In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.

The actin cortex both facilitates and hinders the exocytosis of secretory granules. How cells consolidate these two opposing roles was not well understood. Here we show that antigen activation of mast cells induces oscillations in Ca(2+) and PtdIns(4,5)P(2) lipid levels that in turn drive cyclic recruitment of N-WASP and cortical actin level oscillations. Experimental and computational analysis argues that vesicle fusion correlates with the observed actin and Ca(2+) level oscillations. A vesicle secretion cycle starts with the capture of vesicles by actin when cortical F-actin levels are high, followed by vesicle passage through the cortex when F-actin levels are low, and vesicle fusion with the plasma membrane when Ca(2+) levels subsequently increase. Thus, cells employ oscillating levels of Ca(2+), PtdIns(4,5)P(2) and cortical F-actin to increase secretion efficiency, explaining how the actin cortex can function as a carrier as well as barrier for vesicle secretion.
Coordinated oscillations in cortical actin and Ca2+ correlate with cycles of vesicle secretion

BACKGROUND MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways.
RESULTS This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets.
CONCLUSION In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study (http://crdd.osdd.net:8081/RISCbinder/).
Prediction of guide strand of microRNAs from its sequence and secondary structure

Objective To determine whether risk factors in childhood and early adulthood affect later mammographic breast density. Methods: Subjects were 628 women who attended a medical examination at the University of Glasgow Student Health Service (19481968), responded to a questionnaire (2001) and had a screening mammogram in Scotland (19892002). Mammograms (median age of 59years) were classified using a six category classification (SCC) of breast density percent. Logistic regression was used to determine associations between risk factors and having a high-risk mammogram (25 dense). Results: In multi-variable analyses, high-risk mammograms were associated with parity (adjusted odds ratio (OR) per child: 0.77 (95 confidence interval (CI) 0.610.99)), age at first birth, OR per year: 1.05 (0.991.11), smoking at university, OR smokers versus non-smokers: 0.58 (0.360.92) and body mass index (BMI) while at university, OR per 1kg/m20.75 (0.690.82). No associations with SCC were found for age at menarche, birth weight, oral contraceptive (OC) use, height, leg length or exercise at age 20. Conclusions: We confirm previous findings that breast density is affected by reproductive events and some anthropometric measures, however most of the risk factors acting throughout the life course which we examined were not closely related to adult breast density.
Life course breast cancer risk factors and adult breast density (United Kingdom)