Unveiling Clusters of RNA Transcript Pairs Associated with Markers of Alzheimer's Disease Progression

Background: One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer's disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes. Methodology/Principal Findings: We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis. Conclusions/Significance: Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation. © 2012 Arefin et al

Prevalence of Iron Deficiency Anemia and its Biochemical Parameters among the Selected School- going Under-priviledged Children in Dhaka City

Iron deficiency is a serious health complication particularly in developing countries, which is usually caused due to poor nutrition, genetic disorders and chronic infections. Compared to developed countries prevalence of anaemia in developing and underdeveloped countries is very high, and children are the ones which are mostly affected. In this paper an attempt has been made to study the prevalence of anaemia among some school-going children in Dhaka. An attempt has also been made to assess the severity of anaemia and iron status among the school-going underprivileged children by measuring serum iron (SI), serum TIBC and serum ferritin (SF) and explore a relationship between haemoglobin level and various parameters of iron nutrition. A substantial number of indicators have been used in determining the iron deficiency.Results obtained from the study show that two thirds of the study children are anaemic due to haemoglobin level below 12 gm/dl. However, majority of them had mild anaemia (haemoglobin level between 10.0 to 11.9 gm/dl) and only a few of them had moderate anaemia (haemoglobin level between 7.0 to 9.9 gm/dl). None of the study population had severe anaemia (haemoglobin level below 7.0 gm/dl). Results also show that only 10 of the study population (6%) were found to have significantly low serum iron, low serum ferritin and high serum iron binding capacity (TIBC) as compared to that of the students who had normal haemoglobin level.DOI: http://dx.doi.org/10.3329/jom.v14i2.19657 J Medicine 2013, 14(2): 130-134</jats:p

Relative neighborhood graphs uncover the dynamics of social media engagement

© Springer International Publishing AG 2016. In this paper, we examine if the Relative Neighborhood Graph (RNG) can reveal related dynamics of page-level social media metrics. A statistical analysis is also provided to illustrate the application of the method in two other datasets (the Indo-European Language dataset and the Shakespearean Era Text dataset). Using social media metrics on the world’s ‘top check-in locations’ Facebook pages dataset, the statistical analysis reveals coherent dynamical patterns. In the largest cluster, the categories ‘Gym’, ‘Fitness Center’, and ‘Sports and Recreation’ appear closely linked together in the RNG. Taken together, our study validates our expectation that RNGs can provide a “parameterfree” mathematical formalization of proximity. Our approach gives useful insights on user behaviour in social media page-level metrics as well as other applications

Clustering nodes in large-scale biological networks using external memory algorithms

Novel analytical techniques have dramatically enhanced our understanding of many application domains including biological networks inferred from gene expression studies. However, there are clear computational challenges associated to the large datasets generated from these studies. The algorithmic solution of some NP-hard combinatorial optimization problems that naturally arise on the analysis of large networks is difficult without specialized computer facilities (i.e. supercomputers). In this work, we address the data clustering problem of large-scale biological networks with a polynomial-time algorithm that uses reasonable computing resources and is limited by the available memory. We have adapted and improved the MSTkNN graph partitioning algorithm and redesigned it to take advantage of external memory (EM) algorithms. We evaluate the scalability and performance of our proposed algorithm on a well-known breast cancer microarray study and its associated dataset. © 2011 Springer-Verlag

Identifying communities of trust and confidence in the charity and not-for-profit sector: A memetic algorithm approach

© 2014 IEEE. In this study we analyse complete networks derived from field survey and market research through proposing an efficient methodology based on proximity graphs and clustering techniques enhanced with a new community detection algorithm. The specific context is the charity and Not-For-Profit sector in Australia and consumer behaviours within this context. To investigate the performance of this methodology we conduct experiments on the network extracted from a dataset that contains responses of 1,550 individual Australians to 43 questions in a quantitative survey conducted on behalf of the Australian Charities and Not-for-Profits Commission to study the public trust and confidence in Australian charities. Here, we generate the distance matrix by computing the Spearman correlation coefficient as a similarity metric among individuals. Then, several types of k-Nearest Neighbour (kNN) graphs were calculated from the distance matrix and the new community detection algorithm detected groups of consumers by optimizing a quality function called 'modularity'. Comparison of obtained results with the results of the BGLL algorithm, a heuristic given by the publicly available package Gephi and the MST-kNN algorithm, a graph-based approach to compute clusters that has several applications in bioinformatics and finance, reveals that our methodology is effective in partitioning of complete graphs and detecting communities. The combined results indicate that behavioural models that investigate trust in charities may need to be aware of intrinsic differences among subgroups as revealed by our analysis

Post COVID Mucormycosis Prevention With Oro-Nasal Application of Povidone Iodine (Pvp-I)

In March 2020 WHO declared COVID-19 as pandemic while April 2021 Indian authority concede to declare a significantly increased incidence of “Black Fungus” among the COVID-19 patients. The primary route of infection for Mucormycosis or fungal infection following COVID-19 is inhalation or ingestion resulting accumulation of the agents in nose, para-nasal sinuses and mouth. The spores of black fungus intrude neural and vascular structure, which cause mucosal necrosis due to thrombosis in vasculature. The extension of the disease then increases through further destruction of bones of para-nasal sinus as well as neural and vascular route dissemination. We thought about removing, neutralizing or destroying the culprit fungi from its route of entry zone, i.e. nose and mouth. Povidone Iodine (PVP-I) is a microbicidal agent having effective fungicidal as well as virucidal and bactericidal property. PVP-I can be used in both oral and nasal cavity safely. Efficacy and safety of PVP-I is proved in nose in case of COVID-19. PVP-I is proved effective against different fungi at different concentration at different site. So, we recommend Povidone Iodine nasal spray or irrigation and mouthwash for gargling for these vulnerable group of patients in large scale to prevent post-COVID mucormycosis or fungal infection. J Dhaka Med Coll. 2021; 30(2) : 176-179</jats:p