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The special niche of endophytic fungi promotes their potential to produce antitumor compounds with novel structure and significant bioactivity for screening of new antitumor drugs. In our previous studies, we isolated a Fusarium strain from the roots of the medicinal plant Nothapodytes pittosporoides and identified it as Fusarium nematophilum. We found that the crude extract of F. nematophilum had significant antitumor activity on A549 cancer cells, and overexpressing the global regulatory factor FnVeA (the VeA gene of the fungus F. nematophilum) resulted in a significant increase in the antitumor activity, which was approximately fivefold higher than wild strain for relative inhibition rate. In FnVeAOE, the accumulation of indole, alkene, alkaloid, steroid, and flavonoid metabolites with potential antitumor activity was significantly upregulated compared with wild type via metabolomic analysis. Moreover, the transcriptome analysis showed that 134 differential genes were considered to be closely related to the biosynthesis of antitumor substances, of which 59 differential genes were considered as candidate key genes, and related to tryptophan dimethylallyltransferase, cytochrome P450 monooxygenase, polyketide synthases, and transcription factors. Taken together, we suggest that FnVeA may regulate the biosynthesis of antitumor substances by mediating the expression of genes related to secondary metabolic pathways in F. nematophilum.

The global regulatory factor LaeA has been shown to be involved in the biosynthesis of secondary metabolites in various fungi. In a previous work, we isolated an endophytic fungus from Artemisia annua, and its extract had a significant inhibitory effect on the A549 cancer cell line. Phylogenetic analysis further identified the strain as Alternaria alstroemeria. Overexpression of AalaeA gene resulted in significantly increased antitumor activity of this strain's extract. The 3-(4, 5- dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide assay results showed that the inhibition rate of the AalaeA

Three Rosellinia species were collected from southern China. Based on morphological and phylogenetic analyses, two are introduced as new species while the other is reported as a new record. Rosellinia qiongensis sp. nov. and R. verticillata sp. nov. belong to the R. emergens group. Rosellinia qiongensis differs from its similar species by its dimension of ascospores and three short germ slits on ascospores. Rosellinia verticillata differs from species in this group by its ascospores with whole spore length germ silt and surrounded by a thin slimy sheath. All taxa are made available to illustrations and descriptions.

The global regulatory factor LaeA has been shown to be involved in the biosynthesis of secondary metabolites in various fungi. In a previous work, we isolated an endophytic fungus from Artemisia annua, and its extract had a significant inhibitory effect on the A549 cancer cell line. Phylogenetic analysis based on ITS sequences further identified the strain as Alternaria alstroemeria. Overexpression of the global regulatory factor AalaeA gene resulted in significantly increased antitumor activity of this strain’s extract. The 3 - (4, 5- dimethylthiazol − 2 - yl) -2, 5 - diphenyltetrazolium bromide (MTT) assay results showed that the inhibition rate of the AalaeAOE29 mutant extract on A549 cancer cells was significantly higher than that of the WT extract, as the IC50 decreased from 195.0 µg/mL to 107.4 µg/mL, and the total apoptosis rate was enhanced. Overexpression of the AalaeA gene significantly increased the contents of myricetin, geraniol, ergosterol and 18 other antitumor compounds as determined by metabolomic analysis. Transcriptomic analysis revealed significant changes in 95 genes in the mutant strain, including polyketide synthases (PKSs), nonribosomal peptide synthases (NRPSs), cytochrome P450s, glycosyltransferases (GTs), acetyl-CoA acetyltransferases and others. These results suggested that AaLaeA mediated the antitumor activity of the metabolites in A. alstroemeria by regulating multiple metabolic pathways.

The special niche of endophytic fungi promotes their potential to produce antitumor compounds with novel structure and significant bioactivity for screening of new antitumor drugs. In our previous studies, we isolated a

A number of studies have demonstrated that endophytic fungi have the potential to produce antitumor active substances with novel structures and significant activities. Our previous studies found that an endophytic Fusarium solani derived from the stem of the medicinal plant Nothapodytes pittosporoides (Oliv.) Sleum had good antitumor activity. Furthermore, overexpression of the global regulator FsveA (FsveAOE) in F. solani led to significant inhibition ratio increasement of human alveolar adenocarcino -ma cell (A549). In comparison with the wild strain (WT), the inhibition rate of FsveAOE was obviously increased ~ 14.69%, and the apoptosis ratio was significantly increased 4.86-fold. A metabolomic analysis revealed that the overexpression of FsveA resulted in a significant increasement in several antitumor secondary metabolites of fungi, including alkaloids (Geldanamycin, Acadesine, Adenosine), terpenoids (Lanosterin, Illudin M), carboxylic acid derivatives (L-Arginine, L-Methionine), phenolic (4-Allyl-2-methoxyphenol) and flavonoid metabolites (Epicatechin gallate). Additionally, the transcriptome analysis also displayed that expression pattern of 48 genes-related to the antitumor activity were significantly changed in FsveAOE, mainly involving glycosyl hydrolases, the Zn(2)-Cys(6) class, Cytochrome P450 monooxygen -ase, 3-isopropylmalate dehydratase, and polyketide synthases. Therefore, we conclude that FsveA mediate multiple secondary metabolic pathways to up-regulate the production of antitumor substance in endophytic F. solani.

A number of studies have demonstrated that endophytic fungi have the potential to produce antitumor active substances with novel structures and significant activities. In our previous studies, we isolated a Fusarium strain from the stem of the medicinal plant Nothapodytes pittosporoides (Oliv.). In this study, we identified this strain as Fusarium solani and found that its crude extract has significant antitumor activity against human alveolar adenocarcinoma cells (A549). We overexpressed the global regulatory factor VeA in F. solani (VeA

This paper introduces a condition-based maintenance method combined with long short-term memory network for offshore wind turbine. According to the ranking of offshore wind turbine components using multiple indicators (failure rate, repair time, and maintenance cost), the optimization object focuses on four critical components, namely, rotor, pitch system, gearbox, and generator. Long short-term memory network is implemented to evaluate system condition and predict potential risks, then the preventive maintenance can be performed on the component that reaches the reliability threshold. The repair activity provides an advance maintenance opportunity for the other components, sharing the fix maintenance costs and the downtime. A maintenance decision process is presented in this paper, aiming to achieve the maximum cost savings. Calculated and comparative results demonstrate that the policy proposed in this article is superior in validity and accuracy.

Xylariales are common endophytes of

With the development of offshore wind power, the reliability analysis of offshore wind turbines is increasingly significant due to the system complexity and negative impacts in harsh operating conditions. In this study, the Fault Tree Analysis method is adopted for both qualitative and quantitative evaluation of semi-submersible floating offshore wind turbine failure characteristics. The floating offshore wind turbine is divided into several assemblies, including support structures, pitch and hydraulic system, gearbox, generator and the other systems. Failure rates of relevant offshore structures are collected from previous studies, reports and reliability databases. On this basis, the quantitative assessment of Minimum Cut Sets and Importance Measures are achieved. The calculated results are generally in conformity with statistical data, indicating that most of the failures are caused by several basic factors. Marine conditions, especially the salt-spray and high wind speed, show the most significant impact on floating offshore wind turbine performance.

Gearbox in offshore wind turbines is a component with the highest failure rates during operation. Analysis of gearbox repair policy that includes economic considerations is important for the effective operation of offshore wind farms. From their initial perfect working states, gearboxes degrade with time, which leads to decreased working efficiency. Thus, offshore wind turbine gearboxes can be considered to be multi-state systems with the various levels of productivity for different working states. To efficiently compute the time-dependent distribution of this multi-state system and analyze its reliability, application of the non-homogeneous continuous-time Markov process (NHCTMP) is appropriate for this type of object. To determine the relationship between operation time and maintenance cost, many factors must be taken into account, including maintenance processes and vessel requirements. Finally, an optimal repair policy can be formulated based on this relationship.

The load and corrosion caused by the harsh marine environment lead to the severe degradation of offshore equipment and to their compromised security and reliability. In the quantitative risk analysis, the failure models are difficult to establish through traditional statistical methods. Hence, the calculation of the occurrence probability of small sample events is often met with great uncertainty. In this study, the Bayesian statistical method is implemented to analyze the oil and gas leakages of FPSO internal turret, which is a typical small sample risk but could lead to severe losses. According to the corresponding failure mechanism, two Bayesian statistical models using the Weibull distribution and logarithmic normal distribution as the population distribution are established, and the posterior distribution of the corresponding parameters is calculated. The optimal Bayesian statistical model is determined according to the Bayesian information criterion and Akaike criterion. On the basis of the determined optimal model, the corresponding reliability index is solved to provide basic data for the subsequent risk assessments of FPSO systems.

Three species possessing characteristics of Graphostromataceae were observed in China. Morphology of the described species with illustrations and their phylogeny based on regions of internal transcribed spacers (ITS), the second largest subunit of the RNA polymerase II (RPB2), β-tubulin (TUB2) and α-actin (ACT) are provided. Two new species and one new record from China were identified. Morphologically, Biscogniauxia glaucae sp. nov. differs from B. atropunctata var. maritima, B. citriformis var. macrospora, B. fuscella and B. mediterranea by its stromata with raised margins, with clear outlines, with punctate ostioles openings and ascospores which are equilateral with broadly rounded ends, with a straight germ slit on the more concave side, nearly full spore length, lacking appendages and sheathes. ITS sequence difference between Graphostroma guizhouensis sp. nov and type strain of G. platystomum is 7%, which support identifying it as a new species. Camillea broomeana with scanning electron microscope description of ascospores was illustrated as a new record from China. Cryptostroma was proposed in Graphostromataceae based on molecular data. Vivantia was accepted in Graphostromataceae based on its morphological characteristics and Nodulisporiurn anamorphs which is similar with that of Biscogniauxia.

Xylariales are common endophytes of Dendrobium. However, xylarialean species resolution remains difficult without sequence data and poor sporulation on artificial media and asexual descriptions for only several species and old type material. The surface-sterilized and morph-molecular methods were used for fungal isolation and identification. A total of forty-seven strains were identified as twenty-three species belonging to Apiosporaceae, Hypoxylaceae, Induratiaceae, and Xylariaceae. Five new species—Annulohypoxylon moniliformis, Apiospora dendrobii, Hypoxylon endophyticum, H. officinalis and Nemania dendrobii were discovered. Three tentative new species were speculated in Xylaria. Thirteen known fungal species from Hypoxylon, Nemania, Nigrospora, and Xylaria were also identified. Another two strains were only identified at the genus and family level (Induratia sp., Hypoxylaceae sp.). This study recorded 12 new hosts for xylarialean endophytes. This is the first report of Xylariales species as endophytes from Dendrobium aurantiacum var. denneanum, D. cariniferum, D. harveyanum, D. hercoglossum, D. moniliforme, and D. moschatum. Dendrobium is associated with abundant xylarialean taxa, especially species of Hypoxylon and Xylaria. We recommend the use of oat agar with low concentrations to induce sporulation of Xylaria strains.

This paper aims to introduce a novel method in terms of risk analysis and control of FPSO oil and gas processing system. The Failure Mode and Effect Analysis (FMEA) is implemented to identify the critical equipment and typical failure modes, in order to improve the accuracy and pertinence of model establishment. A Petri Net model is then developed based on FMEA results and the correlation analysis between different components. The input reliability data are primarily collected from Offshore and Onshore Reliability Data (OREDA), and the maintenance information is assessed by industry experts, using fuzzy synthetic evaluation method. This paper focuses on the accuracy, rapidity and feasibility in the modeling and solution process, and considers the influence of weather factors on the maintenance operation and limited maintenance crew number, forming a complete set of reliability and maintenance strategy optimization analysis method of FPSO oil and gas processing system. The results contain system reliability, availability, and maintenance information. The proposed approach can reveal the risk feature of the system and provide corresponding risk control scheme.

A well-established condition-based maintenance (CBM) method based on condition monitoring information can be used to reduce maintenance costs by decimating unnecessary maintenance actions, reducing system downtime, and minimizing unexpected failures. In this paper, we propose an opportunistic CBM optimization approach for offshore wind turbines (OWTs) in which economic dependence exists among the components that are subjected to condition monitoring. An artificial neural network is used to predict life percentage by leveraging the condition monitoring information. A conditional failure probability value that is derived from the predicted failure-time distribution of the component was adopted to represent the deterioration of OWTs. Our maintenance method can be defined by a threshold with two-level failure probability. We propose a simulation method that can be used to calculate the optimal threshold values to minimize the long-term maintenance cost. Failure information and maintenance cost of OWTs are collected from existing articles to illustrate the proposed approach. Results show that the opportunistic CBM strategy can be effective and is established in the wind power industry. Moreover, the expense comparison between onshore and offshore WTs demonstrates the importance of this method.

Dropped objects is one of the most hazardous accidents of floating production storage offloading (FPSO) due to the potentially severe consequences, therefore, the quantitative calculation of dropped objects impact risk, which is featured with uncertainties, is essential. Monte Carlo Simulation (MCS) is a method for this intention with flexibility and efficiency. However, wide calculating resources are needed for MCS-based accident analysis, especially when multiple random variables are concerned. Moreover, dropped objects collision damage models are perplexed and usually pronounced by the Finite Element Method (FEM), which is of a computational complexity. To address this issue, this paper considered a combined methodology: the Artificial Neural Network (ANN) adjusted by genetic algorithm (GA) is united with MCS to analyze dropped objects collision failure probability quantitatively. A time-dependent progressive simulation is employed to forecast the structural response caused by dropped objects collision. Then the ANN-GA is trained based upon the collision data and used as an alternative for the computational FEM performs with the MCS methodology. The risk level for the dropped objects collision of FPSO is assessed by comparing the proposed method and the DNV rules, and a qualitative uncertainty analysis is also conducted in this quantitative failure analysis.

Risk assessment of Floating Offshore Wind Turbines (FOWTs) relies on failure statistics, which are usually collected from various source. In order to quantify the information uncertainties, this paper introduces the Fuzzy Fault Tree Analysis (FFTA) to assess the risk of semi-submersible FOWTs. A Fault Tree (FT) model is adopted to analyze the risk evolution mechanism of the FOWT and the fuzzy set theory is employed to process the failure statistics. As the results of quantitative analysis, the failure intervals of the FOWT and critical assemblies are calculated. The risk measure named Fuzzy Critical Importance (FCI) is utilized to compute the sequence of the basic fault events and identify critical components. A comparison analysis is performed and the results indicate that the present FFTA method is more in line with practical and has the merit in error reduction of failure rate figured out.

This paper presents a Generalized Stochastic Petri Net (GSPN) approach to assess risk of FPSO topsides, considering the weather window and imperfect maintenance effect. The FPSO topside is considered as a multi-component system consisting of several assemblies. A GSPN model is developed according to system identification and the correlation between different components. The reliability, availability, and maintenance information of the system are derived using the model, based on failures collected from existing literature and database. An operation and maintenance cost model is established, and an optimized maintenance strategy is finally determined based on the GSPN outcomes. The results indicate that the proposed approach is an effective tool to evaluate the system risk and save the maintenance expenditure of FPSO topsides.

Floating offshore wind turbine (FOWT) is complexly structured by interdependent subsystems and experiences negative impacts in harsh operating conditions. During the risk and reliability analysis, two issues have to be addressed: system failure mode complexity and mutual correlation. We conducted risk assessment through a modified Failure Modes and Effects Analysis (FMEA) method, named correlation-FMEA,to study the connection between failure modes and its effect on the failure probability of the entire system. A series of failure modes with high priority were determined by conventional FMEA, and the corresponding connections were analyzed to obtain the correlation coefficients using the reliability index vector method. The data used in our research comes from field operation in China. Probability Network Evaluation Technique (PNET) was used to get the weakest failure modes set of the system based on those coefficients. With the results, suggestions for floating wind turbine design were provided regarding aspects of safety and reliability.

Two new endophytic species, Colletotrichumjishouensesp. nov. and. C.tongrenensesp. nov. were isolated from Nothapodytespittosporoides in Guizhou and Hunan provinces, China. Detailed descriptions and illustrations of these new taxa are provided and morphological comparisons with similar taxa are explored. Phylogenetic analysis with combined sequence data (ITS, GAPDH, ACT and TUB2) demonstrated that both species formed distinct clades in this genus. This is the first record of Colletotrichum species from N.pittosporoides in China.

It is increasingly necessary to perform risk analysis in marine structures, to ensure system safety, as they are large and complex. In view of the shortcomings of failure mode and effect analysis (FMEA), a modified fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) approach is proposed that is based on fuzzy evidence reasoning (FER), and considers the risk factor rating and relative weight. The presented method is used to prioritize the risk of equipment failure modes for the floating production storage and offloading system (FPSO) oil and gas processing system. The subjective weights and objective weights of occurrence (O), severity (S), and detectability (D) have been considered comprehensively. The subjective experience of the experts and the objective information reflected by the O, S, and D ratings are all included in the weights, making the ranking results closer to reality. The results can be scientific references for decision-makers in risk identification.

Operation and maintenance (O&M) costs account for a large proportion of the total costs for offshore wind energy. Performing a reasonable maintenance strategy is an effective approach to reduce O&M costs and gain more profits. In this paper, an opportunistic maintenance strategy for offshore wind turbine systems considering maintenance intervals of each subsystem is proposed to minimize the total maintenance cost. First, a Non-homogeneous Continuous-Time Markov Process based state transition model is established to study degradation process of subsystems. The influence of maintenance time schedule on the maintenance cost is studied to obtain the optimal maintenance intervals of each subsystems. Then, an opportunistic maintenance model considering economic dependencies between multiple subsystems is proposed to optimize the maintenance strategy by combining maintenance activities of individual subsystems to a grouping maintenance activity. A numerical example is used to indicate the significant effectiveness of the maintenance model. The result shows that the total maintenance cost of an offshore wind turbine system will be reduced by adopting the opportunistic maintenance strategy when compared with conventional preventive maintenance strategy.

Wang, L.; Sun, L.; Kang, J.; Cai, B.; Wang, Y., and Wu, Y., 2019. Risk identification and control of oil and gas leakage in marine environment. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 50–54. Coconut Creek (Florida), ISSN 0749-0208.Floating production storage and offloading (FPSO), an important facility for offshore oil and gas production, has great effects on the marine environment. The oil and gas (O&G) processing system on the FPSO faces high risks of leakage, which bring potential loss of property and pollution of marine environment. To solve the problem, this paper proposes measures to prevent and control the O&G leakage risks of FPSO based on the bow-tie model. First, the effects of O&G leakage on marine environment were discussed. Next, a disaster chain model was established to describe how the risks lead to O&G leakage. Furthermore, the bow-tie model was adopted to illustrate the whole process of risk accidents, from the potential causes to the possible outcomes. On this basis, safety barriers, i.e. control measures, were designed to prevent the development of accidents.

Floating Production Storage and Offloading (FPSO) is being widely used in offshore petroleum production. As a key subsystem, the offloading system often fails and causes marine disaster. The aim of this study was to investigate the failure events and failure rate to determine the efficient maintenance strategy, in particular maintenance interval and overhaul cycle. Failure Mode and Effects Analysis (FMEA) method was applied for the reliability analysis of the main failure events and their interrelations. In order to guarantee the accuracy, the analysis is based on four primary subsystem of offloading system: platform safety system, offloading hose system, center control system and auxiliary system. The logic structure of the main failure events was demonstrated by dynamic fault tree analysis (DFTA) in order to obtain the minimal cut sets, mean time between failure (MTBF) and failure probability by numerical analysis method. At last, group maintenances theory is applied to obtain the maintenance interval and overhaul cycle for the FPSO to achieve global optimum and feasibility. The result suggested the overhaul cycle is 4.8 years. After the comparison with actual engineering data, it is proved to be precise and this method is stable to be applied for engineering practice.

The failure of floating wind turbine can suspend the operation of the offshore wind turbine generator, which will severely affect the generated energy and cause huge economic loss. Hence, the reliability evaluation of floating wind turbine is of vital importance. RPN calculation which adopts the traditional FMEA method carries significant limitations and cannot solve failure sequence in case RPN is the same. Therefore, the paper makes use of fuzzy FMEA method based on Related Weights and TOPSIS Theory to make fuzzy evaluation on each failure mode of floating wind turbine as well as construct a failure matrix which adopts triangular fuzzy number. This paper utilizes Related Weights that come from different experts to different failure modes and TOPSIS Theory to calculate similarity algorithm to replace RPN value to sequence the risks. Results show that fuzzy FMEA method that applies Related Weights and TOPSIS Theory can more accurately judge the degree of risks of floating wind turbine system's failure mode to avoid the effect of subjectivity on evaluation results to the extreme extent. In this way, the more effective maintenance strategy can be formulated so as to improve the system's reliability.

The reliability analysis of offshore wind turbines is increasingly significant due to the system complexity and negative impacts in harsh operating conditions. Maintenance processes are required to ensure the system safety and performance. In this paper, an opportunistic maintenance strategy for an offshore wind turbine electrical and electronic system is proposed, considering imperfect maintenance and preventive maintenance durations. When one component reaches its reliability threshold, opportunistic preventive maintenance can be implemented for others. Based on the rolling horizon approach, the maintenance planning can be updated to take short-term information into account, which could be changed with time. By selecting the best combination, the maintenance schedule during the mission time is finally determined. Failure information is collected from previous literature studies to accomplish the calculations. The results show that the maintenance cost is significantly reduced by applying opportunistic m...