In this task, you are given an abstract of article. Your task is to generate label "True" if abstract is structured, otherwise generate "False". A structured abstract is composed of a topic sentence (or key sentence), relevant supporting sentences, and a closing (or transition) sentence. This structure is key to keeping your abstract focused on the main idea and creating a clear and concise image.

There is a growing interest in the cell-cell communication roles in cancer mediated by secreted vesicles termed exosomes. In this study, we examined whether exosomes produced by cancer cells could transmit information to normal stromal fibroblasts and trigger a cellular response. We found that some cancer-derived exosomes could trigger elevated -smooth muscle actin expression and other changes consistent with the process of fibroblast differentiation into myofibroblasts. We show that TGF- is expressed at the exosome surface in association with the transmembrane proteoglycan betaglycan. Although existing in a latent state, this complex was fully functional in eliciting SMAD-dependent signaling. Inhibiting either signaling or betaglycan expression attenuated differentiation. While the kinetics and overall magnitude of the response were similar to that achieved with soluble TGF-, we identified important qualitative differences unique to the exosomal route of TGF- delivery, as exemplified by a significant elevation in fibroblast FGF2 production. This hitherto unknown trigger for instigating cellular differentiation in a distinctive manner has major implications for mechanisms underlying cancer-recruited stroma, fibrotic diseases, and wound-healing responses.
False

Expression quantitative trait loci (eQTL) mapping aims to determine genomic regions that regulate gene transcription. Expression QTL is used to study the regulatory structure of normal tissues and to search for genetic factors in complex diseases such as cancer, diabetes, and cystic fibrosis. A modern eQTL dataset contains millions of SNPs and thousands of transcripts measured for hundreds of samples. This makes the analysis computationally complex as it involves independent testing for association for every transcript-SNP pair. The heavy computational burden makes eQTL analysis less popular, often forces analysts to restrict their attention to just a subset of transcripts and SNPs. As larger genotype and gene expression datasets become available, the demand for fast tools for eQTL analysis increases. We present a new method for fast eQTL analysis via linear models, called Matrix eQTL. Matrix eQTL can model and test for association using both linear regression and ANOVA models. The models can include covariates to account for such factors as population structure, gender, and clinical variables. It also supports testing of heteroscedastic models and models with correlated errors. In our experiment on large datasets Matrix eQTL was thousands of times faster than the existing popular software for QTL/eQTL analysis. Matrix eQTL is implemented as both Matlab and R packages and thus can easily be run on Windows, Mac OS, and Linux systems. The software is freely available at the following address: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL
False

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/).
True