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

An Empirical Analysis of Facebook’s Free Basics Program

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Study on the correlation of prevalence of ocular disease to psoriasis and other concomitant diseases in patients with psoriasis in Saudi Arabia

Objective: Ocular diseases are chronic type associated with multiple factors. This research investigates correlation of prevalence of ocular disease to different demographic variables, medical history and psoriasis as well as other concomitant diseases in patients with psoriasis.Design: A survey-based study was conducted.Setting: The research was conducted among patients with ocular diseases in a hospital of Saudi Arabia.Participants: The study was approached to be conducted among 120 patients with psoriasis and age≥ 18 years. 101 patients admitted to participate.Intervention: Questionnaire was prepared to get information from patients. Observations of Ophthalmologists and dermatologists were recorded. Statistical package for social sciences (SPSS version 22) was used for data entry and analysis. Odd ratios and Chi-square test were used to analyze the correlations.Results: Our study has found significant correlation of prevalence of diabetes mellitus and ocular complication and significant correlation of prevalence of diabetes mellitus and cataract. We also observed significant correlation between the prevalence of hypoparathyroidism and ocular complication and significant correlation between the prevalence of having hypoparathyroidism and cataract. Having dyslipidemia and ocular complication was found to be significantly correlated. Our study found association between presence of cataract and topical steroid use. Our findings have focused on the treatment modalities commonly used in Saudi Arabia. Conclusion: Our research findings indicate that proper management of the correlated diseases can prevent the occurrence and severity of ocular diseases

Study on the correlation of prevalence of ocular disease to different demographic variables, medical history and concomitant diseases.

Random blood sugar, cholesterol, and triglycerides were analyzed by taking the blood sample from each patient. The average of the parameters was recorded, and the standard deviation (SD) was calculated. The parameters have been presented in the study as mean± SD. Blood sample analysis revealed that the average random blood glucose level (mg/dl) was 113.3±26.9 (Mean ±SD). The average cholesterol level was 189.35± 49.7 mg/dl, and the average triglycerides level was 124.25±50.0 mg/dl. Average systolic and diastolic blood pressure was found to be 126.0 ± 15.5 mmHg and 81.43 ± 8.0 mmHg, respectively. The patients were examined by the dermatologist. The dermatologists observed the signs, and the patients were asked about the presence of the symptoms to evaluate the severity of psoriasis and psoriatic arthropathy. The patients were asked about the duration for which they had been suffering from psoriasis. On average, a patient was found to be suffering from psoriasis for 11.4 ±9.9 years (the duration ranged from a minimum of 1 year to a maximum of 45 years). The doctor screened the patients for the number of sites involved and evaluated the severity of psoriasis via psoriasis area and severity index (PASI) score. Average Psoriasis Area and Severity Index (PASI) for the patients, as recorded by the dermatologists, was found to be 5.94 ± 6.8. The patients who have psoriasis were found to be under different treatment modalities. Figure 1 shows that the most used treatment approach was Methotrexate (MTX) (31%) and Topical formulations (31%). T he next one is Humira prescribed to 16% of patients. Cyclosporine were found to be recommended to D ijobet, Netigasone, 8%, 6%, and 4% of patients, respectively. Each of Enbrel, cream, and ointment was prescribed for 2% of patients separately

'Neuroinflammation' differs categorically from inflammation: transcriptomes of Alzheimer's disease, Parkinson's disease, schizophrenia and inflammatory diseases compared

'Neuroinflammation' has become a widely applied term in the basic and clinical neurosciences but there is no generally accepted neuropathological tissue correlate. Inflammation, which is characterized by the presence of perivascular infiltrates of cells of the adaptive immune system, is indeed seen in the central nervous system (CNS) under certain conditions. Authors who refer to microglial activation as neuroinflammation confuse this issue because autoimmune neuroinflammation serves as a synonym for multiple sclerosis, the prototypical inflammatory disease of the CNS. We have asked the question whether a data-driven, unbiased in silico approach may help to clarify the nomenclatorial confusion. Specifically, we have examined whether unsupervised analysis of microarray data obtained from human cerebral cortex of Alzheimer's, Parkinson's and schizophrenia patients would reveal a degree of relatedness between these diseases and recognized inflammatory conditions including multiple sclerosis. Our results using two different data analysis methods provide strong evidence against this hypothesis demonstrating that very different sets of genes are involved. Consequently, the designations inflammation and neuroinflammation are not interchangeable. They represent different categories not only at the histophenotypic but also at the transcriptomic level. Therefore, non-autoimmune neuroinflammation remains a term in need of definition

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

Accelerated vascular aging in chronic kidney disease: the potential for novel therapies

The pathophysiology of vascular disease is linked to accelerated biological aging and a combination of genetic, lifestyle, biological, and environmental risk factors. Within the scenario of uncontrolled artery wall aging processes, CKD (chronic kidney disease) stands out as a valid model for detailed structural, functional, and molecular studies of this process. The cardiorenal syndrome relates to the detrimental bidirectional interplay between the kidney and the cardiovascular system. In addition to established risk factors, this group of patients is subjected to a plethora of other emerging vascular risk factors, such as inflammation, oxidative stress, mitochondrial dysfunction, vitamin K deficiency, cellular senescence, somatic mutations, epigenetic modifications, and increased apoptosis. A better understanding of the molecular mechanisms through which the uremic milieu triggers and maintains early vascular aging processes, has provided important new clues on inflammatory pathways and emerging risk factors alike, and to the altered behavior of cells in the arterial wall. Advances in the understanding of the biology of uremic early vascular aging opens avenues to novel pharmacological and nutritional therapeutic interventions. Such strategies hold promise to improve future prevention and treatment of early vascular aging not only in CKD but also in the elderly general population