Calibration-free blood pressure estimation based on a convolutional neural network.

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.

Development strategy of non-GMO organism for increased hemoproteins in Corynebacterium glutamicum: a growth-acceleration-targeted evolution.

Heme, found in hemoproteins, is a valuable source of iron, an essential mineral. The need for an alternative hemoprotein source has emerged due to the inherent risks of large-scale livestock farming and animal proteins. Corynebacterium glutamicum, regarded for Qualified Presumption of Safety or Generally Recognized as Safe, can biosynthesize hemoproteins. C. glutamicum single-cell protein (SCP) can be a valuable alternative hemoprotein for supplying heme iron without adversely affecting blood fat levels. We constructed the chemostat culture system to increase hemoprotein content in C. glutamicum SCP. Through adaptive evolution, hemoprotein levels could be naturally increased to address oxidative stress resulting from enhanced growth rate. In addition, we used several specific plasmids containing growth-accelerating genes and the hemA promoter to expedite the evolutionary process. Following chemostat culture for 15 days, the plasmid in selected descendants was cured. The evolved strains showed improved specific growth rates from 0.59 h-1 to 0.62 h-1, 20% enhanced resistance to oxidative stress, and increased heme concentration from 12.95 µg/g-DCW to 14.22-15.24 µg/g-DCW. Notably, the putative peptidyl-tRNA hydrolase-based evolved strain manifested the most significant increase (30%) of hemoproteins. This is the first report presenting the potential of a growth-acceleration-targeted evolution (GATE) strategy for developing non-GMO industrial strains with increased bio-product productivity.

iCSDB: an integrated database of CRISPR screens.

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.

Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent.

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s.

NuA3 HAT antagonizes the Rpd3S and Rpd3L HDACs to optimize mRNA and lncRNA expression dynamics.

In yeast, NuA3 histone acetyltransferase (NuA3 HAT) promotes acetylation of histone H3 lysine 14 (H3K14) and transcription of a subset of genes through interaction between the Yng1 plant homeodomain (PHD) finger and H3K4me3. Although NuA3 HAT has multiple chromatin binding modules with distinct specificities, their interdependence and combinatorial actions in chromatin binding and transcription remain unknown. Modified peptide pulldown assays reveal that the Yng1 N-terminal region is important for the integrity of NuA3 HAT by mediating the interaction between core subunits and two methyl-binding proteins, Yng1 and Pdp3. We further uncover that NuA3 HAT contributes to the regulation of mRNA and lncRNA expression dynamics by antagonizing the histone deacetylases (HDACs) Rpd3S and Rpd3L. The Yng1 N-terminal region, the Nto1 PHD finger and Pdp3 are important for optimal induction of mRNA and lncRNA transcription repressed by the Set2-Rpd3S HDAC pathway, whereas the Yng1 PHD finger-H3K4me3 interaction affects transcriptional repression memory regulated by Rpd3L HDAC. These findings suggest that NuA3 HAT uses distinct chromatin readers to compete with two Rpd3-containing HDACs to optimize mRNA and lncRNA expression dynamics.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network.

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.

Lichenicola cladoniae gen. nov., sp. nov., a member of the family Acetobacteraceae isolated from an Antarctic lichen.

Two Gram-stain-negative, facultative anaerobic, chemoheterotrophic, pink-coloured, rod-shaped and non-motile bacterial strains, PAMC 26568 and PAMC 26569T, were isolated from an Antarctic lichen. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strains PAMC 26568 and PAMC 26569T belong to the family Acetobacteraceae and the most closely related species are Gluconacetobacter takamatsuzukensis (96.1 %), Gluconacetobacter tumulisoli (95.9 %) and Gluconacetobacter sacchari (95.7 %). Phylogenomic and genomic relatedness analyses showed that strains PAMC 26568 and PAMC 26569T are clearly distinguished from other genera in the family Acetobacteraceae by average nucleotide identity values (<72.8 %) and the genome-to-genome distance values (<22.5 %). Genomic analysis revealed that strains PAMC 26568 and PAMC 26569T do not contain genes involved in atmospheric nitrogen fixation and utilization of sole carbon compounds such as methane and methanol. Instead, strains PAMC 26568 and PAMC 26569T possess genes to utilize nitrate and nitrite and certain monosaccharides and disaccharides. The major fatty acids (>10 %) are summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c; 40.3-40.4 %), C18 : 1 2OH (22.7-23.7 %) and summed feature 2 (C14 : 0 3OH and/or C16 : 1 iso I; 12.0 % in PAMC 26568). The major respiratory quinone is Q-10. The genomic DNA G+C content of PAMC 26568 and PAMC 26569T is 64.6 %. Their distinct phylogenetic position and some physiological characteristics distinguish strains PAMC 26568 and PAMC 26569T from other genera in the family Acetobacteraceae supporting the proposal of Lichenicola gen. nov., with the type species Lichenicola cladoniae sp. nov. (type strain, PAMC 26569T=KCCM 43315T=JCM 33604T).

Rpd3L HDAC links H3K4me3 to transcriptional repression memory.

Transcriptional memory is critical for the faster reactivation of necessary genes upon environmental changes and requires that the genes were previously in an active state. However, whether transcriptional repression also displays 'memory' of the prior transcriptionally inactive state remains unknown. In this study, we show that transcriptional repression of ∼540 genes in yeast occurs much more rapidly if the genes have been previously repressed during carbon source shifts. This novel transcriptional response has been termed transcriptional repression memory (TREM). Interestingly, Rpd3L histone deacetylase (HDAC), targeted to active promoters induces TREM. Mutants for Rpd3L exhibit increased acetylation at active promoters and delay TREM significantly. Surprisingly, the interaction between H3K4me3 and Rpd3L via the Pho23 PHD finger is critical to promote histone deacetylation and TREM by Rpd3L. Therefore, we propose that an active mark, H3K4me3 enriched at active promoters, instructs Rpd3L HDAC to induce histone deacetylation and TREM.

Genome-wide transcriptome profiling of the medicinal plant Zanthoxylum planispinum using a single-molecule direct RNA sequencing approach.

High-throughput RNA sequencing has revolutionized transcriptome-based studies of candidate genes, key pathways and gene regulation in non-model organisms. We analyzed full-length cDNA sequences in Zanthoxylum planispinum (Z. planispinum), a medicinal herb in major parts of East Asia. The full-length mRNA derived from tissues of leaf, early fruit and maturing fruit stage were sequenced using PacBio RSII platform to identify isoform transcriptome. We obtained 51,402 unigenes, with average 1781 bp per gene in 82.473 Mb gene lengths. Among 51,402, 3963 unigenes showed variety of isoform. By selection of one representative gene among each of the various isoforms, we finalized 46,306 unique gene set for this herb. We identified 76 cytochrome P450 (CYP450) and related isoforms that are of the wide diversity in the molecular function and biological process. These transcriptome data of Z. planispinum will provide a good resource to study metabolic engineering for the production of valuable medicinal drugs and phytochemicals.

EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism.

Recently, studies that analyze emotions based on physiological signals, such as electroencephalogram (EEG), by applying a deep learning algorithm have been actively conducted. However, the study of sequence modeling considering the change of emotional signals over time has not been fully investigated. To consider long-term interaction of emotion, in this study, we propose a long short-term memory network to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak-end rule in psychology. We used 32-channel EEG data from the DEAP database. Two-level (low and high) and three-level (low, middle, and high) classification experiments were performed on the valence and arousal emotion models. The results show accuracies of 90.1% and 87.9% using the two-level classification for the valence and arousal models with four-fold cross validation, respectively. In the case of the three-level classification, these values were obtained as 83.5% and 82.6%, respectively. Additional experiments were conducted using a network combining a convolutional neural network (CNN) submodule with the proposed model. The obtained results showed accuracies of 90.1% and 88.3% in the case of the two-level classification and 86.9% and 84.1% in the case of the three-level classification for the valence and arousal models with four-fold cross validation, respectively. In 10-fold cross validation, there were 91.8% for valence and 91.6% for arousal accuracy, respectively.

Nibricoccus aquaticus gen. nov., sp. nov., a new genus of the family Opitutaceae isolated from hyporheic freshwater.

A yellow-coloured, Gram-strain-negative, non-motile, cocci-shaped, strictly aerobic bacterium, designated HZ-65T, was isolated from hyporheic freshwater in the Republic of Korea. Strain HZ-65T grew at 15-37 °C (optimum, 25-30 °C), pH 5.5-9.0 (optimum, pH 7.0) and 0-0.5 % NaCl (w/v; optimum at 0 % NaCl). Phylogenetic analysis based on the 16S rRNA gene showed that strain HZ-65T is a member of family Opitutaceae and is closely related to Opitutus terrae PB90-1T (94.0 % similarity), Cephaloticoccus primus CAG34T (93.0 %), and Cephaloticoccus capnophilus CV41T (92.7 %), while the similarities to other Opitutaceae-type strains were lower than 90.0 %. The DNA G+C content was 62.2 mol% and the quinone present was menaquinone-7. The predominant fatty acids were iso-C14 : 0, anteiso-C15 : 0, C16 : 0, and iso-C16 : 0, representing 70 % of the total fatty acids. The major polar lipid profile consisted of phosphatidylethanolamine, phosphatidylglycerol and diphosphatidylglycerol. Analysis of the HZ-65T genome revealed the presence of 300 genes that are involved in carbohydrate-active enzymes, which indicates the metabolic potential to degrade polysaccharides. The phenotypic, chemotaxonomic, genetic, and phylogenetic properties suggest that strain HZ-65T represents a novel species in a new genus within the family Opitutaceae, for which the name Nibricoccus aquaticus gen. nov., sp. nov., is proposed. The type strain of Nibricoccus aquaticus is HZ-65T (KACC 19333T=NBRC 112907T).

Paucibacter aquatile sp. nov. isolated from freshwater of the Nakdong River, Republic of Korea.

A Gram-negative, aerobic, motile, and rod-shaped bacterial strain designated CR182T was isolated from freshwater of the Nakdong River, Republic of Korea. Optimal growth conditions for this novel strain were found to be: 25-30 °C, pH 6.5-8.5, and 3% (w/v) NaCl. Phylogenetic analysis based on 16S rRNA gene sequence indicates that the strain CR182T belongs to type strains of genus Paucibacter. Strain CR182T showed 98.0% 16S rRNA gene sequence similarity with Paucibacter oligotrophus CHU3T and formed a robust phylogenetic clade with this species. The average nucleotide identity value between strain CR182T and P. oligotrophus CHU3T was 78.4% and the genome-to-genome distance was 22.2% on average. The genomic DNA G+C content calculated from the genome sequence was 66.3 mol%. Predominant cellular fatty acids of strain CR182T were summed feature 3 (C16:1 ω7c and/or C16:1 ω6c) (31.2%) and C16:0 (16.0%). Its major respiratory quinine was ubiquinone Q-8. Its polar lipids consisted of diphosphatidylglycerol, phosphatidylethanolamine, and two unidentified phospholipids. Its genomic DNA G+C content was 66.3%. Based on data obtained from this polyphasic taxonomic study, strain CR182T represents a novel species belonging to genus Paucibacter, for which a name of P. aquatile sp. nov. is proposed. The type strain is CR182T (= KCCM 90284T = NBRC 113032T).

Leucothrix arctica sp. nov., isolated from Arctic seawater.

A Gram-stain-negative, non-motile, oxidase- and catalase-positive, rod-shaped bacterium was isolated from a coastal seawater sample from the Arctic Circle and designated strain IMCC9719T. On the basis of 16S rRNA gene sequence analysis, it was shown that strain IMCC9719T belonged to the genus Leucothrix and was closely related to the type strains of Leucothrix pacifica (97.6 % similarity) and Leucothrix mucor (95.1 %), while the strain shared <90.6 % sequence similarity with other bacterial species. The average nucleotide identity and genome-to-genome distance values between strain IMCC9719T and L. pacifica JCM 18388T were 71.7 and 16.9 %, respectively. The DNA G+C content of strain IMCC9719T was 43.5 mol%. Optimum growth of strain IMCC9719T was observed at 15 °C, at pH 7.5-8.5 and in the presence of 2.0-2.5 % (w/v) NaCl. The major fatty acids were summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c), summed feature 8 (C18 : 1ω6c and/or C18 : 1ω7c) and C16 : 0. Cells of strain IMCC9719T contained diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, an unidentified polar lipid, an unidentified aminophospholipid and two unidentified phospholipids. The major respiratory quinone was ubiquinone-8 (Q-8). Based on the taxonomic data collected in this study, strain IMCC9719T represents a novel species of the genus Leucothrix, for which the name Leucothrix arctica sp. nov. is proposed. The type strain is IMCC9719T (=KACC 18010T=NBRC 110382T).

Deinococcus koreensis sp. nov., a gamma radiation-resistant bacterium isolated from river water.

A gamma radiation-resistant, Gram-stain-negative, rod-shaped bacterial strain, designated SJW1-2T, was isolated from freshwater samples collected from the Seomjin River, Republic of Korea. The 16S rRNA gene sequence analyses showed that strain SJW1-2T was most closely related to Deinococcus metalli 1PNM-19T (94.3 % sequence similarity) and formed a robust phylogenetic clade with other species of the genus Deinococcus. The optimum growth pH and temperature for the isolate were pH 7.0-7.5 and 25 °C, respectively. Strain SJW1-2T exhibited high resistance to gamma radiation. The predominant respiratory quinone was MK-8. The polar lipid profile consisted of different unidentified glycolipids, two unidentified lipids, two unidentified phospholipids and an unidentified phosphoglycolipid. The major peptidoglycan amino acids were alanine, d-glutamic acid, glycine and l-ornithine. The predominant fatty acids (>10 %) were summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c) (25.2 %) and C16 : 0 (21.2 %), and the DNA G+C content was 69.5 mol%. On the basis of phenotypic, genotypic and phylogenetic analyses, strain SJW1-2T (=KACC 19332T=NBRC 112908T) represents a novel species of the genus Deinococcus, for which the name Deinococcus koreensis sp. nov. is proposed.

Planktotalea arctica sp. nov., isolated from Arctic seawater.

A Gram-staining-negative, non-motile, non-pigmented, rod-shaped bacterium was isolated from an Arctic coastal seawater sample and was designated strain IMCC9565T. Analysis of the 16S rRNA gene sequence of strain IMCC9565T revealed that the closest phylogenetic neighbours of the strain were members of the genus Planktotalea. Furthermore, the strain formed a robust clade with Planktotalea frisia SH6-1T, with which it shared 97.9 % 16S rRNA gene sequence similarity. Determination of genomic relatedness based on average nucleotide identity and genome-to-genome distance showed that strain IMCC9565T was distantly related to P. frisia, meaning the Arctic strain represents a novel species. Optimum growth of strain IMCC9565T was observed at 20 °C, pH 7.0 and in the presence of 2 % (w/v) NaCl. The major respiratory isoprenoid quinone was ubiquinone-10 (Q-10) and the major polar lipids consisted of phosphatidylglycerol, phosphatidylcholine, one unidentified aminolipid and two unidentified lipids. The principal fatty acids were C18 : 1ω7c and/or C18 : 1ω6c, C18 : 1ω7c 11-methyl and C16 : 0, and the DNA G+C content was 57.1 mol%. Based on these data, Planktotalea arctica sp. nov. is proposed to accommodate the bacterial isolate and the type strain is IMCC9565T (=KACC 18009T=NBRC 110393T).

Eionea flava sp. nov., isolated from coastal seawater, and emended description of the genus Eionea.

A Gram-staining-negative, non-motile, catalase- and oxidase-positive, facultatively aerobic bacterium, designated IMCC1962(T) was isolated from a surface seawater sample from the Yellow Sea. Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain IMCC1962(T )belonged to the genus Eionea, forming a robust clade with members of the genus, and was most closely related to Eionea nigra (97.3% similarity). DNA-DNA relatedness between strain IMCC1962(T) and Eionea nigra DSM 19752(T) was 21.8-26.3%, which indicated strain IMCC1962(T) represents a novel genomic species of the genus Eionea. The G+C content of the DNA of strain IMCC1962(T) was 48.7 mol%. The major isoprenoid quinone was ubiquinone Q-8 and major fatty acids were C16 : 1ω7c and/or C16 : 1ω6c (43.4%), C18 : 1ω7c and/or C18 : 1ω6c (19.3%) and C16 : 0 (17.2%). The polar lipids found in strain IMCC1962(T) were phosphatidylethanolamine, phosphatidylglycerol, aminophospholipid, unknown phospholipid, and four unknown polar lipids. Strain IMCC1962(T) and Eionea nigra DSM 19752(T) differed from each other in diverse phenotypic characteristics including motility, colony colour and enzyme activities. On the basis of phenotypic and genotypic data, strain IMCC1962(T) ( = KACC 17481(T)= NBRC 109703(T)) represents a novel species of the genus Eionea, for which the name Eionea flava sp. nov. is proposed. An emended description of the genus Eionea is also provided.

Mesonia aquimarina sp. nov., a marine bacterium isolated from coastal seawater.

A Gram-staining-negative, aerobic, yellow, non-flagellated, non-gliding, rod-shaped bacterium, designated strain IMCC1021(T), was isolated off the coast of the East Sea. Optimal growth of strain IMCC1021(T) was observed at 25 °C, pH 7.0-7.5 and in the presence of 3.0-3.5 % (w/v) NaCl. Phylogenetic analysis based on 16S rRNA gene sequences revealed that the strain belonged to the genus Mesonia, showing a close relationship with Mesonia mobilis KMM 6059(T) (97.4 % similarity) followed by Mesonia phycicola MDSW-25(T) (96.1 %). DNA-DNA relatedness between strain IMCC1021(T) and M. mobilis KMM 6059(T) was 17.5 % (reciprocal 7.4 %), which indicated that the new isolate represents a novel genomic species of the genus Mesonia. The major fatty acids were iso-C15 : 0 and iso-C17 : 0 3-OH, and the G+C content of the genomic DNA was 41.4 mol%. Strain IMCC1021(T) contained menaquinone-6 (MK-6) as the respiratory quinone, and polar lipids comprising phosphatidylethanolamine, two unknown aminolipids and three unknown polar lipids. On the basis of the phylogenetic distinctions and differential phenotypic characteristics, it is suggested that strain IMCC1021(T) ( = KCTC 32320(T) = NBRC 109485(T)) should be assigned to the genus Mesonia as the type strain of a novel species, for which the name Mesonia aquimarina sp. nov. is proposed.

Lentisphaera profundi sp. nov., isolated from deep-sea water.

A Gram-staining-negative, aerobic, non-motile, coccus-shaped bacterium, designated SAORIC-696T, was isolated from deep-sea water at a depth of 1700 m in the western North Pacific Ocean. Optimal growth of strain SAORIC-696T was observed at 15 °C, pH 7.0 and in the presence of 2 % (w/v) NaCl. Strain SAORIC-696T formed a robust phylogenetic clade with members of the genus Lentisphaera. The 16S rRNA gene sequence similarity showed that strain SAORIC-696T was most closely related to Lentisphaera marina (98.0 % similarity) and Lentisphaera araneosa (97.3 %). The DNA-DNA relatedness between SAORIC-696T and two species of the genus Lentisphaera was only 27-42 %. The DNA G+C content of strain SAORIC-696T was 43.1 mol% and predominant cellular fatty acids were C16 : 1ω9c (36.8 %), C14 : 0 (22.5 %) and C14 : 0 3-OH and/or iso-C16 : 1 I (10.8 %). Strain SAORIC-696T contained MK-7 as the only respiratory quinone. On the basis of taxonomic data collected in this study, it was concluded that strain SAORIC-696T represents a novel species of the genus Lentisphaera, for which the name Lentisphaera profundi sp. nov. is proposed, with the type strain SAORIC-696T ( = NBRC 110692T = KCTC 42681T).

Aurantivirga profunda gen. nov., sp. nov., isolated from deep-seawater, a novel member of the family Flavobacteriaceae.

A Gram-stain-negative, aerobic, proteorhodopsin-containing, orange, rod-shaped bacterium, designated SAORIC-234T, was isolated from deep seawater in the Pacific Ocean. 16S rRNA gene sequence analysis revealed that the strain could be affiliated with the family Flavobacteriaceae of the phylum Bacteroidetes and shared less than 94.6 % similarity with other species of the family with validly published names. The phenotypic characteristics of this novel isolate, such as growth properties and enzyme activities, could be differentiated from those of other species. The strain was non-motile, oxidase-positive and catalase-negative. The G+C content of the genomic DNA was determined to be 34.8 mol% and menaquinone-6 (MK-6) was the predominant isoprenoid quinone. The predominant fatty acids were iso-C15 : 0, iso-C15 : 1 G, iso-C16 : 0 3-OH, iso-C17 : 0 3-OH and iso-C15 : 0 3-OH. The major polar lipids comprised phosphatidylethanolamine, three unknown aminolipids and three unknown lipids. On the basis of the taxonomic data collected in this study, it was concluded that strain SAORIC-234T represents a novel genus and species in the family Flavobacteriaceae, for which the name Aurantivirga profunda gen. nov., sp. nov. is proposed. The type strain of the type species, Aurantivirga profunda sp. nov., is SAORIC-234T ( = NBRC 110606T = KACC 18400T).