Variable Subregions Research Articles

Variable Sub-Region Canonical Variate Analysis for Dynamic Process Monitoring

When all process variables are used to establish one data-based model, some variables have little contributions to the model. When these variables are grouped into a small local variable set to develop a data-based model, their contributions will become large. It is called local variable characteristic. Standard canonical variate analysis (CVA) based process monitoring method doesn’t consider local process variable characteristic. To solve this problem, a variable sub-region based canonical variate analysis (V-CVA) is proposed for dynamic process monitoring by enhancing the information of local variables. The proposed method combines variable sub-region division and Bayesian fusion. First, standard CVA is performed by using all process variables. Then, K-means clustering is adopted to divide process variables into sub-regions based on the mutual information of process variables and canonical variables. For each variable sub-region, CVA is conducted to compute local process monitoring statistics. Finally, local statistics are fused to build ensemble statistics based on the idea of Bayesian inference. Compared with the standard CVA method, the proposed V-CVA method can emphasize the information of local variables, and has better monitoring performance in the canonical variable feature subspace and the prediction residual subspace. Two examples demonstrate the effectiveness of the proposed method.

Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments.

BackgroundTaxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. Quantitative Insights Into Microbial Ecology (QIIME) and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean, and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene.FindingsWe show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently <2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost 2 and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline.ConclusionsOur results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision, and/or computational performance required.

RNase E forms a complex with polynucleotide phosphorylase in cyanobacteria via a cyanobacterial-specific nonapeptide in the noncatalytic region

RNase E, a central component involved in bacterial RNA metabolism, usually has a highly conserved N-terminal catalytic domain but an extremely divergent C-terminal domain. While the C-terminal domain of RNase E in Escherichia coli recruits other components to form an RNA degradation complex, it is unknown if a similar function can be found for RNase E in other organisms due to the divergent feature of this domain. Here, we provide evidence showing that RNase E forms a complex with another essential ribonuclease-the polynucleotide phosphorylase (PNPase)-in cyanobacteria, a group of ecologically important and phylogenetically ancient organisms. Sequence alignment for all cyanobacterial RNase E proteins revealed several conserved and variable subregions in their noncatalytic domains. One such subregion, an extremely conserved nonapeptide (RRRRRRSSA) located near the very end of RNase E, serves as the PNPase recognition site in both the filamentous cyanobacterium Anabaena PCC7120 and the unicellular cyanobacterium Synechocystis PCC6803. These results indicate that RNase E and PNPase form a ribonuclease complex via a common mechanism in cyanobacteria. The PNPase-recognition motif in cyanobacterial RNase E is distinct from those previously identified in Proteobacteria, implying a mechanism of coevolution for PNPase and RNase E in different organisms.

A software pipeline for processing and identification of fungal ITS sequences.

BackgroundFungi from environmental samples are typically identified to species level through DNA sequencing of the nuclear ribosomal internal transcribed spacer (ITS) region for use in BLAST-based similarity searches in the International Nucleotide Sequence Databases. These searches are time-consuming and regularly require a significant amount of manual intervention and complementary analyses. We here present software – in the form of an identification pipeline for large sets of fungal ITS sequences – developed to automate the BLAST process and several additional analysis steps. The performance of the pipeline was evaluated on a dataset of 350 ITS sequences from fungi growing as epiphytes on building material.ResultsThe pipeline was written in Perl and uses a local installation of NCBI-BLAST for the similarity searches of the query sequences. The variable subregion ITS2 of the ITS region is extracted from the sequences and used for additional searches of higher sensitivity. Multiple alignments of each query sequence and its closest matches are computed, and query sequences sharing at least 50% of their best matches are clustered to facilitate the evaluation of hypothetically conspecific groups. The pipeline proved to speed up the processing, as well as enhance the resolution, of the evaluation dataset considerably, and the fungi were found to belong chiefly to the Ascomycota, with Penicillium and Aspergillus as the two most common genera. The ITS2 was found to indicate a different taxonomic affiliation than did the complete ITS region for 10% of the query sequences, though this figure is likely to vary with the taxonomic scope of the query sequences.ConclusionThe present software readily assigns large sets of fungal query sequences to their respective best matches in the international sequence databases and places them in a larger biological context. The output is highly structured to be easy to process, although it still needs to be inspected and possibly corrected for the impact of the incomplete and sometimes erroneously annotated fungal entries in these databases. The open source pipeline is available for UNIX-type platforms, and updated releases of the target database are made available biweekly. The pipeline is easily modified to operate on other molecular regions and organism groups.

T-cell responses to the trypanosome variant surface glycoprotein are not limited to hypervariable subregions.

Variable subregions within the variant surface glycoprotein (VSG) coat displayed by African trypanosomes are predicted sites for T- and B-cell recognition. Hypervariable subregion 1 (HV-1) is localized to an internal amphipathic alpha helix in VSG monomers and may have evolved due to selective pressure by host T-cell responses to epitopes within this subregion. The prediction of T-cell receptor-reactive sites and major histocompatibility complex class II binding motifs within the HV-1 subregion, coupled with the conservation of amino acid residues in other regions of the molecule sufficient to maintain secondary and tertiary VSG structure, prompted us to test the hypothesis that Th cells may preferentially recognize HV-1 subregion peptides. Thus, we examined the fine specificity of VSG-specific T-cell lines, T-cell hybridomas, and Th cells activated during infection. Our results demonstrate that T-cell epitopes are distributed throughout the N-terminal domain of VSG but are not clustered exclusively within HV-1 or other hypervariable subregions. In contrast, T-cell-reactive sites were not detected within the relatively conserved C-terminal domain of VSG. Overall, this study is the first to dissect the fine specificity of T-cell responses to the trypanosome VSG and suggests that evolution of a conserved HV-1 region may be unrelated to selective pressures exerted by host T-cell responses. This study also demonstrates that T cells do not recognize the relatively invariant C-terminal region of the VSG molecule during infection, suggesting that it could serve as a potential subunit vaccine to provide variant cross-specific immunity for African trypanosomiasis.

Modelling the secondary structures of slippage-prone hypervariable RNA regions: the example of the tiger beetle 18S rRNA variable region V4.

Variable regions within ribosomal RNAs frequently vary in length as a result of incorporating products of slippage. This makes constructing secondary structure models problematic because base homology is difficult or impossible to establish between species. Here, we model such a region by comparing the results of the MFOLD suboptimal folding algorithm for different species to identify conserved structures. Based on the reconstruction of base change on a phylogenetic tree of the species and comparison against null models of character change, we devise a statistical analysis to assess support of these structures from compensatory and semi-compensatory (i.e. G.C to G.U or A.U to G.U) mutations. As a model system we have used variable region V4 from cicindelid (tiger beetle) small subunit ribosomal RNAs (SSU rRNAs). This consists of a mixture of conserved and highly variable subregions and has been subject to extensive comparative analysis in the past. The model that results is similar to a previously described model of this variable region derived from a different set of species and contains a novel structure in the central, highly variable part. The method we describe may be useful in modelling other RNA regions that are subject to slippage.

Manipulation of the ‘Zinc cluster’ region of transcriptional activatorLEU3be site-directed mutagenesis

The transcriptional activator LEU3 of Saccharomyces cerevisiae belongs to a family of lower eukaryotic DNA binding proteins with a well-conserved DNA binding motif known as the Zn(II)2Cys6 binuclear cluster. We have constructed mutations in LEU3 that affect either one of the conserved cysteines (Cys47) or one of several amino acids located within a variable subregion of the DNA binding motif. LEU3 proteins with a mutation at Cys47 were very poor activators which could not be rescued by supplying Zn(II) to the growth medium. Mutations within the variable subregion were generally well-tolerated. Only two of seven mutations in this region generated poor activators, and both could be reactivated by Zn(II) supplements. Three of the other five mutations gave rise to activators that were better than wild type. One of these, His50Cys, exhibited a 1.5 fold increase in in vivo target gene activation and a notable increase in the affinity for target DNA. The properties of the His50Cys mutant are discussed in terms of a variant structure of the DNA binding motif. During the course of this work, evidence was obtained suggesting that only one of the two LEU3 protein-DNA complexes routinely seen actually activates transcription. The other (which may contain an additional protein factor) does not.

The complete sequence of the human β-myosin heavy chain gene and a comparative analysis of its product

We have isolated and sequenced the gene and the cDNA coding for the human cardiac β-myosin heavy chain (designated MYH7). The gene is 22,883 bp long. The 1935 amino acids of this protein ( M r 223,111) are encoded by 38 exons. The 5′ untranslated region (86 bp) is split by two introns. The 3′ untranslated region is 114 bp long. Three Alu repeats were identified within the gene and a fourth one in the 3′ flanking intergenic region. The molecular organization of this gene reflects the conservative pattern with respect to size, coding ratio, and number or position of introns characteristic of vertebrate sarcomeric myosin heavy chain genes. The protein sequence of the human β-heavy chain was compared with corresponding (homologous) sequences of rabbit, rat, and hamster as well as with the (heterologous) embryonic heavy chain sequences of rat, chicken, and man. The results show that protein subregions responsible for basic functions of myosin heavy chains (nucleotide binding and actin binding) are very similar in homologous and heterologous heavy chains. Regions that differ in their primary sequences in heterologous heavy chains appear to be highly conserved within mammalian β-myosin heavy chains. Constant and variable subregions of heavy chains are discussed in terms of functional significance and evolutionary relatedness.

Variable region vector quantization

AbstractGeneralized algorithms for vector quantization are presented and their convergences are proved. The generalized vector quantization is called variable region vector quantization since the method allows adjusted variable dimensional vectors covering variable subregions of source data.Algorithm I is the generalization of the LBG algorithm into the variable region case. This is called full‐gain variable region vector quantization. Algorithm II, on the other hand, is the variable region generalization of the gain‐shape type. The formation of each variable subregion is due to the grouping of elements so that the resulting set of variable dimensional super‐vectors has the minimum distortion to a given codebook. Algorithm III considers encoding‐decoding. Algorithm IV gives the suboptimal minimization for the alleviation of computational load.Methods presented here are applicable to various pattern handling including artificial neural nets. However, data compression terminologies are adopted in this research. All of the foregoing algorithms are introduced without any physical entity to data. Later, the cases of speech and images are discussed using their specific natures. The examples of the region optimization on speech and images are given.

Genetic nomenclature for the immunoglobulin loci of the mouse

In the proposed system for genetic nomenclature of the immunoglobulin loci of the mouse, the heavy-, kappa-, and lambda-chain regions are designatedIgh, Igk, andIgl, respectively. The constant subregions are designatedIgh-C, Igk-C, andIgl-C, and individual loci in these subregions are designated by Arabic numbers following the hyphen and assigned chronologically, for example,Igh-1, Igh-2, Igl-1, etc. The variable subregions are designatedIgh-V, Igk-V, andIgl-V, and individual loci in these subregions are designated by a two- or three-letter symbol or two letters and a number following the hyphen, for example,Igh-Dex, Igh-Pc, andIgk-Ef 1. The symbol following the hyphen for variable-region loci should be related to the antigen for which the immunoglobulin is specific or to the method used for recognizing the variants.