Feature Diminution by Ant Colonized Relative Reduct Algorithm for improving the Success Rate for IVF Treatment

Infertility is the most common problem faced by today’s generation. The factors like environment, genetic or personal characteristics are responsible for these problems. Different infertility treatments like IVF, IUI etc are used to treat those infertile people. But the cost and emotions beyond each and every cycle of IVF treatment is very high and also the success rate differs from person to person. So, there is a need to find a system which would predict the outcome of IVF to motivate the people both in psychologically and financially. Many Data Mining techniques are applied to predict the outcome of the IVF treatment. Reducing the unwanted features which affects the quality of result is one of the significant tasks in Data Mining. This paper proposes a hybrid algorithm named Ant Colonized Relative Reduct Algorithm (ACRRA) which combines the core features of Ant Colony Optimization Algorithm and Relative Reduct Theory for Feature Reduction. In this work, the proposed Algorithm is compared with the existing related algorithms. It is evident from the results that the proposed algorithm achieved its target of reducing the features to minimum numbers without compromising the core knowledge of the system to estimate the success rate

CRUDE OIL FOULING: PETRONAS REFINERIES EXPERIENCE

Managing crude oil fouling has been a challenge in PETRONAS refineries. Over the past several years, numerous initiatives have been conducted in order to have better control of the situation [1][2][3][4]. The control strategy currently implemented is to periodically clean the heat exchanger based on the heat exchanger monitoring parameter and supported by antifouling chemical injection program. A review was conducted in one of PETRONAS’ refineries in order to come up with a better fouling control strategy. The review is separated into two parts. Firstly, the plant operational data for two selected hot pre-heat exchanger trains was analyzed. From the analysis, deposition build-up is apparent for only one exchanger train despite both trains receiving the same crude blend. It seems that there are several significant parameters which caused one of the exchanger to fall into fouling threshold region. EXPRESS [5] software was used to evaluate the fouling threshold region for the heat exchanger. Secondly, foulant sample obtained from the heat exchangers were subjected to analytical testing to investigate the constituent of the foulant. The analyses show that it is mainly organic in nature with a minor portion of the inorganic content made up of mostly corrosion products, salt and sand

Cardiosphere-derived cells suppress allogeneic lymphocytes by production of PGE2 acting via the EP4 receptor

derived cells (CDCs) are a cardiac progenitor cell population, which have been shown to possess cardiac regenerative properties and can improve heart function in a variety of cardiac diseases. Studies in large animal models have predominantly focussed on using autologous cells for safety, however allogeneic cell banks would allow for a practical, cost-effective and efficient use in a clinical setting. The aim of this work was to determine the immunomodulatory status of these cells using CDCs and lymphocytes from 5 dogs. CDCs expressed MHC I but not MHC II molecules and in mixed lymphocyte reactions demonstrated a lack of lymphocyte proliferation in response to MHC-mismatched CDCs. Furthermore, MHC-mismatched CDCs suppressed lymphocyte proliferation and activation in response to Concanavalin A. Transwell experiments demonstrated that this was predominantly due to direct cell-cell contact in addition to soluble mediators whereby CDCs produced high levels of PGE2 under inflammatory conditions. This led to down-regulation of CD25 expression on lymphocytes via the EP4 receptor. Blocking prostaglandin synthesis restored both, proliferation and activation (measured via CD25 expression) of stimulated lymphocytes. We demonstrated for the first time in a large animal model that CDCs inhibit proliferation in allo-reactive lymphocytes and have potent immunosuppressive activity mediated via PGE2

Computational Integration of Homolog and Pathway Gene Module Expression Reveals General Stemness Signatures

The stemness hypothesis states that all stem cells use common mechanisms to regulate self-renewal and multi-lineage potential. However, gene expression meta-analyses at the single gene level have failed to identify a significant number of genes selectively expressed by a broad range of stem cell types. We hypothesized that stemness may be regulated by modules of homologs. While the expression of any single gene within a module may vary from one stem cell type to the next, it is possible that the expression of the module as a whole is required so that the expression of different, yet functionally-synonymous, homologs is needed in different stem cells. Thus, we developed a computational method to test for stem cell-specific gene expression patterns from a comprehensive collection of 49 murine datasets covering 12 different stem cell types. We identified 40 individual genes and 224 stemness modules with reproducible and specific up-regulation across multiple stem cell types. The stemness modules included families regulating chromatin remodeling, DNA repair, and Wnt signaling. Strikingly, the majority of modules represent evolutionarily related homologs. Moreover, a score based on the discovered modules could accurately distinguish stem cell-like populations from other cell types in both normal and cancer tissues. This scoring system revealed that both mouse and human metastatic populations exhibit higher stemness indices than non-metastatic populations, providing further evidence for a stem cell-driven component underlying the transformation to metastatic disease

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants

<p>Abstract</p> <p>Background</p> <p>Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated by a need for a deeper understanding of organ transplant rejection, we combine the data from five separate studies to compare acute rejection versus stability after solid organ transplantation, and use this data to examine approaches to multiplex meta analysis.</p> <p>Results</p> <p>We demonstrate that a commonly used parametric effect size estimate approach and a commonly used non-parametric method give very different results in prioritizing genes. The parametric method providing a meta effect estimate was superior at ranking genes based on our gold-standard of identifying immune response genes in the transplant rejection datasets.</p> <p>Conclusion</p> <p>Different methods of multiplex analysis can give substantially different results. The method which is best for any given application will likely depend on the particular domain, and it remains for future work to see if any one method is consistently better at identifying important biological signal across gene expression experiments.</p

Comparison study of microarray meta-analysis methods

<p>Abstract</p> <p>Background</p> <p>Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods.</p> <p>Results</p> <p>We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable.</p> <p>Conclusions</p> <p>Existing meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.</p