Collective Oscillations of Vortex Lattices in Rotating Bose-Einstein Condensates

The complete low-energy collective-excitation spectrum of vortex lattices is discussed for rotating Bose-Einstein condensates (BEC) by solving the Bogoliubov-de Gennes (BdG) equation, yielding, e.g., the Tkachenko mode recently observed at JILA. The totally symmetric subset of these modes includes the transverse shear, common longitudinal, and differential longitudinal modes. We also solve the time-dependent Gross-Pitaevskii (TDGP) equation to simulate the actual JILA experiment, obtaining the Tkachenko mode and identifying a pair of breathing modes. Combining both the BdG and TDGP approaches allows one to unambiguously identify every observed mode.Comment: 5 pages, 4 figure

Neural network based damage detection using substructure technique

Many researchers have been studying the feasibility of using Artificial Neural Networks (ANN) in structural health monitoring and damage detection. It has been proven by both numerical simulation and laboratory test data that ANN can give reliable prediction of structural conditions. The main drawback of using ANN in structural condition monitoring is the requirement of enormous computational effort. Consequently almost all the previous work described in the literature limited the structural members to a small number of large elements in the ANN model. This may result in the ANN model being insensitive to local damage, especially when this local damage is small. To overcome this problem, this study presents an approach to detect small structural damage by using ANN progressively. It uses the substructure technique together with a two-stage ANN to detect the location and extent of the damage. It starts by dividing the structure into a few substructures. The condition of each substructure is examined. Those substructures with condition change identified are further subdivided and their condition examined. By doing this progressively, the location and severity of low level structural damage can be detected. Modal parameters such as frequencies and mode shapes are used as the input to the ANN. To demonstrate the effectiveness of this approach, a two-span continuous concrete slab structure is used as an example. Different damage scenarios are introduced by reducing the local stiffness of the selected elements at different locations along the structure. The results show that this technique successfully detects simulated damage in the structure

The Moses–Littenberg meta-analytical method generates systematic differences in test accuracy compared to hierarchical meta-analytical models

AbstractObjectiveTo compare meta-analyses of diagnostic test accuracy using the Moses–Littenberg summary receiver operating characteristic (SROC) approach with those of the hierarchical SROC (HSROC) model.Study Design and SettingTwenty-six data sets from existing test accuracy systematic reviews were reanalyzed with the Moses–Littenberg model, using equal weighting (“E-ML”) and weighting by the inverse variance of the log DOR (“W-ML”), and with the HSROC model. The diagnostic odds ratios (DORs) were estimated and covariates added to both models to estimate relative DORs (RDORs) between subgroups. Models were compared by calculating the ratio of DORs, the ratio of RDORs, and P-values for detecting asymmetry and effects of covariates on DOR.ResultsCompared to the HSROC model, the Moses–Littenberg model DOR estimates were a median of 22% (“E-ML”) and 47% (“W-ML”) lower at Q*, and 7% and 42% lower at the central point in the data. Instances of the ML models giving estimates higher than the HSROC model also occurred. Investigations of heterogeneity also differed; the Moses–Littenberg models on average estimating smaller differences in RDOR.ConclusionsMoses–Littenberg meta-analyses can generate lower estimates of test accuracy, and smaller differences in accuracy, compared to mathematically superior hierarchical models. This has implications for the usefulness of meta-analyses using this approach. We recommend meta-analysis of diagnostic test accuracy studies to be conducted using available hierarchical model–based approaches

Simulation study of magnetic holes at the Earth's collisionless bow shock

Recent observations by the Cluster and Double Star spacecraft at the Earth's bow shock have revealed localized magnetic field and density holes in the solar wind plasma. These structures are characterized by a local depletion of the magnetic field and the plasma density, and by a strong increase of the plasma temperature inside the magnetic and density cavities. Our objective here is to report results of a hybrid-Vlasov simulations of ion-Larmor-radius sized plasma density cavities with parameters that are representative of the high-beta solar wind plasma at the Earth's bow shock. We observe the asymmetric self-steepening and shock-formation of the cavity, and a strong localized temperature increase (by a factor of 5–7) of the plasma due to reflections and shock surfing of the ions against the collisionless shock. Temperature maxima are correlated with density minima, in agreement with Cluster observations. For oblique incidence of the solar wind, we observe efficient acceleration of ions along the magnetic field lines by the shock drift acceleration process

Performance of methods for meta-analysis of diagnostic test accuracy with few studies or sparse data

Hierarchical models such as the bivariate and hierarchical summary receiver operating characteristic (HSROC) models are recommended for meta-analysis of test accuracy studies. These models are challenging to fit when there are few studies and/or sparse data (for example zero cells in contingency tables due to studies reporting 100% sensitivity or specificity); the models may not converge, or give unreliable parameter estimates. Using simulation, we investigated the performance of seven hierarchical models incorporating increasing simplifications in scenarios designed to replicate realistic situations for meta-analysis of test accuracy studies. Performance of the models was assessed in terms of estimability (percentage of meta-analyses that successfully converged and percentage where the between study correlation was estimable), bias, mean square error and coverage of the 95% confidence intervals. Our results indicate that simpler hierarchical models are valid in situations with few studies or sparse data. For synthesis of sensitivity and specificity, univariate random effects logistic regression models are appropriate when a bivariate model cannot be fitted. Alternatively, an HSROC model that assumes a symmetric SROC curve (by excluding the shape parameter) can be used if the HSROC model is the chosen meta-analytic approach. In the absence of heterogeneity, fixed effect equivalent of the models can be applied

Physical soil quality indicators for monitoring British soils

The condition or quality of soils determines its ability to deliver a range of functions that support ecosystem services, human health and wellbeing. The increasing policy imperative to implement successful soil monitoring programmes has resulted in the demand for reliable soil quality indicators (SQIs) for physical, biological and chemical soil properties. The selection of these indicators needs to ensure that they are sensitive and responsive to pressure and change e.g. they change across space and time in relation to natural perturbations and land management practices. Using a logical sieve approach based on key policy-related soil functions, this research assessed whether physical soil properties can be used to indicate the quality of British soils in terms of its capacity to deliver ecosystem goods and services. The resultant prioritised list of physical SQIs were tested for robustness, spatial and temporal variability and expected rate of change using statistical analysis and modelling. Six SQIs were prioritised; packing density, soil water retention characteristics, aggregate stability, rate of erosion, depth of soil and soil sealing. These all have direct relevance to current and likely future soil and environmental policy and are appropriate for implementation in soil monitoring programs

CD32-RNA Co-localizes with HIV-RNA in CD3+ Cells Found within Gut Tissues from Viremic and ART-Suppressed Individuals.

BackgroundIdentifying biomarkers for cells harboring replication-competent HIV is a major research priority. Recently, there have been mixed reports addressing the possibility that CD32-expressing T cells are enriched for HIV. There is growing evidence that CD32 expression increases with cellular activation that may be related to, but not necessarily specific for, infection with HIV. However, the relationship of CD32 expression to HIV-infection in subtypes of tissue-resident leukocytes is unclear.MethodsFirst, we used duplex chromogenic in situ hybridization to identify cells actively transcribing RNA for both CD32 and HIV on human gut tissues. Then we performed multiplexed immunofluorescence and in situ hybridization (mIFISH) on sections from the same tissues to determine the phenotype of individual cells co-expressing HIV-RNA and CD32-RNA.ResultsHIV-RNA+ cells were more abundant in tissues from viremic individuals than in those receiving suppressive anti-retroviral therapy (ART). However, staining by both methods indicated that a higher proportion of HIV-RNA+ cells co-expressed CD32-RNA in ART-suppressed individuals than in those with viremia. The majority of HIV-RNA+ cells were CD3+.ConclusionsOur data suggest that the transcription of CD32-RNA is correlated with HIV transcriptional activity in CD3+ cells found within human gut tissue. Whether or not up-regulation of CD32-RNA is a direct result of HIV transcription or more global T-cell activation remains unclear

NETWORKED2‐Subfamily Proteins Regulate the Cortical Actin Cytoskeleton of Growing Pollen Tubes and Polarised Pollen Tube Growth

We have recently characterised NET2A as a pollen‐specific actin‐binding protein which binds F‐actin at the plasma membrane of growing pollen tubes. However, the role of NET2 proteins in pollen development and fertilisation have yet to be elucidated. To further characterise the role of Arabidopsis NET2 proteins in pollen development and fertilisation, we analysed the subcellular localisation of NET2A over the course of pollen grain development, and investigated the role of the NET2 family using net2 loss‐of‐function mutants. We observed NET2A to localise to the F‐actin cytoskeleton in developing pollen grains as it underwent striking structural reorganisations at specific stages of development and during germination, and pollen tube growth. Furthermore, net2 loss‐of‐function mutants exhibited striking morphological defects in the early stages of pollen tube growth, arising from frequent alterations to pollen tube growth trajectory. We observed defects in the cortical actin cytoskeleton and actin‐driven subcellular processes in net2 mutant pollen tubes. We demonstrate that NET2 proteins are essential for normal actin‐driven pollen development highlighting an important role for the NET2 family members in regulating pollen tube growth during fertilisation