Construction of genetic map in barley using sequence-related amplified polymorphism markers, a new molecular marker technique

Sequence-related amplified polymorphism (SRAP) markers, a novel polymerase chain reaction (PCR)-based molecular marker technique, were successfully applied in map construction, cultivar identification, diversity evaluation, comparative genomics and gene location of different plant species. The molecular genetic map of SRAP markers in Steptoe / Morex doubled haploid (DH) population was constructed in this study, using 216 SRAP markers and 312 simple sequence repeat (SSR) markers. Overall, 21 of the 216 SRAP markers generated 78 polymorphic loci, and 98 of 312 SSR markers produced 107 polymorphic loci. Among the 185 loci, 175 loci (70 SRAP loci and 105 SSR loci) were assigned to nine linkage groups. The map covered 1475 cM with a mean density of 8.7 cM per locus. In total, 33 of all the loci (17.84%) showed significant segregation distortion. Moreover, 23 of the 33 loci (69.7%) skewed towards the parent Steptoe, whereas the remaining loci (21.3%) deviated towards the parent Morex and some of these distorted loci tended to cluster at the end of linkage groups, while others were dispersed on linkage groups in a decentralized fashion. The three putative segregation distortion regions (SDRs) were detected on chromosomes 2H, 4H and 5H, respectively. This linkage map indicates its importance in quantitative trait loci (QTLs) mapping, marker-assisted selection (MAS) and integrative analysis for further genetic studies with other linkage maps in barley.Keywords: Barley, sequence-related amplified polymorphism (SRAP), molecular genetic map, simple sequence repeat (SSR), doubled haploid (DH) populatio

Preparation of Bulk 13

Arc-discharge has been widely used in the bulk production of various carbon nanomaterials, especially for structurally more robust single-walled carbon nanotubes. In this paper, the same bulk-production technique was applied to the synthesis of significantly 13C-enriched graphitic materials, from which graphene oxides similarly enriched with 13C were prepared and characterized. The results demonstrate that arc-discharge is a convenient method to produce bulk quantities of 13C-enriched graphene materials from relatively less expensive precursors (largely amorphous 13C powders)

Pediatric endobronchial inflammatory myofibroblastic tumor: a case report and review of the literature

Inflammatory myofibroblastic tumor (IMT) belongs to the group of soft tissue tumor and could occur at any anatomical site from the central nervous system to gastrointestinal tract. The lung and abdomen are commonly affected sites, however, pulmonary IMT is predominantly located within the parenchyma rather than presenting as endobronchial lesion. IMTs may occur in any age group, but they are observed most commonly in children and adolescents. Here, we present a case of IMT arising from the left main stem bronchus in a 10-year-old girl

A Deep Paraphrase Identification Model Interacting Semantics with Syntax

Paraphrase identification is central to many natural language applications. Based on the insight that a successful paraphrase identification model needs to adequately capture the semantics of the language objects as well as their interactions, we present a deep paraphrase identification model interacting semantics with syntax (DPIM-ISS) for paraphrase identification. DPIM-ISS introduces the linguistic features manifested in syntactic features to produce more explicit structures and encodes the semantic representation of sentence on different syntactic structures by means of interacting semantics with syntax. Then, DPIM-ISS learns the paraphrase pattern from this representation interacting the semantics with syntax by exploiting a convolutional neural network with convolution-pooling structure. Experiments are conducted on the corpus of Microsoft Research Paraphrase (MSRP), PAN 2010 corpus, and PAN 2012 corpus for paraphrase plagiarism detection. The experimental results demonstrate that DPIM-ISS outperforms the classical word-matching approaches, the syntax-similarity approaches, the convolution neural network-based models, and some deep paraphrase identification models