NMR Shows Why a Small Chemical Change Almost Abolishes the Antimicrobial Activity of Glycocin F.

Glycocin F (GccF), a ribosomally synthesized, post-translationally modified peptide secreted by Lactobacillus plantarum KW30, rapidly inhibits the growth of susceptible bacteria at nanomolar concentrations. Previous studies have highlighted structural features important for its activity and have shown the absolute requirement for the Ser18 O-linked GlcNAc on the eight-residue loop linking the two short helices of the (C-X6-C)2 structure. Here, we show that an ostensibly very small chemical modification to Ser18, the substitution of the Cα proton with a methyl group, reduces the antimicrobial activity of GccF 1000-fold (IC50 1.5 µM cf. 1.5 nM). A comparison of the GccFα-methylSer18 NMR structure (PDB 8DFZ) with that of the native protein (PDB 2KUY) showed a marked difference in the orientation and mobility of the loop, as well as a markedly different positioning of the GlcNAc, suggesting that loop conformation, dynamics, and glycan presentation play an important role in the interaction of GccF with as yet unknown but essential physiological target molecules.

Thermococcus argininiproducens sp. nov., an arginine biosynthesis archaeal species isolated from the Central Indian Ocean ridge.

A strictly anaerobic hyperthermophilic archaeon, designated strain IOH2T, was isolated from a deep-sea hydrothermal vent (Onnuri vent field) area on the Central Indian Ocean Ridge. Strain IOH2T showed high 16S rRNA gene sequence similarity to Thermococcus sibiricus MM 739T (99.42 %), Thermococcus alcaliphilus DSM 10322T (99.28 %), Thermococcus aegaeus P5T (99.21 %), Thermococcus litoralis DSM 5473T (99.13 %), 'Thermococcus bergensis' T7324T (99.13 %), Thermococcus aggregans TYT (98.92 %) and Thermococcus prieurii Bio-pl-0405IT2T (98.01 %), with all other strains showing lower than 98 % similarity. The average nucleotide identity and in silico DNA-DNA hybridization values were highest between strain IOH2T and T. sibiricus MM 739T (79.33 and 15.00 %, respectively); these values are much lower than the species delineation cut-offs. Cells of strain IOH2T were coccoid, 1.0-1.2 µm in diameter and had no flagella. Growth ranges were 60-85 °C (optimum at 80 °C), pH 4.5-8.5 (optimum at pH 6.3) and 2.0-6.0 % (optimum at 4.0 %) NaCl. Growth of strain IOH2T was enhanced by starch, glucose, maltodextrin and pyruvate as a carbon source, and elemental sulphur as an electron acceptor. Through genome analysis of strain IOH2T, arginine biosynthesis related genes were predicted, and growth of strain IOH2T without arginine was confirmed. The genome of strain IOH2T was assembled as a circular chromosome of 1 946 249 bp and predicted 2096 genes. The DNA G+C content was 39.44 mol%. Based on the results of physiological and phylogenetic analyses, Thermococcus argininiproducens sp. nov. is proposed with type strain IOH2T (=MCCC 4K00089T=KCTC 25190T).

Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition.

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform.

Alkalibacter rhizosphaerae sp. nov., a CO-utilizing bacterium isolated from tidal flat sediment, and emended description of the genus Alkalibacter.

A novel anaerobic, rod-shaped, non-motile bacterium, designated strain ES005T, was isolated from tidal flat sediments near the rhizosphere of Phragmites australis at Eulsukdo Island, Republic of Korea. A polyphasic approach revealed that cells of the strain were Gram-stain-positive, catalase- and oxidase-negative, non-spore-forming rods. Phylogenetic analyses based on 16S rRNA gene sequences revealed that strain ES005T belonged to the family Eubacteriaceae, class Clostridia and showed the highest sequence similarity to Alkalibacter mobilis (97.52 %) and followed by Alkalibacter saccharofermentans Z-79820T (96.72%). The OrthoANI value between strain ES005T and A. mobilis was 69.67 %. Strain ES005T grew optimally at 33-37 °C, at pH 6.0-7.0 and in the presence of 1-2 % (w/v) NaCl. Growth in 12.5 % CO atmosphere was observed. Acetate and formate were end products of fructose fermentation and growth on CO. The major cellular fatty acids of strain ES005T were C14 : 0 (39.1 %) and C16 : 0 (26.6 %). The major polar lipids were diphoshatidylgycerol, phosphatidylglycerol and three unidentified phospholipids. The DNA G+C content of strain ES005T was 46.9 mol%. Based on the phenotypic, phylogenetic, genomic and chemotaxonomic features of the isloate, strain ES005T represents a novel species, for which the name Alkalibacter rhizosphaerae sp. nov. is proposed. The type strain is ES005T (=KCTC 25246T=JCM 34530T).

Proteiniclasticum aestuarii sp. nov., isolated from tidal flat sediment, and emended descriptions of the genus Proteiniclasticum and Proteiniclasticum ruminis.

A novel bacterium, designated SCR006T, was isolated from tidal flat sediment from Suncheon Bay, Republic of Korea. Cells of strain SCR006T were strictly anaerobic, motile cocci, Gram-reaction-negative, and catalase- and oxidase-negative. Growth was observed at 4-41 °C (optimum, 34-37 °C), at pH 6.5-10.0 (optimum, pH 7.0-7.5) and in presence of 0-8 % NaCl (optimum, 0-2 %). Fermentation products of peptone-yeast-glucose medium were acetate and ethanol. Results of phylogenetic analyses based on 16S rRNA gene sequences indicated that strain SCR006T had high sequence similarity to Proteiniclasticum ruminis D3RC-2T (97.9 %), followed by Youngiibacter multivorans DSM 6139T (95.9 %) and Youngiibacter fragilis 232.1T (95.0 %). The average nucleotide identity value between strain SCR006T and P. ruminis DSM 24773T was 72.7 %, which strongly supported that strain SCR006T reresents a novel species within the genus Proteiniclasticum. The major cellular fatty acids are iso-C15 : 0 (27.2 %) and anteiso-C15 : 0 (16.9 %). The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, two unidentified phospholipids, an unidentified aminolipid and five unidentified lipids. The genomic size was 3.2 Mb with genomic DNA G+C content of 45.6 mol%. The results of 16S rRNA-based and genome-based phylogenetic tree analyses indicated that SCR006T should be assigned to the genus Proteiniclasticum. Strain SCR006T could be distinguished from P. ruminis D3RC-2T by its growth conditions, cell morphology and genomic characteristics. Based on the phenotypic, phylogenetic, genomic and chemotaxonomic features, strain SCR006T represents a novel species, for which the name Proteiniclasticum aestuarii sp. nov. is proposed, with the type strain SCR006T (=KCTC 25245T= JCM 34531T).

Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets.

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets.

Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition.

The training of Human Activity Recognition (HAR) models requires a substantial amount of labeled data. Unfortunately, despite being trained on enormous datasets, most current models have poor performance rates when evaluated against anonymous data from new users. Furthermore, due to the limits and problems of working with human users, capturing adequate data for each new user is not feasible. This paper presents semi-supervised adversarial learning using the LSTM (Long-short term memory) approach for human activity recognition. This proposed method trains annotated and unannotated data (anonymous data) by adapting the semi-supervised learning paradigms on which adversarial learning capitalizes to improve the learning capabilities in dealing with errors that appear in the process. Moreover, it adapts to the change in human activity routine and new activities, i.e., it does not require prior understanding and historical information. Simultaneously, this method is designed as a temporal interactive model instantiation and shows the capacity to estimate heteroscedastic uncertainty owing to inherent data ambiguity. Our methodology also benefits from multiple parallel input sequential data predicting an output exploiting the synchronized LSTM. The proposed method proved to be the best state-of-the-art method with more than 98% accuracy in implementation utilizing the publicly available datasets collected from the smart home environment facilitated with heterogeneous sensors. This technique is a novel approach for high-level human activity recognition and is likely to be a broad application prospect for HAR.

Epileptic-Net: An Improved Epileptic Seizure Detection System Using Dense Convolutional Block with Attention Network from EEG.

Epilepsy is a complex neurological condition that affects a large number of people worldwide. Electroencephalography (EEG) measures the electrical activity of the brain and is widely used in epilepsy diagnosis, but it usually requires manual inspection, which can be hours long, by a neurologist. Several automatic systems have been proposed to detect epilepsy but still have some unsolved issues. In this study, we proposed a dynamic method using a deep learning model (Epileptic-Net) to detect an epileptic seizure. The proposed method is largely heterogeneous and comprised of the dense convolutional blocks (DCB), feature attention modules (FAM), residual blocks (RB), and hypercolumn technique (HT). Firstly, DCB is used to get the discriminative features from the EEG samples. Then, FAM extracts the essential features from the samples. After that, RB learns more vital parts as it entirely uses information in the convolutional layer. Finally, HT retains the efficient local features extracted from the layers situated at the different levels of the model. Its performance has been evaluated on the University of Bonn EEG dataset, divided into five distinct classes. The proposed Epileptic-Net achieves the average accuracy of 99.95% in the two-class classification, 99.98% in the three-class classification, 99.96% in the four-class classification, and 99.96% in classifying the complicated five-class problem. Thus the proposed approach shows more competitive results than the existing model to detect epileptic seizures. We also hope that this method can support experts in achieving objective and reliable results, lowering the misdiagnosis rate, and assisting in decision-making.

HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI.

Nowadays WiFi based human activity recognition (WiFi-HAR) has gained much attraction in an indoor environment due to its various benefits, including privacy and security, device free sensing, and cost-effectiveness. Recognition of human-human interactions (HHIs) using channel state information (CSI) signals is still challenging. Although some deep learning (DL) based architectures have been proposed in this regard, most of them suffer from limited recognition accuracy and are unable to support low computation resource devices due to having a large number of model parameters. To address these issues, we propose a dynamic method using a lightweight DL model (HHI-AttentionNet) to automatically recognize HHIs, which significantly reduces the parameters with increased recognition accuracy. In addition, we present an Antenna-Frame-Subcarrier Attention Mechanism (AFSAM) in our model that enhances the representational capability to recognize HHIs correctly. As a result, the HHI-AttentionNet model focuses on the most significant features, ignoring the irrelevant features, and reduces the impact of the complexity on the CSI signal. We evaluated the performance of the proposed HHI-AttentionNet model on a publicly available CSI-based HHI dataset collected from 40 individual pairs of subjects who performed 13 different HHIs. Its performance is also compared with other existing methods. These proved that the HHI-AttentionNet is the best model providing an average accuracy, F1 score, Cohen's Kappa, and Matthews correlation coefficient of 95.47%, 95.45%, 0.951%, and 0.950%, respectively, for recognition of 13 HHIs. It outperforms the best existing model's accuracy by more than 4%.

STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI.

Human activity recognition (HAR) has emerged as a significant area of research due to its numerous possible applications, including ambient assisted living, healthcare, abnormal behaviour detection, etc. Recently, HAR using WiFi channel state information (CSI) has become a predominant and unique approach in indoor environments compared to others (i.e., sensor and vision) due to its privacy-preserving qualities, thereby eliminating the need to carry additional devices and providing flexibility of capture motions in both line-of-sight (LOS) and non-line-of-sight (NLOS) settings. Existing deep learning (DL)-based HAR approaches usually extract either temporal or spatial features and lack adequate means to integrate and utilize the two simultaneously, making it challenging to recognize different activities accurately. Motivated by this, we propose a novel DL-based model named spatio-temporal convolution with nested long short-term memory (STC-NLSTMNet), with the ability to extract spatial and temporal features concurrently and automatically recognize human activity with very high accuracy. The proposed STC-NLSTMNet model is mainly comprised of depthwise separable convolution (DS-Conv) blocks, feature attention module (FAM) and NLSTM. The DS-Conv blocks extract the spatial features from the CSI signal and add feature attention modules (FAM) to draw attention to the most essential features. These robust features are fed into NLSTM as inputs to explore the hidden intrinsic temporal features in CSI signals. The proposed STC-NLSTMNet model is evaluated using two publicly available datasets: Multi-environment and StanWiFi. The experimental results revealed that the STC-NLSTMNet model achieved activity recognition accuracies of 98.20% and 99.88% on Multi-environment and StanWiFi datasets, respectively. Its activity recognition performance is also compared with other existing approaches and our proposed STC-NLSTMNet model significantly improves the activity recognition accuracies by 4% and 1.88%, respectively, compared to the best existing method.

Oricola thermophila sp. nov., a marine bacterium isolated from tidal flat sediment and emended description of the genus Oricola Hameed et al. 2015.

A Gram-stain-negative, facultatively anaerobic, rod-shaped (1.8-4.4×0.5-0.7 µm) and motile marine bacterium, designated as MEBiC13590T, was isolated from tidal flat sediment sampled at Incheon City, on the west coast of the Republic of Korea. The 16S rRNA gene sequence analysis revealed that strain MEBiC13590T showed high similarity to Oricola cellulosilytica CC-AMH-0T (98.2 %), followed by Oceaniradius stylonematis StC1T (97.5 %); however, it clustered with Oricola cellulosilytica. The phylogenomic tree inferred by the up-to-date bacterial core gene set suggested that strain MEBiC13590T shared a phyletic line with Oricola cellulosilytica. Average nucleotide identity and digital DNA-DNA hybridization values (75.0 and 19.3 %, respectively) between strain MEBiC13590T and Oricola cellulosilytica CC-AMH-0T were below the respective species delineation cutoffs. Growth was observed at 22-50 °C (optimum, 45 °C), at pH 5-9 (optimum, pH 7) and with 1-6 % (optimum, 3 %) NaCl. The predominant cellular fatty acids were C16 : 0 (7.6 %), C18 : 0 (12.2 %), 11-methyl C18 : 1 ω7c (5.7 %), C19 : 0 cyclo ω6c and summed feature 8 (comprising C18 : 1 ω7c and/or C18 : 1 ω6c; 38 %). The DNA G+C content was 63.5 mol%. The major respiratory quinone was Q-10. Several phenotypic characteristics such as growth temperature, oxygen requirement, enzyme activities of urease, gelatinase, lipase (C14), α-chymotrypsin, acid phosphatase, ß-galactosidase, ß-glucosidase etc. differentiate strain MEBiC13590T from Oricola cellulosilytica CC-AMH-0T. Based on this polyphasic taxonomic data, strain MEBiC13590T should be classified as representing a novel species in the genus Oricola for which the name Oricola thermophila sp. nov. is proposed . The type strain is MEBiC13590T (=KCCM 43313T=JCM 33661T).

Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm.

R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively separates the QRS components and all non-objective components from the ECG signal. Based on local peaks in QRS components, the AWS algorithm adaptively determines the Region of Interest (ROI). The Feature Extraction process computes the statistical properties of energy, frequency, and noise from each ROI. The Sequential Forward Selection (SFS) procedure is used to find the best subsets of features. Based on these characteristics, an ensemble of decision tree algorithms detects the R peaks. Finally, the R peak position on the initial ECG signal is adjusted using the R location correction (RLC) algorithm. The proposed method has an experimental accuracy of 99.94%, a sensitivity of 99.98%, positive predictability of 99.96%, and a detection error rate of 0.06%. Given the high efficiency in detection and fast processing speed, the proposed approach is ideal for intelligent medical and wearable devices in the diagnosis of CVDs.

Description of Luteithermobacter gelatinilyticus gen. nov., sp. nov., and Paremcibacter congregatus gen. nov., comb. nov. via reclassification of the genus Emcibacter.

Strain MEBiC09520T, which was isolated from a tidal sediment in Incheon, Korea, is a pale yellow, rod-shaped bacterium, cells of which are 0.4-0.5 µm in width and 1.5-2 µm in length. Strain MEBiC09520T shared 95.17 and 92.57% 16S rRNA gene sequence similarity with Emcibacter nanhaiensis and E. congregatus, respectively. It grew optimally at pH 6.0, at 55 °C and with 2.5-3.5% (w/v) NaCl. Its polar lipid components included phosphatidylethanolamine (PE), diphosphatidylglycerol (DPG), phosphatidylglycerol (PG), an unidentified phospholipid (PL), three unidentified aminolipids (ALs) and two unidentified lipids (L). The fatty acids C16:0, C19:0 cyclo ω8c, C14:0 2-OH and summed feature 8 (C18:1ω7c and/or C18:1ω6c) were predominantly present in its cell wall. Strain MEBiC09520T was thermophilic, while E. nanhaiensis and E. congregatus were mesophilic. Although E. nanhaiensis showed no nitrate reduction activity, MEBiC09520T and E. congregatus showed a positive reaction. These strains differed in carbohydrate utilization. In particular, E. congregatus was able to thrive on various carbohydrate substrates as compared to the other strains. The average nucleotide identity value was 69.92% between strain MEBiC09520T and E. congregatus ZYLT, 70.38% between E. congregatus ZYLT and E. nanhaiensis HTCJW17T, and 72.83% between strain MEBiC09520 and E. nanhaiensis HTCJW17T. Considering these differences, strain MEBiC09520T (=KCCM 43320T=MCCC 1K03920T) is suggested to represent and novel species of a new genus, Luteithermobacter gelatinilyticus gen. nov., sp. nov., and E. congregatus should be reclassified as Paremcibacter congregatus gen. nov., comb. nov.

A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition.

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment's result surpasses the previously proposed approaches with an average accuracy of more than 93%.

A Versatile Boc Solid Phase Synthesis of Daptomycin and Analogues Using Site Specific, On-Resin Ozonolysis to Install the Kynurenine Residue.

A de novo solid-phase synthesis of the cyclic lipodepsipeptide daptomycin via Boc chemistry was achieved. The challenging ester bond formation between the nonproteinogenic amino acid kynurenine was achieved by esterification of a threonine residue with a protected tryptophan. Subsequent late-stage on-resin ozonolysis, inspired by the biomimetic pathway, afforded the kynurenine residue directly. Synthetic daptomycin possessed potent antimicrobial activity (MIC100 =1.0 µg mL-1 ) against S. aureus, while five other daptomycin analogues containing (2R,3R)-3-methylglutamic acid, (2S,4S)-4-methylglutamic acid or canonical glutamic acid at position twelve prepared using this new methodology were all inactive, clearly establishing that the (2S,3R)-3-methylglutamic acid plays a key role in the antimicrobial activity of daptomycin.

Roseithermus sacchariphilus gen. nov., sp. nov. and proposal of Salisaetaceae fam. nov., representing new family in the order Rhodothermales.

A novel bacterium with cells that were pinkish-cream-coloured, aerobic, rod-shaped, 0.62-1.00 µm wide and 2.3-3.3 µm long, designated as strain MEBiC09517T, was isolated from Buksung-Po, a small port in Incheon, Republic of Korea. Strain MEBiC09517T had low 16S rRNA gene sequence similarity to validly reported strains; among them, Rubrivirgaprofundi SAORIC-476T displayed highest sequence similarity (89.9 %). Nevertheless, the novel strain shared a phylogenetic line with members of the genus Rhodothermus, not the genus Rubrivirga. Optimum growth conditions of strain MEBiC09517T were at 50-55 °C, pH 7 and in 2.0-4.0 % salt concentration. Strain MEBiC09517T was found to be an obligate marine bacterium that requires KCl, MgCl2 and CaCl2 as well as NaCl for growth. A phosphatidylethanolamine, a diphosphatidylglycerol, three glycolipids and four unidentified lipids were the strain's predominant polar lipid components. The fatty acid of the cell wall mainly consisted of carbons with 16 or 18 chain lengths such as C16 : 0, C18 : 0, C18 : 1 and summed feature 3 (C16 : 1ω6c and/or C16 : 1ω7c). The predominant menaquinone was MK-7. The DNA G+C content is 68.65 mol%. Strain MEBiC09517T differs from genera of the order Rhodothermales in terms of fatty acid composition, growth conditions, and range of carbon source utilization. Based on phylogenetic analysis using the strain's 16S rRNA gene sequence and results of physiological tests, strain MEBiC09517T (KCCM=43267T, JCM=32374T) is proposed as Roseithermus sacchariphilus gen. nov., sp. nov. Additionally, the novel family Salisaetaceae fam. nov. based on phylogenetic analysis and physiological characteristics is suggested.

Parashewanella tropica sp. nov., a mesophilic bacterium isolated from a marine sponge from Chuuk lagoon, Federated States of Micronesia, and emended description of the genus Parashewanella.

A mesophilic, straight-rod-shaped, non-flagellated bacterium, designated MEBiC05444T, was isolated from a marine sponge collected from Chuuk lagoon, Federated States of Micronesia. The strain was Gram-negative, catalase- and oxidase-positive, and facultative anaerobic. The isolate aerobically grew at 8-38 °C (optimum, 24-32 °C), pH 4.0-10.0 (pH 7.0-7.5) with an absolute requirement for Na+ up to 6 % (w/v) NaCl (2 %). Phylogenetic analyses based on 16S rRNA gene sequences revealed that MEBiC05444T belonged to the family Shewanellaceae, within the class Gammaproteobacteria. Strain MEBiC05444T showed highest 16S rRNA gene sequence similarity to Parashewanella curva C51T, followed by [Shewanella] irciniae UST040317-058T and Parashewanella spongiae HJ039T (98.9 %, 97.2 and 95.7 %, respectively). In the phylogenetic tree based on the 16S rRNA gene sequences, MEBiC05444T formed a cluster with P. curva C51T, but the average nucleotide identity value between the two strains was 82 %, thus confirming their separation at species level. The major fatty acids were iso-C15 : 0 (19.7 %), summed feature 3 (composed of C16 : 1 ω7c and/or C16 : 1ω6c; 16.1 %) and C17 : 1ω8c (10.2 %). The only detected respiratory quinone was ubiquinone Q-8. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol, diphosphatidylglycerol, three unidentified aminoglycolipids, two unidentified glycolipids, an unidentified aminoglycophospholipid and an unidentified lipid. The genomic DNA G+C content of strain MEBiC05444T was 40.8 mol%. Based on the results of polyphasic analysis, the strain represents a novel species of the genus Parashewanella, distinct from P. curva C51T, [Shewanella]irciniae UST040317-058T and P. spongiae HJ039T for which the name Parashewanellatropica sp. nov. is proposed with type strain MEBiC05444T (=KCCM 43304T=JCM 16653T).

Anaerosacchariphilus polymeriproducens gen. nov., sp. nov., an anaerobic bacterium isolated from a salt field.

A Gram-stain-negative oval-rod-shaped, spore-forming anaerobic bacterium, designated as strain MCWD5T, was isolated from sediment of a salt pond in the Republic of Korea (35° 7' 18″ N 126° 19' 4″ E). The 16S rRNA gene sequence analysis revealed that strain MCWD5T had low similarity values to members in the family Lachnospiraceae, such as Robinsoniella peoriensis PPC31T (94.8 %), Ruminococcusgauvreauii CCRI-16110T (94.2 %) and Lachnotalea glycerini DLD10T (94.0 %), and its phylogenetic position is unstable. The strain could grow at 20-42 °C (optimum, 38-42 °C), pH 5.5-10.0 (pH 7.0) and with 0-6 % (2.0 %) NaCl. Strain MCWD5T could not use nitrate, nitrite, sulfate or sulfite as electron acceptors. The strain could utilize various carbohydrates, such as arabinose, cellobiose, glucose, etc., and polymers such as pectin and starch. The major fatty acids of strain MCWD5T were C14 : 0, C16 : 0, C16 : 1ω7c, C18 : 1ω7c DMA and summed feature 8 (C17 : 1ω8c and/or C17 : 2), which was clearly different from those of related genera. The major polar lipids were diphosphatidyglycerol, phosphatidyglycerol and an unknown phospholipid. Based on the results of phylogenetic, physiologic and chemotaxonomic studies, Anaerosacchariphilus polymeriproducens gen. nov., sp. nov. with the type strain MCWD5T (=KCTC 15595T=DSM 105757T) is proposed in the family Lachnospiraceae.

Marinirhabdus citrea sp. nov., a marine bacterium isolated from a seaweed.

A gram-stain-negative, aerobic, rod-shaped (1.3-1.9×0.3-0.5 µm) and non-motile marine bacterium, designated MEBiC09412T, was isolated from seaweed collected at Yeonggwang County, South Korea. 16S rRNA gene sequence analysis demonstrated that strain MEBiC09412T shared high sequence similarity with Marinirhabdus gelatinilytica NH83T (95.4 %). Growth was observed at 17-38 °C (optimum 30 °C), at pH 4.0-8.5 (optimum pH 7.0) and with 0.5-6.0 % (w/v; optimum 2.5 %) NaCl. The predominant cellular fatty acids were iso-C15 : 0 (27.4 %), iso-C15 : 1 G (9.6 %), anteiso-C15 : 0 (14.6 %), iso-C16 : 0 (6.2 %), iso-C17 : 0 3OH (13.2 %) and summed feature 3 (comprising C16 : 1ω6c and/or C16 : 1ω7c; 7.4 %). The DNA G+C content was determined to be 43.1 mol%, while the major respiratory quinone was menaquinone-6. Several phenotypic characteristics such as indole production, the oxidizing patterns of several carbohydrtaes (of glucose, fructose, sucrose, maltose, mannose etc.) and organic acids, and the enzyme activities of α-chymotrypsin and α-glucosidase differentiated strain MEBiC09412T from M. gelatinilytica NH83T. On the basis of this polyphasic taxonomic data, strain MEBiC09412T should be classified as a novel species of the genus Marinirhabduswith the suggested name Marinirhabdus citrea sp. nov. The type strain is MEBiC09412T (=KCCM 43216T=JCM 31588T).

Flavobacterium sediminis sp. nov., a starch-degrading bacterium isolated from tidal flat sediment.

A yellow-pigmented bacterium with the ability to degrade starch, designated MEBiC07310T, was isolated from tidal flat sediment collected in Taean County, Republic of Korea. Phylogenetic analysis based on the 16S rRNA gene sequence indicated that strain MEBiC07310T was affiliated with the genus Flavobacterium in the phylum Bacteroidetes and showed that the strain was most closely related to Flavobacterium haoranii LQY-7T (96.8 % similarity), followed by Flavobacterium indicum GPTSA 100-9T (95.2 %) and Flavobacterium urocaniciphilum YIT 12746T (94.6 %). Genome-based analysis of the average nucleotide identity (ANI) and in silico DNA-DNA hybridization (DDH) of strain MEBiC07310T compared with F. haoranii LQY-7T and F. indicum GPTSA 100-9T yielded ANI values of 77.0 and 73.3 % and DDH values of 18.0±2.7 and 16.1±3.6 %, respectively. The DNA G+C content of strain MEBiC07310T was 35.2 mol%. Cells of the strain were aerobic, Gram-stain-negative and rod-shaped, and negative for flexirubin-type pigments. Growth was observed at 17-43 °C (optimum 32 °C), at pH 5.0-8.0 (optimum pH 7.0) and with 0-3 % (w/v) NaCl (optimum 1 %). The major fatty acids (>10 %) of strain MEBiC07310T were iso-C15 : 0, iso-C17 : 0 3-OH, summed feature 1 (iso-C15 : 1 H and/or C13 : 0 3-OH) and summed feature 3 (C16 : 1ω6c and/or C16 : 1ω7c). The major respiratory quinone was menaquinone MK-6. Based on its phenotypic and genotypic characteristics, strain MEBiC07310T should be classified as representing a novel species of the genus Flavobacterium, for which the name Flavobacterium sediminis sp. nov. is proposed. The type strain is MEBiC07310T (=KCTC 62132T=JCM 32291T).