Search

Hyperpigmentation disorders causing emotional distress require the topical use of depigmenting agents of natural origin. In this study, the anti-melanogenic effects of the Lilium lancifolium root extract (LRE) were investigated in B16F10 cells. Consequently, a non-cytotoxic concentration of the extract reduced intracellular melanin content and tyrosinase activity in a dose-dependent manner, correlating with the diminished expression of core melanogenic enzymes within cells. LRE treatment also inhibited cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB)/microphthalmia-associated transcription factor signaling, which regulates the expression of tyrosinase-related genes. Upon examining these findings from a molecular mechanism perspective, LRE treatment suppressed the phosphorylation of protein kinase A (PKA), p38, and extracellular signal-related kinase (ERK), which are upstream regulators of CREB. In addition, L-phenylalanine and regaloside A, specifically identified within the LRE using liquid chromatography-mass spectrometry, exhibited inhibitory effects on melanin production. Collectively, these results imply that LRE potentially suppresses cAMP-mediated melanogenesis by downregulating PKA/CREB and mitogen-activated protein kinase (MAPK)/CREB signaling pathways. Therefore, it can be employed as a novel therapeutic ingredient of natural origin to ameliorate hyperpigmentation disorders.

Background Melatonin, a neurohormone, maybe involved in physiological processes, such as antioxidation, anti-inflammation, and hair growth. In the present study, we investigated the effects of melatonin on proliferation and intracellular signaling in DP cells using a three-dimensional (3D) spheroid culture system that mimics the in vivo hair follicle system. Methods DP cells were incubated in monolayer (2D) and 3D spheroid culture systems. The expression levels of melatonin receptors in DP cells were analyzed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting. The effect of melatonin on the hair-inductive property of DP cells was analyzed using a WST-1-based proliferation assay, determination of DP spheroid size, expression analysis of DP signature genes, and determination of β-catenin stabilization in DP cells. The AKT/GSK3β/β-catenin signaling pathway associated with melatonin-induced β-catenin stabilization in DP cells was investigated by analyzing changes in upstream regulator proteins, including AKT, GSK3β, and their phosphorylated forms. Results The expression levels of the melatonin receptors were higher in human DP cells than in human epidermal keratinocytes and human dermal fibroblast cells. Comparing the expression level according to the human DP cell culture condition, melatonin receptor expression was upregulated in the 3D culture system compared to the traditional two-dimensional monolayer culture system. Cell viability analysis showed that melatonin concentrations up to 1 mM did not affect cell viability. Moreover, melatonin increased the diameter of DP cell 3D spheroids in a dose-dependent manner. Immunoblotting and qRT-PCR analysis revealed that melatonin upregulated the expression of hair growth-related genes, including alkaline phosphatase, bone morphogenetic protein 2, versican, and wingless-int 5A, in a melatonin receptor-dependent manner. Cell fractionation analysis showed that melatonin increased the nuclear localization of β-catenin. This result correlated with the increased transcriptional activation of T-cell factor/lymphoid enhancer factor-responsive luciferase induced by melatonin treatment. Interestingly, melatonin induced the phosphorylation of protein kinase B/AKT at serine 473 residue and GSK-3β at serine 9 residue. To determine whether AKT phosphorylation at serine 473 induced β-catenin nuclear translocation through GSK3β phosphorylation at serine 9, the PI3K/AKT inhibitor LY294002 was cotreated with melatonin. Immunoblotting showed that LY294002 inhibited melatonin-induced phosphorylation of GSK3β at serine 9 residue and β-catenin activation. Conclusion Collectively, this report suggests that melatonin promotes growth properties by activating the AKT/GSK3β/β-catenin signaling pathway through melatonin receptors.

Glucose metabolism is a key metabolic pathway that orchestrates cellular homeostasis by generating ATP, nucleotides, and amino acids. Abnormal glucose signaling has been found in many diseases including cancers and inflammatory diseases. According to recent report, glycolysis contributes to pathogenesis of psoriasis and ablation of Glut1 attenuates animal models of psoriasis. While we were screening a molecular target for atopic dermatitis, we found the levels of glucose transporters including Glut1 (SLC2a1) and Glut3 (SLC2a3) are highly expressed in skin biopsies of dermatitis patients from multiple datasets. We demonstrated that administration of 2-deoxy-d-glucose (2DG) ameliorates animal models of 12-o-tetradecanoylphorbol-13-acetate (TPA) and oxazolone induced dermatitis using morphological and histological analysis. These results suggest that inhibition of glucose metabolism ameliorates dermatitis in animal models.

BACKGROUND: Accumulating evidence indicates that reactive oxygen species (ROS) are an important etiological factor for the induction of dermal papilla cell senescence and hair loss, which is also known alopecia. Arctiin is an active lignin isolated from Arctium lappa and has anti-inflammation, anti-microbial, and anti-carcinogenic effects. In the present study, we found that arctiin exerts anti-oxidative effects on human hair dermal papilla cells (HHDPCs). RESULTS: To better understand the mechanism, we analyzed the level of hydrogen peroxide (H2O2)-induced cytotoxicity, cell death, ROS production and senescence after arctiin pretreatment of HHDPCs. The results showed that arctiin pretreatment significantly inhibited the H2O2-induced reduction in cell viability. Moreover, H2O2-induced sub-G1 phase accumulation and G2 cell cycle arrest were also downregulated by arctiin pretreatment. Interestingly, the increase in intracellular ROS mediated by H2O2 was drastically decreased in HHDPCs cultured in the presence of arctiin. This effect was confirmed by senescence associated-beta galactosidase (SA-β-gal) assay results; we found that arctiin pretreatment impaired H2O2-induced senescence in HHDPCs. Using microRNA (miRNA) microarray and bioinformatic analysis, we showed that this anti-oxidative effect of arctiin in HHDPCs was related with mitogen-activated protein kinase (MAPK) and Wnt signaling pathways. CONCLUSIONS: Taken together, our data suggest that arctiin has a protective effect on ROS-induced cell dysfunction in HHDPCs and may therefore be useful for alopecia prevention and treatment strategies.

In this study, the protective functions of bacteria-free conditioned media from Bifidobacterium and Lactobacillus species against ultraviolet radiation-induced skin ageing and associated cellular damage were investigated. The effects of ultraviolet radiation-induced reactive oxygen species production were suppressed by all conditioned media; particularly, the loss of cell viability and downregulation of collagen gene expression were significantly reversed by the conditioned media from B. longum and B. lactis. Further exa mination of potential anti-pigmentation effects revealed that the B. lactis-derived conditioned media significantly inhibited tyrosinase activity and alpha-melanocyte-stimulating hormone-induced melanin production in human epidermal melanocytes. Further, the conditioned media suppressed the phosphorylation of extracellular signal- related kinase, which functions as an upstream regulator of melanogenesis. Therefore, B. lactis-derived conditioned media can potentially protect against cellular damage involved in skin-ageing processes.

Abstract The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, $$f\left( {RH} \right)$$ f RH , and the hygroscopic mass growth, $$GM_{VIS}$$ G M VIS , which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled $$f\left( {RH} \right)$$ f RH agreed well with the observed $$f\left( {RH} \right)$$ f RH in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.

Digital public displays installed in various locations provide valuable information for the passers-by. However, the static characteristic of the digital public display limits the consumption of the displayed content to a small area. Personal mobile devices such as smartphones are now capable of interacting with digital public displays, which enables the passers-by to "take-away" the content and consume it on-the-go. This process requires device binding, content selection, and transfer between the two devices. In this paper, we propose a device binding method which utilizes the content brightness changing pattern as a unique content ID on the public display and an illuminance sensor on the mobile to bind and transfer between two devices. We conducted performance evaluations for binding algorithm robustness in different conditions. Also comparative studies among other binding interaction methods were conducted. Our results show that our proposed method performed stably across the various conditions and overall performance in interaction completion time and error rate was similar or superior to the existing methods.

Lately, pose estimation based on learning-based Visual Odometry (VO) methods, where raw image data are provided as the input of a neural network to get 6 Degrees of Freedom (DoF) information, has been intensively investigated. Despite its recent advances, learning-based VO methods still perform worse than the classical VO that consists of feature-based VO methods and direct VO methods. In this paper, a new pose estimation method with the help of a Gated Recurrent Unit (GRU) network trained by pose data acquired by an accurate sensor is proposed. The historical trajectory data of the yaw angle are provided to the GRU network to get a yaw angle at the current timestep. The proposed method can be easily combined with other VO methods to enhance the overall performance via an ensemble of predicted results. Pose estimation using the proposed method is especially advantageous in the cornering section which often introduces an estimation error. The performance is improved by reconstructing the rotation matrix using a yaw angle that is the fusion of the yaw angles estimated from the proposed GRU network and other VO methods. The KITTI dataset is utilized to train the network. On average, regarding the KITTI sequences, performance is improved as much as 1.426% in terms of translation error and 0.805 deg/100 m in terms of rotation error.

Ultraviolet (UV) radiation is a major inducer of skin aging and accumulated exposure to UV radiation increases DNA damage in skin cells, including dermal fibroblasts. In the present study, we developed a novel DNA repair regulating material discovery (DREAM) system for the high-throughput screening and identification of putative materials regulating DNA repair in skin cells. First, we established a modified lentivirus expressing the luciferase and hypoxanthine phosphoribosyl transferase (HPRT) genes. Then, human dermal fibroblast WS-1 cells were infected with the modified lentivirus and selected with puromycin to establish cells that stably expressed luciferase and HPRT (DREAM-F cells). The first step in the DREAM protocol was a 96-well-based screening procedure, involving the analysis of cell viability and luciferase activity after pretreatment of DREAM-F cells with reagents of interest and post-treatment with UVB radiation, and vice versa. In the second step, we validated certain effective reagents identified in the first step by analyzing the cell cycle, evaluating cell death, and performing HPRT-DNA sequencing in DREAM-F cells treated with these reagents and UVB. This DREAM system is scalable and forms a time-saving high-throughput screening system for identifying novel anti-photoaging reagents regulating DNA damage in dermal fibroblasts.

Autonomous driving is a complex problem that requires intelligent decision making, and it has recently garnered significant interest due to its potential advantages in convenience and safety. In autonomous driving, conventional path planning to reach a destination is a time-consuming challenge. Therefore, learning-based approaches have been successfully applied to the controller or decision-making aspects of autonomous driving. However, these methods often lack explainability, as passengers cannot discern where the vehicle is headed. Additionally, most experiments primarily focus on highway scenarios, which do not effectively represent road curvature. To address these issues, we propose a reinforcement-learning-based trajectory learning in the Frenet frame (RLTF), which involves learning trajectories in the Frenet frame. Learning trajectories enable the consideration of future states and enhance explainability. We demonstrate that RLTF achieves a 100% success rate in the simulation environment, considering future states on curvy roads with continuous obstacles while overcoming issues associated with the Frenet frame.

The use of augmented reality (AR) continues to increase, particularly in marketing and advertising, where virtual objects are showcased in the AR world, thereby expanding its various applications. In this paper, a method of linking coordinate systems to connect the metaverse with the real world is proposed and a system for correcting and displaying virtual objects in the AR environment is implemented. The proposed method calculates errors to accurately represent virtual objects in AR and presents a method to show these objects without errors. The proposed method was verified through experiments to successfully display virtual objects in AR. To minimize localization errors, semantic segmentation was used to recognize objects and estimate buildings, thereby correcting the device location. An error correction expression is also presented. The proposed system is designed to correct and display virtual objects in AR, with confirmed functionality for location correction.

An automotive 2.1 μm CMOS image sensor has been developed with a full-depth deep trench isolation and an advanced readout circuit technology. To achieve a high dynamic range, we employ a sub-pixel structure featuring a high conversion gain of a large photodiode and a lateral overflow of a small photodiode connected to an in-pixel storage capacitor. With the sensitivity ratio of 10, the expanded dynamic range could reach 120 dB at 85 °C by realizing a low random noise of 0.83 e- and a high overflow capacity of 210 ke-. An over 25 dB signal-to-noise ratio is achieved during HDR image synthesis by increasing the full-well capacity of the small photodiode up to 10,000 e- and suppressing the floating diffusion leakage current at 105 °C.

Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.

Defect inspection is important to ensure consistent quality and efficiency in industrial manufacturing. Recently, machine vision systems integrating artificial intelligence (AI)-based inspection algorithms have exhibited promising performance in various applications, but practically, they often suffer from data imbalance. This paper proposes a defect inspection method using a one-class classification (OCC) model to deal with imbalanced datasets. A two-stream network architecture consisting of global and local feature extractor networks is presented, which can alleviate the representation collapse problem of OCC. By combining an object-oriented invariant feature vector with a training-data-oriented local feature vector, the proposed two-stream network model prevents the decision boundary from collapsing to the training dataset and obtains an appropriate decision boundary. The performance of the proposed model is demonstrated in the practical application of automotive-airbag bracket-welding defect inspection. The effects of the classification layer and two-stream network architecture on the overall inspection accuracy were clarified by using image samples collected in a controlled laboratory environment and from a production site. The results are compared with those of a previous classification model, demonstrating that the proposed model can improve the accuracy, precision, and F1 score by up to 8.19%, 10.74%, and 4.02%, respectively.

Abstract We have rationally designed and developed a fully automated desktop furnace system that enables programmable chemical vapour deposition growth of carbon nanotubes with controlled height, density, and pattern architecture. Comprising several essential components involving a heating furnace, mass flowmeters, and computer controller, the developed system realizes controlled and practical carbon nanotube growth without resort to expensive and ponderous instruments. By programming, modifying, and loading the reusable recipes in the developed system, systematic and reproducible growths of carbon nanotubes with desired morphology and dimension can be performed. Growth results with controlled height, density, and pattern are demonstrated through the actual operations, confirming the validity and usefulness of the developed system towards various practical applications.

Long Term Evolution (LTE) networks that are composed of macrocells and femtocells can provide an efficient solution to not only extend coverage of macrocells but also deal with the growth of traffic within macrocells. LTE is now being considered to be a vital connectivity solution for the success of the Internet of Things (IoT) because it can provide broadband connectivity to the growing number of sensing and monitoring devices and even the wireless sensor networks (WSNs). However, it is still challenging to properly allocate radio frequency resources in the handover procedure between a macrocell and a femtocell. In this paper, we propose a new handover algorithm that increases the efficient utilization of a radio frequency resource and thereby maximizes the capacity of the overall LTE network, including the femtocells within network. The handover decision criteria take into account the strength of the received signal, the radio resource reuse, and the overall capacity of the network throughput. The performance of the proposed algorithm is verified through a simulation, and the simulation results indicate that the proposed handover algorithm improves the reusability of the cell bandwidth and increases the overall capacity of the network.

In an antenna for a UHF RFID reader of wireless sensor networks (WSN), receiver sensitivity in sensing multitags from remote distances is an important performance index. This study designed a dual structured Z -slot antenna with optimized receiver sensitivity to enhance the sensitivity to a circularly polarized antenna with an isotropic pattern for a UHF RFID. Through analysis of performance in the designed antenna, the following was verified: return loss ( S 11 ) was about −62.21 dB at 925.25 MHz, antenna gain was 7.36 dBi, and Δ P r , isotropic gain deviation, was 1.3 dB. Impedance matching was about 50.069 Ω at 925.25 MHz and VSWR was from 1.001 to 1.028. Through this research it was discovered that this can be applied to the design of all RFID readers of WSN. Based on the above results, it is suggested that a circularly polarized Z -slot antenna which can enhance receiver sensitivity over a wide range can be widely applied to UHF RFID readers of WSN.

This paper presents a selective cooperative transmission scheme (abbreviated SCT) for ad hoc network with directional antennas that leverages the benefits of directional-only antenna approach and cooperative communication. The main feature of SCT is its adaptability to the channel condition in the network. In other words, when the node sends data, SCT determines its transmission strategy on either direct or cooperative transmission via a relay node called a forwarder, depending on the transmission time. Simulation results are provided to validate the effectiveness of the proposed scheme.

This paper presents a numerical analysis of latency and jitter for IEEE 802.11e wireless local area networks (WLANs) in a saturation condition, by using a Markov model. We use this model to explicate how the enhanced distributed coordination function (EDCF) differentiates classes of service and to characterize the probability distribution of the medium access control (MAC) layer packet latency and jitter, on which the quality of the voice over Internet protocol (VoIP) calls is dependent. From the proposed analytic model, we can estimate the available number of nodes determining the system performance, in order to satisfy user demands on the latency and jitter.

This paper presents an analytical model for the performance evaluation of an IEEE 802.11e network in non-saturation environments. We first characterize the probability distribution of the MAC layer packet service time. Based on the probability distribution model of the MAC layer packet service time, we then study the queuing performance of the wireless local area networks (WLANs) at different traffic loads based on the IEEE 802.11e MAC protocol. The numerical results show that we can choose a feasible number and bandwidth of the node which determines the system performance that a user demands.