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However, the questionnaire method we applied in the second phase of the survey has shown to deliver. The students found the value of the method in solving a problem by correcting errors or shortcomings and eventually answering correctly. Methods and research on them, however, present the way to acquiring mathematical knowledge through problems. For safer conclusions, we expect as a learning community the results of research in cognitive science and neuroscience around problem solving. The last two areas of educational research, cognitive science and neuro-education, are expected to provide answers for the transition of knowledge from one level to another.Cancer research has yielded tremendous gains over the last two decades with remarkable results addressing this worldwide major public health problem. Continuous technological developments and persistent research has led to significant progress in targeted therapies. This paper focuses on the study of mathematical models that describe in the most optimal way the development of malignant tumours induced in experimental animals of a particular species following chemical carcinogenesis with a complete carcinogen factor known as 3,4-benzopyrene. The purpose of this work is to study the phenomenon of chemical carcinogenesis, inhibition and growth of malignant tumours.The majority of risk genetic variants for common and complex neuropsychiatric traits lie within noncoding regions. Previous efforts have linked risk variants to specific genes by leveraging transcriptome data and expression quantitative trait loci. Most recently, the generation of large-scale epigenome data and the availability of epigenome quantitative trait loci provide a powerful discovery tool for assigning a functional role to the genetic variation in neuropsychiatric traits. In this talk, we will focus on advances in integration of epigenome datasets with the risk of common and complex neuropsychiatric traits.The availability of numerical data grows from 1 day to another in a remarkable way. New technologies of high-throughput Next-Generation Sequencing (NGS) are producing DNA sequences. Next-Generation Sequencing describes a DNA sequencing technology which has revolutionized genomic research. In this paper, we perform some experiments using a cloud infrastructure framework, namely, Apache Spark, in some sequences derived from the National Center for Biotechnology Information (NCBI). The problems we examine are some of the most popular ones, namely, Longest Common Prefix, Longest Common Substring, and Longest Common Subsequence.The fusion of artificial neural networks and fuzzy logic systems allows researchers to model real-world problems through the development of intelligent and adaptive systems. Artificial neural networks are able to adapt and learn by adjusting the interconnections between layers, while fuzzy logic inference systems provide a computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The combined use of those adaptive structures is known as "neuro-fuzzy" systems. In this paper, the basic elements of both approaches are analyzed, noticing that this blending could be applied for pattern recognition in medical applications.Eight computer science students, novice programmers, who were in the first semester of their studies, participated in a field study in order to explore potential differences in their brain activity during programming with a visual versus a textual programming language. The students were asked to develop two specific programs in both programming languages (a total of four tasks). Measurements of cerebral activity were performed by the electroencephalography (EEG) imaging method. According to data analysis, it appears that the type of programming language did not affect the students' brain activity.The continuing development of robotics on the one hand and, on the other hand, the estimated relative growth in the number of elderly individuals suffering from neurodegenerative diseases raises the question of which contribution these powerful systems could have to assist or even improve the diagnostic procedure. Up to now many research groups have designed protocols that use robotic systems that measure the performance of the subject through tracking tasks with and without a synchronized cognitive or motor task. Also, new robotic noninvasive technologies are designed for the diagnosis of neurodegenerative diseases, through the analysis of eye movements. These assessments can distinguish individuals with the disease from age-matched controls. [https://www.selleckchem.com/products/pkr-in-c16.html C16 research buy] The objective of this review is to evaluate the effects and efficiency of robot interventions in the diagnosis of these devastating neurological diseases.MotivationNeurodegenerative diseases (NDs), including amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, occur as a result of neurodegenerative processes. Thus, it has been increasingly appreciated that many neurodegenerative conditions overlap at multiple levels. However, traditional clinicopathological correlation approaches to better classify a disease have met with limited success. Discovering this overlap offers hope for therapeutic advances that could ameliorate many ND simultaneously. In parallel, in the last decade, systems biology approaches have become a reliable choice in complex disease analysis for gaining more delicate biological insights and have enabled the comprehension of the higher order functions of the biological systems.ResultsToward this orientation, we developed a systems biology approach for the identification of common links and pathways of ND, based on well-established and novel topological and functional measures. For this purpose, a molecular pathway network was constructed, using molecular interactions and relations of four main neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease). Our analysis captured the overlapped subregions forming molecular subpathways fully enriched in these four NDs. Also, it exported molecules that act as bridges, hubs, and key players for neurodegeneration concerning either their topology or their functional role.ConclusionUnderstanding these common links and central topologies under the perspective of systems biology and network theory and greater insights are provided to uncover the complex neurodegeneration processes.We use extensive computer simulations to study synchronization phenomena in networks of biological neurons. Each individual neuron is modeled using the leaky integrate-and-fire (LIF) scheme, while many neurons are coupled nonlocally in a network. In this system chimera states develop, which are complex states consisting of coexisting synchronous and asynchronous network areas. We study the influence of the network size on the properties and the form of chimera states. link2 We show that for constant coupling strength, the number of the synchronous/asynchronous domains depends quantitatively on the coupling ratio. This dependence allows to extract synchronization properties in large ensembles of neurons after extrapolating from simulations of small networks. Since computer simulations of even small neuron networks are highly demanding in memory and CPU time, this property is particularly important in view of the large number of neurons involved in any cognitive function. In total, the number of neurons in the human brain is of the order of 1010, and each of them is connected with an average of 103 other neurons.Blockchain is a linearly linked, distributed, and very robust data structure. Originally proposed as part of the Bitcoin distributed stack, it can be applied in a number of fields, most notably in smart contracts, social media, secure IoT, and cryptocurrency mining. It ensures data integrity by distributing strongly encrypted data in widely redundant segments. Each new insertion requires verification and approval by the majority of the users of the blockchain. Both encryption and verification are computationally intensive tasks which cannot be solved with ordinary off-the-shelf CPUs. This has resulted in a renewed scientific interest in secure distributed communication and coordination protocols. Mobile health applications are growing progressively popular and have the enormous advantage of timely diagnosis of certain conditions. However, privacy concerns have been raised as mobile health applications by default have access to highly sensitive personal data. This chapter presents concisely how blockchain can be applied to mobile health applications in order to enhance privacy.The blood plasma flow through a swarm of red blood cells in capillaries is modeled as an axisymmetric Stokes flow within inverted prolate spheroidal solid-fluid unitary cells. The solid internal spheroid represents a particle of the swarm, while the external spheroid surrounds the spheroidal particle and contains the analogous amount of fluid that corresponds to the fluid volume fraction of the swarm. Analytical expansions for the components of the flow velocity are obtained by introducing a stream function ψ which satisfies the fourth-order partial differential equation E4ψ = 0. We assume nonslip conditions on the internal inverted spheroidal boundary which is also impermeable, while on the external spheroidal surface, we assume continuity of the tangential velocity component and nil vorticity. In order to solve the problem at hand, we employ the method of Kelvin inversion, under which, the initial problem, formulated in the inverted prolate spheroidal coordinates, is transformed to an equivalent one in the prolate spheroidal coordinates, where the solution space of the equation E4ψ = 0 is already known from our previously published work. The solution for the original problem is obtained by using the inverse Kelvin transformation and the effect of this transform to the Stokes operator (Dassios, IMA J Appl Math 74427-438, 2009). Finally, the analytical solution for the stream function ψ is given through a series expansion of specific combinations of Gegenbauer functions of mixed order, multiplied by the Euclidean distance on the first and on the third power, in a so-called R-separable form.Monoclonal antibodies (mAbs) constitute a promising class of therapeutics, since ca. 25% of all biotech drugs in development are mAbs. Even though their therapeutic value is now well established, human- and murine-derived mAbs do have deficiencies, such as short in vivo lifespan and low stability. link3 However, the most difficult obstacle to overcome, toward the exploitation of mAbs for disease treatment, is the prevention of the formation of protein aggregates. ANTISOMA is a pipeline for the reduction of the aggregation tendency of mAbs through the decrease in their intrinsic aggregation propensity, based on an automated amino acid substitution approach. The method takes into consideration the special features of mAbs and aims at proposing specific point mutations that could lead to the redesign of those promising therapeutics, without affecting their epitope-binding ability. The method is available online at http//bioinformatics.biol.uoa.gr/ANTISOMA .