Tensor join of hypergraphs and its spectra

In this paper, we introduce three operations on hypergraphs by using tensors. We show that these three formulations are equivalent and we commonly call them as the tensor join. We show that any hypergraph can be viewed as a tensor join of hypergraphs. Tensor join enable us to obtain several existing and new classes of operations on hypergraphs. We compute the adjacency, the Laplacian, the normalized Laplacian spectrum of weighted hypergraphs constructed by this tensor join. Also we deduce some results on the spectra of hypergraphs in the literature. As an application, we construct several pairs of the adjacency, the Laplacian, the normalized Laplacian cospectral hypergraphs by using the tensor join

The transcriptional cofactor PCAF as mediator of the interplay between p53 and HIF-1 alpha and its role in the regulation of cellular energy metabolism

Energy production is a very important function for the cells to maintain homeostasis, survive and proliferate. Cellular energy can be produced either through oxidative phosphorylation (OXPHOS) in the presence of oxygen or glycolysis in its absence. Cancer cells, even in the presence of oxygen prefer to produce energy through glycolysis and this confers them a survival advantage. Energy metabolism has recently attracted the interest of several laboratories as targeting the pathways for energy production in cancer cells could be an efficient anticancer treatment. For that purpose the role of various transcription factors in determining the pathway of energy production has been investigated extensively and there is evidence to suggest that oncogenic transcription factors promote glycolysis whereas tumour suppressors demote it. In line with this notion, the master regulator of cellular response to hypoxia, the Hypoxia Inducible Factor 1 (HIF-1) has been shown to induce the expression of a variety of genes encoding enzymes involved in glucose metabolism as well as OXPHOS favouring energy production through glucose metabolism in hypoxic cells. The tumour suppressor p53 on the other hand inhibits glycolysis and stimulates OXPHOS. One of the pathways through which p53 exerts these effects, is by inducing the inhibitor of glycolysis TIGAR and the cytochrome c oxidase assembly factor SCO2 gene expression under DNA damage conditions. However, the regulation of the expression of these genes in hypoxic conditions has been only partially elucidated. We hypothesised that under hypoxic conditions, TIGAR and SCO2 gene expression might be differentially regulated in cells bearing mutated p53 and in these cells the involvement of HIF-1 could be crucial. Indeed under hypoxia mimicking conditions, the TIGAR and SCO2 protein and mRNA levels were found to be modulated differentially in p53 wild type and mutant cell lines. The bioinformatics analysis revealed the presence of hypoxia responsive elements (HREs) within the regulatory region of the promoters of TIGAR and SCO2 genes. Firefly reporter assays and chromatin immunoprecipitation (ChIP) assays have indicated that HIF-1 plays a crucial role in the regulation of TIGAR gene expression. The direct involvement of HIF-1 in the regulation of SCO2 gene expression requires further investigation. We and others have recently reported that PCAF is a common cofactor for p53 and HIF-1α, regulating the protein stability and transcription target selectivity of both transcription factors thereby orchestrating the balance between life and death in cancer cells. We hypothesised that PCAF plays a similar role in the regulation of cellular energy metabolism by differentially targeting HIF-1α and p53 to the promoter of TIGAR and SCO2 genes. In this study we present evidence to support the notion that PCAF plays an import role in the regulation of TIGAR and SCO2 gene expression under hypoxic mimicking conditions. This conclusion was supported by assessing the functional consequences of PCAFwt and PCAFΔHAT overexpression on the intracellular lactate production, cellular oxygen consumption, NAD+/NADH ratio and ROS generation in cells under hypoxia mimicking conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The role of glucocorticoid receptor phosphorylation in Mcl-1 and NOXA gene expression

<p>Abstract</p> <p>Background</p> <p>The cyclin-dependent kinase (CDK) and mitogen-activated protein kinase (MAPK) mediated phosphorylation of glucocorticoid receptor (GR) exerts opposite effects on GR transcriptional activity and affects other posttranslational modifications within this protein. The major phosphorylation site of human GR targeted by MAPK family is the serine 226 and multiple kinase complexes phosphorylate receptor at the serine 211 residue. We hypothesize that GR posttranslational modifications are involved in the determination of the cellular fate in human lymphoblastic leukemia cells. We investigated whether UV signalling through alternative GR phosphorylation determined the cell type specificity of glucocorticoids (GCs) mediated apoptosis.</p> <p>Results</p> <p>We have identified putative Glucocorticoid Response Elements (GREs) within the promoter regulatory regions of the Bcl-2 family members NOXA and Mcl-1 indicating that they are direct GR transcriptional targets. These genes were differentially regulated in CEM-C7-14, CEM-C1-15 and A549 cells by glucocorticoids and JNK pathway. In addition, our results revealed that the S211 phosphorylation was dominant in CEM-C7-14, whereas the opposite was the case in CEM-C1-15 where prevalence of S226 GR phosphorylation was observed. Furthermore, multiple GR isoforms with cell line specific patterns were identified in CEM-C7-14 cells compared to CEM-C1-15 and A549 cell lines with the same antibodies.</p> <p>Conclusions</p> <p>GR phosphorylation status kinetics, and site specificity as well as isoform variability differ in CEM-C7-14, CEM-C1-15, and A549 cells. The positive or negative response to GCs induced apoptosis in these cell lines is a consequence of the variable equilibrium of NOXA and Mcl-1 gene expression potentially mediated by alternatively phosphorylated GR, as well as the balance of MAPK/CDK pathways controlling GR phosphorylation pattern. Our results provide molecular base and valuable knowledge for improving the GC based therapies of leukaemia.</p

Materials and meta-data for thermochemical electrolysis.

One of the important challenges is to develop coating materials for thermochemical containment vessels and pipes that encounters the highly corrosive and harsh environment produced by the molten salt at high temperature. Overall assessment based on the evidence from previous investigations in this area is that none of the high-performance structural materials (coating, substrates) are likely to survive for an extended period in the high temperature corrosive environment. There are innovative means and methods which could be considered to have sustainable coating-substrate assembly and extended lifetime

Differential regulation of cell death pathways by the microenvironment correlates with chemoresistance and survival in leukaemia

Glucocorticoids (GCs) and topoisomerase II inhibitors are used to treat acute lymphoblastic leukaemia (ALL) as they induce death in lymphoid cells through the glucocorticoid receptor (GR) and p53 respectively. Mechanisms underlying ALL cell death and the contribution of the bone marrow microenvironment to drug response/resistance remain unclear. The role of the microenvironment and the identification of chemoresistance determinants were studied by transcriptomic analysis in ALL cells treated with Dexamethasone (Dex), and Etoposide (Etop) grown in the presence or absence of bone marrow conditioned media (CM). The necroptotic (RIPK1) and the apoptotic (caspase-8/3) markers were downregulated by CM, whereas the inhibitory effects of chemotherapy on the autophagy marker Beclin-1 (BECN1) were reduced suggesting CM exerts cytoprotective effects. GCs upregulated the RIPK1 ubiquitinating factor BIRC3 (cIAP2), in GC-sensitive (CEM-C7-14) but not in resistant (CEM-C1-15) cells. In addition, CM selectively affected GR phosphorylation in a site and cell-specific manner. GR is recruited to RIPK1, BECN1 and BIRC3 promoters in the sensitive but not in the resistant cells with phosphorylated GR forms being generally less recruited in the presence of hormone. FACS analysis and caspase-8 assays demonstrated that CM promoted a pro-survival trend. High molecular weight proteins reacting with the RIPK1 antibody were modified upon incubation with the BIRC3 inhibitor AT406 in CEM-C7-14 cells suggesting that they represent ubiquitinated forms of RIPK1. Our data suggest that there is a correlation between microenvironment-induced ALL proliferation and altered response to chemotherapy

Enriching the student model in an intelligent tutoring system

An intelligent tutoring system is a computer-based self-learning system which provides personalized learning content to students based on their needs and preferences. The importance of a students' affective component in learning has motivated adaptive ITS to include learners' affective states in their student models. Learner-centered emotions such as frustration, boredom, and confusion are considered in computer learning environments like ITS instead of other basic emotions such as happiness, sadness and fear. In our research we detect and respond to students' frustration while they interact with an ITS. The existing approaches used to identify affective states include human observation, self-reporting, modeling affective states, face-based emotion recognition systems, and analyzing data from physical and physiological sensors. Among these, data-mining approaches and affective state modeling are feasible for the large scale deployment of ITS. Systems using data-mining approaches to detect frustration have reported high accuracy, while systems that detect frustration by modeling affective states not only detect a student's affective state but also the reason for that state. In our approach we combine these approaches. We begin with the theoretical definition of frustration, and operationalize it as a linear regression model by selecting and appropriately combining features from log file data. We respond to students' frustration by displaying messages which motivate students to continue the session instead of getting more frustrated. These messages were created to praise the student's effort, attribute the results to external factors, to show sympathy for failure and to get feedback from the students. The messages were displayed based on the reasons for frustration. We have implemented our research in Mindspark, which is a mathematics ITS with a large scale deployment, developed by Educational Initiatives, India. The facial observations of students were collected using human observers, in order to build a ground truth database for training and validating the frustration model. We used 932 facial observations data from 27 students to create and validate our frustration model. Our approach shows comparable results to existing data-mining approaches and also with approaches that model the reasons for the students' frustration. Our approach to responding to frustration was implemented in three schools in India. Data from 188 students from the three schools, collected across two weeks was used for our analysis. The number of frustration instances per session after implementing our approach were analyzed. Our approach to responding to frustration reduced the frustration instances statistically significantly--(p < 0.05)--in Mindspark sessions. We then generalized our theory-driven approach to detect other affective states. Our generalized theory-driven approach was used to create a boredom model which detects students' boredom while they interact with an ITS. The process shows that our theory-driven approach is generalizable to model not only frustration but also to model other affective states.Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of the Indian Institute of Technology, Bombay, India and Monash University, Australia

Investigating behavioral patterns of procrastinators in a Wiki-based activity

Procrastination is one of the issues affecting more than half of the student population and is known to impact them negatively. It is also one of the major reasons for failure and dropout. Therefore, several studies have been conducted in this domain to understand when and why students procrastinate. The existing studies use self-reported procrastination scales and/or digital traces of student interactions recorded in learning environments to identify procrastination behavior. The majority of the extant studies leverage individual tasks such as assignments submission, quizzes attempted, course materials assessed by a student, etc., to study such behavior. This paper uses group-based collaborative wiki activity to explore the procrastination behavior among the students. This study will help us explore student behavior in a group activity. The results would help us investigate if the student’s behavior changes when it comes to a group activity. The results would be beneficial for instructors, practitioners, and educational researchers to know if group activity could be utilized to overcome procrastination behavio

Enriching the student model in an intelligent tutoring system

An intelligent tutoring system is a computer-based self-learning system which provides personalized learning content to students based on their needs and preferences. The importance of a students' affective component in learning has motivated adaptive ITS to include learners' affective states in their student models. Learner-centered emotions such as frustration, boredom, and confusion are considered in computer learning environments like ITS instead of other basic emotions such as happiness, sadness and fear. In our research we detect and respond to students' frustration while they interact with an ITS. The existing approaches used to identify affective states include human observation, self-reporting, modeling affective states, face-based emotion recognition systems, and analyzing data from physical and physiological sensors. Among these, data-mining approaches and affective state modeling are feasible for the large scale deployment of ITS. Systems using data-mining approaches to detect frustration have reported high accuracy, while systems that detect frustration by modeling affective states not only detect a student's affective state but also the reason for that state. In our approach we combine these approaches. We begin with the theoretical definition of frustration, and operationalize it as a linear regression model by selecting and appropriately combining features from log file data. We respond to students' frustration by displaying messages which motivate students to continue the session instead of getting more frustrated. These messages were created to praise the student's effort, attribute the results to external factors, to show sympathy for failure and to get feedback from the students. The messages were displayed based on the reasons for frustration. We have implemented our research in Mindspark, which is a mathematics ITS with a large scale deployment, developed by Educational Initiatives, India. The facial observations of students were collected using human observers, in order to build a ground truth database for training and validating the frustration model. We used 932 facial observations data from 27 students to create and validate our frustration model. Our approach shows comparable results to existing data-mining approaches and also with approaches that model the reasons for the students' frustration. Our approach to responding to frustration was implemented in three schools in India. Data from 188 students from the three schools, collected across two weeks was used for our analysis. The number of frustration instances per session after implementing our approach were analyzed. Our approach to responding to frustration reduced the frustration instances statistically significantly--(p < 0.05)--in Mindspark sessions. We then generalized our theory-driven approach to detect other affective states. Our generalized theory-driven approach was used to create a boredom model which detects students' boredom while they interact with an ITS. The process shows that our theory-driven approach is generalizable to model not only frustration but also to model other affective states. Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy of the Indian Institute of Technology, Bombay, India and Monash University, Australia