Temperature in One-Dimensional Bosonic Mott insulators

The Mott insulating phase of a one-dimensional bosonic gas trapped in optical lattices is described by a Bose-Hubbard model. A continuous unitary transformation is used to map this model onto an effective model conserving the number of elementary excitations. We obtain quantitative results for the kinetics and for the spectral weights of the low-energy excitations for a broad range of parameters in the insulating phase. By these results, recent Bragg spectroscopy experiments are explained. Evidence for a significant temperature of the order of the microscopic energy scales is found.Comment: 8 pages, 7 figure

A Critical Review of Neural Networks for the Use with Spectroscopic Data

In recent years, neural networks have found increased use in the analysis of crystallographic characterization data, such as X-ray diffraction (XRD) patterns. Previous work has shown that neural networks can successfully identify crystalline phases from XRD patterns and classify their symmetry, even in multiphase mixtures. When compared with classical machine learning methods, such as Support Vector Machines or Decision Trees, CNNs show improved performance in the classification of XRD patterns and can even handle experimental artifacts such as peak shifts caused by strain, whereas the classification models would fail. Such an approach is readily extended to other spectroscopic techniques, including NMR, Raman or NIR. Those network models usually employ a convolutional neural network (CNN) architecture which has been developed for the use with images. Despite these promising results, our work reveals several key limitations of the CNN architecture with respect to spectroscopic analysis, and we show that these limitations can lead to failed classifications on relatively simple patterns. Convolutional layers are demonstrated to have very little benefit for classification, and their only important contribution comes from the pooling operations that shrink the size of the input while keeping relevant information regarding peak intensities. Those pooling operations compensate for peak shifts, and therefore classical models applied to the shrunken input perform equally well as the presented neural networks. Nonetheless, we show how to adapt various parameters in the neural networks, such as the choice of activation function, to train more robust models with improved accuracy on classification tasks. Based on our findings, we believe that neural networks have their place for use with spectroscopic data but require careful design of their architecture to handle peculiarities inherent to spectral data

Neighborhood Social Conditions Mediate the Association Between Physical Deterioration and Mental Health

This study investigates how neighborhood deterioration is associated with stress and depressive symptoms and the mediating effects of perceived neighborhood social conditions. Data come from a community survey of 801 respondents geocoded and linked to a systematic on‐site assessment of the physical characteristics of nearly all residential and commercial structures around respondents' homes. Structural equation models controlling for demographic effects indicate that the association between neighborhood deterioration and well‐being appear to be mediated through social contact, social capital, and perceptions of crime, but not through neighborhood satisfaction. Specifically, residential deterioration was mediated by social contact, then, social capital and fear of crime. Commercial deterioration, on the other hand, was mediated only through fear of crime. Additionally, data indicate that the functional definition of a “neighborhood” depends on the characteristics measured. These findings suggest that upstream interventions designed to improve neighborhood conditions as well as proximal interventions focused on social relationships, may promote well‐being.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/117109/1/ajcp9139.pd

Serum (1 → 3)-β-d-glucan measurement as an early indicator of Pneumocystis jirovecii pneumonia and evaluation of its prognostic value

AbstractPneumocystis jirovecii (carinii) pneumonia (PJP) is a major cause of disease in immunocompromised individuals. However, until recently no reliable and specific serological parameters for the diagnosis of PJP have been available. (1 → 3)-β-d-Glucan (BG) is a cell wall component of P. jirovecii and of various other fungi. Data from the past few years have pointed to serum measurement of BG as a promising new tool for the diagnosis of PJP. We therefore conducted a retrospective study on 50 patients with PJP and 50 immunocompromised control patients to evaluate the diagnostic performance of serum BG measurement. Our results show an excellent diagnostic performance with a sensitivity of 98.0% and a specificity of 94%. While the positive predictive value was only 64.7%, the negative predictive value was 99.8% and therefore a negative BG result almost rules out PJP. BG levels were already strongly elevated in an average of 5 days and up to 21 days before microbiological diagnosis demonstrating that the diagnosis could have been confirmed earlier. BG levels at diagnosis and maximum BG levels during follow-up did not correlate with the outcome of patients or with the P. jirovecii burden in the lung as detected by Real-Time PCR. Therefore, absolute BG levels seem to be of no prognostic value. Altogether, BG is a reliable parameter for the diagnosis of PJP and could be used as a preliminary test for patients at risk before a bronchoalveolar lavage is performed

Systematic Mapping of the Hubbard Model to the Generalized t-J Model

The generalized t-J model conserving the number of double occupancies is constructed from the Hubbard model at and in the vicinity of half-filling at strong coupling. The construction is realized by a self-similar continuous unitary transformation. The flow equation is closed by a truncation scheme based on the spatial range of processes. We analyze the conditions under which the t-J model can be set up and we find that it can only be defined for sufficiently large interaction. There, the parameters of the effective model are determined.Comment: 16 pages, 13 figures included. v2: Order of sections changed. Calculation and discussion of apparent gap in Section IV.A correcte

First sequence-confirmed case of infection with the new influenza A(H1N1) strain in Germany

Here, we report on the first sequence-confirmed case of infection with the new influenza A(H1N1) virus in Germany. Two direct contacts of the patient were laboratory-confirmed as cases and demonstrate a chain of direct human-to-human transmission

Microscopic model for Bose-Einstein condensation and quasiparticle decay

Sufficiently dimerized quantum antiferromagnets display elementary S=1 excitations, triplon quasiparticles, protected by a gap at low energies. At higher energies, the triplons may decay into two or more triplons. A strong enough magnetic field induces Bose-Einstein condensation of triplons. For both phenomena the compound IPA-CuCl3 is an excellent model system. Nevertheless no quantitative model was determined so far despite numerous studies. Recent theoretical progress allows us to analyse data of inelastic neutron scattering (INS) and of magnetic susceptibility to determine the four magnetic couplings J1=-2.3meV, J2=1.2meV, J3=2.9meV and J4=-0.3meV. These couplings determine IPA-CuCl3 as system of coupled asymmetric S=1/2 Heisenberg ladders quantitatively. The magnetic field dependence of the lowest modes in the condensed phase as well as the temperature dependence of the gap without magnetic field corroborate this microscopic model.Comment: 6 pages, 5 figure

Relationships Among Disease, Social Support, and Perceived Health: A Lifespan Approach

We examined the relationship between the cumulative presence of major disease (cancer, stroke, diabetes, heart disease, and hypertension), social support, and self‐reported general and emotional well‐being in a community representative sample of predominantly White and African American respondents (N = 1349). Across all ages, greater presence of disease predicted poorer reported general health, and predicted lower emotional well‐being for respondents 40 and above. In contrast, social support predicted better‐reported general and emotional well‐being. We predicted that different types of social support (blood relatives, children, friends, community members) would be relatively more important for health in different age groups based on a lifespan or life stage model. This hypothesis was supported; across all ages, social support was related to better reported general and emotional health, but sources of support differed by age. Broadly, those in younger age groups tended to list familial members as their strongest sources of support, whereas older group members listed their friends and community members. As a whole, social support mediated the effect of disease on reported well‐being, however, moderated mediation by type of support was not significant. The results are consistent with a lifespan approach to changing social ties throughout the life course.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116357/1/ajcp9758.pd