The influence of particle size on supercritical extraction of dog rose (Rosa canina) seed oil

The aim of this research was to determine the effect of particle size on dog rose seed oil recovered by the method of Supercritical Fluid Extraction (SFE). Supercritical extraction implemented using dog rose seed particles (d1 < 0.27 mm; 0.27 < d2 < 0.7 mm; 0.7 mm < d3 < 1.4 mm) at 30 MPa, 50 °C, 0.228 L/min flow rate of supercritical carbon dioxide and at the 40 °C separation temperature. The experiments were carried out over the period of 120 min. The results showed that maximum oil obtained at smallest particle sizes (maximal absolute yield-7.63 g from 78.2 g seed). The highest volume of the residue in oil was with particles d1 < 0.27 mm. The GC analyses revealed the oil from dog rose seeds was rich of unsaturated fatty acids. Keywords: Supercritical CO2 extraction, Dog rose, Dog rose seed, Residue content, Fatty acid, Linoleic aci

Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data

Abstract Background Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, F ST, and I n) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities. Results We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases. Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification

Characterization of the chloroplast genome of the Osmanthus didymopetalus

In this study, we assembled the chloroplast genome of Osmanthus didymopetalus (Oleaceae), a rare evergreen tree native to Hainan, China. The genome of O. didymopetalus was 155,155 bp in length and contained a pair of inverted repeats (IR, 25,697–25,704 bp) regions, which were separated by the small single copy (SSC, 17,591 bp) and the large single copy (LSC, 86,225 bp) regions. The cp genome encoded 133 genes including 88 protein-coding genes, 37 tRNA genes, and eight rRNA ribosomal genes. The overall GC content of O. didymopetalus chloroplast genome is 37.8%. Phylogenetic results showed that O. didymopetalus was more closely to O. yunnanensis, O. fragrans and O. insularis. This study will be beneficial for the evolutionary study and phylogenetic reconstruction of Osmanthus

A Multi-Information Dissemination Model Based on Cellular Automata

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform

Segmentally variable density perforation optimization model for horizontal wells in heterogeneous reservoirs

A 3D three-phase segmentally variable density perforation optimization model for horizontal wells is built by coupling reservoir fluid filtration, near wellbore inflow and wellbore conduit flow based on reservoir numerical simulation. The effects of 8 factors (filtration difference between heel/toe and middle intervals, imperforated interval, permeability heterogeneity, oil layer thickness heterogeneity, porosity heterogeneity, wellbore pressure drawdown, maximum perforation density, and perforation optimization principles) on perforation density and inflow profile, and that of fluid viscosity, casing diameter and pipe wall coarseness on well bore pressure drawdown, are analyzed for segmentally variable density perforation of horizontal wells. Results show that filtration difference between heel/toe and middle intervals, imperforated interval, permeability heterogeneity and oil layer thickness heterogeneity have significant effects on segmentally variable density perforation. In particular, different perforation density optimizations may occur when filtration difference between heel/toe and middle interval is not considered; imperforated interval may affect inflow profile; well bore pressure drawdown can be ruled out for segmentally variable density perforation of most horizontal wells onshore in China. The contrast between predicted and actual production of Well AT9-7H in the Tahe Oilfield indicates that the model is highly accurate. Key words: horizontal well, segmentally variable density perforation, reservoir numerical simulation, imperforated interval, inflow profile, heterogeneit

Crystallization, rheological behavior and mechanical properties of carbon nanotube/metallocene polypropylene composites

In this paper, metallocene polypropylene (mPP) composites filled with carbon nanotubes (CNTs) were prepared using twin-screw extruder. The crystallization behavior, mechanical properties and rheological behavior were characterized by a differential scanning calorimetry (DSC), universal material testing machine and rotational rheometer. The results of DSC indicated that the effect of CNTs on heterogeneous nucleation of mPP was very obvious and the crystallizability of the resin matrix was improved after adding CNTs, especially the initial crystallization temperature ( T _0 ), crystallization temperature ( T _c ) increased by 9.63 °C and 8.28 °C when the CNTs content was 1.25 wt%. The yield stress and elastic modulus increased to 33.98 MPa and 1605.6 MPa as the CNTs concentration increased to 1.0 wt% in contrast to that of the neat mPP. The results of SEM images showed that the better dispersion and adhesion of CNTs into polymer matrix. The results of rotational rheometer proved that interactions increased between CNTs and mPP as the content of CNTs increasing

Bioactive Isopimarane Diterpenes from the Fungus, Epicoccum sp. HS-1, Associated with Apostichopus japonicus

One new isopimarane diterpene (1), together with two known compounds, 11-deoxydiaporthein A (2) and iso-pimara-8(14),15-diene (3) were isolated from the culture of Epicoccum sp., which was associated with Apostichopus japonicus. Their structures were determined by the analysis of 1D and 2D NMR, as well as mass spectroscopic data. The absolute configuration of Compound 1 was deduced by a single-crystal X-ray diffraction experiment using CuKα radiation. In the bioactivity assay, both Compounds 1 and 2 exhibited α-glucosidase inhibitory activity with IC50 values of 4.6 ± 0.1 and 11.9 ± 0.4 μM, respectively. This was the first report on isopimarane diterpenes with α-glucosidase inhibitory activity

Characterization and human microfold cell assay of fish oil microcapsules: Effect of spray drying and freeze-drying using konjac glucomannan (KGM)-soybean protein isolate (SPI) as wall materials

The properties of microencapsulated fish oil, prepared with spray drying and freeze-drying processes using emulsions of konjac glucomannan (KGM) and soybean protein isolate (SPI) was investigated. Comparative analysis of microcapsules showed the encapsulation efficiency of spray drying and freeze-drying were 90.10% and 83.52% respectively. Scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) results exhibited that microcapsules prepared with spray drying were spherical particles with a compact structure, while microcapsules prepared with freeze-drying were irregular particles with a pore-like structure. Release kinetics test further indicated retention rate of core materials for microcapsules prepared with spray drying were better than with freeze-drying. In addition, a human epithelial microfold cell (M-cell) transcytotic assay demonstrated that the M-cells had greater transport activity for the exogenous microcapsules

Exploring the Potential Mechanism of Artemisinin and Its Derivatives in the Treatment of Osteoporosis Based on Network Pharmacology and Molecular Docking

Objective. This study is aimed at predicting and contrasting the mechanisms of artemisinin (ARS), dihydroartemisinin (DHA), artesunate (ART), artemether (ARM), and arteether (ARE) in the treatment of osteoporosis (OP) using network pharmacology and molecular docking. Methods. The targets of ARS, DHA, ART, ARM, and ARE were obtained from the SwissTargetPrediction. The targets related to OP were obtained from the TTD, DrugBank, Genecards, and DisGeNet databases. Then, the anti-OP targets of ARS, DHA, ART, ARM, and ARE were obtained and compared using the Venn diagram. Afterward, the protein-protein interaction (PPI) networks were built using the STRING database, and Cytoscape was used to select hub targets. Moreover, molecular docking validated the binding association between five molecules and hub targets. Finally, GO enrichment and KEGG pathway enrichment were conducted using the DAVID database. The common pathways of five molecules were analysed. Results. A total of 28, 37, 36, 27, and 33 anti-OP targets of ARS, DHA, ART, ARM, and ARE were acquired. EGFR, EGFR, CASP3, MAPK8, and CASP3 act as the top 1 anti-OP targets of ARS, DHA, ART, ARM, and ARE, respectively. MAPK14 is the common target of five molecules. All five molecules can bind well with these hubs and common targets. Meanwhile, functional annotation showed that MAPK, Serotonergic synapse, AMPK, prolactin, and prolactin signaling pathways are the top 1 anti-OP pathway of ARS, DHA, ART, ARM, and ARE, respectively. IL-17 signaling pathway and prolactin signaling pathway are common anti-OP pathways of five molecules. Besides, GO enrichment showed five biological processes and three molecular functions are common anti-OP mechanisms of five molecules. Conclusion. ARS, DHA, ART, ARM and ARE can treat OP through multi-targets and multi pathways, respectively. All five molecules can treat OP by targeting MAPK14 and acting on the IL-17 and prolactin signaling pathways