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Pergularia daemia hydro-ethanolic remove protects against pentylenetetrazole kindling-induced seizures, oxidative tension, and also neuroinflammation inside mice.
Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.In this paper we present an approach toward human action detection for activities of daily living (ADLs) that uses a convolutional neural network (CNN). The network is trained on discrete Fourier transform (DFT) images that result from raw sensor readings, i.e., each human action is ultimately described by an image. More specifically, we work using 3D skeletal positions of human joints, which originate from processing of raw RGB sequences enhanced by depth information. The motion of each joint may be described by a combination of three 1D signals, representing its coefficients into a 3D Euclidean space. All such signals from a set of human joints are concatenated to form an image, which is then transformed by DFT and is used for training and evaluation of a CNN. We evaluate our approach using a publicly available challenging dataset of human actions that may involve one or more body parts simultaneously and for two sets of actions which resemble common ADLs.There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, the earlier the diagnosis, the most effective the treatment. (Semi)-automated structural magnetic resonance imaging (MRI) processing approaches are very popular in AD research. Mild cognitive impairment (MCI) is considered to be a stage between normal cognitive ageing and dementia. MCI can often be the prodromal stage of AD. Around 10-15% of MCI patients convert to AD per year. In this study, we used three supervised machine learning (ML) techniques to differentiate MCI converters (MCIc) from MCI non-converters (MCInc) and predict their conversion rates from baseline MRI data (cortical thickness (CTH) and hippocampal volume (HCV)). A total of 803 participants from the ADNI cohort were included in this study (188 AD, 107 MCIc, 257 MCInc and 156 healthy controls (HC)). We studied the classification abilities of three different WEKA classifiers (support vector machine (SVM), decision trees (J48) and Naive Bayes (NB)). selleck inhibitor We built six different classification models, three models based on CTH and three based on HCV (CTH-SVM, CTH-J48, CTH-NB, HCV-SVM, HCV-J48 and HCV-NB). For the classification experiments, we obtained up to 71% sensitivity and up to 56% specificity. The prediction of conversion showed accuracy for up to 84%. The value of certain multivariate models derived from the classification experiments has exhibited robust and effective results in MCIc identification. However, there was a limitation in this study since we could not compare the CTH with the HCV models seeing as the data used originated from different subjects. As future direction, we propose the creation of a model that would combine various features with data originating from the same subjects, thus being a far more reliable and accurate prognostic tool.Fuzzy logic is an innovative scientific field with several successful applications. Genetic algorithms and fuzzy logic systems fusion provide real-world problems modeling through the development of intelligent and adaptive systems. Moreover, the statistical analysis of the epidemiology of infectious diseases, which combines fuzzy logic aspects, is vital for perceiving their evolution and control potential. Author's objective is initially to provide a review of the efficiency of fuzzy logic applications. The advanced implementation of fuzzy logic theory in epidemiology and the application of fuzzy logic for controlling genetic algorithms within strategies based on the human experience and knowledge known as fuzzy logic controllers (FLCs) are analyzed. Outcomes of this review study show that not only can fuzzy sets be efficiently implemented in epidemiology but also prove the effectiveness of fuzzy genetic algorithms applications, thus suggesting that fuzzy logic applications are a really promising field of research.Many times, we have found ourselves in situations where because we cannot physically be in a specific place, at a given time, we struggle to extract information which is saved on any kind of computer but is powered off. Such an example is when someone is abroad and needs to access a specific set of data that are saved on a computer back home. A solution to that problem is the well-known WOL - Wake On Lan - functionality, but that alone is not enough. Firstly, the WOL is effective only if the target device is being located in the same local network (LAN) with the device which is trying to send the "magic packet" to it. Secondly, it is only effective over the Internet under very specific circumstances. The second way hides some very serious dangers behind it. Thus, in this paper, we introduce the WOL Ecosystem. WOL Ecosystem is being consisted of a RESTful API, running on a server and a client, that is running on a raspberry pi, which in consultation are capable of remotely waking up any computer, that supports the WOL functionality safely and, of course, over the Internet.Over the past 5 years, a significant number of studies focused on computer programming and code writing (software development, code comprehension, program debugging, code optimization, developer training), using the capabilities of brain imaging techniques and of biomarkers. With the use of the aforementioned techniques, researchers have explored the role of programming experience and knowledge, the relation between coding and writing, and the possibilities of improving program debugging with machine learning techniques. In this paper, a review of existing literature and discussion of research issues that should be examined in the future are explored. Research may link the neuroscientific field with training issues in programming, so as to contribute to the learning process.Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.This study presents an educational scenario for the learning of the conic section, the ellipse. The scenario was designed based on the results of neuroeducation research and upon the principles of differentiated instruction. The proposition includes utilization of multiple representational tools as well as several tangible tools, the use of which can support the context of differentiated instruction according to the principles of cognitive neurosciences. In addition, it includes a large number of activities derived from the real world and other disciplines. The proposed scenario lasts four teaching periods, during which students will have the opportunity to discover, to experiment with, and above all to collaboratively pursue learning while choosing their own learning path in the context of differentiated instruction.Mobile health applications are steadily gaining momentum in the modern world given the omnipresence of various mobile or Wi-Fi connections. Given that the bandwidth of these connections increases over time, especially in conjunction with advanced modulation and error-correction codes, whereas the latency drops, the cooperation between mobile applications becomes gradually easier. This translates to reduced computational burden and heat dissipation for each isolated device but at the expense of increased privacy risks. This chapter presents a configurable and scalable edge computing architecture for cooperative digital health mobile applications.The scope of this paper is to propose a new integrated methodology for the evaluation of the strategic performance of a healthcare organization. In order to find the optimal strategy for an emergency department, we propose the combination of BSC, simulation, and UTASTAR algorithm. Through the simulation model, the stakeholders have the ability to evaluate the effect of their decisions on a number of KPIs that are important for the successful implementation of strategy on the ED. This method is able to provide a set of completed results (e.g., scores, weights, value functions, etc.), which may help the organization to evaluate and revise its strategy.Bifenthrin (BF) and acetochlor (AT) are widely used as an insecticide and herbicide, respectively, which are introduced to the aquatic environment as a natural result. Although the thyroid active substances may coexist in the environment, their joint effects on fish have not been identified. We examined the joint toxicity of BF and AT in zebrafish (Danio rerio) in this study. An acute lethal toxicity test indicated that the median lethal concentration (LC50) values of BF and AT under 96 h treatment were 0.40 and 4.56 µmol L-1, respectively. The binary mixture of BF + AT displayed an antagonistic effect on the acute lethal toxicity. After 14 days post fertilization (dpf) with exposure to individual pesticides at sub-lethal concentrations of, no effects were observed on the catalase (CAT) and peroxidase (POD) activities, while the binary mixtures (except for the 7.2 × 10-3 µmol L-1 BF + 1.2 × 10-2 µmol L-1 AT exposure group) significantly induced the CAT activity. The superoxide dismutase (SOD) activity and triiodothyronine (T3) level were significantly increased in all exposure groups.
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