To examine the association of metabolic syndrome (MetS) and its components with knee pain severity trajectories.
Data from a population-based cohort study were utilised. Baseline blood pressure, ...glucose, triglycerides and high-density lipoprotein (HDL) cholesterol were measured. MetS was defined according to the National Cholesterol Education Program-Adult Treatment Panel III criteria. Radiographic knee osteoarthritis (ROA) was assessed by X-ray. Pain severity was measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain questionnaire at each time-point. Group-based trajectory modelling was used to identify pain trajectories and multi-nominal logistic regression was used for analysis. Mediation analysis was performed to assess whether body mass index (BMI)/central obesity mediated the association between MetS, its components and pain trajectories.
Among 985 participants (Mean ± SD age: 62.9 ± 7.4, 50% female), 32% had MetS and 60% had ROA. Three pain trajectories were identified: ‘Minimal pain’ (52%), ‘Mild pain’ (33%) and ‘Moderate pain’ (15%). After adjustment for potential confounders, central obesity increased risk of belonging to both ‘Mild pain’ and ‘Moderate pain’ trajectories as compared to the ‘Minimal pain’ trajectory group, but MetS relative risk ratio (RRR): 2.26, 95%CI 1.50–3.39, hypertriglyceridemia (RRR: 1.75, 95%CI 1.16–2.62) and low HDL (RRR: 1.67, 95%CI 1.10–2.52) were only associated with ‘Moderate pain’ trajectory. BMI/central obesity explained 37–70% of these associations. Results were similar in those with ROA.
MetS and its components are predominantly associated with worse pain trajectories through central obesity, suggesting that the development and maintenance of worse pain trajectories may be caused by MetS.
In this study we compile and present results from the factor analysis of 43 Aerosol Mass Spectrometer (AMS) datasets (27 of the datasets are reanalyzed in this work). The components from all sites, ...when taken together, provide a holistic overview of Northern Hemisphere organic aerosol (OA) and its evolution in the atmosphere. At most sites, the OA can be separated into oxygenated OA (OOA), hydrocarbon-like OA (HOA), and sometimes other components such as biomass burning OA (BBOA). We focus on the OOA components in this work. In many analyses, the OOA can be further deconvolved into low-volatility OOA (LV-OOA) and semi-volatile OOA (SV-OOA). Differences in the mass spectra of these components are characterized in terms of the two main ions m/z 44 (CO2+) and m/z 43 (mostly C2H3O+), which are used to develop a new mass spectral diagnostic for following the aging of OA components in the atmosphere. The LV-OOA component spectra have higher f44 (ratio of m/z 44 to total signal in the component mass spectrum) and lower f43 (ratio of m/z 43 to total signal in the component mass spectrum) than SV-OOA. A wide range of f44 and O:C ratios are observed for both LV-OOA (0.17±0.04, 0.73±0.14) and SV-OOA (0.07±0.04, 0.35±0.14) components, reflecting the fact that there is a continuum of OOA properties in ambient aerosol. The OOA components (OOA, LV-OOA, and SV-OOA) from all sites cluster within a well-defined triangular region in the f44 vs. f43 space, which can be used as a standardized means for comparing and characterizing any OOA components (laboratory or ambient) observed with the AMS. Examination of the OOA components in this triangular space indicates that OOA component spectra become increasingly similar to each other and to fulvic acid and HULIS sample spectra as f44 (a surrogate for O:C and an indicator of photochemical aging) increases. This indicates that ambient OA converges towards highly aged LV-OOA with atmospheric oxidation. The common features of the transformation between SV-OOA and LV-OOA at multiple sites potentially enable a simplified description of the oxidation of OA in the atmosphere. Comparison of laboratory SOA data with ambient OOA indicates that laboratory SOA are more similar to SV-OOA and rarely become as oxidized as ambient LV-OOA, likely due to the higher loadings employed in the experiments and/or limited oxidant exposure in most chamber experiments.
Cell expansion is crucial for plant growth. It is well known that the phytohormone ethylene functions in plant development as a key modulator of cell expansion. However, the role of ethylene in the ...regulation of this process remains unclear. In this study, 2,189 ethylene-responsive transcripts were identified in rose (Rosa hybrida) petals using transcriptome sequencing and microarray analysis. Among these transcripts, an NAC (for no apical meristem NAM, Arabidopsis transcription activation factor ATAF, and cup-shaped cotyledon CUC)-domain transcription factor gene, RhNAC100, was rapidly and dramatically induced by ethylene in the petals. Interestingly, accumulation of the RhNAC100 transcript was modulated by ethylene via microRNA164-dependent posttranscriptional regulation. Overexpression of RhNAC100 in Arabidopsis (Arabidopsis thaliana) substantially reduced the petal size by repressing petal cell expansion. By contrast, silencing of RhNAC100 in rose petals using virus-induced gene silencing significantly increased petal size and promoted cell expansion in the petal abaxial subepidermis (P < 0.05). Expression analysis showed that 22 out of the 29 cell expansion-related genes tested exhibited changes in expression in RhNAC100-silenced rose petals. Moreover, of those genes, one cellulose synthase and two aquaporin genes (Rosa hybrida Cellulose Synthase2 and R. hybrida Plasma Membrane Intrinsic Protein1;1/2;1) were identified as targets of RhNAC100. Our results suggest that ethylene regulates cell expansion by fine-tuning the microRNA164/RhNAC100 module and also provide new insights into the function of NAC transcription factors.
The Alpha Magnetic Spectrometer (AMS) is a precision particle physics detector on the International Space Station (ISS) conducting a unique, long-duration mission of fundamental physics research in ...space. The physics objectives include the precise studies of the origin of dark matter, antimatter, and cosmic rays as well as the exploration of new phenomena. Following a 16-year period of construction and testing, and a precursor flight on the Space Shuttle, AMS was installed on the ISS on May 19, 2011. In this report we present results based on 120 billion charged cosmic ray events up to multi-TeV energies. This includes the fluxes of positrons, electrons, antiprotons, protons, and nuclei. These results provide unexpected information, which cannot be explained by the current theoretical models. The accuracy and characteristics of the data, simultaneously from many different types of cosmic rays, provide unique input to the understanding of origins, acceleration, and propagation of cosmic rays.
Abstract
We present the second release of value-added catalogues of the LAMOST Spectroscopic Survey of the Galactic Anticentre (LSS-GAC DR2). The catalogues present values of radial velocity Vr, ...atmospheric parameters – effective temperature Teff, surface gravity log g, metallicity Fe/H, α-element to iron (metal) abundance ratio α/Fe (α/M), elemental abundances C/H and N/H and absolute magnitudes MV and $M_{K_{\rm s}}$ deduced from 1.8 million spectra of 1.4 million unique stars targeted by the LSS-GAC since 2011 September until 2014 June. The catalogues also give values of interstellar reddening, distance and orbital parameters determined with a variety of techniques, as well as proper motions and multiband photometry from the far-UV to the mid-IR collected from the literature and various surveys. Accuracies of radial velocities reach 5 km s−1 for the late-type stars, and those of distance estimates range between 10 and 30 per cent, depending on the spectral signal-to-noise ratios. Precisions of Fe/H, C/H and N/H estimates reach 0.1 dex, and those of α/Fe and α/M reach 0.05 dex. The large number of stars, the contiguous sky coverage, the simple yet non-trivial target selection function and the robust estimates of stellar radial velocities and atmospheric parameters, distances and elemental abundances make the catalogues a valuable data set to study the structure and evolution of the Galaxy, especially the solar-neighbourhood and the outer disc.
Hydraulic tomography (HT) is a recently developed technology for characterizing high‐resolution, site‐specific heterogeneity using hydraulic data (nd) from a series of cross‐hole pumping tests. To ...properly account for the subsurface heterogeneity and to flexibly incorporate additional information, geostatistical inverse models, which permit a large number of spatially correlated unknowns (ny), are frequently used to interpret the collected data. However, the memory storage requirements for the covariance of the unknowns (ny × ny) in these models are prodigious for large‐scale 3‐D problems. Moreover, the sensitivity evaluation is often computationally intensive using traditional difference method (ny forward runs). Although employment of the adjoint method can reduce the cost to nd forward runs, the adjoint model requires intrusive coding effort. In order to resolve these issues, this paper presents a Reduced‐Order Successive Linear Estimator (ROSLE) for analyzing HT data. This new estimator approximates the covariance of the unknowns using Karhunen‐Loeve Expansion (KLE) truncated to nkl order, and it calculates the directional sensitivities (in the directions of nkl eigenvectors) to form the covariance and cross‐covariance used in the Successive Linear Estimator (SLE). In addition, the covariance of unknowns is updated every iteration by updating the eigenvalues and eigenfunctions. The computational advantages of the proposed algorithm are demonstrated through numerical experiments and a 3‐D transient HT analysis of data from a highly heterogeneous field site.
Key Points
The Reduced‐Order Successive Linear Estimator can solve highly parameterized geostatistical inverse problem
Approximation of covariance leads to affordable memory and computational cost
The uncertainty of the estimates can be updated every iteration efficiently
AbstractPermeability of subsurface porous media is one of the primary factors that affect fluid transport in porous rock. However, accurate prediction of rock permeability is a challenging task due ...to its intricate pore network. Development of digital rocks provides an effective approach to reveal and characterize the pore network. In this paper, a combination of digital rock petrophysics and ensemble machine learning (ML) models is proposed to improve the permeability prediction of subsurface porous media. The permeability of the numerically generated porous samples as outputs was determined by the lattice Boltzmann method (LBM). The five most important parameters (porosity, tortuosity, fractal dimension, average pore diameter, and coordination number) were selected as inputs for the permeability prediction. To improve the accuracy, feature selection and ML methods comparisons were conducted. Three feature selection methods based on expert knowledge, correlation coefficient, and importance score were compared. Moreover, a comparison was performed on six ML methods (support vector machine, artificial neural network, decision tree, random forest, gradient-boosting machine, and Bayesian ridge regression) that were optimized by particle swarm optimization (PSO). The results indicated that (1) the feature selection based on the expert knowledge obtained a higher performance than the groups based on the correlation coefficient and importance score, implying the importance of expert knowledge on feature selection, and thus on ML performance; (2) artificial neural network with hyperparameter tuning achieved the best performance in predicting permeability; and (3) the optimized ML method outperformed the empirical equations in predicting permeability. In conclusion, this study provides a fast and reliable approach predicting permeability of subsurface porous media based on numerically generated porous images. Moreover, the proposed framework can be further extended to determine other petrophysical properties, for example, the relative permeability and thermal conductivity.
Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at ...different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site‐specific ones (e.g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site‐specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site‐specific large‐scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometer‐scale‐fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.
Key Points
Sequential kriging incorporates site‐specific large‐scale geologic features
Residual covariance includes uncertainties of the geologic zones, zone properties, and heterogeneity within the zone
Residual covariance reflecting geologic information improves hydraulic tomographic survey analysis
With the photometric data from the SDSS survey, the spectroscopic data from the SDSS/SEGUE and the LAMOST surveys, and the astrometric data from the Gaia DR2, we have identified 67 highly probable ...member stars of the GD-1 cold stellar stream spread along almost its entire length (i.e., from 126° to 203° in R.A.). With the accurate spectroscopic (i.e., metallicity and line-of-sight velocity) and astrometric (i.e., proper motions) information, the position-velocity diagrams, i.e., φ1- , φ1- δ, and φ1-vgsr, of the GD-1 stream are well mapped. The stream has an average metallicity Fe/H = −1.96. The rich information of member stars of the stream now available allow one not only to model its origin, but also to place strong constraints on the mass distribution and the gravitational potential of the Milky Way.
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