for Early Dysgraphia Risk Detection with a Tablet Application
by Eugenio Lomurno, Linda Greta Dui, Madhurii Gatto, Matteo Bollettino, Matteo Matteucci and Simona Ferrante
Life 2023, 13(3), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/life13030598 - 21 Feb 2023
Cited by 3 | Viewed by 2013
Abstract
Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent [...] Read more.
Dysgraphia is a neurodevelopmental disorder specific to handwriting. Classical diagnosis is based on the evaluation of speed and quality of the final handwritten text: it is therefore delayed as it is conducted only when handwriting is mastered, in addition to being highly language-dependent and not always easily accessible. This work presents a solution able to anticipate dysgraphia screening when handwriting has not been learned yet, in order to prevent negative consequences on the individuals’ academic and daily life. To quantitatively measure handwriting-related characteristics and monitor their evolution over time, we leveraged the Play-Draw-Write iPad application to collect data produced by children from the last year of kindergarten through the second year of elementary school. We developed a meta-model based on deep learning techniques (ensemble techniques and Quasi-SVM) which receives as input raw signals collected after a processing phase based on dimensionality reduction techniques (autoencoder and Time2Vec) and mathematical tools for high-level feature extraction (Procrustes Analysis). The final dysgraphia classifier can identify “at-risk” children with 84.62% Accuracy and 100% Precision more than two years earlier than current diagnostic techniques. Full article
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18 pages, 669 KiB  
Article
Practical Understanding of Cancer Model Identifiability in Clinical Applications
by Tin Phan, Justin Bennett and Taylor Patten
Life 2023, 13(2), 410; https://0-doi-org.brum.beds.ac.uk/10.3390/life13020410 - 01 Feb 2023
Cited by 3 | Viewed by 1607
Abstract
Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used [...] Read more.
Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability. Full article
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20 pages, 2013 KiB  
Article
A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture
by Tooba Rashid, Muhammad Sultan Zia, Najam-ur-Rehman, Talha Meraj, Hafiz Tayyab Rauf and Seifedine Kadry
Life 2023, 13(1), 133; https://0-doi-org.brum.beds.ac.uk/10.3390/life13010133 - 03 Jan 2023
Cited by 4 | Viewed by 1953
Abstract
The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the [...] Read more.
The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option. Full article
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17 pages, 4235 KiB  
Article
Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs
by Xin-Jian Xu, Hong-Xiang Gao, Liu-Cun Zhu and Rui Zhu
Life 2023, 13(1), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/life13010076 - 27 Dec 2022
Cited by 1 | Viewed by 1046
Abstract
Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can [...] Read more.
Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses. Full article
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17 pages, 6132 KiB  
Article
Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm
by Ramya Mohan, Seifedine Kadry, Venkatesan Rajinikanth, Arnab Majumdar and Orawit Thinnukool
Life 2022, 12(11), 1848; https://0-doi-org.brum.beds.ac.uk/10.3390/life12111848 - 11 Nov 2022
Cited by 8 | Viewed by 1747
Abstract
Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects [...] Read more.
Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier. Full article
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10 pages, 707 KiB  
Article
An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens
by Xu Yang, Hongsheng Ma, Keyan Gao and Hui Ge
Life 2022, 12(8), 1134; https://0-doi-org.brum.beds.ac.uk/10.3390/life12081134 - 28 Jul 2022
Viewed by 1221
Abstract
It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited [...] Read more.
It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms. Full article
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13 pages, 2302 KiB  
Article
Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network
by Usman Yunus, Javeria Amin, Muhammad Sharif, Mussarat Yasmin, Seifedine Kadry and Sujatha Krishnamoorthy
Life 2022, 12(8), 1126; https://0-doi-org.brum.beds.ac.uk/10.3390/life12081126 - 27 Jul 2022
Cited by 16 | Viewed by 2301
Abstract
Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and [...] Read more.
Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works. Full article
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16 pages, 1206 KiB  
Article
4D-Dynamic Representation of DNA/RNA Sequences: Studies on Genetic Diversity of Echinococcus multilocularis in Red Foxes in Poland
by Dorota Bielińska-Wąż, Piotr Wąż, Anna Lass and Jacek Karamon
Life 2022, 12(6), 877; https://0-doi-org.brum.beds.ac.uk/10.3390/life12060877 - 10 Jun 2022
Viewed by 1584
Abstract
The 4D-Dynamic Representation of DNA/RNA Sequences, an alignment-free bioinformatics method recently developed by us, has been used to study the genetic diversity of Echinococcus multilocularis in red foxes in Poland. Sequences of three mitochondrial genes, i.e., NADH dehydrogenase subunit 2 (nad2), [...] Read more.
The 4D-Dynamic Representation of DNA/RNA Sequences, an alignment-free bioinformatics method recently developed by us, has been used to study the genetic diversity of Echinococcus multilocularis in red foxes in Poland. Sequences of three mitochondrial genes, i.e., NADH dehydrogenase subunit 2 (nad2), cytochrome b (cob), and cytochrome c oxidase subunit 1 (cox1), are analyzed. The sequences are represented by sets of material points in a 4D space, i.e., 4D-dynamic graphs. As a visualization of the sequences, projections of the graphs into 3D space are shown. The differences between 3D graphs corresponding to European, Asian, and American haplotypes are small. Numerical characteristics (sequence descriptors) applied in the studies can recognize the differences. The concept of creating descriptors of 4D-dynamic graphs has been borrowed from classical dynamics; these are coordinates of the centers or mass and moments of inertia of 4D-dynamic graphs. Based on these descriptors, classification maps are constructed. The concentrations of points in the maps indicate one Polish haplotype (EmPL9) of Asian origin. Full article
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13 pages, 2113 KiB  
Article
Novel DERMA Fusion Technique for ECG Heartbeat Classification
by Qurat-ul-ain Mastoi, Teh Ying Wah, Mazin Abed Mohammed, Uzair Iqbal, Seifedine Kadry, Arnab Majumdar and Orawit Thinnukool
Life 2022, 12(6), 842; https://0-doi-org.brum.beds.ac.uk/10.3390/life12060842 - 06 Jun 2022
Cited by 16 | Viewed by 2003
Abstract
An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from [...] Read more.
An electrocardiogram (ECG) consists of five types of different waveforms or characteristics (P, QRS, and T) that represent electrical activity within the heart. Identification of time intervals and morphological appearance of the waves are the major measuring instruments to detect cardiac abnormality from ECG signals. The focus of this study is to classify five different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC), to identify the exact condition of the heart. Prior to the classification, extensive experiments on feature extraction were performed to identify the specific events from ECG signals, such as P, QRS complex, and T waves. This study proposed the fusion technique, dual event-related moving average (DERMA) with the fractional Fourier-transform algorithm (FrlFT) to identify the abnormal and normal morphological events of the ECG signals. The purpose of the DERMA fusion technique is to analyze certain areas of interest in ECG peaks to identify the desired location, whereas FrlFT analyzes the ECG waveform using a time-frequency plane. Furthermore, detected highest and lowest components of the ECG signal such as peaks, the time interval between the peaks, and other necessary parameters were utilized to develop an automatic model. In the last stage of the experiment, two supervised learning models, namely support vector machine and K-nearest neighbor, were trained to classify the cardiac condition from ECG signals. Moreover, two types of datasets were used in this experiment, specifically MIT-BIH Arrhythmia with 48 subjects and the newly disclosed Shaoxing and Ningbo People’s Hospital (SPNH) database, which contains over 10,000 patients. The performance of the experimental setup produced overwhelming results, which show around 99.99% accuracy, 99.96% sensitivity, and 99.9% specificity. Full article
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18 pages, 1083 KiB  
Article
An Integrated Algorithmic MADM Approach for Heart Diseases’ Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting
by Atiqe Ur Rahman, Muhammad Saeed, Mazin Abed Mohammed, Sujatha Krishnamoorthy, Seifedine Kadry and Fatma Eid
Life 2022, 12(5), 729; https://0-doi-org.brum.beds.ac.uk/10.3390/life12050729 - 13 May 2022
Cited by 15 | Viewed by 3505
Abstract
The possibility neutrosophic hypersoft set (pNHs-set) is a generalized version of the possibility neutrosophic soft set (pNs-set). It tackles the limitations of the pNs-set regarding the use of the multi-argument approximate function. This function maps sub-parametric tuples to a power set of the [...] Read more.
The possibility neutrosophic hypersoft set (pNHs-set) is a generalized version of the possibility neutrosophic soft set (pNs-set). It tackles the limitations of the pNs-set regarding the use of the multi-argument approximate function. This function maps sub-parametric tuples to a power set of the universe. It emphasizes the partitioning of each attribute into its respective attribute-valued set. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. In this study, after characterizing some elementary notions and algebraic operations of the pNHs-set, Sanchez’s method (a classical approach for medical diagnosis) is modified under the pNHs-set environment. A modified algorithm is proposed for the medical diagnosis of heart diseases by integrating the concept of the pNHs-set and the modified Sanchez’s method. The authenticity of the proposed algorithm is evaluated through its implementation in a real-world scenario with real data from the Cleveland data set for heart diseases. The beneficial aspects of the proposed approach are evaluated through a structural comparison with some pertinent existing approaches. Full article
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24 pages, 8397 KiB  
Article
Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects
by Dovilė Komolovaitė, Rytis Maskeliūnas and Robertas Damaševičius
Life 2022, 12(3), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/life12030374 - 04 Mar 2022
Cited by 21 | Viewed by 5159
Abstract
Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. [...] Read more.
Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more. Full article
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14 pages, 305 KiB  
Article
A Computational Model of Similarity Analysis in Quality of Life Research: An Example of Studies in Poland
by Agnieszka Bielińska, Piotr Wa̧ż and Dorota Bielińska-Wa̧ż
Life 2022, 12(1), 56; https://0-doi-org.brum.beds.ac.uk/10.3390/life12010056 - 01 Jan 2022
Cited by 2 | Viewed by 1121
Abstract
Due to the multidimensional structure of the results of similarity studies, their analysis is often difficult. Therefore, a compact and transparent presentation of these results is essential. The purpose of the present study is to propose a graphical representation of the results of [...] Read more.
Due to the multidimensional structure of the results of similarity studies, their analysis is often difficult. Therefore, a compact and transparent presentation of these results is essential. The purpose of the present study is to propose a graphical representation of the results of similarity analysis in studies on the quality of life. The results are visualized on specific diagrams (maps), where a large amount of information is presented in a compact form. New similarity maps obtained using a computational method, correspondence analysis, are shown as a convenient tool for comparative studies on the quality of life of different groups of individuals. The usefulness of this approach to the description of changes of the quality of life after the retirement threshold in different domains is demonstrated. The World Health Organization Quality of Life-BREF questionnaire was used to evaluate individuals in Poland. By analyzing clusters on the similarity maps, two groups (employees and retirees) were classified according to their quality of life in different domains. By comparing the structures of the classification maps containing the information about the whole system considered, it is clearly seen which factors are important in the comparative studies. For the considered problems, the uncertainty coefficients describing the effect size and preserving the information on the symmetry of the variables that were used for the creation of the contingency tables were evaluated. Full article
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Review

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19 pages, 13722 KiB  
Review
Hot Spots and Trends in the Relationship between Cancer and Obesity: A Systematic Review and Knowledge Graph Analysis
by Le Gao, Tian Yang, Ziru Xue and Chak Kwan Dickson Chan
Life 2023, 13(2), 337; https://0-doi-org.brum.beds.ac.uk/10.3390/life13020337 - 27 Jan 2023
Cited by 3 | Viewed by 2100
Abstract
Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and [...] Read more.
Cancer is one of the most difficult medical problems in today’s world. There are many factors that induce cancer in humans, and obesity has become an important factor in inducing cancer. This study systematically and quantitatively describes the development trend, current situation and research hotspot of the relationship between cancer and obesity by using document statistics and knowledge graph visualization technology. Through the visualization technology analysis of knowledge graph in this study, the research hotspot and knowledge base source of the relationship between cancer and obesity in the last 20 years have been ascertained. Obesity-related factors, such as immunity, insulin, adiponectin, adipocytokines, nonalcoholic fatty liver and inflammatory reaction, may affect the occurrence of obesity and increase the risk of cancer. Obesity-related cancers include respiratory cancer, colorectal cancer, hepatocellular cancer, prostate cancer, gastric cancer, etc. Our research provides direction and basis for future research in this field, as well as technical and knowledge basis support for experts and researchers in related medical fields. Full article
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Other

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12 pages, 3257 KiB  
Case Report
Quantitative Complexity Theory (QCT) in Integrative Analysis of Cardiovascular Hemodynamic Response to Posture Change
by Paweł Krzesiński, Jacek Marczyk, Bartosz Wolszczak, Grzegorz Gerard Gielerak and Francesco Accardi
Life 2023, 13(3), 632; https://0-doi-org.brum.beds.ac.uk/10.3390/life13030632 - 24 Feb 2023
Viewed by 1121
Abstract
The explanation of physiological mechanisms involved in adaptation of the cardiovascular system to intrinsic and environmental demands is crucial for both basic science and clinical research. Computational algorithms integrating multivariable data that comprehensively depict complex mechanisms of cardiovascular reactivity are currently being intensively [...] Read more.
The explanation of physiological mechanisms involved in adaptation of the cardiovascular system to intrinsic and environmental demands is crucial for both basic science and clinical research. Computational algorithms integrating multivariable data that comprehensively depict complex mechanisms of cardiovascular reactivity are currently being intensively researched. Quantitative Complexity Theory (QCT) provides quantitative and holistic information on the state of multi-functional dynamic systems. The present paper aimed to describe the application of QCT in an integrative analysis of the cardiovascular hemodynamic response to posture change. Three subjects that underwent head-up tilt testing under beat-by-beat hemodynamic monitoring (impedance cardiography) were discussed in relation to the complexity trends calculated using QCT software. Complexity has been shown to be a sensitive marker of a cardiovascular hemodynamic response to orthostatic stress and vasodilator administration, and its increase has preceded changes in standard cardiovascular parameters. Complexity profiling has provided a detailed assessment of individual hemodynamic patterns of syncope. Different stimuli and complexity settings produce results of different clinical usability. Full article
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