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Silicon is an essential or beneficial element in many organisms. Silicon uptake by vegetation strongly influences terrestrial silicon dynamics; however, little is known about the changes in plant silicon cycling during secondary succession. We hypothesized that the community-weighted mean (CWM) leaf silicon concentration changes along a secondary successional gradient because of turnover of species that differ in silicon uptake. We also hypothesized that the concentration of water-soluble soil silicon should increase with silicon input to the upper soil layer by increasing leaf litterfall. This study tested these predictions using chronosequence plots with stand ages of 16–100 years of secondary succession in a cool-temperate forest in Hokkaido, Japan. We measured the levels of leaf silicon in 36 woody species, leaf litter silicon, and water-extractable soil silicon and examined their correlations with stand age. The leaf silicon concentration varied by 17-fold from 0.6 to 10.3 mg Si g−1, with strong phylogenetic signals among woody species. Reflecting the turnover of species and change in their abundance, the CWM leaf and leaf litter silicon concentrations increased with stand age. In contrast, water-extractable soil silicon concentration decreased despite an increase in leaf-litter silicon flux. The water-extractable soil silicon concentration was strongly and positively correlated with the soil carbon concentration, which decreased with stand age. Our results suggest that during secondary succession, forest silicon dynamics are largely associated with changes in the abundance of silicon-accumulating woody plants and soil carbon dynamics, which potentially influences other silicon-dependent organisms in the ecosystem.

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.

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.

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.

ABSTRACT Mercury superfamily proteins, i.e. inner membrane-spanning proteins (MerC, MerE, MerF and MerT) and a periplasmic mercury-binding protein (MerP), transport mercury into the cytoplasm. A previous study demonstrated that a Mer transporter homolog exhibits cadmium transport activity; based on this, the present study aimed to evaluate the cadmium transport activity of MerC, MerE, MerF and MerT and the effects of MerP co-expression in Escherichia coli. Bacteria expressing MerC, MerE, MerF or MerT without MerP were more sensitive to cadmium and significantly absorbed more cadmium than did the control strain. Expression of MerP in combination with MerC, MerE, MerF or MerT increased the bacterial sensitivity to cadmium and cadmium accumulation compared to a single expression of MerC, MerE, MerF or MerT. Cadmium uptake mediated by MerC, MerE, MerF or MerT was inhibited under cold or acidic conditions. These findings suggest that MerC, MerE, MerF and MerT are broad-spectrum heavy metal transporters that mediate both mercury and cadmium transport into cells and that MerP accelerates the cadmium transport ability of MerC, MerE, MerF and MerT. The mercury transport proteins MerC, MerE, MerF and MerT present Cd transport activity and co-expression of MerP accelerated this activity in E. coli.

Background Impaired multi-ciliated cell (MCC) differentiation, observed in various airway diseases, causes prolonged inflammation and further airway complications due to malfunction of the airway defense mechanism. YAP/TAZ-TEAD signaling mediates multiple mechanisms associated with MCC differentiation. However, YAP reportedly has both negative and positive roles in MCC differentiation, putatively due to its dual functions in the cytoplasm and nucleus. We hypothesize inhibition of nuclear YAP, while preserving cytoplasmic YAP functions, restores MCCs in various airway disorders. Here, we investigated the relationship between YAP nuclear localization and the presence of MCCs across patients. In addition, we identified compounds that promote MCC differentiation based on the inhibitory function of YAP nuclear localization. Method Tracheal specimens were collected from patients with different conditions (smoking, irradiation, tracheotomy, dysphagia), and YAP nuclear localization in MCCs was assessed by immunohistochemistry at areas with varying MCC compositions. To identify compounds that efficiently inhibit nuclear localization of YAP, airway epithelial cells were treated with multiple YAP inhibitors and YAP nuclear localization was assessed by immunofluorescence-based image analysis. The effects of the compounds on MCC differentiation were evaluated using air-liquid interface (ALI) culture and rat airway squamous metaplasia models. Results In the human trachea, YAP nuclear localization in MCCs was negatively correlated with the proportion of MCCs in the epithelium, regardless of patient condition. Among known YAP inhibitors, verteporfin and atorvastatin efficiently inhibited YAP nuclear localization in cultured airway epithelial cells. Additionally, verteporfin promoted MCC differentiation in ALI culture and in vivo squamous metaplasia models. Conclusion Our findings suggest YAP nuclear localization in human airway MCCs is broadly associated with decreased MCCs regardless of patient condition, and verteporfin improves airway MCC differentiation.

Haptics applications are receiving increasing attention in entertainment, medical support systems, and various industries. Three-dimensional (3D) haptics is important to provide users real experiences. Conventional haptic devices consist of many motors and mechanical elements grounded in an environment. Therefore, they are large in size and heavy. Haptic devices using asymmetric vibrations can display illusion forces with mobile structures. However, they need additional structures (comprising actuators) to generate a 3D illusion force; however, the operational mechanism becomes complex. To solve this problem, we propose the use of a 3-degree-of-freedom (3DOF) oscillatory actuator that can generate a 3DOF vibration using only one actuator. This study describes the basic characteristics and operating verification of the 3DOF oscillatory actuator. The static thrust characteristics are quantified and analyzed using a finite element method. The dynamics are calculated based on numerical simulations using a dynamic model. The prototype’s experimental results show that the 3DOF actuator can generate 3DOF vibration.

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.

3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless, currently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore, this paper proposes a novel deep learning-based end-to-end Point-wise LiDAR and Image Multimodal Fusion Network (PMNet) for 3D segmentation of aerial point cloud by fusing aerial image features. PMNet respects basic characteristics of point cloud such as unordered, irregular format and permutation invariance. Notably, multi-view 3D scanned data can also be trained using PMNet since it considers aerial point cloud as a fully 3D representation. The proposed method was applied on two datasets (1) collected from the urban area of Osaka, Japan and (2) from the University of Houston campus, USA and its neighborhood. The quantitative and qualitative evaluation shows that PMNet outperforms other models which use non-fusion and multimodal fusion (observational-level fusion and feature-level fusion) strategies. In addition, the paper demonstrates the improved performance of the proposed model (PMNet) by over-sampling/augmenting the medium and minor classes in order to address the class-imbalance issues.

The pro-fibrotic effects of glucocorticoids may lead to a suboptimal therapeutic response for vocal fold (VF) pathology. Targeting macrophage-fibroblast interactions is an interesting therapeutic strategy; macrophages alter their phenotype to mediate both inflammation and fibrosis. In the current study, we investigated concentration-dependent effects of methylprednisolone on the fibrotic response, with an emphasis on YAP/TAZ-TEAD signaling, and inflammatory gene expression in VF fibroblasts in physical contact with macrophages. We sought to provide foundational data to optimize therapeutic strategies for millions of patients with voice/laryngeal disease-related disability. Following induction of inflammatory (M(IFN/LPS)) and fibrotic (M(TGF)) phenotypes, THP-1-derived macrophages were seeded onto HVOX vocal fold fibroblasts. Cells were co-cultured ± 0.3–3000 nM methylprednisolone ± 3 µM verteporfin, a YAP/TAZ inhibitor. Inflammatory ( CXCL10 , TNF , PTGS2 ) and fibrotic genes ( ACTA2 , CCN2 , COL1A1 ) in fibroblasts were analyzed by real-time polymerase chain reaction after cell sorting. Ser211-phosphorylated glucocorticoid receptor (S211-pGR) was assessed by Western blotting. Nuclear localization of S211-pGR and YAP/TAZ was analyzed by immunocytochemistry. Methylprednisolone decreased TNF and PTGS2 in fibroblasts co-cultured with M(IFN/LPS) macrophages and increased ACTA2 and CCN2 in fibroblasts co-cultured with M(IFN/LPS) and M(TGF). Lower concentrations were required to decrease TNF and PTGS2 expression and to increase S211-pGR than to increase ACTA2 and CCN2 expression and nuclear localization of S211-pGR. Methylprednisolone also increased YAP/TAZ nuclear localization. Verteporfin attenuated upregulation of CCN2 , but not PTGS2 downregulation. High concentration methylprednisolone induced nuclear localization of S211-pGR and upregulated fibrotic genes mediated by YAP/TAZ activation.