Men's passage to fatherhood: an analysis of the contemporary relevance of transition theory

This paper presents a theoretical analysis of men's experiences of pregnancy, birth and early fatherhood. It does so using a framework of ritual transition theory and argues that despite its earlier structural-functionalist roots, transition theory remains a valuable framework, illuminating contemporary transitions across the life course. The paper discusses the historical development of transition or ritual theory and, drawing upon data generated during longitudinal ethnographic interviews with men undergoing the transition to fatherhood, analyses its relevance in understanding contemporary experiences of fatherhood

Pharmaceutical electrochemistry: the electrochemical detection of aspirin utilising screen printed graphene electrodes as sensors platforms.

A sensitive electrochemical sensor was designed for acetyl salicylic acid detection using graphene modified Screen Printed Electrodes. The electrochemical response of the sensor with graphene was improved compared to Screen Printed Electrodes without graphene and displayed an excellent analytical performance for the detection of acetyl salicylic acid. The high acetyl salicylic acid loading capacity on the electrode surface and the outstanding electric conductivity of graphene were also discussed in this manuscript. When a range of different concentrations of acetyl salicylic acid from 0.1 to 100 μM into a pH 4 buffer solution (N defined as the sample size N = 9) were plotted against the oxidation peak a linear response was observed. The detection limit was found to be 0.09 μM based on (3-σ/slope). Screen Printed Graphene electrodes sensors methodology is shown to be useful for quantifying low levels of acetyl salicylic acid in a buffer solution as well as in biological matrixes such as human oral fluid. A linear response was obtained over a range of concentrations from 10 to 150 μM into a human oral fluid solution (N = 10) giving a detection limit of 8.7 μM

Characterization of microRNAs Identified in a Table Grapevine Cultivar with Validation of Computationally Predicted Grapevine miRNAs by miR-RACE

BACKGROUND: Alignment analysis of the Vv-miRNAs identified from various grapevine cultivars indicates that over 30% orthologous Vv-miRNAs exhibit a 1-3 nucleotide discrepancy only at their ends, suggesting that this sequence discrepancy is not a random event, but might mainly derive from divergence of cultivars. With advantages of miR-RACE technology in determining precise sequences of potential miRNAs from bioinformatics prediction, the precise sequences of vv-miRNAs predicted computationally can be verified with miR-RACE in a different grapevine cultivar. This presents itself as a new approach for large scale discovery of precise miRNAs in different grapevine varieties. METHODOLOGY/PRINCIPAL FINDINGS: Among 88 unique sequences of Vv-miRNAs from bioinformatics prediction, 83 (96.3%) were successfully validated with MiR-RACE in grapevine cv. 'Summer Black'. All the validated sequences were identical to their corresponding ones obtained from deep sequencing of the small RNA library of 'Summer Black'. Quantitative RT-PCR analysis of the expressions levels of 10 Vv-miRNA/target gene pairs in grapevine tissues showed some negative correlation trends. Finally, comparison of Vv-miRNA sequences with their orthologs in Arabidopsis and study on the influence of divergent bases of the orthologous miRNAs on their targeting patterns in grapevine were also done. CONCLUSION: The validation of precise sequences of potential Vv-miRNAs from computational prediction in a different grapevine cultivar can be a new way to identify the orthologous Vv-miRNAs. Nucleotide discrepancy of orthologous Vv-miRNAs from different grapevine cultivars normally does not change their target genes. However, sequence variations of some orthologous miRNAs in grapevine and Arabidopsis can change their targeting patterns. These precise Vv-miRNAs sequences validated in our study could benefit some further study on grapevine functional genomics

Using graph theory to analyze biological networks

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care