3,187 research outputs found

    Spinor-Vector Duality in Heterotic String Orbifolds

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    The three generation heterotic-string models in the free fermionic formulation are among the most realistic string vacua constructed to date, which motivated their detailed investigation. The classification of free fermion heterotic string vacua has revealed a duality under the exchange of spinor and vector representations of the SO(10) GUT symmetry over the space of models. We demonstrate the existence of the spinor-vector duality using orbifold techniques, and elaborate on the relation of these vacua to free fermionic models.Comment: 20 pages. v2 minor corrections. Version to appear on JHEP. v3 misprints correcte

    A new method to quantify and compare the multiple components of fitness-A study case with kelp niche partition by divergent microstage adaptations to Temperature

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    Point 1 Management of crops, commercialized or protected species, plagues or life-cycle evolution are subjects requiring comparisons among different demographic strategies. The simpler methods fail in relating changes in vital rates with changes in population viability whereas more complex methods lack accuracy by neglecting interactions among vital rates. Point 2 The difference between the fitness (evaluated by the population growth rate.) of two alternative demographies is decomposed into the contributions of the differences between the pair-wised vital rates and their interactions. This is achieved through a full Taylor expansion (i.e. remainder = 0) of the demographic model. The significance of each term is determined by permutation tests under the null hypothesis that all demographies come from the same pool. Point 3 An example is given with periodic demographic matrices of the microscopic haploid phase of two kelp cryptic species observed to partition their niche occupation along the Chilean coast. The method provided clear and synthetic results showing conditional differentiation of reproduction is an important driver for their differences in fitness along the latitudinal temperature gradient. But it also demonstrated that interactions among vital rates cannot be neglected as they compose a significant part of the differences between demographies. Point 4 This method allows researchers to access the effects of multiple effective changes in a life-cycle from only two experiments. Evolutionists can determine with confidence the effective causes for changes in fitness whereas population managers can determine best strategies from simpler experimental designs.CONICYT-FRENCH EMBASSADY Ph.D. gran

    Cortical microstructure in primary progressive aphasia: a multicenter study

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    Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA).In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rating scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach.The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cortical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity.Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in PPA.© 2022. The Author(s)

    On dynamic network entropy in cancer

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    The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte

    Phenotypic redshifts with self-organizing maps: A novel method to characterize redshift distributions of source galaxies for weak lensing

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    Wide-field imaging surveys such as the Dark Energy Survey (DES) rely on coarse measurements of spectral energy distributions in a few filters to estimate the redshift distribution of source galaxies. In this regime, sample variance, shot noise, and selection effects limit the attainable accuracy of redshift calibration and thus of cosmological constraints. We present a new method to combine wide-field, few-filter measurements with catalogs from deep fields with additional filters and sufficiently low photometric noise to break degeneracies in photometric redshifts. The multi-band deep field is used as an intermediary between wide-field observations and accurate redshifts, greatly reducing sample variance, shot noise, and selection effects. Our implementation of the method uses self-organizing maps to group galaxies into phenotypes based on their observed fluxes, and is tested using a mock DES catalog created from N-body simulations. It yields a typical uncertainty on the mean redshift in each of five tomographic bins for an idealized simulation of the DES Year 3 weak-lensing tomographic analysis of σΔz=0.007\sigma_{\Delta z} = 0.007, which is a 60% improvement compared to the Year 1 analysis. Although the implementation of the method is tailored to DES, its formalism can be applied to other large photometric surveys with a similar observing strategy.Comment: 24 pages, 11 figures; matches version accepted to MNRA

    What Facilitates Return to Work? Patients Experiences 3 Years After Occupational Rehabilitation

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    Objective We have limited knowledge about the specific elements in an occupational rehabilitation programme that facilitate the process leading to return to work (RTW) as perceived by the patients. The aim of the study was to explore individual experiences regarding contributing factors to a successful RTW, 3 years after a resident occupational rehabilitation programme. Methods The study is based on interviews of 20 individuals who attended an occupational rehabilitation programme 3 years earlier. Ten informants had returned to work (RTW) and ten were receiving disability pension (DP). Data were analysed by systematic text condensation inspired by Giorgi’s phenomenological analysis. Results The core categories describing a successful RTW process included positive encounters, increased self-understanding and support from the surroundings. While the informants on DP emphasized being seen, heard and taken seriously by the professionals, the RTW group highlighted being challenged to increase self-understanding that promoted new acting in every-day life. Being challenged on self-understanding implied increased awareness of own identity, values and resources. Support from the surroundings included support from peer participants, employer and social welfare system. Conclusion Successful RTW processes seem to comprise positive encounters, opportunities for increased self-understanding and support from significant others. An explicit focus on topics like identity, own values and resources might improve the outcome of the rehabilitation process

    A Study of D0 --> K0(S) K0(S) X Decay Channels

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    Using data from the FOCUS experiment (FNAL-E831), we report on the decay of D0D^0 mesons into final states containing more than one KS0K^0_S. We present evidence for two Cabibbo favored decay modes, D0KS0KS0Kπ+D^0\to K^0_SK^0_S K^- \pi^+ and D0KS0KS0K+πD^0\to K^0_SK^0_S K^+ \pi^-, and measure their combined branching fraction relative to D0Kˉ0π+πD^0\to \bar{K} ^0\pi^+\pi^- to be Γ(D0KS0KS0K±π)Γ(D0Kˉ0π+π)\frac{\Gamma(D^0\to K^0_SK^0_SK^{\pm}\pi^{\mp})}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)} = 0.0106 ±\pm 0.0019 ±\pm 0.0010. Further, we report new measurements of Γ(D0KS0KS0KS0)Γ(D0Kˉ0π+π)\frac{\Gamma(D^0\to K^0_SK^0_SK^0_S)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)} = 0.0179 ±\pm 0.0027 ±\pm 0.0026, Γ(D0K0Kˉ0)Γ(D0Kˉ0π+π)\frac{\Gamma(D^0\to K^0\bar{K} ^0)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)} = 0.0144 ±\pm 0.0032 ±\pm 0.0016, and Γ(D0KS0KS0π+π)Γ(D0Kˉ0π+π)\frac{\Gamma(D^0\to K^0_SK^0_S\pi^+\pi^-)}{\Gamma(D^0\to \bar{K} ^0\pi^+\pi^-)} = 0.0208 ±\pm 0.0035 ±\pm 0.0021 where the first error is statistical and the second is systematic.Comment: 11 pages, 3 figures, typos correcte
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