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Methanol is assimilated through the serine cycle to generate acetyl-CoA without carbon loss. However, a highly active serine cycle requires high consumption of reducing equivalents and ATP, thereby leading to the impaired efficiency of methanol conversion to reduced chemicals. In the present study, a genome-scale flux balance analysis (FBA) predicted that the introduction of the heterologous ribulose monophosphate (RuMP) cycle, a more energy-efficient pathway for methanol assimilation, could theoretically increase growth rate by 31.3% for the model alphaproteobacterial methylotroph Methylorubrum extorquens AM1. Based on this analysis, we constructed a novel synergistic assimilation pathway in vivo by incorporating the RuMP cycle into M. extroquens metabolism with the intrinsic serine cycle. We demonstrated that the operation of the synergistic pathway could increase cell growth rate by 16.5% and methanol consumption rate by 13.1%. This strategy rewired the central methylotrophic metabolism through adjusting core gene transcription, leading to a pool size increase of C2 to C5 central intermediates by 1.2- to 3.6-fold and an NADPH cofactor improvement by 1.3-fold. The titer of 3-hydroxypropionic acid (3-HP), a model product in the newly engineered chassis of M. extorquens AM1, was increased to 91.2 mg/L in shake-flask culture, representing a 3.1-fold increase compared with the control strain with only the serine cycle. The final titer of 3-HP was significantly improved to 0.857 g/L in the fed-batch bioreactor, which was more competitive compared with the other 3-HP producers using methane and CO2 as C1 sources. Collectively, our current study demonstrated that engineering the synergistic methanol assimilation pathway was a promising strategy to increase the carbon assimilation and the yields of reduced chemicals in diverse host strains for C1 microbial cell factories.

Candida albicans and Cryptococcus neoformans, human-pathogenic fungi found worldwide, are receiving increasing attention due to high morbidity and mortality in immunocompromised patients. In the present work, 110 fungus pairs were constructed by coculturing 16 wood-decaying basidiomycetes, among which coculture of Trametes robiniophila Murr and Pleurotus ostreatus was found to strongly inhibit pathogenic fungi through bioactivity-guided assays. A combination of metabolomics and molecular network analysis revealed that 44 features were either newly synthesized or produced at high levels in this coculture system and that 6 of the features that belonged to a family of novel and unusual linear sesterterpenes contributed to high activity with MICs of 1 to 32 μg/ml against pathogenic fungi. Furthermore, dynamic 13C-labeling analysis revealed an association between induced features and the corresponding fungi. Unusual sesterterpenes were 13C labeled only in P. ostreatus in a time course after stimulation by the coculture, suggesting that these sesterterpenes were synthesized by P. ostreatus instead of T. robiniophila Murr. Sesterterpene compounds 1 to 3 were renamed postrediene A to C. Real-time reverse transcription-quantitative PCR (RT-qPCR) analysis revealed that transcriptional levels of three genes encoding terpene synthase, farnesyl-diphosphate farnesyltransferase, and oxidase were found to be 8.2-fold, 88.7-fold, and 21.6-fold higher, respectively, in the coculture than in the monoculture, indicating that biosynthetic gene cluster 10 was most likely responsible for the synthesis of these sesterterpenes. A putative biosynthetic pathway of postrediene A to postrediene C was then proposed based on structures of sesterterpenes and molecular network analysis. IMPORTANCE A number of gene clusters involved in biosynthesis of secondary metabolites are presumably silent or expressed at low levels under conditions of standard laboratory cultivation, resulting in a large gap between the pool of discovered metabolites and genome capability. This work mimicked naturally occurring competition by construction of an artificial coculture of basidiomycete fungi for the identification of secondary metabolites with novel scaffolds and excellent bioactivity. Unusual linear sesterterpenes of postrediene A to C synthesized by P. ostreatus not only were promising lead drugs against human-pathogenic fungi but also highlighted a distinct pathway for sesterterpene biosynthesis in basidiomycetes. The current work provides an important basis for uncovering novel gene functions involved in sesterterpene synthesis and for gaining insights into the mechanism of silent gene activation in fungal defense.

Methanol, commercially generated from methane, is a renewable chemical feedstock that is highly soluble, relatively inexpensive, and easy to handle. The concept of native methylotrophic bacteria serving as whole cell catalysts for production of chemicals and materials using methanol as a feedstock is highly attractive. In recent years, the available omics data for methylotrophic bacteria, especially for Methylobacterium extorquens, the most well-characterized model methylotroph, have provided a solid platform for rational engineering of methylotrophic bacteria for industrial production. In addition, there is a strong interest in converting the more traditional heterotrophic production platforms toward the use of single carbon substrates, including methanol, through metabolic engineering. In this chapter, we review the recent progress toward achieving the desired growth and production yields from methanol, by genetically engineered native methylotrophic strains and by the engineered synthetic methylotrophs.

Candida albicans and Cryptococcus neoformans, human-pathogenic fungi found worldwide, are receiving increasing attention due to high morbidity and mortality in immunocompromised patients. In the present work, 110 fungus pairs were constructed by coculturing 16 wood-decaying basidiomycetes, among which coculture of Trametes robiniophila Murr and Pleurotus ostreatus was found to strongly inhibit pathogenic fungi through bioactivity-guided assays. A combination of metabolomics and molecular network analysis revealed that 44 features were either newly synthesized or produced at high levels in this coculture system and that 6 of the features that belonged to a family of novel and unusual linear sesterterpenes contributed to high activity with MICs of 1 to 32 μg/ml against pathogenic fungi. Furthermore, dynamic (13)C-labeling analysis revealed an association between induced features and the corresponding fungi. Unusual sesterterpenes were (13)C labeled only in P. ostreatus in a time course after stimulation by the coculture, suggesting that these sesterterpenes were synthesized by P. ostreatus instead of T. robiniophila Murr. Sesterterpene compounds 1 to 3 were renamed postrediene A to C. Real-time reverse transcription-quantitative PCR (RT-qPCR) analysis revealed that transcriptional levels of three genes encoding terpene synthase, farnesyl-diphosphate farnesyltransferase, and oxidase were found to be 8.2-fold, 88.7-fold, and 21.6-fold higher, respectively, in the coculture than in the monoculture, indicating that biosynthetic gene cluster 10 was most likely responsible for the synthesis of these sesterterpenes. A putative biosynthetic pathway of postrediene A to postrediene C was then proposed based on structures of sesterterpenes and molecular network analysis. IMPORTANCE A number of gene clusters involved in biosynthesis of secondary metabolites are presumably silent or expressed at low levels under conditions of standard laboratory cultivation, resulting in a large gap between the pool of discovered metabolites and genome capability. This work mimicked naturally occurring competition by construction of an artificial coculture of basidiomycete fungi for the identification of secondary metabolites with novel scaffolds and excellent bioactivity. Unusual linear sesterterpenes of postrediene A to C synthesized by P. ostreatus not only were promising lead drugs against human-pathogenic fungi but also highlighted a distinct pathway for sesterterpene biosynthesis in basidiomycetes. The current work provides an important basis for uncovering novel gene functions involved in sesterterpene synthesis and for gaining insights into the mechanism of silent gene activation in fungal defense.

Background Butadiene is a platform chemical used as an industrial feedstock for the manufacture of automobile tires, synthetic resins, latex and engineering plastics. Currently, butadiene is predominantly synthesized as a byproduct of ethylene production from non-renewable petroleum resources. Although the idea of biological synthesis of butadiene from sugars has been discussed in the literature, success for that goal has so far not been reported. As a model system for methanol assimilation, Methylobacterium extorquens AM1 can produce several unique metabolic intermediates for the production of value-added chemicals, including crotonyl-CoA as a potential precursor for butadiene synthesis. Results In this work, we focused on constructing a metabolic pathway to convert crotonyl-CoA into crotyl diphosphate, a direct precursor of butadiene. The engineered pathway consists of three identified enzymes, a hydroxyethylthiazole kinase (THK) from Escherichia coli, an isopentenyl phosphate kinase (IPK) from Methanothermobacter thermautotrophicus and an aldehyde/alcohol dehydrogenase (ADHE2) from Clostridium acetobutylicum. The Km and kcat of THK, IPK and ADHE2 were determined as 8.35 mM and 1.24 s−1, 1.28 mM and 153.14 s−1, and 2.34 mM and 1.15 s−1 towards crotonol, crotyl monophosphate and crotonyl-CoA, respectively. Then, the activity of one of rate-limiting enzymes, THK, was optimized by random mutagenesis coupled with a developed high-throughput screening colorimetric assay. The resulting variant (THKM82V) isolated from over 3000 colonies showed 8.6-fold higher activity than wild-type, which helped increase the titer of crotyl diphosphate to 0.76 mM, corresponding to a 7.6% conversion from crotonol in the one-pot in vitro reaction. Overexpression of native ADHE2, IPK with THKM82V under a strong promoter mxaF in M. extorquens AM1 did not produce crotyl diphosphate from crotonyl-CoA, but the engineered strain did generate 0.60 μg/mL of intracellular crotyl diphosphate from exogenously supplied crotonol at mid-exponential phase. Conclusions These results represent the first step in producing a butadiene precursor in recombinant M. extorquens AM1. It not only demonstrates the feasibility of converting crotonol to key intermediates for butadiene biosynthesis, it also suggests future directions for improving catalytic efficiency of aldehyde/alcohol dehydrogenase to produce butadiene precursor from methanol. Electronic supplementary material The online version of this article (10.1186/s12934-018-1042-4) contains supplementary material, which is available to authorized users.

In this work, the 2H, 18O and 226Ra values in groundwater and surface water in the Minjiang River estuary were investigated in the dry and wet seasons. The δ18O values in the dry season were always higher than those in the wet season in both groundwater and surface water because of the presence of evaporation in the water cycle process. During the dry season, the δ18O values in groundwater on the southern bank of the Minjiang River are much higher than those on the northern bank because evaporation is more intense in the farmland of the southern bank than in the urbanized northern bank. The δ18O values in the estuarine water exhibit a good positive correlation with salinity, with a coefficient of 0.96 (p = 0.05) in both seasons. The 226Ra activities in the estuarine water increase with increasing salinity because of desorption from riverine suspended particles. The 226Ra activity reaches a peak value at a salinity of 20.5. Based on a three-endmember model, the average proportions of the estuarine water are calculated to be 0.02 for groundwater, 0.39 for river water and 0.59 for seawater. From this mixing ratio, the groundwater discharge into the estuary is estimated to be 9.31 × 106 m3 d−1 in the wet season.

The co-culture of Trametes versicolor and Ganoderma applanatum is a model of intense basidiomycete interaction, which induces many newly synthesized or highly produced features. Currently, one of the major challenges is an identification of the origin of induced features during the co-culture. Herein, we report a 13C-dynamic labeling analysis used to determine an association of induced features and corresponding fungus even if the identities of metabolites were not available or almost nothing was known of biochemical aspects. After the co-culture of T. versicolor and G. applanatum for 10 days, the mycelium pellets of T. versicolor and G. applanatum were sterilely harvested and then mono-cultured in the liquid medium containing half fresh medium with 13C-labeled glucose as carbon source and half co-cultured supernatants collected on day 10. 13C-labeled metabolome analyzed by LC-MS revealed that 31 induced features including 3-phenyllactic acid and orsellinic acid were isotopically labeled in the mono-culture after the co-culture stimulation. Twenty features were derived from T. versicolor, 6 from G. applanatum, and 5 features were synthesized by both T. versicolor and G. applanatum. 13C-labeling further suggested that 12 features such as previously identified novel xyloside [N-(4-methoxyphenyl)formamide 2-O-beta-D-xyloside] were likely induced through the direct physical interaction of mycelia. Use of molecular network analysis combined with 13C-labeling provided an insight into the link between the generation of structural analogs and producing fungus. Compound 1 with m/z 309.0757, increased 15.4-fold in the co-culture and observed 13C incorporation in the mono-culture of both T. versicolor and G. applanatum, was purified and identified as a phenyl polyketide, 2,5,6-trihydroxy-4, 6-diphenylcyclohex-4-ene-1,3-dione. The biological activity study indicated that this compound has a potential to inhibit cell viability of leukemic cell line U937. The current work sets an important basis for further investigations including novel metabolites discovery and biosynthetic capacity improvement.

In recent years, the detection technology based on machine learning algorithms for distributed denialof-service (DDoS) attacks has made great progress. However, previous methods fail to make full use of contextual information and rely heavily on the probability distribution of the input data. To avoid those pitfalls, the Conditional Random Fields (CRF) model is introduced in this paper for DDoS attacks detection. Firstly, the CRF is trained to build the classification model for DDoS attacks based on three groups of statistical features including conditional entropy, flag ratios and protocol ratios. Then, the trained CRF models are used to identify the attacks with model inference. Experimental results demonstrate that, the proposed approach can accurately distinguish between attacks and normal network traffic, and is more robust to resist disturbance of background traffic than its counterparts.

XML has been widely used for information exchange and storage as the de facto data representation format nowadays. Several XML query languages such XPath, XQuery and XML-QL have been proposed. Many structural join algorithms have been proposed for processing XPath queries, Although holistic twig join algorithms has been proved to be I/O optimal in terms of input and output sizes for queries with only ancestor-descendant edges, it cannot control the size of intermediate results for queries with parent-child edges. We address the problem of efficient path queries with mixed of ancestor-descendant and parent-child edges on a simple but novel index, called BI (i.e. Binary Index) based on Dewey labeling scheme. And we propose a new holistic path join algorithm, namely PSBI, which has the same performance as PathStack for query path with only ancestor-descendant edges, but it is significantly more efficient than PathStack for queries with the presence of parent-child edges. Experimental results demonstrate that the PSBI and BI index has a substantial performance improvement compared to original PathStack algorithm.

As XPath is the core of most XML query languages, the efficiency of processing XPath expression has been a main part of the cost in XML queries. However, most existing XPath processing algorithms, which don't take index structure into account, spend lots of costs on spaces and time. This paper proposes an efficient XPath query processing mechanism based on structural index that makes full use of XML structural index to quickly retrieve XML data. Coming together with the mechanism, the Compressed XPath Query Tree based on structural index is proposed, which significantly reduces many join operations. Then query algorithms are used to deal with all the structural relationship using our mechanism. Finally, experiments will run the algorithm on real XML datasets and query workloads to report the performance of our mechanism and show the efficiency compared with other mechanisms.

Evaluating classifier performances is a crucial problem in pattern recognition and machine learning. In this paper, we propose a new measure, i.e. confusion entropy, for evaluating classifiers. For each class cl"i of an (N+1)-class problem, the misclassification information involves both the information of how the samples with true class label cl"i have been misclassified to the other N classes and the information of how the samples of the other N classes have been misclassified to class cl"i. The proposed measure exploits the class distribution information of such misclassifications of all classes. Both theoretical analysis and statistical experiments show the proposed measure is more precise than accuracy and RCI. Experimental results on some benchmark data sets further confirm the theoretical analysis and statistical results and show that the new measure is feasible for evaluating classifier performances.

Cancer classification is the critical basis for patient-tailored therapy. Conventional histological analysis tends to be unreliable because different tumors may have similar appearance. The advances in microarray technology make individualized therapy possible. Various machine learning methods can be employed to classify cancer tissue samples based on microarray data. However, few methods can be elegantly adopted for generating accurate and reliable as well as biologically interpretable rules. In this paper, we introduce an approach for classifying cancers based on the principle of minimal rough fringe. For training rough hypercuboid classifiers from gene expression data sets, the method dynamically evaluates all available genes and sifts the genes with the smallest implicit regions as the dimensions of implicit hypercuboids. An unseen object is predicted to be a certain class if it falls within the corresponding class hypercuboid. Based upon the method, ensemble rough hypercuboid classifiers are subsequently constructed. Experimental results on some open cancer gene expression data sets show that the proposed method is capable of generating accurate and interpretable rules compared with some other machine learning methods. Hence, it is a feasible way of classifying cancer tissues in biomedical applications.

In this letter we propose a scheme of entanglement concentration via entanglement swapping in the quantum dot (QD) plus double-sided cavity system with the help of an input photon. With our scheme, bipartite maximally entangled states among two spatially separated particles can be generated without direct interaction and initial entanglement. Moreover, four-particle maximally entangled states can be created too. Contrary to previous work investigating entanglement concentration in QD systems, the non-maximally entangled state to be concentrated can be unknown for us; furthermore, the complicated Bell-state measurement of entanglement swapping is replaced by single-photon detection, which will greatly reduce the realisation complexity of the current scheme.

Pruning decision trees is deemed an effective way of solving over-fitting in practice. Pruned decision trees usually have simpler structure and are expected to have higher generalization ability at the expense of classification accuracy. Nowadays, various pruning methods are available. However, the problem of how to make a trade-off between structural simplicity and classification accuracy has not been well solved. In this paper, we firstly propose a method to evaluate structural complexities of decision trees in pruning process. Based upon the method, we introduce a new measure for post-pruning decision trees, which takes into account both classification accuracy and structural complexity. The experimental results on 20 benchmark data sets from the UCI machine learning data repository show that the proposed method is competitively feasible for pruning decision trees.

High field Fowler-Nordheim (F-N) stress effects on interface-trap density and emission cross sections in n-MOSFETs have been studied using three-level charge pumping (3LCP). The results show that 3LCP is sensitive to changes in trap cross section as a function of energy in the bandgap. An asymmetric change in electron and hole emission cross sections following F-N tunneling injection is found. The work also provides further insight into the influence of hot electrons on interface trap generation in MOSFETs in both the upper and lower bandgap following electrical stress.

In this paper, we propose a scheme of remote quantum state preparation and transfer that use a double-sided cavity system, in which a singly charged quantum dot is embedded in a double-sided optical microcavity with partially reflective top and bottom mirrors. The implementation of the scheme mainly depends on the interaction between the input single-photon pulse and the spins of electrons in the coupling system. Discussions about the effect of the cavity loss, side leakage and exciton–cavity coupling strength for the fidelity of generated states show that the fidelity can remain high enough by controlling these parameters. Therefore, the current scheme is feasible in the experiment.

A special SRAM has been designed as a yield enhancement vehicle in a 0.35 micrometer CMOS technology. Extra design rules were added to encourage process defects on certain places and discourage them on others. From the failure signature of a memory cell (0 or 1 failure) and its failure extent (single cell, double cell, bitline, wordline, ...) one can uniquely determine the process related cause of the failure. A dedicated test program has been developed to find the most common failures in a memory cell (e.g. floating bitline, bitline shorted to ground or Vdd, shorts between the nodes of the cell, ...). The innovating characteristics of the design allow to link these failures in an SRAM with high probability to a process related defect and its location within the memory cell. By simply testing the SRAM the main cause of failure can be found which can help to drive yield improvement, without doing intensive failure analysis. In this paper the design philosophy and the test methodology of this SRAM are described, illustrated with some examples of process related defects that proved the usefulness and the strength of the design and the test program.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.