ABSTRACT
This paper investigates the application of ensemble learning techniques, specifically meta-learning, in intrusion detection systems (IDS) for the Internet of Medical Things (IoMT). It underscores the existing challenges posed by the heterogeneous and dynamic nature of IoMT environments, which necessitate adaptive, robust security solutions. By harnessing meta-learning alongside various ensemble strategies such as stacking and bagging, the paper aims to refine IDS mechanisms to effectively counter evolving cyber threats. The study proposes a performance-driven weighted meta-learning technique for dynamic assignment of voting weights to classifiers based on accuracy, loss, and confidence levels. This approach significantly enhances the intrusion detection capabilities for the IoMT by dynamically optimizing ensemble IDS models. Extensive experiments demonstrate the proposed model's superior performance in terms of accuracy, detection rate, F1 score, and false positive rate compared to existing models, particularly when analyzing various sizes of input features. The findings highlight the potential of integrating meta-learning in ensemble-based IDS to enhance the security and integrity of IoMT networks, suggesting avenues for future research to further advance IDS performance in protecting sensitive medical data and IoT infrastructures.
ABSTRACT
Lipases are important biocatalysts and ubiquitous in plants, animals, and microorganisms. The high growth rates of microorganisms with low production costs have enabled the wide application of microbial lipases in detergent, food, and cosmetic industries. Herein, a novel lipase from Lacticaseibacillus rhamnosus IDCC 3201 (Lac-Rh) was isolated and its activity analyzed under a range of reaction conditions to evaluate its potential industrial application. The isolated Lac-Rh showed a molecular weight of 24 kDa and a maximum activity of 3438.5 ± 1.8 U/mg protein at 60 °C and pH 8. Additionally, Lac-Rh retained activity in alkaline conditions and in 10% v/v concentrations of organic solvents, including glycerol and acetone. Interestingly, after pre-incubation in the presence of multiple commercial detergents, Lac-Rh maintained over 80% of its activity and the stains from cotton were successfully removed under a simulated laundry setting. Overall, the purified lipase from L. rhamnosus IDCC 3201 has potential for use as a detergent in industrial applications. KEY POINTS: ⢠A novel lipase (Lac-Rh) was isolated from Lacticaseibacillus rhamnosus IDCC 3201 ⢠Purified Lac-Rh exhibited its highest activity at a temperature of 60 °C and a pH of 8, respectively ⢠Lac-Rh remains stable in commercial laundry detergent and enhances washing performance.
Subject(s)
Detergents , Enzyme Stability , Lacticaseibacillus rhamnosus , Lipase , Lipase/metabolism , Lipase/chemistry , Lipase/genetics , Lacticaseibacillus rhamnosus/enzymology , Lacticaseibacillus rhamnosus/genetics , Lacticaseibacillus rhamnosus/chemistry , Hydrogen-Ion Concentration , Detergents/chemistry , Temperature , Molecular Weight , Bacterial Proteins/genetics , Bacterial Proteins/chemistry , Bacterial Proteins/metabolismABSTRACT
Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.
ABSTRACT
This study presents the synthesis and characterization of non-isocyanate polyurethanes (NIPU) derived from the copolymerization of cyclic-carbonated soybean oil (CSBO) and cyclic carbonate (CC)-terminated poly(ether carbonate) (RCC). Using a double-metal cyanide catalyst, poly(ether carbonate) polyol was first synthesized through the copolymerization of carbon dioxide and propylene oxide. The terminal hydroxyl group was then subjected to a substitution reaction with a five-membered CC group using glycerol-1,2-carbonate and oxalyl chloride, yielding RCC. Attempts to prepare NIPU solely using RCC and diamine were unsuccessful, possibly due to the low CC functionality and the aminolysis of RCC's linear carbonate repeating units. However, when combined with CSBO, solid NIPUs were successfully obtained, exhibiting good thermal stability along with enhanced mechanical properties compared to conventional CSBO-based NIPU formulations. Overall, this study underscores the potential of leveraging renewable resources and carbon capture technologies to develop sustainable NIPUs with tailored properties, thereby expanding their range of applications.
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This study explores the potential for optimizing a sustainable manufacturing process that maintains the essential characteristics of conventional liposomes using food-grade solvents and components. The focus was comparing the physicochemical, morphological, and interfacial properties of liposomes produced with these food-grade ingredients to those made by conventional methods. It was found that there was no significant difference in particle size (195.87 ± 1.40 nm) and ζ-potential (-45.13 ± 0.65 mV) between liposomes made from food-grade and conventional materials. The manufacturing process for liposomes, utilizing food-grade solvents and components, was optimized through the application of Plackett-Burman design and response surface methodology. This approach helped identify key parameters (soy lecithin, ß-sitosterol, W/O ratio) and their optimal values (3.17 g, 0.25 g, 1:2.59). These findings suggest that it is possible to enhance the use of liposomes as an effective and safe delivery system in the food industry, adhering to the strict guidelines set by regulatory agencies.
Subject(s)
Lecithins , Liposomes , Particle Size , Liposomes/chemistry , Lecithins/chemistry , Sitosterols/chemistry , Microfluidics/instrumentation , Glycine max/chemistryABSTRACT
Hydrothermal pretreatment is useful for microfibrillated cellulose (MFC) preparation due to its safety, but the remaining hemicellulose might affect MFC properties. This study aimed to investigate the effect of centrifugation time on hemicellulose removal and the physicochemical properties of MFC obtained after hydrothermal pretreatment and micro-fibrillation. In this study, centrifugation was applied to the MFC suspension at varying duration times. Composition analysis and Fourier transform infrared spectra indicated that fractionated MFC has no hemicellulose content after 10, 20, and 30 min centrifugation. It also showed an approximately 5 times higher than 0.5 % g/g of initial solid concentration, indicated by a lower gel concentration point, than unfractionated MFC. Scanning electron microscope images of the fractionated MFC for 30 min (MFC2C) presented thin, long cellulose fibrils of 517 nm in average diameter and 635-10,000 nm in length that induced a slower sedimentation rate. MFC2C dispersion was also improved by autoclave sterilization by regulating cellulose structure, rheology, and crystallinity. As a result, MFC dispersibility can be enhanced by removing hemicellulose through simple centrifugation.
Subject(s)
Cellulose , Edible Seaweeds , Rhodophyta , Cellulose/chemistryABSTRACT
The Internet of Medical Things (IoMT) is a growing trend within the rapidly expanding Internet of Things, enhancing healthcare operations and remote patient monitoring. However, these devices are vulnerable to cyber-attacks, posing risks to healthcare operations and patient safety. To detect and counteract attacks on the IoMT, methods such as intrusion detection systems, log monitoring, and threat intelligence are utilized. However, as attackers refine their methods, there is an increasing shift toward using machine learning and deep learning for more accurate and predictive attack detection. In this paper, we propose a fuzzy-based self-tuning Long Short-Term Memory (LSTM) intrusion detection system (IDS) for the IoMT. Our approach dynamically adjusts the number of epochs and utilizes early stopping to prevent overfitting and underfitting. We conducted extensive experiments to evaluate the performance of our proposed model, comparing it with existing IDS models for the IoMT. The results show that our model achieves high accuracy, low false positive rates, and high detection rates, indicating its effectiveness in identifying intrusions. We also discuss the challenges of using static epochs and batch sizes in deep learning models and highlight the importance of dynamic adjustment. The findings of this study contribute to the development of more efficient and accurate IDS models for IoMT scenarios.
Subject(s)
Internet of Things , Humans , Internet , Intelligence , Machine Learning , Memory, Long-TermABSTRACT
We investigated the potential of Inula britannica extract encapsulated in liposomes as a functional food ingredient with enhanced bioavailability and stability. Inula britannica, known for its anti-inflammatory properties and various health benefits, was encapsulated using a liposome mass production manufacturing method, and the physical properties of liposomes were evaluated. The liposomes exhibited improved anti-inflammatory effects in lipopolysaccharide-activated RAW 264.7 macrophages, suppressing the production of pro-inflammatory mediators such as nitric oxide and prostaglandin E2 and downregulating the expression of iNOS and COX-2 transcription factors. Additionally, we observed reduced production of pro-inflammatory cytokines TNF-α, IL-6, and IL-1ß, and modulation of the NF-κB and mitogen-activated protein kinase signaling pathways. These findings suggest that Inula britannica extract encapsulated in liposomes could serve as a valuable functional food ingredient for managing and preventing inflammation-related disorders, making it a promising candidate for incorporation into various functional food products. The enhanced absorption and stability provided by liposomal encapsulation can enable better utilization of the extract's beneficial properties, promoting overall health and well-being.
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In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.
Subject(s)
Internet of Things , Humans , Algorithms , Biometry , Computer SecurityABSTRACT
This study investigated the effect of graphite nanoplatelet (GNP) size and dispersion on the thermal conductivities and tensile strengths of epoxy-based composites. GNPs of four different platelet sizes, ranging from 1.6 to 3 µm, were derived by mechanically exfoliating and breaking expanded graphite (EG) particles using high-energy bead milling and sonication. The GNPs were used as fillers at loadings of 0-10 wt%. As the GNP size and loading amount increased, the thermal conductivities of the GNP/epoxy composites increased, but their tensile strengths decreased. However, interestingly, the tensile strength reached a maximum value at the low GNP content of 0.3% and thereafter decreased, irrespective of the GNP size. Our observations of the morphologies and dispersions of the GNPs in the composites indicated that the thermal conductivity was more likely related to the size and loading number of fillers, whereas the tensile strength was more influenced by the dispersion of fillers in the matrix.
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Ligularia fischeri, a leafy edible plant found in damp shady regions, has been used as an herbal medicine and is also consumed as a horticultural crop. In this study, we investigated the physiological and transcriptomic changes, especially those involved in phenylpropanoid biosynthesis, induced by severe drought stress in L. fischeri plants. A distinguishing characteristic of L. fischeri is a color change from green to purple due to anthocyanin biosynthesis. We chromatographically isolated and identified two anthocyanins and two flavones upregulated by drought stress using liquid chromatography-mass spectrometry and nuclear magnetic resonance analyses in this plant for the first time. In contrast, all types of caffeoylquinic acids (CQAs) and flavonol contents were decreased under drought stress. Further, we performed RNA sequencing to examine the molecular changes in these phenolic compounds at the transcriptome level. In an overview of drought-inducible responses, we identified 2,105 hits for 516 distinct transcripts as drought-responsive genes. Moreover, differentially expressed genes (DEGs) associated with phenylpropanoid biosynthesis accounted for the greatest number of both up- and downregulated DEGs by Kyoto Encyclopedia of Genes and Genomes enrichment analysis. We identified 24 meaningful DEGs based on the regulation of phenylpropanoid biosynthetic genes. Potential drought-responsive genes included upregulated flavone synthase (LfFNS, TRINITY DN31661 c0 g1 i1) and anthocyanin 5-O-glucosyltransferase (LfA5GT1, TRINITY DN782 c0 g1 i1), which could contribute to the high levels of flavones and anthocyanins under drought stress in L. fischeri. In addition, the downregulated shikimate O-hydroxycinnamolytransferase (LfHCT, TRINITY DN31661 c0 g1 i1) and hydroxycinnamoyl-CoA quinate/shikimate transferase (LfHQT4, TRINITY DN15180 c0 g1 i1) genes led to a reduction in CQAs. Only one or two BLASTP hits for LfHCT were obtained for six different Asteraceae species. It is possible that the HCT gene plays a crucial role in CQAs biosynthesis in these species. These findings expand our knowledge of the response mechanisms to drought stress, particularly regarding the regulation of key phenylpropanoid biosynthetic genes in L. fischeri.
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Phytoremediation of metals from water (WM) and nutrient (NM) media exposed to waste metal cutting fluid (WMCF) along with temperature (T) and humidity (H) stress was tested using Azolla imbricata (Roxb.) Nakai. In the absence of WMCF, biomass was higher in NM than in WM during all tests. Surprisingly, opposite results were noted in the presence of WMCF, with growth failing at exposure to > 0.1% and > 0.5% in NM and WM, respectively. Further, correlation analysis of the growth data following WM exposure revealed that biomass was affected positively by T and negatively by H and metal accumulation. Simultaneously, metal accumulation was affected negatively by T and positively by H. The average accumulations of Al, Cd, Cr, Fe, Pb, and Zn across all T/H tests were 540, 282, 71, 1645, 2494 and 1110 mg·kg-1, respectively. The observed bioconcentration factor indicated that A. imbricata acts as a hyperaccumulator or accumulator of Zn (>10) and as either accumulator (>1) or excluder (<1) of the other metals. Overall, the phytoremediation performance of A. imbricata in multi-metal-contaminated WMCF was high in WM under all environmental conditions. Therefore, the use of WM is an economically feasible approach for the removal of metals from WMCF.
Subject(s)
Metals, Heavy , Soil Pollutants , Metals, Heavy/analysis , Biodegradation, Environmental , Humidity , Temperature , Water/analysis , Soil Pollutants/analysisABSTRACT
Decision-making in the peer-to-peer loan market has not been studied as extensively as traditional lending mostly because of the perceived risk in dealing with low credit borrowers seeking funding alternatives. We develop a machine learning-based approach to test the viability and usefulness in peer-to-peer loan repayment predictions among low credit borrowers. This analysis provides potential benefits that could strengthen the lending market with a more reliable method of identifying applications from promising candidates with low credit. Here an experiment will be performed to measure the performance of a model used for classifying peer-to-peer loan data. The aim is to aid the repayment prediction capabilities of peer lenders when analyzing low credit applicants. A binary classification algorithm is used to build the model and applied to actual historical loan data to evaluate performance. Experiment results, visualizations, and key performance indicators are discussed in the work to influence confidence in using the method proposed.
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Dry eye syndrome (DES) affects the cornea, causes pain and hypersensitivity to light. Although inflammation and endoplasmic reticulum stress are known to be involved, the detailed mechanisms remain unknown. DES is characterized by a decrease in corneal thickness, tear volume, and lacrimal gland size, and damage to corneal cells. Tisochrysis lutea is a microalga that has been shown to reduce immune factors. Therefore, we hypothesized that T. lutea could ameliorate DES. We investigated the role of T. lutea in scopolamine-induced DES in BALB/c mice. Oral administration of T. lutea increased corneal thickness, tear volume, and size of the corneal cells, and reduced damage to the corneal cells. Furthermore, treatment of ARPE-19 human retinal pigmented epithelial cells with T. lutea reduced expression of the inflammatory factor, NF-κB, MAPK, and AKT. T. lutea may be used therapeutically to reduce the symptoms of DES.
Subject(s)
Dry Eye Syndromes , Lacrimal Apparatus , Mice , Animals , Humans , Dry Eye Syndromes/diagnosis , NF-kappa B/pharmacology , Tears/metabolism , Lacrimal Apparatus/metabolism , Cornea/metabolism , Mice, Inbred BALB CABSTRACT
Although plastics have benefited our lives in terms of cost and convenience, the disposal of end-of-life plastics poses environmental problems, such as microplastics (MPs). Although the separation (e.g., filtration) and staining of MPs with fluorescent dye/solvent are generally accepted steps to observe MPs in an environmental matrix, in this study, an in situ selective fluorescent illumination of the MPs in water was attempted with the aid of surfactant. Nonpolar fluorescent dye in combination with surfactant affords nanometer-sized dye particles in water, which adsorb on MPs and penetrate the polymer matrix for effective staining and stable fluorescent behaviors. The effects of different staining parameters, including different dyes, surfactants, staining temperatures, staining times, dye/surfactant ratios, dye/MP ratios, and MP concentrations in aqueous solutions were investigated to better understand staining conditions. More interestingly, non-adsorbed free dye molecules in the staining solution were almost completely fluorescence-quenched by introducing the quenching agent, aniline, while the fluorescence intensity of the stained MP was maintained. By staining MPs with a dye/surfactant combination and subsequently quenching with aniline, in situ selective fluorescent illumination of the MPs in water was successfully achieved, which may eliminate the tedious separation/filtration procedure of MPs to accomplish the quick detection or monitoring of MPs.
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BACKGROUND: Skin prick tests are widely used to diagnose allergic sensitization. The influence of obesity on the skin prick test result has not been clearly established, even though the association between allergic disease and obesity is relatively well known. OBJECTIVE: To determine whether a change in body mass index (BMI) contributes to skin reactivity to histamine and allergens in a skin prick test, we performed a 2-year follow-up study on Korean children. METHODS: Skin prick tests for common aeroallergens were performed on elementary school students from Jeju Island, Korea. BMI was calculated using weight and height after measuring both, and demographic characteristics were surveyed. The same tests were repeated after 2 years. RESULTS: The sensitization rate increased during the 2 years between tests and the children's mean BMI also increased, along with their age. The wheal sizes induced by Dermatophagoides pteronyssinus, Dermatophagoides farinae, Japanese cedar, and histamine were significantly increased during 2 years; however, only the histamine reaction associated with increased BMI had statistical significance. Furthermore, other variables-including the number of sensitized allergens-were not related to histamine skin reactivity. CONCLUSION: Histamine skin reactivity increased in children over time and some allergens showed increased specific reactions; however, BMI gain is a specific predictor of histamine reactivity. Further studies are needed to elucidate the clinical significance of these changes.
Subject(s)
Allergens , Histamine , Body Mass Index , Child , Follow-Up Studies , Humans , Republic of Korea/epidemiology , Skin TestsABSTRACT
BACKGROUND: A correlation between the harmful effects of air pollutants and atopic dermatitis has been reported. There are few studies on the correlation between the concentration of heavy metals in the indoor atmosphere and symptoms of atopic dermatitis. METHODS: Twenty-two homes of children showing atopic dermatitis symptoms were enrolled, and eighteen homes with similarly aged children without symptoms or a history of atopic dermatitis participated as a control group. We measured the concentrations of various air pollutants (particulate matter 10, carbon dioxide, carbon monoxide, formaldehyde, nitrogen dioxide, volatile organic compounds (VOCs), ozone, radon, bacterial aerosols, and mold) as well as various heavy metals, such as lead, cadmium, and mercury, in the living room and children's bedroom of each home. RESULTS: Lead was more commonly detected in the indoor air in houses of children with atopic dermatitis (15/22) as compared to in the control group (3/18) (chi square test, p = 0.002). In adjusted logistic regression analysis, VOCs and lead were significantly associated with atopic dermatitis (p < 0.05). CONCLUSION: Our study shows that lead in indoor air might be associated with atopic dermatitis, even if the concentrations of airborne lead are below the safety levels suggested by health guidelines.
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Whitening research is of particular interest in the cosmetics market. The main focus of whitening research is on melanogenesis inhibition through tyrosinase activity. The mechanism of melanogenesis is involved with tyrosinase activity and p-PKC signaling. In this study, we used Momordica cochinchinensis (Lour.) spreng, a tropical fruit found throughout Southeast Asia, to investigate the inhibitory effect of melanogenesis. M. cochinchinensis contains a high concentration of polyphenols, flavonoids, and unsaturated fatty acids, which might be related to antioxidant activity. This study aimed to determine whether M. cochinchinensis extracts inhibit melanin synthesis in melan-A cells by inhibiting tyrosinase activity and p-PKC signaling. M. cochinchinensis was divided into pulp and aril and extracted under various conditions, and it was confirmed that all pulp and aril extracts have high contents of both phenols and flavonoids. Melan-A cells were treated with PMA for three days to induce melanin synthesis. After PMA treatment, M. cochinchinensis extracts were added to cultured media in a dose-dependent manner. Melanin contents and MTS were used to determine the amount of melanin in live cells. M. cochinchinensis extracts were evaluated for their effects on tyrosinase activity and p-PKC signaling pathways by Western blotting. It was found that M. cochinchinensis extract treatment decreased the amount of melanin and suppressed p-PKC expression. Additionally, tyrosinase activity was reduced after M. cochinchinensis extract treatment in a dose-dependent manner. Therefore, it was concluded that M. cochinchinensis could be used in antimelanogenesis and functional cosmetic materials to improve whitening.
Subject(s)
Antioxidants/pharmacology , Melanins/biosynthesis , Momordica , Monophenol Monooxygenase/antagonists & inhibitors , Plant Extracts/pharmacology , Signal Transduction/drug effects , Flavonoids/pharmacology , Humans , Phenols/pharmacologyABSTRACT
This review describes the preparation of nonedible vegetable oil (NEVO)-based polyols and their application in anticorrosive and antimicrobial polyurethane (PU) coatings. PUs are a class of versatile polymers made up of polyols and isocyanates. Renewable vegetable oils are promising resources for the development of ecofriendly polyols and the corresponding PUs. Researchers are interested in NEVOs because they provide an alternative to critical global food issues. The cultivation of plant resources for NEVOs can also be popularized globally by utilizing marginal land or wastelands. Polyols can be prepared from NEVOs following different conversion routes, including esterification, etherification, amidation, ozonolysis, hydrogenation, hydroformylation, thio-ene, acrylation, and epoxidation. These polyols can be incorporated into the PU network for coating applications. Metal surface corrosion and microbial growth are severe problems that cause enormous economic losses annually. These problems can be overcome by NEVO-based PU coatings, incorporating functional ingredients such as corrosion inhibitors and antimicrobial agents. The preferred coatings have great potential in high performance, smart, and functional applications, including in biomedical fields, to cope with emerging threats such as COVID-19.
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Streptomyces spp. have been major contributors of novel natural products that are used in many application areas. We found that the nojirimycin (NJ) producer JCM 3382 has antimicrobial activity against Staphylococcus aureus via cellular degradation. Genome analysis revealed 30 biosynthetic gene clusters, including those responsible for producing antibiotics, including an azasugar NJ. In-depth MS/MS analysis confirmed the production of 1-deoxynojirimycin (DNJ) along with NJ. In addition, the production of tambromycins, setomimycin, and linearmycins was verified by spectroscopic analyses, including LC-MS and NMR. The distribution of the clusters of genes coding for antibiotics in 2061 Streptomyces genomes suggested potential producers of tambromycin, setomimycin, and linearmycin. For a DNJ gene cluster, homologs of gabT1 and gutB1 were commonly found; however, yktC1 was identified in only 112 genomes. The presence of several types of clusters suggests that different strains may produce different types of azasugars. Chemical-profile-inspired comparative genome analysis may facilitate a more accurate assessment of the biosynthetic potential to produce secondary metabolites.