The Effect of Organic Vegetable Mixed Juice on Blood Circulation and Intestine Flora: Randomized, Double-Blinded, Placebo-Controlled Clinical Trial.

Epidemiological evidence suggests that fruit and vegetable intake significantly positively affects cardiovascular health. Since vegetable juice is more accessible than raw vegetables, it attracts attention as a health functional food for circulatory diseases. Therefore, this study measured blood lipids, antioxidants, blood circulation indicators, and changes in the microbiome to confirm the effect of organic vegetable mixed juice (OVJ) on improving blood circulation. This 4-week, randomized, double-blinded, placebo-controlled study involved adult men and women with borderline total cholesterol (TC) and low-density lipoprotein (LDL) levels. As a result, blood lipid profile indicators, such as TC, triglycerides, LDL cholesterol, and apolipoprotein B, decreased (p < 0.05) in the OVJ group compared with those in the placebo group. Additionally, the antioxidant biomarker superoxide dismutase increased (p < 0.05). In contrast, systolic and diastolic blood viscosities, as blood circulation-related biomarkers, decreased (p < 0.05) in the OVJ group compared with those in the placebo group. After the intervention, a fecal microbiome analysis confirmed differences due to changes in the intestinal microbiome composition between the OVJ and placebo groups. In conclusion, our research results confirmed that consuming OVJ improves blood circulation by affecting the blood lipid profile, antioxidant enzymes, and microbiome changes.

The Effects of Lilium lancifolium Thunb. on the Alleviation of Joint Pain: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial.

Arthritis is mainly a geriatric disease that causes joint pain and lowers the quality of life. This clinical trial was performed to evaluate the efficacy of Lilium lancifolium Thunb. (HY-LL) in alleviating joint pain. Six candidate anti-inflammatory components including regaloside A were identified in HY-LL using HPLC analysis. All participants were assigned to the HY-LL or the placebo group and took tablets twice a day for 12 weeks. As a result, pain VAS and K-WOMAC total scores significantly decreased after 12 weeks compared to the baseline in the HY-LL group, with a statistically significant difference between the two groups (p = 0.043, 0.043). The K-WOMAC sub-scores for pain and function showed a statistically significant improvement in the HY-LL group compared to the placebo group (p = 0.023, 0.047). Furthermore, the participants' overall quality of life improved after 12 weeks of HY-LL consumption (p = 0.024). However, no significant differences were observed in the blood biomarkers. Therefore, this study demonstrated the positive effect of 12 weeks of HY-LL consumption on joint pain and quality of life.

Probiotic Consortium Confers Synergistic Anti-Inflammatory Effects in Inflammatory Disorders.

The composition and diversity of gut microbiota significantly influence the immune system and are linked to various diseases, including inflammatory and allergy disorders. While considerable research has focused on exploring single bacterial species or consortia, the optimal strategies for microbiota-based therapeutics remain underexplored. Specifically, the comparative effectiveness of bacterial consortia versus individual species warrants further investigation. In our study, we assessed the impact of the bacterial consortium MPRO, comprising Lactiplantibacillus plantarum HY7712, Bifidobacterium animalis ssp. lactis HY8002, and Lacticaseibacillus casei HY2782, in comparison to its individual components. The administration of MPRO demonstrated enhanced therapeutic efficacy in experimental models of atopic dermatitis and inflammatory colitis when compared to single strains. MPRO exhibited the ability to dampen inflammatory responses and alter the gut microbial landscape significantly. Notably, MPRO administration led to an increase in intestinal CD103+CD11b+ dendritic cells, promoting the induction of regulatory T cells and the robust suppression of inflammation in experimental disease settings. Our findings advocate the preference for bacterial consortia over single strains in the treatment of inflammatory disorders, carrying potential clinical relevance.

Effect of gut microbiota-derived metabolites and extracellular vesicles on neurodegenerative disease in a gut-brain axis chip.

A new perspective suggests that a dynamic bidirectional communication system, often referred to as the microbiome-gut-brain axis, exists among the gut, its microbiome, and the central nervous system (CNS). This system may influence brain health and various brain-related diseases, especially in the realms of neurodevelopmental and neurodegenerative conditions. However, the exact mechanism is not yet understood. Metabolites or extracellular vesicles derived from microbes in the gut have the capacity to traverse the intestinal epithelial barrier or blood-brain barrier, gaining access to the systemic circulation. This phenomenon can initiate the physiological responses that directly or indirectly impact the CNS and its function. However, reliable and controllable tools are required to demonstrate the causal effects of gut microbial-derived substances on neurogenesis and neurodegenerative diseases. The integration of microfluidics enhances scientific research by providing advanced in vitro engineering models. In this study, we investigated the impact of microbe-derived metabolites and exosomes on neurodevelopment and neurodegenerative disorders using human induced pluripotent stem cells (iPSCs)-derived neurons in a gut-brain axis chip. While strain-specific, our findings indicate that both microbial-derived metabolites and exosomes exert the significant effects on neural growth, maturation, and synaptic plasticity. Therefore, our results suggest that metabolites and exosomes derived from microbes hold promise as potential candidates and strategies for addressing neurodevelopmental and neurodegenerative disorders.

A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy.

Purpose: Thyroid eye disease (TED) is an autoimmune condition with an array of clinical manifestations, which can be complicated by compressive optic neuropathy. It is important to identify patients with TED early to ensure close monitoring and treatment to prevent potential permanent disability or vision loss. Deep learning artificial intelligence (AI) algorithms have been utilized in ophthalmology and in other fields of medicine to detect disease. This study aims to introduce a deep learning model to evaluate orbital computed tomography (CT) images for the presence of TED and potential compressive optic neuropathy. Design: Retrospective review and deep learning algorithm modeling. Subjects: Patients with TED with dedicated orbital CT scans and with an examination by an oculoplastic surgeon over a 10-year period at a single academic institution. Patients with no TED and normal CTs were used as normal controls. Those with other diagnoses, such as tumors or other inflammatory processes, were excluded. Methods: Orbital CTs were preprocessed and adopted for the Visual Geometry Group-16 network to distinguish patients with no TED, mild TED, and severe TED with compressive optic neuropathy. The primary model included training and testing of all 3 conditions. Binary model performance was also evaluated. An oculoplastic surgeon was also similarly tested with single and serial images for comparison. Main Outcome Measures: Accuracy of deep learning model discernment of region of interest for CT scans to distinguish TED versus normal control, as well as TED with clinical signs of optic neuropathy. Results: A total of 1187 photos from 141 patients were used to develop the AI model. The primary model trained on patients with no TED, mild TED, and severe TED had 89.5% accuracy (area under the curve: range, 0.96-0.99) in distinguishing patients with these clinical categories. In comparison, testing of an oculoplastic surgeon in these 3 categories showed decreased accuracy (70.0% accuracy in serial image testing). Conclusions: The deep learning model developed in the study can accurately detect TED and further detect TED with clinical signs of optic neuropathy based on orbital CT. The model proved superior compared with human expert grading. With further optimization and validation, this TED deep learning model could help guide frontline health care providers in the detection of TED and help stratify the urgency of a referral to an oculoplastic surgeon and endocrinologist. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

A deep learning approach using an ensemble model to autocreate an image-based hip fracture registry.

Objectives: With more than 300,000 patients per year in the United States alone, hip fractures are one of the most common injuries occurring in the elderly. The incidence is predicted to rise to 6 million cases per annum worldwide by 2050. Many fracture registries have been established, serving as tools for quality surveillance and evaluating patient outcomes. Most registries are based on billing and procedural codes, prone to under-reporting of cases. Deep learning (DL) is able to interpret radiographic images and assist in fracture detection; we propose to conduct a DL-based approach intended to autocreate a fracture registry, specifically for the hip fracture population. Methods: Conventional radiographs (n = 18,834) from 2919 patients from Massachusetts General Brigham hospitals were extracted (images designated as hip radiographs within the medical record). We designed a cascade model consisting of 3 submodules for image view classification (MI), postoperative implant detection (MII), and proximal femoral fracture detection (MIII), including data augmentation and scaling, and convolutional neural networks for model development. An ensemble model of 10 models (based on ResNet, VGG, DenseNet, and EfficientNet architectures) was created to detect the presence of a fracture. Results: The accuracy of the developed submodules reached 92%-100%; visual explanations of model predictions were generated through gradient-based methods. Time for the automated model-based fracture-labeling was 0.03 seconds/image, compared with an average of 12 seconds/image for human annotation as calculated in our preprocessing stages. Conclusion: This semisupervised DL approach labeled hip fractures with high accuracy. This mitigates the burden of annotations in a large data set, which is time-consuming and prone to under-reporting. The DL approach may prove beneficial for future efforts to autocreate construct registries that outperform current diagnosis and procedural codes. Clinicians and researchers can use the developed DL approach for quality improvement, diagnostic and prognostic research purposes, and building clinical decision support tools.

Development of High-Performance Polymer Electrolyte Membranes through the Application of Quantum Dot Coatings to Nafion Membranes.

Proton exchange membrane water electrolysis (PEMWE) generates oxygen and hydrogen at the anode and cathode, respectively, by conducting protons generated at the anode to the cathode through a proton exchange membrane (PEM). The performance of PEMWE can be improved with faster catalytic reactions at each electrode; thus, the development of a PEM with excellent ionic conductivity and physicochemical stability is essential. Nafion, a type of perfluoro-sulfonic acid polymer, is the most widely used PEM material. However, despite its excellent conductivity and chemical stability, it exhibits high hydrogen permeability due to its structural characteristics. Quantum dots (QDs) have a hydrophilic functional group that can act as an ion conductor and are extremely compatible with the hydrophilic cluster of Nafion due to their characteristic nanosized structure. In this study, various compositions of N-doped carbon quantum dots (CQDs) containing hydrophilic functional groups were coated on a Nafion-212 membrane. The resulting series of CQD-coated Nafion membranes exhibited improvements in morphology and ionic conductivity as well as reductions in hydrogen permeability. In particular, the Nafion membrane coated with 0.75 wt % of N-doped CQD (CQD-cNafion-0.75) exhibited improved mechanical properties and higher oxidation stability compared to Nafion-212. It also displayed higher ionic conductivity of 240.3 mS cm-1 at 80 °C and reduced hydrogen permeability (about 10% reduction) compared to Nafion-212. In addition, the performance of single-cell PEMWE using the CQD-cNafion-0.75 membrane was found to be approximately 1.2 times higher than Nafion-212.

Investigation of memory-enhancing effects of Streptococcus thermophilus EG007 in mice and elucidating molecular and metagenomic characteristics using nanopore sequencing.

Over the past decades, accumulating evidences have highlighted the gut microbiota as a key player in the brain functioning via microbiota-gut-brain axis, and accordingly, the beneficial role of several probiotic strains in cognitive ability also have been actively investigated. However, the majority of the research have demonstrated the effects against age-related cognitive decline or neurological disease. To this end, we aimed to investigate lactic acid bacteria strains having beneficial effects on the cognitive function of healthy young mice and elucidate underlying characteristics by carrying out nanopore sequencing-based genomics and metagenomics analysis. 8-week consumption of Streptococcus thermophilus EG007 demonstrated marked enhancements in behavior tests assessing short-term spatial and non-spatial learning and memory. It was revealed that EG007 possessed genes encoding various metabolites beneficial for a health condition in many aspects, including gamma-aminobutyric acid producing system, a neurotransmitter associated with mood and stress response. Also, by utilizing 16S-23S rRNA operon as a taxonomic marker, we identified more accurate species-level compositional changes in gut microbiota, which was increase of certain species, previously reported to have associations with mental health or down-regulation of inflammation or infection-related species. Moreover, correlation analysis revealed that the EG007-mediated altered microbiota had a significant correlation with the memory traits.

A scalable artificial intelligence platform that automatically finds copy number variations (CNVs) in journal articles and transforms them into a database: CNV extraction, transformation, and loading AI (CNV-ETLAI).

BACKGROUND: Although copy number variations (CNVs) are infrequent, each anomaly is unique, and multiple CNVs can appear simultaneously. Growing evidence suggests that CNVs contribute to a wide range of diseases. When CNVs are detected, assessment of their clinical significance requires a thorough literature review. This process can be extremely time-consuming and may delay disease diagnosis. Therefore, we have developed CNV Extraction, Transformation, and Loading Artificial Intelligence (CNV-ETLAI), an innovative tool that allows experts to classify and interpret CNVs accurately and efficiently. METHODS: We combined text, table, and image processing algorithms to develop an artificial intelligence platform that automatically extracts, transforms, and organizes CNV information into a database. To validate CNV-ETLAI, we compared its performance to ground truth datasets labeled by a human expert. In addition, we analyzed the CNV data, which was collected using CNV-ETLAI via a crowdsourcing approach. RESULTS: In comparison to a human expert, CNV-ETLAI improved CNV detection accuracy by 4% and performed the analysis 60 times faster. This performance can improve even further with upscaling of the CNV-ETLAI database as usage increases. 5,800 CNVs from 2,313 journal articles were collected. Total CNV frequency for the whole chromosome was highest for chromosome X, whereas CNV frequency per 1 Mb of genomic length was highest for chromosome 22. CONCLUSIONS: We have developed, tested, and shared CNV-ETLAI for research and clinical purposes (https://lmic.mgh.harvard.edu/CNV-ETLAI). Use of CNV-ETLAI is expected to ease and accelerate diagnostic classification and interpretation of CNVs.

Positive Effect of Lactobacillus acidophilus EG004 on Cognitive Ability of Healthy Mice by Fecal Microbiome Analysis Using Full-Length 16S-23S rRNA Metagenome Sequencing.

Evidence for the concept of the "gut-brain axis" (GBA) has risen. Many types of research demonstrated the mechanism of the GBA and the effect of probiotic intake. Although many studies have been reported, most were focused on neurodegenerative disease and, it is still not clear what type of bacterial strains have positive effects. We designed an experiment to discover a strain that positively affects brain function, which can be recognized through changes in cognitive processes using healthy mice. The experimental group consisted of a control group and three probiotic consumption groups, namely, Lactobacillus acidophilus, Lacticaseibacillus paracasei, and Lacticaseibacillus rhamnosus. Three experimental groups fed probiotics showed an improved cognitive ability by cognitive-behavioral tests, and the group fed on L. acidophilus showed the highest score. To provide an understanding of the altered microbial composition effect on the brain, we performed full 16S-23S rRNA sequencing using Nanopore, and operational taxonomic units (OTUs) were identified at species level. In the group fed on L. acidophilus, the intestinal bacterial ratio of Firmicutes and Proteobacteria phyla increased, and the bacterial proportions of 16 species were significantly different from those of the control group. We estimated that the positive results on the cognitive behavioral tests were due to the increased proportion of the L. acidophilus EG004 strain in the subjects' intestines since the strain can produce butyrate and therefore modulate neurotransmitters and neurotrophic factors. We expect that this strain expands the industrial field of L. acidophilus and helps understand the mechanism of the gut-brain axis. IMPORTANCE Recently, the concept of the "gut-brain axis" has risen and suggested that microbes in the GI tract affect the brain by modulating signal molecules. Although many pieces of research were reported in a short period, a signaling mechanism and the effects of a specific bacterial strain are still unclear. Besides, since most of the research was focused on neurodegenerative disease, the study with a healthy animal model is still insufficient. In this study, we show using a healthy animal model that a bacterial strain (Lactobacillus acidophilus EG004) has a positive effect on mouse cognitive ability. We experimentally verified an improved cognitive ability by cognitive behavioral tests. We performed full 16S-23S rRNA sequencing using a Nanopore MinION instrument and provided the gut microbiome composition at the species level. This microbiome composition consisted of candidate microbial groups as a biomarker that shows positive effects on cognitive ability. Therefore, our study suggests a new perspective for probiotic strain use applicable for various industrialization processes.

Complete Genome Sequence of the Newly Developed Lactobacillus acidophilus Strain With Improved Thermal Adaptability.

Lactobacillus acidophilus (L. acidophilus) is a representative probiotic and is widely used in many industrial products for its beneficial effects on human and animal health. This bacterium is exposed to harsh environments such as high temperatures for manufacturing industrial products, but cell yield under high temperatures is relatively low. To resolve this issue, we developed a new L. acidophilus strain with improved heat resistance while retaining the existing beneficial properties through the adaptive laboratory evolution (ALE) method. The newly developed strain, L. acidophilus EG008, has improved the existing limit of thermal resistance from 65°C to 75°C. Furthermore, we performed whole-genome sequencing and comparative genome analysis of wild-type and EG008 strains to unravel the molecular mechanism of improved heat resistance. Interestingly, only two single-nucleotide polymorphisms (SNPs) were different compared to the L. acidophilus wild-type. We identified that one of these SNPs is a non-synonymous SNP capable of altering the structure of MurD protein through the 435th amino acid change from serine to threonine. We believe that these results will directly contribute to any industrial field where L. acidophilus is applied. In addition, these results make a step forward in understanding the molecular mechanisms of lactic acid bacteria evolution under extreme conditions.

MarkIt: A Collaborative Artificial Intelligence Annotation Platform Leveraging Blockchain For Medical Imaging Research.

Current research on medical image processing relies heavily on the amount and quality of input data. Specifically, supervised machine learning methods require well-annotated datasets. A lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. We developed MarkIt, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. Our platform handles both Digital Imaging and Communications in Medicine (DICOM) and non-DICOM images, and allows users to annotate them for classification and object detection tasks in an efficient manner. MarkIt can accelerate the annotation process and keep track of user activities to calculate a fair reward. A proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest X-ray studies for multi-label classification. We calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. We hypothesize that MarkIt allows the typically arduous annotation task to become more efficient. In addition, MarkIt can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. The platform is publicly available for testing on https://markit.mgh.harvard.edu.

Prescribing patterns of polypharmacy in Korean pediatric patients.

BACKGROUND: Several studies have examined the risk and health outcomes related to polypharmacy among the elderly. However, information regarding polypharmacy among pediatric patients is lacking. OBJECTIVE: The aim of this study was to investigate the prevalence of polypharmacy and its related factors among the pediatric population of South Korea. METHODS: We used national claim data from the Health Insurance Review and Assessment Service-Pediatric Patients Sample (HIRA-PPS) in Korea originating from 2012 through 2016. Polypharmacy was defined as a daily average of two or more drugs used yearly. Complex chronic conditions (CCCs) were examined to evaluate concomitant chronic diseases in pediatric patients. Age-specific contraindications and potential drug-drug interactions were assessed according to criteria established by the Korea Institute of Drug Safety & Risk Management (KIDS). Descriptive statistics and logistic regression were conducted to analyze the status of polypharmacy and its associated risk factors in pediatric patients. RESULTS: The 5-year prevalence of pediatric polypharmacy in pediatric patients was 3.7%. The prevalence of polypharmacy was much higher in younger pediatric patients: 9.5% for patients between the ages of 1-7 years, 0.9% for ages 6-11 years, and 1.1% for ages 12-19 years. Pediatric patients with CCCs, Medical Aid benefits, or a hospital admission history had a significantly higher prevalence of polypharmacy when compared to their counterparts without those conditions. The most commonly prescribed drugs were respiratory agents (29%) followed by anti-allergic drugs (18.7%), central nervous system agents (15.9%), antibiotics (10.1%), and gastrointestinal drugs (7.7%). There was a positive correlation between the daily average number of inappropriate prescriptions and the degree of polypharmacy, especially in pediatric patients between the ages of 1-7 years. Contraindications and potential drug-drug interactions occurred in 11.0% and 10.1% of patients exposed to polypharmacy, respectively. CONCLUSIONS: One in ten pediatric patients under the age of 7 years was prescribed two or more concurrent drugs on average per day. Furthermore, pediatric patients exposed to polypharmacy showed an increased risk of inappropriate drug use. The implementation of a medication review system that considers pediatric patient polypharmacy exposure would reduce inappropriate drug use and prevent unwanted adverse outcomes.

Effect of late-life weight change on dementia incidence: a 10-year cohort study using claim data in Korea.

BACKGROUND: The association between body mass index (BMI) in late-life and dementia risk remains unclear. We investigated the association between BMI changes over a 2-year period and dementia in an elderly Korean population. METHODS: We examined 67 219 participants aged 60-79 years who underwent BMI measurement in 2002/2003 and 2004/2005 as part of the National Health Insurance Service-Health Screening Cohort. Baseline characteristics including BMI, socioeconomic status and cardiometabolic risk factors were measured at baseline (2002/2003). The difference between BMI at baseline and at the next health screening (2004/2005) was used to calculate the BMI change. After 2 years, the incidence of dementia was monitored for a mean 5.3 years from 2008 to 2013. Multivariate HRs for dementia incidence were estimated on the basis of baseline BMI and its changes after adjusting for various other risk factors. A subgroup analysis was conducted to determine the effects of baseline BMI and BMI changes. RESULTS: We demonstrated a significant association between late-life BMI changes and dementia in both sexes (men: >-10% HR=1.26, 95% CI 1.08 to 1.46, >+10% HR=1.25, 95% CI 1.08 to 1.45; women: >-10% HR=1.15, 95% CI 1.03 to 1.29, >+10% HR=1.17, 95% CI 1.05 to 1.31). However, the baseline BMI was not associated with dementia, except in underweight men. After stratification based on the baseline BMI, the BMI increase over 2 years was associated with dementia in men with a BMI of <25 kg/m2 and women with a BMI of 18.5-25 kg/m2, but not in the obese subgroup in either sex. However, BMI decrease was associated with dementia in those with a BMI of ≥18.5 kg/m2, but not in the underweight subgroup in either sex. CONCLUSION: Both weight gain and weight loss may be significant risk factors associated with dementia. Continuous weight control and careful monitoring of weight changes are necessary to prevent dementia development.

A CT metal artifact reduction algorithm based on sinogram surgery.

BACKGROUND: Streak artifacts in computed tomography (CT) images caused by metallic objects limit the wider use of CT imaging technologies. There have been various attempts to improve CT images containing streak artifacts; however, most of them generate additional artifacts or do not completely eradicate existing artifacts. OBJECTIVE: In this paper, we propose a novel algorithm which reduces streak artifacts in CT images. METHODS: Using CT numbers reconstructed from a given sinogram, we extract the metal part M and the surrounding area C with similar CT numbers. By filling in the area C âˆª M with the evaluated average CT number of C, we obtain a modified CT image. Using forward projection of the modified CT image, we generate a sinogram containing information about the anatomical structure. We undertake sinogram surgery to remove the metallic effects from the sinogram, after which we repeat the procedure. RESULTS: We perform numerical experiments using various simulated phantoms and patient images. For a quantitative analysis, we use the relative l∞ error and the relative l2 error. In simulated phantom experiments, all l∞ errors and l2 errors approach 10% and 1% of the initial errors, respectively. Moreover, for the patient image simulations, all l∞ errors are decreased by a factor of 20 while the l2 errors are decreased less than 5%. We observe that the proposed algorithm effectively reduces the metal artifacts. CONCLUSIONS: In this paper, we propose a metal artifact reduction algorithm based on sinogram surgery to reduce metal artifacts without additional artifacts. We also provide empirical convergence of our algorithm.

Exploring evidence of positive selection signatures in cattle breeds selected for different traits.

Since domestication, the genome landscape of cattle has been changing due to natural and artificial selection forces resulting in several general and specialized cattle breeds of the world. Identifying genomic regions affected due to these forces in livestock gives an insight into the history of selection for economically important traits and genetic adaptation to specific environments of the populations under consideration. This study explores the genes/genomic regions under selection in relation to the phenotypes of Holstein, Hanwoo, and N'Dama cattle breeds using Tajima's D, XP-CLR, and XP-EHH population statistical methods. The whole genomes of 10 Holstein (South Korea), 11 Hanwoo (South Korea), and 10 N'Dama (West Africa-Guinea) cattle breeds re-sequenced to ~11x coverage and retained 37 million SNPs were used for the study. Selection signature analysis revealed 441, 512, and 461 genes under selection from Holstein, Hanwoo, and N'Dama cattle breeds, respectively. Among all these, seven genes including ARFGAP3, SNORA70, and other RNA genes were common between the breeds. From each of the gene lists, significant functional annotation cluster terms including milk protein and thyroid hormone signaling pathway (Holstein), histone acetyltransferase activity (Hanwoo), and renin secretion (N'Dama) were enriched. Genes that are related to the phenotypes of the respective breeds were also identified. Moreover, significant breed-specific missense variants were identified in CSN3, PAPPA2 (Holstein), C1orf116 (Hanwoo), and COMMD1 (N'Dama) genes. The genes identified from this study provide an insight into the biological mechanisms and pathways that are important in cattle breeds selected for different traits of economic significance.

Comparative genome analysis of Lactobacillus plantarum GB-LP3 provides candidates of survival-related genetic factors.

Lactobacillus plantarum is found in various environmental niches such as in the gastrointestinal tract of an animal host or a fermented food. This species isolated from a certain environment is known to possess a variety of properties according to inhabited environment's adaptation. However, a causal relationship of a genetic factor and phenotype affected by a specific environment has not been systematically comprehended. L. plantarum GB-LP3 strain was isolated from Korean traditional fermented vegetable and the whole genome of GB-LP3 was sequenced. Comparative genome analysis of GB-LP3, with other 14 L. plantarum strains, was conducted. In addition, genomic island regions were investigated. The assembled whole GB-LP3 genome contained a single circular chromosome of 3,206,111bp with the GC content of 44.7%. In the phylogenetic tree analysis, GB-LP3 was in the closest distance from ZJ316. The genomes of GB-LP3 and ZJ316 have the high level of synteny. Functional genes that are related to prophage, bacteriocin, and quorum sensing were found through comparative genomic analysis with ZJ316 and investigation of genomic islands. dN/dS analysis identified that the gene coding for phosphonate ABC transporter ATP-binding protein is evolutionarily accelerated in GB-LP3. Our study found that potential candidate genes that are affected by environmental adaptation in Korea traditional fermented vegetable.