Atmospheric nitrogen inputs and losses along an urbanization gradient from Boston to Harvard Forest,MA

Urbanization alters nitrogen (N) cycling, but the spatiotemporal distribution and impact of these alterations on ecosystems are not well-quantified. We measured atmospheric inorganic N inputs and soil leaching losses along an urbanization gradient from Boston, MA to Harvard Forest in Petersham, MA. Atmospheric N inputs at urban sites (12.3 ± 1.5 kg N ha^?1 year^?1) were significantly greater than non-urban (5.7 ± 0.5 kg N ha^?1 year^?1) sites with NH₄ ⁺ (median value of 77 ± 4 %) contributing thrice as much as NO₃ ^?. Proximity to urban core correlated positively with NH₄ ⁺ (R² = 0.57, p = 0.02) and total inorganic N inputs (R² = 0.61, p = 0.01); on-road CO₂ emissions correlated positively with NO ₃ ^? inputs (R² = 0.74, p = 0.003). Inorganic N leaching rates correlated positively with atmospheric N input rates (R² = 0.61, p = 0.01), but did not differ significantly between urban and non-urban sites (p > 0.05). Our empirical measurements of atmospheric N inputs are greater for urban areas and less for rural areas compared to modeled regional estimates of N deposition. Five of the nine sites had NO ₃ ^? leached that came almost entirely from nitrification, indicating that the NO₃ ^? in leachate came from biological processes rather than directly passing through the soil. A significant proportion (17–100 %) of NO ₃ ^? leached from the other four sites came directly from the atmosphere. Surprisingly, the four sites where atmospheric sources made up the largest proportion of leachate NO₃ ^? also had relatively low N leaching rates, suggesting that atmospheric N inputs added to terrestrial ecosystems can move to multiple sinks and losses simultaneously, rather than being lost via leaching only after abiotic and biotic sinks have become saturated. This study improves our understanding of atmospheric N deposition and leaching in urban ecosystems, and highlights the need to incorporate urbanization effects in N deposition models.     

Dynamic properties of SOD1 mutants can predict survival time of patients carrying familial amyotrophic lateral sclerosis

One of the reasons for the death of motor neurons of the brain and spinal cord in patients with amyotrophic lateral sclerosis is known to be formation of subcellular protein aggregates that are caused by mutations in the SOD1 gene. Patient survival time was earlier shown to have limiting correlation with thermostability change of SOD1 mutant forms of patients’ carriers. We hypothesized that aggregation of mutant SOD1 may occur not only due to the protein destabilization, but through formation of novel interatomic bonds which stabilize “pathogenic” conformations of the mutant as well. To estimate these effects in the present paper, we performed statistical analysis of occupancy of intramolecular hydrogen bonds, hydrogen bonds between the protein and water molecules, and water bridges with use of molecular dynamics simulation for 38 mutant SOD1 forms. Multiple regression model based on these kinds of bonds demonstrated correlation with patient survival time significantly better (R = .9, p-value < 10^?11) than the thermostability of SOD1 mutants only. It was shown that the occupancy of intramolecular hydrogen bonds between amino acid residues is a key determinant (R = .89, p-value < 10^?10) in predicting patients’ survival time.     

Differential interactions and structural stability of chitosan oligomers with human serum albumin and α-1-glycoprotein

Chitosan is a naturally occurring deacetylated derivative of chitin with versatile biological activities. Here, we studied the interaction of chitosan oligomers with low degree of polymerization such as chitosan monomer (CM), chitosan dimer (CD), and chitosan trimer (CT) with human serum albumin (HSA) a major blood carrier protein and α-1-glycoprotein (AGP). Since, HSA and AGP are the two important plasma proteins that determine the drug disposition and affect the fate of distribution of drugs. Fluorescence emission spectra indicated that CM, CD, and CT had binding constants of K_CM = 6.2 ± .01 × 10⁵ M^?1, K_CD = 5.0 ± .01 × 10⁴ M^?1, and K_CT = 1.6 ± .01 × 10⁶ M^?1_, respectively, suggesting strong binding with HSA. However, binding of chitooligomers with AGP was insignificant. Thermodynamic and molecular docking analysis indicated that hydrogen bonds and also hydrophobic interaction played an important role in stabilizing the HSA-chitooligomer complexes with free energies of ?7.87, ?6.35, and ?8.4?Kcal/mol for CM, CD, and CT, respectively. Further, circular dichroism studies indicated a minor unfolding of HSA secondary structure, upon interaction with chitooligomers, which are supported with fluctuations of root mean square deviation (RMSD) and radius of gyration (R_g) of HSA. Docking analysis revealed that all three chitooligomers were bound to HSA within subdomain IIA (Site I). In addition, RMSD and R_g analysis depicted that HSA-chitooligomer complexes stabilized at around 4.5 ns. These results suggest that HSA might serve as a carrier in delivering chitooligomers to target tissues than AGP which has pharmacological importance.     

Allometric biomass,nutrient and carbon stock models for <Emphasis Type="Italic">Kandelia candel</Emphasis> of the Sundarbans,Bangladesh

Key message

Present study recommends DBH as independent variable of the derived allometric models and Biomass = a + b DBH ² has been selected for total above-ground biomass, nutrients and carbon stock.

Abstract

Kandelia candel (L.) Druce is a shrub to small tree of the Sundarbans mangrove forest of Bangladesh. The aim of the study was to derive the allometric models for estimating above-ground biomass, nutrient and carbon stock in K. candel. A total of eight linear models with 64 regression equations were tested to derive the allometric models for biomass of each part of plant; and nutrients and carbon stock in total above-ground biomass. The best fitted allometric models were selected by considering the values of R ², CV, R _mse, MS_error, S _a, S _b, F value, AICc and Furnival Index. The selected allometric models were Biomass = 0.014 DBH² + 0.03; √Biomass = 0.29 DBH ? 0.21; √Biomass = 0.66 √DBH ? 0.57; √Biomass = 1.19 √DBH ? 1.02; Biomass = 0.21 DBH² + 0.12 for leaves, branches, bark, stem without bark and total above-ground biomass, respectively. The selected allometric models for Nitrogen, Phosphorous, Potassium and Carbon stock in total above-ground biomass were N = 0.39 DBH² + 0.49, P = 0.77 DBH² + 0.14, K = 0.87 DBH² + 0.07 and C = 0.09 DBH² + 0.05, respectively. The derived allometric models have included DBH as a single independent variable, which may give quick and accurate estimation of the above-ground biomass, nutrient and carbon stock in this species. This information may also contribute to a broader study of nutrient cycling, nutrient budgeting and carbon sequestration of the studied forest.

     

Can uranium follow the iron-acquisition pathway? Interaction of uranyl-loaded transferrin with receptor 1

Transferrin receptor 1 (R_D) binds iron-loaded transferrin and allows its internalization in the cytoplasm. Human serum transferrin also forms complexes with metals other than iron, including uranium in the uranyl form (UO₂ ²⁺). Can the uranyl-saturated transferrin (TUr₂) follow the receptor-mediated iron-acquisition pathway? In cell-free assays, TUr₂ interacts with R_D in two different steps. The first is fast, direct rate constant, k ₁ = (5.2 ± 0.8) × 10⁶ M^?1 s^?1; reverse rate constant, k _?1 = 95 ± 5 s^?1; and dissociation constant K ₁ = 18 ± 6 μM. The second occurs in the 100-s range and leads to an increase in the stability of the protein–protein adduct, with an average overall dissociation constant K _d = 6 ± 2 μM. This kinetic analysis implies in the proposed in vitro model possible but weak competition between TUr₂ and the C-lobe of iron-loaded transferrin toward the interaction with R _D.     

Titanium dioxide nanoparticles preferentially bind in subdomains IB,IIA of HSA and minor groove of DNA

Titanium dioxide nanoparticles (TiO₂-NPs) interaction with human serum albumin (HSA) and DNA was studied by UV–visible spectroscopy, spectrofluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) to analyze the binding parameters and protein corona formation. TEM revealed protein corona formation on TiO₂-NPs surface due to adsorption of HSA. Intrinsic fluorescence quenching data suggested significant binding of TiO₂-NPs (avg. size 14.0 nm) with HSA. The Stern–Volmer constant (K_sv) was determined to be 7.6 × 10² M^?1 (r² = 0.98), whereas the binding constant (K_a) and number of binding sites (n) were assessed to be 5.82 × 10² M^?1 and 0.97, respectively. Synchronous fluorescence revealed an apparent decrease in fluorescence intensity with a red shift of 2 nm at Δλ = 15 nm and Δλ = 60 nm. UV–visible analysis also provided the binding constant values for TiO₂-NPs–HSA and TiO₂-NPs-DNA complexes as 2.8 × 10² M^?1 and 5.4 × 10³ M^?1. The CD data demonstrated loss in α-helicity of HSA and transformation into β-sheet, suggesting structural alterations by TiO₂-NPs. The docking analysis of TiO₂-NPs with HSA revealed its preferential binding with aromatic and non-aromatic amino acids in subdomain IIA and IB hydrophobic cavity of HSA. Also, the TiO₂-NPs docking revealed the selective binding with A-T bases in minor groove of DNA.     

Pharmacophore mapping,molecular docking and QSAR studies of structurally diverse compounds as CYP2B6 inhibitors

Pharmacophore mapping, molecular docking and quantitative structure–activity relationship (QSAR) studies were carried out for a structurally diverse set of 48 compounds as CYP2B6 inhibitors. The generated best pharmacophore hypotheses from the three methods of conformer generation (FAST, BEST and conformer algorithm based on energy screening and recursive buildup) indicate the importance of two features, namely, hydrogen bond acceptor [electron-rich centre] and ring aromaticity. The distance between the two centres of the important features for ideal inhibitors varied from 5.82 to 6.03 Å. The chemometric tools used for the QSAR analysis were genetic function approximation (GFA) and genetic partial least squares. The developed QSAR models indicate the importance of an electron-rich centre, size of molecule, impact of branching and ring system and distribution of charges in the molecular surface. The docking study confirms the importance of an electron-rich centre for binding with the iron atom of the cytochrome enzyme. A GFA model with spline option was found to be the best model based on internal validation as well as the r ² _{m (overall)} criterion (Q ² = 0.772, r ² _{m (overall)} = 0.774). According to the external prediction statistics (R ² _pred = 0.876), another GFA-derived model with spline option outperforms the remaining models.     

Structural and functional studies on a variant of cystatin purified from brain of Capra hircus

Cystatins, known for their ubiquitous presence in mammalian system are thiol protease inhibitors serving important physiological functions. Here, we present a variant of cystatin isolated from brain of Capra hircus (goat) which is glycosylated but lacks disulphide bonds. Caprine brain cystatin (CBC) was isolated using alkaline treatment, ammonium sulphate fractionation (40–60%) and gel filtration chromatography on Sephacryl S-100HR column with an overall yield of 26.29% and 322-fold purification. The inhibitor gave a molecular mass of ~44 kDa as determined by SDS-PAGE and gel filtration behaviour. The Stokes radius and diffusion coefficient of CBC were 27.14 Å and 8.18 × 10^?7 cm² s^?1, respectively. Kinetic data revealed that CBC inhibited thiol proteases reversibly and competitively, with the highest inhibition towards papain (K_i = 4.10 nM) followed by ficin and bromelain. CBC possessed 34.7% α-helical content as observed by CD spectroscopy. UV, fluorescence, CD and FTIR spectroscopy revealed significant conformational change upon CBC-papain complex formation. Isothermal titration calorimetry (ITC) was used to measure the thermodynamic parameters – ΔH, ΔS, ΔG along with N (binding stoichiometry) for CBC-papain complex formation. Binding stoichiometry (N = .97 ± .07 sites) for the CBC-papain complex indicates that cystatin is surrounded by nearly one papain molecule. Negative ΔH (?5.78 kcal mol^?1) and positive ΔS (11.01 cal mol^?1 deg^?1) values suggest that the interaction between CBC and papain is enthalpically as well as entropically favoured process. The overall negative ΔG (?9.19 kcal mol^?1) value implies a spontaneous CBC-papain interaction.     

Hydrolytic Enzyme Production is Associated with Candida Albicans Biofilm Formation from Patients with Type 1 Diabetes

Oral candidosis is common in patients with diabetes mellitus, as yeasts, particularly Candida albicans, have the propensity to colonise, form biofilms and release hydrolytic enzymes which cause inflammation. This study aimed to investigate these characteristics in isolates from three groups of patients with type 1 diabetes: individuals with better controlled diabetes (BCD; ≥6 <8%), individuals with poorly controlled diabetes (PCD; ≥8%) and non-diabetics (ND; HbA_1c <5.9%). The biomass (Bm), phospholipase (P_z), haemolysin (H_z) and proteinase (Pr_z) were assessed using a microtitre biofilm assay and agar-based hydrolytic enzyme assays. Biofilm formation was significantly increased in the PCD group compared to ND and BCD groups (P < 0.05). No significant differences in P_z levels were observed between groups, whereas both H_z and Pr_z were significantly greater in the diabetes groups than in the healthy control group (P < 0.05). Statistically significant correlations were found to exist between the HbA_1c levels of the patients and the Bm (R = 0.384; P = 0.033), haemolysin activity (R = ?0.455; P = 0.010) and proteinase activity (R =  ?0.531; P = 0.002). There was no apparent correlation between the Bm and P_z activity (R = ?0.305; P = 0.053) or H_z activity (R = ?0.100; P = 0.296). However, a negative correlation was found between Bm and Pr_z values (R = ?0.343; P = 0.030). These data suggest that biofilm formation is likely to play a role in the pathogenicity of oral candidosis, and in patients with diabetes, this may be due to the ability of C. albicans to adapt to the altered physiological environment. The production of hydrolytic enzymes is independently associated with this growth modality.     

Environmental variables controlling soil respiration on diurnal, seasonal and annual time-scales in a mixed mountain forest in Switzerland

Studies on soil respiration in mountain forests are rather scarce compared to their broad distribution. Therefore, we investigated daily, seasonal and annual soil respiration rates in a mixed forest (Lägeren), located at about 700 m in the Swiss Jura mountains, during 2 years (2006 and 2007). Soil respiration (SR) was measured continuously with high temporal resolution (half-hourly) at one single point (SR_automated) and periodically with high spatial resolution (SR_manual) at 16 plots within the study site. Both, SR_automated and SR_manual showed a similar seasonal cycle. SR strongly depended on soil temperature in 2007 (R ² = 0.82–0.92), but less so in 2006 (R ² = 0.56–0.76) when SR was water limited during a summer drought. Including soil moisture improved the fit of the 2006 model significantly (R ² = 0.78–0.97). Total annual SR for the study site was estimated as 869 g C m^?2 year^?1 for 2006 and as 907 g C m^?2 year^?1 for 2007 (uncertainty <10% at the 95% confidence interval, determined by bootstrapping). Selected environmental conditions were assessed in more detail: (1) Rapid, but contrasting changes of SR were found after summer rainfall. Depending on soil moisture at pre-rain conditions, summer rain could either cause a pulse of CO₂ from the soil or an abrupt decrease of SR_automated due to water logging of soil pores. (2) Two contrasting winter seasons resulted in SR being about 60–70% (31.2–44.6 g C m^?2) higher during a mild winter (2007) compared to a harsh winter (2006). (3) Analysing SR for selected periods on a diurnal scale revealed a counter-clockwise hysteresis with soil surface temperatures. This indication of a time-lagged response of SR to temperature was further supported by a very strong relationship (R ² = 0.86–0.90) of SR to soil temperature with a time-lag of 2–4 h.     

Response of soil respiration to temperature and soil moisture: Effects of different vegetation types on a small scale in the eastern Loess Plateau of China

Soil respiration is affected by vegetation and environmental conditions. The purpose of this study was to investigate the effect of vegetation type on soil respiration, temperature and water content, and their correlations on a small scale. We measured soil respiration rate (R_s) over a 3-year period at biweekly intervals in three plots in the eastern Loess Plateau of China, with the same soil texture but different vegetation types: pine forest, grassland, and shrub land. Simultaneously, soil temperature (T_s) at 10 cm depth and soil water content (W_s) within 10 cm depth were measured. The seasonal course of R_s and T_s showed a similar temporal variation in the three plots, with higher values in summer and autumn and lower values in winter and spring. No significant differences (P>0.05) were found between plots, except for W_s. The mean cumulative release of CO₂ efflux from March to December was 962.5, 1027.5, and 1166.5 g C m^? 2 a^? 1 for plots 1, 2, and 3, respectively, with no significant difference between plots. The fitted exponential equations of R_s versus T_s from the 3-year data-set were significant (P < 0.05) with an R² of 0.72, 0.64, and 0.72 for plots 1, 2, and 3, respectively. The calculated Q₁₀ from the parameters of the fitted equation was 3.57, 3.52, and 3.61, and the R₁₀ was 2.36, 2.03, and 2.37 μmol CO₂ m^? 2 s^? 1 for plots 1, 2, and 3, respectively. Compared with the T_s, the correlations between R_s and W_s were not significant for the three plots. However, if the T_s was above 10°C, then their correlation was significant, and W_s had an impact on R_s. Four combined regression equations including two variables of T_s and W_s could be well established to model correlations between R_s and both T_s and W_s. Our study demonstrated that the exponential and power model fitted best and no significant different correlations of combined equations existed between the three plots. These results show that vegetation type had little impact on R_s, T_s, W_s, and their correlations, as well as on related parameters such as Q₁₀ and R₁₀. Therefore, while doing R_s research in a horizontal patchy vegetation conditions on a small area, the sampling location of measurements should focus on vertical dominant vegetation and ignore patch vegetation so as to reduce field work load.     

Performance evaluation of an integrated constructed wetland used to treat a contaminated aquatic environment

The treatment performance of an integrated constructed wetland (ICW) that was in operation for 3 years was evaluated. Artificial neural network modeling was used to predict contaminant treatment efficiencies based on easily measured field parameters. The estimates for average yearly removals of total phosphorus (TP), total nitrogen (TN), chemical oxygen demand (COD), and total suspended solids (TSS) were 0.81 ± 0.18, 7.17 ± 1.62, 63.80 ± 17.41, and 126.12 ± 48.61 g m^?2 d^?1, respectively. Removal velocities of contaminants were determined from analyses of inlet–outlet datasets. The areal removal rate constants were 0.46, 0.73, 0.44, and 0.82 m d^?1 for TP, TN, COD, and TSS, respectively. The presence of high background concentrations of contaminants (TP: 0.01 mg L^?1, TN: 0.13 mg L^?1, COD: 6.43 mg L^?1, TSS: 14.83 mg L^?1) indicated that the water in the ICW was mesotrophic. Statistical methods (i.e., principal component analysis (PCA), forward selection, and correlation analysis) were used to select optimal input subsets for different contaminants. These data subsets were subsequently used for model development. To find the optimal network architectures, a genetic algorithm was introduced to the learning processes. The models were competent at providing reasonable matches between the measured and the predicted effluent concentrations of TP (R² = 0.9711), TN (R² = 0.8875), COD (R² = 0.9359), and TSS (R² = 0.9164). The results of the models provided information that will be useful for the design and modification of constructed wetlands.