Transfer print techniques for heterogeneous integration of photonic components

The essential functionality of photonic and electronic devices is contained in thin surface layers leaving the substrate often to play primarily a mechanical role. Layer transfer of optimised devices or materials and their heterogeneous integration is thus a very attractive strategy to realise high performance, low-cost circuits for a wide variety of new applications. Additionally, new device configurations can be achieved that could not otherwise be realised. A range of layer transfer methods have been developed over the years including epitaxial lift-off and wafer bonding with substrate removal. Recently, a new technique called transfer printing has been introduced which allows manipulation of small and thin materials along with devices on a massively parallel scale with micron scale placement accuracies to a wide choice of substrates such as silicon, glass, ceramic, metal and polymer. Thus, the co-integration of electronics with photonic devices made from compound semiconductors, silicon, polymer and new 2D materials is now achievable in a practical and scalable method. This is leading to exciting possibilities in microassembly. We review some of the recent developments in layer transfer and particularly the use of the transfer print technology for enabling active photonic devices on rigid and flexible foreign substrates

Ordovician tectonics of the South European Variscan Realm: new insights from Sardinia

Although much is known about the Ordovician tectonics of the South European Variscides, aspects of their geodynamic evolution and palaeogeographic reconstruction remain uncertain. In Sardinia, Variscan tectonic units include significant vestiges of Ordovician evolution, such as a fold system that affected only the Cambrian-Lower Ordovician successions, and are cut by a regional angular unconformity. A comparison of the stratigraphy and tectonic structures of the successions below and above the Lower Ordovician unconformity and a reinterpretation of biostratigraphic data allow us to identify significant differences between the stacked tectonic units. The unconformity is sealed as follows: (i) in the Sulcis-Iglesiente Unit (Variscan External Zone, SW Sardinia) by Middle-Upper Ordovician continental and tidal deposits; and (ii) in the Sarrabus and Gerrei units (part of the Variscan Nappe Zone, SE Sardinia) by Middle-Upper Ordovician calc-alkaline volcanic rocks. Therefore, at the same time, one tectonic unit was situated close to a rifting setting and the others were involved in a convergent margin. Of note are the different durations associated with the unconformities in the tectonic units (17 Myr in the Sulcis-Iglesiente Unit, 6 Myr in the Sarrabus and Gerrei units) and the occurrence (or absence) of glacio-marine deposits indicating that the units were located at different palaeo-latitudes during the Ordovician. These results suggest that the SW and SE Sardinia blocks did not share the same geodynamic setting during the Ordovician, implying that they were situated in different palaeogeographic positions at this time and subsequently amalgamated during the Variscan Orogeny. Furthermore, stratigraphic and tectonic correlations with neighbouring areas, such as the eastern Pyrenees, imply alternative palaeogeographic reconstructions to those proposed previously for some peri-Mediterranean Variscan terranes

On a formula of Gammelgaard for Berezin-Toeplitz quantization

We give a proof of a slightly refined version of Gammelgaard's graph theoretic formula for Berezin-Toeplitz quantization on (pseudo-)Kaehler manifolds. Our proof has the merit of giving an alternative approach to Karabegov-Schlichenmaier's identification theorem. We also identify the dual Karabegov-Bordemann-Waldmann star product.Comment: 18 page

An explicit formula for the Berezin star product

We prove an explicit formula of the Berezin star product on Kaehler manifolds. The formula is expressed as a summation over certain strongly connected digraphs. The proof relies on a combinatorial interpretation of Englis' work on the asymptotic expansion of the Laplace integral.Comment: 19 pages, to appear in Lett. Math. Phy

Increased entropy of signal transduction in the cancer metastasis phenotype

Studies into the statistical properties of biological networks have led to important biological insights, such as the presence of hubs and hierarchical modularity. There is also a growing interest in studying the statistical properties of networks in the context of cancer genomics. However, relatively little is known as to what network features differ between the cancer and normal cell physiologies, or between different cancer cell phenotypes. Based on the observation that frequent genomic alterations underlie a more aggressive cancer phenotype, we asked if such an effect could be detectable as an increase in the randomness of local gene expression patterns. Using a breast cancer gene expression data set and a model network of protein interactions we derive constrained weighted networks defined by a stochastic information flux matrix reflecting expression correlations between interacting proteins. Based on this stochastic matrix we propose and compute an entropy measure that quantifies the degree of randomness in the local pattern of information flux around single genes. By comparing the local entropies in the non-metastatic versus metastatic breast cancer networks, we here show that breast cancers that metastasize are characterised by a small yet significant increase in the degree of randomness of local expression patterns. We validate this result in three additional breast cancer expression data sets and demonstrate that local entropy better characterises the metastatic phenotype than other non-entropy based measures. We show that increases in entropy can be used to identify genes and signalling pathways implicated in breast cancer metastasis. Further exploration of such integrated cancer expression and protein interaction networks will therefore be a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Microarray data analysis in neoadjuvant biomarker studies in estrogen receptor-positive breast cancer

Microarray data have been widely utilized to discover biomarkers predictive of response to endocrine therapy in estrogen receptor-positive breast cancer. Typically, these data have focused on analyses conducted on the diagnostic specimen. However, dynamic temporal changes in gene expression associated with treatment may deliver significant improvements to the current generation of predictive models. We present and discuss some statistical issues relevant to the paper by Taylor and colleagues, who conducted studies to model the prognostic potential of gene expression changes that occur after endocrine treatment

Photophysics of Two-Dimensional Perovskites—Learning from Metal Halide Substitution

Whereas their photophysics exhibits an intricate interplay of carriers with the lattice, most reports have so far relied on single compound studies. With the exception of variations of the organic spacer cations, the effect of constituent substitution on the photophysics and the nature of emitting species, in particular, has remained largely under-explored. Here PEA$_2$PbBr$_4$, PEA$_2$PbI$_4$, and PEA$_2$SnI$_4$ are studied through a variety of optical spectroscopy techniques to reveal a complex set of excitonic transitions at low temperature. We attribute the emergence of weak high energy features to a vibronic progression breaking Kasha's rule and highlight that the responsible phonons cannot be accessed through simple Raman spectroscopy. Bright peaks at lower energy are due to two distinct excitons, of which the upper is a convolution of a bright exciton and a localised state, whereas the lower is attributed to shallow defects. Our study offers deeper insights into the photophysics of two-dimensional perovskites through compositional substitution and highlights critical limits to the communities' current understanding of the photophysics of these compounds

Intimate partner violence against women in rural Vietnam - different socio-demographic factors are associated with different forms of violence: Need for new intervention guidelines?

Background: This population-based study investigated the different forms, magnitude and risk factors of men's violence against women in intimate relationships in a rural part of northern Vietnam and whether a difference in risk factors were at hand for the different forms of violence. Vietnam has undergone a rapid transition in the last 20 years, moving towards a more equal situation for men and women however, Confucian doctrine is still strong and little is known about men's violence against women within the Vietnamese family. Methods: This is a cross-sectional population-based study that used a questionnaire developed by the World Health Organisation for investigating women's health and violence against women in different settings. Face-to face structured interviewing was performed and 883 married women, aged 17 to 60 participated. Bi- and multivariate analyses was used for risk factor assessment. Results: The lifetime prevalence of physical violence was 30.9 percent and past year prevalence was 8.3 per cent, while the corresponding figures for physical and sexual violence combined was 32.7 and 9.2 percent. The lifetime prevalence was highest for psychological abuse ( 27.9 percent) as a single entity. In most cases the violence was of a severe nature and exercised as repeated acts over time. Woman's low educational level, husband's low education, low household income and the husband having more than one wife/partner were risk factors for lifetime and past year physical/sexual violence. The pattern of factors associated with psychological abuse alone was however different. Husband's low professional status and women's intermediate level of education appeared as risk factors. Conclusion: Men's violence against women in intimate relationships is commonly occurring in rural Vietnam. There is an obvious need of preventive and treatment activities. Our findings point at that pure psychological abuse is different from physical/sexual violence in terms of differing characteristics of the perpetrators and it might be that also different strategies are needed to reduce and prevent this violence

Analyzing the regulation of metabolic pathways in human breast cancer

<p>Abstract</p> <p>Background</p> <p>Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.</p> <p>Methods</p> <p>For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.</p> <p>Results</p> <p>Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.</p> <p>Conclusion</p> <p>We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.</p