Cold stress, a major abiotic factor, positively modulates the synthesis of artemisinin in Artemisia annua and influences the biosynthesis of other secondary metabolites. To elucidate the changes in the synthesis of secondary metabolites under low-temperature conditions, we conducted dynamic transcriptomic and metabolite quantification analyses of A. annua leaves. The accumulation of total organic carbon (TOC) in leaves under cold stress provided ample precursors for secondary metabolite synthesis. Short-term exposure to low temperature induced a transient increase in jasmonic acid synthesis, which positively regulates the artemisinin biosynthetic pathway, contributing to artemisinin accumulation. Additionally, transcripts of genes encoding key enzymes and transcription factors in both the phenylpropanoid and artemisinin biosynthetic pathways, including PAL, C4H, ADS, and DBR2, exhibited similar expression patterns, suggesting a coordinated effect between these pathways. Prolonged exposure to low temperature sustained high levels of phenylpropanoid synthesis, leading to significant increases in lignin, flavonoids, and anthocyanin. Conversely, the final stage of the artemisinin biosynthetic pathway is inhibited under these conditions, resulting in elevated levels of dihydroartemisinic acid and artemisinic acid. Collectively, our study provides insights into the parallel transcriptional regulation of artemisinin and phenylpropanoid biosynthetic pathways in A. annua under cold stress.

This study comprehensively investigated the occurrence, sources, and potential toxicity risks of 16 PAHs in main urban area surface soils of Changchun, northeast China. The 16 total PAHs (Σ16PAHs) concentrations in surface soils decreased significantly along the commercial traffic district, industrial zone, residential zone, park zone, and outskirt farmland, ranging from 46.6 to 8870.8 ng/g, with a mean value of 1480.1ng/g and 4-ring PAHs were always the dominant constituents in all land used area soils. Based on ArcGIS mapping, higher concentrations of PAHs are mainly concentrated in the northeast region and the central historic district soils of Changchun city. Mixed pyrogenic sources and coal combustion were the dominant sources and contributed approximately 45% and 30% of the total soil PAHs, respectively. Toxicity risk assessment based on the toxic equivalent concentrations (BaPeq) of soil PAHs indicated the potential toxicity risks detected in this study were relatively moderate compared with other global cities, while some special attention still should be paid to hotspots with high PAHs concentrations surrounding the commercial traffic area and industrial zone in the northeast pattern of Changchun city. This study could be potentially useful for local governments targeted to control toxicity exposure, promote actions to alleviate PAHs contamination.

With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.

Q-learning usually carries out global path planning in grid environment, which is difficult to satisfy the requirements of vehicle dynamics in practice. In this paper, a local path planning for intelligent vehicle based on Q-learning algorithm is proposed. Firstly, the vehicle-road model is established. The information of lane boundary and lane center line is obtained by interpolation method, and the position relationship between main vehicle and environment vehicle is determined. Secondly, the variables that can reflect the driving state of the vehicle and the relationship with the surrounding vehicle position are determined to describe the vehicle states. According to the actual driving situation and the mechanical structure of the vehicle, the action that the vehicle can take is determined. Then, the reward and punishment function is designed through the optimization objectives: driving in the lane, no collision with the surrounding vehicles and so on. Finally, the vehicle path discrete sequence is obtained after training, it is necessary to smooth the path to ensure that the vehicle can track the planned path well. In this paper, the cubic spline interpolation is used to process the discrete path sequence into smooth continuous path. In order to verify the effectiveness of the planning method, the path tracking simulation is carried out on MATLAB/CarSim software. The results show that the path solved by this method can satisfy the requirements of vehicle dynamics.

INTRODUCTION:Esophageal squamous cell carcinoma (ESCC) represents an aggressive malignancy often accompanied with a poor prognosis. Owing to the poor mortality and morbidity rates associated with this malignancy, a deeper understanding of the finer molecular changes that occur in ESCC is required in order to identify novel potential targets for early detection and therapy. At present the mechanism by which ESCC functions on a molecular level is not fully understood. Hence, the aim of the present study was to ascertain as to whether microRNA-384 (miR-384) influences the progression of ESCC.MATERIAL AND METHODS:Bioinformatics analysis was initially conducted to identify ESCC-related differentially expressed genes and predict regulatory miRs. After the target relationship between miR-384 and LIMK1 had been verified, the expression of miR-384 and LIMK1 in the EC9706 cell line was altered in an attempt to investigate the regulatory roles of miR-384 in the expression of the LIMK1/cofilin signaling pathway-related genes, cell proliferation, invasion, cell cycle distribution and apoptosis, in addition to lymph node metastasis (LNM) and tumor growth in nude mice.RESULTS:Microarray-based gene expression profiling indicated that miR-384 affected the progression of ESCC through the LIMK1-mediated LIMK1/cofilin signaling pathway. Furthermore, miR-384 and Bax were observed to be poorly expressed, while LIMK1, cofilin and Bcl-2 were highly expressed in ESCC. The obtained evidences indicating that miR-384 targeted and negatively regulated LIMK1. Upregulation of miR-384 or LIMK1 inhibition was determined to block the LIMK1/cofilin signaling pathway, repress cell proliferation, invasion, cell cycle, LNM and tumor growth, while promote cell apoptosis in ESCC.CONCLUSION:Collectively, based on the key findings of the study, miR-384 could sequester LIMK1, which acts to suppress activation of the LIMK1/cofilin signaling pathway, thus ultimately inhibiting the development and progression of ESCC.

BACKGROUND:The aim of this study was to investigate the important role of pathway crosstalk and pathway dysfunction in the high-metastasis process of lung cancer cells, by using the microarray expression profiles of lung cancer cells at different metastasis levels. METHODS:The gene expression profile GSE10096 was downloaded from the Gene Expression Omnibus database, including 4 nonmetastasis samples, 3 low-metastasis samples (M1) and 3 high-metastasis samples (M5) of lung cancer cells. After the conversion from probe level to expression values using Jetset, the data were identified by limma package in R language to screen differentially expressed genes (DEGs). The pathways of DEGs were further enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network of genes related to the core pathway (pathway in cancer) and its neighbor pathways was constructed. Based on the PPI network, significantly changed pathway crosstalk and pathways were analyzed. RESULTS:Compared with those in the M1 lung cancer cells, the pathways hsa00564 (glycerophospholipid metabolism) and hsa0098 (metabolism of xenobiotics by cytochrome P450) of the M5 lung cancer cells showed significant functional changes. The dysfunction of pathway crosstalk mainly occurred between pathways hsa0098 and hsa04916 (melanogenesis pathway) and other pathways. CONCLUSIONS:The results of our analysis indicate the significance of pathway crosstalk dysfunction and pathway dysfunction of M1 and M5 lung cancer cells as shown by bioinformatics methods. The present findings have the potential to lead to the study of the mechanisms of lung cancer in future.

BackgroundTo explore the molecular mechanisms of the anti-cancer effect of curcumin in human lung squamous cell carcinoma (LSQCC) SK-MES-1 cells.MethodsCell viability was determined using MTT assay. Ribonucleic acid sequencing was performed to measure expression levels of transcripts in LSQCC cells treated with 15mol/L curcumin (treatment groups) or an equal amount of dimethylsulfoxide (control). Cuffdiff software was used to identify differentially expressed genes (DEGs) in treatment groups, followed by enrichment analysis of DEGs using the Database for Annotation, Visualization and Integration Discovery. The protein-protein interaction (PPI) networks for up and downregulated DEGs were constructed by Cytoscape software using Search Tool for the Retrieval of Interacting Genes data to identify hub nodes.ResultsCurcumin significantly reduced cell viability in LSQCC cells. In total, 380 DEGs including 154upregulated and 126 downregulated genes were found in the treatment groups. The upregulated genes were enriched in base excision repair (BER, such as PCNA, POLL, and MUTYH) and Janus kinase-signal transducer and activator of transcription (JAT-STAT) signaling pathways (such as AKT1 and STAT5A), while the downregulated genes were enriched in nine pathways, including the vascular endothelial growth factor (VEGF) signaling pathway (such as PTK2, VEGFA, MAPK1, and MAPK14) and mitogen-activated protein kinase (MAPK) signaling pathway (ARRB2, MAPK1, MAPK14, and NFKB1). PCNA and AKT1 were the hub nodes in the PPI network of upregulated genes while MAPK1, MAPK14, VEGFA, and NFKB1 were the hub nodes in the PPI network of downregulated genes.ConclusionsCurcumin might exert anti-cancer effects on LSQCC via regulating BER, JAT-STAT, VEGF, and MAPK signaling pathways.

Rhodiola sachalinensis is an endangered species with important medicinal value. We used inter-simple sequence repeat (ISSR) and methylation-sensitive amplified polymorphism (MSAP) markers to analyze genetic and epigenetic differentiation in different populations of R. sachalinensis, including three natural populations and an ex situ population. Chromatographic fingerprint was used to reveal HPLC fingerprint differentiation. According to our results, the ex situ population of R. sachalinensis has higher level genetic diversity and greater HPLC fingerprint variation than natural populations, but shows lower epigenetic diversity. Most genetic variation (54.88%) was found to be distributed within populations, and epigenetic variation was primarily distributed among populations (63.87%). UPGMA cluster analysis of ISSR and MSAP data showed identical results, with individuals from each given population grouping together. The results of UPGMA cluster analysis of HPLC fingerprint patterns was significantly different from results obtained from ISSR and MSAP data. Correlation analysis revealed close relationships among altitude, genetic structure, epigenetic structure, and HPLC fingerprint patterns (R-2 = 0.98 for genetic and epigenetic distance; R-2 = 0.90 for DNA methylation level and altitude; R-2 = -0.95 for HPLC fingerprint and altitude). Taken together, our results indicate that ex situ population of R. sachalinensis show significantly different genetic and epigenetic population structures and HPLC fingerprint patterns. Along with other potential explanations, these findings suggest that the ex situ environmental factors caused by different altitude play an important role in keeping hereditary characteristic of R. sachalinensis.

In order to improve the real-time, precision and interactivity of collision detection, based on detailed study of intelligent optimization algorithm technology, we propose a parallel ant colony optimization algorithm, which is introduced into improved random collision detection algorithm, in preliminary testing phase we use balancing bounding box tree first to rule out disjoint objects quickly, use parallel thought[1] to accelerate the speed of collision detection, regard the basic unit and leaves of object as "ants", and then traverse the search. Compare to traditional serial and parallel collision detection algorithm and partial parallel collision detection algorithm, especially for large-scale optimization problems, in the premise does not affect the accuracy and interactivity, the algorithm accelerate the collision detection efficiency further and reduce the time complexity.

In this study, Rhododendron aureum Georgi was scattered at four different altitudes on the north slope of Changbai Mountain in China to identify differentially expressed genes in response to environmental stress, and the differential display method was adopted to reverse transcribe the total RNA from its leaves. Four cDNA fragments were sequenced and affirmed to be up-regulated with increasing of altitudinal gradient (environmental stress), which were named ESIG4, ESIG2, ESIG10 and ESIG8, respectively. ESIG4 had 70% homology to fatty acid hydroxylase 1 (FAH1) and 74% homology to fatty acid hydroxylase 2 (FAH2), which participated in Bax inhibitor-1-mediated suppression of cell death in Arabidopsis thaliana. ESIG2 had 64% homology to mRNA sequence of sunflower under drought conditions. ESIG10 had 49% homology to response protein induced by stresses in Arabidopsis thaliana or barley. ESIG8 had 51% homology to Se-responsive gene in a Se-hyperaccumulator Astragalus racemosus. Based on these data and literatures, we discussed the stress tolerance mechanism of Rhododendron aureum Georgi located at the high altitude.

Ant colony optimization (ACO) is a population-based metaheuristic technique to solve combination optimization problems effectively. However, how to improve the performance of ACO algorithms is still an active research topic. Though there are many algorithms solving TSPs effectively, there is an application bottleneck that the ACO algorithm costs too much time in order to get an optimal solution. This paper revised pheromones in local and global update mode--a fast ACO algorithm for solving TSPs is presented in this paper. Firstly, a new pheromone increment model called ant constant, which keeps energy conversation of ants, is introduced to embody the pheromone difference of different candidate paths. Meanwhile, a pheromone diffusion model, which is based on info fountain of a path, is established to reflect the strength field of the pheromone diffusion faithfully, and it strengthens the collaboration among ants. Experimental results on different benchmark data sets show that the proposed algorithm can not only get better optimal solutions but also enhance greatly the convergence speed.

Collision detection is one of the major issues of virtual reality, including calculating the distance between two objects in space. In this paper, the proposed method based upon the technique of splitting the NURBS surfaces. Firstly, through the interpreting factor to adaptive subdivision surface, and then gradually subdivision surface for the control points with increment algorithm surrounded by constructing a convex hull, based on the distance between the convex hull the GJK algorithm instead of the bounding box algorithm, improve the speed of the algorithm. Through the experiments indicate that the algorithm efficiency has improved a lot, more content to complex interactive virtual system in real-time.

PSO algorithm is easy to operate and to realize, and it has got many scholars' attention once proposed. In recent years there has appeared many improved PSO algorithm, but it cann't get the global optimal answer in probability 1. Thinking over the problem using probability theory, this paper can achieve the optimal answer as far as possible big Priori probability, and experiments show that the improved PSO algorithm has avoided local minima successfully and got higher search rates.

To improve real-time performance and accuracy are key aspects of collision detection. In view of that conventional algorithms of collision detection spend a lot of detection time, this paper presents a advanced algorithm. We adapts a parallel method based on MPI. At the same time, we use temporal-spatial coherence and spatial subdivision algorithm. First, we subdivide the space into a series of voxels, and then we detect the state of the object. If the state is changed, we should build its list which is used to store its adjacent objects in voxel. We can begin with mark points. These mark points has independence, so the parallel method based on MPI can be used to speed up the collision detection. In a word, this algorithm reduces the times of collision detection and the traversing depth of the bounding box tree. The results of experiment prove that this method has real-time performance and superiority.

This paper puts forward a kind of hybrid algorithm-combining the hierarchical bounding volume method and the bounding volume coordinate chain method in the virtual environment. This algorithm can improve the efficiency of collision detection, rigid body and the software both can be applied to with the collision detection. Then we built the task trees by traversing the mixed hierarchical bounding volumes and speeded up the collision detection algorithm by applying a parallel computing and introduce a space-time correlations concept to accelerate the speed of updating of the bounding box at the same time. Objects that intersect precisely in the testing process, combined with hierarchical bounding box tree compression and storage, storage space by reducing the algorithm to improve the detection algorithm speed. Experimental results show that the method compared with the single bounding method level, it advantages, and objects in the detection of more cases can reduce the execution time required for the algorithm.