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.

[Q]: 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/).
[A]: Prediction of guide strand of microRNAs from its sequence and secondary structure

[Q]: CD4(+)CD25(+) regulatory T cells (T regs) play a major role in the maintenance of self-tolerance and immune suppression, although the mechanisms controlling T reg development and suppressor function remain incompletely understood. Herein, we provide evidence that Kruppel-like factor 10 (KLF10/TIEG1) constitutes an important regulator of T regulatory cell suppressor function and CD4(+)CD25(-) T cell activation through distinct mechanisms involving transforming growth factor (TGF)-beta1 and Foxp3. KLF10 overexpressing CD4(+)CD25(-) T cells induced both TGF-beta1 and Foxp3 expression, an effect associated with reduced T-Bet (Th1 marker) and Gata3 (Th2 marker) mRNA expression. Consistently, KLF10(-/-) CD4(+)CD25(-) T cells have enhanced differentiation along both Th1 and Th2 pathways and elaborate higher levels of Th1 and Th2 cytokines. Furthermore, KLF10(-/-) CD4(+)CD25(-) T cell effectors cannot be appropriately suppressed by wild-type T regs. Surprisingly, KLF10(-/-) T reg cells have reduced suppressor function, independent of Foxp3 expression, with decreased expression and elaboration of TGF-beta1, an effect completely rescued by exogenous treatment with TGF-beta1. Mechanistic studies demonstrate that in response to TGF-beta1, KLF10 can transactivate both TGF-beta1 and Foxp3 promoters, implicating KLF10 in a positive feedback loop that may promote cell-intrinsic control of T cell activation. Finally, KLF10(-/-) CD4(+)CD25(-) T cells promoted atherosclerosis by approximately 2-fold in ApoE(-/-)/scid/scid mice with increased leukocyte accumulation and peripheral pro-inflammatory cytokines. Thus, KLF10 is a critical regulator in the transcriptional network controlling TGF-beta1 in both CD4(+)CD25(-) T cells and T regs and plays an important role in regulating atherosclerotic lesion formation in mice.
[A]: Kruppel-like factor KLF10 targets transforming growth factor-beta1 to regulate CD4(+)CD25(-) T cells and T regulatory cells.

[Q]: Metastasis is a multistep process responsible for most cancer deaths, and it can be influenced by both the immediate microenvironment (cellcell or cellmatrix interactions) and the extended tumour microenvironment (for example vascularization). Hypoxia (low oxygen) is clinically associated with metastasis and poor patient outcome, although the underlying processes remain unclear. Microarray studies have shown the expression of lysyl oxidase (LOX) to be elevated in hypoxic human tumour cells. Paradoxically, LOX expression is associated with both tumour suppression and tumour progression, and its role in tumorigenesis seems dependent on cellular location, cell type and transformation status. Here we show that LOX expression is regulated by hypoxia-inducible factor (HIF) and is associated with hypoxia in human breast and head and neck tumours. Patients with high LOX-expressing tumours have poor distant metastasis-free and overall survivals. Inhibition of LOX eliminates metastasis in mice with orthotopically grown breast cancer tumours. Mechanistically, secreted LOX is responsible for the invasive properties of hypoxic human cancer cells through focal adhesion kinase activity and cell to matrix adhesion. Furthermore, LOX may be required to create a niche permissive for metastatic growth. Our findings indicate that LOX is essential for hypoxia-induced metastasis and is a good therapeutic target for preventing and treating metastases.
[A]:
Lysyl oxidase is essential for hypoxia-induced metastasis