Twinning superlattices in indium phosphide nanowires

Here, we show that we control the crystal structure of indium phosphide (InP) nanowires by impurity dopants. We have found that zinc decreases the activation barrier for 2D nucleation growth of zinc-blende InP and therefore promotes the InP nanowires to crystallise in the zinc blende, instead of the commonly found wurtzite crystal structure. More importantly, we demonstrate that we can, by controlling the crystal structure, induce twinning superlattices with long-range order in InP nanowires. We can tune the spacing of the superlattices by the wire diameter and the zinc concentration and present a model based on the cross-sectional shape of the zinc-blende InP nanowires to quantitatively explain the formation of the periodic twinning.Comment: 18 pages, 4 figure

Membrane connectivity estimated by digital image analysis of HER2 immunohistochemistry is concordant with visual scoring and fluorescence in situ hybridization results: algorithm evaluation on breast cancer tissue microarrays

<p>Abstract</p> <p>Introduction</p> <p>The human epidermal growth factor receptor 2 (HER2) is an established biomarker for management of patients with breast cancer. While conventional testing of HER2 protein expression is based on semi-quantitative visual scoring of the immunohistochemistry (IHC) result, efforts to reduce inter-observer variation and to produce continuous estimates of the IHC data are potentiated by digital image analysis technologies.</p> <p>Methods</p> <p>HER2 IHC was performed on the tissue microarrays (TMAs) of 195 patients with an early ductal carcinoma of the breast. Digital images of the IHC slides were obtained by Aperio ScanScope GL Slide Scanner. Membrane connectivity algorithm (HER2-CONNECT™, Visiopharm) was used for digital image analysis (DA). A pathologist evaluated the images on the screen twice (visual evaluations: VE1 and VE2). HER2 fluorescence <it>in situ </it>hybridization (FISH) was performed on the corresponding sections of the TMAs. The agreement between the IHC HER2 scores, obtained by VE1, VE2, and DA was tested for individual TMA spots and patient's maximum TMA spot values (VE1max, VE2max, DAmax). The latter were compared with the FISH data. Correlation of the continuous variable of the membrane connectivity estimate with the FISH data was tested.</p> <p>Results</p> <p>The pathologist intra-observer agreement (VE1 and VE2) on HER2 IHC score was almost perfect: kappa 0.91 (by spot) and 0.88 (by patient). The agreement between visual evaluation and digital image analysis was almost perfect at the spot level (kappa 0.86 and 0.87, with VE1 and VE2 respectively) and at the patient level (kappa 0.80 and 0.86, with VE1max and VE2max, respectively). The DA was more accurate than VE in detection of FISH-positive patients by recruiting 3 or 2 additional FISH-positive patients to the IHC score 2+ category from the IHC 0/1+ category by VE1max or VE2max, respectively. The DA continuous variable of the membrane connectivity correlated with the FISH data (HER2 and CEP17 copy numbers, and HER2/CEP17 ratio).</p> <p>Conclusion</p> <p>HER2 IHC digital image analysis based on membrane connectivity estimate was in almost perfect agreement with the visual evaluation of the pathologist and more accurate in detection of HER2 FISH-positive patients. Most immediate benefit of integrating the DA algorithm into the routine pathology HER2 testing may be obtained by alerting/reassuring pathologists of potentially misinterpreted IHC 0/1+ versus 2+ cases.</p

Human oral viruses are personal, persistent and gender-consistent.

Viruses are the most abundant members of the human oral microbiome, yet relatively little is known about their biodiversity in humans. To improve our understanding of the DNA viruses that inhabit the human oral cavity, we examined saliva from a cohort of eight unrelated subjects over a 60-day period. Each subject was examined at 11 time points to characterize longitudinal differences in human oral viruses. Our primary goals were to determine whether oral viruses were specific to individuals and whether viral genotypes persisted over time. We found a subset of homologous viral genotypes across all subjects and time points studied, suggesting that certain genotypes may be ubiquitous among healthy human subjects. We also found significant associations between viral genotypes and individual subjects, indicating that viruses are a highly personalized feature of the healthy human oral microbiome. Many of these oral viruses were not transient members of the oral ecosystem, as demonstrated by the persistence of certain viruses throughout the entire 60-day study period. As has previously been demonstrated for bacteria and fungi, membership in the oral viral community was significantly associated with the sex of each subject. Similar characteristics of personalized, sex-specific microflora could not be identified for oral bacterial communities based on 16S rRNA. Our findings that many viruses are stable and individual-specific members of the oral ecosystem suggest that viruses have an important role in the human oral ecosystem

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; <37 weeks) or (2) early preterm birth (ePTB; <32 weeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

The evolutionary ecology of complex lifecycle parasites: linking phenomena with mechanisms

Many parasitic infections, including those of humans, are caused by complex lifecycle parasites (CLPs): parasites that sequentially infect different hosts over the course of their lifecycle. CLPs come from a wide range of taxonomic groups-from single-celled bacteria to multicellular flatworms-yet share many common features in their life histories. Theory tells us when CLPs should be favoured by selection, but more empirical studies are required in order to quantify the costs and benefits of having a complex lifecycle, especially in parasites that facultatively vary their lifecycle complexity. In this article, we identify ecological conditions that favour CLPs over their simple lifecycle counterparts and highlight how a complex lifecycle can alter transmission rate and trade-offs between growth and reproduction. We show that CLPs participate in dynamic host-parasite coevolution, as more mobile hosts can fuel CLP adaptation to less mobile hosts. Then, we argue that a more general understanding of the evolutionary ecology of CLPs is essential for the development of effective frameworks to manage the many diseases they cause. More research is needed identifying the genetics of infection mechanisms used by CLPs, particularly into the role of gene duplication and neofunctionalisation in lifecycle evolution. We propose that testing for signatures of selection in infection genes will reveal much about how and when complex lifecycles evolved, and will help quantify complex patterns of coevolution between CLPs and their various hosts. Finally, we emphasise four key areas where new research approaches will provide fertile opportunities to advance this field