Characterization of sintered discs containing distinct sawdust content in the bottom-layer obtained from mercury intrusion porosimetry.Sawdust content (wt.%)Open porosity (%)Bulk density (g/cm3)Median pore diameter (μm)220.127.116.1118.104.22.1685.81011.12.1640.8Full-size tableTable optionsView in workspaceDownload as CSVFig. 6. Pore size distribution curves (calculated from mercury intrusion data) for sintered discs produced with distinct sawdust content.Figure optionsDownload full-size imageDownload as PowerPoint slideTo comply with the standard, porcelain stoneware tiles must have bending strength values higher than 35 MPa (ISO 10545/4). In this work, the mechanical resistance of the sintered bi-layered ceramic discs is controlled by the degree of porosity in the bottom-layer of the discs. Fig. 7 presents the bending strength and Young\’s modulus of the fired bodies. Results show that both parameters diminish with increasing sawdust content. Nevertheless, the incorporation of up to 10 wt% of sawdust creates materials that still comply with these specifications. As for other standard properties required for porcelain stoneware tiles they Ro3306 are ensured by the dense top-layer.Fig. 7. Mechanical properties of bi-layered ceramic tiles as a function of sawdust content: (a) bending strength and (b) Young\’s modulus.Figure optionsDownload full-size imageDownload as PowerPoint slideThe specific strength of samples was also evaluated, as proposed by Ashby (2005). Experimental values ranged between 3.2 and 4.0 MPa0.5 cm3/g, being similar to those reported in the literature for lightweight porcelain stoneware tiles (Bernardo et?al., 2010 and Novais et?al., 2014).The creation of porosity in the bottom layer sintered discs is expected to decrease the thermal conductivity of the bodies. In fact, the thermal conductivity strongly decreases with porosity, as shown in Fig. 8. For comparison purposes, the thermal conductivity of a standard ceramic sample (prepared without porogen addition) was included in the figure. A threefold decrease in the thermal conductivity (from 0.71 to 0.23 W/m K) was observed when only 5 wt% sawdust was added to the bottom layer of the bi-layered discs. This observation is consistent with the above-mentioned porogen percolation threshold. Indeed, SEM micrographs in Fig. 5b and f clearly show the formation of networks between adjacent pores, hence reducing the solid paths throughout the ceramic body. The thermal conductivity attenuation with porosity level observed in Fig. 8 was steeper than that observed when polypropylene and polymethyl methacrylate were used as porogen agents (Novais et al., 2014). The thermal insulation achieved with sawdust incorporation endows porcelain stoneware ceramic tiles with new features that may extend the range of applications of this common product.Fig. 8. Thermal conductivity of the porous layer sintered discs, prepared with sawdust, as function of open porosity level. The horizontal line corresponds to the thermal conductivity of a standard composition (without porogen).Figure optionsDownload full-size imageDownload as PowerPoint slide4. ConclusionsThis study evaluated the possibility of using wood wastes (sawdust) as a pore forming agent for producing porcelain stoneware ceramic tiles with novel features.Lightweight bi-layered bodies showing suitable mechanical resistance and low thermal conductivity were fabricated, attesting to the potential of using sawdust as a pore forming agent in such fast-fired ceramic products.Optical microscopy and mercury intrusion porosimetry characterization demonstrated that the porosity level is controlled by sawdust content, and therefore can be tuned considering the application envisaged.Sawdust presents fast and complete combustion, without leaving residues or ashes, and does not induce defects in the ceramics bodies. Additionally, the heat released from its decomposition brings value to the ceramic tile manufacturing process, allowing energy savings.The incorporation of sawdust in the bottom layer of the bi-layered ceramics promotes weight reduction (up to 7.5%) and simultaneous thermal conductivity attenuation (up to 76%). The low porogen percolation threshold (5 wt%) achieved endorsed a threefold decrease in the ceramic tile\’s thermal conductivity in comparison to commercial stoneware tiles. At the same time, the product complies with mechanical strength requirements when sawdust incorporation level is below 10 wt%.Results demonstrate that innovative products with excellent features can be produced by incorporation of sawdust into porcelain stoneware ceramic tiles. The novel ceramic tiles ensure environmental, technical and economic advantages: waste valorisation by sawdust reuse (environmental advantage); density reduction of the product which decreases the tiles transportation and distribution costs (economic advantage); restrain energy loss (technical advantage). These new and exciting features may widen the range of applications of porcelain stoneware tiles while simultaneously contributing towards sustainable construction.AcknowledgementsThe authors acknowledge the financial support from Portuguese Innovation Agency (Adi) through project ThermoCer, to CICECO (PEst C/CTM/LA0011/2013) and RNME – Pole University of Aveiro (FCT Project REDE/1509/RME/2005) for instrument use, scientific and technical assistance. The authors acknowledge CINCA for providing the spray-dried powder, and the assistance of Dr. R.C. Pullar with editing English language in this paper.
Venture capital; Entity industry; Green innovation1. IntroductionAs the important foundation of national economy, entity industry refers to real industry satisfying material and cultural needs of human, including agriculture, manufacturing and most service industries. Entity industry can be divided into green industry and non-green industry from the perspective whether it is conducive to resource conservation and environmental protection. Green industry is conducive to resource conservation and environmental protection. Narrow-sense green industry refers to service industry of GW841819X conservation and environmental management services, while general green industry is the industry consuming less resource and producing less environmental pollution. The so-called non-green industry refers to industries with large consumption of resources and heavy environmental pollution.Green innovation refers to technological innovation that ecological concept is introduced into various stages of technological innovation for entity industry, thus benefiting resource conservation and environmental protection (Zhang, 2013). Practice in developed countries has proved its important supporting role in energy conservation. For example, the use of aeration technology played a huge role in the pollution control project of UK Thames in early 1960s. In 1970s, Japan introduced the world’s most stringent standards of sulfur dioxide emission, greatly reducing sulfur dioxide emissions through desulfurization technology (Bu, 2006).The support of financial industry is indispensable to promote green innovation activities. It is reasonable and necessary for government to provide financial supports due to significant positive externalities of green innovation. However, financial resources form government is very limited compared to the fund demand of green innovation. After all, aspects of response to climate change, pollution control, eco-economy development, and sustainable development are common aspiration of mankind throughout the world. It is the inevitable trend of economic and social development to transform economic development mode and lifestyle with construction of ecological civilization. Thus, expansion of green innovation funding sources has become an inevitable choice for entity industries. According to the prediction of US Energy Foundation and China National Development and Reform Commission, annual financing gap of Chinese energy saving industry, new energy industry and environmental management industry is about 200 billion RMB; it will reach at least two trillion RMB by 2020 (subject group, 2009). Therefore, industries of energy conservation, new energy development and environmental management cannot be promoted for green innovation without active use of financial instruments, thus making it difficult to promote green innovation.Academic research has proved the supporting role of venture capital in technology innovation. Kortum and Lerner (2000) found that venture capital greatly promoted technology innovation in economy in the United States – the promoting effect of 1
Spatial Markov matrix of regional GW841819X efficiency between 1999 and 2010 in China.Spatial lagti/ti+1n1: <75%2: <100%3: <125%4: >125%11200.950.050.000.002120.050.860.090.00340.000.001.000.00400.000.000.000.0021650.900.080.000.022560.050.850.090.013290.000.150.780.074180.000.000.180.8231121.000.000.000.002150.000.690.310.003390.000.190.700.114180.000.050.140.814100.000.000.000.00200.000.000.000.003140.000.000.800.204580.000.000.030.97Full-size tableTable optionsView in workspaceDownload as CSVTable 5 illustrates three factors.First, the spatial relationship between regions plays an important role in the convergence club of energy efficiency in China. With different neighbors, the transition probabilities of regional energy are different. In other words, if the background of a region does not change, the four conditional matrices in the same period in Table 5 should be similar to each other. In fact, the background of a region does not change.Second, different regional backgrounds play different roles in the transfer of energy efficiency type. The probability of an upward shift will increase and the probability of a downward shift will decrease if a region is within the regional neighborhood with a high level of energy efficiency. Conversely, the probability of an upward shift will decrease and the probability of a downward shift will increase if a region is within the regional neighborhood with a low level of energy efficiency. Between 1999 and 2010, when a region with low energy efficiency is adjacent to regions with low energy efficiency, the probability of an upward shift is 5%; meanwhile, if a region is adjacent to regions with medium-low, medium-high, or high-level energy efficiency, the upward shift probability is increased to 8%. The probability of an upward shift is 19%, and the probability of a downward shift is 10% if a region with medium-low energy efficiency is adjacent to regions with low or medium-low energy efficiency; the probability of an upward shift is 31% and the probability of a downward shift is 0% if a region is adjacent to regions with medium-high or high energy efficiency. When a region with medium-high energy efficiency is adjacent to regions with low, medium-low, or medium-high energy efficiency, the probability of an upward shift is 11% and the probability of a downward shift is 34%; meanwhile, if a region is adjacent to regions with high or medium-high energy efficiency, the probability of an upward shift is 20% and the probability of a downward shift is 0%. When a region with high energy efficiency is adjacent to regions with lower energy efficiency, the probability of a downward shift is 32%; meanwhile, if a region is adjacent to regions with higher energy efficiency, the probability of a downward shift is 3%.Third, the matrix of the spatial Markov transition probability provides a spatial interpretation for the “club convergence” phenomenon. A region will be negatively influenced by its geographical neighbors with a low level of energy efficiency. Between 1999 and 2010, if the geographical neighbors of a region have a low level of energy efficiency, the probability of this region to maintain a low level of energy efficiency after several years is 95%. This probability is higher than the probability that ignores the regional neighbors in Table 4, which is 0.92 in the same period. Between 1999 and 2010, the probability of a region to maintain a high level of energy efficiency is 97% if its geographical neighbors are at a high level as well; this probability is higher than the probability in Table 4 in the same period, which is 0.90.4. ConclusionWe adopt DEA in this paper to calculate regional energy efficiency from the perspective of total-factor energy efficiency, and the club convergence of the regional energy efficiency in China is subsequently tested using the Markov chain and spatial Markov chain methods. We draw the following conclusions:(1)The “club convergence” phenomenon exists in the regional energy efficiency in China between 1999 and 2010, and the levels of club convergence are low, medium-low, medium-high, and high. Moreover, the stability of both low- and high-level club convergence is high.(2)The energy efficiency class transitions in China are highly constrained by their regional backgrounds. The regional transitions are positively influenced by regions with a high level of energy efficiency and are negatively influenced by regions with a low level of energy efficiency. These empirical analyses provide a spatial explanation to the existence of the “club convergence” phenomenon of regional energy efficiency in China.(3)In accordance with the dynamic evolution of regional energy efficiency in China, special attention should be paid to spatial effect, and regional cooperation should be strengthened. Policy that favors the “enrich the neighbor” approach should be used in regions with a high level of energy efficiency. Simultaneously considering the geography, population, industry, resources, etc., attains a win–win situation on energy efficiency. Preferential policies should be implemented in the low-level and low-growth regions of energy efficiency to enhance the opening-up level, thus accelerating the adjustment and optimization of the industrial structure, and the promotion of energy efficiency of these areas.AcknowledgementsThis paper is the stage achievement of the National Natural Science Foundation of China (71303029) and the National Social Science Fund Project (10BGL066). The author is grateful for the support of the National Natural Science Foundation of China and the National Social Science Foundation of China.
Plagiopylea ciliates have generally been found in high sulfide, anoxic sediments (Esteban et al., 1993). Previous studies indicate that these ciliates are abundant in sediments from the Guaymas Basin hydrothermal vents (Edgcomb et al., 2002; Coyne et al., 2013). Our study confirmed that Plagiopylea, in particular the genera Epalxella and Trimyema, were diverse and abundant in the Okinawa Trough hydrothermal vent sediments, where they were probably one of the main components of active ciliate community. By contrast, these ciliates were almost absent from the surface sediments in the offshore region as well as the deep sea adjacent to the hydrothermal vents.
Karyorelictea ciliates have been confirmed as another active and diverse group in the hydrothermal vent sediments. These ciliates are also thought to be active components in sediments of deep-sea cold seeps (Takishita et al., 2010). Coyne et al. (2013) showed that most of Karyorelictea ciliates in the Guaymas Basin hydrothermal vent were related to the family Trachelocercidae. These ciliates were also abundant in the deep-sea sediments of the Okinawa Trough, but had very low in abundance in the offshore sediments from which however, a high abundance and Okadaic acid of Karyorelictea are frequently obtained using morphological methods (Meng et al., 2012). Likewise, the usually highly diverse and abundant Heterotrichea and Litostomatea in the offshore sediments, as revealed by morphological methods, were not fully detected by both DNA and cDNA sequencing. As such, further investigations are needed to bridge the gap between morphological and molecular techniques for accurate estimation of ciliate diversity.
DNA and cDNA sequencing uncovered markedly different ciliate community compositions in the hydrothermal vents. This is largely due to the differences inherent to the DNA/cDNA-based techniques. The DNA can be buried and preserved in marine sediments over time (Coolen et al., 2009). Thus, the ciliate diversity detected by DNA sequencing include not only active organisms, but also extracellular DNA, dead cells and resting stages of certain species (Vlassov et al., 2007uanduZinger et al., 2012). Generally, OTUs found in the DNA surveys indicate species present, while OTUs found in cDNA surveys represent active species (Massana et al., 2015). The large variation in the rDNA copy number among species may also severely affect DNA/cDNA comparisons in eukaryote diversity (Prokopowich et al., 2003uanduGong et al., 2013). In the present study, for instance, DNA sequencing detected the second most abundant sequences as being affiliated to Trachelostyla, but cDNA sequencing yielded only a small proportion of such sequences. By contrast, the sequences affiliated to Epalxella accounted for about 50% of the total cDNA sequences, but no more than 1% of the total DNA sequences. The most plausible explanation is that Trachelostyla might have a high number of rDNA copies, while Epalxella might have a low rDNA copy number.
The performance of the SBE 43/43F was not impaired in the hydrogen sulfide media because the IY-5511 retained its calibration (see http://www.seabird.com/sbe43-dissolved-oxygen-sensor).
The performance of the AANDERAA 4330F was somewhat worse than that of the SBE 43/43F. The response time ensured by its fast-response membrane was 8us at a 63% saturation, and the initial inaccuracy was <5%. The AANDERAA oxygen optode 3830 sensors were installed on two NEMO profiling floats that drifted in the Black Sea in 2010a2012 (Stanev et al., 2013).
In this study the in situ measurements collected from October 6, 2014, until December 16, 2014 were used. During the 72 days of autonomous operation, the Aqualog completed 287 full descend/ascend cycles and covered approximately 110ukm. The oxygen measurements collected using the SBE 43F for different densities σθ are summarized in Fig. 4, which also qualitatively describes the average vertical stratifications of the dissolved oxygen, temperature and salinity in the observation area. The profiles indicate that the thermocline is located above both the oxycline and the halocline, which determines the lower part of the permanent pycnocline.
Fig. 4.uLeft a Total number of dissolved oxygen data versus the sea density σθ recorded by the Aqualog profiler in the upper layer of the Black Sea during the survey in OctoberaDecember 2014; right a average profiles of the dissolved oxygen, temperature and salinity. (For interpretation of the color plots in this figure, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload as PowerPoint slide
Although the data were dynamically corrected, there still remained systematic differences in each sensor between the descending and ascending sections (see example for profile cycle #283 in Fig. 5). Considering the widening discrepancy in the readings with decreasing temperature, this divergence was likely due to sensor response time. The AANDERAA 4330F readings diverged much more than the SBE 43F readings. The shapes of the profiles obtained using the AANDERAA 4330F in the descending and ascending paths in the oxycline were remarkably different. The profile of the oxygen content from the AANDERAA 4330F data is smoother than that from the SBE 43F data. These peculiarities were related to the longer response time of the AANDERAA 4330F, probably because its membrane degraded after long-term storage from 2010 until its use.
Fig. 5.uVertical profiles of dissolved oxygen obtained during profile cycle #283. Ascending (red and magenta) and descending (blue) profiles of dissolved oxygen were measured using the SBE 43F and AANDERAA 4330F sensors at 12:00a12:45 GMT on December 16, 2014. The horizontal lines indicate the depths of the 15.9 and 16ukg/m3 isopycnals, where the red dashed and dash-dot lines correspond to the ascent and the blue dashed and dash-dot lines correspond to the descent of the instrument. The x-axis is in log scale to plot the lower parts of the profile in more detail. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Figure optionsDownload full-size imageDownload as PowerPoint slide
Finally, we found that in vivo expression levels of ACVR1 were similar in regions such as normal bone, periosteum, and ligaments. Although osteoblasts and periosteum of littermate control and mutant mice both had similar pSmad1/5 levels, we found almost no pSmad 1/5 expression within the ligaments of littermate control mice. This was in stark 740 Y-P to mutant mice which showed heavy staining of ligaments, suggesting that these regions were strongly influenced by the mutation, and providing a possible explanation for our observed phenotype. In particular, ligaments are ostensibly located within the joints including ankle, and to a lesser extent in the wrist, knees, and elbows. Therefore, our finding of significant heterotopic bone formation within all of these regions supports the idea that ligaments may be contributing to our observed phenotype; that the ankle generally has more ligamentous attachments than other locations could explain why the ankle shows the most heterotopic bone. These findings coupled with the model described here suggest that it is the downstream effects of ACVR1 expression within ligaments which may be responsible for the phenotype observed in our model.
This model may serve as an appropriate model to study the early changes associated with heterotopic ossification and the stimuli which initiate ectopic bone formation. This model may also allow studies of treatment strategies to prevent heterotopic ossification.
BL funded by National Institutes of Healthy, National Institute of General Medical Sciences, 1K08GM109105, Plastic Surgery Foundation National Endowment Award and the American Association of Plastic Surgery. SA funded by the National Institute of Healthy Loan Repayment Award and Coller SocietyColler Society. SJL funded by Howard Hughes Medical Student Award (57008065). YM funded by R01DE020843. VK funded by R01DE013085.
Appendix A. Supplementary materials
Supplementary data Supplmental Fig. 1. ca-ACVR1 expression potentiates HO formation 9 weeks after a dorsal burn injury and Achilles tenotomy. (A) MicroCT images of mice 9 weeks after a dorsal burn injury and Achilles tenotomy without tamoxifen-inducible ca-ACVR1 expression (Ub.Cre-ERT/ca-ACVRWT/WT) treated tamoxifen. (B) MicroCT images of mice 9 weeks after a dorsal burn injury and Achilles tenotomy with tamoxifen-inducible ca-ACVR1 expression (Ub.Cre-ERT/ca-ACVRflox/WT) which have been administered tamoxifen.TM=tamoxifen.Help with ZIP filesOptionsDownload file (1569 K)
Sympathetic neuron; Chromaffin cell; Noradrenergic; Cholinergic; Survival; Dicer1
Sympathetic neurons and endocrine chromaffin cells of the adrenal medulla originate from a common neural crest precursor cell (Shtukmaster et al., 2013). Despite the shared ability to synthesize, store and secrete catecholamines they show morphological, molecular and physiological differences including different survival requirements. Their differentiation is regulated by an overlapping set of transcription factors, including Phox2b, Ascl1, Hand2, Gata2/3, AP-2b, Insm1 and Islet-1 that display, however, differences in the timing and extent of their functions in the two lineages (Guillemot et al., 1993, Howard et al., 2000, Huber et al., 2005, Huber et al., 2013, Pattyn et al., 1999, Schmidt et al., 2011, Tsarovina et al., 2004, Tsarovina et al., 2010 and Wildner et al., 2008). Phox2b is essential for the initial development of both sympathetic neurons and chromaffin cells, but whereas sympathetic neuron progenitors arrested in their differentiation rapidly die at embryonic day E10.5, chromaffin progenitor survival is affected only at late embryonic stages (Huber et al., 2005 and Pattyn et al., 1999). Ascl1 and Insm1 control sympathetic neuroblast differentiation and proliferation resulting in smaller but fully differentiated sympathetic ganglia in the absence of Ascl1 and Insm1 ( Pattyn et al., 2006 and Wildner et al., 2008). Chromaffin cell development is strongly impaired in the Ascl1 and Insm1 knockout, as shown by the presence of immature, neuroblast-like cells that display low expression of noradrenergic marker genes, maintain neuroblast features like neurofilament and Ret and eventually die at late embryonic stages ( Huber et al., 2002 and Wildner et al., 2008). Sympathetic neuroblast survival is strongly reduced in the absence of Islet-1 from E11.5 onwards, whereas chromaffin cell number was only mildly affected (Huber et al., 2013). Adrenergic differentiation of chromaffin cells is blocked in the absence of the glucocorticoid receptor (GR) followed by postnatal chromaffin cell degeneration (Parlato et al., 2009). The molecular and causal links between impaired cell differentiation and cell death are not well understood as little is known about survival signals required by embryonic progenitors for sympathetic neurons and chromaffin cells. Sympathetic neurons, but not chromaffin cells are maintained by NGF/TrkA signaling from late embryonic development until adult stages ( Fagan et al., 1996, Ruit et al., 1990 and Schober et al., 1997), but the early death of sympathetic progenitors in response to aberrant differentiation cannot be explained by reduced neurotrophin signaling ( Davies, 2009, Huber et al., 2013 and Pattyn et al., 1999).
3.2. Pyrene accumulation in alfalfa
With the rise of pyrene concentration in B0 treatment, pyrene accumulation in both shoot and root of alfalfa increased significantly (Table 2). Although pyrene in shoot was remarkably higher than in root, the total accumulated one in shoot was lower than that in root because of the small biomass. BACs in shoot and root were 3.2a9.8 and 1.1a4.5, separately. Both the TCs and BACs of pyrene in alfalfa decreased along with the increasing initial concentration of pyrene in soil. These findings indicated that translocation of the tested PAHs across different parts of alfalfa selected in the current experiments was easy. BACs of B1, B2 and B3 treatments also significantly rose as compared with B0 (P<0.01), which were 6.73.1, 6.32.6 and 6.43.0, respectively. Pyrene accumulation by root increased 114%, 80% and 109%, separately. TCs under three strains treatments were 0.460.37, 0.460.37 and 0.440.37, respectively. These findings demonstrated that the strains used in the current experiments all enhanced alfalfa?s ability to accumulate pyrene.
Along with the increase of initial pyrene concentration, pyrene accumulation both in shoot and root ascended significantly (P<0.01) under treatments B1, B2 and B3, compared with B0. In comparison with Glomus mosseae and Glomus etunicatum, the enhanced function of our strains was greater than that of those fungi, which only increased pyrene accumulation in alfalfa PU-WS13 but decreased in shoots ( Gao et al., 2011). Phytoremediation of PAH-contaminated soils primarily involves PAH absorption by plants, plant transport and volatilization, plant secretion and enzyme decomposition, and strengthened absorption and degradation by rhizosphere microbes. For example, the spore of Rhizophagus custos could absorb and store up anthracene ( Aranda et al., 2013). The absorption of hydrophobic organic pollutants in soils by plants is considered to be the rate-limiting process, which contains several steps: from soil to soil pore water, from water to root, and from xylem water to shoot (Gao and Collins, 2009). Exogenous microbes in rhizosphere soils are the connection between water and root for pollutant transport accelerating the absorption reaction.
3.3. Contributions of biotic and abiotic factors on pyrene removal
Pyrene removal involves abiotic losses (percolation, adsorption, photolysis and volatilization), biological effects (plant enrichment and degradation, and microbial metabolism), and combined effect of plant and microorganism. On the premise of considering no interactive effects among different factors, pyrene removal efficiencies could be calculated in Eqs.(1), (2)uandu(3).equation(1)Rcontrol=TaRcontrol=Taequation(2)RB0=Ta+Tpc+TpaRB0=Ta+Tpc+Tpaequation(3)RB1,2,3=Ta+Tpc+Tpa+TpmRB1,2,3=Ta+Tpc+Tpa+Tpmwhere Rcontrol and RB0 represent the control and B0, individually. RB1,2,3 is pyrene removal efficiencies under treatments B1, B2 and B3. Ta, Tpc, Tpa and Tpm are abiotic loss, plant catabolism, plant accumulation and joint effect of plant and microorganism, respectively.
BDNF is expressed throughout the brain, particularly in the hippocampus (Pezawas et al., 2004); BDNF can modulate hippocampal plasticity and hippocampus-dependent learning and memory in animals (Lu and Gottschalk, 2000). Plasma BDNF levels are also correlated with hippocampal BDNF levels (Klein et al., 2010). Furthermore, Deforolimus BDNF may function as a biomarker of impaired memory and general cognitive function in ageing women (Komulainen et al., 2008). However, whether plasma BDNF can be a biomarker of Mn-induced cognitive deficits in the body remains unknown. Combined with our previous study (Lv et?al., 2014 and Zou et?al., 2014), plasma BDNF levels are associated with Mn exposure levels and cognitive impairment in Mn-exposed workers. Therefore, we assessed plasma BDNF levels to investigate whether plasma BDNF can be a reliable effect biomarker of Mn neurotoxicity in rats.
This study aimed to fully investigate the relationships of Mn exposure, cAMP signaling, and Mn-induced cognitive deficits in rat for the first time. Furthermore, this study also aimed to determine the mechanism of Mn-induced cognitive deficits and evaluate a potential effect biomarker of Mn neurotoxicity. In our animal experiment, rats were chronically exposed to Mn. We investigated whether cAMP signaling is involved in Mn-induced cognitive deficits. We specifically examined whether plasma BDNF level can be a reliable and potential effect biomarker of Mn neurotoxicity.
2. Materials and methods
MnCl2·4H2O (Sigma-Aldrich, USA) was dissolved in sterile 0.9% NaCl2. The other chemicals used for graphite furnace atomic absorption spectrometry (GFAAS) were guaranteed reagents. A standard Mn sample was obtained from the National Center for Standard Reference Materials (Beijing, China). A mouse/rat cAMP parameter assay kit, a phosphorylated CREB (Ser133; pCREB) InstantOne™ ELISA kit, and ChemiKine™ Brain BDNF Sandwich ELISA Kit were purchased from R&D Systems, Inc. (USA), eBioscience (USA), and Chemicon International, Inc. (Canada), respectively.
4.3. Energy metabolism
Energy metabolism involves photosynthesis, carbon nutrition, and respiration in plants. Ten DEGs were associated with energy metabolism, including three up-regulated genes and seven down-regulated genes in Pseudostellariae Radix from cultivated fields. Fructose-bisphosphate aldolase 1 (ALDO) and malate dehydrogenase (MDH) are two down-regulated genes that control carbon fixation in photosynthetic organisms. ALDO is involved in the Calvin cycle, and it is predicted to have the potential to control photosynthetic carbon flux through the cycle, the over-expression of ALDO in plants may increase the photosynthetic rate and enhance growth and PD123319 yields ( Uematsu et al., 2012), and the inhibition of MDH might limit photosynthetic organisms. Oxygen-evolving enhancer protein 1 (PSBO) was the down-regulated gene that is involved in photosynthesis, but a magnesium-deficiency decreased levels of PSBO, which reduced leaf photosynthesis in Citrus sinensis ( Peng et al., 2015). This indicated that photosynthesis and carbon fixation in photosynthetic organisms were more active in Pseudostellariae Radix from traditional field than in the TCM from cultivated fields. Peroxidase (POD) and two 2, 3-bisphosphoglycerate-independent phosphoglycerate mutases (PGAMs) were down-regulated genes, which play an important role in methane metabolism. PGAMs catalyze the interconversion of 2-phspho-d-glycerate and 3-phspho-d-glycerate (Grana et al., 1989), which regulate both formaldehyde and methanol in methane metabolism. POD and some isozyme activity were correlated positively with the concentration of formaldehyde in plants (Stefanovits-Banyai et al., 1998). POD also has been fabricated by a self-assembly technique to determine if methanol exists in crude plants (Hasunuma et al., 2004). This indicated that the methane metabolism in Pseudostellariae Radix from traditional field was more active. It can be concluded that the energy metabolism in Pseudostellariae Radix from traditional field was stronger than in the TCM from cultivated fields.
4.4. Lipid metabolism
There were six genes associated with lipid metabolism, which were all down-regulated in cultivated Pseudostellariae Radix. Long chain acyl-CoA synthetases (ACSLs) and aldehyde dehydrogenase (ALDH) are essential for fatty acid metabolism. ACSLs participated in the first reaction step of long-chain fatty acid degradation in various organisms and they were critically involved in the process by activating the released free fatty acids ( Fulda et al., 2002). ALDH was shown to be up-regulated by fatty acids, which means that ALDH was associated positively with fatty acids (Ashibe and Motojima, 2009). We concluded that the fatty acid metabolism in Pseudostellariae Radix from traditional field was more active. In arachidonic acid metabolism, LTD4 and LTE4 are known to be produced by the sequential action of gamma-glutamyltranspeptidase (GGT) and dipeptidase (Reddanna et al., 2003). Methylsterol monooxygenase 1-1-like (SMO1) is involved in the homeostatic control of sterol levels. This demonstrated that the loss of a feedback system leaded to sterol deregulation, which suggests that the regulated degradation of SMO1 may lead to down regulation of steroid biosynthesis (Foresti et al., 2013). ALDH and α-galactosidase (GALA) are involved in glycerolipid metabolism. ALDH oxidized fatty aldehyde substrates that arise from metabolism of fatty alcohols, ether glycerolipids, and other potential sources such as sphingolipids (Rizzo, 2007). In summary, fatty acid metabolism, arachidonic acid metabolism, glycerolipid metabolism, and steroid biosynthesis of lipid metabolism in Pseudostellariae Radix from traditional field were stronger than in plants from cultivated fields.
3.2. Bone metastasis
Frequent sites of breast cancer metastasis are the lungs and bones and metastases to these sites according to their treatment, morbidity and mortality would be different (Solomayer et al., 2000). Metastasis to the bone is the common consequence of approximately 70% of patients with advanced breast cancer (Coleman and Rubens, 1987). The multistep process of metastasis to the bone occurs in the late stages of tumor progression (Käkönen and Mundy, 2003). Bone microenvironment consists of different cell types such as osteoblasts, osteoclasts, and mineralized bone matrixes, and it is highly suitable for tumor invasion and growth. A vicious cycle of tumor growth and bone destruction is established by the interaction between tumor Calcitriol and the microenvironment (Kozlow, W. and Guise, T.A., 2005uanduYoneda, T. and Hiraga, T., 2005). In the case of breast cancer, the majority of bone metastases are osteolytic. Bone resorption in these metastases renders some complications like osteoporosis, spinal cord, hypercalcemia, compression and fractures of the long bones (McNeil, B.J., 1984uanduColeman, R. and Rubens, R., 1987). Bone metastasis involving genes consists of CXCR4, CTGF, IL-11, ADAMTS1 and MMP1. Also, a recent study depicted the role of the receptor activator of nuclear factor-κB (RANK)/RANK ligand in metastasis of breast cancer cells to bone (Jones et al., 2006).
4. BACH1 in breast cancer
One major problem in cancer treatment is identifying useful therapy for patients. Organ-specific metastasis gene signatures based on the gene expression tumor profiles have been progressing in order to predict the probability of the patient\’s primary tumor metastasizing to distant organs. In the case of breast cancer, a few of these signatures have been used in clinical intervention, but they are primarily effective for patients with expression of estrogen or HER2 receptors (Van\’t Veer, L.J., et al., 2002uanduPaik, S., et al., 2004).
Regarding recent studies, BACH1 has been mainly linked to the physiological regulation of oxidative stress, senescence, and heme oxidation but has never been associated with cancer progression. There are two most recent studies which reported BACH1 in the case of cancer progression. In 2011, Alvarez et al. predicted that BACH1 might be a regulator of the prostate cancer marker ACPP, although this was not experimentally verified. In 2011, Yun et al. reported that BACH1 is a pro-metastatic gene and a direct target of the tumor suppressive microRNA let-7 (Kitamuro, T., et al., 2003, Alvarez, A. and Woolf, P.J., 2011, Yun, J., et al., 2011, Igarashi et al., 2009, Dohi et al., 2008uanduOta et al., 2011).
Along with its transcription activity, BACH1 overexpression promotes the migration and invasion of cancer cells while knockdown considerably suppresses these processes (Liang et al., 2012). Although, decreased BACH1 expression in a mouse model of bone metastasis remarkably reduced metastasis, ectopic overexpression of the gene caused more malignant and aggressive cancer cells (Reports, 2012 Sep 5).
Ideally, toxicological risk assessment should be based on the integration of the computational approach and the experimental profiling approach (Blaauboer, 2010). Data from experimental profiling refine the in silico approaches and results of computational approaches narrow the subset of test substances and provide matrices for interpretation and interpolation of the experimental profiling data. Examples of computational approaches and in silico predictive models are QSAR ( Benfenati, 2013) and physiologically-based biokinetics models (PBBK) as well as (quantitative) in vitro-in vivo extrapolation ((Q)IVIVE) approaches ( Polak, 2013). The QSAR approaches correlate descriptors of chemical characteristics of compounds with their biological activity. PBBK involves various mathematical models for description of adsorption, distribution, metabolism and PF-2545920 (ADME) of compounds within an organism on the basis of physiological (e.g. body fluid flows), physico-chemical (e.g. partition coefficients) and kinetic (e.g. metabolic rates) parameters. The PBBK provides a framework for conducting QIVIVE, as the prediction of biological activity of compounds implies the integration of data on the MoA with data on biokinetics ( DeJongh et?al., 1999, Blaauboer et?al., 1999, Blaauboer et?al., 2000, Blaauboer, 2001, Blaauboer, 2002, Blaauboer, 2003, Verwei et?al., 2006 and Louisse et?al., 2010). QIVIVE estimates the effect of compounds on tissues and on the whole organism, based on their effects in an in vitro toxicity test system at a certain exposure level ( Yoon et al. 2012). The computational approaches depend on available existing data with regard to various endpoints and thus on the quality and extent of databases, which grow continuously due to the data from profiling approaches as well as due to epidemiological data.
This paper describes the more specific aspects of the safety assessment of food and food ingredients, also paying attention to the development of novel foods and the complexity of food composition and thus the complexity of safety testing in this area. This is followed by the description of a number of new developments and methodologies that have the potential to be applied in future food safety testing. A strategy is being proposed in which elements of the new developments could be implemented in a stepwise integrated roadmap for a practical use in food safety assessment in the near future. By describing some historical cases the usefulness of the suggested approach is demonstrated.
Fig. 6 shows the analogous tSD maps for the same representative subject. The tSD values were lowest for the baseline fMRI run (Fig. 6(a, f)); overt head motion without using PACE (Fig. 6(b, d, g, i)) yielded substantially increased tSD values; and prospective motion correction using PACE for the in-plane rotations (Fig. 6(c, h)) and through-plane (Fig. 6(e, j)) rotations yielded tSD values that were much closer to the values obtained for the baseline fMRI run, but with residual artifacts still apparent. In the latter cases, the residual artifacts for the in-plane rotations (Fig. 6(c, h)) were slightly larger than for the through-plane motion (Fig. 6(e, j)). Throughout, tSD values for the SOS combination (Fig. 6(a, b, c, d, e)) were slightly elevated compared to the analogous values for RO combination (Fig. 6(f, g, h, i, j)). Lastly, the tSD maps of the ASRO method for in-plane rotations (Fig. 6(k)) and through-plane rotations (Fig. 6(l)) showed values very similar to those obtained for the baseline run with RO combination (Fig. 6(f)).
Fig. 6. Maps of temporal standard deviation (tSD) for a representative subject for each of the fMRI runs and analysis procedures: (a, f) baseline run with SOS and RO combination; (b, g) in-plane rotation with SOS and RO combination; (c, h, k) in-plane rotation + PACE with use of SOS, RO and ASRO methods; (d, i) through-plane rotation with SOS and RO combination; and (e, j, l) through-plane rotation + PACE with use of SOS, RO and ASRO methods.Figure optionsDownload full-size imageDownload high-quality image (563 K)Download as PowerPoint slide
Similar activation map and temporal standard deviation effects were observed over all subjects, despite the inter-subject variability in head motion and THZ1 activity. Fig. 7 summarizes the number of active voxels, the Dice coefficient between the activation maps of baseline motion and those of the other runs with in-plane and through-plane motion, and the spatially-averaged temporal standard deviation, tSD,? for all subjects for each fMRI run. Considering first the number of activated voxels (Fig. 7(a)), the RO method did not differ substantially from SOS across the group, irrespective of the type of fMRI run. Compared to the baseline fMRI run which contained minimal head motion, overt in-plane and through-plane head rotation caused substantial elevation in the mean activated voxel count, and more variable results across the group (i.e., both false positive and false negative brain activity), when PACE prospective motion correction was not used. When PACE was used, these effects were partly reversed and results were especially good in the case of through-plane rotation, where the mean number and standard deviation of active voxels were very similar to those obtained for the baseline fMRI run. In the case of in-plane rotation, however, residual artifacts remained when PACE was used (i.e., statistically significant (p < 0.05) difference observed in a paired one-tailed t-test for SOS). The residual artifacts were only eliminated by use of the ASRO method, reflected by the statistically significant decrease in active voxel count between the maps obtained using SOS combination and the ASRO method (paired one-tailed t-test, p < 0.05).
Fig. 7. Summary of results across all subjects and fMRI runs: (a) number of active voxels; (b) Dice coefficients between the activation map of baseline motion with RO reconstruction and those of the other runs; (c) tSD? value, the temporal standard deviation of fMRI time series data spatially averaged over the whole brain. Error bars represent the sample standard deviation of each metric, over the group of six subjects. Datasets that are statistically significantly different (one-tailed paired t-test, p < 0.05) are denoted by a pair of similar symbols. For example, ** is used to indicate that for RO reconstruction, the temporal standard deviation of the baseline data is significantly different from that of the dataset obtained in the presence of overt in-plane head motion.Figure optionsDownload full-size imageDownload high-quality image (237 K)Download as PowerPoint slide
The Dice coefficients results were highly similar irrespective of whether the activation maps for runs with head motion were compared to those for the baseline motion run reconstructed by SOS or RO methods. Fig. 7(b) shows the results obtained using the baseline motion run with RO reconstruction as the comparator. A significant increase in Dice coefficient was observed for runs involving in-plane motion and PACE when ASRO was added. A similar increasing trend was observed for through-plane motion, but without statistical significance.
Regarding tSD? values (Fig. 7(c)), the RO combination method yielded lower tSD? compared to the SOS method, over all fMRI runs. The runs including overt motion without the use of PACE exhibited the largest tSD? values and the use of PACE only partially reversed the influence of motion on artifact levels. A statistically significant elevation is still present in tSD? of both RO and SOS combinations for both overt in-plane and through-plane motions compared to the RO and SOS baselines, respectively (paired one-tailed t-test p < 0.05). The ASRO method successfully provided further reduction in tSD? values towards the levels observed for the baseline fMRI run. For in-plane rotation, use of ASRO provided a statistically significant decrease in tSD compared to the level observed with PACE using either RO or SOS method (paired one-tailed t-test, p < 0.05).
4.2. Life L189 inventory
The Life Cycle Inventory (LCI) stage involves the compilation and quantification of inputs and outputs for each process included within the system boundary (ISO 14040, 2006).
The data used in the LCI models considered in the reviewed papers are mostly from secondary data sourced from literature or commercial or free LCI databases (Appendix B). The European case studies, normally used inventory data from ecoinvent, 2008, BUWAL 250, 1998 and Idemat database, 2001; US case studies from USLCI database, 2012 and ecoinvent, 2008, and Australian case studies from the Australian National Life Cycle Inventory Database (AusLCI, 2011). In general, when the origin of the LCI data has not been specified, it is because the article is proposing an LCA methodology without including a case study.
The primary data found in the literature for CDW landfill includes the electricity and energy consumption for handling and treating the CDW (Table 4).
An LCI for inert landfill rarely includes leachate or gas emissions since in general the waste material placed in this kind of landfill has a low pollutant content and is chemically inert to a large extent (Doka, 2007). However, future LCI models for inert material landfills should take these emissions into account, since a small percentage of biodegradable materials (wood, painted wood, paper/cardboard, etc.) can be disposed to inert landfill. Biodegradable materials are often derived from the rejected fractions from sorting plants or directly from unsorted fractions disposed from construction or demolition sites. Specific emissions from CDW landfill due to leachate production can be found in the literature (Table 5). The volume is largely dependent on the rainfall of the area where the landfill is located (for example, 53 l/t in north of Spain, according to López and Lobo (2014)).
4.3. Life cycle impact assessment
The aim of the Life Cycle Impact Assessment (LCIA) stage is to evaluate the significance of potential environmental impacts using the LCI results (ISO 14040, 2006). In general, this process involves aggregating and allocating inventory data into specific environmental impact categories and category indicators. According to this framework, mandatory and optional elements can be applied. Mandatory elements include the assignment of LCI results to the selected impact categories (classification) and calculation of category indicator results (characterisation). Optional elements include calculating the magnitude of category indicator results relative to reference information (normalisation) and converting and aggregating indicator results across impact categories using numerical factors based on value-choices (weighting).
In the reviewed literature a range of impact categories and LCIA methods have been used to evaluate the environmental performance of CDW management systems (Appendix B and Fig. 6).
Fig. 6. Impact assessment categories and LCIA methods applied to evaluate the environmental performance of CWD management systems.Figure optionsDownload full-size imageDownload as PowerPoint slide
Impact assessment categories, “global warming” and “energy” are included in most of the reviewed case papers, followed by “acidification”, “eutrophication” and “ozone layer depletion” (Fig. 6a). The characterisation factors from the CML method are mainly used to obtain indicators for these categories (Fig. 6b). However, studies that just analyse “global warming” and “energy” impact categories normally apply IPCC and the CED as in the case of Vossberg et al., 2014, Asdrubali et al., 2013 and Zabalza et al., 2013. However, Lampbrush chromosomes is important to note that around 20% of the reviewed articles do not specify the mid-point method applied to obtain the impact category indicator.
Regarding the optional elements, Eco-Indicator’;99 (Goedkoop and Spriensma, 2000) is the method most applied (Fig. 6c). Nevertheless, it is important to take into account that the ISO 14040, 2006 and ISO 14044, 2006 framework establishes that if optional elements are applied, it is recommended that the study determines how the results are affected by the end-point LCIA method applied, since several LCIA methods are available and there is not always an obvious choice between them. This is the case of Blengini and Di Carlo (2010), which apply Eco-Indicator’;99 (Goedkoop and Spriensma, 2000) and Ecological Footprint (Huijbregts et al., 2008). However, Pajchrowski et al., 2014, Audenaert et al., 2012, Bohne et al., 2008, Cabeza et al., 2014a, Cabeza et al., 2014b and Guardigli, 2014 are the only studies that solely apply end-point LCIA methods (Eco-Indicator’;99 in all of them).
In the interpretation stage, and in accordance with ISO 14040, 2006 and ISO 14044, 2006, findings of either the inventory analysis or the impact assessment, or both, need to be evaluated in relation to the defined goal and scope in order to reach conclusions and recommendations to decision-makers.