Characterization of protein-protein interactions between the nucleocapsid protein and membrane protein of the avian infectious bronchitis virus

Avian infectious bronchitis virus (IBV) is one of the major viral respiratory diseases of chickens. Better understanding of the molecular mechanism of viral pathogenesis may contribute significantly to the development of prophylactic, therapeutic and diagnostic reagents as well as help in infection control. Avian IBV belongs to the Coronaviridaes and is similar to the other known coronaviruses. Previous studies have indicated that protein–protein interactions between nucleocapsid (N) and the membrane (M) proteins in coronavirus are related to coronavirus viral assembly. However, cases of IBV are seldom reported. In this study, yeast two-hybrid and  co-immunoprecipitation techniques were applied to investigate possible interactions between IBV N and M proteins. We found that interaction of the N and M proteins took place in vivo and the residues 168 – 225 of the M protein and the residues 150 - 210 of the N protein were determined to be involved in their interaction. These results may provide some useful information on the molecular mechanism of IBV’s N and M proteins, which will facilitate therapeutic strategies aiming at the disruption of the association between membrane and nucleocapsid proteins and indicate a new drug target for IBV.Key words: Co-immunoprecipitation, membrane protein, nucleocapsid protein, protein-protein interaction, yeast two-hybrid

Ultrastructure of Ediacaran cloudinids suggests diverse taphonomic histories and affinities with non-biomineralized annelids

Cloudinids have long been considered the earliest biomineralizing metazoans, but their affinities have remained contentious and undetermined. Based on well-preserved ultrastructures of two taxa, we here propose new interpretations regarding both their extent of original biomineralization and their phylogenetic affinity. One of these taxa is a new cloudinid from Mongolia, Zuunia chimidtsereni gen. et sp. nov., which exhibits key characteristics of submicrometric kerogenous lamellae, plastic tubewall deformation, and tube-wall delamination. Multiple carbonaceous lamellae are also discovered in Cloudina from Namibia and Paraguay, which we interpret to have originated from chitinous or collagenous fabrics. We deduce that these cloudinids were predominantly originally organic (chitinous or collagenous), and postmortem decay and taphonomic mineralization resulted in the formation of aragonite and/or calcite. Further, based on our ultrastructural characterization and other morphological similarities, we suggest that the cloudinids should most parsimoniously be assigned to annelids with originally organic tubes

Tremadocian (Ordovician) reclined graptolites from Baishan, North China

The Erdaopuzi section in the Baishan area of Jilin Province, northeast China, contains abundant late Tremadoc graptolites. However, there are some taxonomic controversies regarding the characters of the hand specimens that are difficult to identify. In this paper, we present our restudy of isolated graptolites from acid residue and discuss the taxonomic problems of the subgenus Clonograptus (Neoclonograptus) Zhao and Zhang. Four genera and six species were identified, including Adelograptus tenellus (Linnarsson), Ancoragraptus gracilis (Zhao and Zhang), Dictyonema sp., Psigraptus jacksoni Rickards and Stait, Psigraptus lenzi Jackson, Psigraptus arcticus Jackson. According to the significant evolution of morphological characters, two graptolite zones (from bottom to top) can be identified in ascending order in the study area, i.e. Adelograptus Zone and the Psigraptus Zone

Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer Interface Classification

Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods