Identification of in vivo metabolites of Citri Sarcodactylis Fructus by UHPLC-Q/Orbitrap HRMS.

INTRODUCTION: Citri Sarcodactylis Fructus has the effects of relieving cough, removing phlegm, and reducing asthma, but little is known about the metabolic and distribution of its chemical constituents in vivo. Therefore, it is necessary to study the metabolism of Citri Sarcodactylis Fructus in vivo. OBJECTIVE: We aimed to (1) analyze the distribution of prototype compounds and metabolites of the chemical constituents of Citri Sarcodactylis Fructus in rat and (2) infer the metabolites and metabolic pathways of the chemical constituents. MATERIALS AND METHODS: A C18 column (3 × 100 mm, 2.6 µm) was used. The mobile phase was water containing 0.1% formic acid (eluent A) and acetonitrile containing 0.1% formic acid (eluent B) at a discharge rate of 0.3 mL/min. Mass spectra of biological samples were collected in electrospray ionization (ESI) positive ion mode in the m/z 100-1500 scan range. The obtained biological samples were then subjected to chemical analysis, including plasma, urine, feces, and heart, liver, spleen, lungs, kidneys, stomach, and small intestine tissues. Prototype compounds and metabolites were identified. RESULTS: In all, 40 prototype compounds and 78 metabolites, including 26 phase I metabolites and 52 phase II metabolites, were identified using UHPLC-Q/Orbitrap HRMS. Eight possible metabolic pathways (reduction, hydrolysis, dehydration, methylation, hydroxylation, sulfation, glucuronidation, and demethylation) were proposed. The prototype compounds were predominantly distributed in lung tissues. The metabolites were mainly distributed in plasma and kidney tissues. CONCLUSION: We systematically investigated the metabolites of Citri Sarcodactylis Fructus in vivo. We suggest metabolic pathways that might be relevant for further metabolic studies and screening of active ingredients of Citrus Sarcodactylis Fructus in vivo.

Comparative study of characteristic compounds of three species of truffle.

The Panxi area in Sichuan Province is the main area for the production of truffles in China, and several species of truffle are known to exist in this region. Nevertheless, it is unclear what the differences in chemical composition between the truffles are. Using an ultra-high-performance liquid chromatography quadrupole/orbitrap high-resolution mass spectrometry coupled with Compound Discoverer 3.0, we identified chemical components in three mainly known truffles from the Panxi region. Further analysis of chemical composition differences was conducted using principal component analysis, and orthogonal partial least squares discriminant analysis. Note that, 78.9% of the variance was uncovered by the principal component analysis model. As a result of the orthogonal partial least squares discriminant analysis model, the three species of truffles (Tuber pesudohimalayense, Tuber indicum, and Tuber sinense) from Panxi were better discriminated, with R2 X, R2 Y, and Q2 being 0.821, 0.993, and 0.947, respectively. In this study, 87 components were identified. T. pesudohimalayense contained significantly higher levels of nine different compounds than the other two species. Hence, it was possible to identify similarities and differences between three species of truffles from Panxi in terms of chemical composition. This can be used as a basis for quality control.

Collaborative and Multilevel Feature Selection Network for Action Recognition.

The feature pyramid has been widely used in many visual tasks, such as fine-grained image classification, instance segmentation, and object detection, and had been achieving promising performance. Although many algorithms exploit different-level features to construct the feature pyramid, they usually treat them equally and do not make an in-depth investigation on the inherent complementary advantages of different-level features. In this article, to learn a pyramid feature with the robust representational ability for action recognition, we propose a novel collaborative and multilevel feature selection network (FSNet) that applies feature selection and aggregation on multilevel features according to action context. Unlike previous works that learn the pattern of frame appearance by enhancing spatial encoding, the proposed network consists of the position selection module and channel selection module that can adaptively aggregate multilevel features into a new informative feature from both position and channel dimensions. The position selection module integrates the vectors at the same spatial location across multilevel features with positionwise attention. Similarly, the channel selection module selectively aggregates the channel maps at the same channel location across multilevel features with channelwise attention. Positionwise features with different receptive fields and channelwise features with different pattern-specific responses are emphasized respectively depending on their correlations to actions, which are fused as a new informative feature for action recognition. The proposed FSNet can be inserted into different backbone networks flexibly, and extensive experiments are conducted on three benchmark action datasets, Kinetics, UCF101, and HMDB51. Experimental results show that FSNet is practical and can be collaboratively trained to boost the representational ability of existing networks. FSNet achieves superior performance against most top-tier models on Kinetics and all models on UCF101 and HMDB51.

Analysis of the differences in the chemical composition of monascus rice and highland barley monascus.

Monascus rice (MR) and highland barley monascus (HBM), the monascus fermented products, are applied in food and medicine to reduce cholesterol and promote digestion. Due to the fermentation substrates, their compositions are different. However, the exact differences have not been reported to date. By UPLC-Q-Orbitrap HRMS analysis, multiple components of twenty batches of MR and HBM samples were identified. In total, 100 components were confirmed (e.g., monacolins, pigments, decalin derivatives, amino acids). Then, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to filter the components of MR and HBM. In the PCA model, 88.1% of the total variance was uncovered. The OPLS-DA model showed better discrimination between MR and HBM, and the values of R2X, R2Y, and Q2 were 0.837, 0.996, and 0.956, respectively. Based on the value of the variable importance in projection (VIP) and the result of the t-test, 424 components (VIP > 1, p < 0.05) were acquired. Finally, 11 differential components were selected as the characteristic compounds to discriminate between MR and HBM: the content of 9 monacolins (3-hydroxy-3,5-dihydrodehydromonacolin K, monacolin K, dehydromonacolin K, dehydromonacolin J hydroxy acid, monacophenyl, dihydromonacolin J hydroxy acid, monacolin L, dehydromonacolin J, and monacolin R) in HBM was higher than the content in MR, but the content of 2 pigments (ankaflavin and monascin) was lower in HBM and higher in MR. The findings revealed the similarities and differences in the chemical composition between MR and HBM, which is expected to provide a basis for quality control of HBM.

Analysis of the characteristic compounds of Citri Sarcodactylis Fructus from different geographical origins.

INTRODUCTION: Citri Sarcodactylis Fructus (CSF) is widely used as a food ingredient and a traditional Chinese medicine. In China, CSF is cultivated in many places, including Sichuan, Guangdong, Zhejiang, and Fujian provinces. The types and chemical contents of CSF from different origins may vary greatly due to the difference in climate and environmental conditions. Therefore, comparing the chemical composition of CSF from various places is vital. OBJECTIVE: To rapidly select potential characteristic compounds for differentiating CSF from different origins. MATERIAL AND METHODS: Thirty-one batches of CSF samples from different regions were analysed using ultra-performance liquid chromatography with hybrid quadrupole-orbitrap high-resolution mass spectrometry. Thereafter, chemometric methods, including principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA), were employed to find differential metabolites among the CSF samples from various origins. RESULTS: PCA revealed 77.9% of the total variance and divided all CSF samples into three categories corresponding to their origins. OPLS-DA displayed better discrimination of CSF from different sources, with R2 X, R2 Y, and Q2 of 0.801, 0.985, and 0.849, respectively. Finally, 203 differential metabolites were obtained from CSF from different origins using the variable importance in projection of the OPLS-DA model, 30 of which were identified, and five coumarin compounds were selected as marker compounds discriminating CSF from different origins. CONCLUSION: This work provides a practical strategy for classifying CSF from different origins and offers a research foundation for the quality control of CSF.

Global and Local Knowledge-Aware Attention Network for Action Recognition.

Convolutional neural networks (CNNs) have shown an effective way to learn spatiotemporal representation for action recognition in videos. However, most traditional action recognition algorithms do not employ the attention mechanism to focus on essential parts of video frames that are relevant to the action. In this article, we propose a novel global and local knowledge-aware attention network to address this challenge for action recognition. The proposed network incorporates two types of attention mechanism called statistic-based attention (SA) and learning-based attention (LA) to attach higher importance to the crucial elements in each video frame. As global pooling (GP) models capture global information, while attention models focus on the significant details to make full use of their implicit complementary advantages, our network adopts a three-stream architecture, including two attention streams and a GP stream. Each attention stream employs a fusion layer to combine global and local information and produces composite features. Furthermore, global-attention (GA) regularization is proposed to guide two attention streams to better model dynamics of composite features with the reference to the global information. Fusion at the softmax layer is adopted to make better use of the implicit complementary advantages between SA, LA, and GP streams and get the final comprehensive predictions. The proposed network is trained in an end-to-end fashion and learns efficient video-level features both spatially and temporally. Extensive experiments are conducted on three challenging benchmarks, Kinetics, HMDB51, and UCF101, and experimental results demonstrate that the proposed network outperforms most state-of-the-art methods.

Microstructure and Properties of Nano-Hydroxyapatite Reinforced WE43 Alloy Fabricated by Friction Stir Processing.

This research mainly focuses on the successful fabrication of nano-hydroxyapatite (nHA) reinforced WE43 alloy by two-pass friction stir processing (FSP). Microstructure evolution, mechanical properties, and in vitro corrosion behavior of FSPed WE43/nHA composite and FSPed WE43 alloy were studied. The results show that nHA particles are effectively dispersed in the processing zone, and the well-dispersed nHA particles can enhance the grain refine effect of FSP. The average grain sizes of FSPed WE43 alloy and WE43/nHA composite are 5.7 and 3.3 µm, respectively. However, a slight deterioration in tensile strength and yield strength is observed on the WE43/nHA composite, compared to the FSPed WE43 alloy, which is attributed to the locally agglomerated nHA particles and the poor quality of interfacial bonding between nHA particles and matrix. The electrochemical test and in vitro immersion test results reveal that the corrosion resistance of the WE43 alloy is greatly improved after FSP. With the addition of nHA particles, the corrosion resistance of the WE43/nHA composite shows an even greater improvement.

Investigation of sub-phonon lifetime pulse amplification in active frequency matching stimulated Brillouin scattering.

We demonstrate Brillouin amplification of sub-phonon lifetime Stokes pulses based on an active frequency matching method. The main purpose is to extend Brillouin amplification to further applications requiring shorter pulse widths and break the phonon lifetime limit. A combination of theoretical simulations and experiments is used to achieve this goal. As a result, the Brillouin transient gain is identified as the key parameter to achieve sub-phonon lifetime Brillouin amplification. The experimental results agree well with the theoretical simulation.

Active frequency matching in stimulated Brillouin amplification for production of a 2.4 J, 200 ps laser pulse.

A frequency matching Brillouin amplification in high-power solid-state laser systems is proposed. The energy extraction efficiency could be maintained at a high level in a non-collinear Brillouin amplification structure using an exact Stokes frequency shift. Laser pulses having a width of 200 ps and energy of 2.4 J were produced. This method can be used to transfer energy from a long pulse to a short pulse through a high-power solid-state laser system.

High-quality near-field beam achieved in a high-power laser based on SLM adaptive beam-shaping system.

We demonstrate a high-power laser system with a high-quality near-field beam by using a liquid-crystal spatial light modulator (SLM). An efficient spatial beam shaping algorithm is discussed which can improve the output nearfield beam quality effectively. Both small-signal and large-signal amplification situation of the laser are considered in the beam shaping algorithm. The experimental results show that the near field fluence modulation of output is improved from 1.99:1 to 1.26:1 by using the liquid-crystal SLM. Obvious uniform spatial fluence distribution and near-field beam quality improvement are observed.

First-principles study of spontaneous polarization in SrBi2Ta2O9.

We investigate the spontaneous polarization in SrBi2Ta2O9 by analyzing the maximally localized Wannier functions using a first-principles method. The calculated spontaneous polarization in ferroelectric SrBi(2)Ta(2)O(9) along the polarized a axis is 23.8 µC cm( - 2), with an electronic contribution of 8.4 µC cm( - 2). The electronic contribution of each atom to the spontaneous polarization in the unit cell is quantitatively evaluated. The Bi component plays the most important role in the complex chemical bonding and the total polarization. The chemical bonding in the Ta-O octahedra and bismuth oxide slabs of ferroelectric SrBi2Ta2O9 is also addressed.