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Open AccessReview Impacts of Indoxyl Sulfate and p-Cresol Sulfate on Chronic Kidney Disease and Mitigating Effects of AST-120
Received: 30 July 2018 / Revised: 7 September 2018 / Accepted: 8 September 2018 / Published: 11 September 2018
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Abstract
Uremic toxins, such as indoxyl sulfate (IS) and p-cresol, or p-cresyl sulfate (PCS), are markedly accumulated in the organs of chronic kidney disease (CKD) patients. These toxins can induce inflammatory reactions and enhance oxidative stress, prompting glomerular sclerosis and interstitial fibrosis, to aggravate
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  • 778| 563| 785| 13| 202| 236| 19| 643| 957| 271| [...] Read more.
    Uremic toxins, such as indoxyl sulfate (IS) and p-cresol, or p-cresyl sulfate (PCS), are markedly accumulated in the organs of chronic kidney disease (CKD) patients. These toxins can induce inflammatory reactions and enhance oxidative stress, prompting glomerular sclerosis and interstitial fibrosis, to aggravate the decline of renal function. Consequently, uremic toxins play an important role in the worsening of renal and cardiovascular functions. Furthermore, they destroy the quantity and quality of bone. Oral sorbent AST-120 reduces serum levels of uremic toxins in CKD patients by adsorbing the precursors of IS and PCS generated by amino acid metabolism in the intestine. Accordingly, AST-120 decreases the serum IS levels and reduces the production of reactive oxygen species by endothelial cells, to impede the subsequent oxidative stress. This slows the progression of cardiovascular and renal diseases and improves bone metabolism in CKD patients. Although large-scale studies showed no obvious benefits from adding AST-120 to the standard therapy for CKD patients, subsequent sporadic studies may support its use. This article summarizes the mechanisms of the uremic toxins, IS, and PCS, and discusses the multiple effects of AST-120 in CKD patients. Full article
    (This article belongs to the Section Uremic Toxins)
    Figures

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    Figure 1
    <p>Mechanisms of IS pathology. IS enters renal tubular cells or VSMCs through OAT1 and OAT3. It induces free radical production, reduces nitric oxide levels, and activates AHR to produce ROS that damage cells. In the cardiovascular system, damaged endothelial cells and VSMCs secrete elevated amounts of NADPH and Nox4, while secreting less KLOTHO, a protective role in the kidney. This causes dysfunction of endothelial cells and osteoblastic phenotyped-VSMCs, which ultimately lead to atherosclerosis and arteriosclerosis. In the kidney, injured tubular cells and mesangial cells secrete various cytokines to promote EMT transition, which results in tubular and interstitial cell fibrosis. In the bone, at early-stage CKD, hyperphosphatemia, hypocalcemia, vitamin D deficiency, FGF23 elevation, and parathyroid hormone (PTH) elevation lead to bone fragility and fracture. IS potentiates this condition. Upon further worsening of the renal function, the viability and function of osteoblasts and osteoclasts are impaired and PTH is secreted, leading to reduced bone quantity. This is called CKD-MBD, or renal osteodystrophy. IS causes deterioration of some material properties of the bone. Changes in these material properties perturb bone elasticity, leading to a decline in bone quality. This disease concept is called “uremic osteoporosis”. AHR, aryl hydrocarbon receptor; CBF-1, core binding factor 1; CKD-MBD, chronic kidney disease-mineral bone disease; CYP1A1, cytochrome P450 family 1 subfamily A member 1; EMT, epithelial-to-mesenchymal transition; FGF23, fibroblast growth factor 23; ICAM-1, intercellular adhesion molecule 1; IS, indoxyl sulfate; NADPH, nicotinamide adenine dinucleotide phosphate hydrogen; NF-κB, nuclear factor kappa B; NO, nitric oxide; Nox4, NADPH Oxidase 4; OAT, organic anion transporters; ROS, reactive oxygen species; PAI-1, plasminogen activation inhibitor -1; PTH, parathyroid hormone; TGF-β1, transforming growth factor β-1; VSMCs, vascular smooth muscle cells.</p>
    Full article ">
    Open AccessArticle Association of Anabolic Effect of Calcitriol with Osteoclast-Derived Wnt 10b Secretion
    Nutrients 2018, 10(9), 1164; https://doi.org/10.3390/nu10091164
    Received: 18 July 2018 / Revised: 21 August 2018 / Accepted: 23 August 2018 / Published: 25 August 2018
    PDF Full-text (32107 KB) | HTML Full-text | XML Full-text
    Abstract
    Canonical Wnt (Wingless/Integrated) signaling is crucial in bone development and the Wnt ligand can promote osteoblast differentiation from mesenchymal progenitor cells. Calcitriol, an active vitamin D3, is used clinically for treatment of secondary hyperparathyroidism (SHPT) in chronic kidney disease (CKD) patients. The bone
    [...] Read more.
    Canonical Wnt (Wingless/Integrated) signaling is crucial in bone development and the Wnt ligand can promote osteoblast differentiation from mesenchymal progenitor cells. Calcitriol, an active vitamin D3, is used clinically for treatment of secondary hyperparathyroidism (SHPT) in chronic kidney disease (CKD) patients. The bone effects of calcitriol in SHPT remains uncertain. We hypothesized that calcitriol improves bone mass by suppressing osteoclast activity, and simultaneously promoting Wnt ligand secretion. We designed a cross-sectional study in maintenance hemodialysis patients to explore the effects of calcitriol on different bone turnover markers and specifically emphasized the Wnt 10b levels. Then, we explored the source of Wnt 10b secretion by using osteoclasts and osteoblasts treated with calcitriol in cell culture studies. Finally, we explored the effects of calcitriol on bone microarchitectures in CKD mice, using the 5/6 nephrectomy CKD animal model with analysis using micro-computed tomography. Calcitriol promoted the growth of both trabecular and cortical bones in the CKD mice. Wnt 10b and Procollagen 1 N-terminal Propeptide (P1NP) significantly increased, but Tartrate-resistant acid phosphatase 5b (Trap 5b) significantly decreased in the calcitriol-treated maintenance hemodialysis patients. Calcitriol enhanced Wnt 10b secretion from osteoclasts in a dose-dependent manner. Treatment of SHPT with calcitriol improved the bone anabolism by inhibiting osteoclasts and promoting osteoblasts that might be achieved by increasing the Wnt 10b level. Full article
    Figures

    Figure 1

    Figure 1
    <p>Comparison of serum Wnt 10b levels to the use of calcitriol in hemodialysis patients with PTH &lt; 300 pg/mL group (left) and PTH &gt; 300 pg/mL group (right). Values are means ± SD. (Blue: No calcitriol; green: Calcitriol use). In patients with PTH &gt; 300 pg/mL, Wnt 10b was significantly greater in patients treated with calcitriol than those not treated with calcitriol. Because the PTH value was measured at the end of three months’ calcitriol treatment, there were 55 patients with PTH &lt; 300 pg/mL given calcitriol. Wnt 10b was also significantly greater in patients treated with calcitriol than those not treated with calcitriol. ** <italic>p</italic> &lt; 0.01.</p>
    Full article ">Figure 2
    <p>The comparison of serum Wnt 10b level to the use of calcitriol in patients with skeletal bone resistance disease favored (Left, PTH &gt; 300 pg/mL and ALP &lt; 155 IU/L) and high bone turnover disease favored (Right, PTH &gt; 300 pg/mL and ALP &gt; 155 IU/L). Values are means ± SD. (Blue: No calcitriol; green: Calcitriol). Wnt 10b was significantly increased in both skeletal bone resistance disease group and high bone turnover disease group with calcitriol treatment. ** <italic>p</italic> &lt; 0.01.</p>
    Full article ">Figure 3
    <p>Comparison of serum protein level of Wnt inhibitors (DKK-1 and sclerostin) to the use of calcitriol in different PTH group. ((<bold>A</bold>). PTH &lt; 150 pg/mL, (<bold>B</bold>). PTH 151–300 pg/mL, (<bold>C</bold>). PTH &gt; 300 pg/mL.) Values are means ± SD. (Blue: No calcitriol; green: Calcitriol). DKK-1 was significantly lower in the PTH &lt; 150 pg/mL subgroup ((<bold>A</bold>). <italic>p</italic> &lt; 0.05) and sclerostin was significantly lower in both the PTH 151–300 pg/mL subgroup ((<bold>B</bold>). <italic>p</italic> &lt; 0.01) and PTH &gt; 300 pg/mL subgroup ((<bold>C</bold>). <italic>p</italic> &lt; 0.05). * <italic>p</italic> &lt; 0.05, ** <italic>p</italic> &lt; 0.01.</p>
    Full article ">Figure 4
    <p>The comparison of serum bone turnover marker in patient without calcitriol use in 25(OH)D level &lt; 30 ng/mL vs. &gt; 30 ng/mL group. Values are means ± SD. (Blue: 25(OH)D &lt; 30 ng/mL; green: 25(OH)D &gt; 30 ng/mL). Patients with 25(OH)D &gt; 30 ng/mL had significantly higher Wnt 10b levels but lower DKK-1, Trap 5b, and OPG levels compared to the group with 25(OH)D &lt; 30 ng/mL. * <italic>p</italic> &lt; 0.05, ** <italic>p</italic> &lt; 0.01.</p>
    Full article ">Figure 5
    <p>The comparison of serum Wnt 10b level to the serum 25(OH)D level in patients without (left) or with (right) calcitriol treatment. Values are means ± SD. (Blue: 25(OH)D &lt; 30 ng/mL; green: 25(OH)D &gt; 30 ng/mL). Wnt 10b was increased significantly in patients with 25(OH)D &gt; 30 ng/mL if they did not receive calcitriol treatment in the past three months, but this significance was lost in the calcitriol treatment group. ** <italic>p</italic> &lt; 0.01.</p>
    Full article ">Figure 6
    <p>Tartrate-resistant acid phosphatase (Trap) stain of the osteoclast. Calcitriol inhibits the fusion ability and the number of TRAP-positive differentiated osteoclast cell in a dose-dependent manner. Multinuclear TRAP-positive cells containing more than three nuclei were scored as osteoclasts. Data are mean number ± SD of osteoclasts. (* <italic>p</italic> &lt; 0.05 versus control group, <italic>n</italic> = 3).</p>
    Full article ">Figure 7
    <p>Confocal analysis of immunofluorescent labeling of Wnt 10b (green) was greater in osteoclasts treated with 1, 10, or 100 nM calcitriol compared to control. Actin was labeled with Cy3 (red). Bar = 20 μm.</p>
    Full article ">Figure 8
    <p>Wnt10b and Wnt 16 protein expression in calcitriol-treated osteoclasts. With calcitriol treatment of 10 to 100 nM overnight, Western blot analysis showed a significant increase in Wnt 10b expression. Wnt 16 expression in osteoclasts after different doses of calcitriol was not significantly different at the protein level. Actin protein served as a loading control. Data are means ± SD. (* <italic>p</italic> &lt; 0.05 versus control group, <italic>n</italic> = 3).</p>
    Full article ">Figure 9
    <p>Wnt10b and Wnt 16 protein expression in calcitriol-treated osteoblasts. After preosteoblast 7F2 cells were stimulated with 100 μg/mL ascorbic acid and 10 mM β-glycerol phosphate, Western blot analysis showed both Wnt 10b and Wnt 16 expression in osteoblast at different doses of calcitriol was not significantly different at the protein level. Actin protein served as a loading control. Data are means ± SD (<italic>n</italic> = 3).</p>
    Full article ">Figure 10
    <p>Three-dimensional (3D) image obtained via Bruker micro-tomography. (<bold>A</bold>) Trabecular bone mass (yellow part) of femur bone was greater after calcitriol treatment. (<bold>B</bold>) The growth of trabecular and cortical bone all increased significantly, especially calcitriol dose of 150 IU/kg for one month. (Blue: Sham operated control; green: CKD mice, No calcitriol; yellow: CKD mice, 25 IU/kg calcitriol use; red: CKD mice, 150 IU/kg calcitriol use). All experiments in this figure use mice in the C57/Bl6 background. (* <italic>p</italic> &lt; 0.05, <italic>n</italic> = 3).</p>
    Full article ">
    Open AccessArticle Evaluation of the Polarimetric-Radar Quantitative Precipitation Estimates of an Extremely Heavy Rainfall Event and Nine Common Rainfall Events in Guangzhou
    Atmosphere 2018, 9(9), 330; https://doi.org/10.3390/atmos9090330
    Received: 26 April 2018 / Revised: 6 August 2018 / Accepted: 20 August 2018 / Published: 22 August 2018
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    Abstract
    The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even
    [...] Read more.
    The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation <50 mm) in this extremely heavy rainfall event. However, it severely underestimated heavy rain (>50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve Dm and Nw from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on Dm and Nw. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when Dm and Nw are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(KDP, ZDR) underestimates most heavy rain. Therefore, R(AH), which is least sensitive to DSD variations, replaces R(KDP, ZDR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China. Full article
    (This article belongs to the Section Climatology and Meteorology)
    Figures

    Figure 1

    Figure 1
    <p>Distributions of (<bold>a</bold>) total rainfall observed at the automatic weather stations (AWSs) and (<bold>b</bold>) the average radar reflectivity for 1900–2400 UTC on 6 May 2017; (<bold>c</bold>) the enlarged area denoted by the square in (<bold>b</bold>). In (<bold>a</bold>), precipitation within the area enclosed by circles is evaluated. In (<bold>b</bold>), the small square indicates the location of the Yangjiang disdrometer (21.84° N, 111.98° E, 90 m above mean sea level). In (<bold>c</bold>), the triangle denotes the radar location (23.00° N, 113.36° E, 179 m), the “+” symbols indicate the three AWSs (23.28° N, 113.57° E, 75 m; 23.23° N, 113.60° E, 48 m; 23.28° N, 113.62° E, 50 m) where total rainfall exceeded 300 mm, and the small square indicates the location of the disdrometer; LG denotes the Luogang disdrometer (23.22° N, 113.48° E, 71 m).</p>
    Full article ">Figure 2
    <p>Radar reflectivity at 0.5° elevation during rainfall event on 6 May 2017: (<bold>a</bold>) 1900 UTC, (<bold>b</bold>) 2000 UTC, (<bold>c</bold>) 2100 UTC, (<bold>d</bold>) 2200 UTC, (<bold>e</bold>) 2300 UTC, and (<bold>f</bold>) 2400 UTC.</p>
    Full article ">Figure 3
    <p>Radial velocity at 0.5° elevation during rainfall event on 6 May 2017: (<bold>a</bold>) 1900 UTC, (<bold>b</bold>) 2000 UTC, (<bold>c</bold>) 2100 UTC, (<bold>d</bold>) 2200 UTC, (<bold>e</bold>) 2300 UTC, and (<bold>f</bold>) 2400 UTC.</p>
    Full article ">Figure 4
    <p>Flowchart describing the precipitation-estimation algorithm.</p>
    Full article ">Figure 5
    <p>Scatterplots of rain variables (<italic>D</italic><sub>m</sub> and <italic>N</italic><sub>w</sub>) and radar variables (<italic>Z</italic><sub>H</sub> and <italic>Z</italic><sub>DR</sub>) calculated from drop size distribution (DSD) data. The red points are mean data value, which are fitted to the black data points by using a three-order polynomial fit with the least-squares method. (<bold>a</bold>) <italic>D</italic><sub>m</sub> vs. <italic>Z</italic><sub>DR</sub> (Equation (16)), and (<bold>b</bold>) ratio of <italic>N</italic><sub>w</sub> to <italic>Z</italic><sub>H</sub> vs. <italic>Z</italic><sub>DR</sub> (Equation (17)). Correlation coefficient (CC) and normalized relative bias (NB) for the retrieved rain variables versus the observations are shown in this figure.</p>
    Full article ">Figure 6
    <p>Hourly rainfall accumulations at stations where the largest rainfall accumulations were observed. (<bold>a</bold>–<bold>c</bold>) show the QPE results of the algorithm at stations 1–3, respectively. The column in blue indicates an AWS observation, and the column in red indicates the radar estimate.</p>
    Full article ">Figure 7
    <p>Occurrence frequencies of (<bold>a</bold>) <italic>Z</italic><sub>DR</sub> versus <italic>Z</italic><sub>H</sub> and (<bold>b</bold>) <italic>K</italic><sub>DP</sub> versus <italic>Z</italic><sub>H</sub> from polarimetric radar. The average <italic>Z</italic><sub>DR</sub>–<italic>Z</italic><sub>H</sub> and <italic>K</italic><sub>DP</sub>–<italic>Z</italic><sub>H</sub> relationships were calculated from DSDs at the Yangjiang (black curves) and Luogang stations (red curves) for this rainfall event.</p>
    Full article ">Figure 8
    <p>(<bold>a</bold>) <italic>D</italic><sub>m</sub> retrieved from radar observations versus DSD data. (<bold>b</bold>) log<sub>10</sub><italic>N</italic><sub>w</sub> retrieved from radar observations versus DSD data.</p>
    Full article ">Figure 9
    <p>(<bold>a</bold>) Occurrence frequencies of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub>. Two black rectangles correspond to the maritime and continental convective clusters, respectively, reported by Bringi et al. [<xref ref-type="bibr" rid="B40-atmosphere-09-00330">40</xref>]. The red “+” symbol indicates the average value of log<sub>10</sub><italic>N</italic><sub>w</sub> (4.06) and <italic>D</italic><sub>m</sub> (1.44 mm) retrieved from radar data and the red “×” symbol indicates the average value of log<sub>10</sub><italic>N</italic><sub>w</sub> (3.54) and <italic>D</italic><sub>m</sub> (1.90 mm) retrieved from Yangjiang DSD data for convective precipitation. (<bold>b</bold>) The bias between rain rate retrieved from radar data and rain rate observed at AWSs of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub> for hourly accumulation &lt;50 mm. (<bold>c</bold>) Biases for hourly accumulation &gt;50 mm. (<bold>d</bold>) Rain rate observed at AWSs of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub>. The pink rectangles indicate rain rate observed at station 1 during 2000–2400 UTC on 6 May 2017 and the red rectangles indicate rain rate observed at station 2 during 2100–2300 UTC on 6 May 2017 and that observed at station 3 during 2200–2300 UTC on 6 May 2017, during which underestimations occurred in each corresponding station. (<bold>e</bold>) Rain rate observed at AWSs of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub> for hourly accumulation &lt;50 mm. (<bold>f</bold>) Rain rate observed at AWSs of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub> for hourly accumulation &gt;50 mm. Note that all these data are averaged from two adjacent data in time (average time: 12 min).</p>
    Full article ">Figure 10
    <p><italic>R</italic><sub>QPE</sub> (rain rate calculated with the QPE algorithm) minus <italic>R</italic><sub>Gauge</sub> (rain rate measured at the AWS) at the three stations with the heaviest rainfall. (<bold>a</bold>–<bold>c</bold>) show the values for stations 1–3, respectively. The different colors represent the various estimators used during this rainfall event.</p>
    Full article ">Figure 11
    <p>Time series of polarimetric radar (PR) variables at the three stations where the heaviest rainfall occurred. The columns from left to right represent stations 1–3, and the rows from top to bottom show <italic>Z</italic><sub>H</sub> (<bold>a</bold>–<bold>c</bold>), <italic>Z</italic><sub>DR</sub> (<bold>d</bold>–<bold>f</bold>), <italic>K</italic><sub>DP</sub> (<bold>g</bold>–<bold>i</bold>), <italic>ρ</italic><sub>HV</sub> (<bold>j</bold>–<bold>l</bold>), and Hcl (<bold>m</bold>–<bold>o</bold>). The panels (<bold>p</bold>–<bold>r</bold>) in the bottom (sixth) row show the time series of rain rate observed (solid lines) at each station and those derived from the radar data (red and blue dash lines: estimated by using algorithms presented in <xref ref-type="sec" rid="sec3dot1-atmosphere-09-00330">Section 3.1</xref> and <xref ref-type="sec" rid="sec6-atmosphere-09-00330">Section 6</xref>, respectively). The black lines denote the observation heights used for precipitation estimation.</p>
    Full article ">Figure 12
    <p>(<bold>a</bold>) Rain rate observed at the AWSs of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub> derived from the PR data. Most high rain rate data are distributed in the red circle. (<bold>b</bold>) NE decreased percentage ((NE<sub>before</sub> ? NE<sub>after</sub>)/NE<sub>before</sub> × 100%) after algorithm improvement of log<sub>10</sub><italic>N</italic><sub>w</sub> versus <italic>D</italic><sub>m</sub> derived from the PR data. Note that all these data are averaged from two adjacent data in time (average time: 12 min).</p>
    Full article ">Figure 13
    <p>Hourly rainfall accumulations at stations where the largest rainfall accumulations were observed. (<bold>a</bold>–<bold>c</bold>) show the results of the algorithms at stations 1–3, respectively. The column in blue indicates an AWS observation, the column in red indicates the radar estimate based on the unimproved algorithm, and the column in green indicates the radar estimate based on the improved algorithm.</p>
    Full article ">
    Open AccessArticle Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan
    Remote Sens. 2018, 10(8), 1304; https://doi.org/10.3390/rs10081304
    Received: 7 July 2018 / Revised: 10 August 2018 / Accepted: 16 August 2018 / Published: 18 August 2018
    PDF Full-text (8428 KB) | HTML Full-text | XML Full-text
    Abstract
    In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as
    [...] Read more.
    In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas. Full article
    Figures

    Figure 1

    Figure 1
    <p>The map of the study area, Urayasu City, Japan. (<bold>a</bold>) The geographic location of Urayasu City; (<bold>b</bold>) the distribution and development history of the reclaimed areas, namely Moto-Machi (old town) outlined in green, Naka-Machi (central town) outlined in yellow and Shin-Machi (new town) outlined in red. A to G represent the reclaimed areas at different times. The background image is a Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS-2 PALSAR-2) intensity image acquired on 4 December 2014; and (<bold>c</bold>) the topography of the study area [<xref ref-type="bibr" rid="B36-remotesensing-10-01304">36</xref>].</p>
    Full article ">Figure 2
    <p>The temporal and spatial baseline distributions of the SAR interferograms from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data sets (a–e), where each acquisition is represented by a diamond associated to an ID number; the green diamonds represent the valid acquisitions and the yellow diamonds represent the selected master image of persistent scatterers interferometry (PSI) and super master image of the small baseline subset (SBAS). (<bold>a</bold>) Time–position plot of PSI interferograms generated by the ERS-1/-2 data, with 24 January 2000 as the master image; (<bold>b</bold>) time–baseline plot of SBAS interferograms generated by the ERS-1/-2 data, with 2 August 1999 as the super master image; (<bold>c</bold>) time–position plot of PSI interferograms generated by the ALOS PALSAR data, with 5 August 2009 as the master image; (<bold>d</bold>) time–position Delaunay 3D plot of SBAS interferograms generated by the ALOS PALSAR data, with March 20, 2009 as the super master image; (<bold>e</bold>) time–position Delaunay 3D plot of SBAS interferograms generated by the ALOS-2 PALSAR-2 data, with 4 December 2014 as the super master image; and (<bold>f</bold>) the histogram of the average coherence for the three satellite datasets. These connections in (<bold>d</bold>,(<bold>e</bold>) are a subset of the whole main network and represent such interferograms that will be unwrapped in a 3D way.</p>
    Full article ">Figure 3
    <p>Line of sight (LOS) displacement velocity in Urayasu City from 1993 to 2006 for the ERS-1/-2 data: (<bold>a</bold>) Estimated mean displacement velocity using the PSI method; (<bold>b</bold>) estimated mean displacement velocity using the SBAS method. The background image is an ERS-2 intensity image acquired on 24 May 1999. The red points P1 to P8 are the selected points to show the time-series LOS displacements estimated by the PSI and SBAS measurements in (<bold>a</bold>,<bold>b</bold>), respectively.</p>
    Full article ">Figure 4
    <p>Histogram distribution for the ERS-1/-2-derived displacement rates from May 1993 to February 2006: (<bold>a</bold>) the corresponding histogram of the PSI measurements from the ERS-1/-2 data; and (<bold>b</bold>) the corresponding histogram of the SBAS measurements from the ERS-1/-2 data.</p>
    Full article ">Figure 5
    <p>Time-series LOS displacement plots of the PSI and SBAS measurements from the ERS-1/-2 data (<bold>a</bold>–<bold>h</bold>) for the selected points P1 to P8, which are indicated by red points in <xref ref-type="fig" rid="remotesensing-10-01304-f003">Figure 3</xref>.</p>
    Full article ">Figure 6
    <p>Mean LOS displacement velocity in Urayasu City from 2006 to 2010 for the PALSAR data: (<bold>a</bold>) estimated mean displacement velocity using the PSI method; (<bold>b</bold>) estimated mean displacement velocity using the SBAS method. The background image is a PALSAR-2 intensity image acquired on 04 December 2014. The red points P1 to P8 are the selected points to show the time-series LOS displacements estimated by the PSI and SBAS measurements in (<bold>a</bold>,<bold>b</bold>), respectively. A-G represent the reclaimed areas and districts which described in <xref ref-type="table" rid="remotesensing-10-01304-t001">Table 1</xref>.</p>
    Full article ">Figure 7
    <p>Histogram distribution for the PALSAR-derived results from June 2006 to December 2010. (<bold>a</bold>) The corresponding histogram of the PSI measurements from the PALSAR data; and (<bold>b</bold>) the corresponding histogram of the SBAS measurements from the PALSAR data.</p>
    Full article ">Figure 8
    <p>Time-series LOS displacement plots of the PSI and SBAS measurements (<bold>a</bold>–<bold>h</bold>) for points P1 to P8, which are indicated as red points in <xref ref-type="fig" rid="remotesensing-10-01304-f006">Figure 6</xref>a,b, respectively.</p>
    Full article ">Figure 9
    <p>Mean LOS displacement velocity in Urayasu City from 2014 to 2017 for the PALSAR-2 data. The background image is a PALSAR-2 intensity image acquired on 4 December 2014. P1–P1’ to P6–P6’ are the selected profiles to show the displacement velocities at different sites.</p>
    Full article ">Figure 10
    <p>The corresponding histogram of the SBAS measurements from the PALSAR-2 data.</p>
    Full article ">Figure 11
    <p>Mean LOS displacement velocities for the three observation periods (<bold>a</bold>–<bold>f</bold>) along the six profiles whose positions are indicated as purple lines in <xref ref-type="fig" rid="remotesensing-10-01304-f009">Figure 9</xref>.</p>
    Full article ">Figure 12
    <p>Comparison between InSAR-derived linear subsidence velocity and leveling measured linear subsidence velocity during the three InSAR observation periods: (<bold>a</bold>,<bold>b</bold>) ERS-1/-2 derived linear subsidence rate (May 1993 to February 2006) and leveling-derived linear subsidence rate (January 1993 to January 2006); (<bold>c</bold>,<bold>d</bold>) PALSAR-derived linear subsidence rate (June 2006 to December 2010) and leveling-derived linear subsidence rate (January 2006 to January 2011); (<bold>e</bold>) PALSAR-2-derived linear subsidence rate (December 2014 to December 2016) and leveling-derived linear subsidence rate (January 2015 to January 2017); and (<bold>f</bold>) spatial distribution of leveling points in Urayasu City.</p>
    Full article ">Figure 13
    <p>The spatial distribution map of difference of land subsidence rates during the three observation periods: (<bold>a</bold>) ERS-1/-2 derived subsidence rate using the SBAS method; (<bold>b</bold>) difference between ERS-1/-2 and PALSAR derived subsidence rates (subtracting ERS-1/-2 from PALSAR); (<bold>c</bold>) difference between PALSAR and PALSAR-2 derived subsidence rates (subtracting PALSAR from PALSAR-2); (<bold>d</bold>) difference between ERS-1/-2 and PALSAR-2 derived subsidence rates (subtracting ERS-1/-2 from PALSAR-2).</p>
    Full article ">Figure 14
    <p>Depth of the upper surface of the solid geological stratum (<bold>a</bold>) in Urayasu City (adapted from the public report by the technical committee of Urayasu City [<xref ref-type="bibr" rid="B54-remotesensing-10-01304">54</xref>]). The points refer to the locations of borehole sites; (<bold>b</bold>) soil cross sections along the A–A’ line. FS + AS refer to filled sandy soil and alluvial sand layers, and AC and DS refer to the alluvial clay layer and diluvial dense sandy layer, respectively. The borehole investigation data were obtained from the Chiba Prefecture [<xref ref-type="bibr" rid="B55-remotesensing-10-01304">55</xref>].</p>
    Full article ">Figure 15
    <p>Subsidence rate map (2006–2010) generated with ALOS PALSAR data overlaid on a Google Earth image. The green polygons indicate the park area, red polygons indicate the location of high-rise buildings, the yellow polygon shows the highly populated residential area. The blue polygon indicates the border of Urayasu City and corresponds to the location of <xref ref-type="fig" rid="remotesensing-10-01304-f014">Figure 14</xref>a, and the A–A’ line corresponds to the soil cross section in <xref ref-type="fig" rid="remotesensing-10-01304-f014">Figure 14</xref>a,b.</p>
    Full article ">
    Open AccessArticle Does the Exhaustion of Resources Drive Land Use Changes? Evidence from the Influence of Coal Resources-Exhaustion on Coal Resources–Based Industry Land Use Changes
    Sustainability 2018, 10(8), 2698; https://doi.org/10.3390/su10082698
    Received: 3 July 2018 / Revised: 30 July 2018 / Accepted: 31 July 2018 / Published: 1 August 2018
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    Abstract
    Analyzing the spatial-temporal changes of resources–based industrial land is essential to the transformation and development of resources–exhausted cities. In this paper, we studied coal resources–based industrial land use changes and their driving factors in a typical coal resources–exhausted city, Anyuan District, Pingxiang city.
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    Analyzing the spatial-temporal changes of resources–based industrial land is essential to the transformation and development of resources–exhausted cities. In this paper, we studied coal resources–based industrial land use changes and their driving factors in a typical coal resources–exhausted city, Anyuan District, Pingxiang city. The changes between coal resources–based industrial land and other land-use types were analyzed. The logistic regression models were applied to identify the main driving factors and quantify their contributions to coal resources–based industrial land-use changes during the two periods of 2003–2008 and 2008–2013. The results show that coal resources–based industrial land declined by 34.37% during the period 2008–2013 as coal resources were being exhausted. Altitude, distance to roads, distance to town, population density change, fixed-asset investment per area change, and GDP per capita change drove coal resources–based industrial land-use changes. However, the patterns of the driving effects differed, and even the same factors had different influences on coal resources–based industrial land-use changes during the two periods. The changes in the driving factors can be seen as responses to socioeconomic transformation and development in the city, which is experiencing the exhaustion of coal resources. As a result of the comprehensive effects of these driving factors, coal resources–based industrial land use has changed in complex ways. Full article
    Figures

    Figure 1

    Figure 1
    <p>Location of Anyuan District.</p>
    Full article ">Figure 2
    <p>Changes in coal resources–based industrial land in Anyuan District.</p>
    Full article ">Figure 3
    <p>Area for each type of land use in Anyuan District during the period 2003–2013.</p>
    Full article ">Figure 4
    <p>Vegetation covers change from 2003 to 2013. (<bold>a</bold>): vegetation cover in 2003; (<bold>b</bold>): vegetation cover in 2008; (<bold>c</bold>): vegetation cover in 2013.</p>
    Full article ">
    Open AccessArticle Manzamine A Exerts Anticancer Activity against Human Colorectal Cancer Cells
    Mar. Drugs 2018, 16(8), 252; https://doi.org/10.3390/md16080252
    Received: 11 June 2018 / Revised: 24 June 2018 / Accepted: 27 July 2018 / Published: 29 July 2018
    PDF Full-text (4289 KB) | HTML Full-text | XML Full-text | Supplementary Files
    Abstract
    Marine sponges are known to produce numerous bioactive secondary metabolites as defense strategies to avoid predation. Manzamine A is a sponge-derived β-carboline-fused pentacyclic alkaloid with various bioactivities, including recently reported anticancer activity on pancreatic cancer. However, its cytotoxicity and mode of action against
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    Marine sponges are known to produce numerous bioactive secondary metabolites as defense strategies to avoid predation. Manzamine A is a sponge-derived β-carboline-fused pentacyclic alkaloid with various bioactivities, including recently reported anticancer activity on pancreatic cancer. However, its cytotoxicity and mode of action against other tumors remain unclear. In this study, we exhibit that manzamine A reduced cell proliferation in several colorectal cancer (CRC) cell lines. To further investigate the manzamine A triggered molecular regulation, we analyzed the gene expression with microarray and revealed that pathways including cell cycle, DNA repair, mRNA metabolism, and apoptosis were dysregulated. We verified that manzamine A induced cell cycle arrest at G0/G1 phase via inhibition of cyclin-dependent kinases by p53/p21/p27 and triggered a caspase-dependent apoptotic cell death through mitochondrial membrane potential depletion. Additionally, we performed bioinformatics analysis and demonstrated that manzamine A abolished epithelial–mesenchymal transition process. Several mesenchymal transcriptional factors, such as Snail, Slug, and Twist were suppressed and epithelial marker E-cadherin was induced simultaneously in HCT116 cells by manzamine A, leading to the epithelial-like phenotype and suppression of migration. These findings suggest that manzamine A may serve as a starting point for the development of an anticancer drug for the treatment of metastatic CRC. Full article
    (This article belongs to the collection Marine Compounds and Cancer) Printed Edition available
    Figures

    Figure 1

    Figure 1
    <p>Manz A reduced cell proliferation in colorectal cancer cells. (<bold>A</bold>) HCT116, DLD-1, and HT-29 cells were treated with Manz A at various concentrations of 0, 0.5, 1, 2.5, and 5 μM for 24 h. Cell viability (%) was measured using MTS cell proliferation assay and data was expressed as percentage of absorbance from Manz A treated cells compared to DMSO treated ones; (<bold>B</bold>) Colony formation assay was performed to determine the long-term effects of Manz A on the growth of HCT116 cells. Cells were pre-treated with 0.1% DMSO or 5 μM Manz A for 24 h and left for 7 days to grow. Colonies were then stained with Giemsa. The data were expressed as the mean ± SD of three independent experiments. * <italic>p</italic> &lt; 0.05, ** <italic>p</italic> &lt; 0.01, *** <italic>p</italic> &lt; 0.001.</p>
    Full article ">Figure 2
    <p>The enrichment map of gene ontology biological processes (GOBPs) enriched in GSEA result. GSEA was performed on microarray data to enrich terms in GOBP. Enrichment results were visualized with Cytoscape 3.0 using Enrichment Map plugin. Each node indicates an enriched term and edges represent overlapped genes between terms. The color of node boarder and edge were shown according to enrichment FDR and similarity, respectively. Blue and red nodes refer to enriched functions in DMSO vehicle control (CTL) and Manz A treatment. The size of node and edge width represents the number of genes enriched in each term and overlapped between terms, respectively.</p>
    Full article ">Figure 3
    <p>Manz A induced G<sub>0</sub>/G<sub>1</sub> phase arrest. (<bold>A</bold>) HCT116 cells were treated with 5 μM Manz A for 24 h and then subjected to DNA content analysis by flow cytometry. The cell cycle distribution was quantified with model fitting in FlowJo; (<bold>B</bold>) Cell cycle distributions from three independent experiments is shown. * <italic>p</italic> &lt; 0.05, ** <italic>p</italic> &lt; 0.01, *** <italic>p</italic> &lt; 0.001; (<bold>C</bold>) Representative western blot analyses of the cell cycle regulator levels in response to Manz A treatment in HCT116 cells. ACTIN was used as an internal control; (<bold>D</bold>) Relative expression level of each protein was densitometrically estimated and normalized to that of ACTIN under the same treatment. Data is presented in log<sub>2</sub> ratio of the protein level in Manz A treated cells to that in DMSO treated ones from three independent results.</p>
    Full article ">Figure 4
    <p>Manz A induced apoptosis in HCT116 cells. Cells were treated with 5 μM Manz A for 24 h. (<bold>A</bold>) Cells were harvested and stained with Annexin V-FITC and propidium iodide (PI). The fluorescent signal was measured by flow cytometry. A representative result is shown in the left panel and statistics analysis is at the right panel; (<bold>B</bold>) Cells were subjected to mitochondrial membrane potential detection by JC-1 and flow cytometry; (<bold>C</bold>) Cells were subjected to caspase 3/7 activity assay by DNA fluorescent dye containing caspase 3/7 cleavage site. Values are expressed as the mean ± SD from three independent experiments. * <italic>p</italic> &lt; 0.05, ** <italic>p</italic> &lt; 0.01, *** <italic>p</italic> &lt; 0.001.</p>
    Full article ">Figure 5
    <p>EMT was inactivated in Manz A treated HCT116 cells and normal colorectal tissues. Genes involved in EMT inactivation and activation were collected as two gene sets for GSEA to test the gene expression pattern between (<bold>A</bold>) MA-treated and DMSO-treated HCT116 cells or (<bold>C</bold>) the pattern between CRC and healthy control clinical tissues. Relative gene expression was ranked in descending order and colored from red to blue in response to MA (<bold>A</bold>) or occurrence of disease (<bold>C</bold>). Green line indicates the profile of running ES score and black lines represent the positions of gene set members on the rank ordered list according to their relative levels in Manz A (MA) compared to DMSO vehicle control (CTL) or CRC tumors (CRC) compared to biopsies from healthy controls (Normal). Relative expression levels of genes involved in EMT inactivation and activation were compared in (<bold>B</bold>) MA-treated HCT116 and (<bold>D</bold>) clinical tissues. Data is shown in mean expression of Z-transformed expression level. * <italic>p</italic> &lt; 0.05.</p>
    Full article ">Figure 6
    <p>Manz A reversed epithelial-mesenchymal transition (EMT) and decreased cell mobility in HCT116 cells. (<bold>A</bold>) Transwell assay of HCT116 cells. Cells were pre-treated with 5 μM Manz A or DMSO for 24 h before seeded onto 24-well transwells. Migrated cell were stained and counted after 8 h; (<bold>B</bold>) Immunofluorescence of stained E-cadherin and β-catenin in HCT116 cells were monitored in the presence or absence of Manz A. Bars indicate 40 μm (<bold>C</bold>) Western blot analyses of the EMT regulator levels in response to Manz A treatment in HCT116 cells. ACTIN was used as an internal control. Relative expression level of each protein was densitometrically estimated and normalized to that of ACTIN under the same treatment; (<bold>D</bold>) Relative mRNA expression of EMT-related genes in HCT116 cells in response to Manz A treatment. The mRNA level of GAPDH was used as an internal control. ** <italic>p</italic> &lt; 0.01, *** <italic>p</italic> &lt; 0.001.</p>
    Full article ">
    Open AccessArticle Influence of Pre-Stress Magnitude on Fatigue Crack Growth Behavior of Al-Alloy
    Materials 2018, 11(8), 1267; https://doi.org/10.3390/ma11081267
    Received: 25 June 2018 / Revised: 18 July 2018 / Accepted: 21 July 2018 / Published: 24 July 2018
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    Abstract
    From tensile overload to shot peening, there have been many attempts to extend the fatigue properties of metals. A key challenge with the cold work processes is that it is hard to avoid generation of harmful effects (e.g., the increase of surface roughness
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    From tensile overload to shot peening, there have been many attempts to extend the fatigue properties of metals. A key challenge with the cold work processes is that it is hard to avoid generation of harmful effects (e.g., the increase of surface roughness caused by shot peening). Pre-stress has a positive effect on improving the fatigue property of metals, and it is expected to strength Al-alloy without introducing adverse factors. Four pre-stresses ranged from 120 to 183 MPa were incorporated in four cracked extended-compact tension specimens by application of different load based on the measured stress–strain curve. Fatigue crack growth behavior and fractured characteristic of the pre-stressed specimens were investigated systematically and were compared with those of an as-received specimen. The results show that the pre-stress ranged from 120 to 183 MPa significantly improved the fatigue resistance of Al-alloy by comparison with that of the as-received specimen. With increasing pre-stress, the fatigue life first increases, then decrease, and the specimen with pre-stress of 158 MPa has the longest fatigue life. For the manner of pre-stress, no adverse factor was observed for increasing fatigue property, and the induced pre-stress reduced gradually till to disappear during subsequent fatigue cycling. Full article
    Figures

    Graphical abstract

    Graphical abstract
    Full article ">Figure 1
    <p>(<bold>a</bold>) The geometry and detailed dimensions of the Al-alloy specimen for measuring the stress–strain curve (in mm); and (<bold>b</bold>) the experimental result.</p>
    Full article ">Figure 2
    <p>The fabrication of a pre-stressed specimen for fatigue testing: (<bold>a</bold>) E-CT specimen (in mm); (<bold>b</bold>) specimen with a fatigue pre-crack; and (<bold>c</bold>) application of pre-stress.</p>
    Full article ">Figure 3
    <p>The da/dN-Δ<italic>K</italic><sub>app</sub> curves for the comparison between the diverse pre-stress specimens and the as-received specimen: (<bold>a</bold>) the as-received specimen and the specimen with pre-stress = 120 MPa; (<bold>b</bold>) the as-received specimen and the specimen with pre-stress = 137 MPa; (<bold>c</bold>) the as-received specimen and the specimen with pre-stress = 158 MPa; and (<bold>d</bold>) the as-received specimen and the specimen with pre-stress = 183 MPa.</p>
    Full article ">Figure 4
    <p>The da/dN-Δ<italic>K</italic><sub>app</sub> curves for direct comparison (<bold>a</bold>) between the four pre-stressed specimens and the as-received specimen; and (<bold>b</bold>) between 158 MPa pre-stress and 183 MPa pre-stress.</p>
    Full article ">Figure 5
    <p>The as-received specimen: (<bold>a</bold>) macrograph showing the detailed SEM locations; (<bold>b</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 20 MPa<inline-formula> <mml:math id="mm5" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>; (<bold>c</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 30 MPa<inline-formula> <mml:math id="mm6" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 1.7 μm); (<bold>d</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 40 MPa<inline-formula> <mml:math id="mm7" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 3.2 μm); and (<bold>e</bold>) unstable propagation zone.</p>
    Full article ">Figure 6
    <p>The pre-stressed specimen with pre-stress of 137 MPa: (<bold>a</bold>) macrograph showing the detailed SEM locations; (<bold>b</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 20 MPa<inline-formula> <mml:math id="mm8" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 0.3 μm); (<bold>c</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 30 MPa<inline-formula> <mml:math id="mm9" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 1.7 μm); (<bold>d</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 40 MPa<inline-formula> <mml:math id="mm10" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 3.8 μm); and (<bold>e</bold>) unstable propagation zone (striation spacing ≈ 4.7 μm).</p>
    Full article ">Figure 7a
    <p>The pre-stressed specimen with pre-stress of 158 MPa: (<bold>a</bold>) macrograph showing the detailed SEM locations; (<bold>b</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 20 MPa<inline-formula> <mml:math id="mm11" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 0.2 μm); (<bold>c</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 30 MPa<inline-formula> <mml:math id="mm12" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 1.4 μm); (<bold>d</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 40 MPa<inline-formula> <mml:math id="mm13" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 3.2 μm); and (<bold>e</bold>) unstable propagation zone.</p>
    Full article ">Figure 7b
    <p>The pre-stressed specimen with pre-stress of 158 MPa: (<bold>a</bold>) macrograph showing the detailed SEM locations; (<bold>b</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 20 MPa<inline-formula> <mml:math id="mm11" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 0.2 μm); (<bold>c</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 30 MPa<inline-formula> <mml:math id="mm12" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 1.4 μm); (<bold>d</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 40 MPa<inline-formula> <mml:math id="mm13" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 3.2 μm); and (<bold>e</bold>) unstable propagation zone.</p>
    Full article ">Figure 8
    <p>The pre-stressed specimen with pre-stress of 183 MPa: (<bold>a</bold>) macrograph showing the detailed SEM locations; (<bold>b</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 20 MPa<inline-formula> <mml:math id="mm14" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula>; (<bold>c</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 30 MPa<inline-formula> <mml:math id="mm15" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 2.6 μm); (<bold>d</bold>) Δ<italic>K</italic><sub>app</sub> ≈ 40 MPa<inline-formula> <mml:math id="mm16" display="block"> <mml:semantics> <mml:mrow> <mml:msqrt> <mml:mi mathvariant="normal">m</mml:mi> </mml:msqrt> </mml:mrow> </mml:semantics> </mml:math> </inline-formula> (striation spacing ≈ 4.9 μm); and (<bold>e</bold>) unstable propagation zone.</p>
    Full article ">Figure 9
    <p>The relationship between the pre-stress magnitude and the ratio of N<sub>f-pre-stress</sub> and N<sub>f</sub>.</p>
    Full article ">
    Open AccessArticle A Novel Carrier Loop Algorithm Based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) for Weak TC-OFDM Signals
    Sensors 2018, 18(7), 2256; https://doi.org/10.3390/s18072256
    Received: 16 May 2018 / Revised: 25 June 2018 / Accepted: 9 July 2018 / Published: 13 July 2018
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    Abstract
    Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an
    [...] Read more.
    Digital broadcasting signals represent a promising positioning signal for indoors applications. A novel positioning technology named Time & Code Division-Orthogonal Frequency Division Multiplexing (TC-OFDM) is mainly discussed in this paper, which is based on China mobile multimedia broadcasting (CMMB). Signal strength is an important factor that affects the carrier loop performance of the TC-OFDM receiver. In the case of weak TC-OFDM signals, the current carrier loop algorithm has large residual carrier errors, which limit the tracking sensitivity of the existing carrier loop in complex indoor environments. This paper proposes a novel carrier loop algorithm based on Maximum Likelihood Estimation (MLE) and Kalman Filter (KF) to solve the above problem. The discriminator of the current carrier loop is replaced by the MLE discriminator function in the proposed algorithm. The Levenberg-Marquardt (LM) algorithm is utilized to obtain the MLE cost function consisting of signal amplitude, residual carrier frequency and carrier phase, and the MLE discriminator function is derived from the corresponding MLE cost function. The KF is used to smooth the MLE discriminator function results, which takes the carrier phase estimation, the angular frequency estimation and the angular frequency rate as the state vector. Theoretical analysis and simulation results show that the proposed algorithm can improve the tracking sensitivity of the TC-OFDM receiver by taking full advantage of the characteristics of the carrier loop parameters. Compared with the current carrier loop algorithms, the tracking sensitivity is effectively improved by 2–4 dB, and the better performance of the proposed algorithm is verified in the real environment. Full article
    (This article belongs to the Special Issue Selected Papers from UPINLBS 2018)
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    <p>TC-OFDM signal Frame Structure.</p>
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    <p>Conventional carrier loop structure.</p>
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    <p>The principle of MLE.</p>
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    <p>LM algorithm flow chart.</p>
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    <p>The proposed carrier loop structure based on MLE and KF.</p>
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    <p>The Relationship between Loss of Lock Probability and SNR.</p>
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    <p>The RMS Frequency Tracking Error with SNR under different sample observations.</p>
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    <p>The residual carrier and phase convergence curve estimated by LM algorithm.</p>
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    <p>Frequency error comparison results by MLE and MLE&amp;KF.</p>
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    <p>Comparison results of frequency estimation errors by three algorithms under different SNR.</p>
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    <p>Comparison of the tracking probabilities between the three algorithms.</p>
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    <p>Each component of the modified base stations.</p>
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    <p>The TC-OFDM receiver. (<bold>a</bold>) is the internal and external structure of the TC-OFDM receiver; and (<bold>b</bold>) is the communication between the positioning receiver and the mobile phone.</p>
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    <p>Actual test diagram of the tracking sensitivity between the three algorithms.</p>
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    <p>The base station distribution of the test environment on the campus.</p>
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    <p>The RMSE positioning accuracy error in horizontal direction.</p>
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    Open AccessArticle A Fiber Bragg Grating-Based Anemometer
    Sensors 2018, 18(7), 2213; https://doi.org/10.3390/s18072213
    Received: 26 May 2018 / Revised: 28 June 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
    PDF Full-text (3428 KB) | HTML Full-text | XML Full-text
    Abstract
    A novel fiber anemometer based on two pairs of fiber gratings is experimentally demonstrated and can simultaneously detect wind speed and wind direction. One pair of gratings, which are separated by 90° in space, is fixed on a small stainless steel pipe driven
    [...] Read more.
    A novel fiber anemometer based on two pairs of fiber gratings is experimentally demonstrated and can simultaneously detect wind speed and wind direction. One pair of gratings, which are separated by 90° in space, is fixed on a small stainless steel pipe driven by a rotating disc for measuring the wind-direction angle. The other pair is composed of a sensing and a matched grating. The frequency of the spectrum-shifted of the sensing grating to overlap with that of the matched grating is employed for determining the wind speed. The errors in the wind-speed and wind-angle measurements are experimentally demonstrated to be less than 1%. The proposed fiber anemometer with a simple and durable structure can be applied in wind-powered electricity generators. Full article
    (This article belongs to the Section Physical Sensors)
    Figures

    Graphical abstract

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    <p>Configuration of the wind-speed sensing head.</p>
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    <p>Sensing concept for measuring wind speed.</p>
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    <p>Experimental setup of the sensing head for measuring wind speed.</p>
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    <p>Sensing concept for measuring wind direction.</p>
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    <p>Experimental setup for measuring wind direction.</p>
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    <p>Experimental setup for measuring both wind speed and direction.</p>
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    <p>Oscilloscope diagrams for wind speeds (<bold>a</bold>) 200; (<bold>b</bold>) 400; (<bold>c</bold>) 600; and (<bold>d</bold>) 800 rpm.</p>
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    <p>Plots of (<bold>a</bold>) frequency and wind speed versus rotational speed and (<bold>b</bold>) grating wavelength shift versus wind-direction angle.</p>
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    <p>Relationship curves between the real and experimental (<bold>a</bold>) rotational speed, (<bold>b</bold>) wind speed, and (<bold>c</bold>) wind-direction angle.</p>
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    Open AccessArticle Seasonal Dynamics of Stem Radial Increment of Pinus taiwanensis Hayata and Its Response to Environmental Factors in the Lushan Mountains, Southeastern China
    Forests 2018, 9(7), 387; https://doi.org/10.3390/f9070387
    Received: 14 May 2018 / Revised: 19 June 2018 / Accepted: 26 June 2018 / Published: 29 June 2018
    PDF Full-text (3274 KB) | HTML Full-text | XML Full-text | Supplementary Files
    Abstract
    Seasonal radial-increment records can help to elucidate how tree growth responds to climate seasonality. Such knowledge is critical for understanding the complex growth-climate relationships in subtropical China. We hypothesize that under subtropical monsoon climate characterized by mild winters and hot summers, summer drought
    [...] Read more.
    Seasonal radial-increment records can help to elucidate how tree growth responds to climate seasonality. Such knowledge is critical for understanding the complex growth-climate relationships in subtropical China. We hypothesize that under subtropical monsoon climate characterized by mild winters and hot summers, summer drought constrains stem radial increment, which generally results in growth-limiting factors switching from temperatures in spring and early summer to precipitation in summer and autumn. Here, we monitored intra-annual dynamics of stem radial increment with band dendrometers in a montane stand of Taiwan pine (Pinus taiwanensis Hayata) from Lushan Mountains for two consecutive years (2016–2017). A pronounced bimodal seasonal pattern of stem radial increment was observed in 2016. However, it was less clear in 2017 when late-summer rainfall events occurred in early August. Changing growth-climate relationships were detected throughout the two growing seasons. Stem increments were consistently positively correlated with temperatures before early July, whereas the growth-temperature dependency was gradually weakened and more variable after early July. Conversely, stem increments were significantly correlated with precipitation and soil moisture since early July, indicating that moisture variables were the main factor limiting stem increments in dry period. More precipitation was received in the dry period (July–November) of 2017 as compared with the year 2016, which favoured a wider annual increment in 2017, although growing-season temperature and precipitation was similar between years. Our study suggests a seasonal shift in growth-limiting factors in subtropical forests, which should be explicitly considered in forecasting responses of tree growth to climatic warming. Full article
    (This article belongs to the Section Forest Ecophysiology and Biology)
    Figures

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    Figure 1
    <p>Monthly maximum (Ta_max), mean (Ta), minimum (Ta_min) temperatures and monthly precipitation (P) of Lushan from 1980 to 2011.</p>
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    <p>Schematic figure of the diurnal cycle of stem radius variation recorded by automatic dendrometer in Taiwan pine. The diurnal cycles are divided into three distinct phases: contraction, recovery and increment. The magnitude and duration of stem increment are indicated. Each circle represents an hourly measurement recorded in August 2017.</p>
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    <p>Annual courses of hourly radial records of monitored trees (<bold>a</bold>,<bold>e</bold>), air and soil temperatures (Ta, mean air temperature; Ta_max, maximum air temperature; Ta_min, minimum air temperature; Ts, mean soil temperature) (<bold>b</bold>,<bold>f</bold>), precipitation (P) and soil moisture (SM) (<bold>c</bold>,<bold>g</bold>), vapor pressure deficit (VPD) and solar radiation (Ra) (<bold>d</bold>,<bold>h</bold>) during 2016 and 2017.</p>
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    <p>Observed and modeled stem radial variations (<bold>a</bold>,<bold>c</bold>) and associated daily growth rate (<bold>b</bold>,<bold>d</bold>) of Taiwan pine in 2016 and 2017. Dots and lines in top panels represent raw measurements of daily maximum radius and Gompertz function modeled curves, respectively. Dashed lines in the bottom panels indicate daily growth rates equal to 2 μm day<sup>?1</sup>, which correspond to the dendrometer accuracy.</p>
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    <p>Time series of stem increments of four trees extracted for the growing periods of (<bold>a</bold>) 2016 and (<bold>b</bold>) 2017.</p>
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    <p>Variations of monthly mean magnitude (<bold>a</bold>) and duration (<bold>b</bold>) of stem increment during the two growing seasons. Different letters indicate significant difference in magnitude and duration of stem increment among months in 2016 (uppercase) and 2017 (lowercase) at <italic>p</italic> &lt; 0.05. Error bars indicate ± SD of mean.</p>
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    <p>Moving correlations (21 days window) between stem increments and environmental variables of mean air temperature (<bold>a</bold>,<bold>e</bold>), maximum air temperature (<bold>b</bold>,<bold>f</bold>), precipitation (<bold>c</bold>,<bold>g</bold>), and soil moisture (<bold>d</bold>,<bold>h</bold>) during the growing seasons in 2016 and 2017. Spearman correlations were calculated for relationships between stem increments and all environmental variables. Horizontal thin dashed lines indicate the significance level at <italic>p</italic> = 0.05. Vertical thick dashed lines indicate the timing of shifts in the correlations between stem increments and environmental variables. The shaded regions indicate the growing seasons.</p>
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    Open AccessArticle A Thin Film Flexible Supercapacitor Based on Oblique Angle Deposited Ni/NiO Nanowire Arrays
    Nanomaterials 2018, 8(6), 422; https://doi.org/10.3390/nano8060422
    Received: 1 June 2018 / Revised: 8 June 2018 / Accepted: 9 June 2018 / Published: 11 June 2018
    PDF Full-text (3593 KB) | HTML Full-text | XML Full-text | Supplementary Files
    Abstract
    With high power density, fast charging-discharging speed, and a long cycling life, supercapacitors are a kind of highly developed novel energy-storage device that has shown a growing performance and various unconventional shapes such as flexible, linear-type, stretchable, self-healing, etc. Here, we proposed a
    [...] Read more.
    With high power density, fast charging-discharging speed, and a long cycling life, supercapacitors are a kind of highly developed novel energy-storage device that has shown a growing performance and various unconventional shapes such as flexible, linear-type, stretchable, self-healing, etc. Here, we proposed a rational design of thin film, flexible micro-supercapacitors with in-plane interdigital electrodes, where the electrodes were fabricated using the oblique angle deposition technique to grow oblique Ni/NiO nanowire arrays directly on polyimide film. The obtained electrodes have a high specific surface area and good adhesion to the substrate compared with other in-plane micro-supercapacitors. Meanwhile, the as-fabricated micro-supercapacitors have good flexibility and satisfactory energy-storage performance, exhibiting a high specific capacity of 37.1 F/cm3, a high energy density of 5.14 mWh/cm3, a power density of up to 0.5 W/cm3, and good stability during charge-discharge cycles and repeated bending-recovery cycles, respectively. Our micro-supercapacitors can be used as ingenious energy storage devices for future portable and wearable electronic applications. Full article
    (This article belongs to the Special Issue Metallic Nanostructures)
    Figures

    Graphical abstract

    Graphical abstract
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    <p>The schematic fabrication process of flexible micro-supercapacitors (MSCs). (<bold>a</bold>–<bold>f</bold>) Steps of preparing substrate, lithography, sputtering Ti/Au, oblique angle-depositing Ni nanowires annealing and packaging, respectively; (<bold>g</bold>) photograph of the as-fabricated flexible supercapacitor device.</p>
    Full article ">Figure 2
    <p>(<bold>a</bold>,<bold>b</bold>) Top-view and cross-section images of the nanostructured layer prepared on a polyimide (PI) substrate; (<bold>c</bold>) transmission electron microscopy (TEM) image of the oblique nanowire arrays; (<bold>d</bold>) high-resolution TEM (HRTEM) image of the nanowire; (<bold>e</bold>) X-ray diffractometer (XRD) patterns of the nanowires on the substrate; (<bold>f</bold>) Raman spectra measured on the surface of nanowires using a 532-nm laser; (<bold>g</bold>) Ni 2p; (<bold>h</bold>) O 1s and (<bold>i</bold>) C 1s X-ray photoelectron spectroscopy (XPS) spectra.</p>
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    <p>(<bold>a</bold>) Photos of the MSC devices; (<bold>b</bold>) capacitance-voltage (CV) curves at various scan rates; (<bold>c</bold>) Galvanostatic charge-discharge (GCD) at different currents measured in the voltage window of 0–1 V; (<bold>d</bold>) comparison of capacitances of the MSC devices at varied galvanostatic charge-discharge current densities; (<bold>e</bold>) capacitance retention on cycle number at a current of 4 A/cm<sup>3</sup>; (<bold>f</bold>) energy and powder densities of the MSC devices.</p>
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    <p>(<bold>a</bold>) Photos of a MSC at different bending states; (<bold>b</bold>) CV curves at 100 mV/s in straight and different bending states, respectively; (<bold>c</bold>) charge/discharge curves at a current of 2 A/cm<sup>3</sup> in straight and different bending states, respectively; (<bold>d</bold>) capacitance performance under the different bending states.</p>
    Full article ">Figure 5
    <p>The integrated MSCs system based on six individual devices: (<bold>a</bold>) device position on the substrate; (<bold>b</bold>) CV curves of one MSC and an integrated arrays of six MSCs at scan rates of 100 mV/s and 300 mV/s, respectively; (<bold>c</bold>) galvanostatic CD curves of an array system of one MSC and six MSCs at the currents of 2 A/cm<sup>3</sup> and 4 A/cm<sup>3</sup>, respectively; (<bold>d</bold>) photos of an integrated MSCs system; (<bold>e</bold>) flexibility performance of the integrated MSCs system based on six individual devices at different bending states; (<bold>f</bold>) the capacitance stability of the MSCs during repeated bending-recovery cycles at a galvanostatic current of 4 A/cm<sup>3</sup>.</p>
    Full article ">
    Open AccessArticle Berberine Protects against NEFA-Induced Impairment of Mitochondrial Respiratory Chain Function and Insulin Signaling in Bovine Hepatocytes
    Int. J. Mol. Sci. 2018, 19(6), 1691; https://doi.org/10.3390/ijms19061691
    Received: 8 May 2018 / Revised: 31 May 2018 / Accepted: 5 June 2018 / Published: 6 June 2018
    PDF Full-text (3574 KB) | HTML Full-text | XML Full-text
    Abstract
    Fatty liver is a major lipid metabolic disease in perinatal dairy cows and is characterized by high blood levels of non-esterified fatty acid (NEFA) and insulin resistance. Berberine (BBR) has been reported to improve insulin sensitivity in mice with hepatic steatosis. Mitochondrial dysfunction
    [...] Read more.
    Fatty liver is a major lipid metabolic disease in perinatal dairy cows and is characterized by high blood levels of non-esterified fatty acid (NEFA) and insulin resistance. Berberine (BBR) has been reported to improve insulin sensitivity in mice with hepatic steatosis. Mitochondrial dysfunction is considered a causal factor that induces insulin resistance. This study investigates the underlying mechanism and the beneficial effects of BBR on mitochondrial and insulin signaling in bovine hepatocytes. Revised quantitative insulin sensitivity check index (RQUICKI) of cows with fatty liver was significantly lower than that of healthy cows. Importantly, the Akt and GSK3β phosphorylation levels, protein levels of PGC-1α and four of the five representative subunits of oxidative phosphorylation (OXPHOS) were significantly decreased in cows with fatty liver using Western Blot analysis. In bovine hepatocytes, 1.2 mmol/L NEFA reduced insulin signaling and mitochondrial respiratory chain function, and 10 and 20 umol/L BBR restored these changes. Furthermore, activation of PGC-1α played the same beneficial effects of BBR on hepatocytes treated with NEFA. BBR treatment improves NEFA-impaired mitochondrial respiratory chain function and insulin signaling by increasing PGC-1α expression in hepatocytes, which provides a potential new strategy for the prevention and treatment of fatty liver in dairy cows. Full article
    (This article belongs to the Special Issue Liver Damage and Repair)
    Figures

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    Figure 1
    <p>The chemical structure of berberine (BBR).</p>
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    <p>Dairy cows were classified into a healthy group and a fatty liver group. (<bold>A</bold>) The blood levels of NEFA; (<bold>B</bold>) Hepatic TG content; (<bold>C</bold>) H&amp;E staining (×10) in liver sections in the healthy group; (<bold>D</bold>) H&amp;E staining (×10) in liver sections in the fatty liver group. Quantified data are mean ± SD; ** <italic>p</italic> &lt; 0.01 versus control group.</p>
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    <p>Dairy cows with fatty liver displayed impaired hepatic insulin signaling and mitochondrial respiratory chain function. (<bold>A</bold>) The value of revised quantitative insulin sensitivity check index (RQUICKI) in healthy and fatty liver cows; (<bold>B</bold>–<bold>D</bold>) Western blot analysis and quantification of key molecules of the insulin signaling pathway, PGC-1α, and five representative subunits of oxidative phosphorylation (OXPHOS) complexes in the liver of healthy and fatty liver cows. β-Actin served as an internal control. Quantified data are mean ± SD; * <italic>p</italic> &lt; 0.05 versus control group; ** <italic>p</italic> &lt; 0.01 versus control group.</p>
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    <p>BBR decreased NEFA-induced TG accumulation in bovine hepatocytes. Oil red O staining (×40): (<bold>A</bold>) The normal bovine hepatocytes; (<bold>B</bold>) The bovine hepatocytes were pretreated with 1.2 mmol/L NEFA for 9 h; (<bold>C</bold>) After NEFA treatment, the hepatocytes were treated with 10 μmol/L BBR for another 24 h; (<bold>D</bold>) After NEFA treatment, the hepatocytes were treated with 20 μmol/L BBR for another 24 h; (<bold>E</bold>) BBR treatment significantly reduced the NEFA-induced increase in TG content. Quantified data are mean ± SD; ** <italic>p</italic> &lt; 0.01 versus control group; <sup>##</sup> <italic>p</italic> &lt; 0.01 versus NEFA group.</p>
    Full article ">Figure 5
    <p>BBR improved the NEFA-induced mitochondrial respiratory chain function damage in bovine hepatocytes. Hepatocytes were assigned to four groups as follows: A control group, a 1.2 mmol/L NEFA treatment group, a 1.2 mmol/L NEFA and 10 μmol/L BBR treatment group, a 1.2 mmol/L NEFA and 20 μmol/L BBR treatment group. (<bold>A</bold>–<bold>F</bold>) Western blot analysis and quantification of five representative subunits of OXPHOS complexes, β-Actin served as an internal control. Quantified data are mean ± SD; ** <italic>p</italic> &lt; 0.01 versus control group; <sup>##</sup> <italic>p</italic> &lt; 0.01 versus NEFA group.</p>
    Full article ">Figure 6
    <p>BBR improved the NEFA-induced insulin signaling damage in bovine hepatocytes. Hepatocyte treatment is described as in <xref ref-type="fig" rid="ijms-19-01691-f005">Figure 5</xref>. (<bold>A</bold>–<bold>C</bold>) Western blot analysis and quantification of key molecules of the insulin signaling pathway, β-Actin served as an internal control. Quantified data are mean ± SD; ** <italic>p</italic> &lt; 0.01 versus control group; <sup>##</sup> <italic>p</italic> &lt; 0.01 versus NEFA group.</p>
    Full article ">Figure 7
    <p>Effects of BBR on the expression of PGC-1α in bovine hepatocytes. Hepatocyte treatment is described in <xref ref-type="fig" rid="ijms-19-01691-f005">Figure 5</xref>. (<bold>A</bold>,<bold>B</bold>) Western blot analysis and quantification of PGC-1α, β-Actin served as an internal control; (<bold>C</bold>) PGC-1α mRNA expression level changed in different groups. Quantified data are mean ± SD; ** <italic>p</italic> &lt; 0.01 versus control group; <sup>##</sup> <italic>p</italic> &lt; 0.01 versus NEFA group.</p>
    Full article ">Figure 8
    <p>Activation of PGC-1α increased the beneficial effects of BBR on mitochondrial respiratory chain function and insulin signaling damage induced by NEFA. Hepatocytes were assigned to 3 groups as follows: A 1.2 mmol/L NEFA group, a 1.2 mmol/L NEFA + 20 μmol/L BBR treatment group, and a 1.2 mmol/L NEFA + 10 μmol/L ZLN005 treatment group. (<bold>A</bold>,<bold>B</bold>) Western blot analysis and quantification of key molecules of the insulin signaling pathway, PGC-1α, and five representative subunits of OXPHOS complexes, and β-Actin served as an internal control; (<bold>C</bold>) TG content in bovine hepatocytes. Quantified data are mean ± SD; <sup>##</sup> <italic>p</italic> &lt; 0.01 versus NEFA group.</p>
    Full article ">
    Open AccessArticle Characterization of Phosphorus in a Toposequence of Subtropical Perhumid Forest Soils Facing a Subalpine Lake
    Forests 2018, 9(6), 294; https://doi.org/10.3390/f9060294
    Received: 1 May 2018 / Revised: 21 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
    PDF Full-text (1956 KB) | HTML Full-text | XML Full-text
    Abstract
    The productivity of forests is often considered to be limited by the availability of phosphorus (P). Knowledge of the role of organic and inorganic P in humid subtropical forest soils is lacking. In this study, we used chemical fractionation and 31P nuclear
    [...] Read more.
    The productivity of forests is often considered to be limited by the availability of phosphorus (P). Knowledge of the role of organic and inorganic P in humid subtropical forest soils is lacking. In this study, we used chemical fractionation and 31P nuclear magnetic resonance (NMR) spectroscopy to characterize the form of P and its distribution in undisturbed perhumid Taiwan false cypress (Chamaecyparis formosensis Matsum.) forest soils. The toposequence of transects was investigated for the humic layer from summit to footslope and lakeshore. The clay layer combined with a placic-like horizon in the subsoil may affect the distribution of soil P because both total P and organic P (Po) contents in all studied soils decreased with soil depth. In addition, Po content was negatively correlated with soil crystalline Fe oxide content, whereas inorganic P (Pi) content was positively correlated with soil crystalline Fe oxide content and slightly increased with soil depth. Thus, Pi may be mostly adsorbed by soil crystalline Fe oxides in the soils. Among all extractable P fractions, the NaOH-Po fraction appeared to be the major component, followed by NaHCO3-Po; the resin-P and HCl-Pi fractions were lowest. In addition, we found no typical trend for Pi and Po contents in soils with topographical change among the three sites. From the 31P-NMR spectra, the dominant Po form in soils from all study sites was monoesters with similar spectra. The 31P-NMR findings were basically consistent with those from chemical extraction. Soil formation processes may be the critical factor affecting the distribution of soil P. High precipitation and year-round high humidity may be important in the differentiation of the P species in this landscape. Full article
    (This article belongs to the Special Issue Carbon, Nitrogen and Phosphorus Cycling in Forest Soils)
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    Graphical abstract

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    <p>Correlations of contents of P<sub>i</sub> and P<sub>o</sub> with Fe<sub>d</sub> (<bold>a</bold>) and Fe<sub>o</sub> (<bold>b</bold>) in the pedon samples collected from the three sampling sites. * Statistically significant at <italic>p</italic> &lt; 0.05.</p>
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    <p>Correlations of contents of P<sub>i</sub> and P<sub>o</sub> with Al<sub>d</sub> (<bold>a</bold>) and Al<sub>o</sub> (<bold>b</bold>) in the pedon samples collected from the three sampling sites. * Statistically significant at <italic>p</italic> &lt; 0.05.</p>
    Full article ">Figure 3
    <p><sup>31</sup>P-NMR spectra for NaOH-EDTA extracts from soils at different sites. a: phosphonate, b: inorganic orthophosphate, c: orthophosphate monoesters, d: orthophosphate diesters, e: pyrophosphate.</p>
    Full article ">Figure 4
    <p>Proportion of extracted P in various classes from humic samples at different sites determined by <sup>31</sup>P-NMR spectroscopy. Error bars indicate standard deviation.</p>
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    Open AccessArticle Comparative in Silico Analysis of Ferric Reduction Oxidase (FRO) Genes Expression Patterns in Response to Abiotic Stresses, Metal and Hormone Applications
    Molecules 2018, 23(5), 1163; https://doi.org/10.3390/molecules23051163
    Received: 18 April 2018 / Revised: 4 May 2018 / Accepted: 9 May 2018 / Published: 12 May 2018
    Cited by 1 | PDF Full-text (3808 KB) | HTML Full-text | XML Full-text | Supplementary Files
    Abstract
    The ferric reduction oxidase (FRO) gene family is involved in various biological processes widely found in plants and may play an essential role in metal homeostasis, tolerance and intricate signaling networks in response to a number of abiotic stresses. Our study describes the
    [...] Read more.
    The ferric reduction oxidase (FRO) gene family is involved in various biological processes widely found in plants and may play an essential role in metal homeostasis, tolerance and intricate signaling networks in response to a number of abiotic stresses. Our study describes the identification, characterization and evolutionary relationships of FRO genes families. Here, total 50 FRO genes in Plantae and 15 ‘FRO like’ genes in non-Plantae were retrieved from 16 different species. The entire FRO genes have been divided into seven clades according to close similarity in biological and functional behavior. Three conserved domains were common in FRO genes while in two FROs sub genome have an extra NADPH-Ox domain, separating the function of plant FROs. OsFRO1 and OsFRO7 genes were expressed constitutively in rice plant. Real-time RT-PCR analysis demonstrated that the expression of OsFRO1 was high in flag leaf, and OsFRO7 gene expression was maximum in leaf blade and flag leaf. Both genes showed vigorous expressions level in response to different abiotic and hormones treatments. Moreover, the expression of both genes was also substantial under heavy metal stresses. OsFRO1 gene expression was triggered following 6 h under Zn, Pb, Co and Ni treatments, whereas OsFRO7 gene expression under Fe, Pb and Ni after 12 h, Zn and Cr after 6 h, and Mn and Co after 3 h treatments. These findings suggest the possible involvement of both the genes under abiotic and metal stress and the regulation of phytohormones. Therefore, our current work may provide the foundation for further functional characterization of rice FRO genes family. Full article
    Figures

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    <p>The FRO gene families. (<bold>A</bold>) Systematic evolutionary relationships of 16 different species among eight lineages within the subgroup. (<bold>B</bold>) List of FRO genes per specie within group. (<bold>C</bold>) The unrooted maximum-likelihood phylogenetic tree of FRO family members was inferred from the amino acid sequence alignment of FRO proteins. The seven conserved clades are marked by different colors and represented as Clade-I, Clade-II, Clade-III, Clade-IV, Clade-V, Clade-VI and Clade-VII. Scale bar represents 0.2 amino acid substitution per site.</p>
    Full article ">Figure 2
    <p>Amino acid sequences alignment analysis of Ferric_reduct, FAD_binding_8 and NAD_binding_6 domains of FRO proteins and the consensuses are shown in small letters. The two motifs HPFT in FAD binding domain and GPYG in NAD binding domain are well conserved in <italic>OsFRO1</italic> and <italic>OsFRO7</italic> protein sequences.</p>
    Full article ">Figure 3
    <p>Chromosomal localization of FRO genes Families (<bold>A</bold>), <italic>Arabidopsis thaliana</italic>; (<bold>B</bold>), <italic>Brachypodium distachyon</italic>; (<bold>C</bold>), <italic>Oryza sativa</italic>; (<bold>D</bold>), <italic>Zea mays</italic>; (<bold>E</bold>), <italic>Physcomitrella patens</italic>; (<bold>F</bold>), <italic>Chlamydomonas reinhardtii</italic>; (<bold>G</bold>), <italic>Vitis vinifera</italic>; (H), <italic>Physcomitrella patens</italic>; (<bold>I</bold>), <italic>Chlorella variabilis NC64</italic>; (<bold>J</bold>), <italic>Coccomyxa subellipsoidea C-169</italic>; (<bold>K</bold>), <italic>Populus trichocarpa</italic>; (<bold>L</bold>), <italic>Volvox carteri</italic>; (<bold>M</bold>), <italic>Selaginella moellendorffii</italic>; (<bold>N</bold>), <italic>Homo sapiens</italic>; (<bold>O</bold>), <italic>Saccharomyces cerevisiae</italic>; respectively. The graphical view was drawn from each gene ID and scaffolds information and position of each gene are indicated by line, whereas scale bar represents the total length of chromosome.</p>
    Full article ">Figure 4
    <p>Developmental expression profile of <italic>OsFRO1</italic> &amp; <italic>OsFRO7</italic> rice genes. (<bold>A</bold>) The expression profiles obtained from the database of Rice as reported by Genevestigator v3, demonstrating different expression levels of <italic>OsFRO1</italic> &amp; <italic>OsFRO7</italic> genes in different tissues. Results were given as heat maps from green to red reflecting relative signal values; where dark green boxes represent stronger down-regulated expression and dark red boxes represents stronger up-regulation; (<bold>B</bold>) The graphs indicate tissue specific expression level in rice plant. The samples were collected in different developmental stages and were analyzed through qRT-PCR. Data are means represents from three independent qRT-PCR amplifications; (<bold>C</bold>) The tissue specific banding pattern of <italic>OsFRO1</italic> &amp; <italic>OsFRO7</italic> rice genes through Semi-quantitative RT-qPCR analysis, <italic>OsActineI</italic> was used as standard control to normalized the data.</p>
    Full article ">Figure 5
    <p>(<bold>A</bold>) Inducible expression profile of <italic>OsFRO1</italic> and <italic>OsFRO7</italic> in response to abiotic stresses, as reported by Genevestigator v3, demonstrating different expression levels of <italic>OsFRO1</italic> and <italic>OsFRO7</italic> genes in different tissues. Results were given as heat maps from green to red reflecting relative signal values; where dark green boxes represent stronger down-regulated expression and dark red boxes represents stronger up-regulation; (<bold>B</bold>) Log 2-ratio and fold changes in the expression of <italic>OsFRO1</italic> and <italic>OsFRO7</italic> genes under abiotic stresses; (<bold>C</bold>) Inducible expression profile of <italic>OsFRO1</italic> in response to treatment with NaCl, Heat, Cold and PEG (6000); (<bold>D</bold>) Inducible expression profile of <italic>OsFRO7</italic> in response to treatment with NaCl, Heat, Cold and PEG (6000); (<bold>E</bold>) Inducible expression profile of <italic>OsFRO1</italic> in response to hormones treatment ABA, MeJa and SA; (<bold>F</bold>) Inducible expression profile of <italic>OsFRO7</italic> in response to hormones treatments ABA, MeJa and SA. Mean values represents from two independent qRT-PCR amplifications; (<bold>G</bold>) Reverse transcriptase analysis and banding pattern of <italic>OsFRO1</italic> and <italic>OsFRO7</italic> genes in response to treatment with NaCl, Heat, Cold and PEG (6000); (<bold>H</bold>) RT-PCR analysis and banding pattern of <italic>OsFRO1</italic> and of <italic>OsFRO7</italic> genes under different hormones treatments. Where <italic>OsActineI</italic> was used as standard control to normalized the data.</p>
    Full article ">Figure 6
    <p>Inducible expression patterns of rice FRO family genes under different heavy metals stresses. The 18 days old rice seedlings were exposed to FeSo<sub>4</sub>·7H<sub>2</sub>O (7 mM), CdCl<sub>2</sub> (0.5 mM), PbNo<sub>3</sub> (1 mM), K<sub>2</sub>Cr<sub>2</sub>O<sub>7</sub> (1 mM), NiCl<sub>2</sub> (1 mM), MnSo<sub>4</sub> (2 mM), CoCl<sub>2</sub> (1 mM) and Zn(No<sub>3</sub>)<sub>2</sub> (5 mM) treatments for 24 h. (<bold>A</bold>,<bold>B</bold>) represents the expression profile of <italic>OsFRO1</italic> and (<bold>C</bold>,<bold>D</bold>) represents the expression pattern of <italic>OsFRO7.</italic> The samples were taken at (0 h, 3 h, 6 h,12 h and 24 h) duration, RNA was extracted and analysis were performed through qRT-PCR and Semi RT-qPCR. Mean values represents from two independent qRT-PCR amplifications, where <italic>OsActineI</italic> was used as standard control to normalize the data.</p>
    Full article ">Figure 7
    <p>The enzymatic activity and morphological changes in rice shoots under Fe and Cr stresses. (<bold>A</bold>) Shows the changes in SOD, POD, CAT and MDA under Fe and Cr toxic level; (<bold>B</bold>) The mean of shoot length and roots length under stress conditions. The graph represents the mean of three replicates; (<bold>C</bold>) The physical appearance in terms of shoot (cm) and root (cm) of young seedlings and changes after heavy metal stresses after one week.</p>
    Full article ">
    Open AccessArticle Dietary α-Mangostin Provides Protective Effects against Acetaminophen-Induced Hepatotoxicity in Mice via Akt/mTOR-Mediated Inhibition of Autophagy and Apoptosis
    Int. J. Mol. Sci. 2018, 19(5), 1335; https://doi.org/10.3390/ijms19051335
    Received: 30 March 2018 / Revised: 25 April 2018 / Accepted: 26 April 2018 / Published: 1 May 2018
    PDF Full-text (3990 KB) | HTML Full-text | XML Full-text
    Abstract
    Acetaminophen overdose-induced hepatotoxicity is the most common cause of acute liver failure in many countries. Previously, alpha-mangostin (α-MG) has been confirmed to exert protective effects on a variety of liver injuries, but the protective effect on acetaminophen-induced acute liver injury (ALI) remains largely
    [...] Read more.
    Acetaminophen overdose-induced hepatotoxicity is the most common cause of acute liver failure in many countries. Previously, alpha-mangostin (α-MG) has been confirmed to exert protective effects on a variety of liver injuries, but the protective effect on acetaminophen-induced acute liver injury (ALI) remains largely unknown. This work investigated the regulatory effect and underlying cellular mechanisms of α-MG action to attenuate acetaminophen-induced hepatotoxicity in mice. The increased serum aminotransferase levels and glutathione (GSH) content and reduced malondialdehyde (MDA) demonstrated the protective effect of α-MG against acetaminophen-induced hepatotoxicity. In addition, α-MG pretreatment inhibited increases in tumor necrosis factor (TNF-α) and interleukin-1β (IL-1β) caused by exposure of mice to acetaminophen. In liver tissues, α-MG inhibited the protein expression of autophagy-related microtubule-associated protein light chain 3 (LC3) and BCL2/adenovirus E1B protein-interacting protein 3 (BNIP3). Western blotting analysis of liver tissues also proved evidence that α-MG partially inhibited the activation of apoptotic signaling pathways via increasing the expression of Bcl-2 and decreasing Bax and cleaved caspase 3 proteins. In addition, α-MG could in part downregulate the increase in p62 level and upregulate the decrease in p-mTOR, p-AKT and LC3 II /LC3 I ratio in autophagy signaling pathways in the mouse liver. Taken together, our findings proved novel perspectives that detoxification effect of α-MG on acetaminophen-induced ALI might be due to the alterations in Akt/mTOR pathway in the liver. Full article
    (This article belongs to the Section Bioactives and Nutraceuticals)
    Figures

    Graphical abstract

    Graphical abstract
    Full article ">Figure 1
    <p>HPLC analysis of an extract obtained by STE of mangosteen pericarp. (<bold>A</bold>) The standard of α-mangostin; (<bold>B</bold>) α-mangostin in the HPLC; (<bold>C</bold>) comparison of different extraction techniques. All data are expressed as mean ± S.D., <italic>n</italic> = 3. ** <italic>p</italic> &lt; 0.01 vs. STE method.</p>
    Full article ">Figure 2
    <p>Inhibition of APAP-induced acute liver injury by α-MG. Representative sections of liver stained with hematoxylin and eosin (H&amp;E), (100×, 400×) for histopathological observations. Hepatocellular necrosis was marked by arrows (<bold>A</bold>). The degrees of damage were accessed by necrosis scores (<bold>C</bold>). Analysis of apoptosis in mice was evaluated by TUNEL staining (<bold>B</bold>). Representative liver sections were stained with TUNEL (400×). The TUNEL positive cells percentage is shown in (<bold>D</bold>); All data are expressed as mean ± S.D., <italic>n</italic> = 8. ** <italic>p</italic> &lt; 0.01 vs. normal group; ## <italic>p</italic> &lt; 0.01 or # <italic>p</italic> &lt; 0.05 vs. APAP group.</p>
    Full article ">Figure 3
    <p>Effects of α-MG on serum enzyme activity, oxidative stress, and inflammatory responses. ALT (<bold>A</bold>); AST (<bold>B</bold>); GSH (<bold>C</bold>); MDA (<bold>D</bold>); TNF-α (<bold>E</bold>) and IL-1β (<bold>F</bold>) levels from mice in each experimental group were determined by commercial kits. All data are expressed as mean ± S.D., <italic>n</italic> = 8. *** <italic>p</italic> &lt; 0.001 or ** <italic>p</italic> &lt; 0.01 vs. normal group; ### <italic>p</italic> &lt; 0.01 or ## <italic>p</italic> &lt; 0.01 vs. APAP group.</p>
    Full article ">Figure 4
    <p>Inhibition of APAP-induced autophagy by α-MG. The expression of LC3 (<bold>A</bold>) and BNIP3 (<bold>B</bold>) in liver tissues from each experimental group was determined by immunofluorescence; The fluorescence intensity of LC3 (<bold>C</bold>) and BNIP3 (<bold>D</bold>) (green fluorescent) was accessed by photodensitometry. Representative immunofluorescence images were taken at 200×. DAPI (4′,6-diamidino-2-phenylindole4, blue) was used as a nuclear counterstain. All data are expressed as mean ± S.D., <italic>n</italic> = 8. ** <italic>p</italic> &lt; 0.01 vs. normal group; ## <italic>p</italic> &lt; 0.01 vs. APAP group.</p>
    Full article ">Figure 5
    <p>Suppression of apoptotic and autophagic pathway by α-MG using Western blotting analysis. The intensity of Bax, Bcl-2, caspase 3, cleaved caspase 3, Akt, p-Akt, m-TOR, p-m-TOR, p62 and LC3 II /LC3 I ratio were standardized to that of β-actin (<bold>A</bold>,<bold>B</bold>); Quantitative analysis of scanning densitometry for Bax (<bold>C</bold>); Bcl-2 (<bold>D</bold>); cleaved caspase 3 (<bold>E</bold>); p-Akt (<bold>F</bold>); m-TOR (<bold>G</bold>); p62 (<bold>H</bold>) and LC3 II /LC3 I ratio (<bold>I</bold>); were performed. All data are expressed as mean ± S.D., <italic>n</italic> = 8. ** <italic>p</italic> &lt; 0.01 vs. normal group; ## <italic>p</italic> &lt; 0.01 or # <italic>p</italic> &lt; 0.05 vs. APAP group.</p>
    Full article ">Figure 6
    <p>Model of action of APAP and α-MG.</p>
    Full article ">

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