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.

Input: Consider Input: AIMS The present study aims to investigate the interaction between nitric oxide (NO) and hydrogen sulfide (H(2)S), the two important gaseous mediators in rat hearts.
METHODS AND RESULTS Intracellular calcium in isolated cardiomyocytes was measured with a spectrofluorometric method using Fura-2. Myocyte contractility was measured with a video edge system. NaHS (50 M, an H(2)S donor) had no significant effect on the resting calcium level, electrically induced (EI) calcium transients, and cell contractility in ventricular myocytes. Stimulating endogenous NO production with l-arginine or exogenous application of NO donors [sodium nitroprusside (SNP) and 2-(N,N-diethylamino)-diazenolate-2-oxide] decreased myocyte twitch amplitudes accompanied by slower velocities of both cell contraction and relaxation. Surprisingly, NaHS reversed the negative inotropic and lusitropic effects of the above three NO-increasing agents. In addition, the mixture of SNP + NaHS increased, whereas SNP alone decreased, the resting calcium level and the amplitudes of EI calcium transients. Angeli's salt, a nitroxyl anion (HNO) donor, mimicked the effect of SNP + NaHS on calcium handling and myocyte contractility. Three thiols, N-acetyl-cysteine, l-cysteine, and glutathione, abolished the effects of HNO and SNP + NaHS on myocyte contraction. Neither Rp-cAMP [a protein kinase A (PKA) inhibitor] nor Rp-cGMP [a protein kinase G (PKG) inhibitor] affected the effects of SNP + NaHS, suggesting a cAMP/PKA- or cGMP/PKG-independent mechanism.
CONCLUSION H(2)S may interact with NO to form a thiol sensitive molecule (probably HNO) which produces positive inotropic and lusitropic effects. Our findings may shed light on the interaction of NO and H(2)S and provide new clues to treat cardiovascular diseases.

Output: Hydrogen sulfide interacts with nitric oxide in the heart: possible involvement of nitroxyl.

Input: Consider Input: The PANTHER (protein annotation through evolutionary relationship) classification system (http://www.pantherdb.org/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments. The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs). Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways. The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests. It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. In the 2013 release of PANTHER (v.8.0), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability. This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system.

Output: Large-scale gene function analysis with the PANTHER classification system

Input: Consider Input: The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
Output: Oncogenic pathway signatures in human cancers as a guide to targeted therapies