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- Ferrari, A. C., et al.
(author)
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Science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems
- 2015
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In: Nanoscale. - : Royal Society of Chemistry (RSC). - 2040-3372 .- 2040-3364. ; 7:11, s. 4598-4810
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Journal article (peer-reviewed)abstract
- We present the science and technology roadmap for graphene, related two-dimensional crystals, and hybrid systems, targeting an evolution in technology, that might lead to impacts and benefits reaching into most areas of society. This roadmap was developed within the framework of the European Graphene Flagship and outlines the main targets and research areas as best understood at the start of this ambitious project. We provide an overview of the key aspects of graphene and related materials (GRMs), ranging from fundamental research challenges to a variety of applications in a large number of sectors, highlighting the steps necessary to take GRMs from a state of raw potential to a point where they might revolutionize multiple industries. We also define an extensive list of acronyms in an effort to standardize the nomenclature in this emerging field.
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- Jolma, A, et al.
(author)
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Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities
- 2010
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In: Genome research. - : Cold Spring Harbor Laboratory. - 1549-5469 .- 1088-9051. ; 20:6, s. 861-873
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Journal article (peer-reviewed)abstract
- The genetic code—the binding specificity of all transfer-RNAs—defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the ∼1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.
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| 4. |
- Kivioja, R., et al.
(author)
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Risk factors for early-onset ischemic stroke: A case-control study
- 2018
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In: Journal of the American Heart Association. - 2047-9980. ; 7:21
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Journal article (peer-reviewed)abstract
- Background-Recent studies have shown an increasing prevalence of vascular risk factors in young adults with ischemic stroke (IS). However, the strength of the association between all vascular risk factors and early-onset IS has not been fully established. Methods and Results-We compared 961 patients with a first-ever IS at 25 to 49 years to 1403 frequency-matched stroke-free controls from a population-based cohort study (FINRISK). Assessed risk factors included an active malignancy, atrial fibrillation, cardiovascular disease, current smoking status, a family history of stroke, high low-density lipoprotein cholesterol, high triglycerides, low high-density lipoprotein cholesterol, hypertension, and type 1 and type 2 diabetes mellitus. We performed subgroup analyses based on age, sex, and IS etiology. In a fully adjusted multivariable logistic regression analysis, significant risk factors for IS consisted of atrial fibrillation (odds ratio [OR], 10.43; 95% confidence interval [CI], 2.33–46.77], cardiovascular disease (OR, 8.01; 95% CI, 3.09–20.78), type 1 diabetes mellitus (OR, 6.72; 95% CI, 3.15–14.33), type 2 diabetes mellitus (OR, 2.31; 95% CI, 1.35–3.95), low high-density lipoprotein cholesterol (OR, 1.81; 95% CI, 1.37–2.40), current smoking status (OR, 1.81; 95% CI, 1.50–2.17), hypertension (OR, 1.43; 95% CI, 1.17–1.75), and a family history of stroke (OR, 1.37; 95% CI, 1.04–1.82). High low-density lipoprotein cholesterol exhibited an inverse association with IS. In the subgroup analyses, the most consistent associations appeared for current smoking status and type 1 diabetes mellitus. Conclusions-Our study establishes the associations between 11 vascular risk factors and early-onset IS, among which atrial fibrillation, cardiovascular disease, and both type 1 and 2 diabetes mellitus in particular showed strong associations. © 2018 The Authors.
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| 9. |
- Palin, K, et al.
(author)
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Contribution of allelic imbalance to colorectal cancer
- 2018
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 3664-
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Journal article (peer-reviewed)abstract
- Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole-genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge. Unbiased CRISPR-Cas9 knockout and activation screens identified in total 79 genes within AI peaks regulating cell growth. Genetic and functional data implicate loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers.
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