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.

Example Input: BACKGROUND Late April 2009, human infection with variant influenza virus A(H1N1)v emerged in the Northern Americas posing a threat that this virus may become the next pandemic influenza virus.
OBJECTIVES To prepare laboratories for surge capacity for molecular diagnosis of patients suspected for A(H1N1)v infection in the Netherlands. STUDY DESIGN A panel of 10 blinded specimens containing seasonal A(H1N1) or A(H3N2), or A/Netherlands/602/2009(H1N1)v influenza virus, or negative control was distributed to the outbreak assistance laboratories (OAL) together with influenza virus A (M-gene), swine influenza virus A (NP-gene) and influenza virus A(H1N1)v (H1v-gene) specific primers and probes and protocol (CDC Atlanta, USA). Laboratories were asked to implement and test this protocol.
RESULTS All OAL were able to detect A(H1N1)v using the CDC M-gene reagents, the majority with similar sensitivity as the in-house M-gene based assays. RT-PCRs used in routine diagnostic setting in the OAL specifically designed to detect H1, H3, or NS1 from seasonal influenza A viruses, did not or at very low level cross-react with A(H1N1)v. The CDC swine NP-gene and H1v-gene RT-PCRs showed somewhat reduced sensitivity compared to the CDC and in-house M-gene RT-PCRs. In contrast, in-house developed A(H1N1)v specific H1v-gene and N1v-gene RT-PCRs showed equal sensitivity to CDC and in-house M-gene RT-PCRs.
CONCLUSIONS The Dutch OAL are prepared for detection and specific identification of A(H1N1)v, although some level of cross-reactivity was observed with seasonal influenza viruses. Additionally, M-gene based generic influenza A virus detection is recommended to be able to detect emerging influenza A viruses in routine settings.
Example Output: Preparing the outbreak assistance laboratory network in the Netherlands for the detection of the influenza virus A(H1N1) variant.

Example Input: Glioblastoma multiforme (GBM) is a lethal brain tumour in adults and children. However, DNA copy number and gene expression signatures indicate differences between adult and paediatric cases. To explore the genetic events underlying this distinction, we sequenced the exomes of 48 paediatric GBM samples. Somatic mutations in the H3.3-ATRX-DAXX chromatin remodelling pathway were identified in 44% of tumours (21/48). Recurrent mutations in H3F3A, which encodes the replication-independent histone 3 variant H3.3, were observed in 31% of tumours, and led to amino acid substitutions at two critical positions within the histone tail (K27M, G34R/G34V) involved in key regulatory post-translational modifications. Mutations in ATRX (-thalassaemia/mental retardation syndrome X-linked) and DAXX (death-domain associated protein), encoding two subunits of a chromatin remodelling complex required for H3.3 incorporation at pericentric heterochromatin and telomeres, were identified in 31% of samples overall, and in 100% of tumours harbouring a G34R or G34V H3.3 mutation. Somatic TP53 mutations were identified in 54% of all cases, and in 86% of samples with H3F3A and/or ATRX mutations. Screening of a large cohort of gliomas of various grades and histologies (n = 784) showed H3F3A mutations to be specific to GBM and highly prevalent in children and young adults. Furthermore, the presence of H3F3A/ATRX-DAXX/TP53 mutations was strongly associated with alternative lengthening of telomeres and specific gene expression profiles. This is, to our knowledge, the first report to highlight recurrent mutations in a regulatory histone in humans, and our data suggest that defects of the chromatin architecture underlie paediatric and young adult GBM pathogenesis.
Example Output: Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma

Example Input: Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure-activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.
Example Output:
Extended-connectivity fingerprints.