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]: 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.
[A]: Large-scale gene function analysis with the PANTHER classification system

[Q]: RATIONALE Fibrosis is mediated partly by extracellular matrix-depositing fibroblasts in the heart. Although these mesenchymal cells are reported to have multiple embryonic origins, the functional consequence of this heterogeneity is unknown.
OBJECTIVE We sought to validate a panel of surface markers to prospectively identify cardiac fibroblasts. We elucidated the developmental origins of cardiac fibroblasts and characterized their corresponding phenotypes. We also determined proliferation rates of each developmental subset of fibroblasts after pressure overload injury.
METHODS AND RESULTS We showed that Thy1(+)CD45(-)CD31(-)CD11b(-)Ter119(-) cells constitute the majority of cardiac fibroblasts. We characterized these cells using flow cytometry, epifluorescence and confocal microscopy, and transcriptional profiling (using reverse transcription polymerase chain reaction and RNA-seq). We used lineage tracing, transplantation studies, and parabiosis to show that most adult cardiac fibroblasts derive from the epicardium, a minority arises from endothelial cells, and a small fraction from Pax3-expressing cells. We did not detect generation of cardiac fibroblasts by bone marrow or circulating cells. Interestingly, proliferation rates of fibroblast subsets on injury were identical, and the relative abundance of each lineage remained the same after injury. The anatomic distribution of fibroblast lineages also remained unchanged after pressure overload. Furthermore, RNA-seq analysis demonstrated that Tie2-derived and Tbx18-derived fibroblasts within each operation group exhibit similar gene expression profiles.
CONCLUSIONS The cellular expansion of cardiac fibroblasts after transaortic constriction surgery was not restricted to any single developmental subset. The parallel proliferation and activation of a heterogeneous population of fibroblasts on pressure overload could suggest that common signaling mechanisms stimulate their pathological response.
[A]: Developmental heterogeneity of cardiac fibroblasts does not predict pathological proliferation and activation.

[Q]: BACKGROUND There is no doubt that good bimanual performance is very important for skilled handball playing. The control of the non-dominant hand is especially demanding since efficient catching and throwing needs both hands.
METHODOLOGY/HYPOTHESES We investigated training-induced structural neuroplasticity in professional handball players using several structural neuroimaging techniques and analytic approaches and also provide a review of the literature about sport-induced structural neuroplastic alterations. Structural brain adaptations were expected in regions relevant for motor and somatosensory processing such as the grey matter (GM) of the primary/secondary motor (MI/supplementary motor area, SMA) and somatosensory cortex (SI/SII), basal ganglia, thalamus, and cerebellum and in the white matter (WM) of the corticospinal tract (CST) and corpus callosum, stronger in brain regions controlling the non-dominant left hand.
RESULTS Increased GM volume in handball players compared with control subjects were found in the right MI/SI, bilateral SMA/cingulate motor area, and left intraparietal sulcus. Fractional anisotropy (FA) and axial diffusivity were increased within the right CST in handball players compared with control women. Age of handball training commencement correlated inversely with GM volume in the right and left MI/SI and years of handball training experience correlated inversely with radial diffusivity in the right CST. Subcortical structures tended to be larger in handball players. The anatomical measures of the brain regions associated with handball playing were positively correlated in handball players, but not interrelated in control women.
DISCUSSION/CONCLUSION Training-induced structural alterations were found in the somatosensory-motor network of handball players, more pronounced in the right hemisphere controlling the non-dominant left hand. Correlations between handball training-related measures and anatomical differences suggest neuroplastic adaptations rather than a genetic predisposition for a ball playing affinity. Investigations of neuroplasticity specifically in sportsmen might help to understand the neural mechanisms of expertise in general.
[A]:
Structural Brain Correlates Associated with Professional Handball Playing