Search

[Display omitted] • BT substitute was prepared in a pilot-scale PBR using interesterified plant oils. • The physical properties of BT substitute have been improved compared to BT. • The volatile profiles of BT substitute-based hotpots has no significant difference with BT ones. Edible beef tallow (BT) has been widely used in Sichuan hotpot due to its unique flavor and texture. However, BT should not be consumed in excess caused by its trans -fatty acids and cholesterol issues. In this study, a BT substitute was prepared after enzymatic interesterification in a pilot-scale packed-bed reactor using soybean oil and fully hydrogenated palm oil (4:3, w/w) as feedstock. The products were characterized against BT in terms of fatty acid/triacylglycerol compositions, solid fat content, polymorphism, and melting/crystallization behaviors to select the most promising BT substitute. The optimal flow rate was 120 mL/min. Changes in volatile compounds during stir-frying and simmering were also investigated for Sichuan hotpots made with these two oils. The volatile compounds of BT substitute were similar to that of natural BT. The findings will contribute to expanding the base oil categories of Sichuan hotpot oils. [ABSTRACT FROM AUTHOR]

The NADC34-like porcine reproductive and respiratory syndrome virus 2 (PRRSV-2) first emerged in China in 2017 and has the potential to become the dominant PRRSV strain in China. Here, a novel PRRSV-2, SCcd2020, was isolated from diseased piglets in Sichuan province, southwest China in 2020. The complete viral genome was determined and analyzed. An ORF5-based phylogenetic analysis showed that SCcd2020 clustered with NADC34-like strains, whereas the genome sequence clustered the isolate with NADC30-like viruses and it contains a discontinuous 131-aa deletion in NSP2 when compared to NADC30 strain. Notably, recombination analyses indicated that SCcd2020 is a multiple recombinant virus from NADC30-like, NADC34-like and JXA1-like strains, which is the first description of Chinese domestic HP-PRRSV involving the recombination event of an NADC34-like strain. Importantly, an animal challenge study in 4-week-old piglets showed that SCcd2020 causes high fever and severe hemorrhagic pneumonia with pulmonary consolidation and edema, and it has a high mortality rate (60%), which indicated that SCcd2020 is a highly pathogenic PRRSV strain. The study reports the emergence of a novel highly pathogenic NADC34-like recombinant strain, and it highlights the importance of monitoring newly emerging PRRSV strains in China. • A novel NADC34-like PRRSV was isolated from diseased piglets in southwest China in 2020 and denominated as SCcd2020. • SCcd2020 is a multiple recombinant virus from a NADC30-like, a NADC34-like and a Chinese domestic JXA1-like strain. • Animal experiment demonstrated SCcd2020 is a highly pathogenic PRRSV strain with a high mortality (60%). [ABSTRACT FROM AUTHOR]

• We conducted a comparative analysis of soil phosphorus fractions changes under unstable landforms. The aim was to clarify the differences between study area and Walker and Syers (1976) model. • Variation partitioning analysis (VPA) was employed to discern the impact of environmental factors (climate, microbes, and soil) on soil phosphorus fraction within unstable landforms. • Structural equation models (SEMs) were employed to investigate the cycling of soil phosphorus and the availability of bioavailable phosphorus within unstable landforms. Landslides significant influence on bedrock weathering, pedogenesis, and ecological succession, thereby playing a pivotal role in driving biogeochemical cycles. Landslide chronosequences have traditionally served as invaluable study systems for investigating vegetation succession and exploring soil development and nutrient dynamics. In this study, we analyzed soil phosphorus fractions across a 22,000-year landslide chronosequence. Our investigation examined the impact of environmental factors on these fractions and elucidated the supply of bioavailable phosphorus. Our findings revealed a decline in the concentration of all phosphorus fractions with soil age. Notably, even at an age exceeding 22,000 years, primary mineral phosphorus constituted 23 % of the total phosphorus pool at the later stages of soil development, while organic phosphorus accounted for a mere 7 %. This discrepancy can be attributed to frequent landslides replenishing phosphorus stocks. These findings indicate that the Walker and Syers model may not adequately anticipate alterations in soil phosphorus fractions under geologically unstable landscapes. Environmental factors had limited impact on primary mineral phosphorus concentrations. Yet, they significantly influenced occluded and nonoccluded phosphorus. Changes in organic phosphorus concentrations cannot be fully explained by the examined environmental factors, as organic phosphorus in the soil primarily originates from plant litter decomposition. The sources of bioavailable phosphorus in the investigated region were elucidated using structural equation modeling. The bioavailable phosphorus in the study area is mainly derived from nonoccluded phosphorus rather than organic phosphorus. This observation can be attributed to the distinct landform conditions, which sustain elevated levels of primary mineral phosphorus during soil development. Consequently, the continuous availability of nonoccluded phosphorus ensures a reliable supply of bioavailable phosphorus. In conclusion, the landslide chronosequence serves as a vital example of long-term ecosystem development under unstable landforms. Its refinement of the Walker and Syers model under unstable landscapes. [ABSTRACT FROM AUTHOR]

Microseismic monitoring is a widely used technique in coal mine safe production. Microseismic events classification is one of the most important steps in microseismic monitoring. CNN has been proven to be the potential tool to identify microseismic events automatically. When the noise is serious and the signal is weak, the recognition ability of CNN is limited. Some scholars use denoising methods, such as wavelet transform, to enhance the signal-to-noise ratio (SNR) of the input data. However, the effective signal will be broken if the incorrect threshold parameters are set in this wavelet transform-based denoising method. In this paper, we intend to make the data volume of the time-frequency graph obtained by WPT as the input of CNN. But it may lead to a dimensional disaster if taking the time-frequency domain signals as input directly. To utilize the effective information extraction and reduce the data dimension, we propose a dual-channel CNN model by combining time domain information and wavelet packet decomposition coefficients (T-WPD CNN). The wavelet packet decomposition coefficients highlight the characteristics of the signal and suppress the characteristics of noise. In addition, the coefficients have the same dimension as the original signal. These advantages are useful for enhancing the performance of CNN. Two field datasets are used to test the network. One from Australia and another from Jiayang coal mine, Sichuan, China. The feature map of the convolutional layer with different inputs are shown to illustrate the influence effect of raw time domain signal and WPD coefficient on the classification performance. The final results show that the T-WPD CNN is superior to the traditional CNN method in accuracy and robustness. • The network combines time-domain information and wavelet packet decomposition coefficients of the microseismic data. • The more obvious feature map of the microseismic is extracted, which makes the classification performance better. • The wavelet packet decomposition coefficients can avoid the dimensional disaster of the input data. [ABSTRACT FROM AUTHOR]