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Listening to music is a crucial tool for relieving stress and promoting relaxation. However, the limited options available for stress-relief music do not cater to individual preferences, compromising its effectiveness. Traditional methods of curating stress-relief music rely heavily on measuring biological responses, which is time-consuming, expensive, and requires specialized measurement devices. In this paper, a deep learning approach to solve this problem is introduced that explicitly uses convolutional neural networks and provides a more efficient and economical method for generating large datasets of stress-relief music. These datasets are composed of Mel-scaled spectrograms that include essential sound elements (such as frequency, amplitude, and waveform) that can be directly extracted from the music. The trained model demonstrated a test accuracy of 98.7%, and a clinical study indicated that the model-selected music was as effective as researcher-verified music in terms of stress-relieving capacity. This paper underlines the transformative potential of deep learning in addressing the challenge of limited music options for stress relief. More importantly, the proposed method has profound implications for music therapy because it enables a more personalized approach to stress-relief music selection, offering the potential for enhanced emotional well-being.

The present study introduces a systematic approach using analytical quality by design (AQbD) methodology for the development of a qualified liquid chromatographic analytical method, which is a challenge in herbal medicinal products due to the intrinsic complex components of botanical sources. The ultra-high-performance liquid chromatography-photodiode array-mass spectrometry (UHPLC-PDA-MS) technique for 11 flavonoids in Genkwa Flos was utilized through the entire analytical processes, from the risk assessment study to the factor screening test, and finally in method optimization employing central composite design (CCD). In this approach, column temperature and mobile solvent slope were found to be critical method parameters (CMPs) and each of the eleven flavonoid peaks’ resolution values were used as critical method attributes (CMAs) through data mining conversion formulas. An optimum chromatographic method in the design space was calculated by mathematical and response surface methodology (RSM). The established chromatographic condition is as follows: acetonitrile and 0.1% formic acid gradient elution (0–13 min, 10–45%; 13–13.5 min, 45–100%; 13.5–14 min, 100–10%; 14–15 min, 10% acetonitrile), column temperature 28℃, detection wavelength 335 nm, and flow rate 0.35 mL/min using C 18 (50 × 2.1 mm, 1.7 μm) column. A validation study was also performed successfully for apigenin 7- O -glucuronide, apigenin, and genkwanin. A few important validation results were as follows: linearity over 0.999 coefficient of correlation, detection limit of 2.87–22.41, quantitation limit of 8.70–67.92, relative standard deviation of precision less than 0.22%, and accuracy between 100.13 and 102.49% for apigenin, genkwanin, and apigenin 7- O -glucuronide. In conclusion, the present design-based approach provide a systematic platform that can be effectively applied to ensure pharmaceutically qualified analytical data from complex natural products based botanical drug.

The purpose of this research was to identify metabolite change during barley ( Hordeum vulgare ) germination and reveal active principles for the anti-wrinkle activity. Barley was germinated with deionized water (DW) and mineral-rich water (MRW) for the comparison of the effect of mineral contents on the metabolites changes during germination. The effects of germinated barley extracts (GBEs) on collagen production and collagenase inhibition were evaluated in vitro using human dermal fibroblasts (HDFs). A pronounced anti-wrinkle activity was observed in the test group treated with the MRW-GBEs. In order to find out the active components related to the anti-wrinkle activity, an orthogonal projection to latent structure-discriminant analysis (OPLS-DA) was performed, using the data from secondary metabolites profiling conducted by UPLC–PDA–ESI–MS. The anti-wrinkle activity of MRW-GBEs was revealed to be associated with the increase of oligomeric compounds of procyanidin and prodelphinidin, indicating that it can be used as an active ingredient for anti-wrinkle agents.

As a result of advances in sequencing technology, the role of gut microbiota in the mechanism of type-2 diabetes mellitus (T2DM) has been revealed. Studies showing wide distribution of microbiome throughout the human body, even in the blood, have motivated the investigation of the dynamics in gut microbiota across the humans. Particularly, extracellular vesicles (EVs), lipid bilayer structures secreted from the gut microbiota, have recently come into the spotlight because gut microbe-derived EVs affect glucose metabolism by inducing insulin resistance. Recently, intestine hyper-permeability linked to T2DM has also been associated with the interaction between gut microbes and leaky gut epithelium, which increases the uptake of macromolecules like lipopolysaccharide from the membranes of microbes leading to chronic inflammation. In this article, we firstly investigate the co-occurrence of stool microbes and microbe-derived EVs across serum and urine in human subjects (N = 284), showing the dynamics and stability of gut derived EVs. Stool EVs are intermediate, while the bacterial composition in both urine and serum EVs is distinct from the stool microbiome. The co-occurrence of microbes was compared between patients with T2DM (N = 29) and matched in healthy subjects (N = 145). Our results showed significantly higher correlations in patients with T2DM compared to healthy subjects across stool, serum, and urine, which could be interpreted as the dysfunction of intestinal permeability in T2DM. Therefore, the significant correlation of EVs might give insight into the pathophysiological mechanisms of T2DM, as well as the role of EVs as a biomarker in the intestinal permeability of T2DM.

Abstract A group of bacterial strains formerly known as CDC group M-5 are opportunistic pathogens to humans. In 1993, a name, Neisseria weaveri, was proposed by two independent studies to harbor CDC group M-5 strains, namely N. weaveri Holmes et al. 1993 and N. weaveri Andersen et al. 1993, with two different ‘type’ strains. However, no study has been conducted on to the relatedness of the two ‘type’ strains, although the close relationship of the two taxa has long been accepted unofficially. Formally, the status of the name N. weaveri Andersen et al. 1993 is illegitimate because it is a later homonym of N. weaveri Holmes et al., 1993; but the name of the strain is still validly published. In this study, we attempt to resolve the confusion caused by the apparent duplication of the species N. weaveri (with different type strains) using whole genome shotgun sequencing. We also sought to gain insight into the genetic characteristics of N. weaveri by conducting comparative genomics. On the basis of genomic similarities revealed through a comparative genomic study, we propose that N. weaveri Andersen et al. 1993 should be re-classified as a later heterotypic synonym of N. weaveri Holmes et al., 1993.

Viruses have evolved sophisticated mechanisms to manipulate host cell processes and utilize intracellular organelles to facilitate their replication. These complex interactions between viruses and cellular organelles allow them to hijack the cellular machinery and impair homeostasis. Moreover, viral infection alters the cell membrane’s structure and composition and induces vesicle formation to facilitate intracellular trafficking of viral components. However, the research focus has predominantly been on the immune response elicited by viruses, often overlooking the significant alterations that viruses induce in cellular organelles. Gaining a deeper understanding of these virus-induced cellular changes is crucial for elucidating the full life cycle of viruses and developing potent antiviral therapies. Exploring virus-induced cellular changes could substantially improve our understanding of viral infection mechanisms.

Mass spectrometry-based molecular imaging has been utilized to map the spatial distribution of target metabolites in various matrixes. Among the diverse mass spectrometry techniques, matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) is the most popular for molecular imaging due to its powerful spatial resolution. This unparalleled high resolution, however, can paradoxically act as a bottleneck when the bio-imaging of large areas, such as a whole plant, is required. To address this issue and provide a more versatile tool for large scale bio-imaging, direct analysis in real-time-time of flight-mass spectrometry (DART-TOF-MS), an ambient ionization MS, was applied to whole plant bio-imaging of a medicinal plant, Ephedrae Herba. The whole aerial part of the plant was cut into 10–20 cm long pieces, and each part was further cut longitudinally to compare the contents of major ephedra alkaloids between the outer surface and inner part of the stem. Using optimized DART-TOF-MS conditions, molecular imaging of major ephedra alkaloids of the whole aerial part of a single plant was successfully achieved. The concentration of alkaloids analyzed in this study was found to be higher on the inner section than the outer surface of stems. Moreover, side branches, which are used in traditional medicine, represented a far higher concentration of alkaloids than the main stem. In terms of the spatial metabolic distribution, the contents of alkaloids gradually decreased towards the end of branch tips. In this study, a fast and simple macro-scale MS imaging of the whole plant was successfully developed using DART-TOF-MS. This application on the localization of secondary metabolites in whole plants can provide an area of new research using ambient ionization mass spectroscopy and an unprecedented macro-scale view of the biosynthesis and distribution of active components in medicinal plants.

Shadow work continues to witness a significant uptick in the context of mobile shopping. Therefore, we question whether shadow work perceived by mobile shoppers may become a bigger problem, create fatigue for mobile shoppers, and lead them to discontinue the use of mobile shopping apps. This study examines the relationship between shadow work and the discontinuance of mobile shopping apps. Data from a total of 266 completed surveys were collected by a market research firm. We adopted partial least squares structural equation modeling (PLS-SEM) to assess both the measurement and structural components of the model. The results show that both information overload and system feature overload positively influence individuals’ shadow work. This study explores the concept of shadow work in the context of mobile shopping apps. Specifically, the study developed the relationships between the antecedents and consequences of shadow work in the mobile shopping context. The main contribution of our study is that it introduces an integrative model of shadow work in the mobile shopping context, highlighting the importance of shadow work.

In wire arc additive manufacturing (AM), as in arc welding, arc heat thermally deforms substrates and articles. For industrial applications, deformation characteristics of various materials must be understood and appropriate materials and methods of reducing deformation must be devised. Therefore, angular distortions of different materials were investigated through bead-on-plate welding and finite element analysis. A model that simplifies temperature-dependent properties was developed to establish relationships between thermomechanical properties and angular distortion. A simplified model of temperature-dependent properties was used, and angular distortion characteristics were extensively investigated for different material properties and heat inputs. Coefficient of thermal expansion, density, and specific heat all notably affected angular distortion depending on heat input conditions. Results showed that during wire arc AM, flatness of both substrates and articles could vary depending on material properties, heat input, substrate thickness, and bead accumulation. Study findings can provide insight into deformation characteristics of new materials and how to mitigate thermal distortions.

As a result of advances in sequencing technology, the role of gut microbiota in the mechanism of type-2 diabetes mellitus (T2DM) has been revealed. Studies showing wide distribution of microbiome throughout the human body, even in the blood, have motivated the investigation of the dynamics in gut microbiota across the humans. Particularly, extracellular vesicles (EVs), lipid bilayer structures secreted from the gut microbiota, have recently come into the spotlight because gut microbe-derived EVs affect glucose metabolism by inducing insulin resistance. Recently, intestine hyper-permeability linked to T2DM has also been associated with the interaction between gut microbes and leaky gut epithelium, which increases the uptake of macromolecules like lipopolysaccharide from the membranes of microbes leading to chronic inflammation. In this article, we firstly investigate the co-occurrence of stool microbes and microbe-derived EVs across serum and urine in human subjects (N = 284), showing the dynamics and stability of gut derived EVs. Stool EVs are intermediate, while the bacterial composition in both urine and serum EVs is distinct from the stool microbiome. The co-occurrence of microbes was compared between patients with T2DM (N = 29) and matched in healthy subjects (N = 145). Our results showed significantly higher correlations in patients with T2DM compared to healthy subjects across stool, serum, and urine, which could be interpreted as the dysfunction of intestinal permeability in T2DM. Therefore, the significant correlation of EVs might give insight into the pathophysiological mechanisms of T2DM, as well as the role of EVs as a biomarker in the intestinal permeability of T2DM.