Comprehensive population synthesis predictions for massive binary stars in the Small Magellanic Cloud

The past and ongoing gravitational wave detections have fostered a wide interest in understanding the formation of binary black holes (BBHs). Several formation scenarios have been proposed, including the evolution of isolated massive binaries. While most of the observed merging black holes are at cosmological distances, and thus likely at low metallicity, the Small Magellanic Cloud (SMC) — one of the satellite galaxies of the Milky Way — provides a unique laboratory to probe the binary scenario thanks to its rich massive star population and low metallicity. In this thesis, we provide comprehensive synthetic SMC populations of massive binary stars throughout all their different evolutionary stages, based on a dense grid of more than 50,000 detailed binary evolution models. As stars expand when they age, mass transfer occurs in most of our models. This leads to a spin-up of the mass gainers to produce rapidly rotating so called Be/Oe emission line stars, which are abundant in the SMC. The mass donors, if massive enough, may form so called Wolf-Rayet (WR) stars, which are hydrogen deficient and have emission line dominated spectra. Our models predict 7 so called WR + main sequence star (MS) binaries in the SMC, which roughly matches the observed number, even though our models produce more long-period WR binaries than observed. At the same time, our synthetic population contains ~ 200 BH+MS binaries, mostly associated with Be/Oe stars, which are so far undetected. We show that this is not in contradiction to the small number of wind-accreting BH+MS binaries observed through their X-ray emission. We also predict a neutron star (NS) +MS population, for which our models reproduce the orbital period distribution of the observed rich Be/X-ray binary population, but not their number. On the other hand, we predict a so far undiscovered group of X-ray quiet NS binaries with orbital periods below 10 days. We then use semi-analytic methods to investigate which of our BH+MS binaries can evolve into a BH+WR system, which of those evolve further into a BBH, and which of those can merge within the Hubble time. Our results predict a high BH companion fraction for WR stars, and 2-3 BH+WR binaries in the SMC. Our prediction on merging BBHs is sensitive to several model assumptions. The main features of the observed merging BBH population can be reproduced by our model, with common envelope evolution and stable mass transfer evolution contributing about equally. In conclusion, while our model in part agrees with the observed populations, it also raises new questions. In particular, there is an apparent lack of long-period massive evolved binaries with WR or BH components. As those are harder to detect than the shorter period counterparts, we hope that future observing campaigns will have the power to resolve this issue

Comparison of form-deprived myopia and lens-induced myopia in guinea pigs

<b>AIM:</b> To study the efficacy difference between form-deprived myopia (FDM) and lens-induced myopia (LIM), the degree of myopia, axial length and pathological changes of the posterior sclera from guinea pigs were evaluated.<b>METHODS:</b> Four-week pigmented guinea pigs were randomly assigned into 3 groups, including normal control (<i>n</i>=6), FDM group with monocular cover (<i>n</i>=11) and LIM group with monocular -7D lens treatment (<i>n</i>=11). FDM group was form-deprived while LIM group was lens-induced for 14 d. Refractive error and axial length were measured prior to and post treatment, respectively. Morphological changes of sclera were examined using both light and electronic microscopes.<b>RESULTS:</b> After 14d treatment, refractive errors for FDM group and LIM group were -3.05±0.71D and -2.12±1.29D, respectively, which were significantly more myopic than that of normal controls and fellow control eyes (<i>P</i>&lt;0.01). As for axial length, it was 7.93±0.03 mm for FDM group and 7.89±0.06 mm for LIM group, which were significantly longer than both normal and fellow controls (<i>P</i>&lt;0.01). With respect to both refractory error and axial length, the differences between FDM group and LIM group were not significant (<i>P</i>&gt;0.05). Under light microscope, both FDM group and LIM group showed thinned sclera, disarrangement of fibrosis and enlarged disassociation between fibers. Consistently, ultrastructural examination showed degenerated fibroblasts and thinned fibers in posterior sclera.<b>CONCLUSION:</b>Following two weeks of myopia induction in guinea pigs, with regard to the degree of myopia, axial length and pathological alterations, there was no significant difference between FDM and LIM models. Therefore, FDM and LIM are equally effective and useful as a model of experimental myopia and guinea pigs are ideal animals for induction of experimental myopia because their high sensitivity to both form-deprivation and lens-induction

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table