Quality evaluation of Shenmaidihuang Pills based on the chromatographic fingerprints and simultaneous determination of seven bioactive constituents.

In this study, we aimed to establish a comprehensive and practical quality evaluation system for Shenmaidihuang pills. A simple and reliable high-performance liquid chromatography coupled with photodiode array detection method was developed both for fingerprint analysis and quantitative determination. In fingerprint analysis, relative retention time and relative peak area were used to identify the common peaks in 18 samples for investigation. Twenty one peaks were selected as the common peaks to evaluate the similarities of 18 Shenmaidihuang pills samples with different manufacture dates. Furthermore, similarity analysis was applied to evaluate the similarity of samples. Hierarchical cluster analysis and principal component analysis were also performed to evaluate the variation of Shenmaidihuang pills. In quantitative analysis, linear regressions, injection precisions, recovery, repeatability and sample stability were all tested and good results were obtained to simultaneously determine the seven identified compounds, namely, 5-hydroxymethylfurfural, morroniside, loganin, paeonol, paeoniflorin, psoralen, isopsoralen in Shenmaidihuang pills. The contents of some analytes in different batches of samples indicated significant difference, especially for 5-hydroxymethylfurfural. So, it was concluded that the chromatographic fingerprint method obtained by high-performance liquid chromatography coupled with photodiode array detection associated with multiple compounds determination is a powerful and meaningful tool to comprehensively conduct the quality control of Shenmaidihuang pills.

Ion exchange-assisted surface passivation toward highly stable red-emitting fluoride phosphors for light-emitting diodes.

A facile and environmentally friendly ion exchange-assisted surface passivation (IASP) strategy is presented for synthesizing red emitting Mn4+-activated fluoride phosphors. A substantial, pristine Mn4+-free shell layer, applied as a coating to Mn4+ doped potassium fluorosilicate K2SiF6:Mn4+ (KSFM) phosphors, enhances both water resistance and luminescence efficiency. The stability test of fluoride in water at ambient temperature and boiling water demonstrates that IASP-treated KSFM phosphors are highly water resistant. Furthermore, both the negative thermal temperature (NTQ) fitting results and the photoluminescence (PL) decay confirm that the IASP process effectively passivates surface defects, leading to enhanced luminescence performance. The maximum internal quantum yield (QYi) of the IASP-KSFM phosphor is 94.24%. A white LED realized a high color rendering index (CRI) of 93.09 and luminous efficiency (LE) of 149.48 lm/W. This work presented a novel technique for the development of stable fluoride phosphors and has the potential to increase the use of KSFM phosphors in plant supplementary lighting systems and white light-emitting diodes.

Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks.

Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed model is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). The RNN is used as a deconvolution operator on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the spatially varying weights for the RNN. As a result, the RNN is spatial-aware and can implicitly model the deblurring process with spatially varying kernels. To better exploit properties of the spatially varying RNN, we develop both one-dimensional and two-dimensional RNNs for deblurring. The third component, based on a CNN, reconstructs the final deblurred feature maps into a restored image. In addition, the whole network is end-to-end trainable. Quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art deblurring algorithms.

Transboundary Pathogenic microRNA Analysis Framework for Crop Fungi Driven by Biological Big Data and Artificial Intelligence Model.

Plant fungal diseases have been affecting the world's agricultural production and economic levels for a long time, such as rice blast, gray tomato mold, potato late blight etc. Recent studies have shown that fungal pathogens transmit microRNA as an effector to host plants for infection. However, bioassay-based verification analysis is time-consuming and challenging, and it is difficult to analyze from a global perspective. With the accumulation of fungal and plant-related data, data analysis methods can be used to analyze pathogenic fungal microRNA further. Based on the microRNA expression data of fungal pathogens infecting plants before and after, this paper discusses the selection strategy of sample data, the extraction strategy of pathogenic fungal microRNA, the prediction strategy of a fungal pathogenic microRNA target gene, the bicluster-based fungal pathogenic microRNA functional analysis strategy and experimental verification methods. A general analysis pipeline based on machine learning and bicluster-based function module was proposed for plant-fungal pathogenic microRNA.The pipeline proposed in this paper is applied to the infection process of Magnaporthe oryzae and the infection process of potato late blight. It has been verified to prove the feasibility of the pipeline. It can be extended to other relevant crop pathogen research, providing a new idea for fungal research on plant diseases. It can be used as a reference for understanding the interaction between fungi and plants.

Investigation of the influence mechanism of rock damage on rock fragmentation and cutting performance by the discrete element method.

Rock damage is one of the key factors in the design and model choice of mining machinery. In this paper, the influence of rock damage on rock fragmentation and cutting performance was studied using PFC2D. In PFC2D software, it is feasible to get rock models with different damage factors by reducing the effective modulus, tensile and shear strength of bond by using the proportional factors. A linear relationship was obtained between the proportion factor and damage factor. Furthermore, numerical simulations of rock cutting with different damage factors were carried out. The results show that with the increase of damage factor, the rock cutting failure mode changes from tensile failure to brittle failure, accompanied by the propagation of macro cracks, the formation of large debris and a notable decrease in the peak cutting force. The mean cutting force is negatively correlated with the damage factor. Besides this, the instability of cutting force was evaluated by the fluctuation index and the pulse number of unit displacement. It was found that the cutting force was quite stable when the damage factor was 0.3, which improves the reliability of cutting machines. Finally, the cutting energy consumption of rock cutting with different damage factors was analysed. The results reveal that an increase of damage factor can raise the rock cutting efficiency. The aforementioned findings play a significant role in the development of assisted rock-breaking technologies and the design of cutting head layout of mining machinery.

Low-Light Image Enhancement via a Deep Hybrid Network.

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and the salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder-decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. The experimental results show that the proposed network favorably performs against the state-of-the-art low-light image enhancement algorithms.

Mining Magnaporthe oryzae sRNAs With Potential Transboundary Regulation of Rice Genes Associated With Growth and Defense Through Expression Profile Analysis of the Pathogen-Infected Rice.

In recent years, studies have shown that phytopathogenic fungi possess the ability of cross-kingdom regulation of host plants through small RNAs (sRNAs). Magnaporthe oryzae, a causative agent of rice blast, introduces disease by penetrating the rice tissues through appressoria. However, little is known about the transboundary regulation of M. oryzae sRNAs during the interaction of the pathogen with its host rice. Therefore, investigation of the regulation of M. oryzae through sRNAs in the infected rice plants has important theoretical and practical significance for disease control and production improvement. Based on the high-throughput data of M. oryzae sRNAs and the mixed sRNAs during infection, the differential expressions of sRNAs in M. oryzae before and during infection were compared, it was found that expression levels of 366 M. oryzae sRNAs were upregulated significantly during infection. We trained a SVM model which can be used to predict differentially expressed sRNAs, which has reference significance for the prediction of differentially expressed sRNAs of M. oryzae homologous species, and can facilitate the research of M. oryzae in the future. Furthermore, fifty core targets were selected from the predicted target genes on rice for functional enrichment analysis, the analysis reveals that there are nine biological processes and one KEGG pathway associated with rice growth and disease defense. These functions correspond to thirteen rice genes. A total of fourteen M. oryzae sRNAs targeting the rice genes were identified by data analysis, and their authenticity was verified in the database of M. oryzae sRNAs. The 14 M. oryzae sRNAs may participate in the transboundary regulation process and act as sRNA effectors to manipulate the rice blast process.

Numerical simulation of induced cutting in deep coal.

Deep coal cutting is a hot research topic at present. In this paper, the cutting technology of three-drum shearer was proposed based on previous studies. Besides, the influence of confining pressure on coal cutting performance was studied by using the discrete element method, and the induction effect of central cutting on coal cutting performance was discussed. Moreover, coal cutting with different boundary conditions was simulated with the aid of PFC2D software. The results show that as the confining pressure increases, the model dominated by tensile failure does not change, but the crack gradually develops from the vertical direction to the free surface of coal. The cutting debris first increases and then decreases; so does the cutting force. Under the effect of central cutting, the crack tends to develop towards the free surface of coal more, and both the peak cutting force and the specific energy consumption increase with the increase of confining pressure. Induced by central cutting, with the increase of confining pressure, the reduction value of peak cutting force increases first and then decreases while the reduction value of cutting specific energy consumption increases.