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As an important equipment for the separation, store, and transport of the oil and natural gas in energy industry, the pressure vessels are subjected to harsh service environments of sulfide stress cracking (SSC). In this work, the effect of the cooling rate on the SSC susceptibility of a Q345 pressure vessel steel is studied. A pronounced distinction in microstructure is presented as the cooling rate is increased from ≈1 to ≈15 °C s−1. At ≈1 °C s−1, the microstructure is consisted of polygonal‐like ferrite and distinct banded pearlite. At ≈5 °C s−1, the microstructure is consisted of polygonal‐like ferrite and faintly banded pseudo‐pearlite. At ≈15 °C s−1, the microstructure is acicular ferrite with a complete elimination of banded structure. The cooling rate decides the level of the banded structure. As the cooling rate increases, the banded structure becomes relatively slight and even disappears. Strength in sort ascending is ≈1, ≈ 5, and ≈15 °C s−1. The SSC failure happens at ≈1 and ≈5 °C s−1, while does not happen at ≈15 °C s−1. The strength is not the only dominant factor responsible for the SSC failure. The SSC is preferentially generated at the banded structure. The elimination of the banded structure is necessary to improve the SSC resistance of the Q345 pressure vessel steel. [ABSTRACT FROM AUTHOR]

Interactions between plants and insects are among the most important life functions for all organism at a particular natural community. Usually a large number of samples are required to identify insect diets in food web studies. Previously, Sanger sequencing and next generation sequencing (NGS) with short DNA barcodes were used, resulting in low species‐level identification; meanwhile the costs of Sanger sequencing are expensive for metabarcoding together with more samples. Here, we present a fast and effective sequencing strategy to identify larvae of Lepidoptera and their diets at the same time without increasing the cost on Illumina platform in a single HiSeq run, with long‐multiplex‐metabarcoding (COI for insects, rbcL, matK, ITS and trnL for plants) obtained by Trinity assembly (SHMMT). Meanwhile, Sanger sequencing (for single individuals) and NGS (for polyphagous) were used to verify the reliability of the SHMMT approach. Furthermore, we show that SHMMT approach is fast and reliable, with most high‐quality sequences of five DNA barcodes of 63 larvae individuals (54 species) recovered (full length of 100% of the COI gene and 98.3% of plant DNA barcodes) using Trinity assembly (up‐sized to 1015 bp). For larvae diets identification, 95% are reliable; the other 5% failed because their guts were empty. The diets identified by SHMMT approach are 100% consistent with the host plants that the larvae were feeding on during our collection. Our study demonstrates that SHMMT approach is reliable and cost‐effective for insect‐plants network studies. This will facilitate insect‐host plant studies that generally contain a huge number of samples. [ABSTRACT FROM AUTHOR]

Abstract: Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two‐step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA‐based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%–100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity‐related projects with large DNA sequence data sets. [ABSTRACT FROM AUTHOR]

Species identification via DNA barcodes has recently become an important and routine task in many biodiversity projects using DNA sequence data., Here, we present BarcodingR, an integrated software package that provides a comprehensive implementation of species identification methods, including artificial intelligence, fuzzy-set, Bayesian and kmer-based methods, that are not readily available in other packages., BarcodingR additionally provides new functions for barcode evaluation, barcoding gap analysis, delimitation comparison analysis, species membership analysis and consensus identification., Comparison with other barcoding methods using 11 empirical data sets indicates that on average, FZKMER (implemented in BarcodingR) and one extant barcoding method BRONX outperform all other methods examined in this study. Two other methods, BP and FZ (both implemented in BarcodingR), present similar performance as SVM and BLOG, respectively, and all display better performance than that of Jrip., The software of BarcodingR is open source under GNU General Public License and freely available for all major operating systems. [ABSTRACT FROM AUTHOR]