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Background: Studies on the relationship between thyroid function and non-alcoholic fatty liver disease (NAFLD) among euthyroid subjects have produced conflicting results.Objectives: The aim of this study was to investigate the association between thyroid function and the presence of NAFLD in a large-sample middle-aged euthyroid subjects.Methods: A total of 2576 euthyroid subjects who underwent health check-up were included. NAFLD was diagnosed by hepatic ultrasonography. Conventional risk factors for NAFLD were assessed as well as serum levels of TSH, FT3 and FT4.Results: Levels of FT3 were significantly higher in NAFLD group (5.12 ± 0.58 versus 4.84 ± 0.58 pmol/L, adjustedp = 0.000) than non-NAFLD group, while levels of TSH and FT4 were comparable between NAFLD and non-NAFLD groups (TSH: 2.13 ± 0.90 versus 2.20 ± 0.93 mIU/L, adjustedp = 0.190; FT4: 16.41 ± 2.04 versus 16.18 ± 2.06 pmol/L, adjustedp = 0.146, respectively). Levels of FT3 were positively correlated with components of metabolic syndrome. Multivariate logistic regression analysis revealed that high level of FT3 was an independent predictor for NAFLD (odds ratio: 1.253,p = 0.040). The relationship between FT4 and NAFLD in women was different according to menopausal status, with negative association in pre-menopausal women (OR: 0.777, 95% CI: 0.617–0.979,p = 0.032) and null association in post-menopausal women (OR: 1.037, 95% CI: 0.841–1.277,p = 0.736).Conclusions: Our findings suggested that high levels of FT3 were significantly associated with NAFLD among middle-aged euthyroid subjects independently of known metabolic risk factors. A negative correlation of serum FT4 level with NAFLD was only observed in pre-menopausal women. [ABSTRACT FROM AUTHOR]

• A ultiscale reweighted convolutional coding neural network (MRCON-Net) for reducing artifact noise in low-dose CT imaging is proposed. • In this work, w extend traditional CSC to its reweighted convolutional learning form. Furthermore, we use dilated convolution to extract multiscale image features, which allows our single model to capture the correlations between features of different scales. • To automatically adjust the elements in the feature code to correct the obtained solution, channel attention is utilized to learn appropriate weights. Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue. A multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights. The visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results. Compared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements. [ABSTRACT FROM AUTHOR]

• R. pseudoacacia increased the fine particles and reduced coarse particles content. • R. pseudoacacia significantly reduced D 0 , D 1 , D 2 but increased Δα and Δf. • R. pseudoacacia suppress the coarsening and deterioration of the surface. The soil particle size distribution (PSD) is a fundamental physical property that can affect soil nutrients, soil structure characterization and soil hydraulic properties. However, the effect of plant expansion on soil PSD is not clear. Therefore, in this study, fractal theory was applied to quantitatively describe the PSD. On Mount Tai, artificial afforestation with Robinia pseudoacacia is helpful in restoring ecosystems and improving soil quality. However, clonal spread allows R. pseudoacacia to easily escape cultivation, leading to the formation of mixed forests. Therefore, exploring the effect of R. pseudoacacia expansion on soil PSD is important. The results show that 1) R. pseudoacacia expansion increased the clay and silt contents and reduced the sand content; 2) R. pseudoacacia expansion significantly reduced the capacity dimension, entropy dimension, and correlation dimension and increased the width and asymmetry of the singularity spectra but was unrelated to the entropy dimension/capacity dimension; and 3) the capacity dimension was significantly positively correlated with the sand content and significantly negatively correlated with the silt and clay contents, but the asymmetry of the soil PSD was inconsistent with the capacity dimension. In summary, R. pseudoacacia can reduce the heterogeneity of the PSD by increasing the distribution range and density of the PSD. Our results provide important insight into the effect of plant expansion on soil PSD. [ABSTRACT FROM AUTHOR]

The structural, magnetic, and magnetocaloric properties of Ni 44 Mn 45-x Sn 11 Fe x (x = 0, 1, 2 and 3) Heusler alloys have been studied detailly by means of X-ray diffraction, differential scanning calorimetry, and magnetization measurements. The martensitic transition temperature (T M) and austenite Curie temperature (T A C) increase gradually with increasing Fe content. Large differences in magnetization between the ferromagnetic austenite and weakly magnetic martensite phases and obvious thermal hysteresis have also been observed during the martensite transformation. With the magnetic field change up to 0–7 T, the maximum value of magnetic entropy changes reaches 26.7 and 23 J kg−1K−1 for x = 1 and 2, respectively, which are much larger than the undoped sample (9.8 J kg−1K−1). Whereas, the effective refrigerant capacity RC eff reduced due to Fe doping increased the hysteresis loss. The alloy for x = 1 has highest RC value of 172 (255) J kg−1 for a magnetic field change of 0–5 T (0–7 T), furthermore, a large magnetic entropy change and moderate RC eff has been obtained around room temperature. • Fe-doped Ni44Mn45-xSn11Fex (x = 0, 1, 2 and 3) Heusler alloys were fabricated. • Structural and magnetic properties of Ni44Mn45-xSn11Fex were investigated. • Considerable magnetocaloric performances in Ni44Mn44Sn11Fe were realized. [ABSTRACT FROM AUTHOR]

• Proposed a method to estimate dual-energy CT images from traditional single energy CT image together with a single view projection at a different energy level. • Deep-learning algorithm is used to perform material-decomposition-like operations. • The approach can provide superior material-specific images with significantly reduced noise compared with standard DECT imaging. [Display omitted] Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners. [ABSTRACT FROM AUTHOR]

• The optimum ball-milling time is 3–5 h to obtain amorphous alloy. • The theory of the energy barrier and schematic diagram of atomic arrangement were adopted to interpret the amorphization and crystallization mechanisms. • The uniform microstructure was obtained by annealed at 700 ℃ for 20 min. • The coercivity (H cj) and the remanence (B r) are increased by 1.28 kOe and 23.6 emu/g, respectively. The phase transformation mechanism and magnetic properties of the Sm-Fe alloy powders produced by melt-spinning and high-energy ball milling are investigated. The amorphous phase is stable between 2 and 5 h, and the SmFe 9 phase is precipitated from the amorphous matrix after milled for 5 h. The time of 3–5 h is optimal to obtain the largest number of short-range/medium-range order in the amorphous alloy, which can contribute to the formation of uniform microstructure in the subsequent crystallization annealing process. The magnetic properties of the nitrided powders produced by crystallizing the amorphous precursors are improved compared to the properties of those produced by direct melt-spinning. The coercivity (H cj) and the remanence (B r) are increased by 1.28 kOe and 23.6 emu/g, respectively. This kind of micro-regulation plays a key role in improving the magnetic properties after nitrogenating and lays the foundation for the production of bulk materials in the future. [ABSTRACT FROM AUTHOR]

Through analyzing the transient components and transient characteristics in transient zero-sequence current (TZSC), a novel fault feeder detection method based on the transient correlation of non-power frequency components (NPFCs) for the resonant grounded system is proposed. Firstly, using variational mode decomposition combined with fast Fourier transformation (VMD–FFT) to decompose the TZSC, by removing the power frequency components and noise signals, the transient NPFCs can be obtained. Secondly, to reflect the overall changing trend between faulty and healthy currents, the moving average filter is introduced to smooth the NPFCs; in this way, the fault transient features can be accurately revealed. Finally, the faulty feeder can be detected by comparing the threshold with the maximum difference value of comprehensive correlation coefficient of NPFCs. The detection results show that the proposed fault detection method can accurately select the faulty feeder; it is unaffected by fault resistances, fault phase angles, etc. Moreover, the detection method can resist noise interference. [ABSTRACT FROM AUTHOR]

• Weak shallow detachment in the Longmen Shan belt can lead the deformation into the foreland. • Duplex detachment has controls on the out-of-sequence style of the Longmen Shan belt. • The current structural pattern of the Longmen Shan belt is produced by upper crustal thrusting. The topic of the Longmen Shan fold-and-thrust belt has been vigorously debated due to the steep topography in contrast with the low convergence rate and earthquake hazards. The Longmen Shan fold-and-thrust belt is characterized by two main detachments: a strong lower detachment at 15–17 km depth in the hinterland and a weak upper detachment at ~7 km depth in the Sichuan Basin. Nevertheless, how the two detachments control deformation in the Longmen Shan remains unclear. In this study, we focus on the mechanical strengths of the two detachments in the study area and design three analog models to investigate the kinematics and mechanisms of the Longmen Shan fold-and-thrust belt. All three model experiments have the same strong lower detachments at the basement, but different upper detachments. The results indicate that for Model 1 with no upper detachment and Model 2 with a strong frictional upper detachment, the strain and deformation only concentrate near the mobile backwall, and particle image velocimetry analysis reveals that both models deform in the forward in-sequence style. However, for Model 3 with a weak ductile upper detachment, the strain and deformation propagate into the foreland, and the model deforms in the out-of-sequence style consistent with the Longmen Shan fold-and-thrust belt. The model results indicate that the spatial relation of strong lower detachment and weak upper detachment may be one of the important factors producing the current structural pattern and the out-of-sequence style of the Longmen Shan fold-and-thrust belt. [ABSTRACT FROM AUTHOR]

• The magnetism and magnetocaloric effect (MCE) were investigated in Er 4 CoCd compound. • A large magnetocaloric effect in Er 4 CoCd compound was observed. • The origin of MCE and its potential application in Er 4 CoCd were discussed. The Er-based compound Er 4 CoCd was synthesized successfully and its structural, magnetic together with magnetocaloric properties were investigated in detail. The Er 4 CoCd crystallized in a Gd 4 RhIn-type structure with the space group of F 4 ¯ 3 m , which was confirmed by Rietveld refinement. A ferromagnetic transition was observed around its Curie temperature (T C) of 12.5 K accompanied by a large magnetocaloric effect (MCE). The performance of MCE for Er 4 CoCd was evaluated by the isothermal maximal magnetic entropy (- Δ S M max) , the temperature averaged entropy change (TEC) at 10 K temperature lift [ TEC (10 K)], and the relative cooling power (RCP). The values of - Δ S M max , TEC (10 K), and RCP of Er 4 CoCd are 20.4 J/kg K, 19.2 J/kg K, and 503 J/kg under the magnetic field change (Δ H) of 0–5 T, respectively. [ABSTRACT FROM AUTHOR]