Glycoconjugate-Specific Developmental Changes in the Horse Vomeronasal Organ.

The vomeronasal organ (VNO) is a tubular pheromone-sensing organ in which the lumen is covered with sensory and non-sensory epithelia. This study used immunohistochemistry and lectin histochemistry techniques to evaluate developmental changes, specifically of the glycoconjugate profile, in the horse VNO epithelium. Immunostaining analysis revealed PGP9.5 expression in some vomeronasal non-sensory epithelium (VNSE) cells and in the vomeronasal receptor cells of the vomeronasal sensory epithelium (VSE) in fetuses, young foals, and adult horses. Olfactory marker protein expression was exclusively localized in receptor cells of the VSE in fetuses, young foals, and adult horses and absent in VNSE. To identify the glycoconjugate type, lectin histochemistry was performed using 21 lectins. Semi-quantitative analysis revealed that the intensities of glycoconjugates labeled with WGA, DSL, LEL, and RCA120 were significantly higher in adult horse VSE than those in foal VSE, whereas the intensities of glycoconjugates labeled with LCA and PSA were significantly lower in adult horse VSE. The intensities of glycoconjugates labeled with s-WGA, WGA, BSL-II, DSL, LEL, STL, ConA, LCA, PSA, DBA, SBA, SJA, RCA120, jacalin, and ECL were significantly higher in adult horse VNSE than those in foal VNSE, whereas the intensity of glycoconjugates labeled with UEA-I was lower in adult horse VNSE. Histochemical analysis of each lectin revealed that various glycoconjugates in the VSE were present in the receptor, supporting, and basal cells of foals and adult horses. A similar pattern of lectin histochemistry was also observed in the VNSE of foals and adult horses. In conclusion, these results suggest that there is an increase in the level of N-acetylglucosamine (labeled by WGA, DSL, LEL) and galactose (labeled by RCA120) in horse VSE during postnatal development, implying that they may influence the function of VNO in adult horses.

Functional Analysis of Membrane-Associated Scaffolding Tight Junction (TJ) Proteins in Tumorigenic Characteristics of B16-F10 Mouse Melanoma Cells.

Tight junction (TJ) proteins (Tjps), Tjp1 and Tjp2, are tight junction-associated scaffold proteins that bind to the transmembrane proteins of tight junctions and the underlying cytoskeleton. In this study, we first analyzed the tumorigenic characteristics of B16-F10 melanoma cells, including cell proliferation, migration, invasion, metastatic potential, and the expression patterns of related proteins, after the CRISPR-Cas9-mediated knockout (KO) of Tjp genes. The proliferation of Tjp1 and Tjp2 KO cells significantly increased in vitro. Other tumorigenic characteristics, including migration and invasion, were significantly enhanced in Tjp1 and Tjp2 KO cells. Zonula occludens (ZO)-associated protein Claudin-1 (CLDN-1), which is a major component of tight junctions and functions in controlling cell-to-cell adhesion, was decreased in Tjp KO cells. Additionally, Tjp KO significantly stimulated tumor growth and metastasis in an in vivo mouse model. We performed a transcriptome analysis using next-generation sequencing (NGS) to elucidate the key genes involved in the mechanisms of action of Tjp1 and Tjp2. Among the various genes affected by Tjp KO-, cell cycle-, cell migration-, angiogenesis-, and cell-cell adhesion-related genes were significantly altered. In particular, we found that the Ninjurin-1 (Ninj1) and Catenin alpha-1 (Ctnna1) genes, which are known to play fundamental roles in Tjps, were significantly downregulated in Tjp KO cells. In summary, tumorigenic characteristics, including cell proliferation, migration, invasion, tumor growth, and metastatic potential, were significantly increased in Tjp1 and Tjp2 KO cells, and the knockout of Tjp genes significantly affected the expression of related proteins.

Inhibition of Urban Particulate Matter-Induced Airway Inflammation by RIPK3 through the Regulation of Tight Junction Protein Production.

Urban particulate matter (UPM) is a high-hazard cause of various diseases in humans, including in the respiratory tract, skin, heart, and even brain. Unfortunately, there is no established treatment for the damage caused by UPM in the respiratory epithelium. In addition, although RIPK3 is known to induce necroptosis, its intracellular role as a negative regulator in human lungs and bronchial epithelia remains unclear. Here, the endogenous expression of RIPK3 was significantly decreased 6 h after exposure to UPM. In RIPK3-ovexpressed cells, RIPK3 was not moved to the cytoplasm from the nucleus. Interestingly, the overexpression of RIPK3 dramatically decreased TEER and F-actin formation. Its overexpression also decreased the expression of genes for pro-inflammatory cytokines (IL-6 and IL-8) and tight junctions (ZO-1, -2, -3, E-cadherin, and claudin) during UPM-induced airway inflammation. Importantly, overexpression of RIPK3 inhibited the UPM-induced ROS production by inhibiting the activation of iNOS and eNOS and by regulating mitochondrial fission processing. In addition, UPM-induced activation of the iκB and NF-κB signaling pathways was dramatically decreased by RIPK3, and the expression of pro-inflammatory cytokines was decreased by inhibiting the iκB signaling pathway. Our data indicated that RIPK3 is essential for the UPM-induced inflammatory microenvironment to maintain homeostasis. Therefore, we suggest that RIPK3 is a potential therapeutic candidate for UPM-induced pulmonary inflammation.

Ergothioneine alleviates senescence of fibroblasts induced by UVB damage of keratinocytes via activation of the Nrf2/HO-1 pathway and HSP70 in keratinocytes.

Ultraviolet B (UVB) irradiation induces skin damage and photoaging through several deleterious effects, including generation of reactive oxygen species (ROS), apoptosis of epidermal cells, inflammation, and collagen degradation in fibroblasts. Ergothioneine (EGT) is a naturally occurring amino acid with potential biological properties. We evaluated whether EGT protects against UVB-induced photoaging using a keratinocyte/fibroblast co-culture system. Keratinocytes were pretreated with EGT, irradiated with UVB, and co-cultured with fibroblasts. In keratinocytes, ROS production and apoptosis were assessed. We also analyzed the Nrf2/HO-1 pathway, HSP70, proapoptotic proteins, and paracrine cytokines by Western blotting and real-time PCR. Collagen degradation-related genes and senescence were also assessed in fibroblasts. EGT pretreatment of keratinocytes significantly inhibited downregulation of the Nrf2/HO-1 pathway and HSP70, and protected keratinocytes by suppressing production of ROS and cleavage of proapoptotic proteins, including caspase-8 and PARP. Furthermore, EGT significantly reduced the paracrine cytokines, including IL-1ß, IL-6, and TNF-α. In co-cultures of fibroblasts with EGT-treated keratinocytes, the expression levels of collagen degradation-related genes and fibroblast senescence were significantly decreased; however, synthesis of procollagen type I was significantly increased. Our results confirm that EGT suppresses the modification of collagen homeostasis in fibroblasts by preventing downregulation of the Nrf2/HO-1 pathway and HSP70 in keratinocytes following UVB irradiation.

Corroded Bolt Identification Using Mask Region-Based Deep Learning Trained on Synthesized Data.

The performance of a neural network depends on the availability of datasets, and most deep learning techniques lack accuracy and generalization when they are trained using limited datasets. Using synthesized training data is one of the effective ways to overcome the above limitation. Besides, the previous corroded bolt detection method has focused on classifying only two classes, clean and fully rusted bolts, and its performance for detecting partially rusted bolts is still questionable. This study presents a deep learning method to identify corroded bolts in steel structures using a mask region-based convolutional neural network (Mask-RCNN) trained on synthesized data. The Resnet50 integrated with a feature pyramid network is used as the backbone for feature extraction in the Mask-RCNN-based corroded bolt detector. A four-step data synthesis procedure is proposed to autonomously generate the training datasets of corroded bolts with different severities. Afterwards, the proposed detector is trained by the synthesized datasets, and its robustness is demonstrated by detecting corroded bolts in a lab-scale steel structure under varying capturing distances and perspectives. The results show that the proposed method has detected corroded bolts well and identified their corrosion levels with the most desired overall accuracy rate = 96.3% for a 1.0 m capturing distance and 97.5% for a 15° perspective angle.

Capsule-Like Smart Aggregate with Pre-Determined Frequency Range for Impedance-Based Stress Monitoring.

In this article, a new capsule-like smart aggregate (CSA) is developed and verified for impedance-based stress monitoring in a pre-determined frequency range of less than 100 kHz. The pros and cons of the existing smart aggregate models are discussed to define the requirement for the improved CSA model. The conceptual design and the impedance measurement model of the capsule-like smart aggregate (CSA) are demonstrated for concrete damage monitoring. In the model, the interaction between the CSA and the monitored structure is considered as the 2-degrees of freedom (2-DOF) impedance system. The mechanical and impedance responses of the CSA are described for two conditions: during concrete strength development and under compressive loadings. Next, the prototype of the CSA is designed for impedance-based monitoring in concrete structures. The local dynamic properties of the CSA are numerically simulated to pre-determine the sensitive frequency bands of the impedance signals. Numerical and experimental impedance analyses are performed to investigate the sensitivity of the CSA under compressive loadings. The changes in the impedance signals of the CSA induced by the compressive loadings are analyzed to assess the effect of loading directions on the performance of the CSA. Correlations between statistical impedance features and compressive stresses are also made to examine the feasibility of the CSA for stress quantification.

Raspberry Pi Platform Wireless Sensor Node for Low-Frequency Impedance Responses of PZT Interface.

A wireless impedance monitoring system, called SSeL-Pi, is designed to have cheap, mobile, and handy practical features as compared to wired commercial impedance analyzers. A Raspberry Pi platform impedance sensor node is designed to measure signals at a low-frequency range of up to 100 kHz. The low-frequency impedance measurement via the proposed node has been combined with a new PZT interface technique for measuring local responses sensitive to structural damage. The new PZT interface can work as a surface-mounted or embedded sensor, and its local dynamic characteristics are numerically analyzed to pre-determine an effective impedance resonant frequency range of less than 100 kHz. Next, a software scheme was designed to visualize the input/output parameters of the proposed SSeL-Pi system (i.e., Raspberry Pi platform and PZT interface) and automate signal acquisition procedures of the impedance sensor node. The calibration for impedance signals obtained from the proposed system was performed by a series of procedures, from acquiring real and imaginary impedance to adjusting them with respect to a commercial impedance analyzer (HIOKI-3532). The feasibility of the wireless impedance monitoring system was experimentally evaluated for PZT interfaces that were subjected to various compressive loadings. The consistent results analyzed from signals measured by the SSeL-Pi and HIOKI 3532 systems were observed. Additionally, the strong relationships between impedance features (frequency shift and RMSD index) and compressive stresses of the PZT interfaces showed the potential for axial force/stress variation monitoring in real structures using the Raspberry Pi platform impedance sensor node and developed PZT interface.

Developmental Formation of the GABAergic and Glycinergic Networks in the Mouse Spinal Cord.

Gamma-aminobutyric acid (GABA) and glycine act as inhibitory neurotransmitters. Three types of inhibitory neurons and terminals, GABAergic, GABA/glycine coreleasing, and glycinergic, are orchestrated in the spinal cord neural circuits and play critical roles in regulating pain, locomotive movement, and respiratory rhythms. In this study, we first describe GABAergic and glycinergic transmission and inhibitory networks, consisting of three types of terminals in the mature mouse spinal cord. Second, we describe the developmental formation of GABAergic and glycinergic networks, with a specific focus on the differentiation of neurons, formation of synapses, maturation of removal systems, and changes in their action. GABAergic and glycinergic neurons are derived from the same domains of the ventricular zone. Initially, GABAergic neurons are differentiated, and their axons form synapses. Some of these neurons remain GABAergic in lamina I and II. Many GABAergic neurons convert to a coreleasing state. The coreleasing neurons and terminals remain in the dorsal horn, whereas many ultimately become glycinergic in the ventral horn. During the development of terminals and the transformation from radial glia to astrocytes, GABA and glycine receptor subunit compositions markedly change, removal systems mature, and GABAergic and glycinergic action shifts from excitatory to inhibitory.

Optimal Localization of Smart Aggregate Sensor for Concrete Damage Monitoring in PSC Anchorage Zone.

This study investigates the feasibility of smart aggregate (SA) sensors and their optimal locations for impedance-based damage monitoring in prestressed concrete (PSC) anchorage zones. Firstly, numerical stress analyses are performed on the PSC anchorage zone to determine the location of potential damage that is induced by prestressing forces. Secondly, a simplified impedance model is briefly described for the SA sensor in the anchorage. Thirdly, numerical impedance analyses are performed to explore the sensitivities of a few SA sensors in the anchorage zone under the variation of prestressing forces and under the occurrence of artificial damage events. Finally, a real-scale PSC anchorage zone is experimentally examined to evaluate the optimal localization of the SA sensor for concrete damage detection. Impedance responses measured under a series of prestressing forces are statistically quantified to estimate the performance of damage monitoring via the SA sensor in the PSC anchorage.

Piezoelectric Sensor-Embedded Smart Rock for Damage Monitoring in a Prestressed Anchorage Zone.

In this paper, a piezoelectric sensor-embedded smart rock is proposed for the electromechanical impedance monitoring of internal concrete damage in a prestressed anchorage zone. Firstly, a piezoelectric sensor-embedded smart rock is analyzed for impedance monitoring in concrete structures. An impedance measurement model is analyzed for the PZT (lead zirconate titanate)-embedded smart rock under compression in a concrete member. Secondly, a prototype of the smart rock embedded with a PZT sensor is designed in order to ascertain, sensitively, the variations of the impedance signatures induced by concrete damage in an anchorage zone. Thirdly, the performance of the smart rock is estimated from a numerical analysis and experimental tests. Variations in the impedance signals under compressive test cases are analyzed in order to predetermine the sensitive frequency band for the impedance monitoring. Lastly, an experiment on an anchorage zone embedded with the smart rocks and surface-mounted PZT sensors is conducted for the impedance measurement under a series of loading cases. The impedance variations are quantified in order to comparatively evaluate the feasibility of the sensor-embedded smart rock for the detection of internal concrete damage in the anchorage zone. The results show that the internal concrete damage was successfully detected using the PZT-embedded smart rock, thus enabling the application of the technique for anchorage zone health monitoring.

Smart PZT-Embedded Sensors for Impedance Monitoring in Prestressed Concrete Anchorage.

This study investigates the feasibility evaluation of smart PZT-embedded sensors for impedance-based damage monitoring in prestressed concrete (PSC) anchorages. Firstly, the concept of impedance-based damage monitoring for the concrete anchorage is concisely introduced. Secondly, a prototype design of PZT-embedded rebar and aggregate (so-called smart rebar-aggregate) is chosen to sensitively acquire impedance responses-induced local structural damage in anchorage members. Thirdly, an axially loaded concrete cylinder embedded with the smart rebar-aggregate is numerically and experimentally analyzed to investigate their performances of impedance monitoring. Additionally, empirical equations are formulated to represent the relationships between measured impedance signatures and applied compressive stresses. Lastly, an experimental test on a full-scale concrete anchorage embedded with smart rebar-aggregates at various locations is performed to evaluate the feasibility of the proposed method. For a sequence of loading cases, the variation in impedance responses is quantified to evaluate the accuracy of smart rebar-aggregate sensors. The empirical equations formulated based on the axially loaded concrete cylinder are implemented to predict compressive stresses at sensor locations in the PSC anchorage.

Correction: Pham et al. Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model. Sensors 2020, 20, 3382.

The authors wish to make the following correction to this paper [...].

Monitoring of Corroded and Loosened Bolts in Steel Structures via Deep Learning and Hough Transforms.

In this study, a regional convolutional neural network (RCNN)-based deep learning and Hough line transform (HLT) algorithm are applied to monitor corroded and loosened bolts in steel structures. The monitoring goals are to detect rusted bolts distinguished from non-corroded ones and also to estimate bolt-loosening angles of the identified bolts. The following approaches are performed to achieve the goals. Firstly, a RCNN-based autonomous bolt detection scheme is designed to identify corroded and clean bolts in a captured image. Secondly, a HLT-based image processing algorithm is designed to estimate rotational angles (i.e., bolt-loosening) of cropped bolts. Finally, the accuracy of the proposed framework is experimentally evaluated under various capture distances, perspective distortions, and light intensities. The lab-scale monitoring results indicate that the suggested method accurately acquires rusted bolts for images captured under perspective distortion angles less than 15° and light intensities larger than 63 lux.

Sensor Fault Diagnosis for Impedance Monitoring Using a Piezoelectric-Based Smart Interface Technique.

For a structural health monitoring (SHM) system, the operational functionality of sensors is critical for successful implementation of a damage identification process. This study presents experimental and analytical investigations on sensor fault diagnosis for impedance-based SHM using the piezoelectric interface technique. Firstly, the piezoelectric interface-based impedance monitoring is experimentally conducted on a steel bolted connection to investigate the effect of structural damage and sensor defect on electromechanical (EM) impedance responses. Based on the experimental analysis, sensor diagnostic approaches using EM impedance features are designed to distinguish the sensor defect from the structural damage. Next, a novel impedance model of the piezoelectric interface-driven system is proposed for the analytical investigation of sensor fault diagnosis. Various parameters are introduced into the EM impedance formulation to model the effect of shear-lag phenomenon, sensor breakage, sensor debonding, and structural damage. Finally, the proposed impedance model is used to analytically estimate the change in EM impedance responses induced by the structural damage and the sensor defect. The analytical results are found to be consistent with experimental observations, thus evidencing the feasibility of the novel impedance model for sensor diagnosis and structural integrity assessment. The study is expected to provide theoretical and experimental foundations for impedance monitoring practices, using the piezoelectric interface technique, with the existence of sensor faults.

Bolt-Loosening Monitoring Framework Using an Image-Based Deep Learning and Graphical Model.

In this study, we investigate a novel idea of using synthetic images of bolts which are generated from a graphical model to train a deep learning model for loosened bolt detection. Firstly, a framework for bolt-loosening detection using image-based deep learning and computer graphics is proposed. Next, the feasibility of the proposed framework is demonstrated through the bolt-loosening monitoring of a lab-scaled bolted joint model. For practicality, the proposed idea is evaluated on the real-scale bolted connections of a historical truss bridge in Danang, Vietnam. The results show that the deep learning model trained by the synthesized images can achieve accurate bolt recognitions and looseness detections. The proposed methodology could help to reduce the time and cost associated with the collection of high-quality training data and further accelerate the applicability of vision-based deep learning models trained on synthetic data in practice.

HNG, A Humanin Analogue, Promotes Hair Growth by Inhibiting Anagen-to-Catagen Transition.

The hair follicle goes through repetitive cycles including anagen, catagen, and telogen. The interaction of dermal papilla cells (DPCs) and keratinocytes regulates the hair cycle and hair growth. Humanin was discovered in the surviving brain cells of patients with Alzheimer's disease. HNG, a humanin analogue, activates cell growth, proliferation, and cell cycle progression, and it protects cells from apoptosis. This study was performed to investigate the promoting effect and action mechanisms of HNG on hair growth. HNG significantly increased DPC proliferation. HNG significantly increased hair shaft elongation in vibrissa hair follicle organ culture. In vivo experiment showed that HNG prolonged anagen duration and inhibited hair follicle cell apoptosis, indicating that HNG inhibited the transition from the anagen to catagen phase mice. Furthermore, HNG activated extracellular signal-regulated kinase (Erk)1/2, Akt, and signal transducer and activator of transcription (Stat3) within minutes and up-regulated vascular endothelial growth factor (VEGF) levels on DPCs. This means that HNG could induce the anagen phase longer by up-regulating VEGF, which is a Stat3 target gene and one of the anagen maintenance factors. HNG stimulated the anagen phase longer with VEGF up-regulation, and it prevented apoptosis by activating Erk1/2, Akt, and Stat3 signaling.

Joint Banknote Recognition and Counterfeit Detection Using Explainable Artificial Intelligence.

We investigated machine learning-based joint banknote recognition and counterfeit detection method. Unlike existing methods, since the proposed method simultaneously recognize banknote type and detect counterfeit detection, it is significantly faster than existing serial banknote recognition and counterfeit detection methods. Furthermore, we propose an explainable artificial intelligence method for visualizing regions that contributed to the recognition and detection. Using the visualization, it is possible to understand the behavior of the trained machine learning system. In experiments using the United State Dollar and the European Union Euro banknotes, the proposed method shows significant improvement in computation time from conventional serial method.

Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization.

We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method.

Local Strand-Breakage Detection in Multi-Strand Anchorage System Using an Impedance-Based Stress Monitoring Method-Feasibility Study.

This study investigates the feasibility of impedance-based stress monitoring method for local-strand breakage detection in multi-strand anchorage systems. Firstly, stress fields of a multi-strand anchorage system are numerically analyzed to examine anchorage's responses sensitive to local strand breakage. Secondly, an impedance-based stress monitoring technique via the PZT interface is outlined. Thirdly, a novel hoop-type PZT interface is designed for the multi-strands anchorage to monitor the stress variation induced by the strand breakage. Local dynamic responses of the hoop-type PZT interface are analyzed to predetermine the effective frequency ranges. Finally, the numerical feasibility of the proposed method is verified on a seven-strand anchorage system under various strand breakage cases. Variations in impedance responses are statistically quantified, and broken strands are localized by linear tomography analysis of damage indices. A lab-scale experiment is also conducted on a multi-strands anchorage to evaluate the realistic performance of the hoop PZT interface for impedance-based stress monitoring method.

Sensing Region Characteristics of Smart Piezoelectric Interface for Damage Monitoring in Plate-Like Structures.

For impedance-based damage detection practices, the sensing range of piezoelectric devices is an important parameter that should be determined before real implementations. This study presents numerical and experimental analyses for characterizing the sensing region of a smart PZT (lead⁻zirconate⁻titanate) interface for damage monitoring in plate-like structures. First, a finite element (FE) model of the PZT interface mounted on a plate structure is established. The impedance responses of the PZT interface are numerically simulated under different damage locations inflicted in the plate domain. The impedance features are extracted from the impedance signatures to analyze the sensing distance and the damage detectability of the PZT interface. Next, the splice plate of a bolted connection is selected as a practical plate-like structure for the experimental examination of the PZT interface's sensing region on a limited plate domain. The damage sensitivity behavior of the PZT interface is analyzed with respect to the damage location on the splice plate. An FE analysis of the corresponding PZT interface-splice plate system is also conducted to support the experimental results.