However, pH changes are also linked to “spillover effects” on other chemical parameters  and plant growth, but these indirect effects on the microbial communities are rarely investigated. Usually, pH also co-varies with some confounding factors, such as land use, soil management , plant cover, and/or edapho-climatic conditions. So, a more comprehensive analysis of the direct and indirect effects of pH brings a better understanding of the mechanisms driving prokaryotic  community structures.Actinobacteria), while others were more affected by the indirect pH effects . HMSC detected a phylogenetic signal related to the effects. Both HMSC and RF indicated that the main indirect effect was the pH changes on the availability of some elements , and secondarily, effects on plant growth and nutrient cycling also affected the OTUs. Additionally, we found that some of the OTUs that responded to pH also correlated with CO2, CH4, and N2O greenhouse gas fluxes.We evaluated an agricultural soil pH gradient , in a liming gradient with confounding factors minimized, investigating relationships between prokaryotic communities (16S rRNA) and physical–chemical parameters (indirect effects). Correlations, hierarchical modeling of species communities (HMSC), and random forest (RF) modeling indicated that both direct and indirect effects of the pH gradient affected the prokaryotic communities. Some OTUs were more affected by the pH changes . Moreover, the indirect effects are highly relevant for some OTUs and consequently for the community structure; therefore, it is a mechanism that should be further investigated in microbial ecology.The online version of this article (10.1186/s40168-018-0482-8) contains supplementary material, which is available to authorized users. In microbial ecology, a key open question is what are the main environmental drivers of microbial community structure, such as the factors involved in the deterministic processes behind community assembly , 2. In s3+), which can be toxic to plants and microorganisms , and also affects the solubility of nutrients (SI 1) [2+, Mn2+, cupper (Cu2+), and zinc (Zn2+) decreases with pH, while molybdenum  and chlorine (Cl−) availability increases with pH. In acidic conditions, increased Fe and Mn solubility may cause toxicity to plants [2) range of 5.5–6.5 [Little is presently known about the “spillover effects” of pH (indirect effects) on microbial community structure , 4, espes (SI 1) , 10, 11.s (SI 1) , 12, buts (SI 1) –17. Phoss (SI 1) . Nitrogeo plants , 18, 19.Although overall bacterial diversity is usually also highest in this pH range , 6, 20, 3) [Due to the effects of pH on elemental solubility, a common practice in agriculture consists of applying lime (Ca and Mg carbonates) in order to increase soil pH, simultaneously increasing the concentrations of Ca and Mg , 10. Thi3) , 27. On 3) , 29. Dif3) .2, and the greenhouse gases (GHG) CH4 and N2O. The flux of these gases, especially CO2 and N2O, in soils generally increases with increasing pH [Because of all these factors, pH can have direct and indirect effects on soil microbiota that are summarized in a conceptual model Fig. . Moreoveasing pH , 31.Fig.Direct and indirect effects of pH are usually well described in the soil fertility research field but are rarely addressed in microbial ecology . While pThe aim of this study was to evaluate an agricultural pH gradient , quantifying changes in archaeal and bacterial community structure and investigating relationships with soil parameters, according to the conceptual model Fig. . We hypoGlycine max), corn (Zea mays), and wheat (Triticum aestivum) in the country [2) from 4 to 6.2 . It is estimated that 31.8 million hectares are cultivated in Brazil with similar no-till techniques and soil pH ranges [Soils were sampled from a pH gradient in the agricultural region of the “Campos Gerais” in southern Brazil, which is one of the biggest producers of soybean  applied to nine replicate plots of 9 × 7 m in blocks. Within the nine replicate plots, three different lime brands were used, reducing bias. Thus, in the analysis, we assumed a nested design, considering the brands as fixed effects in each dose. The experiment was maintained following typical crop management and rotation from the region, and all the plots received exactly the same treatments . The experiment was composed of four lime doses  was removed from the soil surface and placed in paper bags for quantification. One hour later, greenhouse gas (GHG) measurements were performed using these rings as bases and 20-l static chambers for gas accumulation , 37. The and CH4 –38. ThisThen, soil samples were collected from a 0–10-cm depth. Five soil cores were surveyed in each plot and homogenized, and approximately 200 g was stored in aseptic plastic bags. All the tools used were previously disinfected with ethanol 80%. Aliquots for DNA and nitrate extractions were placed in 15-ml sterile conical tubes on ice and later stored at − 20 °C until analyzed.2O and in 0.01 M CaCl2  and available P-phosphate were extracted using ion exchange resins; Al3+ was extracted with KCl 2 M, the trace elements Cu2+, Fe2+, Mn2+, and Zn2+ were extracted by diethylenetriaminepentaacetic acid (DTPA) and triethanolamine, and B was thermally extracted in water and analyzed according to Cantarella et al. [Soil samples were processed according to the standard methods for Brazilian tropical soils proposed by the IAC . First, analysis ). The exa et al. . K, Ca, a et al. . Cation a et al.  and anala et al. .2 and CH4 were determined by a flame ionization detector (250 °C) and N2O by a Ni-electron capture detector (320 °C). Injector was setting to 250 °C and Porapak columns to 70 °C of temperature. The GC was calibrated with standard gases , and standard gases were read periodically (between every 20 samples), as quality control. The gas molar volume (Vm) was corrected for the headspace chamber air temperature (K) as measured at sampling time. And the gas fluxes (f) were calculated by each gas considering the change in gas concentration in the chamber during the incubation time (ΔC/Δt), the chamber volume (V), the soil area covered by the chamber (A), and the molecular weight of the gas (m), by the equation: f = ΔC/Δt × V/A × m/Vm [Gas samples were analyzed by gas chromatography , and the concentration of COA × m/Vm –38.The 15-ml frozen soil samples were ground in mortars in liquid nitrogen to improve homogeneity and lysis efficiency. Aliquots (250 mg) were then used for extraction with the Power Lyzer Soil DNA Isolation Kit , and DNA concentration was analyzed spectrophotometrically . Later, the V4 region of the 16S rRNA was amplified by PCR using 1 μl of the DNA extracts (49 ± 9 ng of DNA) and the KlenTaq Master Mix 1X (Sigma) and the primers 515F and 806R . PCR conSequencing data were analyzed with the QIIME pipeline . SequencP < 0.05) (SI 2). The operational taxonomy units  were correlated with the chemical data (Pearson and Spearman correlation indices). Also, unweighted UniFrac distances previously obtained from QIIME were used for ordination using PCoA and further correlated with the environmental parameters using Vegan envfit function [Statistical analyses were first performed using the R software , and thefunction .3. In order to measure the influences that are beyond those explained by pH, we regressed the variables in groups G2, G3, and G4 against pH and used the residuals from these models as explanatory variables. Further, due to the experimental design (n = 32), we reduced the number of explanatory variables to avoid overfitting. To do this, we run principal component analysis and included as explanatory variables only the two first principal components of the variables in groups G3 and G4. Thus, the full model consisted of seven explanatory variables: pH (G1), Ca and Mg (G2), and two variables representing G3  and G4 (G4_1 and G4_2).We then analyzed the data with HMSC , an apprR2 [Due to the zero-inflated nature of the data (absences) and the high number of OTUs involved, we constructed two models, one for presence–absence (assuming probit link function and Bernoulli distribution), and another one for abundance . In the presence–absence model, we included only those 248 OTUs that were both present and absent in at least 10 samples and thus showed substantial variation in occurrence. In the abundance model, we included only those 271 OTUs that were present in all samples and thus showed variation in terms of abundance but not occurrence. To quantify the influences of variables other than pH, we also considered four reduced models containing subsets of full model predictors: (i) G1 only, (ii) G1 and G2, (iii) G1, G2, and G3, and (iv) G1, G2, and G4. We examined the predictive powers of these models through fivefold cross-validation  into the HMSC analyses. To assess possible co-occurrence patterns among the OTUs that cannot be attributed to their responses to the environment, we also included a community-level random effect implemented through a latent variable approach .P = 0.05, trees = 500, and permutations = 400). If none of the predictors were significant, no model was produced for the response variable (OTUs). Model performances were measured as variance explained (R2) and validation score, and OTUs that had a validation score lower than 0.1 were excluded from the “The abundance of each OTU was modeled according to the environmental predictors using the machine-learning algorithm RF, which is independent of data distribution . RF algoBacteroides p 0.65, Hydrogenedentes p 0.74, and WD272 p − 0.78; P < 0.001). But the strongest effects were observed at genus level, as will be described in Fig. The direct and indirect effects of a pH gradient on the soil prokaryotic community structure were evaluated. A lime gradient produced alterations in the soil pH (ranging from 4.1 to 6.2) that caused changes in soil chemistry and shifts on the prokaryotic community structures. Some effects were observed at Phylum level Table , includi2 (from 4.2 ± 0.3 in the control to 5.3 ± 5 in the highest lime dose) and the concentration of the co-variables Ca (from 22.9 ± 10.9 in the control to 81.4 ± 31.8 cmolc dm3 in the highest lime dose) and Mg (from 9.6 ± 8.0 to 21.3 ± 14.8 cmolc dm3). Indirect effects of the pH were also detected, accordingly to the correlation between pH with the different variables (P < 0.01): Al (p − 0.98), Fe (p − 0.51), Mn (p − 0.51), Cu (r − 0.45), B (r − 0.54), CEC (r 0.52), and soybean yield (p 0.49) (Table P < 0.05). Perhaps, future studies using litter traps and temporal survey of the gases could bring better correlations. Litter quantity among all plots was 78.8 ± 31.7 g m− 2, nitrate 6.4 ± 1.1, CO2 flux 107 ± 89 mg m2 h− 1, CH4 flux − 7 ± 19 μg m2 h− 1, and N2O flux 13 ± 60 μg m2 h− 1, but no statistical difference was observed between the pH ranges or lime doses , of which 34 OTUs correlated with CO2, 46 with CH4, and 32 with N2O  with pH CaCl2 (r2 0.85), the co-variates Ca (r2 0.75) and Mg (r2 0.26), and the indirect effects 1: Al (r2 0.88), B (r2 0.26), Cu, , Mn (r2 0.37), and Fe (r2 0.49), and the indirect effects 2: soy yield (r2 0.31), NO3 (r2 0.15), K (r2 0.37), and CEC (r2 0.20) . Additionally, the Simpson diversity index positively correlated with pH   had negative correlations with pH, but correlated better with Fe, as supported by the RF analysis  were highly correlated with pH, but some were correlated also with the indirect effects  hadR2 0.06), whereas the abundance model had better predictive power (R2 0.29), so the results are shown for this model . A large proportion of OTUs obtained strong statistical support for either a positive (37 OTUs) or a negative response to pH (23 OTUs) and also to the variables from the group G3  and G4   [Proteobacteria) responded positively (increase in abundance) to group G3 (indirect 1), while rectangle 2 (some Actinobacteria) responded positively to pH. The community-level random effect included in the model captured also residual co-occurrence patterns among the OTUs that were not explained by the environmental covariates  , was 0.6tes Fig. . The restes Fig. .P = 0.05), resulting in overall mean variance explained of 0.32 and mean validation score of 0.12  also shifted simultaneously . Additionally, in the Park Grass experiment in UK, a pH gradient that had confounding factors minimized, community structures were linked to pH, P, C, and N, and the authors suggested that the effect of pH on nutrient availability was the most probable mechanism, in line with our results [Moreover, our data point to a strong influence of pH on the community structure indirectly throughout effects on elemental availability . RF indicated that these OTUs were mostly related to Al, Fe, Zn, and Cu  in soil [Varibacter, Bradyrhizobium, and Rhizobium were highly correlated with Fe (Table 3) that influenced microbial community structure  had higher relative occurrence in low pH, as also reported in a European soil [Actinobacteria may benefit from higher .It could have important applications for agronomic and land reclamation systems. For example, there is a current research focus in identifying which bacterial species are related to plant yield, disease suppression, and environmental services , 54. OnenosZ, mcrA, and pmoA) simultaneously coupled to a temporal survey of the gases could be a better proxy to it, as demonstrated previously [Lastly, based on our conceptual model, we hypothesized that the pH effects on soil chemistry and in the microbial community structures could also affect microbial activity, such as gas fluxes , 31, 38.pH changes in soil co-occur with interactions among soil elements , hiding many indirect effects of pH on soil microbial communities. Thus, in this work, we investigated an important question that is rarely addressed in microbial ecology: is pH directly the main driver for most of the prokaryotic OTU occurrences or is it also a good indicator of the other co-variables that are in fact the relevant drivers for some microbial groups? Our data indicated that pH acts by means of two different mechanisms to drive prokaryotic community structures: (i) first, pH changes are directly associated with changes in some microbial groups, and (ii) the second is related to the “spillover effects” of pH, indirectly affecting microbial community structures through changes in some soil element availability. Moreover, indirect effects are highly relevant for some OTUs, and consequently for the community structure, deserving more attention in microbial ecology. Future studies in controlled conditions  should address this question in detail. Thereby, a better understanding of the ecology of several microbial groups can be more accurately accessed.Additional file 1:Figure S1A. Overall theoretical diagram of expected interactions in the pH range between 4 and 6 with the microbial community structure. Boxes represent soil variables and the arrows the interactions . In this study, the gradient was produced by liming application, with expected direct effect on pH, and Ca and Mg values. The indirect effects are the “spillover” effect of the pH in the other soil and plant variables. Indirect effect 1 is mainly related to the solubility of elements, while indirect effect 2 is related to these effects on plant growth and nutrient cycling. SOM is the abbreviated form for soil organic matter, CEC for cation exchange capacity, and WC for water content. Temperature and WC are considered in this diagram only for the survey day of greenhouse fluxes . Figure S1B. Effect of pH in the relative availability of important ions related to soil fertility . (PDF 526 kb)Theoretical full diagram and rationally. Additional file 2:Table SI2A. Land-use history of the crop fields . Table SI2B. Soil chemical parameters, and plant yield, and greenhouse gas fluxes according to different soil pH (CaCl2) ranges. Table SI2C. Soil chemical parameters, plant yield, and greenhouse gas fluxes according to the lime doses. (PDF 775 kb)Land-use description and soil parameters. Additional file 3:Table SI3A. Predictive performance of different HMSC models based on fivefold cross-validation. All models include community-level random effect at the sample level. Predictive performance is measured by Tjur (2009) R2 for the presence–absence model and correlation for the abundance model. The values presented are averages over the OTUs. Table SI3B. Variance partitioning of the full HMSC models. The values show average (over the OTUs) proportion of variance attributed to each of the predictors. Figure SI3A. HMSC-based estimates of species responses to the environmental covariates. Panel A shows the results for the presence–absence model and panel B for the abundance model. In both cases, the OTUs have been ordered by their phylogeny, as illustrated by the plots. Positive and negative responses are shown by red and blue entries, respectively, and based on posterior mean. The darker red and blue colors corresponding to cases with strong statistical support (posterior probability at least 95%), and the percentages of such OTUs are given on the bottom of the panel. Figure SI3B. HMSC-based estimates of species residual (after accounting for influences of covariates) associations. Panels A and C show the results for the presence–absence model and panels B and D for the abundance model. In panels A and B, the species have been ordered in a way that best shows clusters of associated OTUs, whereas in panels C and D, they have been ordered by the phylogeny (as illustrated in the plots). Positive and negative OTU pairs for which the residual association is positive, with at least 95% posterior probability, are shown by red and blue entries, respectively. Table SI3C. Responses of the OTUs  according to the abundance model to pH, Ca, Mg, indirect effects 1 (PCA1a and PCA1b), and indirect effects 2 (PCA2a and PCA2b). (PDF 1310 kb)Hierarchical modeling of species communities (HMSC). Additional file 4:Table S6 Random Forests (RF) models for each OTU . (XLSX 660 kb)Additional file 5:Bacteria and Archaea Phyla correlations. Table S4 A1 Bacteria Relative Frequency . Table S4 A2 Bacteria Relative Frequency . Table S4 B1 Archaea Relative Frequency (related to Bacteria %). Table S4 B2 Archaea Relative Frequency (Only Archaea). Table S4 C Pearson (r) and Spearman (p) correlation indexes between soil parameters and the relative abundance of archaeal and bacterial phyla . (XLS 57 kb)Additional file 6:OTUs at Genus level (frequency and correlations). Table S5 A Spearman (p) and Pearson (r) correlation indexes between the soil chemical parameters and the relative abundance of archaeal and bacterial OTUs  (XLSX 229 kb)Additional file 7:Phylogenetic tree correspondent to the Figure 3 - HMSC models. (PDF 17 kb)


What wavelengths do undulatory swimmers use during propulsion? In this work we find that a wide range of body/caudal fin (BCF) swimmers, from larval zebrafish and herring to fully–grown eels, use specific wavelength (ratio of wavelength to tail amplitude of undulation) values that fall within a relatively narrow range. The possible emergence of this constraint is interrogated using numerical simulations of fluid–structure interaction. Based on these, it was found that there is an optimal specific wavelength (OSW) that maximizes the swimming speed and thrust generated by an undulatory swimmer. The observed values of specific wavelength for BCF animals are relatively close to this OSW. The mechanisms underlying the maximum propulsive thrust for BCF swimmers are quantified and are found to be consistent with the mechanisms hypothesized in prior work. The adherence to an optimal value of specific wavelength in most natural hydrodynamic propulsors gives rise to empirical design criteria for man–made propulsors. St = 2fa/U, where f is the tail or wing beating frequency, 2a is the maximum tip–to–tip lateral excursion, and U is the forward speed) is a nondimensional number used to describe the kinematics of flying and swimming animals , with corresponding swimming–speed Reynolds number range Re ∈ .In  and 12]12] the alat values for these robotic sheets were higher than those used in our numerical simulations. This is evidence that SW maximizes swimming speed for high Reynolds number animals.The OSWs for these four cases are shown in sSW = 3.45. Additionally we observed, across a large range of Reynolds numbers, that low aspect ratio anguilliform swimmers tend to swim at greater St than high aspect ratio non–anguilliform swimmers. This is consistent with numerical studies relating the emergence of single and double–row wakes to optimal Strouhal numbers for carangiform and anguilliform swimmers, respectively , Relat ∈ , ReLlat, ∈ , and Rewave ∈ . For translation–locked simulations, the various Reynolds numbers ranges were as follows: Re = 0, Relat ∈ , ReLlat, ∈ , and Rewave ∈ . Since the Reynold numbers considered were relatively modest  × 10−3L, while for the refined grid simulation, ΔX1 =  × 10−3L. The mean axial force measurements for each case are q0 = 33.6 mN and q1 = 34.3 mN, which represents a percent change of δ = 2.08%. This gives us confidence that the high Relat simulations carried out in this work are relatively insensitive to grid spacing up to the prescribed δ tolerance.S1 AppendixSection 1 shows the correlation between swimming–speed, wave–speed, and lateral Reynolds numbers. Section 2 discusses the nondimensionalization of the axial force simulation data included in Figs (PDF)Click here for additional data file.S1 Figlat , ReLlat, , and Rewave  for the swimmers considered in  vs. the traveling wave speed λf. Data are available in The maximum fluid axial velocity (EPS)Click here for additional data file.S9 Figa) Net axial force generated by simulations of an undulating plate with L = 1.0 cm, h = 0.1 cm, f = 3 Hz, a = 0.05 with (red) periodic boundary conditions, and (black) wall boundary conditions. Average axial fluid momentum multiplied by undulation frequency over one swimming cycle with periodic boundary conditions (green) as functions of SW. (b) velocity of the traveling wave λf (pink) and change in weighted mass (blue). Data are available in ((EPS)Click here for additional data file.S10 Figz = −0.05 cm) contours of axial fluid velocity for a stationary sheet simulation for various snapshots in time. Black dots represent the Lagrangian points of the undulating body. The plate has L = 1.0 cm, h = 0.1 cm, f = 3 Hz, a = 0.05 cm with (a) & (b) SW = 13.33, (c) & (d) SW = 10, (e) & (f) SW = 5.Mid–sheet ((EPS)Click here for additional data file.S11 Figz = −0.05 cm) contours of normalized axial fluid velocity (ux/umax) for a stationary sheet simulation for various snapshots in time. Black dots represent the Lagrangian points of the undulating body. The plate has L = 1.0 cm, h = 0.1 cm, f = 3 Hz, a = 0.05 cm with (a) & (b) SW = 13.33, (c) & (d) SW = 10, (e) & (f) SW = 5. For each difference SW, umax is taken to be the maximum axial fluid velocity over Ω and over one swimming cycle.Mid–sheet ((EPS)Click here for additional data file.S1 Data(XLS)Click here for additional data file.S2 Data(XLS)Click here for additional data file.S3 Data(XLS)Click here for additional data file.


Cyprinus carpio and Labeo rohita netted from Sardaryab, a tributary of River Kabul. By using atomic absorption spectrometry we assessed different organs including livers, gills, and muscles. Metals studied were chromium, iron, zinc, lead, and copper. Livers of both species showed higher concentrations of metals while muscles showed the least amount. Chromium and iron were the highly concentrated metals in the gills and livers of both species. A quantity of 0.154 ± 0.011, 0.199 ± 0.0079, and 0.024 ± 0.008 μg/g of chromium was found in the gills, livers, and muscles of Cyprinus carpio, respectively. Similarly, the gills, liver, and muscles of Labeo rohita contained 0.133 ± 0.008, 0.165 ± 0.01, and 0.019 ± 0.006 μg/g of Cr, respectively. Iron was highest in carp in the range of 0.086 ± 0.01 in gills and 0.067 ± 0.011 μg/g in muscles, comparatively. All the studied metals were found within the US recommended daily dietary allowances (RDA) limits; hence no immediate risk in their consumption for human was found. The data showed that Cyprinus carpio being omnivorous and bottom feeder stored higher concentrations of metals as compared to Labeo rohita.We examined and compared heavy metals bioaccumulation in Labeo dyocheilus and Wallago attu. Zinc frequently appears everywhere in the environment. About 70 ppm of Zn is found in different strata of the earth [Health and environmental problems arising from heavy metals present in aquatic ecosystem and their bioaccumulation in fishes are very well known . Heavy mhe earth . Zinc alhe earth . Differehe earth . Zinc enhe earth . The homhe earth . This ALhe earth . Essentihe earth . The nathe earth . The exche earth . Heavy mhe earth , 17. Conhe earth .Limnologists are highly concerned about the increasing contaminations of water bodies due to heavy metals which are more dominant in lotic systems towards which industrial wastes are directed. Heavy metals deteriorate the ecological balance of the aquatic environment , 19–21.  Cyprinus carpio and Labeo rohita and to determine which of the species have comparatively higher accumulation of heavy metals from Sardaryab, a tributary of River Kabul.We aimed to investigate heavy metals bioaccumulation in different organs ofFish were collected with the help of cast net from Sardaryab Tapu, River Kabul, one kilometer upstream from Sardaryab Bridge. Morphometric measurements were taken on the spot and then fishes were transported to PCSIR  laboratory, packed in ice bar in a plastic bag.In the laboratory fishes were dissected for gills, liver, and muscles with the help of stainless steel knife on a clean glass. The organs were dried in the sun for one day.3 + 20 mL HCl was added to each beaker containing specific organ samples and they were heated gently on a hot plate 200–250°C to digest the samples completely. The hot plate was kept in ventilation hub because of acidic fumes that originate from the samples and was heated until the solution became clear and transparent. The solution was evaporated up to 0.5 mL and until dense white fumes started after the brown fumes. This is an indication that the digestion is completed. The solution was diluted to 10 mL by adding distilled water and rinsing the beaker [μg/g.The dried organs were weighted and kept in oven at 120°C for one hour and then cooled in a desiccator. One gram (three samples from each organ and each species) from each cooled sample was weighted through electric balance and transferred to beakers of 50 mL capacity thoroughly washed with distilled water. After this 10 mL concentrated HNOe beaker . The sam Cyprinus carpio and Labeo rohita from Sardaryab River Kabul was investigated in the present study and the results are presented in Bioaccumulation of heavy metals Zn, Cr, Cu, Hg, Fe, and Pb in different organs of Cyprinus carpio the quantity of Zn was 0.074 ± 0.01, Cr was 0.154 ± 0.011, Fe was 0.086 ± 0.008, Cu was 0.024 ± 0.004, and Pb was 0.041 ± 0.019 ug/g. In liver of Cyprinus carpio the quantity of Zn was 0.07 ± 0.009, Cr was 0.188 ± 0.007, Fe was 0.08 ± 0.008, Cu was 0.089 ± 0.007, and Pb was 0.142 ± 0.011. In the muscles of the same fish the accumulation of Zn was 0.018 ± 0.004, Cr was 0.024 ± 0.008, Fe was 0.067 ± 0.008, and Cu was 0.016 ± 0.008 μg/g.In gills of Labeo rohita the quantity of Zn was 0.058 ± 0.009, Cr was 0.133 ± 0.008, Fe was 0.08 ± 0.01, Cu was 0.018 ± 0.006, and Pb was 0.024 ± 0.014 ug/g. In liver of Labeo rohita the quantity of Zn was 0.088 ± 008, Cr was 0.165 ± 0.01, Fe was 0.061 ± 0.011, Cu was 0.071 ± 0.01, and Pb was 0.161 ± 0.011 ug/g, while in the muscles of this fish the storage of Zn was 0.02 ± 0.008, Cr was 0.019 ± 0.006, Fe was 0.05 ± 0.01, and Cu was 0.01 ± 0.004 ug/g.Similarly in the gills of Cyprinus carpio as compared to Labeo rohita. Hg was not detected in neither of the species whereas Pb was not detected in muscles of both species. All the investigated heavy metals were found within the US (RDA) permissible dietary limits. Overall order of heavy metals burden in Cyprinus carpio and Labeo rohita was found to be as liver > gills > muscles.Overall concentration of the studied heavy metals was higher inBioaccumulations of heavy metals are used for environmental monitoring largely because aquatic organisms are in direct contact with the contaminated water. Tissue metal concentrations in fish are good indicators of aquatic system exposure to the metal contamination , 26. Hea Cyprinus carpio. In addition to common carp higher level of Cr was also detected in Labeo rohita. Accumulation pattern of zinc was reported in the same order and was highest in liver of Labeo rohita as reported by Palaniappan et al., 2010. All of the heavy metals that we studied here such as Fe, Zn, Cu, Pb, and Cr are also reported by Onwumere and Oladimeji [ Oreochromis niloticus that were in contact with petroleum refinery effluent. In our findings the accumulation of metals was higher in liver and gills. Livers of both species showed higher concentrations of heavy metals while muscles showed the least amount except iron (Fe) which was highest in gills of Labeo rohita. Heavy metals usually mount up in metabolically active tissues [ Labeo rohita. Experimental studies have shown that muscles of Labeo rohita accumulate least metal zinc as compared to other organs [ Cyprinus carpio gut content contains 45% detritus [ Cyprinus carpio makes it more prone to heavy metals bioaccumulation as compared to herbivore Labeo rohita. Water having accumulated heavy metals is a source for bioaccumulation in the gills; being the respiratory route they are in direct contact with water [In the present study we found that Cr and Fe were in higher concentrations followed by Zn in different organs of theladimeji  in Oreoc tissues . The tis tissues . The hig tissues , 32. The tissues . It has  tissues . Mastan  tissues  has alsor organs . Bioaccur organs –36. In adetritus ; such codetritus . Thus thth water , 27. Labeo dyocheilus and Wallago attu. In addition to it, other studies also reported that higher concentration of Cr is a usual trend of bioaccumulation in different fishes such as Channa punctatus [ Wallago attu [It has been a common trend in most cases that accumulation has been the highest in liver and lowest in muscles. Gills, skin, and alimentary canal are the entry points of heavy metals. Active interaction of the tissue type, that is, gills with contaminated water and liver exposure to contaminated food, is another reason for the concentration of heavy metals in the respective tissues . It has unctatus  and Wallago attu . This isAll the studied heavy metals do not exceed the limits set by US (RDA)  on the b Cyprinus carpio stored higher concentration of Cr in all three mentioned organs as compared to Labeo rohita. Hence Labeo rohita is good for consumption as it accumulates least amount of Cr.It is concluded from the current study that


Here we find that the SHG of Nd:YAG laser is achieved at 26.1 °C while the optical damage resistance is higher than 6.5 MW/cm2 in the ZrO2 and MgO co-doped LiNbO3  crystal. Moreover, the monotonic decrease of phase-matching temperature is firstly found with the increase of doping concentration. These unusual properties may be attributed to the formation of Laser has been widely used in many aspects, by now it is difficult to get each frequency that we want, and frequency conversion is an effective way to obtain different frequency laser through a nonlinear optical crystal. MgO-doped LiNbO As one of the most versatile optical crystals, lithium niobate  plays a key role in the second harmonic generation (SHG) of blue-green light from near-infrared lasers owing to its large nonlinearity and capability of temperature-tuned noncritical phase matching (90° phase-matching)8. Unfortunately, optical damage in LiNbO3, also known as photorefractive effect, induced by modest intensities of visible light, sharply decreases the conversion efficiency and hinders its practical usage10. A breakthrough came that the optical damage resistance of LiNbO3 could be improved at least two orders of magnitude when 4.6 mol.% MgO was doped into12. From then on, MgO-doped LiNbO3 (Mg:LN) has become the often used material in frequency conversion, optical waveguide, optical parametric oscillation and terahertz source17. But for infrared-to-visible nonlinear frequency conversion, the phase-matching temperature is usually above 100 °C, so a precise temperature-controlled stove is needed. The extra unit in the laser cavity can increase the complexity of the system and lead to the additional energy loss18. Besides, the elevated operating temperature will also reduce components reliability and shorten service life. Detailed investigations show that the phase-matching temperature of Mg:LN increases with increased MgO concentration, and reaches the maximum near the doping threshold, then decreases with further increased doping concentration19. Therefore, it is possible that phase matching achieves at room temperature (about 25 °C) with a high enough doping concentration of MgO, but such heavily doped Mg:LN crystal is hard to grow with high optical quality20. Later, several ions such as Zn2+, In3+, Sc3+, and Hf4+ were also reported to have the same resistance against optical damage when doped into LiNbO3, but just like Mg:LN, the efficient noncritical phase matching at room temperature is still a problem23. Therefore, room temperature 90° phase-matching has always been a serious challenge for LiNbO3 at present.Miniature, compact and high-power all-solid-state blue-green lasers are widely applied in ocean exploration, medical treatment, optical communication and laser display, etc2-doped LiNbO3 (Zr:LN) has attracted great attentions because of its high optical damage resistance in the visible and even ultraviolet region, low doping threshold (2.0 mol.%) and distribution coefficient close to one26. Up to now, investigations on Zr:LN crystals mainly focus on optical waveguide, defect structure and co-doping with photorefractive impurities29, but their refractive indices and nonlinear optical properties  are rarely reported. In fact, no sign of phase matching in Zr:LN crystals was found when they were heated from room temperature to above 200 °C in our pre-experiments, which implies that the phase-matching temperature of Zr:LN crystals may be lower than room temperature. If that is true, we may obtain room temperature 90° phase-matching by doubly doping with ZrO2 and MgO in LiNbO3. And co-doping with two optical damage resistant ions is conducive to finely tune the optical properties of LiNbO330.In recent years, ZrO2 and MgO co-doped LiNbO3  crystals with various ZrO2 dopants, and the doping concentration of MgO was chosen as 5.0 mol.% because Mg:LN has high phase-matching temperature in this doping level. Our experimental results demonstrate that the phase-matching temperature of Zr,Mg:LN monotonically decreases with increased ZrO2 concentration. And the efficient 90° phase-matching is achieved in Zr,Mg:LN crystal at room temperature without a stove, meanwhile it also has a very high optical damage resistance.In this paper, we grew a series of ZrOTPM on the ZrO2 concentration in the melt for Zr,Mg:LN crystals is depicted in Fig. TPM decreases as the ZrO2 concentration increases, and a straight line can be fitted well to the experimental data. For comparison, the TPM versus the doping concentration of MgO for Mg:LN crystals is presented in Fig. 19. The relationship between the TPM and the impurity concentration is similar to a parabola going downwards, and the maximum temperature stays within the concentration range of 4~6 mol.%. Generally, the TPM versus the doping concentration exposes a more or less expressed threshold behavior corresponding to the sharp change of optical properties, and this similar behavior can be found in other mono or dual doped LN crystals, such as Zn:LN21, Sc:LN22 and Zn,In:LN30. In contrast, Zr,Mg:LN crystals exhibit a significant monotonic, and a simple linear extrapolation from existing data holds over a wider concentration range.Temperature tuned 90° phase-matching was achieved by using a Q-switched Nd:YAG laser with a wavelength of 1064 nm. The dependence of the phase-matching temperature 1.7,Mg5.0:LN crystal is 26.1 °C, which is close to room temperature (25 °C). Figure 2, and maintaining this conversion efficiency for two hours, there is no significant degradation. Overall, we should point out emphatically that if considering the temperature increase (about 2~3 °C) in the continuous harmonic output32, Zr1.7,Mg5.0:LN crystal is particularly well-suited for practical application of laser frequency doubling at room temperature.Please note that the phase-matching temperature in Zr2 increases from 0.5 to 1.7 mol.%, none of Zr,Mg:LN crystals appears noticeable beam smeared, even under the highest focused intensity of 6.5 × 106 W/cm2 in our laboratory, and the optical damage resistance is the same magnitude as that of Zr2.0:LN. However, Mg5.0:LN crystal can only withstand a maximum intensity of 4.1 × 105 W/cm2 under the same conditions. The above results indicate that the optical damage resistance of these Zr,Mg:LN crystals is improved by at least an order of magnitude than that of Mg5.0:LN.In order to measure the optical damage resistance, the distortion of the transmitted light beam through the wafer was observed with a 532 nm laser. Figure n of these crystals were measured by two-beam holography33. Two e-polarized coherent beams at 532 nm were intersected in the wafers with equal intensity of 400 mW/cm2. The change of refractive index Δn was calculated by the equation34d is the crystal thickness, 3.0 mm; and ε = 28 is the relative dielectric constant of the crystal35, and the erasure time constant e of its initial value.To quantitatively characterize the optical damage resistance, the light-induced changes of the refractive index Δn and the photoconductivity 2 concentration for all samples are presented in Fig. 2 into Mg5.0:LN, then changes slightly as the ZrO2 concentration increases. Moreover, the Δn of Zr,Mg:LN is considerably less than that of Mg5.0:LN and even lower than that of Zr2.0:LN. In addition, the 5.0:LN but close to that of Zr2.0:LN. It is well known that the increase of the photoconductivity is primarily responsible for the increase of the optical damage resistance36. Therefore, the results demonstrate again that adding some ZrO2 into Mg5.0:LN can further enhance the optical damage resistance, which is consistent with the results of the transmitted light beam distortion.The change of refractive index Δ− absorption spectra, sensitively reflect the change of defect structure in LiNbO3, which have become an important tool in studying the properties of dopant-related defects. Figure − absorption spectra of CLN and Zr,Mg:LN crystals. As no obvious OH− band shift is observed, a three-peak model37 is employed by Lorentz fitting, and the results are listed in Table 5.0:LN and Zr2.0:LN crystals are also listed. It can be seen from the table that the component peaks of Zr2.0:LN are located at 3475, 3485, and 3495 cm−1, respectively, which agree with the previous results38, and the shift in component peaks is connected with the formation of OH−–2, the relative intensity of OH−–39. As shown in the table, the peak of 3476 cm−1 appears in Zr0.5,Mg:LN and moves to 3478 cm−1 with increased concentration of ZrO2. Based on these analyses, the total content of 0.5 mol.% ZrO2 and 5.0 mol.% MgO in LiNbO3 has reached the doping threshold.The infrared absorption spectra, referring mainly to OH3 is sensitive to the crystal composition and defect40, especially the absorption edge has a maximum violet-shift when the doping concentration reaches the threshold. The UV-visible absorption spectra of Zr,Mg:LN crystals are shown in Fig. −1. From this figure, we can see that the absorption edge of Zr0.5,Mg5.0:LN crystal has a maximum violet-shift, and with the doping concentration of ZrO2 increasing from 0.5 to 1.7 mol.%, the absorption edge has an obvious red-shift. Therefore, we can conclude that the total doping concentration of 0.5 mol.% ZrO2 and 5.0 mol.% MgO in LiNbO3 reaches the threshold level, which is in agreement with the results of OH− absorption spectra.The UV absorption edge of LiNbO2 and 5.0 mol.% MgO has reached the doping threshold, which implies that impurity ions enter normal Nb sites after all − absorption spectra indicates that the peak shifts are related to the formation of OH−–−1 absorption peak36 attributed to the OH− stretching vibration of −–2 can directly influence the threshold level of MgO, namely, Zr4+ ions affect the site occupation of Mg2+ ions. So it can be deuced that the appearance of − absorption band shift. And the amount of 2. Considering the The absorption spectra of Zr,Mg:LN crystals demonstrate that the total content of 0.5 mol.% ZrO2 doping can significantly influence the phase-matching temperature of Mg5.0:LN. In particular, the monotonic decrease of phase-matching temperature on ZrO2 concentration is found for the first time, which is different from the previous reports on doped LiNbO3. As we know, so-called optical damage resistant impurities in visible region such as Mg, Zn, In and Hf can enhance the UV photorefractive effect42, but Zr exhibits excellent optical damage resistance in both visible and UV region. It is thought that the enhanced UV photorefractive effect has direct relationship with doped ions occupying Nb sites43, which means that Zr4+ ions in Nb sites can greatly alter defect structures and properties of LiNbO3. In Zr,Mg:LN crystals, Our results on SHG of Nd:YAG lasers have shown that the additional ZrO3 co-doping with 5.0 mol.% MgO and various ZrO2 concentrations. The experimental results indicate that the phase-matching temperature of Zr,Mg:LN decreases with increased ZrO2 concentration for the first time. And 90° phase-matching of 1064 nm radiation is achieved at room temperature in Zr1.7,Mg5.0:LN crystal, while it holds a high resistance of optical damage at 532 nm, and does not suffer any dark trace damage when exposed to high power laser irradiation for two hours, which will be greatly valuable for engineering applications in compact and efficient high-power green lasers. These excellent properties of Zr,Mg:LN may be attributed to the formation of We grew a series of LiNbO3 crystals were grown along the c-axis by the conventional Czochralski method in air. 0, 0.5, 1.0, 1.5 and 1.7 mol.% ZrO2 were added, respectively, to the congruent melt (Li/Nb = 48.38/51.62) doping with 5.0 mol.% MgO, labeled as Mg5.0:LN, Zr0.5,Mg5.0:LN, Zr1.0,Mg5.0:LN, Zr1.5,Mg5.0:LN and Zr1.7,Mg5.0:LN. After annealing treatment and artificial polarization, the crystals were cut into 3.0 mm and 1.0 mm thick y-plates for the characterization of photorefraction and absorption. Choosing crack-free and uniform bulk crystals were cut into 9.0 × 10.0 × 9.0 mm3 (X × Y × Z) for second harmonic generation, where y-axis was the transmission direction. All samples were optical grade polished on both faces perpendicular to y-axis of the crystal. For comparison, congruent LiNbO3 and 2.0 mol.% ZrO2-doped congruent LiNbO3 crystals were also prepared, labelled as CLN and Zr2.0:LN, respectively.A series of doubly doped LiNbOy-plates were measured at room temperature with a Magna-560 Fourier transform infrared spectrophotometer and a U-4100 spectrophotometer, respectively. The resolution value of this infrared spectrometer was 4.0 cm−1, and the step-length of the UV-Vis. spectrometer was 1.0 nm.The infrared absorption spectra and UV-Visible absorption spectra of 1.0 mm thick c-axis of the crystal perpendicular to the polarization direction of the light, so-called 90° phase-matching. The second harmonic energy was detected with a band-pass filter and a pulse laser energy meter. The bulk crystal was mounted in an oven thermally controlled to a temperature better than ±0.2 °C. The temperature was measured with a Pt-100 thermistor placed in direct contact with the crystal, and a slow heating rate was used about 0.5 °C/min to minimize temperature gradients with the sample. In addition, the distance between the entrance and the exit windows of the crystal surfaces was at least 5.0 cm to minimize the possible temperature gradients along the crystal y-axis. The formula for the conversion efficiency was η = E2/E1, where E1 was the energy of the fundamental wave and E2 was that of the second harmonic wave.The experiments for second harmonic generation were performed with a Q-switched Nd:YAG laser 1064 nm at a 1 Hz repetition rate, 8 ns pulse width. The laser facula diameter was 5.0 mm, and the maximum average output energy was 320 mJ. The fundamental light was directed to the crystal in a geometry with the The datasets generated during the current study are available from the corresponding author on reasonable request.


Osmanthus fragrans Lour. flowers, identifying the degradation products of acteoside and salidroside (major PhGs in O. fragrans flowers) by UPLC–QTOF–MS and studying the anti-hypoxia activity of PhGs after degradation. The degradation of PhGs followed first-order reaction kinetics, and the rate constant of acteoside (4.3 to 203.4 × 10−3 day−1) was higher than that of salidroside (3.9 to 33.3 × 10−3 day−1) in O. fragrans flowers. Salidroside was mainly hydrolyzed to tyrosol during storage, and the degradation products of acteoside were verbasoside, caffeic acid, isoacteoside, etc. In a model of cobalt chloride (CoCl2)-induced hypoxia in PC12 cells, the anti-hypoxia ability of PhGs decreased after degradation, which resulted from the reduction of PhGs contents. Particularly, caffeic acid exhibited stronger anti-hypoxia ability than acteoside and could slightly increase the anti-hypoxia ability of degraded acteoside. The results revealed that high temperature, high pH and light exposure caused PhGs degradation, and thus the anti-hypoxia ability of PhGs reduced.This study was aimed at investigating the chemical stability  of phenylethanoid glycosides (PhGs) in  Osmanthus fragrans (Thunb.) Lour. (sweet osmanthus), belonging to the Oleaceae family1, is widely planted in south and middle China3. Osmanthus fragrans is famous for its fragrant flowers, which are small flowers with four-lobed corolla and varies in colors, such as white, pale yellow, yellow and orange-yellow3. They are widely cultivated as ornamental plants4. In China, O. fragrans flowers have been used as foods 6 and traditional Chinese medicine1 for a long time. In recent studies, the extracts of O. fragrans flowers have showed the abilities of neuroprotection7, anti-aging8, anti-inflammatory9, antioxidantion13 and inhibiting melanogenesis10.O. fragrans flowers were well correlated with phenylethanoid glycosides (PhGs), especially the acteoside 13. PhGs are a class of naturally occurring phenols, which are abundant in O. fragrans flowers with the total phenylethanoid glycoside (TPG) content ranged from 92.66 to 130.57 milligrams of acteoside equivalents (AE) per gram of dry weight (mg AE /g DW)12. Acteoside and salidroside were two main PhGs in O. fragrans flowers with the content of 32.78 to 71.79 mg/g DW and 4.72 to 16.08 mg/g DW12, respectively. Acteoside has the ability of protecting nerve cells, for instance, acteoside protects pheochromocytoma (PC12) neuronal cells against 1-methyl-4-phenylpyridinium ion (MPP+)-induced apoptotic or necrotic death14. Also acteoside can protect the human neuroblastoma SH-SY5Y cells from β-amyloid-induced cell damage15 and MPP+-induced injury16. Salidroside shows similar neuroprotective effects19. Particularly, salidroside can protect PC12 cell from CoCl2-induced hypoxia damage21. Thus, we consider acteoside and PhGs in O. fragrans flowers have the potential ability of anti-hypoxia in PC12 cell.Previous studies revealed that the anti-aging and antioxidant activities of O. fragrans flowers have potential to be used in functional food or medicine. However, acteoside in solution is unstable, and is easy to be destroyed by a number of factors such as temperature23, pH25 and light25 during storage. There is no more available information in previous literatures on the degradation kinetics and degradation products of acteoside and salidroside.Considering the benefits mentioned above, PhGs in O. fragrans flower extracts (OFE); (2) to identify the degradation products of acteoside and salidroside by UPLC–QTOF–MS/MS; (3) to investigate the anti-hypoxia activities of acteoside, salidroside and TPG after degradation.Therefore, the objectives of this work were (1) to evaluate the effects of temperature, pH and light on the degradation kinetics of acteoside, salidroside and TPG in et al.12, which might be due to the different origin of O. fragrans flowers and/or extraction conditions . For thermal stability of TPG, the contents (after 90 days) decreased by 17.64%, 35.39%, 76.90% and 87.00% at 4, 20, 37 and 50 °C, respectively. As for 80 °C, the TPG content decreased by 84.25% after 7 days. It showed that the degradation of TPG accelerated with the elevation of temperatures. Besides temperature, light and pH also affected the TPG stability, and the degradation of TPG increased with light exposure and the elevation of pH values. The degradation of salidroside and acteoside exhibited similar pattern.As shown in Fig. R2 higher than 0.94 for all treatments (Table k) values increased rapidly and the half-life time (t1/2) values decreased  in OFE ranged from 2.1 × 10−3 to 251 × 10−3 day−1 and 2.8 to 330.1 day  and high pH values (pH 9.0). The t1/2 values of salidroside in OFE ranged from 20.8 to 177.7 day. At the same condition, the t1/2 value of acteoside in OFE was lower than that of salidroside, indicating that acteoside was less stable than salidroside. It was attributable to the different molecular structures of salidroside and acteoside. Salidroside is phenylethanoid monosaccharides, while acteoside is phenylethanoid disaccharide and has ester linkage, which is easy to be hydrolyzed. The calculated activation energy (Ea) values of TPG, salidroside and acteoside in OFE were 50.40, 21.63 and 69.14 kJ/mol, respectively , some of these compounds might accelerate the degradation28 of salidroside and acteoside in OFE, compared with the standards.The These findings revealed that the degradation of PhGs follows first-order reaction kinetic, which could be used to predict their contents during storage. Phenylethanoid glycosides were unstable at high temperature, high pH and light exposure conditions, therefore PhGs should be stored in the dark at low temperature and pH.For tentative identification of the degradation products, the degraded salidroside and acteoside were analyzed by UPLC–QTOF–MS/MS. Tyrosol, salidroside, caffeic acid, acteoside and isoacteoside were further identified using standards, and other degradation products were tentatively identified using the MS data. As shown in Fig. β-oxoacteoside, and the degradation products of AD1, AD5 and AD6 were identified as unknown compounds , we found the main degradation products of acteoside were verbasoside, caffeic acid and isoacteoside  of cultured PC12 cells  salidroside showed the maximum protection (81.15 ± 1.33% viability) among the given concentrations of salidroside, slightly higher than that of the treatment with 90 μM salidroside (79.6% viability) in a previous study21. Also, the treatment with 25 μg/mL (83 μM) salidroside showed lower cell viability (71.74 ± 0.93%) than that in the previous study. The cell viabilities of PC12 cells treated with 5, 25 and 50 μg/mL acteoside  were 61.67%, 75.24% and 86.13%, respectively, higher than those of salidroside . It revealed that acteoside could protect PC12 cells against CoCl2-induced hypoxia damage, even stronger than salidroside. In addition, the TPG, salidroside and acteoside contents showed good correlation with anti-hypoxia activities, with correlation coefficients of 0.945, 0.982 and 0.983 (p < 0.01), respectively.The 400 μM CoCl2-induced decrease in cell viabilities of PC12 cells, though the cell viabilities of PC12 cells treated with degraded salidroside were lower, compared with that of undegraded salidroside  than those of acteoside. The caffeic acid and isoacteoside contents also showed good correlation with anti-hypoxia activities, with correlation coefficients of 0.94 and 0.96 (p < 0.01), respectively. We considered that caffeic acid might increase anti-hypoxia ability of degraded acteoside.For the degraded acteoside, the cell viabilities decreased Fig.  as the ak and t1/2 values, followed by pH. It suggested that PhGs should be stored at low temperature, low pH and dark condition. During storage, salidroside was mainly hydrolyzed to tyrosol, and acteoside was hydrolyzed to verbasoside and caffeic acid. Acteoside also could isomerize into isoacteoside, cyclize to oraposide, and oxidize to campneoside II, β-oxoacteoside, etc. The degradation attenuated the anti-hypoxia ability of PhGs, though caffeic acid could slightly increase the anti-hypoxia ability of degraded acteoside. It is necessary to use some methods to increase the stability of PhGs in the further study.In conclusion, the degradation of PhGs  fitted to the first-order reaction kinetics, and temperature had the greatest effect on the 2 was purchased from Sigma-Aldrich . HPLC-grade acetonitrile and guaranteed-grade formic acid were obtained from Merck . Other chemicals and reagents  were purchased from Sinopharm Chemical Regent Co. .Caffeic acid (purity ≥98%), salidroside (purity ≥98%) and acteoside (purity ≥98%) were purchased from Aladdin . Tyrosol (purity ≥98%) and isoacteoside (purity ≥98%) were purchased from Yuanye Biotechnology Co. . CoClO. fragrans var. thunbergii flowers  were extracted with 95% ethanol (100 mL) for 12 h at 20 °C using a method of Jiang et al.12 with modification. The mixture was filtered by vacuum pump . The filtrate was evaporated  at 40 °C under a vacuum to dryness, and dissolved with water to a final concentration of 1 mg flower extract/mL . Salidroside and acteoside were dissolved with water to a concentration of 100 μg/mL (test solution). The test solutions were used directly after preparation.The dried The influence of temperature on the stability was studied at 4 °C, 20 °C, 37 °C, 50 °C and 80 °C at pH value of 6.0. Each test solution was divided into 30 mL portions in sealed glass bottles and kept away from light at 4 °C (in refrigerator), at room temperature (20 °C) and in a water bath , respectively (±2 °C). The total phenylethanoid glycoside (TPG) content was determined at 0, 7, 14, 21, 30, 45, 60 and 90 days except for those stored at 80 °C . The salidroside content was determined at 0, 7, 14, 21, 30, 45, 60 and 90 days. The acteoside content was determined at 0, 7, 14, 21, 30, 45, 60 and 90 days except for those stored at 50 °C  and 80 °C .The effect of pH on the stability was studied at different pHs  at 20 °C. Hydrochloric acid (2 M) and sodium hydroxide (2 M) were used to adjust the pH. Each test solution was divided into 30 mL portions in sealed glass bottles, stored in the dark. The TPG and salidroside contents were determined at 0, 7, 14, 21, 30, 45, 60 and 90 days. The acteoside content was determined at 0, 7, 14, 21, 30, 45, 60 and 90 days except for pH value of 9.0 .A lamp  was used in the light exposure experiment, performed at 20 °C. Each test solution  was divided into 30 mL portions in sealed glass bottles and placed 20 cm under the lamp with a light intensity of 2000 Lux, which was detected by a light meter . The TPG, salidroside and acteoside contents were determined at 0, 7, 14, 21, 30, 45, 60 and 90 days.The treatment was performed in triplicates (n = 3). Samples before storage and collected after completion of storage at above conditions were subjected to MS analysis.et al.12. A Biotek microplate reader  was used to measure the absorbance of the OFE at 334 nm. The contents were expressed as micrograms of acteoside equivalents (AE) per milliliters in OFE solution (μg AE/mL). The concentration range of the calibration series was 5 to 200 μg/mL.The total phenylethanoid glycoside content was determined using a method described by Jiang 13 with modification. The sample was separated on an Agilent ZORBAX Eclipse XDB-C18 column  at 25 °C. The mobile phase consisted of acetonitrile (solvent A) and water . A gradient program was used according to the following profile: 0–1 min, 6% A; 1–4 min, from 6% to 15% A; 4–8 min, from 15% to 20% A; 8–10 min, from 20% to 30% A; 10–12 min, from 30% to 100% A; 12–12.5 min, from 100% to 6% A; 12.5–15 min, 6% A. The flow rate was 0.2 mL/min and the injection volume was 2 μL. The DAD detector was set from 190 nm to 400 nm.The samples were filtered through a 0.22 μm nylon syringe filter  and analyzed by an Agilent 1290 UHPLC instrument  equipped with an autosampler, a binary pump, a column thermostat and a diode-array detector, using a previous method13 with modification. An Agilent ZORBAX Eclipse XDB-C18 column  was used at 25 °C. The mobile phase and gradient program were described as the same as the UHPLC-DAD analysis. The flow rate was 0.2 mL/min and the injection volume was 2 μL. The PDA detector was set 280 nm.The samples were filtered through a 0.22 μm nylon syringe filter  and transferred to an autosampler vial for UPLC–PDA–QTOF–MS analysis. The UPLC analyses were performed on a Waters ACQUITY UPLC  equipped with an autosampler, a binary pump, a column thermostat and a photo-diode array, using a previous method+ mass spectrometer . In the negative ion mode, the source voltage was −4.5 kV and the source temperature was 550 °C; in the positive ion mode, the source voltage was 5.5 kV and the source temperature was 600 °C. The other MS conditions were set as follows: scan range, m/z 50–1500; nebulizer gas (Air), 50 psi; heater gas (Air), 50 psi; curtain gas (N2), 35 psi; maximum allowed error, ± 5 ppm; declustering potential (DP), 100 V; collision energy (CE), 10 V. For MS/MS acquisition mode, the parameters were the same except that the collision energy (CE) was set at 40 ± 20 V, ion release delay (IRD) at 67, ion release width (IRW) at 25. In addition, information-dependent acquisition (IDA)-based auto-MS2 was performed on the 8 most intense metabolite ions in a cycle of full scan (1 s). The exact mass calibration was performed automatically by the Automated Calibration Delivery System before each analysis.The UPLC system coupled with a Triple TOF 560029, ChemSpider30, Metlin Metabolite31 and MassBank32.MS data were acquired using an Analyst® TF 1.6 software (AB Sciex) and processed by PeakView 1.2 (AB Sciex). The degradation products of acteoside and salidroside were tentatively identified based on the reported literatures and free accessible databases, such as Reaxys2 at 37 °C33.The differentiated rat pheochromocytoma cell line PC12 was obtained from the Cell bank of Chinese Academy of Sciences  and cultured in RPMI-1640 medium , supplemented with 100 U/mL penicillin, 100 μg/mL streptomycin and 10% fetal bovine serum  in a humidified incubator with 5% CO34 . PC12 cells were placed in 96-well plates at a density of 1 × 104 cells per well and in a volume of l80 μL. The plates were cultured for 24 h, and 20 μL of samples were added to the experimental groups. The control and model groups were added 20 μL of culture medium. After 24 h of incubation, the culture medium was replaced with 100 μL medium containing 400 µM CoCl2 while the control group was replaced with 100 μL new medium21. After 12 h treatment, 10 µL CCK-8 solution was added to each well and the plates were incubated at 37 °C for an additional 2 h. The absorbance was measured at 450 nm using Biotek microplate reader  and the background absorbance was excluded by performing blank corrections. Cell viability was expressed as a percentage of the untreated group (control = 100%).Cell viability was determined by Cell Counting Kit-8 (CCK-8)35.C0 and C are the PhGs contents (μg/mL) at time t0 and t, respectively; k is the rate constant (day−1); and t is the storage time (day).The degradation kinetics of most biological substances in food system follow the zero-order equation , first-o36  were calculated by the following equations  can be expressed by the Arrhenius equation27 (K0 is frequency factor (day−1); Ea is the Arrhenius activation energy (kJ/mol); R is the universal gas constant (8.314 J/(mol∙K)); and T is absolute temperature . The Ea was calculated according to equation  was used to calculate the parameters of kinetic models.All the analyses were performed in triplicate, and values were reported as the mean ± standard deviation. All results were confirmed from three independent experiments in the cell assay. Statistical analysis was performed using SPSS 20.0 and Excel 2011. One-way analysis of variance (ANOVA) were used to evaluate the significant differences between means, and Supplementary Information


Thiobacillus thioparus THI115. The isotopic fractionation constant  was −2.2±0.2‰. Under experimental conditions performed at parts per million by volume level of COS, the 34ɛ value for intact cells of T. thioparus THI115 was −3.6±0.7‰, suggesting that, based on Rees’ model, the 34ɛ value mainly depended on COS transport into the cytoplasm. The 34ɛ value for intact cells of T. thioparus THI115 was similar to those for Mycobacterium spp. and Williamsia sp., which are known to involve the conserved region of nucleotide sequences encoding the clade D of β-class carbonic anhydrase (β-CA) including COSase. On the other hand, the 34ɛ value was distinct from those for bacteria in the genus Cupriavidus. These results provide an insight into biological COS degradation, which is indispensable for estimating the COS global budget based on the isotope because of the significant contribution of COS degradation by microorganisms harboring β-CA family enzymes.Carbonyl sulfide (COS) is one of the major sources of stratospheric sulfate aerosols, which affect the global radiation balance and ozone depletion. COS-degrading microorganisms are ubiquitous in soil and important for the global flux of COS. We examined the sulfur isotopic fractionation during the enzymatic degradation of COS by carbonyl sulfide hydrolase (COSase) from  Carbonyl sulfide (COS) is the most abundant sulfur compound in the troposphere at nearly 500 parts per trillion by volume (pptv) , 53. Due2) hydrolase (EC 4.4.1.27)  , 72 exhitructure . T. thioeshwater . T. thioy source . During y source , 38, 55.e.g.   and D(CO34S) can be calculated based on the masses of CO32S, CO34S, and H2O. The 34ɛdif value for COS diffusion in liquid water is calculated as D(CO34S)/D(CO32S)−1, and the value is −3.7‰. Thus, the model shows the upper limit of 34ɛnet=−3.7‰ when the ratio of efflux to influx is equivalent to 0 (i.e. kout<<kenz), while the lower limit of 34ɛnet=−2.2‰, 34ɛ value of COSase, when the ratio of efflux to influx is equal to 1 (i.e. kout>>kenz). Thus, the 34ɛnet value of −3.6±0.7‰ obtained in the present experiment using T. thioparus THI115 suggests that the ratio of efflux to influx of this experiment was 0.07, kinto was 0.35 h−1, and kenz (0.33 h−1) was much higher than kout (0.02 h−1). In other words, the main factor controlling the 34ɛ value of bacterial COS degradation was not the enzyme, but diffusion/transport into the cytoplasm. We are the first to show kinto and kout needed in order to estimate influx and efflux for COS degradation by T. thioparus THI115.In this equation, −2 s−1 COS from a wheat farm after harvesting . In addition, NCBI’s CD-Search, a search tool for conserved domains, indicated that only 2 out of 245 CAs of Cupriavidus spp. searched from the NCBI’s protein database were classified to clade D of β-CA, suggesting that Cupriavidus spp. THI414 and THI415 do not have β-CA classified in clade D. Although the number of bacterial isolates examined here was limited, our results suggest that the 34ɛ value for bacteria is closely related to the presence of relevant enzymes.It is important to note that different enzymes for COS degradation may also partially explain the difference in  of β-CA . Genome  of β-CA . In cont2 hydrolase, in phylogenetically diverse organisms (34ɛ value for these organisms is also similar to that for COSase.COS-degrading ability is widespread not only in bacteria , 56 and rganisms , 74, COST. thioparus THI115 possesses other β-CA family enzymes because a Southern blotting analysis using the COSase gene showed the presence of only one copy of the COSase gene in the genome (T. thioparus DSM 505 indicated that there are nucleotide sequences annotated to β-CA, carboxysome CA, γ-CA, and RubisCO (T. thioparus THI115 is in progress in order to obtain information on the involvement of these enzymes.On the other hand, the presence of other COS-degrading enzymes needs to be considered. It is unlikely that e genome . Howevere genome , nitrogee genome , CO dehye genome , which e RubisCO . The X-r RubisCO , 40, 65.33E values were less than those for 33ɛ and 34ɛ values (33E values were 0.1±0.1‰ for COSase and 0.1±0.2‰ for T. thioparus THI115 (ɛ values . 33E vals THI115 , showingT. thioparus THI115 as well as chemoorganotrophic bacteria reported previously did not show MIF, which confirmed that these reactions do not contribute to sulfur isotope anomalies in the atmosphere (Sulfur isotope anomalies originating from the MIF process have been observed in aerosols from the troposphere and in snow pits and ice cores in which aerosols from the stratosphere have been deposited , 63, 64.mosphere , 63, 64.T. thioparus THI115 in the present study indicated biological effects on δ34S value of tropospheric COS and provides insights into biological COS degradation, which is important for estimating the COS global budget. In the present study, we used a purified enzyme of COSase and intact cells of T. thioparus THI115 in order to obtain a clearer understanding of isotopic fractionation in COS degradation by bacteria. Based on Rees’ model, we assumed that isotopic fractionation by T. thioparus THI115 was mainly influenced by the transport of COS into the cytoplasm during the degradation of COS, even at very low concentrations such as atmospheric COS.Isotopic fractionation for COSase and Further studies with a focus on isotopic fractionation by plants, another major sink of atmospheric COS, are indispensable for distinguishing COS fluxes between soil and plants. Moreover, measurements at low COS concentrations, such as atmospheric concentrations, are needed for soil, the second largest sink of COS, and COS-degrading organisms in order to clarify the COS dynamics operating between the troposphere and soil or organisms . Therefo


Acute diarrhea is one of the most serious problems in global public health that causes considerable morbidity and mortality worldwide. Human caliciviruses (HuCV) including norovirus  and sapovirus (SaV), is a leading cause of acute sporadic diarrhea in individuals across all age groups. However, few studies had been conducted clarifying the characteristics of HuCV in diarrhea cases across all age groups in China. Our study was aimed at assessing the HuCV-related diarrhea burden and NoV genotypes distribution in southwest China.The study was conducted in four hospitals in Kunming city, Yunnan province, from June 2014 to July 2015. Stool specimens were collected from 1,121 diarrhea cases and 319 healthy controls in outpatient departments. Reverse transcription polymerase chain reaction (RT-PCR) was used to detect NoV  and SaV. Sequencing was applied to confirm the three viral infections and phylogenetic analysis was performed to determine their genotypes. A structured questionnaire was used to record the demographic information and clinical symptoms of subjects.p < 0.0001, OR = 3.5, 95 % CI 1.8–6.5). The prevalence of the NoV genogroup GII and genotype GII.4 in diarrhea cases was significantly higher than that found in healthy controls . NoV GII  was the most common HuCV subtype in diarrhea cases, followed by SaV  and NoV GI . Of 118 NoV GII strains isolated from diarrhea patients. GII.4  was the predominant strain, followed by GII.3 , GII.12 , GII.17 , and GII.5 . Of the 55 GII.4 strains, the GII.4 Sydney 2012 variant had absolutely predominant prevalence , followed by the NoV GII.4-2006b variant . The GII.4 Orleans 2009 variant was not found in diarrhea cases of the study.HuCV was detected at an 11.0 % infection rate in 1,121 diarrhea cases and at 3.4 % rate in 319 non-diarrhea subjects (NoV GII was the major genogroup and GII.4 was the most predominant strain detected in diarrhea patients. The GII.17 is an emergent variant in sporadic diarrhea and might become the predominant strain in diarrhea cases in the near future. Rapid, accurate detection kits need to be developed to help us find and treat NoV-associated diarrhea in clinical settings in a timely manner. Acute gastroenteritis is still one of the most concerning public health problems that causes considerable morbidity and mortality worldwide , 2. HumaNoV is one of the important enteropathogens associated with acute diarrhea, but it has been underestimated in terms of its burden for many years, for a lack of adequate diagnostic tools until 1993 . AlthougThe prevalence of NoV is about 18.0 % in acute sporadic diarrhea cases worldwide . The detNoV is also known as one of the major leading causes of acute nonbacterial outbreaks of diarrhea. NoV-associated diarrhea outbreaks occurred more than 900 times during the period of 1993–2011 , and invNoV has caused increasing concern in recent years, which has been attributed to a large number of reported outbreaks of diarrhea and to food safety issues , 15. AcuThis study aims to: (i) understand the detection rate of HuCV, (ii) grasp the NoV GII genotype distribution, and (iii) understand the relationship between NoV genotypes and clinical symptoms.An “acute diarrhea case” was defined as any case in which the patient excreted stool more than three times within a 24-hour period and in which the stool form was not normal . A “healthy control” was defined as any case in which the subject was without diarrhea symptom and other gastrointestinal diseases.Four sentinel hospitals were selected for this study, namely the First People’s Hospital of Yunnan Province, the First Affiliated Hospital of Kunming Medical University, the Kunming Children’s Hospital, and Pushan Community Hospital.Stool specimens were collected from subjects with acute diarrhea and without diarrhea by trained doctors or nurses. Sterilized sampling cups were used to collect stools, the amount of fecal samples was more than 3 g or 3 mL, each stool sample was temporarily preserved at −20 °C and was transferred to the laboratory of the local Center for Disease Control, and then a part of each stool specimen was taken out and balanced to 10 % suspension with a phosphate buffer solution. RT-PCR was applied to examine SaV, and NoV GI and GI. A structured questionnaire was used to record the demographic information and clinical symptoms of each subject. This study was conducted from July 2014 to June 2015.A viral nucleic acid extraction kit  was applied to extract the viral nucleic acid from each stool specimen following the manufacturer’s protocol. A 15 % fecal suspension was centrifuged for 5 min at 14,000 g, 150 μL supernatant was used to extract the three viral nucleic acids, and 50 μL extracted RNA was obtained and stored at −70 °C prior to virus detection.A two-step multiplex RT-PCR was performed to detect SaV, and NoV GI and GII. The primes of p289/p290 target the polymerase region as previously described, this pair of primes produces PCR products of 331 bp for SaV and 319 bp for NoV  , 25. Thep < 0.05 was considered to be statistically significant.Data analysis was performed using the Statistical Product and Service Solutions (SPSS v19.0) software . The odds ratios (ORs) and 95 % confidence intervals (CIs) of categorical variables were calculated using the chi-square test or the Fisher’s exact test with two-tailed. A value of A molecular evolutionary genetics analysis was employed to perform the phylogenetic analysis (MEGA v5.2). A phylogenetic tree was constructed using the neighbor-joining method with 1,000 bootstrap replicates. Reference strains were downloaded from GenBank.χ2 = 0.341, p = 0.558). The average ages of patients and healthy controls were 0.6 years and 2.3 years, respectively. The majority of the subjects were from urban areas; 753 cases (67.2 %) and 223 (57.0 %) controls. The average frequency of diarrhea in patients was six episodes (quartile range = 2). Various types of clinical symptoms were observed in the diarrhea cases. Vomiting  was the most common clinical symptom, followed by fever  and dehydration .In the present study, which was conducted from July 2014 to June 2015, 1,440 subjects including children and adults participated. Among those, 1,121 were acute diarrhea patients and 319 were healthy controls. The male to female sex ratios in diarrhea cases and healthy controls were 1.00 and 1.01, respectively (n = 123) in diarrhea cases across all age groups. Among these, the prevalence of NoV GII was 10.5 % (n = 118) in acute gastroenteritis patients of all ages, followed by SaV  and NoV GI . The prevalence of both NoV genogroups (GI and GII) was 10.7 % (n = 120) in diarrhea cases across all ages.The detection rate of HuCV was 11.0 % . No significant differences were observed in NoV GI between diarrhea patients  and healthy controls . SaV was detected in three (0.3 %) diarrhea cases, but was not found in controls (p = 0.472). NoV GII was more prevalent in patients relative to healthy controls across all age groups . NoV genotype GII.4 was found in 55 (4.9 %) diarrhea cases and six (1.9 %) non-diarrhea cases . GII.12 was detected in 25 diarrhea cases and three healthy controls . The detection rate of GII.5 in diarrhea cases was as high as in healthy controls . No significant differences in GII.12 and GII.5 were observed between diarrhea patients and healthy patients .HuCV was detected in 123 (11.0 %) diarrhea cases and 11 (3.4 %) healthy controls . HuCV was more prevalent in patients than in controls aged above five years . NoV GII was more prevalent in patients with diarrhea relative to healthy controls aged above five years . NoV GII was detected in 14.8 % of elderly cases, but neither NoV GI nor SaV was detected in controls over 65 years. No significant differences in these three virus pathogens were observed between cases and controls over 65 years of age  was the leading subtype of HuCV, followed by SaV  and NoV GI . Of the 118 strains of NoV GII isolated from 1,121 diarrhea cases, the predominant genotype was GII.4 , followed by GII.3  and GII.12 . The prevalences of GII.17 and GII.5 were less than 1.0 % in diarrhea cases. Among the 10 strains of NoV GII detected in healthy controls, the top two most prevalent genotypes were GII.12  and GII.5 . Others genotypes were not detected in asymptomatic individuals . GII.12 had an obvious age distribution trend , which was only found in patients aged 0–2 years (3.0 %) and 2–5 years (1.7 %). The prevalence of GII.4 had not age distribution trend . No significant differences in GII.17, GII.3, and GII.5 were observed in diarrhea cases across all age groups , 59 (5.3 %), 21 (1.9 %), 223 (19.9 %), and 27 (2.4 %) diarrhea patients fell into the age ranges of 0–2 years, 2–5 years, 5–14 years, 14–65 years, and over 65 years, respectively. Although NoV GII was more prevalent in children below two years of age and adults above 65 years of age, no significant difference in presence of NoV GII was found between the age groups  had a peak in prevalence in the fall . The prevalences of GII.5 and GII.12 did not show distinct seasonal variation, and the prevalences of GII.3 and GII.17 showed minor seasonal variations across one year, respectively. GII.4 was detected throughout the year and significant differences were observed in different seasons , with a seasonal peak of GII.4  in the fall , GII.3 , and GII.12 ; and dehydration was only associated with GII.4  and GII.3 . Watery stool was more commonly observed in diarrhea patients infected with GII.4, GII.3, and GII.12 ; and mucousy stool was found less frequently in GII.3 and GII.12  than in GII.4 , GII.3 , GII.12 , GII.17 , and GII.5 . Of all the 55 strains of GII.4, the GII.4 Sydney 2012 variant was absolutely predominant , followed by the GII.4-2006b variant . The GII.4 Orleans 2009 variant was not found in diarrhea cases in this study.In the study, 123 strains of HuCV were isolated from acute gastroenteritis cases, of which two (1.6 %) cases were NoV GI, three (2.4 %) cases were classified as SaV, and 118 (95.9 %) cases were classified as NoV GII. Among the 118 strains of NoV GII, five genotypes were identified as followed: GII.4 , GII.12 , and GII.5 . Of the six strains of GII.4, four (80.0 %) strains were of the GII.4 Sydney 2012 variant and two (20.0 %) were of the GII.4-2006b variant. The GII.4 Orleans 2009 variant was not found in healthy controls  case was classified as NoV GI, which was a GI.3 genotype, and 10 (90.9 %) cases were classified as NoV GII, which were divided into GII.4 , but was close to the prevalence in high-mortality countries (14.0 %) . The lowThere were no significant differences in NoV GII prevalence across several age groups, which might suggest that age had no significant influence on NoV infection. Other studies led to similar conclusions , 34. TheIn this study, the GII.17 variant emerged in diarrhea patients. This finding was in accordance with other studies. The GII.17 variant was the prominent genotype in sporadic diarrhea patients, the detection rate of which in diarrhea cases was up to 15.0 %, which was higher than the prevalence of the GII.4 Sydney 2012 strain in sporadic diarrhea patients in Jiangsu Province, China, from the end of 2014 onwards . The novThere were several limitations on our research. Firstly, the subjects were mainly selected from urban areas, and in addition, the diarrhea cases were all selected from outpatient department, which might lead to selection bias in respect of the epidemiological characteristics of HuCV. Secondly, the number of healthy controls was small, hence the OR of the HuCV and NoV genogroups were not highly reliable. Thirdly, genetic characterization of NoV was only based on partial polymerase regions in this study. However, the genotyping of NoV based on only capsid regions or on both regions might provide different results. Therefore, further study involving diarrhea cases selected from urban, rural, outpatient, and inpatients departments will should be conducted to ensure accurate assessment of the NoV disease burden. A 1:1 match case-control study will be a good choice. Meanwhile, genotyping of NoV based on both capsid regions and RNA-dependent RNA polymerase regions should be conducted in order to enhance the accuracy and effectiveness of NoV GII genotyping.In summary, the results show that NoV is one of the major causes of diarrhea, that GII.4 is still the predominant strain, and that GII.17 has emerged in sporadic diarrhea cases and might become a predominant strain, which will lead to more cases and outbreaks of diarrhea in the near future. This will provide new obstacles to preventing NoV-associated diarrhea. Therefore, monitoring the prevalence trends and evolution of GII.17 is necessary.