5-Azacytidine (5-Aza) induces differentiation of mesenchymal stem cells (MSCs) into cardiomyocytes. However, the underlying mechanisms are not well understood. Our previous work showed that 5-Aza induces human bone marrow-derived MSCs to differentiate into cardiomyocytes. Here, we demonstrated that 5-Aza induced cardiac differentiation of human umbilical cord-derived MSCs (hucMSCs) and explored the potential signaling pathway. Our results showed that hucMSCs had cardiomyocyte phenotypes after 5-Aza treatment. In addition, myogenic cells differentiated from hucMSCs were positive for mRNA and protein of desmin, β-myosin heavy chain, cardiac troponin T, A-type natriuretic peptide, and Nkx2.5. Human diploid lung fibroblasts treated with 5-Aza expressed no cardiac-specific genes. 5-Aza did not induce hucMSCs to differentiate into osteoblasts. Further study revealed that 5-Aza treatment activated extracellular signal related kinases (ERK) in hucMSCs, but protein kinase C showed no response to 5-Aza administration. U0126, a specific inhibitor of ERK, could inhibit 5-Aza-induced expression of cardiac-specific genes and proteins in hucMSCs. Increased phosphorylation of signal transducers and activators of transcription 3, and up-regulation of myocyte enhancer-binding factor-2c and myogenic differentiation antigen in 5-Aza-treated hucMSCs were also suppressed by U0126. Taken together, these results suggested that sustained activation of ERK by 5-Aza contributed to the induction of the differentiation of hucMSCs into cardiomyocytes in vitro.

SUMMARYSexually dimorphic breeding tubercles (BTs) are keratinized epidermal structures that form clusters on the dorsal surface of the anterior rays of zebrafish male pectoral fins. BTs appear during sexual maturation and are maintained through regular shedding and renewal of the keratinized surface. Following pectoral fin amputation, BT clusters regenerate after the initiation of revascularization, but concomitantly with a second wave of angiogenesis. This second wave of regeneration forms a web-like blood vessel network that penetrates the supportive epidermis of BTs. Upon analyzing the effects of sex steroids and their inhibitors, we show that androgens induce and estrogens inhibit BT cluster formation in intact and regenerating pectoral fins. Androgen-induced BT formation in females is accompanied by the formation of a male-like blood vessel network. Treatment of females with both androgens and an angiogenesis inhibitor results in the formation of undersized BT clusters when compared with females treated with androgens alone. Overall, the growth and regeneration of large BTs requires a hormonal stimulus and the presence of an additional blood vessel network that is naturally found in males.

Flexible ceramic fibers (FCFs) have been developed for various advanced applications due to their superior mechanical flexibility, high temperature resistance, and excellent chemical stability. In this article, we present an overview on the recent progress of FCFs in terms of materials, fabrication methods, and applications. We begin with a brief introduction to FCFs and the materials for preparation of FCFs. After that, various methods for preparation of FCFs are discussed, including centrifugal spinning, electrospinning, solution blow spinning, self-assembly, chemical vapor deposition, atomic layer deposition, and polymer conversion. Recent applications of FCFs in various fields are further illustrated in detail, including thermal insulation, air filtration, water treatment, sound absorption, electromagnetic wave absorption, battery separator, catalytic application, among others. Finally, some perspectives on the future directions and opportunities for the preparation and application of FCFs are highlighted. We envision that this review will provide readers with some meaningful guidance on the preparation of FCFs and inspire them to explore more potential applications.

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

An open-end winding permanent magnet synchronous motor (PMSM) has a larger range of speed regulation than normal PMSM with the same DC voltage, and the control method is more flexible. It can also manage energy distribution between two power sources without a DC/DC converter. This paper aims at an electric vehicle equipped with OW-PMSM drive system with dual power sources and dual inverters; based on analyzing the external characteristics of each winding mode, we propose a winding mode switching strategy whose torque saturation judgmental algorithm, which is insensitive to motor's parameters, could automatically realize upswitching of the winding mode. The proposed multi-level current hysteresis modulation algorithm could set the major power source and switch it at any time in independent mode, which accomplishes energy distribution between two power sources; its two control methods, low switching frequency method and high power difference method, could achieve different energy distribution effects. Simulation results confirm the validity and effectiveness of the winding mode switching strategy and current modulation method. They also show that an electric vehicle under the proposed control methods has better efficiency than one equipped with a traditional OW-PMSM drive system under traditional control.Control strategies of OW-PMSM and dual inverters have always been a research focus. Winding topology and configurations are widely discussed in order to optimize the motor's working range and efficiency [17][18][19]. and zero-sequence voltage elimination method [23,24] are also under extensive research. Loncarski, J. et al. compared the output current ripple in single and dual inverter motor drives for electric vehicles, concluding that the dual-2L inverter can act as a 3L inverter and offers a significant ripple reduction [2], which can also be observed in this paper. AnQun-tao, Sun Li et al. proposed a dual inverters SVPWM modulation method for current control and in this way voltage vector synthesized in the range of a hexagon and when the bus voltage remains constant, the base speed of the electric motor could be 1.7 times that in Y-connection without generating zero-sequence voltage [7,8]. Zhan H., Heng N. et al. studied a common DC bus-based dual inverters SVPWM modulation strategy. In this way, the amplitude of the voltage vector could be maximized and at the same time a zero-sequence current could be suppressed [23,24]. Welchko studied dual sources-based dual inverters' voltage vectors distribution, and proposed three types of voltage distribution methods to adapt to different working conditions of hybrid vehicles to achieve energy management functions between two power sources [16]. However, the two inverters work independently without coordination, and thus the switching frequency of inverter devices has also doubled, which increases the inverter switching loss. All the studies discussed above have proposed current control methods of dual inverters but division of OW-PMSM winding modes and winding modes ...

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Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. Deep convolutional neural networks (DCNNs) have been proven to be effective in image classification, but their use to identify nutrient deficiencies in rice has received little attention. In the present study, we explore the accuracy of different DCNNs for diagnosis of nutrient deficiencies in rice. A total of 1818 photographs of plant leaves were obtained via hydroponic experiments to cover full nutrition and 10 classes of nutrient deficiencies. The photographs were divided into training, validation, and test sets in a 3 : 1 : 1 ratio. Fine-tuning was performed to evaluate four state-of-the-art DCNNs: Inception-v3, ResNet with 50 layers, NasNet-Large, and DenseNet with 121 layers. All the DCNNs obtained validation and test accuracies of over 90%, with DenseNet121 performing best (validation accuracy = 98.62 ± 0.57%; test accuracy = 97.44 ± 0.57%). The performance of the DCNNs was validated by comparison to color feature with support vector machine and histogram of oriented gradient with support vector machine. This study demonstrates that DCNNs provide an effective approach to diagnose nutrient deficiencies in rice.