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To investigate the effects of COVID-19 on individuals with tinnitus and their views to guide future tinnitus care. A mixed-methods cross-sectional research design. An online survey was completed by 365 individuals with tinnitus from Australia and other countries. Tinnitus was reported to be more bothersome during the pandemic by 36% of respondents, whereas 59% reported no change and 5% reported less bothersome tinnitus. Nearly half of the respondents had received COVID-19 vaccination(s) and 12% of them reported more bothersome tinnitus while 2% developed tinnitus post-vaccination. Australian respondents spent less time in self-isolation or quarantine and saw fewer change in in-person social contact than respondents from other countries. More than 70% of respondents thought that tinnitus care services were insufficient both before and during the pandemic. Regarding their opinions on how to improve tinnitus care in the future, five themes including alleviation of condition, government policies, reduced barriers, self- and public-awareness, and hearing devices were identified. A majority of respondents did not perceive any change in tinnitus perception and one-third of respondents had worsened tinnitus during the pandemic. To improve tinnitus care, better awareness and more accessible resources and management are crucial. [ABSTRACT FROM AUTHOR]

Atherosclerosis (AS) is the most common inducer of cardiovascular diseases, and resveratrol (RSV) has played a protective function in the endothelial injury of AS. This study was to explore the molecular mechanism of RSV in oxidized low-density lipoprotein (ox-LDL)-mediated endothelial dysfunction. Circ_0091822, microRNA-106b-5p (miR-106b-5p) or toll-like receptor (TLR4) levels were examined using reverse transcription-quantitative polymerase chain reaction assay. Cell viability was detected via Cell Counting Kit-8 assay and angiogenesis was assessed by tube formation assay. Cell apoptosis was determined through flow cytometry. The protein analysis was conducted via western blot. Inflammatory cytokines were measured by enzyme-linked immunosorbent assay. The oxidative injury was evaluated using the commercial kits. The binding detection was performed via dual-luciferase reporter assay and RNA pull-down assay. Circ_0091822 was downregulated by RSV in ox-LDL-treated endothelial cells. RSV promoted cell viability and angiogenesis while inhibiting apoptosis, inflammation, and oxidative stress after exposure to ox-LDL. The circ_0091822 knockdown relieved the ox-LDL-induced cell damages. RSV suppressed the ox-LDL-caused endothelial dysfunction via inducing the downregulation of circ_0091822. Circ_0091822 could target miR-106b-5p, and the reversal of circ_0091822 for RSV function was achieved by sponging miR-106b-5p. Circ_0091822 absorbed miR-106b-5p to elevate the level of TLR4. RSV impeded ox-LDL-induced damages by regulating miR-106b-5p/TLR4 axis. All these findings suggested that RSV acted as an inhibitory factor in ox-LDL-induced endothelial injury via downregulating circ_0091822 to upregulate miR-106b-5p-related TLR4. [ABSTRACT FROM AUTHOR]

Linear spectral mixture analysis (LSMA) and regression analysis are the two most conventionally used methods to estimate impervious surfaces at the subpixel scale in an urban area. However, LSMA lacks the sensitivity to pixel brightness, which leads to inter variability of endmembers and affects the ability to distinguish features with a similar spectral signature. This research aims to develop LSMA aided by a regression analysis model to estimate impervious surfaces with higher accuracy. A spectral angle mapping (SAM) based regression analysis model is introduced to reduce errors. Based on high-resolution images and field survey data, the SAM-based regression analysis can estimate non-impervious surface and high-impervious surface densities with high accuracy, while less accurate in impervious surfaces with low/medium density. In contrast, LSMA is able to estimate low/medium-density impervious surfaces with higher accuracy. We propose an improved approach by integrating the two methods, regression analysis aided LSMA, for impervious surface estimation. The proposed method increases the overall accuracy of the impervious surface estimation to 85.24%, which is significantly greater than that of the conventional methods. [ABSTRACT FROM AUTHOR]

This research extends the partially confirmatory approach to accommodate mixed types of data and missingness in a unified framework that can address a wide range of the confirmatory-exploratory continuum in factor analysis. A mix of Bayesian adaptive and covariance Lasso procedures was developed to estimate model parameters and regularize the loading structure and local dependence simultaneously. Several model variants were offered with different constraints for identification. The less-constrained variant can achieve sufficient condition for the more-powerful variant, although loading estimates associated with local dependence can be inflated. Parameter recovery was satisfactory, but the information on local dependence was partially lost with categorical data or missingness. A real-life example illustrated how the models can be used to obtain a more discernible loading pattern and to identify items that do not measure what they are supposed to measure. The proposed methodology has been implemented in the R package LAWBL. [ABSTRACT FROM AUTHOR]

Different from traditional practice that considers factor analysis as either exploratory or confirmatory, different amounts of substantive information can be available in between the confirmatory and exploratory extremes under the partially confirmatory approach. Based on Bayesian Lasso methods, three models were recently proposed for various types of data under the new approach: the partially confirmatory factor analysis (PCFA), generalized PCFA, and partially confirmatory item response model. All models with related variants can be implemented in the R package LAWBL, which is available free of charge. This article introduces the theoretical and statistical foundation of the three models in a unified framework, including model formulation, identification, variants, and Bayesian inference and estimation with regularizations. Didactic examples covering different scenarios are employed to illustrate the implementation of the models and their variants in LAWBL step by step. Guidelines and suggestions are given to researchers and practitioners in a discussion. [ABSTRACT FROM AUTHOR]

This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the immunohistochemical features of different stages of BK virus allograft nephropathy (BKVN) and further elucidate the underlying immunological mechanism involved in the evolution of BKVN. Methods: Fifty-two renal transplant recipients with biopsy proven BKVN were retrospectively selected. According to the third edition of the American Society of Transplantation Infection guidelines, 10 patients were categorized as having mild BKVN (stage A), 25 were moderate (stage B) and 17 were severe (stage C). The differential infiltrations of CD3+ (T lymphocytes), CD4+ (helper T lymphocytes), CD8+ (cytotoxic T lymphocytes), CD20+ (B lymphocytes), CD68+ (macrophages) and CD138+ (plasma cells) cells and the expression of interleukin-2 receptor (IL-2R) and human leukocyte antigen DR (HLA-DR) were compared among the three groups. Results: CD3+, CD4+, CD8+, CD20+, CD138+ and CD68+ cells infiltrations, IL-2R and HLA-DR expression were positive in the BKVN patients. Moreover, with increasing stages of BKVN, the numbers of positively stained inflammatory cells and the expression of IL-2R were significantly increased in the severe group compared to the mild group, whereas no statistically significant differences were observed with regard to HLA-DR expression. Eosinophil and neutrophil infiltration could also be observed in moderate to advanced BKVN. Conclusion: Renal allograft damage caused by BKVN involved T lymphocyte-, B lymphocyte- and mononuclear macrophage-mediated immune responses. Inflammatory cell infiltrations in the renal allograft were probably the driving force for BKVN progression. Additionally, eosinophils and neutrophils may be involved in the pathophysiological mechanism of BKVN. [ABSTRACT FROM AUTHOR]

Objectives: BK virus (BKV) infection has become one of the main complications in renal transplant recipients (RTRs) with the arrival of newer potent immunosuppressive agents. However, reports on the epidemiology of BKV infection and risk factors in Chinese population after renal transplantation are scarce. Methods: From June 2015 to July 2016, living-donor renal transplant recipients (LDRTRs) who routinely received the quantitative BKV DNA testing of urine and plasma samples using quantitative real-time polymerase chain reaction (PCR) for the first time after transplantation were selected, while dialysis patients and healthy living donors during that period served as controls. Potential variables were compared and analyzed using logistic regression model multivariate analysis to assess the BKV infection related factors in LDRTRs. Results: Among the 52 LDRTRs identified, BKV DNA was detected in 16 urine samples (30.8%), significantly higher than that of dialysis patients (6.3%) and healthy living donors (4.2%) (p < .001). Nevertheless, no statistically significant difference wax noted between the latter two groups in urine samples (p = .842). Meanwhile, BKV DNA detection in blood samples was all negative in the three groups. Univariate analysis shown tacrolimus (Tac) trough level and lymphocyte percentage were associated with BKV infection in LDRTRs. Multivariate regression analysis also showed Tac trough level (HR, 1.644; p = .03), lymphocyte percentage (HR, 0.878; p = .026) were associated with BKV infection in LDRTRs. Conclusions: In Chinese population, the incidence of BKV infection increased significantly after living-donor renal transplantation. Significantly increased Tac trough level and decreased lymphocyte percentage might be the risk factors for BKV infection in LDRTRs. [ABSTRACT FROM AUTHOR]

Rubber expansion has been widely explored at various scales due to its increasing environmental and socio-economic impact and the global demand for natural rubber products. However, accurate and up-to-date maps and large-scale monitoring of rubber plantation spatial distribution are not yet available. In this article, we developed a simple algorithm for rapidly and accurately mapping rubber plantations on Hainan Island, China, by combining survey samples and Landsat 8 Operational Land Imager data from 2014 in order to understand their spatial distribution. The results showed that rubber plantations are distinguishable from other land-cover types by band value changes, vegetation index changes, and phenological phase changes (defoliation and foliation) and can be accurately extracted from multi-temporal Landsat images using a decision tree method and Google Earth Engine. This method results in a high overall classification accuracy of 92.17% with a correspondingκof 84.33%. Rubber plantations are concentrated in the northwest of Hainan Island and gradually decrease to the south and east. Most plantations are found in the 50–500-m elevation range, with few found outside this range. We believe that the proposed approach will have significant implications for mapping and monitoring rubber spatial distribution at regional scales. [ABSTRACT FROM PUBLISHER]

Since its first flight in 2007, the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR (PolSAR) data in very high spatial resolution. It is possible to observe small spatial features in this type of data, offering the opportunity to explore structures in the images. In general, the structured scenes would present multimodal or spiky histograms. The finite mixture model has great advantages in modeling data with irregular histograms. In this paper, a type of important statistics called log-cumulants, which could be used to design parameter estimator or goodness-of-fit tests, are derived for the finite mixture model. They are compared with log-cumulants of the texture models. The results are adopted to UAVSAR data analysis to determine which model is better for different land types. [ABSTRACT FROM AUTHOR]

Abstract Postmortem meat tenderization is a process mediated by a series of biochemical reactions related to muscle cell death. Cell death is considered a sign that muscle has started to transform into meat. Mitochondria play a significant role in regulating and executing cell death, as they are an aggregation point for many cell death signals and are also the primary target organelle damaged by tissue anoxia. Mitochondrial damage is likely to have an expanded role in postmortem meat tenderization. This review presents current findings on mitochondrial damage induced by the accumulation of reactive oxygen species during postmortem anaerobic metabolism and on the impact of mitochondrial damage on proteolysis and discusses how this leads to improved tenderness during aging. The underlying mechanisms of mitochondrial regulation of postmortem muscle tenderization likely focus on the mitochondria’s role in postmortem cell death and energy metabolism. The death process of postmortem skeletal muscle cells may exhibit multiple types, possibly involving transformation from autophagy to apoptosis and, ultimately, necroptosis or necrosis. Mitochondrial characteristics, especially membrane integrity and ATP-related compound levels, are closely related to the transformation of multiple types of dead postmortem muscle cells. Finally, a possible biochemical regulatory network in postmortem muscle tenderization is proposed. [ABSTRACT FROM AUTHOR]

Low mathematics achievement is a persistent problem in the United States, and multiplication is a fundamental area in which many students manifest learning difficulties. This study examined the strategic developmental levels of multiplication problem solving among 121 elementary school students in Grades 3 through 5. A latent class analysis modeling was used to identify three valid groups representing different patterns of strategy choices for each of three types of multiplication problems. Findings indicated intra-group variability for problem-solving accuracy, for frequency of using different strategies, and for accuracy of executing direct retrieval/algorithm (DR/AG) strategies. Students demonstrated relative consistency in their strategy choices for solving the three problem types. Students who used DR/AG strategies most frequently showed the highest problem-solving accuracy and the highest accuracy of executing the DR/AG strategies. Students who most frequently relied on incorrect operations or who indicated they did not know how to solve problems demonstrated the lowest problem-solving accuracy among the three groups; the number of students in this group increased with problem difficulty levels. Implications are discussed in terms of identifying students' strategic developmental levels and providing differentiated instruction based on the identified levels. [ABSTRACT FROM AUTHOR]

By analysing the test data of 1029 British secondary school students’ performance on 20 Programme for International Student Assessment English reading items through the generalised deterministic input, noisy ‘and’ gate (G-DINA) model, the study conducted two investigations on exploring the relationships among the five reading comprehension skills defined by six English language experts. Skill groups, skill types and conflicting relationship were discovered in the investigation about the relationship among reading comprehension skills as part of the cognitive structure of the examinees, which provided insights into the sequence of reading skill training. The investigation about the relationship among reading comprehension skills in the problem-solving process, conducted at the item level, conceptualised skill relationship patterns as strategies for solving items. The study also demonstrated that saturated G-DINA model catered to the characteristics of reading comprehension skills and could be applied to tests involving highly interactive and hierarchical skills. [ABSTRACT FROM AUTHOR]

Cognitive diagnosis models (CDMs) are psychometric models developed mainly to assess examinees’ specific strengths and weaknesses in a set of skills or attributes within a domain. By adopting the Generalized-DINA model framework, the recently developed general modeling framework, we attempted to retrofit the PISA reading assessments, a non-cognitive-diagnostic assessment, in three steps: (a) Q-matrices construction based on item content analysis; (b) Q-matrices validation and refinement based on a saturated CDM; and (c) reduced CDMs evaluations based on acceptable Q-matrix. Our research showed that (a) the practice of retrofitting non-cognitive-diagnostic assessment for cognitive diagnostic purpose was feasible; (b) Q-matrix construction and CDM selection were especially important when retrofitting approach was adopted; (c) compensatory and saturated CDMs could fit the PISA reading assessment better than other CDMs; and (d) the application of CDMs to reading assessments may promote research on reading construct. [ABSTRACT FROM PUBLISHER]

In this paper, we propose generalised partial linear single-index mixed models for analysing repeated measures data. A penalised quasi-likelihood approach using P-spline is used to estimate the nonparametric function, linear parameters, and single-index coefficients. Asymptotic properties of the estimators are developed when the dimension of spline basis grows with increasing sample size. Simulation examples and two applications: the study of health effects of air pollution in North Carolina, and treatment effect of naltrexone on health costs for alcohol-dependent individuals, illustrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Recently, the Markov chain Monte Carlo (MCMC) estimation method has become explosively popular in a variety of quantitative research methods. In mediation effect analysis (MEA), the MCMC estimation methods can be a promising tool and an important alternative as compared with traditional methods (e.g., theztest using the delta method and the bias-corrected bootstrapping method) in addressing issues such as nonconvergence and complex modeling. In this article, a subject-level MCMC approach for the single MEA is empirically evaluated and compared with traditional methods through Monte Carlo simulation. The evaluation covers point and interval estimates of both manifest and latent variables across conditions including sample size, effect size, and magnitude of factor loadings. BUGS codes for MEA with both manifest and latent variables are provided that can be easily adapted to fit various MEA models in practice. [ABSTRACT FROM AUTHOR]

The objective of the study is to identify the rice heading date and analyse its spatial characteristics on a regional scale using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) normalized differential vegetation index (NDVI) data and a new approach: quadratic polynomial fitting. The cloud-contaminated NDVI value was identified by reliability data and linearly interpolated with values before and after the cloudy one. The discrete Fourier transformation (DFT) and quadratic polynomial fitting were implemented to generate new time series curves. Rice heading date was retrieved by calculating the day for maximum NDVI. Comparing with DFT, the proposed quadratic polynomial fitting significantly improves the computation efficiency, while providing approximate precision of estimation. In regional analysis, the rice heading date retrieved from polynomial fitting is more consistent than that from DFT. The study also suggests that multi-temporal MODIS NDVI data combined with different methods can retrieve crop phenology information on a large scale. [ABSTRACT FROM AUTHOR]

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R 2 = 0.41 and RMSE/mean = 0.21 for overstorey LAI and R 2 = 0.49 and RMSE/mean = 0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed. [ABSTRACT FROM AUTHOR]

The basic principle of the formation of high-temperature Ni-base alloy reinforced by tungsten (wolfram) carbide (WC) particles using the technique of direct metal laser sintering (DMLS) was introduced. The effect of different process parameters (laser power, scanning rate) on the micro-topography, composition and micro-hardness of the sintered area is studied by means of scanning electron telescope, X-ray diffractometer and micro-hardness tester. At a laser power of 900 W and a scanning rate of 0.7 m/min, the test shows good laser sintering performance and high sintered density. Meanwhile, it indicates that WC particles are molten into dendritic crystals and evenly dispersed in the matrix, and that the reinforced particles are arrested by the solid-liquid interface. The fact that WC particles are well dispersed in the matrix improves the performance of the bulk sintering. According to the test, we have obtained a set of optimal process parameters in production of sintered metal bulks, high in hardness and compact in construction. [ABSTRACT FROM AUTHOR]

Multi-layer spray deposited Al-8.5Fe-1.3V-1.7Si alloy was co-deposited with 15 vol% SiC particulate (SiCP). The microstructure of the composite as-rolled has been determined using transmission electron microscopy (TEM). Elevated temperature properties of the composite have been investigated by comparing the tensile mechanical properties at 20°C, 315°C and 400°C for the composite. The significance of these results for microstructure at elevated temperature, operating strengthening mechanisms and the effects of SiCP reinforcement in such an alloy system, is discussed. [ABSTRACT FROM AUTHOR]

This study investigates a method using multi-temporal, multi-polarization ENVISAT (Environmental Satellite) Advanced Synthetic Aperture Radar (ASAR) data for mapping an agricultural area in a sub-tropical cloud-prone and rainy area of Pearl River Delta in south China. A total of six scenes of HH (radio waves transmitted and received in horizontal polarization) and VV (radio waves transmitted and received in vertical polarization) polarization ASAR data acquired from March to November 2006 were used for land cover classification. Meanwhile, four field surveys of 320 test sites were carried out simultaneously with ASAR image acquisition in May, July, September and November 2006. A decision tree classifier is used to classify seven main types of land cover features including sugarcane, banana fields, lotus ponds, fish ponds, mangrove wetland, seawater and buildings. As a result, a classification map of Nansha Island was generated with overall accuracy of 80% and a kappa coefficient of 77%. The results show that the multi-temporal and multi-polarization ASAR images can have good performance in separating the basic land cover categories in a sub-tropical cloud-prone and rainy area. The decision tree classifier is also approved to work efficiently on satellite SAR images with good classification accuracy. The analysis to get the best combination of radar scenes for the decision tree also proves that multi-temporal radar backscatter information received in the crop growth period is important in improving classification accuracy. [ABSTRACT FROM AUTHOR]

We investigated the relationship between the leaf area index (LAI) of rice and the ENVISAT Advanced Synthetic Aperture Radar (ASAR) vertical/horizontal (VV/HH) polarization ratio. Four alternating polarization ASAR images of swaths IS4 and IS5 over rice fields were used in the study. The VV/HH polarization ratio correlates well with the field-measured LAI and an empirical relationship was established to estimate the LAI of rice using the VV/HH polarization ratio. A theoretical radiative transfer model was adopted to analyse the relationship. The error of the estimated LAI was 0.17 for the test site and a better correlation was found when LAI was less than 3.5. The results suggest that ASAR alternating polarization data can be used to estimate the LAI of rice for wide-area monitoring of rice growth. [ABSTRACT FROM AUTHOR]

Oblique sharp and repetitive stripes in remote sensing imagery can distractingly and obstructively affect the interpretation and application of remote sensing data. A new methodology for removing oblique stripes, based on discrete wavelet transform (DWT), is proposed. DWT has been used to transform a multispectral image into a frequency domain to find the frequency components caused by stripes. The new technique has been tested taking CMODIS imagery as an example and evaluated by a number of indices. The results show that the proposed method can remove oblique stripes effectively and keep most image information. [ABSTRACT FROM AUTHOR]