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Ball milling time is an important factor that affects the rheological properties of shear thickening fluid (STF), which is rarely studied. In this study, SiO2 particles with a diameter of 500 nm and PEG 200 were mixed and milled for 4, 6, 8, 16, and 24 h in an omnidirectional planetary ball mill to obtain the corresponding shear thickening fluids (STFs). Through TGA, SEM, DLS, and other characterization methods, it was found that a long ball milling duration would lead to the surface breakage and wear of SiO2 in STF. The effects of STFs with different milling durations on the inter-yarn friction and puncture resistance of STF-Kevlar composites were also studied. The results show that the STFs produced with a ball milling time of 4 h or 6 h are not uniform, with some semi-solid lump remained. Compared with other milling time durations, the peak viscosity of STF with a milling time of 8 h can better improve the friction between yarns in the fabric and the fabric puncture resistance.

BackgroundThe goal of this study was to develop and validate a radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) preoperatively differentiating luminal and non-luminal molecular subtypes in patients with invasive breast cancer.MethodsOne hundred and thirty-five invasive breast cancer patients with luminal (n = 78) and non-luminal (n = 57) molecular subtypes were divided into training set (n = 95) and testing set (n = 40) in a 7:3 ratio. Demographics and MRI radiological features were used to construct clinical risk factors. Radiomics signature was constructed by extracting radiomics features from the second phase of DCE-MRI images and radiomics score (rad-score) was calculated. Finally, the prediction performance was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsMultivariate logistic regression analysis showed that no clinical risk factors were independent predictors of luminal and non-luminal molecular subtypes in invasive breast cancer patients. Meanwhile, the radiomics signature showed good discrimination in the training set (AUC, 0.86; 95% CI, 0.78–0.93) and the testing set (AUC, 0.80; 95% CI, 0.65–0.95).ConclusionThe DCE-MRI radiomics signature is a promising tool to discrimination luminal and non-luminal molecular subtypes in invasive breast cancer patients preoperatively and noninvasively.

ObjectiveThis study aims to develop and validate the performance of an unenhanced magnetic resonance imaging (MRI)-based combined radiomics nomogram for discrimination between low-grade and high-grade in chondrosarcoma.MethodsA total of 102 patients with 44 in low-grade and 58 in high-grade chondrosarcoma were enrolled and divided into training set (n=72) and validation set (n=30) with a 7:3 ratio in this retrospective study. The demographics and unenhanced MRI imaging characteristics of the patients were evaluated to develop a clinic-radiological factors model. Radiomics features were extracted from T1-weighted (T1WI) images to construct radiomics signature and calculate radiomics score (Rad-score). According to multivariate logistic regression analysis, a combined radiomics nomogram based on MRI was constructed by integrating radiomics signature and independent clinic-radiological features. The performance of the combined radiomics nomogram was evaluated in terms of calibration, discrimination, and clinical usefulness.ResultsUsing multivariate logistic regression analysis, only one clinic-radiological feature (marrow edema OR=0.29, 95% CI=0.11-0.76, P=0.012) was found to be independent predictors of differentiation in chondrosarcoma. Combined with the above clinic-radiological predictor and the radiomics signature constructed by LASSO [least absolute shrinkage and selection operator], a combined radiomics nomogram based on MRI was constructed, and its predictive performance was better than that of clinic-radiological factors model and radiomics signature, with the AUC [area under the curve] of the training set and the validation set were 0.78 (95%CI =0.67-0.89) and 0.77 (95%CI =0.59-0.94), respectively. DCA [decision curve analysis] showed that combined radiomics nomogram has potential clinical application value.ConclusionThe MRI-based combined radiomics nomogram is a noninvasive preoperative prediction tool that combines clinic-radiological feature and radiomics signature and shows good predictive effect in distinguishing low-grade and high-grade bone chondrosarcoma, which may help clinicians to make accurate treatment plans.

ObjectiveThis study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images.Materials and MethodsThe clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis.ResultsMaximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram.ConclusionWe demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization.

Terahertz (THz) metamaterials have attracted great attention due to their widely application potential in smart THz devices; however, most of them are fabricated on rigid substrate and thus limit the exploration of flexible THz electronics. In this paper, a flexible THz metamaterial absorber (MMA) incorporated with phase change material vanadium dioxide (VO2) is proposed. The simulation results indicate that two absorption peaks at around 0.24 THz (marked as A) and 0.46 THz (marked as B) can be observed by designing a I-shaped metamaterial combined with split ring structure. The strong absorption over 92% at 0.24 THz is bending-insensitive, but the absorption at 0.46 THz is bending-sensitive, across the bending angle in the range of 0–50 degrees. Moreover, dynamic modulation of the absorption can be achieved across the insulator-metal phase transition of VO2. Particularly, the absorption of the A-peak can be tuned from 99.4% to 46.9%, while the absorption of the B-peak can be tuned from 39.6% to 99.3%. This work would provide significance for the design of flexible THz smart devices.

Abstract This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR and HR images. They discriminate local patterns expressed by the neighboring pixels along the planar directions but ignore the intrinsic 3D proximity including the depth map. As a special case of general images, the face has limited geometric variations, which we believe that the relevant depth map can be learned and used to guide the face SR task. Motivated by it, we design a network including two branches: one for auxiliary depth map estimation and the other for the main SR task. Adaptive geometric features are further learned from the depth map and used to modulate the mid-level features of the SR branch. The whole network is implemented in an end-to-end trainable manner under the extra supervision of depth map. The supervisory depth map is either a paired one from RGB-D scans or a reconstructed one by a 3D prior model of faces. The experiments demonstrate the effectiveness of the proposed method and achieve improved performance over the state of the arts.

Electronic fitness function (EFF, achieved by the electrical transport properties) as a new quantity to estimate thermoelectric (TE) performance of semiconductor crystals is usually used for screening novel TE materials. In recent years, because of the high EFF values, an increasing number of two-dimensional materials have been predicted to have the potential for TE applications via high-throughput calculations. Among them, the GeS2 monolayer has many interesting physical properties and is being used for industrial applications. Hence, in this work, we systematically investigated the TE performance, including both electronic and thermal transport properties, of the GeS2 monolayer with first-principles calculations. The results show that the structure of the GeS2 monolayer at 700 K is thermally unstable, so we study its TE performance only at 300 and 500 K. As compared with other typical TE monolayers, the GeS2 monolayer exhibits excellent electronic transport properties but a relatively high lattice thermal conductivity of 5.71 W m−1 K−1 at 500 K, and thus an unsatisfactory ZT value of 0.23. Such a low ZT value indicates that it is necessary to consider not only the electron transport properties but also the thermal transport properties to screen the thermoelectric materials with excellent performance through high-throughput calculations.

Rectified linear unit (ReLU) plays an important role in today’s convolutional neural networks (CNNs). In this paper, we propose a novel activation function called Rectified Exponential Unit (REU). Inspired by two recently proposed activation functions: Exponential Linear Unit (ELU) and Swish, the REU is designed by introducing the advantage of flexible exponent and multiplication function form. Moreover, we propose the Parametric REU (PREU) to increase the expressive power of the REU. The experiments with three classical CNN architectures, LeNet-5, Network in Network, and Residual Network (ResNet) on scale-various benchmarks including Fashion-MNIST, CIFAR10, CIFAR100, and Tiny ImageNet demonstrate that REU and PREU achieve improvement compared with other activation functions. Our results show that our REU has relative error improvements over ReLU of 7.74% and 6.08% on CIFAR-10 and 100 with the ResNet, the improvements of PREU is 9.24% and 9.32%. Finally, we use the different PREU variants in the Residual unit to achieve more stable results.

The hemibiotrophic ascomycete fungus Colletotrichum gloeosporioides is the causal agent of anthracnose on numerous plants, and it causes considerable economic losses worldwide. Endocytosis is an essential cellular process in eukaryotic cells, but its roles in C. gloeosporioides remain unknown. In our study, we identified an endocytosis-related protein, CgEnd3, and knocked it out via polyethylene glycol (PEG)-mediated protoplast transformation. The lack of CgEnd3 resulted in severe defects in endocytosis. C. gloeosporioides infects its host through a specialized structure called appressorium, and ΔCgEnd3 showed deficient appressorium formation, melanization, turgor pressure accumulation, penetration ability of appressorium, cellophane membrane penetration, and pathogenicity. CgEnd3 also affected oxidant adaptation and the expression of core effectors during the early stage of infection. CgEnd3 contains one EF hand domain and four calcium ion-binding sites, and it is involved in calcium signaling. A lack of CgEnd3 changed the responses to cell-wall integrity agents and fungicide fludioxonil. However, CgEnd3 regulated appressorium formation and endocytosis in a calcium signaling-independent manner. Taken together, these results demonstrate that CgEnd3 plays pleiotropic roles in endocytosis, calcium signaling, cell-wall integrity, appressorium formation, penetration, and pathogenicity in C. gloeosporioides, and it suggests that CgEnd3 or endocytosis-related genes function as promising antifungal targets.

In order to improve the performance of bearingless brushless DC motor, a closed-loop suspended force controller combining the discrete space voltage vector modulation is applied and the direct torque control is presented in this paper. Firstly, we increase the number of the control vector to reduce the torque ripple. Then, the suspending equation is constructed which is spired by the direct torque control algorithm. As a result, the closed-loop suspended force controller is built. The simulated and experimental results evaluate the performance of the proposed method. The more advantage is that the proposed algorithm can achieve the fast torque response, reduce the torque ripple, and follow ideal stator flux track. Furthermore, the motor which implants the closed-loop suspended force controller cannot onlyobtain the dynamic response rapidly and displacement control accurately, but also has the characteristics of bearingless brushless DC motor (such as simple structure, high energy efficiency, small volume and low failure rate).

The fungus Colletotrichum gloeosporiodes infects plant hosts with a specialized cell called an appressorium, which is melanized and required for plant cell wall penetration. Here, we show that the mitogen-activated protein kinase CgMK1 governs appressorium formation and virulence in the poplar anthracnose fungus C. gloeosporioides. Deletion of CgMK1 impairs aerial hyphal growth and biomass accumulation, and CgMK1 is responsible for the expression of melanin biosynthesis-associated genes. CgMK1 deletion mutants are unable to form appressorium and lose the capacity to colonize either wounded or unwounded poplar leaves, leading to loss of virulence. We demonstrate that the exogenous application of cAMP fails to restore defective appressorium formation in the CgMK1 deletion mutants, suggesting that CgMK1 may function downstream or independent of a cAMP-dependent signal for appressorium formation. Moreover, CgMK1 mutants were sensitive to high osmosis, indicating that CgMK1 plays an important role in stress response. We conclude that CgMK1 plays a vital role in regulating appressorium formation, melanin biosynthesis, and virulence in C. gloeosporiodes. [ABSTRACT FROM AUTHOR]

Abstract??The feasibility of nitrite accumulation in a pilot-scale A/O (anoxic/oxic) nitrogen removal plant treating domestic wastewater was investigated at various dissolved oxygen (DO) concentrations and pH levels. The results showed that the pH was not a useful operational parameter to realize nitrite accumulation. Significant nitrite accumulation was observed at the low DO concentration range of 0.3?0.8?mg/l and the maximum nitrite accumulation ratio of about 90% occurred at a DO concentration of 0.6?mg/l. This suggests a reduction of 22% in the oxygen consumption, and therefore a considerable saving in aeration. However, the nitrite accumulation was destroyed at the high DO concentration and the resumption was very slow. In addition, the average ammonia removal efficiency reached as high as 93% at the low DO level. Moreover, experimental results indicated that nitrogen could be removed by simultaneous nitrification and denitrification (SND) via nitrite in the aerobic zones at the low DO concentration, with the efficiency of 6?12%. [ABSTRACT FROM AUTHOR]