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By definition, if the first derivative of d ln N/d ln R remains constant for a space of R, this is the fractal dimension of the shape, in the present case of the time series trajectory. We found that RRI manifested different fractal dynamics, thus, a complex pattern of progression in these two morbid entities, suggesting the need for further investigation in ANS contribution to tumor pathophysiology.Data collection and analysis is becoming more and more important in our everyday lives. It is widely believed that with the power of data collection, almost every problem can be given a possible solution. Machine learning and artificial intelligence algorithms along with the use of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database that collects data from subjects suffering from Alzheimer's disease (AD) can be utilised for recognising biomarkers' patterns that suggest degenerative behavior. Thus, allowing for early prevention. Computers are able to distinguish patterns over millions of data faster than humans can. The suggested methodology will be able to precisely find subjects that might suffer from such diseases due to certain patterns and biomarkers that suggest such predisposition at an early and thus treatable stage of the disease. Due to the quantity of the data collected, an algorithm will be able to distinguish even the slightest change in a healthy neuron in order to treat it. Other databases can be used for other diseases without the concept being changed.Parkinson's disease (PD) is a complex neurodegenerative disorder, characterized by severe motor symptoms which lead to progressive weakness of motor function caused by prominent loss of dopamine-secreting neurons within the substantia nigra. Compelling neuropathological evidence reveals the accumulation of insoluble protein aggregates, such as α-synuclein and tau, which are important hallmarks of the disease. Protein biochips have great potential to be powerful tools for clinical diagnostics, whereas novel sensing methods implementing biosensors for protein quantification in body fluids are highly required. Herein, the development of a device using a thin film of conductive polymer acid-doped polyaniline that can detect specific biomolecules is examined. The polymer is shown to change conductivity in the presence of proteins, so this direct chemical to electric transduction can be used to quantify concentration alterations. The fabrication of such a device is proposed, so that it can be implemented in rapid screening tests as part of an integrated holistic point-of-care diagnostics model that brings together a multidisciplinary healthcare team of PD experts.Storing information in memory efficiently is one of the most significant challenges in computer science. The two main factors that consist an efficient data structure is the reduction of space and time consumption. There is a plethora of different tools able to reduce the run-time of a process, and Apache Spark is one of these; it is a computing framework that is using clusters to execute a process. There are two key features in this software, a directed acyclic graph (DAG) that maps the execution process and the resilient distributed datasets (RDD), which allow large in-memory computations. In order to construct a data structure, which is space- and time-efficient, we have to utilize the corresponding framework. A comparison of the run-time improvement with the use of Spark is also provided. Finally, to prove the efficacy of this software tool, we construct a space-efficient data structure and compare the run-time with and without its use.While expert systems are artificial intelligence (AI) agents, they share many common characteristics with human experts. As technology progresses, such systems are not just able to make simple decisions following "simplistic" linear logical protocols; they "behave" as real experts in at least two ways by demonstrating superb decision-making skills and by conforming to the social norms for expertise, i.e., they "feel" as human experts. A review of the common characteristics of human experts may have important implications for the direction of the development for such systems. Implications for bioinformatics and future research (especially concerning the accompanying concept of "expert generalist") are also discussed.Neuromyths are an important issue in the context of the educational practice, as the misunderstanding of teachers about how the human brain works lead to educational interventions that can have a negative impact on learning. In this study, neuromyths and the problems leading to education are initially defined and identified. Then, following the presentation of the qualitative study, the findings of the case study conducted on high school students regarding the neuromyth of learning styles were highlighted. From the findings, it seems that although learners may indicate or argue that they fit a particular learning style, it is not shown that they learn better with the preferred learning style. In addition, it appears from the findings of the study that learning through multiple representations is an educational practice that contributes to learning. The above highlights the need to train in-service teachers and educate prospective teachers in the principles of educational neuroscience, so that their educational interventions can have a positive impact on students' learning.In the last two decades, the medical sciences have changed their approach to pathogenesis as well as to the diagnosis and treatment of complex human diseases. The main reason for this change is the explosive development of biomedical technology and research, which produces a huge amount of information and data which are generated at an increasing rate. Toward this direction is the pathway analysis, a thriving research area of systems biology tools and methodologies which aim to unravel the inherent complexity of high-throughput biological data produced by the advent of omics technologies. Through this graph mining approach, we can deal with the complexity of the cellular systems of various diseases such as Alzheimer's disease. In this work, we developed a subpathway analysis method for single-cell RNA-seq experiments which isolates differentially expressed subpathways indicating potentially perturbed biological processes. The differential expression status of each gene is negotiated among well-established RNA-seq differential expression analysis tools in order to minimize false discoveries. Also, we demonstrate the efficacy of our method on a single-cell RNA-seq dataset for temporal tracking of microglia activation in neurodegeneration. Results suggest that our approach succeeds in isolating several perturbed biological processes known to be associated with neurodegeneration.Even before quantum computers were known to the public, quantum-inspired algorithms were already introduced and acquired a well-deserved reputation. Their strength lies on their ability to combine innovative characteristics and superior results. see more In the almost 20 years that the field has existed, a huge number of results have been obtained. This chapter is an attempt to give a succinct but comprehensive review of the current state of affairs in the field of quantum-inspired-based metaheuristics, particularly geared towards those that can be used in existing quantum computers.Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy. It is known that deregulation of adipokine pathways is probably implicated in the ontogenesis of ALL. The present work aims at investigating the role of adiponectin and its effects on an ALL cell line. The CCRF-CEM cells were used as a model. Cells have been treated with adiponectin, with different concentrations up to 72 h. Cytotoxicity and cell cycle distribution were investigated for all concentrations using flow cytometry. Selected concentrations were also used for additional microarray analysis, using a small gene set of cancer-related genes. Lower and higher adiponectin concentrations did not produce an inhibition of proliferation, as well as an increase in cell death. It was found that adiponectin regulated differentially genes, such as CD22, CDH1, IFNG, LCK, MSH2, SPINT2, and others. At the same time, it appeared that adiponectin-related gene expression was more active on chromosomes 18 and 1. Machine learning classification algorithms showed that several genes were grouped together indicating common regulatory mechanisms. The present study showed that adiponectin is able to induce gene differential expression in leukemic cells in vitro, suggesting a possible role in the progression of leukemia. It is also an indication that more studies are required in order to further understand the role of adiponectin and adipokines in general in the role of human neoplasms.Social media platforms have gained ground in the day-to-day life of the vast majority of people globally. Growing evidence suggests that social media overuse can take a pathological form, and users can exhibit behaviors similar to those appearing in several types of addiction. The aim of this study was to validate the Greek version of the Social Media Disorder Scale (SMDS) among young adults. An online survey was conducted among Greek adults, aged 18-29 years old. A total of 251 respondents voluntarily participated. Internal consistency, criterion, and construct validity were examined. Results suggest that the Greek version of SMDS shows good psychometric properties. Internal consistency was above the acceptable margin, with a satisfying Cronbach's α coefficient. Correlations with other addiction-related constructs were found to be moderate. Construct validity of the scale was evaluated with exploratory factor analysis. Exploratory factor analysis resulted in a single factor model, which explained almost half of the total variance. The Greek version of SMDS is a psychometrically sound and valid instrument, which researchers and practitioners can use to assess social media addiction in young adults.Alzheimer's disease affects almost ten million people every year. Negative emotions such as frustration and anxiety can have impact on brain capability in terms of memory functions. Alzheimer's patients experience more negative emotions than healthy older adults. Non-pharmacological treatment such as animal therapy could help Alzheimer patient but has restrictions and requirements. We propose a Virtual Reality Zoo Therapy system in which the patients are immersed in a virtual environment and can interact with animals using their hands. With the immersive experience of virtual reality (VR), patients feel that they are in a real therapy room and can freely interact with animals. This system is controlled by an intelligent agent which tracks the patients' emotions using electroencephalography and commands the animals according to their hand gesture and emotions. Experiments have been done and preliminary results show that it is possible to predict patients' hand gesture and interpret them in order to interact with virtual animals and the Zoo Therapy system can reduce the negative emotions.Diagnosing and preventing Alzheimer's disease is a complex task, partly due to being characterized by a lengthy asymptomatic stage. In order to tackle this, most preclinical studies are multidimensional in nature and largely focus on prevention through lifestyle interventions, such as improving nutrition and introducing physical as well as cognitive exercise. With the widespread use of mobile smart devices today, mobile health applications can help inform high-risk individuals at a low cost, while also aiding in the prevention of cognitive decline through constant virtual coaching services that contribute to lifestyle interventions. Under this light, a mobile application is developed in the context of this paper that provides risk assessment of individuals, daily monitoring of factors that have been found to help prevent cognitive impairment, and individually tailored guidance based on the individual's progress. The developed application is also capable of reassessing users' risk to track their progress, while also providing these services in an intuitive and user-friendly manner, which could enable the future development of more accurate models through the collected data.
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