•To improve the gas permeability of coal seam, the industrial experiment of pulsating hydraulic fracturing (PHF) were carried out.•The pulsating peak pressure is the main influencing factor of the ...fracturing radius.•The initial gas concentration of the fractured hole and the guide hole are 1.2–1.8 times and 1.5–2.2 times of the ordinary hole.•The mineral crystals embedded in the coal are transported to the surface to form an erosion hole, which leads to improving the gas permeability of the coal seam by PHF.
To improve the gas permeability of coal seams, industrial experiments of pulsating hydraulic fracturing (PHF) were carried out, and physical properties, including the reasonable fracture radius, gas extraction concentration, water content, permeability, pore and mineral composition of a coal seam, were investigated. The results show that the pulsating peak pressure is the main influencing factor of the fracturing radius, and the free surfaces of the fractured holes and guide holes can realize the directional penetration of the fractured area in the coal seam. The initial gas concentration of the fractured holes and the guide holes are 1.2–1.8 times and 1.5–2.2 times that of the ordinary hole, respectively. The gas concentration decreases with time, and the decay phase of the ordinary hole is approximately 14days after fracturing, while the fractured holes and guide holes are 38days and 34days, and the gas concentrations are stable at 40% and 50%, respectively. The water content is approximately 2%, which is only 1.1 times that of the original coal seam. At the same time, the permeability coefficient of the coal seam increases by 48–217 times. Due to the erosion of pulsating water, the mineral crystals embedded in the coal are transported to the surface to form an erosion hole, which leads to improving the gas permeability of the coal seam by PHF.
Designing multiobjective evolutionary algorithms (MOEAs) for community detection in complex networks has attracted much attention of researchers recently. However, most of the existing methods focus ...on addressing the task of nonoverlapping community detection, where each node must belong to one and only one community. In fact, communities are often overlapped with each other in many real-world networks, thus it is necessary to design overlapping community detection algorithms. To this end, this paper proposes a mixed representation-based MOEA (MR-MOEA) for overlapping community detection. In MR-MOEA, a mixed individual representation scheme is proposed to fast encode and decode the overlapping divisions of complex networks. Specifically, this mixed representation consists of two parts: one represents all potential overlapping nodes and the other delegates all nonoverlapping nodes. These two parts evolve together to detect the overlapping communities of networks based on different updating strategies suggested in MR-MOEA. We verify the effectiveness of the proposed algorithm MR-MOEA on ten real-world complex networks and the experimental results demonstrate that MR-MOEA is superior over six representative algorithms for overlapping community detection.
Global volcanic and plutonic olivines record the compositional characteristics and physicochemical conditions of the parental magmas. Thus, they have significant potential for use as petrogenetic ...discriminators of the olivine formation environment and prospecting indicators for potential host rocks of magmatic sulfide deposits. Several data visualization approaches have been proposed by researchers to determine olivine origins. However, they can only discriminate specific olivine populations and require the incorporation of trace elements for which data are lacking globally. In this study, a machine-learning method consisting of the random forest algorithm and the synthetic minority oversampling technique (SMOTE) is used to discriminate the crystallization environments of olivine and predict the sulfide potential of olivine-bearing mafic-ultramafic intrusions. We employ a global data set of 24 341 olivine samples from 12 environments to determine the contents of MgO, FeO, Ni, Ca, Mn, and Cr and the Fo number 100 × Mg/(Mg+Fe). The results indicate that the proposed method can classify olivine into genetically distinct populations and distinguish olivine derived from mineralized intrusions from that derived from sulfide-barren intrusions with high accuracies (higher than 99% on average). We develop a dimensionality reduction algorithm to visualize the olivine classifications using low-dimensional vectors and an olivine classifier (accessible at
China University of Geosciences, Beijing). The model is used successfully to identify the contributions of distinct sources to regional magmatism using olivines from the late Permian picrite and basalt along the western margin of the Yangtze block (SW China) and to predict the sulfide potential of the newly discovered Qixin mafic-ultramafic complex in the southern Central Asian Orogenic Belt (NW China). The findings suggest that the proposed approach enables the accurate identification of olivine origins in diferent formation environments and is a reliable indicator suitable for global Ni-Cu-platinum group element (PGE) exploration.
Panax notoginseng (Burk) F.H. Chen is important medicinal plant of the Araliacease family. Triterpene saponins are the bioactive constituents in P. notoginseng. However, available genomic information ...regarding this plant is limited. Moreover, details of triterpene saponin biosynthesis in the Panax species are largely unknown.
Using the 454 pyrosequencing technology, a one-quarter GS FLX titanium run resulted in 188,185 reads with an average length of 410 bases for P. notoginseng root. These reads were processed and assembled by 454 GS De Novo Assembler software into 30,852 unique sequences. A total of 70.2% of unique sequences were annotated by Basic Local Alignment Search Tool (BLAST) similarity searches against public sequence databases. The Kyoto Encyclopedia of Genes and Genomes (KEGG) assignment discovered 41 unique sequences representing 11 genes involved in triterpene saponin backbone biosynthesis in the 454-EST dataset. In particular, the transcript encoding dammarenediol synthase (DS), which is the first committed enzyme in the biosynthetic pathway of major triterpene saponins, is highly expressed in the root of four-year-old P. notoginseng. It is worth emphasizing that the candidate cytochrome P450 (Pn02132 and Pn00158) and UDP-glycosyltransferase (Pn00082) gene most likely to be involved in hydroxylation or glycosylation of aglycones for triterpene saponin biosynthesis were discovered from 174 cytochrome P450s and 242 glycosyltransferases by phylogenetic analysis, respectively. Putative transcription factors were detected in 906 unique sequences, including Myb, homeobox, WRKY, basic helix-loop-helix (bHLH), and other family proteins. Additionally, a total of 2,772 simple sequence repeat (SSR) were identified from 2,361 unique sequences, of which, di-nucleotide motifs were the most abundant motif.
This study is the first to present a large-scale EST dataset for P. notoginseng root acquired by next-generation sequencing (NGS) technology. The candidate genes involved in triterpene saponin biosynthesis, including the putative CYP450s and UGTs, were obtained in this study. Additionally, the identification of SSRs provided plenty of genetic makers for molecular breeding and genetics applications in this species. These data will provide information on gene discovery, transcriptional regulation and marker-assisted selection for P. notoginseng. The dataset establishes an important foundation for the study with the purpose of ensuring adequate drug resources for this species.
Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark ...displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.
Accurate and rapid pattern recognition of epilepsy from intracranial electroencephalogram (iEEG) recordings is important for medical diagnostics. In this paper, three algorithms based on discrete ...wavelet transform (DWT) analysis and parallel probabilistic neural network, SA-PNN, SA-PPNN, and LSA-PPNN, are presented to identify iEEG recordings and detect epileptic seizures. Simulated annealing (SA) and local simulated annealing (LSA) are utilized to optimize network parameters of probabilistic neural network classifier, respectively. The combinations of different features are utilized as the input vectors of classifiers to complete classification tasks. Experiments are conducted to deal with five different classification tasks. Compared with non-parallel probabilistic neural network algorithm (SA-PNN), the running time of parallel probabilistic neural network algorithm (SA-PPNN) is shortened by 2.18 times. Compared with SA-PPNN, the average operating time of LSA-PPNN is reduced by 9.97 times. The reason is that LSA-PPNN trains and optimizes parameters with local data firstly and then brings the parameters into the global training data sets to train the network for a test. As the amount of data increases, the superiority over LSA-PPNN is getting more distinct. Our methods are also compared with other existing relative research. Experimental results prove that our methods are much more competitive. In particular, for the classification task C-D, the classification accuracy of our method reaches 83.3%, which is much higher than previous results.
Ganoderma lucidum is a widely used medicinal macrofungus in traditional Chinese medicine that creates a diverse set of bioactive compounds. Here we report its 43.3-Mb genome, encoding 16,113 ...predicted genes, obtained using next-generation sequencing and optical mapping approaches. The sequence analysis reveals an impressive array of genes encoding cytochrome P450s (CYPs), transporters and regulatory proteins that cooperate in secondary metabolism. The genome also encodes one of the richest sets of wood degradation enzymes among all of the sequenced basidiomycetes. In all, 24 physical CYP gene clusters are identified. Moreover, 78 CYP genes are coexpressed with lanosterol synthase, and 16 of these show high similarity to fungal CYPs that specifically hydroxylate testosterone, suggesting their possible roles in triterpenoid biosynthesis. The elucidation of the G. lucidum genome makes this organism a potential model system for the study of secondary metabolic pathways and their regulation in medicinal fungi.
Internal transcribed spacer of nuclear ribosomal DNA (nrDNA) is already one of the most popular phylogenetic and DNA barcoding markers. However, the existence of its multiple copies has complicated ...such usage and a detailed characterization of intra-genomic variations is critical to address such concerns.
In this study, we used sequence-tagged pyrosequencing and genome-wide analyses to characterize intra-genomic variations of internal transcribed spacer 2 (ITS2) regions from 178 plant species. We discovered that mutation of ITS2 is frequent, with a mean of 35 variants per species. And on average, three of the most abundant variants make up 91% of all ITS2 copies. Moreover, we found different congeneric species share identical variants in 13 genera. Interestingly, different species across different genera also share identical variants. In particular, one minor variant of ITS2 in Eleutherococcus giraldii was found identical to the ITS2 major variant of Panax ginseng, both from Araliaceae family. In addition, DNA barcoding gap analysis showed that the intra-genomic distances were markedly smaller than those of the intra-specific or inter-specific variants. When each of 5543 variants were examined for its species discrimination efficiency, a 97% success rate was obtained at the species level.
Identification of identical ITS2 variants across intra-generic or inter-generic species revealed complex species evolutionary history, possibly, horizontal gene transfer and ancestral hybridization. Although intra-genomic multiple variants are frequently found within each genome, the usage of the major variants alone is sufficient for phylogeny construction and species determination in most cases. Furthermore, the inclusion of minor variants further improves the resolution of species identification.
Camptotheca acuminata is a Nyssaceae plant, often called the "happy tree", which is indigenous in Southern China. C. acuminata produces the terpenoid indole alkaloid, camptothecin (CPT), which ...exhibits clinical effects in various cancer treatments. Despite its importance, little is known about the transcriptome of C. acuminata and the mechanism of CPT biosynthesis, as only few nucleotide sequences are included in the GenBank database.
From a constructed cDNA library of young C. acuminata leaves, a total of 30,358 unigenes, with an average length of 403 bp, were obtained after assembly of 74,858 high quality reads using GS De Novo assembler software. Through functional annotation, a total of 21,213 unigenes were annotated at least once against the NCBI nucleotide (Nt), non-redundant protein (Nr), Uniprot/SwissProt, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Arabidopsis thaliana proteome (TAIR) databases. Further analysis identified 521 ESTs representing 20 enzyme genes that are involved in the backbone of the CPT biosynthetic pathway in the library. Three putative genes in the upstream pathway, including genes for geraniol-10-hydroxylase (CaPG10H), secologanin synthase (CaPSCS), and strictosidine synthase (CaPSTR) were cloned and analyzed. The expression level of the three genes was also detected using qRT-PCR in C. acuminata. With respect to the branch pathway of CPT synthesis, six cytochrome P450s transcripts were selected as candidate transcripts by detection of transcript expression in different tissues using qRT-PCR. In addition, one glucosidase gene was identified that might participate in CPT biosynthesis. For CPT transport, three of 21 transcripts for multidrug resistance protein (MDR) transporters were also screened from the dataset by their annotation result and gene expression analysis.
This study produced a large amount of transcriptome data from C. acuminata by 454 pyrosequencing. According to EST annotation, catalytic features prediction, and expression analysis, novel putative transcripts involved in CPT biosynthesis and transport were discovered in C. acuminata. This study will facilitate further identification of key enzymes and transporter genes in C. acuminata.
Outsourcing logistics operation to third-party logistics has attracted more attention in the past several years. However, very few papers analyzed fuel consumption model in the context of outsourcing ...logistics. This problem involves more complexity than traditional open vehicle routing problem (OVRP), because the calculation of fuel emissions depends on many factors, such as the speed of vehicles, the road angle, the total load, the engine friction, and the engine displacement. Our paper proposed a green open vehicle routing problem (GOVRP) model with fuel consumption constraints for outsourcing logistics operations. Moreover, a hybrid tabu search algorithm was presented to deal with this problem. Experiments were conducted on instances based on realistic road data of Beijing, China, considering that outsourcing logistics plays an increasingly important role in China’s freight transportation. Open routes were compared with closed routes through statistical analysis of the cost components. Compared with closed routes, open routes reduce the total cost by 18.5% with the fuel emissions cost down by nearly 29.1% and the diver cost down by 13.8%. The effect of different vehicle types was also studied. Over all the 60- and 120-node instances, the mean total cost by using the light-duty vehicles is the lowest.
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