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Diclofenac etalhyaluronate, an active pharmaceutical ingredient in JOYCLU® (JCL), serves as a joint function improvement agent in knee and hip osteoarthritis patients. However, frequent cases of anaphylaxis induced by JCL administration have been reported. Recent clinical research suggests the potential utility of the basophil activation test (BAT) in predicting JCL-induced anaphylaxis. Nonetheless, the BAT is deemed impractical for routine diagnostic testing due to complex procedures involving whole blood stimulation and flow cytometry-based analyses. In the study reported here, an IgE crosslinking-induced luciferase expression (EXiLE) test which uses patient sera without complicated procedures, was performed with patients who had received JCL, with or without subsequent anaphylactic symptoms. The results of this test were then compared with those of the BAT reported in a clinical research study. Of the six BAT-positive JCL-induced anaphylaxis-experienced patients, four were positive in the EXiLE test and all non-experienced patients were negative in both the BAT and EXiLE tests, thus illustrating a high concordance rate of 92.3%. Further validation of testing conditions is expected to improve these rates. Notably, complement inactivation treatment led to a positive EXiLE result in a BAT-negative patient. In conclusion, it appears that the EXiLE test exhibits promise as an alternative to BAT for predicting JCL-induced anaphylaxis, and in so doing offers a simpler diagnostic approach.

Abstract InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014. Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy, and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all major urban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detected by Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformation in Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms.

Abstract Methylmercury (MeHg) is converted to inorganic mercury (iHg) in several organs; however, its impact on tissues and cells remains poorly understood. Previously, we established a bacterial organomercury lyase (MerB)-expressing mammalian cell line to overcome the low cell permeability of iHg and investigate its effects. Here, we elucidated the cytotoxic effects of the resultant iHg on autophagy and deciphered their relationship. Treatment of MerB-expressing cells with MeHg significantly increases the mRNA and protein levels of LC3B and p62, which are involved in autophagosome formation and substrate recognition, respectively. Autophagic flux assays using the autophagy inhibitor chloroquine (CQ) revealed that MeHg treatment activates autophagy in MerB-expressing cells but not in wild-type cells. Additionally, MeHg treatment induces the accumulation of ubiquitinated proteins and p62, specifically in MerB-expressing cells. Confocal microscopy revealed that large ubiquitinated protein aggregates (aggresomes) associated with p62 are formed transiently in the perinuclear region of MerB-expressing cells upon MeHg exposure. Meanwhile, inhibition of autophagic flux decreases the MeHg-induced cell viability of MerB-expressing cells. Overall, our results imply that cells regulate aggresome formation and autophagy activation by activating LC3B and p62 to prevent cytotoxicity caused by iHg. These findings provide insights into the role of autophagy against iHg-mediated toxicity.

No reliable serum biomarker for diffuse alveolar damage, a poor prognosis subtype of drug-induced interstitial lung disease, is currently available. Here, the authors show stratifin/14-3-3σ in serum is a promising biomarker for diagnosis of this type of disease.

Abstract Vocal fold (VF) fibrosis is a major cause of intractable voice-related disability and reduced quality of life. Excision of fibrotic regions is suboptimal and associated with scar recurrence and/or further iatrogenic damage. Non-surgical interventions are limited, putatively related to limited insight regarding biochemical events underlying fibrosis, and downstream, the lack of therapeutic targets. YAP/TAZ integrates diverse cell signaling events and interacts with signaling pathways related to fibrosis, including the TGF-β/SMAD pathway. We investigated the expression of YAP/TAZ following vocal fold injury in vivo as well as the effects of TGF-β1 on YAP/TAZ activity in human vocal fold fibroblasts, fibroblast-myofibroblast transition, and TGF-β/SMAD signaling. Iatrogenic injury increased nuclear localization of YAP and TAZ in fibrotic rat vocal folds. In vitro, TGF-β1 activated YAP and TAZ in human VF fibroblasts, and inhibition of YAP/TAZ reversed TGF-β1-stimulated fibroplastic gene upregulation. Additionally, TGF-β1 induced localization of YAP and TAZ in close proximity to SMAD2/3, and nuclear accumulation of SMAD2/3 was inhibited by a YAP/TAZ inhibitor. Collectively, YAP and TAZ were synergistically activated with the TGF-β/SMAD pathway, and likely essential for the fibroplastic phenotypic shift in VF fibroblasts. Based on these data, YAP/TAZ may evolve as an attractive therapeutic target for VF fibrosis.

We aimed to efficiently enhance plant Hg(II) tolerance by the transgenic approach utilizing a bacterial mercury transporter MerC, an Arabidopsis mesophyll specific promoter pRBCS1A, and a vacuolar membrane targeting syntaxin AtVAM3/SYP22. We generated two independent homozygous Arabidopsis pRBCS1A-TCV lines expressing mT-Sapphire-MerC-AtVAM3 under the control of pRBCS1A. Quantitative RT-PCR showed that the transgene was expressed specifically in shoots of pRBCS1A-TCV lines. Confocal analyses further demonstrated the leaf mesophyll specific expression of mT-Sapphire-MerC-AtVAM3. Confocal observation of the protoplast derived from the F1 plants of the pRBCS1A-TCV line and the tonoplast marker line p35S-GFP-δTIP showed the tonoplast colocalization of mT-Sapphire-MerC-AtVAM3 and GFP-δTIP. These results clearly demonstrated that mT-Sapphire-MerC-AtVAM3 expression in Arabidopsis is spatially regulated as designed at the transcript and the membrane trafficking levels. We then examined the Hg(II) tolerance of the pRBCS1A-TCV lines as well as the p35S-driven MerC-AtVAM3 expressing line p35S-CV under the various Hg(II) stress conditions. Short-term (12 d) Hg(II) treatment indicated the enhanced Hg(II) tolerance of both pRBCS1A-TCV and p35S-CV lines. The longer (3 weeks) Hg(II) treatment highlighted the better shoot growth of the transgenic plants compared to the wild-type Col-0 and the pRBCS1A-TCV lines were more tolerant to Hg(II) stress than the p35S-CV line. These results suggest that mesophyll-specific expression of MerC-AtVAM3 is sufficient or even better to enhance the Arabidopsis Hg(II) tolerance. The Hg accumulation in roots and shoots did not differ between the wild-type Col-0 and the MerC-AtVAM3 expressing plants, suggesting that the boosted Hg(II) tolerance of the transgenic lines would be attributed to vacuolar Hg-sequestration by the tonoplast-localized MerC. Further perspectives of the MerC-based plant engineering are also discussed.

Introduction: Postoperative dysphonia is mostly caused by vocal fold scarring, and careful management of vocal fold surgery has been reported to reduce the risk of scar formation. However, depending on the vocal fold injury, treatment of postoperative dysphonia can be challenging. Objective: The goal of the current study was to develop a novel prophylactic regenerative approach for the treatment of injured vocal folds after surgery, using biodegradable gelatin hydrogel microspheres as a drug delivery system for basic fibroblast growth factor. Methods: Videoendoscopic laryngeal surgery was performed to create vocal fold injury in 14 rabbits. Immediately following this procedure, biodegradable gelatin hydrogel microspheres with basic fibroblast growth factor were injected in the vocal fold. Two weeks after injection, larynges were excised for evaluation of vocal fold histology and mucosal movement. Results: The presence of poor vibratory function was confirmed in the injured vocal folds. Histology and digital image analysis demonstrated that the injured vocal folds injected with gelatin hydrogel microspheres with basic fibroblast growth factor showed less scar formation, compared to the injured vocal folds injected with gelatin hydrogel microspheres only, or those without any injection. Conclusion: A prophylactic injection of basic fibroblast growth factor -containing biodegradable gelatin hydrogel microspheres demonstrates a regenerative potential for injured vocal folds in a rabbit model.

Abstract ETS homologous factor (EHF) belongs to the epithelium-specific subfamily of the E26 transformation-specific (ETS) transcription factor family. Currently, little is known about EHF’s function in cancer. We previously reported that ETS1 induces expression of the ZEB family proteins ZEB1/δEF1 and ZEB2/SIP1, which are key regulators of the epithelial–mesenchymal transition (EMT), by activating the ZEB1 promoters. We have found that EHF gene produces two transcript variants, namely a long form variant that includes exon 1 (EHF-LF) and a short form variant that excludes exon 1 (EHF-SF). Only EHF-SF abrogates ETS1-mediated activation of the ZEB1 promoter by promoting degradation of ETS1 proteins, thereby inhibiting the EMT phenotypes of cancer cells. Most importantly, we identified a novel point mutation within the conserved ETS domain of EHF, and found that EHF mutations abolish its original function while causing the EHF protein to act as a potential dominant negative, thereby enhancing metastasis in vivo. Therefore, we suggest that EHF acts as an anti-EMT factor by inhibiting the expression of ZEBs, and that EHF mutations exacerbate cancer progression.

Gadolinium-based contrast agents (GBCAs) are widely used to improve tissue contrast during magnetic resonance imaging. Exposure to GBCAs can result in gadolinium deposition within human tissues and has become a clinical concern because of the potential toxic effects of free gadolinium (Gd3+). Here, we report the impact of a single administration of GBCAs (Omniscan and Gadovist), and Gd3+ on mouse tissues. Five-week-old male BALB/c mice were injected intravenously with GBCAs or Gd3+. Seven days after injection, relatively high levels of gadolinium were detected in the spleen (118.87 nmol/g tissue), liver (83.00 nmol/g tissue), skin (48.56 nmol/g tissue), and kidneys (25.59 nmol/g tissue) of the Gd(NO3)3 (high dose: 0.165 mmol/kg) group; in the bones (11.12 nmol/g tissue), kidneys (7.49 nmol/g tissue), teeth (teeth: 6.18 nmol/g tissue), and skin (2.43 nmol/g tissue) of the Omniscan (high dose: 1.654 mmol/kg) group and in the kidneys (16.36 nmol/g tissue) and skin (4.88 nmol/g tissue) of the Gadovist (high dose: 3.308 mmol/kg) group. Enlargement of the spleen was observed in the Gd3+ group (p

Abstract Similar to the hypertrophic scar and keloids, the efficacy of glucorticoids (GC) for vocal fold injury is highly variable. We previously reported dexamethasone enhanced the pro-fibrotic effects of transforming growth factor (TGF)-β as a potential mechanism for inconsistent clinical outcomes. In the current study, we sought to determine the mechanism(s) whereby GCs influence the fibrotic response and mechanisms underlying these effects with an emphasis on TGF-β and nuclear receptor subfamily 4 group A member 1 (NR4A1) signaling. Human VF fibroblasts (HVOX) were treated with three commonly-employed GCs+ /-TGF-β1. Phosphorylation of the glucocorticoid receptor (GR:NR3C1) and activation of NR4A1 was analyzed by western blotting. Genes involved in the fibrotic response, including ACTA2, TGFBR1, and TGFBR2 were analyzed by qPCR. RNA-seq was performed to identify global changes in gene expression induced by dexamethasone. GCs enhanced phosphorylation of GR at Ser211 and TGF-β-induced ACTA2 expression. Dexamethasone upregulated TGFBR1, and TGFBR2 in the presence of TGF-β1 and increased active NR4A1. RNA-seq results confirmed numerous pathways, including TGF-β signaling, affected by dexamethasone. Synergistic pro-fibrotic effects of TGF-β were observed across GCs and appeared to be mediated, at least partially, via upregulation of TGF-β receptors. Dexamethasone exhibited diverse regulation of gene expression including NR4A1 upregulation consistent with the anti-fibrotic potential of GCs.

Chlorophyll content is one of the essential parameters to assess the growth process of the fruit trees. This present study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional autoencoder (CAE) features of hyperspectral (HS) data. This study also demonstrated the inspection of anomaly among the trees by employing multidimensional scaling on the CAE features and detected outlier trees prior to fit nonlinear regression models. These outlier trees were excluded from the further experiments that helped in improving the prediction performance of CACC. Gaussian process regression (GPR) and support vector regression (SVR) techniques were investigated as nonlinear regression models and used for prediction of CACC. The CAE features were proven to be providing better prediction of CACC when compared with the direct use of HS bands or vegetation indices as predictors. The CACC prediction performance was improved with the exclusion of the outlier trees during training of the regression models. It was evident from the experiments that GPR could predict the CACC with better accuracy compared to SVR. In addition, the reliability of the tree canopy masks, which were utilized for averaging the features' values for a particular tree, was also evaluated.

Water/ice/land region segmentation is an important task for remote sensing, as it analyses the occurrence of water or ice on the earth's surface. Many previous deep learning researches effectively utilized multispectral satellite images for highly accurate water/ice/land region segmentation. However, the deep-learning-based segmentation of synthetic aperture radar images still remains a challenging task due to the unavailability of enough labeled data. In order to overcome this issue, we designed a two-step deep-learning-based transfer learning model that needs a very limited number of labeled samples. The proposed approach consists of two models. The first model is a deep encoder-decoder 6SD to Landsat-8 multispectral translation model (DTF) that translates fully polarimetric PALSAR-2 6SD data to six new features. As for the second model (transfer learning), it utilizes the DTF features to fine-tune the model using the Landsat-8 multispectral pretrained model for water/ice/land segmentation. Hereinafter, the proposed two-step model is referred to as DTF-TL. Also, a qualitative and quantitative analysis was carried out to evaluate the performance of the proposed model (DTF-TL) and compare it with various transfer learning methods. Overall, the DTF-TL model outperformed the other models with consistent and reliable water/ice/land segmentation results in terms of the recall (0.980), precision (0.981), F1-score (0.981), mean intersection over union (0.962), and accuracy (0.989).

Cysteine desulfurases are pyridoxal-5'-phosphate (PLP)-dependent enzymes that mobilize sulfur derived from the l-cysteine substrate to the partner sulfur acceptor proteins. Three cysteine desulfurases, IscS, NifS, and SufS, have been identified in ISC, NIF, and SUF/SUF-like systems for iron-sulfur (Fe-S) cluster biosynthesis, respectively. These cysteine desulfurases have been investigated over decades, providing insights into shared/distinct catalytic processes based on two types of enzymes (type I: IscS and NifS, type II: SufS). This review summarizes the insights into the structural/functional varieties of bacterial and eukaryotic cysteine desulfurases involved in Fe-S cluster biosynthetic systems. In addition, an inactive cysteine desulfurase IscS paralog, which contains pyridoxamine-5'-phosphate (PMP), instead of PLP, is also described to account for its hypothetical function in Fe-S cluster biosynthesis involving this paralog. The structural basis for cysteine desulfurase functions will be a stepping stone towards understanding the diversity and evolution of Fe-S cluster biosynthesis.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized.

Pi-SAR2 is an airborne polarimetric synthetic aperture radar operated by the National Institute of Information and Communications Technology. The polarimetric observation data of Pi-SAR2 are very valuable because of its high resolution, but it cannot be used effectively because the data are not well calibrated with respect to elevation. Therefore, we have calibrated the data according to the observation conditions. The Pi-SAR2 observation data are very large due to its high resolution and require sufficient computational resources to be calibrated. We utilized the AI Bridging Cloud Infrastructure (ABCI), constructed and operated by the National Institute of Advanced Industrial Science and Technology, to calculate them. This paper reports on the calibration, scattering power decomposition, and orthorectification of the Pi-SAR2 observation data using the ABCI.

The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale.

Background: The reported prevalences of IgG autoantibodies (AAbs) to FcεRIα and IgE in sera from patients with chronic spontaneous urticaria (CSU) have varied, and these AAbs are also often observed in healthy control subjects. Regarding the histamine release activity of purified IgG from patients with CSU, the number of examined patients has been small. Thus, we sought to determine the prevalence and FcεRI crosslinking ability of these AAbs in a large number of patients with CSU and non-atopic control (NC) subjects. Methods: We compared the concentrations of anti-IgE and anti-FcεRIα AAbs and the abilities of these AAbs to cause FcεRI aggregation in patients with CSU (n = 134) and NC subjects (n = 55) using ELISA and an in vitro elicitation test, respectively. Results: The concentration of anti-IgE AAbs was significantly different between the NC subjects and the CSU patients (P

For researchers in the plant metal field, the agar reagent used for the solid plate medium is a problematic factor because application of different agar types and even a different lot of the same agar type can mask the plant metal-related phenotypes and impair the reproducibility. In this study, we systematically assessed effects of different agar reagents on metal(loid) sensitivity and element accumulation of the Arabidopsis metal sensitive mutants. Three established mutants (cad1-3, cad1-6, and abcc1/2), and three different types of purified agar reagents (Type A, Type E, and Nacalai) with two independent batches for each reagent were subjected to the analyses. First, we found that element concentrations in the agar reagents largely varied among the agar types. Then the effects of agar reagents on the mutant metal(loid)-sensitivity were examined under As(III), Hg(II), Cd(II), and excess Zn(II) conditions. A significant variation of the mutant metal(loid)-sensitivity was observed among the different agar plates but the variation depended on the combination of metal(loid) stress and agar reagents. Briefly, the type-dependent variation was more evident under As(III) and Hg(II) than Cd(II) or excess Zn(II) conditions. A lot-dependent variation was also observed for Type A and Type E but not for Nacalai: hypersensitive phenotypes of cad1-3, cad1-6, and abcc1/2 under As(III) or Hg(II) treatments were diminished when different batches of the Type A or Type E agar types were used. We also found a significant variation of As and Hg accumulation in the wild-type and cad1-3. Plant As and Hg concentrations were remarkably higher and the difference between the genotypes was more evident when grown with Type A agar plates. We finally analyzed ionomic profiles in the plants exposed to As(III) stress. Agar-type specific ionomic changes in cad1-3 were more observed with the Type A plates than with the Nacalai plates. The presented results overall suggest that suitability of agar reagents for metal(loid)-related phenotyping depends on the experimental design, and an inappropriate selection of agar reagents can mask even very clear phenotypes of the established mutant like cad1-3. We also discuss perspectives on the agar problem in the plant metal study.

Abstract Background Planar cell polarity (PCP) coordinates the patterning and orientation of cells and their structures along tissue planes, and although its acquisition during the formation of airway epithelium has been described, the mechanisms for its maintenance and reconstruction are poorly understood. We aimed to clarify whether ambient environment change by orthotropic autologous transplantation affected PCP at the cellular level. Methods We performed orthotropic autologous transplantation by inverting tracheal segments in rats, and then performed morphological evaluation by microscopy. The PCP of the tracheal epithelium was assessed over time by analyzing the directions of mucociliary transport and ciliary beat, the positional relationship between the basal body and basal foot, and the bias of Vang-like protein 1 (Vangl1) at 2, 4, and 6 months postoperatively. Results After 2 months, the directions of mucociliary transport and ciliary beat were preserved toward the lung in the inverted tracheal segments. The positional relationship between the basal body and the basal foot, and the bias of Vangl1, also indicated preservation of PCP in the inverted tracheal segments. Similar results were obtained at 6 months. Conclusion The PCP of ciliated epithelium was preserved in reversed trachea, even after long-term observation.

Abstract Methylmercury (MeHg) is a widely distributed environmental pollutant that causes a series of cytotoxic effects. However, molecular mechanisms underlying MeHg toxicity are not fully understood. Here, we report that sequestosome1/p62 protects mouse embryonic fibroblasts (MEFs) against low-dose MeHg cytotoxicity via clearance of MeHg-induced ubiquitinated proteins. p62 mRNA and protein expression in MEFs were temporally induced by MeHg exposure p62-deficient MEFs exhibited higher sensitivity to MeHg exposure compared to their wild-type (WT) counterparts. An earlier and higher level of accumulation of ubiquitinated proteins was detected in p62-deficient cells compared with WT MEFs. Confocal microscopy revealed that p62 and ubiquitinated proteins co-localized in the perinuclear region of MEFs following MeHg treatment. Further analysis of MEFs revealed that ubiquitinated proteins co-localized with LC3-positive puncta upon co-treatment with MeHg and chloroquine, an autophagy inhibitor. In contrast, there was minimal co-localization in p62-deficient MEFs. The present study, for the first time, examined the expression and distribution of p62 and ubiquitinated proteins in cells exposed to low-dose MeHg. Our findings suggest that p62 is crucial for cytoprotection against MeHg-induced toxicity and is required for MeHg-induced ubiquitinated protein clearance.

The epithelial-mesenchymal transition (EMT) is a crucial morphological event that occurs during epithelial tumor progression. ZEB1/2 are EMT transcription factors that are positively correlated with EMT phenotypes and breast cancer aggressiveness. ZEB1/2 regulate the alternative splicing and hence isoform switching of fibroblast growth factor receptors (FGFRs) by repressing the epithelial splicing regulatory proteins, ESRP1 and ESRP2. Here, we show that the mesenchymal-like phenotypes of oral squamous cell carcinoma (OSCC) cells are dependent on autocrine FGF-FGFR signaling. Mesenchymal-like OSCC cells express low levels of ESRP1/2 and high levels of ZEB1/2, resulting in constitutive expression of the IIIc-isoform of FGFR, FGFR(IIIc). By contrast, epithelial-like OSCC cells showed opposite expression profiles for these proteins and constitutive expression of the IIIb-isoform of FGFR2, FGFR2(IIIb). Importantly, ERK1/2 was constitutively phosphorylated through FGFR1(IIIc), which was activated by factors secreted autonomously by mesenchymal-like OSCC cells and involved in sustained high-level expression of ZEB1. Antagonizing FGFR1 with either inhibitors or siRNAs considerably repressed ZEB1 expression and restored epithelial-like traits. Therefore, autocrine FGF-FGFR(IIIc) signaling appears to be responsible for sustaining ZEB1/2 at high levels and the EMT phenotype in OSCC cells.

Several laboratories have created rat basophil leukemia (RBL) cell lines stably transfected with the human high affinity IgE receptor (FcεRIH). More recently, humanized RBL cell lines saw the introduction of reporter genes such as luciferase (RS-ATL8) and DsRed (RBL NFAT-DsRed). These reporters are more sensitive than their parental non-reporter humanized RBL cell lines. However, no studies so far have addressed the levels of FcεRIH surface expression on humanized RBL cell lines. This is a critical parameter, as it determines the ability of these cells to be efficiently sensitized with human IgE, hence it should affect the sensitivity of the cell assay-a critical parameter for any diagnostic application. Our purpose was to assess and compare the levels of expression of the transfected FcεRIH chain in humanized RBL cell lines. We compared surface levels of FcεRIαH by flow cytometry, using a fluorescently labelled monoclonal antibody (CRA-1/AER-37) and determined receptor numbers using calibration microspheres. FcεRIαH copy numbers were assessed by qPCR, and the sequence verified. Transfection with FcεRIγH cDNA was assessed for its ability to increase FcεRIαH expression in the NFAT-DsRed reporter. While both SX-38 and RS-ATL8 expressed about 500.000 receptors/cell, RBL 703-21 and NFAT-DsRed had approximately 10- to 30-fold lower FcεRIαH expression, respectively. This was neither related to FcεRIH gene copy numbers, nor to differences in steady state mRNA levels, as determined by qPCR and RT-qPCR, respectively. Instead, FcεRIαH surface expression appeared to correlate with the co-expression of FcεRIγH. Stable transfection of NFAT-DsRed cells with pBJ1 neo-huFcεRI gamma, which constitutively expresses FcεRIγH, increased FcεRIαH chain expression levels. Levels of FcεRIαH surface expression vary greatly between humanized RBL reporter cell lines. This difference will affect the sensitivity of the reporter system when used for diagnostic purposes.

The limitations in obtaining sufficient datasets for training deep learning networks is preventing many applications from achieving accurate results, especially when detecting new constructions using time-series satellite imagery, since this requires at least two images of the same scene and it must contain new constructions in it. To tackle this problem, we introduce Chronological Order Reverse Network (CORN)—an architecture for detecting newly built constructions in time-series SAR images that does not require a large quantity of training data. The network uses two U-net adaptations to learn the changes between images from both Time 1−Time 2 and Time 2−Time 1 formats, which allows it to learn double the amount of changes in different perspectives. We trained the network with 2028 pairs of 256 × 256 pixel SAR images from ALOS-PALSAR, totaling 4056 pairs for the network to learn from, since it learns from both Time 1−Time 2 and Time 2−Time 1. As a result, the network can detect new constructions more accurately, especially at the building boundary, compared to the original U-net trained by the same amount of training data. The experiment also shows that the model trained with CORN can be used with images from Sentinel-1. The source code is available at https://github.com/Raveerat-titech/CORN.

Snail, also called Snai1, is a key regulator of EMT. Snail plays crucial roles in cancer progression, including resistance to anti-tumor drugs and invasion by various cancer cells. Slug, also known as Snai2, is also involved in the aggravation of certain tumors. In this study, we examined the roles of Slug in human oral squamous cell carcinoma (OSCC) cells. Slug is highly expressed in these cells, and Slug siRNA effectively represses anti-tumor drug resistance and invasive properties. In addition, transforming growth factor (TGF)-β upregulates the expression of Snail and Slug and promotes resistance to anti-tumor drugs in OSCC cells. Surprisingly, Slug siRNA appears to upregulate Snail expression considerably in OSCC cells. Snail siRNA also appears to upregulate Slug expression. Thus, either Slug or Snail siRNA alone partially mitigates malignant phenotypes in the presence of TGF-β, whereas both Slug and Snail siRNAs together dramatically suppress them. Therefore, Slug and Snail in tandem, but not alone, are potential therapeutic targets for nucleic acid medicines to treat oral cancer.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3−6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

Remote sensing data can be utilized to help developing countries monitor the use of land. However, the problem of constant cloud coverage prevents us from taking full advantage of satellite optical images. Therefore, we instead opt to use data from synthetic-aperture radar (SAR), which can capture images of the Earth’s surface regardless of the weather conditions. In this study, we use SAR data to identify newly built constructions. Most studies on change detection tend to detect all of the changes that have a similar temporal change characteristic occurring on two occasions, while we want to identify only the constructions and avoid detecting other changes such as the seasonal change of vegetation. To do so, we study various deep learning network techniques and have decided to propose the fully convolutional network with a skip connection. We train this network with pairs of SAR data acquired on two different occasions from Bangkok and the ground truth, which we manually create from optical images available from Google Earth for all of the SAR pairs. Experiments to assign the most suitable patch size, loss weighting, and epoch number to the network are discussed in this paper. The trained model can be used to generate a binary map that indicates the position of these newly built constructions precisely with the Bangkok dataset, as well as with the Hanoi and Xiamen datasets with acceptable results. The proposed model can even be used with SAR images of the same specific satellite from another orbit direction and still give promising results.

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

Background: An in vitro elicitation test employing human high-affinity IgE receptor-expressing rat mast cell lines appears to be a useful method for measuring mast cell activation using a patient's IgE and an allergen; however, such cell lines are sensitive to human complements in the serum. We have recently developed a new luciferase-reporting mast cell line (RS-ATL8) to detect IgE crosslinking-induced luciferase expression (EXiLE) with relatively low quantities of serum IgE. Methods: A total of 30 patients suspected of having egg white (EW) allergy were subjected to an oral food challenge (OFC) test; then, the performances of EW-specific serum IgE (CAP-FEIA), EW-induced degranulation, and EXiLE responses in RS-ATL8 cells were compared using receiver-operating characteristic (ROC) curve analysis. The patients' sera were diluted to 1:100, which causes no cytotoxicity when sensitizing the RS-ATL8 cells for the degranulation and EXiLE tests. Results: The area under the ROC curves was highest in the EXiLE test (0.977), followed by CAP-FEIA (0.926) and degranulation (0.810). At an optimal cutoff range (1.648-1.876) calculated from the ROC curve of the EXiLE test, sensitivity and specificity were 0.944 and 0.917, respectively. A 95% positive predictive value was given at a cutoff level of 2.054 (fold increase in luciferase expression) by logistic regression analysis. Conclusions: In contrast to in vivo tests, the EXiLE test appears to be a useful tool in diagnosing patients suspected of having IgE-dependent EW allergy without the risk of severe systemic reactions.

Accurate attitude information from a satellite image sensor is essential for accurate map projection and reducing computational cost for post-processing of image registration, which enhance image usability, such as change detection. We propose a robust attitude-determination method for pushbroom sensors onboard spacecraft by matching land features in well registered base-map images and in observed images, which extends the current method that derives satellite attitude using an image taken with 2-D image sensors. Unlike 2-D image sensors, a pushbroom sensor observes the ground by changing its position and attitude according to the trajectory of a satellite. To address pushbroom-sensor observation, the proposed method can trace the temporal variation in the sensor attitude by combining the robust matching technique for a 2-D image sensor and a non-linear least squares approach, which can express gradual time evolution of the sensor attitude. Experimental results using images taken from a visible and near infrared pushbroom sensor of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as test image and Landsat-8/OLI images as a base map show that the proposed method can determine satellite attitude with an accuracy of 0.003° (corresponding to the 2-pixel scale of ASTER) in roll and pitch angles even for a scene in which there are many cloud patches, whereas the determination accuracy remains 0.05° in the yaw angle that does not affect accuracy of image registration compared with the other two axes. In addition to the achieved attitude accuracy that was better than that using star trackers (0.01°) regarding roll and pitch angles, the proposed method does not require any attitude information from onboard sensors. Therefore, the proposed method may contribute to validating and calibrating attitude sensors in space, at the same time better accuracy will contribute to reducing computational cost in post-processing for image registration.

Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer’s accuracy of the method was approximately 77.3% and the user’s accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.

Greenhouse Gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two Earth-observing instruments: the Thermal and Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer 2 (TANSO-FTS-2) and TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 can be used to perform cloud discrimination in each band. The cloud discrimination algorithm uses minimum reflectance (Rmin) for comparisons with observed top-of-atmosphere reflectance. The creation of cloud-free Rmin requires 10 CAI or CAI-2 data. Thus, Rmin is created from CAI L1B data for a 30-day period in GOSAT, with a revisit time of 3 days. It is necessary to change the way in which 10 observations are chosen for GOSAT-2, which has a revisit time of 6 days. Additionally, Rmin processing for GOSAT CAI data was updated to version 02.00 in December 2016. Along with this change, the resolution of Rmin changed from 1/30° to 500 m. We examined the impact of changes in Rmin on cloud discrimination results using GOSAT CAI data. In particular, we performed comparisons of: (1) Rmin calculated using different methods to choose the 10 observations and (2) Rmin calculated using different generation procedures and spatial resolutions. The results were as follows: (1) The impact of using different methods to choose the 10 observations on cloud discrimination results was small, except for a few cases, e.g., snow-covered regions and sun-glint regions; (2) Cloud discrimination results using Rmin in version 02.00 were better than results obtained using Rmin in the previous version, apart from some special situations. The main causes of this were as follows: (1) The change of used band from band 2 to band 1 for Rmin calculation; (2) The change of spatial resolution of Rmin from 1/30° to 500-m.

We consider grammar-based text compression with longest first substitution (LFS), where non-overlapping occurrences of a longest repeating factor of the input text are replaced by a new non-terminal symbol. We present the first linear-time algorithm for LFS. Our algorithm employs a new data structure called sparse lazy suffix trees. We also deal with a more sophisticated version of LFS, called LFS2, that allows better compression. The first linear-time algorithm for LFS2 is also presented.

Greenhouse gases Observing SATellite-2 (GOSAT-2) will be launched in fiscal year 2018. GOSAT-2 will be equipped with two sensors: the Thermal and Near-infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer 2 (FTS-2) and the TANSO-Cloud and Aerosol Imager 2 (CAI-2). CAI-2 is a push-broom imaging sensor that has forward- and backward-looking bands to observe the optical properties of aerosols and clouds and to monitor the status of urban air pollution and transboundary air pollution over oceans, such as PM2.5 (particles less than 2.5 micrometers in diameter). CAI-2 has important applications for cloud discrimination in each direction. The Cloud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA1), which applies sequential threshold tests to features is used for GOSAT CAI L2 cloud flag processing. If CLAUDIA1 is used with CAI-2, it is necessary to optimize the thresholds in accordance with CAI-2. However, CLAUDIA3 with support vector machines (SVM), a supervised pattern recognition method, was developed, and then we applied CLAUDIA3 for GOSAT-2 CAI-2 L2 cloud discrimination processing. Thus, CLAUDIA3 can automatically find the optimized boundary between clear and cloudy areas. Improvements in CLAUDIA3 using CAI (CLAUDIA3-CAI) continue to be made. In this study, we examined the impact of various support vectors (SV) on GOSAT-2 CAI-2 L2 cloud discrimination by analyzing (1) the impact of the choice of different time periods for the training data and (2) the impact of different generation procedures for SV on the cloud discrimination efficiency. To generate SV for CLAUDIA3-CAI from MODIS data, there are two times at which features are extracted, corresponding to CAI bands. One procedure is equivalent to generating SV using CAI data. Another procedure generates SV for MODIS cloud discrimination at the beginning, and then extracts decision function, thresholds, and SV corresponding to CAI bands. Our results indicated the following. (1) For the period from November to May, it is more effective to use SV generated from training data from February while for the period from June to October it is more effective to use training data from August; (2) In the preparation of SV, features obtained using MODIS bands are more effective than those obtained using the corresponding GOSAT CAI bands to automatically extract cloud training samples.

The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of their regions of interest (ROI) at full-resolution from all kinds of devices without the need for tedious data downloading, decompressing, and processing. This paper reports on the Landsat-8 automatic image processing system (L-8 AIPS) that incorporates the function of mask developed by United States Geological Survey (USGS), the pan-sharpening technique of spectral summation intensity modulation, the adaptive contrast enhancement technique, as well as the Openlayers and Google Maps/Earth compatible superoverlay technique. Operation of L-8 AIPS enables the most up-to-date Landsat-8 images of Taiwan to be browsed with a clear contrast enhancement regardless of the cloud condition, and in only one hour’s time after receiving the raw data from the USGS Level 1 Product Generation System (LPGS). For any ROI in Taiwan, all historical Landsat-8 images can also be quickly viewed in time series at full resolution (15 m). The debris flow triggered by Typhoon Soudelor (8 August 2015), as well as the barrier lake formed and the large-scale destruction of vegetation after Typhoon Nepartak (7 July 2016), are given as three examples of successful applications to demonstrate that the gap between the user’s needs and the existing Level-1 product from LPGS can be bridged by providing browsable images in near real-time.

Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02°, comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth.

Parasite-specific IgE is thought to correlate with protection against Schistosoma mansoni infection or re-infection. Only a few molecular targets of the IgE response in S. mansoni infection have been characterised. A better insight into the basic mechanisms of anti-parasite immunity could be gained from a genome-wide characterisation of such S. mansoni allergens. This would have repercussions on our understanding of allergy and the development of safe and efficacious vaccinations against helminthic parasites.A complete medium- to high-throughput amenable workflow, including important quality controls, is described, which enables the rapid translation of S. mansoni proteins using wheat germ lysate and subsequent assessment of potential allergenicity with a humanised Rat Basophilic Leukemia (RBL) reporter cell line. Cell-free translation is completed within 90 minutes, generating sufficient amounts of parasitic protein for rapid screening of allergenicity without any need for purification. Antigenic integrity is demonstrated using Western Blotting. After overnight incubation with infected individuals' serum, the RS-ATL8 reporter cell line is challenged with the complete wheat germ translation mixture and Luciferase activity measured, reporting cellular activation by the suspected allergen. The suitability of this system for characterization of novel S. mansoni allergens is demonstrated using well characterised plant and parasitic allergens such as Par j 2, SmTAL-1 and the IgE binding factor IPSE/alpha-1, expressed in wheat germ lysates and/or E. coli. SmTAL-1, but not SmTAL2 (used as a negative control), was able to activate the basophil reporter cell line.This method offers an accessible way for assessment of potential allergenicity of anti-helminthic vaccine candidates and is suitable for medium- to high-throughput studies using infected individual sera. It is also suitable for the study of the basis of allergenicity of helminthic proteins.