How growing tumour impacts intracranial pressure and deformation mechanics of brain

Brain is an actuator for control and coordination. When a pathology arises in cranium, it may leave a degenerative, disfiguring and destabilizing impact on brain physiology. However, the leading consequences of the same may vary from case to case. Tumour, in this context, is a special type of pathology which deforms brain parenchyma permanently. From translational perspective, deformation mechanics and pressures, specifically the intracranial cerebral pressure (ICP) in a tumour-housed brain, have not been addressed holistically in literature. This is an important area to investigate in neuropathy prognosis. To address this, we aim to solve the pressure mystery in a tumour-based brain in this study and present a fairly workable methodology. Using image-based finite-element modelling, we reconstruct a tumour-based brain and probe resulting deformations and pressures (ICP). Tumour is grown by dilating the voxel region by 16 and 30 mm uniformly. Cumulatively three cases are studied including an existing stage of the tumour. Pressures of cerebrospinal fluid due to its flow inside the ventricle region are also provided to make the model anatomically realistic. Comparison of obtained results unequivocally shows that as the tumour region increases its area and size, deformation pattern changes extensively and spreads throughout the brain volume with a greater concentration in tumour vicinity. Second, we conclude that ICP pressures inside the cranium do increase substantially; however, they still remain under the normal values (15 mmHg). In the end, a correlation relationship of ICP mechanics and tumour is addressed. From a diagnostic purpose, this result also explains why generally a tumour in its initial stage does not show symptoms because the required ICP threshold has not been crossed. We finally conclude that even at low ICP values, substantial deformation progression inside the cranium is possible. This may result in plastic deformation, midline shift etc. in the brain

Analysis of Growing Tumor on the Flow Velocity of Cerebrospinal Fluid in Human Brain Using Computational Modeling and Fluid-Structure Interaction

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles

Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

O envolvimento de crianças com necessidades educativas especiais em contexto de creche e de jardim-de-infância

Este estudo pretende analisar o envolvimento observado em 50 crianças com incapacidades integradas em contexto de creche/jardim-de-infância da Área Metropolitana do Porto, comparando os níveis e tipos de envolvimento observado em dois contextos de actividade: na presença da educadora da educação especial e na sua ausência. O perfil de incapacidade das crianças foi obtido com base no Índex de Capacidades (Simeonsson & Bailey, 1991) e os dados de envolvimento observado foram obtidos através da aplicação do Engagement Quality Observation System III (McWilliam & de Kruif,1998). Centrando-se nas competências interactivas da criança em situações de jogo ou em actividades de rotina, o envolvimento tem sido estudado como um factor de aprendizagem e desenvolvimento que ilustra as experiências diárias da criança, bem como a qualidade das oportunidades que lhe são proporcionadas nos seus contextos educativos.Os resultados indicam que a sofisticação, bem como o foco de envolvimento da criança são influenciados por características da criança (idade e grau de incapacidade), bem como por factores do meio educativo em que ela interage (presença da educadora de educação especial), sublinhado a relevância desta linha de investigação e fornecendo evidência empírica acerca do constructo de envolvimento enquanto indicador de processos interactivos com valor desenvolvimenta

Novel centroid selection approaches for KMeans-clustering based recommender systems

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems. An issue with traditional k-means clustering algorithms is that they choose the initial k centroid randomly, which leads to inaccurate recommendations and increased cost for offline training of clusters. The work in this paper highlights how centroid selection in k-means based recommender systems can improve performance as well as being cost saving. The proposed centroid selection method has the ability to exploit underlying data correlation structures, which has been proven to exhibit superior accuracy and performance in comparison to the traditional centroid selection strategies, which choose centroids randomly. The proposed approach has been validated with an extensive set of experiments based on five different datasets (from movies, books, and music domain). These experiments prove that the proposed approach provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided

MIP-based extraction techniques for the determination of antibiotic residues in edible meat samples : Design, performance & recent developments

Misusing or overusing antibiotics in livestock and poultry can result in the accumulation of mentioned drugs in the animal meat. Consequently, its consumption by humans and therefore increasing the risks of antibiotic resistance emergences. In order to decrease these risks, constant monitoring of the meat samples is necessary. Therefore, the concentration of antibiotics needs to be lower than maximum residue limits. As meat is a complex matrix, sample preparation is a mandatory step in the analysis. Molecularly imprinted polymers are one of the extensively studied tools in this aspect. These polymers exhibited great affinity and selectivity towards the target compound/s. In this work, a collection of studies from 2017 to 2021 is reviewed. Inclusion criteria were formed around papers incorporating molecularly imprinted polymers as a means of extraction or detection of antibiotics in meat samples. This review represents different synthesis methods of these polymers and their applications in the extraction and determination of antibiotics from meat samples. It also demonstrates the advantages, gaps and weakness of these systems in the food chemistry field. It can also act as a guide for the design and development of novel polymer-based analytical methods for food applications. Throughout this review, the methods for determination of antibiotic residues in food samples using conventional and novel MIP based techniques are discussed, by coupling MIPs with other analytical techniques, Limit of detection and quantification and recovery rates will improve significantly, which results in designing of platforms in food chemistry analysis with higher efficacy.Peer reviewe

Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

Game player modeling is a paradigm of computational models to exploit players’ behavior and experience using game and player analytics. Player modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the game. Player behavior focuses on dynamic and static information gathered at the time of gameplay. Player experience concerns the association of the human player during gameplay, which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s surroundings. In this paper, player experience modeling is studied based on the board puzzle game “Candy Crush Saga” using cognitive data of players accessed by physiological and peripheral devices. Long Short-Term Memory-based Deep Neural Network (LSTM-DNN) is used to predict players’ effective states in terms of valence, arousal, dominance, and liking by employing the concept of transfer learning. Transfer learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related problems. The homogeneous transfer learning approach has not been implemented in the game domain before, and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’ engagement. Relevant not only from a player’s point of view, it is also a benchmark study for game developers who have been facing problems of “cold start” for innovative games that strengthen the game industrial economy