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DESCRIPTION OF LACTOBACILLUS SUANTSAII SP. NOV. Lactobacillus suantsaii (suan.tsai′ i. N.L. neut. n. suantsaium suan-tsai, the name of a traditional fermented mustard green product in Taiwan; N.L. gen. n. suantsaii of suan-tsai, from which the type strain was isolated). Cells are Gram-stain-positive, catalase and oxidase negative, rod-shaped (0.5--0.6×1.1--7.5 µm), and facultatively anaerobic. Colonies are approximately 2 mm in diameter, with a smooth to rough surface, circular to slightly irregular after 2 days at 37 Ǫ C on MRS agar plates. Homofermentative; no gas is produced from glucose. Both L- (65 %) and D- lactate (35 %) are produced as end products from glucose. In MRS broth, growth occurs at 10--37 Ǫ C, but not at 45 Ǫ C. Growth occurs at pH 4.0--7.0, but no growth at pH 8.0. Growth occurs in the presence of 1.0–2.0 % (w/v) NaCl, but not in the presence of 9 % (w/v) NaCl. Ammonia is not produced from arginine. Nitrate is not reduced. Indole, H 2 S and urease are not produced. Acetoin is produced. Gelatin and aesculin are not hydrolysed. Acid is produced from L- arabinose, D- galactose, D- glucose, D- fructose, N -acetylglucosamine, maltose and gluconate. Acid is not produced from glycerol, erythritol, D- arabinose, L- xylose, D- adonitol, methyl b- D- xylopyranoside, D- mannose, L- sorbose, L- rhamnose, dulcitol, inositol, D- mannitol, D- sorbitol, methyl a- D- mannopyranoside, methyl a- D- glucopyranoside, amygdalin, arbutin, salicin, cellobiose, lactose, melibiose, trehalose, inulin, melezitose, raffinose, arbutin, glycogen, xylitol, gentiobiose, turanose, D- lyxose, D- tagatose, D- fucose, L- fucose, D- arabitol, L- arabitol, and 2- or 5-ketogluconate. Esterase, esterase lipase, valine aminopeptidase, cystine aminopeptidase, trypsin, naphthol-AS-BIphosphohydrolase, a- galactosidase, b- galactosidase, b- glucuronidase, a- glucosidase and b- glucosidase are produced. Cells do not contain meso -diaminopimelic acid in their cell-wall peptidoglycan. The peptidoglycan type is A4a (L- Lys-D- Asp), with the presence of alanine (Ala), aspartic acid (Asp), glutamic acid (Glu) and lysine (Lys) in a molar ratio of 0.5: 1.0: 3.0: 1.0. The major cellular fatty acids are C 16: 0, C 18: 1 Ɯ 9 c, and C 18: 1 Ɯ 7 c and/or C 18: 1 Ɯ 6 c. The type strain is L88 T (=BCRC 12945 T =NBRC 113535 T), which was isolated from suan-tsai, a traditional fermented mustard green, in Hsinchu county, Taiwan, ROC. The DNA G+C content of the type strain is 51.1 mol%.

DESCRIPTION OF LACTOBACILLUS SUANTSAII SP. NOV. Lactobacillus suantsaii (suan.tsai��� i. N.L. neut. n. suantsaium suan-tsai, the name of a traditional fermented mustard green product in Taiwan; N.L. gen. n. suantsaii of suan-tsai, from which the type strain was isolated). Cells are Gram-stain-positive, catalase and oxidase negative, rod-shaped (0.5--0.6��1.1--7.5 ��m), and facultatively anaerobic. Colonies are approximately 2 mm in diameter, with a smooth to rough surface, circular to slightly irregular after 2 days at 37 �� C on MRS agar plates. Homofermentative; no gas is produced from glucose. Both L- (65 %) and D- lactate (35 %) are produced as end products from glucose. In MRS broth, growth occurs at 10--37 �� C, but not at 45 �� C. Growth occurs at pH 4.0--7.0, but no growth at pH 8.0. Growth occurs in the presence of 1.0���2.0 % (w/v) NaCl, but not in the presence of 9 % (w/v) NaCl. Ammonia is not produced from arginine. Nitrate is not reduced. Indole, H 2 S and urease are not produced. Acetoin is produced. Gelatin and aesculin are not hydrolysed. Acid is produced from L- arabinose, D- galactose, D- glucose, D- fructose, N -acetylglucosamine, maltose and gluconate. Acid is not produced from glycerol, erythritol, D- arabinose, L- xylose, D- adonitol, methyl b- D- xylopyranoside, D- mannose, L- sorbose, L- rhamnose, dulcitol, inositol, D- mannitol, D- sorbitol, methyl a- D- mannopyranoside, methyl a- D- glucopyranoside, amygdalin, arbutin, salicin, cellobiose, lactose, melibiose, trehalose, inulin, melezitose, raffinose, arbutin, glycogen, xylitol, gentiobiose, turanose, D- lyxose, D- tagatose, D- fucose, L- fucose, D- arabitol, L- arabitol, and 2- or 5-ketogluconate. Esterase, esterase lipase, valine aminopeptidase, cystine aminopeptidase, trypsin, naphthol-AS-BIphosphohydrolase, a- galactosidase, b- galactosidase, b- glucuronidase, a- glucosidase and b- glucosidase are produced. Cells do not contain meso -diaminopimelic acid in their cell-wall peptidoglycan. The peptidoglycan type is A4a (L- Lys-D- Asp), with the presence of alanine (Ala), aspartic acid (Asp), glutamic acid (Glu) and lysine (Lys) in a molar ratio of 0.5: 1.0: 3.0: 1.0. The major cellular fatty acids are C 16: 0, C 18: 1 �� 9 c, and C 18: 1 �� 7 c and/or C 18: 1 �� 6 c. The type strain is L88 T (=BCRC 12945 T =NBRC 113535 T), which was isolated from suan-tsai, a traditional fermented mustard green, in Hsinchu county, Taiwan, ROC. The DNA G+C content of the type strain is 51.1 mol%., Published as part of Liou, Jong-Shian, Huang, Chien-Hsun, Wang, Chun-Lin, Lee, Ai-Yun, Mori, Koji, Tamura, Tomohiko, Watanabe, Mizuki, Blom, Jochen, Huang, Lina & Watanabe, Koichi, 2019, Lactobacillus suantsaii sp. nov., isolated from suan-tsai, a traditional Taiwanese fermented mustard green, pp. 1484-1489 in International Journal of Systematic and Evolutionary Microbiology 69 (5) on pages 1488-1489, DOI: 10.1099/ijsem.0.003346, http://zenodo.org/record/6223872

High-dimensional data, such as that relating to gene expression in microarray experiments, may contain substantial amount of useful information to be explored. However, the information, relevant variables and their joint interactions are usually diluted by noise due to a large number of non-informative variables. Consequently, variable selection plays a pivotal role for learning in high dimensional problems. Most of the traditional feature selection methods, such as Pearson's correlation between response and predictors, stepwise linear regressions and LASSO are among the popular linear methods. These methods are effective in identifying linear marginal effect but are limited in detecting non-linear or higher order interaction effects. It is well known that epistasis (gene - gene interactions) may play an important role in gene expression where unknown functional forms are difficult to identify. In this thesis, we propose a novel nonparametric measure to first screen and do feature selection based on information from nearest neighborhoods. The method is inspired by Lo and Zheng's earlier work (2002) on detecting interactions for discrete predictors. We apply a backward elimination algorithm based on this measure which leads to the identification of many in influential clusters of variables. Those identified groups of variables can capture both marginal and interactive effects. Second, each identified cluster has the potential to perform predictions and classifications more accurately. We also study procedures how to combine these groups of individual classifiers to form a final predictor. Through simulation and real data analysis, the proposed measure is capable of identifying important variable sets and patterns including higher-order interaction sets. The proposed procedure outperforms existing methods in three different microarray datasets. Moreover, the nonparametric measure is quite flexible and can be easily extended and applied to other areas of high-dimensional data and studies.

Superoxide dismutases (SODs) are important antioxidant enzymes that catalyze the disproportionation of superoxide anion to oxygen and hydrogen peroxide to guard cells against superoxide toxicity. The major pathway for activation of copper/zinc SOD (CSD) involves a copper chaperone for SOD (CCS) and an additional minor CCS-independent pathway reported in mammals. We characterized the CCS-dependent and -independent activation pathways for three CSDs localized in different cellular compartments in Arabidopsis (Arabidopsis thaliana). The main activation pathway for CSD1 in the cytoplasm involved a CCS-dependent and -independent pathway, which was similar to that for human CSD. Activation of CSD2 in chloroplasts depended totally on CCS, similar to yeast (Saccharomyces cerevisiae) CSD. Peroxisome-localized CSD3 via a CCS-independent pathway was similar to nematode (Caenorhabditis elegans) CSD in retaining activity in the absence of CCS. In Arabidopsis, glutathione played a role in CCS-independent activation, as was reported in humans, but an additional factor was required. These findings reveal a highly specific and sophisticated regulation of CSD activation pathways in planta relative to other known CCS-independent activation.

Under Web2.0, everyone is the content producer and consumer concurrently. The users’ contribution to the environment is the key element to enrich the content. The connection among the individuals forms the social network, and become the virtual community which is socially open to every individual who wants to share information and join the community. The anticipation of the web companies in the open environment of Web2.0 is to have a bigger-and-better community. However, in the present phase of development, there are still many questions remaining, such as, will the virtual communities be expanded like the anticipation? Is the inference correct? In this paper, we propose a novel method to conduct the research based on the Particle Swarm Optimization (PSO). By simulating the behaviors of the individuals in the communities, we are able to demonstrate that there’s a limitation of the community development in the Web 2.0 environment.

Seven bifidobacterial strains were isolated from the faeces of two adult males of the two-toed sloth (Choloepus didactylus) housed in Parco Natura Viva, in Italy. Comparative sequence analysis of 16S rRNA and of five housekeeping (hsp60, rpoB, clpC, dnaJ, dnaG) genes revealed that these strains were classified into two clusters. On the basis of 16S rRNA gene sequence similarity, the type strain of Bifidobacterium catenulatum subsp. kashiwanohense DSM 21854T (95.4 %) was the closest neighbour to strain in Cluster I (BRDM 6T), whereas the type strain of Bifidobacterium dentium DSM 20436T (values were in the range of 98‒99.8 %) was the closest neighbour to the other six strains in Cluster II. The average nucleotide identity (ANI) values of BRDM 6T and of strains in Cluster II with the closely related type strains were 76.0 and 98.9 % (mean value) respectively. Therefore, genotyping based on the genome sequence of the strain BRDM 6T combined with phenotypic analyses clearly revealed that the strain BRDM 6T represents a novel species for which the names Bifidobacterium choloepi sp. nov. (BRDM 6T=NBRC 114053T=BCRC 81222T) is proposed.

Corrigendum to Characterization of Bifidobacterium species in feaces of the Egyptian fruit bat: Description of Bifidobacterium vespertilionis sp. nov. and Bifidobacterium rousetti sp. nov.

Background: In eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. In the ubiquitin-proteasome pathway, E3 ligases play important roles by recognizing a specific protein substrate and catalyzing the attachment of ubiquitin to a lysine (K) residue. As more and more experimental data on ubiquitin conjugation sites become available, it becomes possible to develop prediction models that can be scaled to big data. However, no development that focuses on the investigation of ubiquitinated substrate specificities has existed. Herein, we present an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities. Results: In this investigation, totally 6259 experimentally validated ubiquitinated proteins were obtained from dbPTM. After having filtered out homologous fragments with 40% sequence identity, the training data set contained 2658 ubiquitination sites (positive data) and 5532 non-ubiquitinated sites (negative data). Due to the difficulty in characterizing the substrate site specificities of E3 ligases by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. The profile hidden Markov model (profile HMM) was adopted to construct the predictive models learned from the identified substrate motifs. A five-fold cross validation was then used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 73.07%, 65.46%, and 67.93%, respectively. Additionally, an independent testing set, completely blind to the training data of the predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (76.13%) and outperform other ubiquitination site prediction tool. Conclusion: A case study demonstrated the effectiveness of the characterized substrate motifs for identifying ubiquitination sites. The proposed method presents a practical means of preliminary analysis and greatly diminishes the total number of potential targets required for further experimental confirmation. This method may help unravel their mechanisms and roles in E3 recognition and ubiquitin-mediated protein degradation. Refereed/Peer-reviewed

usc Protein O-GlcNAcylation, involving the β-attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of the basis for OGT's substrate specificity should aid understanding how O-GlcNAc contributes to diverse cellular processes. Due to an increasing number of O-GlcNAcylated peptides with site-specific information identified by mass spectrometry (MS)-based proteomics, we were motivated to characterize substrate site motifs of O-GlcNAc transferases. In this investigation, a non-redundant dataset of 410 experimentally verified O-GlcNAcylation sites were manually extracted from dbOGAP, OGlycBase and UniProtKB. After detection of conserved motifs by using maximal dependence decomposition, profile hidden Markov model (profile HMM) was adopted to learn a first-layered model for each identified OGT substrate motif. Support Vector Machine (SVM) was then used to generate a second-layered model learned from the output values of profile HMMs in first layer. The two-layered predictive model was evaluated using a five-fold cross validation which yielded a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. Additionally, an independent testing set from PhosphoSitePlus, which was really non-homologous to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools. A case study indicated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation and has been implemented as a web-based system, OGTSite, which is now freely available at http://csb.cse.yzu.edu.tw/OGTSite/. Refereed/Peer-reviewed