Thermal-Hydraulic performance in a microchannel heat sink equipped with longitudinal vortex generators (LVGs) and nanofluid

In this study, the numerical conjugate heat transfer and hydraulic performance of nanofluids flow in a rectangular microchannel heat sink (RMCHS) with longitudinal vortex generators (LVGs) was investigated at different Reynolds numbers (200-1200). Three-dimensional simulations are performed on a microchannel heated by a constant temperature with five different configurations with different angles of attack for the LVGs under laminar flow conditions. The study uses five different nanofluid combinations of Al2O3 or CuO, containing low volume fractions in the range of 0.5% to 3.0% with various nanoparticle sizes that are dispersed in pure water, PAO (Polyalphaolefin) or ethylene glycol. The results show that for Reynolds number ranging from 100 to 1100, Al2O3-water has the best performance compared with CuO nanofluid with Nusselt number values between 7.67 and 14.7, with an associated increase in Fanning friction factor by values of 0.0219-0.095. For the case of different base fluids, the results show that CuO-PAO has the best performance with Nusselt number values between 9.57 and 15.88, with an associated increase in Fanning friction factor by 0.022-0.096. The overall performance of all configurations of microchannels equipped with LVGs and nanofluid showed higher values than the ones without LVG and water as a working fluid

Robust Estimators in Generalized Pareto Models

This paper deals with optimally-robust parameter estimation in generalized Pareto distributions (GPDs). These arise naturally in many situations where one is interested in the behavior of extreme events as motivated by the Pickands-Balkema-de Haan extreme value theorem (PBHT). The application we have in mind is calculation of the regulatory capital required by Basel II for a bank to cover operational risk. In this context the tail behavior of the underlying distribution is crucial. This is where extreme value theory enters, suggesting to estimate these high quantiles parameterically using, e.g. GPDs. Robust statistics in this context offers procedures bounding the influence of single observations, so provides reliable inference in the presence of moderate deviations from the distributional model assumptions, respectively from the mechanisms underlying the PBHT.Comment: 26pages, 6 figure

Paradigm of tunable clustering using binarization of consensus partition matrices (Bi-CoPaM) for gene discovery

Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.National Institute for Health Researc

Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass.

Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P = 3.1 x 10-12) BMD locus on Chromosome [email protected] Mbp that replicated in two separate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts. Furthermore, its expression was regulated by a local expression QTL (eQTL), which overlapped the BMD association. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp-/- mice displayed increased osteogenic differentiation. Lhfp-/- mice also had elevated BMD due to increased cortical bone mass. Lastly, we identified SNPs in human LHFP that were associated (P = 1.2 x 10-5) with heel BMD. In conclusion, we used GWAS and systems genetics to identify Lhfp as a regulator of osteoblast activity and bone mass

A novel deletion mutation in the TUSC3 gene in a consanguineous Pakistani family with autosomal recessive nonsyndromic intellectual disability

<p>Abstract</p> <p>Background</p> <p>Intellectual disability (ID) is a serious disorder of the central nervous system with a prevalence of 1-3% in a general population. In the past decades, the research focus has been predominantly on X-linked ID (68 loci and 19 genes for non syndromic X linked ID) while for autosomal recessive nonsyndromic ID (NSID) only 30 loci and 6 genes have been reported to date.</p> <p>Methods</p> <p>Genome-wide homozygosity mapping with 500 K Nsp1 array (Affymetrix), CNV analysis, PCR based breakpoint mapping and DNA sequencing was performed to explore the genetic basis of autosomal recessive nonsyndromic ID in a large Pakistani family.</p> <p>Results</p> <p>Data analysis showed linkage at 8p23 locus with common homozygous region between SNPs rs6989820 and rs2237834, spanning a region of 12.494 Mb. The subsequent CNV analysis of the data revealed a homozygous deletion of 170.673 Kb which encompassed the <it>TUSC3 </it>gene.</p> <p>Conclusion</p> <p>We report a novel deletion mutation in <it>TUSC3 </it>gene which is the second gene after <it>TRAPPC9 </it>in which mutation has been identified in more than one family with autosomal recessive NSID. The study will aid in exploring the molecular pathway of cognition.</p

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

Inhibition of complement C5a prevents breakdown of the blood-brain barrier and pituitary dysfunction in experimental sepsis

INTRODUCTION: Septic encephalopathy secondary to a breakdown of the blood-brain barrier (BBB) is a known complication of sepsis. However, its pathophysiology remains unclear. The present study investigated the effect of complement C5a blockade in preventing BBB damage and pituitary dysfunction during experimental sepsis. METHODS: Using the standardised caecal ligation and puncture (CLP) model, Sprague-Dawley rats were treated with either neutralising anti-C5a antibody or pre-immune immunoglobulin (Ig) G as a placebo. Sham-operated animals served as internal controls. RESULTS: Placebo-treated septic rats showed severe BBB dysfunction within 24 hours, accompanied by a significant upregulation of pituitary C5a receptor and pro-inflammatory cytokine expression, although gene levels of growth hormone were significantly attenuated. The pathophysiological changes in placebo-treated septic rats were restored by administration of neutralising anti-C5a antibody to the normal levels of BBB and pituitary function seen in the sham-operated group. CONCLUSIONS: Collectively, the neutralisation of C5a greatly ameliorated pathophysiological changes associated with septic encephalopathy, implying a further rationale for the concept of pharmacological C5a inhibition in sepsis

The rhesus macaque is three times as diverse but more closely equivalent in damaging coding variation as compared to the human

Abstract Background As a model organism in biomedicine, the rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate. Although a draft genome sequence was completed in 2007, there has been no systematic genome-wide comparison of genetic variation of this species to humans. Comparative analysis of functional and nonfunctional diversity in this highly abundant and adaptable non-human primate could inform its use as a model for human biology, and could reveal how variation in population history and size alters patterns and levels of sequence variation in primates. Results We sequenced the mRNA transcriptome and H3K4me3-marked DNA regions in hippocampus from 14 humans and 14 rhesus macaques. Using equivalent methodology and sampling spaces, we identified 462,802 macaque SNPs, most of which were novel and disproportionately located in the functionally important genomic regions we had targeted in the sequencing. At least one SNP was identified in each of 16,797 annotated macaque genes. Accuracy of macaque SNP identification was conservatively estimated to be >90%. Comparative analyses using SNPs equivalently identified in the two species revealed that rhesus macaque has approximately three times higher SNP density and average nucleotide diversity as compared to the human. Based on this level of diversity, the effective population size of the rhesus macaque is approximately 80,000 which contrasts with an effective population size of less than 10,000 for humans. Across five categories of genomic regions, intergenic regions had the highest SNP density and average nucleotide diversity and CDS (coding sequences) the lowest, in both humans and macaques. Although there are more coding SNPs (cSNPs) per individual in macaques than in humans, the ratio of dN/dS is significantly lower in the macaque. Furthermore, the number of damaging nonsynonymous cSNPs (have damaging effects on protein functions from PolyPhen-2 prediction) in the macaque is more closely equivalent to that of the human. Conclusions This large panel of newly identified macaque SNPs enriched for functionally significant regions considerably expands our knowledge of genetic variation in the rhesus macaque. Comparative analysis reveals that this widespread, highly adaptable species is approximately three times as diverse as the human but more closely equivalent in damaging variation.http://deepblue.lib.umich.edu/bitstream/2027.42/112453/1/12863_2011_Article_1004.pd

Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection

Citation: Udukala, D. N., Wang, H. W., Wendel, S. O., Malalasekera, A. P., Samarakoon, T. N., Yapa, A. S., . . . Bossmann, S. H. (2016). Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection. Beilstein Journal of Nanotechnology, 7, 364-373. doi:10.3762/bjnano.7.33Additional Authors: Ortega, R.;Toledo, Y.;Bossmann, L.;Robinson, C.;Janik, K. E.;Koper, O. B.;Motamedi, M.;Zhu, G. H.Proteases, including matrix metalloproteinases (MMPs), tissue serine proteases, and cathepsins (CTS) exhibit numerous functions in tumor biology. Solid tumors are characterized by changes in protease expression levels by tumor and surrounding tissue. Therefore, monitoring protease levels in tissue samples and liquid biopsies is a vital strategy for early cancer detection. Water-dispersable Fe/Fe3O4-core/shell based nanoplatforms for protease detection are capable of detecting protease activity down to sub-femtomolar limits of detection. They feature one dye (tetrakis(carboxyphenyl) porphyrin (TCPP)) that is tethered to the central nanoparticle by means of a protease-cleavable consensus sequence and a second dye (Cy 5.5) that is directly linked. Based on the protease activities of urokinase plasminogen activator (uPA), MMPs 1, 2, 3, 7, 9, and 13, as well as CTS B and L, human breast cancer can be detected at stage I by means of a simple serum test. By monitoring CTS B and L stage 0 detection may be achieved. This initial study, comprised of 46 breast cancer patients and 20 apparently healthy human subjects, demonstrates the feasibility of protease-activity-based liquid biopsies for early cancer diagnosis