Strain engineering and one-dimensional organization of metal-insulator domains in single-crystal VO2 beams

Spatial phase inhomogeneity at the nano- to microscale is widely observed in strongly-correlated electron materials. The underlying mechanism and possibility of artificially controlling the phase inhomogeneity are still open questions of critical importance for both the phase transition physics and device applications. Lattice strain has been shown to cause the coexistence of metallic and insulating phases in the Mott insulator VO2. By continuously tuning strain over a wide range in single-crystal VO2 micro- and nanobeams, here we demonstrate the nucleation and manipulation of one-dimensionally ordered metal-insulator domain arrays along the beams. Mott transition is achieved in these beams at room temperature by active control of strain. The ability to engineer phase inhomogeneity with strain lends insight into correlated electron materials in general, and opens opportunities for designing and controlling the phase inhomogeneity of correlated electron materials for micro- and nanoscale device applications.Comment: 14 pages, 4 figures, with supplementary informatio

Germline-Focused Analysis of Tumour-Only Sequencing: Recommendations from the ESMO Precision Medicine Working Group.

It is increasingly common in oncology practice to perform tumour sequencing using large cancer panels. For pathogenic sequence variants in cancer susceptibility genes identified on tumour-only sequencing, it is often unclear whether they are of somatic or constitutional (germline) origin. There is wide-spread disparity regarding both the extent to which systematic 'germline-focused analysis' is performed upon tumour sequencing data and for which variants follow-up analysis of a germline sample is performed. Here we present analyses of paired sequencing data from 17,152 cancer samples, in which 1494 pathogenic sequence variants were identified across 65 cancer susceptibility genes. From these analyses, the European Society of Medical Oncology Precision Medicine Working Group Germline Subgroup have generated (i) recommendations regarding germline-focused analyses of tumour-only sequencing data, (ii) indications for germline follow-up testing and (iii) guidance on patient information-giving and consent

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging

Broadband Reconfiguration of OptoMechanical Filters

We demonstrate broad-band reconfiguration of coupled photonic crystal nanobeam cavities by using optical gradient force induced mechanical actuation. Propagating waveguide modes that exist over wide wavelength range are used to actuate the structures and in that way control the resonance of localized cavity mode. Using this all-optical approach, more than 18 linewidths of tuning range is demonstrated. Using on-chip temperature self-referencing method that we developed, we determined that 20 % of the total tuning was due to optomechanical reconfiguration and the rest due to thermo-optic effects. Independent control of mechanical and optical resonances of our structures, by means of optical stiffening, is also demonstrated

Key stages in mammary gland development: The mammary end bud as a motile organ

In the rodent, epithelial end buds define the tips of elongating mammary ducts. These highly motile structures undergo repeated dichotomous branching as they aggressively advance through fatty stroma and, turning to avoid other ducts, they finally cease growth leaving behind the open, tree-like framework on which secretory alveoli develop during pregnancy. This review identifies the motility of end buds as a unique developmental marker that represents the successful integration of systemic and local mammotrophic influences, and covers relevant advances in ductal growth regulation, extracellular matrix (ECM) remodeling, and cell adhesion in the inner end bud. An unexpected growth-promoting synergy between insulin-like growth factor-1 and progesterone, in which ducts elongate without forming new end buds, is described as well as evidence strongly supporting self-inhibition of ductal elongation by end-bud-secreted transforming growth factor-β acting on stromal targets. The influence of the matrix metalloproteinase ECM-remodeling enzymes, notably matrix metalloproteinase-2, on end bud growth is discussed in the broader context of enzymes that regulate the polysaccharide-rich glycosaminoglycan elements of the ECM. Finally, a critical, motility-enabling role for the cellular architecture of the end bud is identified and the contribution of cadherins, the netrin/neogenin system, and ErbB2 to the structure and motility of end buds is discussed

Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

Polymer ultrapermeability from the inefficient packing of 2D chains

The promise of ultrapermeable polymers, such as poly(trimethylsilylpropyne) (PTMSP), for reducing the size and increasing the efficiency of membranes for gas separations remains unfulfilled due to their poor selectivity. We report an ultrapermeable polymer of intrinsic microporosity (PIM-TMN-Trip) that is substantially more selective than PTMSP. From molecular simulations and experimental measurement we find that the inefficient packing of the two-dimensional (2D) chains of PIM-TMN-Trip generates a high concentration of both small (<0.7 nm) and large (0.7–1.0 nm) micropores, the former enhancing selectivity and the latter permeability. Gas permeability data for PIM-TMN-Trip surpass the 2008 Robeson upper bounds for O2/N2, H2/N2, CO2/N2, H2/CH4 and CO2/CH4, with the potential for biogas purification and carbon capture demonstrated for relevant gas mixtures. Comparisons between PIM-TMN-Trip and structurally similar polymers with three-dimensional (3D) contorted chains confirm that its additional intrinsic microporosity is generated from the awkward packing of its 2D polymer chains in a 3D amorphous solid. This strategy of shape-directed packing of chains of microporous polymers may be applied to other rigid polymers for gas separations

High Quality Genomic Copy Number Data from Archival Formalin-Fixed Paraffin-Embedded Leiomyosarcoma: Optimisation of Universal Linkage System Labelling

Most soft tissue sarcomas are characterized by genetic instability and frequent genomic copy number aberrations that are not subtype-specific. Oligonucleotide microarray-based Comparative Genomic Hybridisation (array CGH) is an important technique used to map genome-wide copy number aberrations, but the traditional requirement for high-quality DNA typically obtained from fresh tissue has limited its use in sarcomas. Although large archives of Formalin-fixed Paraffin-embedded (FFPE) tumour samples are available for research, the degradative effects of formalin on DNA from these tissues has made labelling and analysis by array CGH technically challenging. The Universal Linkage System (ULS) may be used for a one-step chemical labelling of such degraded DNA. We have optimised the ULS labelling protocol to perform aCGH on archived FFPE leiomyosarcoma tissues using the 180k Agilent platform. Preservation age of samples ranged from a few months to seventeen years and the DNA showed a wide range of degradation (when visualised on agarose gels). Consistently high DNA labelling efficiency and low microarray probe-to-probe variation (as measured by the derivative log ratio spread) was seen. Comparison of paired fresh and FFPE samples from identical tumours showed good correlation of CNAs detected. Furthermore, the ability to macro-dissect FFPE samples permitted the detection of CNAs that were masked in fresh tissue. Aberrations were visually confirmed using Fluorescence in situ Hybridisation. These results suggest that archival FFPE tissue, with its relative abundance and attendant clinical data may be used for effective mapping for genomic copy number aberrations in such rare tumours as leiomyosarcoma and potentially unravel clues to tumour origins, progression and ultimately, targeted treatment