Our aim is to increase the

Our aim is to increase the spatial resolution of the present model framework and to evaluate the impacts using actual decided cutting plans and more detailed forestry models for obtaining future scenarios. We are also working on including estimates of the uncertainties in the model outputs and improving the spatial databases for several ecosystem variables (e.g. soil weathering rates). Integrating b-raf inhibitors protection with the provision of ES is a key element for sustainable land use planning (Vihervaara et al., 2015), and it is therefore also planned to include other biodiversity indicators than the dead wood biomass in our model framework in the future. Spatial age-structure of forests, proportions of deciduous trees, species traits and trophic complexity, for instance, are essential biodiversity variables that may have impact on ecosystem functions and services, and that should be integrated in the model system. However, that requires use of novel data sources and Earth Observation outputs in addition to the approach used in this study.
4. Concluding remarks
Increasing the use of forest bioenergy is an important mitigation strategy against climate change. However, these policies need to be evaluated, planned and implemented taking into account the boundary conditions of the ecological systems affected. These boundary conditions include quantifying the real net C ecosystem budget and emission savings obtained, the long-term supply of nutrients available for sustained forest growth, and impacts on other relevant ecosystem compartments (e.g. nutrient leaching to surface waters and impacts on valuable species). A systems analysis approach is therefore needed to analyse these complex interactions.
The modelling framework developed and documented in the present study allows a quantitative analysis of the impacts of forest harvesting scenarios on several key ecosystem sustainability indicators. The demonstration case of the H?meenlinna municipality shows how there can be conflicting goals between maximising the use of energy-wood and minimising impacts on species diversity, soil carbon and nutrient stores and nutrient leaching. The developed system allows seeking for optimised solutions with respect to different management options and sustainability considerations. Responsible and sustainable natural resources economy is a key policy goal of the Finnish government, and therefore such model systems are increasingly needed. However, the framework is complex and large uncertainties still remain regarding many components, including data sources, scenarios assumptions, simplifications of ecosystem processes, and process rate parameters. Continued work is therefore needed to improve these modelling and evaluation tools and to secure the collection of long-term detailed ecosystem and experimental data needed for the model developments and impact evaluations.
1. Introduction
Lake ecosystems are considered important sentinels of environmental change as they integrate alterations in the catchment and atmosphere (Adrian et al., 2009 and Williamson et al., 2009). Key response variables acting as sentinel variables include a wide range of physical, chemical and biological indicators that are sensitive to climate and land-use change (Adrian et al., 2006 and Adrian et al., 2009). While the effects of anthropogenic pressure on key response variables are reasonably well understood in isolation, it remains a challenge to predict how global change affects the interactions among such variables and, thus, the ecological network of a lake and its stability. The lack of ground-truthed data on species interactions and community network response to stress has been identified as major gap in the bio-monitoring sciences (Gray et al., 2014). To better understand and predict how global change will affect community structure and stability and hence also associated ecosystem processes, it is necessary to assess how ecological networks change over time and under pressure (McMeans et al., 2015).

When comparing global health of

When comparing global health of ecosystems with our selection of trophic indicators, the impacts of the tested management-based scenarios are relatively similar (see Fig. 7). However when looking at the trophic level scale, pressures strongly diverge. Among the three scenarios F = 0.2 (i.e. only for species whose F is currently above F = 0.2) causes the major reduction of fishing impact. In comparison F = M keeps high fishing impact, notably on low and intermediate trophic levels which tend to have high natural mortality potentially above 0.2. For the lowest trophic levels, the current F is below M and thus is not reduced in our management scenario. It is worth mentioning that both F = 0.2 and F = M tend to reduce total catches, by 14% and 10% respectively, but a slight increase in high trophic level AZD1152-HQPA captured can be observed with F = 0.2. This is probably due to a bottom-up effect with the enhancement of potential prey. It is also interesting to note that the F = M scenario, which minimize the total catches losses compared to F = 0.2, tends toward the ‘balanced harvest’ concept ( Zhou et al., 2010 and Jacobsen et al., 2014).
The scenario F = 0.2* (i.e. for all fishable species) provides the same amount of global catches as the current fishing effort, but with a shift toward the low-middle trophic levels (i.e. 2.7 ≤ TL ≤ 3.3). This scenario, characterized by increased fishing mortalities on some low TLs, is in the continuity of the ‘fishing through the marine food web’ process (Essington et al., 2006). Our comparison between F = 0.2* and its alternative would have been more realistic if the proportion of edible species and energetic values would have been taken into account. Theoretically the choice between the two scenarios, an F = 0.2* and F = 0.2 would likely depend on the will of societies to either manage for the long-term economic performance by restricting the impact of fishing on high trophic levels or to convert lower trophic levels into commercial sources of protein. It appears that European societies tend more toward the first objective and therefore to a rather conservative approach (Pinnegar et al., 2002).
We estimate target values for the five tested indicators based on whole-ecosystem models, and for MTL and HTI based on survey data for the Celtic/Biscay demersal community. Regarding models, our results demonstrate that model-specific target values can be estimate for all indicators, and within each ecosystem, as long as a management scenario has been agreed as the operational enforcement of an EAFM. F = M incoherent target values for the Bay of Biscay model are likely due to the structure of the model, which groups a number of demersal exploited species together. That grouping is certainly not the most adapted when one is dealing with fishing effort. It is worth mentioning that this type of incongruity is absent in the three other models as they differ in their structure.
Concerning survey data, Ecosim simulations did not provide good estimations of targets for MTI and API, for two major reasons. Firstly because trophic levels are given to species but great disparities are often found among species composing the trophic groups. Thus, Rogers et al. (2010) advocate that the efficiency of trophic indicators is conditional to the TL precision being used in the estimations. Secondly, because species observed in surveys are mostly included in intermediate trophic levels. Therefore, great care must be taken when dealing with different sources of TL as they can be underestimated in EwE due to the parameterization of the model (Deehr et al., 2014 and Lassalle et al., 2014).

We used NMDS ordination of abundance data of dragonflies species

We used NMDS ordination of abundance data of dragonflies species at reach scale, considering data of all sampling methods, and the envfit function, available in vegan package of R (version 3.1.2), to analyse the influence of environmental variables on the odonate assemblages. We considered 5 environmental variables: mean annual water temperature in the main channel, elevation and width of river corridor as numerical variables, and physiographic context (two categories: Alpine region and alluvial plain) and channel morphology (two categories: single-thread and multiple-thread reaches) as categorical variables. For the physiographic context values, we attributed the value 0 to the reaches located in the alluvial plain and the value 1 to those located in the Alpine region, while for channel morphology we attributed the value 0 to single-thread reaches and 1 to multiple-thread reaches. The ordination used the Bray–Curtis adjusted distances (Clarke et al., 2006), which are specifically designed for the analyses of sparse or denuded assemblages, as it occurred in some of our study reaches. A Kruskal–Wallis test was used to test the difference of the medians of the metrics calculated for the different combination of sampling methods. A Mann–Whitney test was used to test if the median values of the ORI metrics were significantly different between single-thread and multiple-thread reaches. Univariate statistical analyses were carried out using PAST (version 3.04; Hammer et al., 2001).
3. Results
3.1. Odonate gap 26 at reach scale and influence of the environmental variables
A total of 4176 adults, 673 larvae and 721 exuviae were recorded. All adults and 96% of exuviae and 83% of larvae were successfully identified to the species level. Forty-one species were recorded, fifteen zygopterans and twenty-six anisopterans. A total of 39 species were considered as breeding (see Appendix), and dragonfly richness varied between 22 species (Cervere reach, Stura di Demonte River) and 1 breeding species (Cavazzo reach, Tagliamento River), based on data from the three sampling methods. High dragonfly richness was recorded for reaches on the Stura di Demonte, Sesia and Tagliamento rivers, while dragonflies richness was low in the study reaches located along the Brenta, Chiese and Adige rivers.
GLM showed a significant relationship (t = 2.69; p = 0.016) between the number of dragonfly species breeding in the main channel, considering all sampling methods, and the mean annual water temperature of the main channel. GLM was also significant (F = 7.79; p = 0.013). In five study reaches no breeding species were found in the main channel: three of them (Demonte reach, Stura di Demonte River; Borghetto reach, Adige River; Grigno reach, Brenta River) are characterized by the lowest values of mean annual water temperature, lower or equal to 10 °C (Fig. 2). Also in the Cavazzo reach (Tagliamento River) and in the Friola reach (Brenta River) no breeding species were found in the main channel. These reaches have a high mean annual water temperature (i.e. above 10 °C), but they are highly dynamic braided reaches, where the main channel morphology is generally not stable for long periods and they lack riparian and aquatic vegetation. The Demonte reach (Stura di Demonte River) is also characterized by the same morphological conditions. The majority of the study reaches (i.e. four out of five) with no recorded breeding lotic dragonflies species were located in the Alpine region.

Acknowledgements We are grateful to the

We are grateful to the Joseph W. Jones Ecological Research Center and the University of Georgia Graduate School for supporting this research. We would especially like to thank Analie Barnett with The Nature Conservancy for her statistical guidance, as well as many local landowners for their cooperation and help. We also sincerely appreciate the helpful comments of two anonymous reviewers.
1. Introduction
Recreation and tourism in national parks and other protected areas have increased rapidly across the world in the past decade (Balmford et al., 2009). This trend of increase is particularly pronounced in rapidly-industrializing countries, such as China. When the importance of tourism for the local economy is well appreciated, the impacts related to tourism have caught considerable attention from both research communities and resource managers (Liliholm and Romney, 2000, Hall and H?rk?nen, 2006 and Monz et al., 2013). A large number of studies have examined the effects of tourism on the physical environment, vegetation, and wildlife in terrestrial systems (Hall and Trichostatin A H?rk?nen, 2006). Much less attention has been paid to freshwater ecosystems, e.g. lakes, rivers/streams (Hadwen, 2007), although they are among the most attractive locations (Kaplan and Kaplan, 1989) .
Among the limited number of studies on aquatic ecosystems, most examined the impacts of tourism and recreation (e.g. boating and swimming) on water quality, macrophytes, and algae (phytoplankton and periphyton). The effects observed include increases of nutrient levels and chlorophyll (Butler et al., 1996 and Hammitt and Cole, 1998; Hadwen et al., 2010; Monz et al., 2013), degraded physical habitats, and shifts in food web structure (Mosisch and Arthington, 2004). Several studies also highlight the need for biological monitoring as nutrients may stay unchanged under tourism stress while algal Trichostatin A or chlorophyll increased substantially (Hadwen and Bunn, 2004). Only a few studies have examined the effects of tourism on other components of aquatic ecosystems, such as macroinvertebrate and fish assemblages. Kasangaki et al. (2006) found a significant decrease in the species richness of sensitive aquatic insects and water quality in tropical streams affected by tourism and other human disturbances. Escarpinati et al. (2011) reported higher variability among macroinvertebrate samples collected from tourism-impacted river sites than undisturbed sites. Escarpinati et al. (2014) also observed immediate loss of taxa richness and abundances of macroinvertebrates in streams trampled by tourists.
Assessing aquatic assemblages in parks and conservation areas is critical for sustaining both ecosystem health and tourism. Furthermore, lakes and streams in protected areas are often the least-disturbed in their respective regions, and thus can serve as the regional benchmarks for assessing the biological conditions of other water bodies and act as regional refuges of many rare and endangered species for biodiversity conservation. Most scenic water bodies are oligotrophic and highly sensitive to human disturbances, particularly nutrient inputs, to which booming tourism can contribute significantly (Hadwen and Bunn, 2004). However, there is general lack of information on aquatic ecosystems, such as species composition and their spatial–temporal variations in relation to environmental factors, in protected areas in most developing countries, where tourism is growing fast (Balmford et al., 2009). The general low level of disturbances in protected areas also likely presents a challenge to routine monitoring methods (e.g. 300?500 fixed counts from benthic samples only, see Barbour et al., 1999) and national or regional indicators, which are intended for rapid evaluation of biological impairment caused by broad-scale or/and general disturbances, e.g. pollution and land-use change (USEPA, 2006 and Cao and Hawkins, 2011).

bax inhibitor Measurement of cytokine release Following incubation l of media

2.5. Measurement of cytokine release
Following incubation, 90 μl of media was collected from the top (apical) and 600 μl collected from the bottom (basolateral) wells from the first group of IEC monolayers. An additional 135 μl of assay media was added to the media collected from the apical side to produce a total volume of 225 μl of which 200 μl was used to determine IL-6 and IL-8 concentrations. All samples for these and all other analyses described in the subsequent sections were stored at ?20 °C but were analyzed within one week from when they were collected. The concentrations of IL-6 and IL-8 from thawed apical and basolateral supernatants were measured in separate ELISA assays using ELISA kits from R&D Systems, Inc. (Minneapolis, Minnesota, USA). Following concentration measurements by ELISA, the concentration units (pg/ml) were corrected for the different volumes in the apical and basolateral well and reported as pg/side.
2.6. Measurement of TEER
After harvesting cultured supernatant from the first group of IEC monolayers treated with the different bax inhibitor or controls overnight, the inserts were washed two times with assay media and equilibrated in assay media for an additional 30 min at 37 °C, 5% CO2. TEER was measured in duplicate for each well using the previously discussed voltmeter.
2.7. MTT cytotoxicity assay
After harvesting culture supernatants and recording TEER measurements the assay media was discarded from the top well and Transwell™ inserts were transferred to an empty 24-well plate. The conversion of water soluble MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) to an insoluble formazan for each IEC monolayer was determined using Vybrant? MTT Cell Proliferation Assay Kit (Invitrogen).
Following overnight incubation with the indicated proteins or controls at 37 °C, 5% CO2, 7.1 μl of apical supernatant and 42.9 μl of basolateral supernatant were collected from each well from the second group of IEC monolayers. These samples were combined for measurement of the amount of lactate dehydrogenase (LDH) released. Transwell™ inserts were transferred into a 24-well plate containing 0.1% TritonX-100 for lysis of IEC monolayers and measurement of cell associated LDH. LDH activity was determined using an In Vitro Toxicology Assay Kit for Lactate Dehydrogenase (Sigma) and reported as percentage of LDH release from total LDH concentration.
Following removal of the Transwell™ inserts for LDH analysis, two individual 10 μl volume aliquots were sampled and measured from each basolateral well and placed into 3 ml scintillation fluid (BD Biosciences, San Jose, California, USA). Duplicate 10 μl samples of the start solution used to treat the apical surface of the second group of IEC monolayers were also placed into 3 ml of scintillation fluid each. Counts per minute (cpm) were measured in each sample using a liquid scintillation counter (LS 6500 Multi-purpose Scintillation Counter, Beckman Coulter, Inc., Brea, California, USA) to quantify [3H]-inulin. The percentage of [3H]-inulin movement from apical to basolateral wells was calculated using the amount of [3H]-inulin that crossed the IEC barrier overnight divided by the amount of [3H]-inulin added to the apical surface at the start of the experiment.

With respect to the clinical chemistry parameters evaluated DHA EE

With respect to the clinical chemistry parameters evaluated, DHA-EE-related effects were limited to generally dose-related reductions in cholesterol and triglycerides levels. These effects were not unexpected due to the known lipid-lowering action of DHA and EPA (Harris, 1989, Saito et?al., 2012, Ryan et?al., 2009 and Kastelein et?al., 2014). The observed clinical chemistry changes support -the overall beneficial effects of DHA supplementation on the lipid profile in mammals. Although dogs are “high-density lipoprotein mammals” and resistant to atherogenesis (NRC, 2006), the observed lipid lowering effect is important for applications of DHA and EPA in human clinical practice.
In conclusion, the present study provided evidence of safety for DHA-EE administered at doses up to 2000 mg/kg bw/day to beagle dogs for up to 9 months. DHA-EE administration did not have any toxicologically relevant effects on clinical observations, hematology and clinical chemistry, necropsy and microscopic findings, as well as on ECG measurements or electrical rhythm in dogs in this akt inhibitors chronic duration study. Combined with the in vitro and in vivo genetic toxicity tests results, dog telemetry, rat respiratory and chronic toxicity studies, the extensive toxicological evaluation of DHA-EE demonstrated an excellent safety profile of the purified DHA.
Sources of support
DSM (Dahms, Beilstein and Salem are DSM employees; Bonnette is employed by Charles River Labs which conducted the study).
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Benzo(a)pyrene; Catechin hydrate; Genotoxicity; Apoptosis; Histopathology
B(a)P, Benzo(a)pyrene; PMS, Post-mitochondrial supernatant; CYPOR, NADPH-cytochrome P450 reductase; mEH, Microsomal epoxide hydrolase; GPx, Glutathione peroxidase; GSH, Reduced Glutathione; GR, Glutathione reductase; GSSG, Oxidized glutathione; GST, Glutathione-S-transferase; LDH, Lactate dehydrogenase; LPO, Lipid peroxidation; MDA, Malondialdehyde; SOD, Superoxide dismutase; ROS, Reactive oxygen species; CAT, Catalase; QR, Quinone reductase
1. Introduction
Polycyclic aromatic hydrocarbons (PAHs) ubiquitously present as pollutants in the environment are carcinogenic in nature (Jernström et al., 1996). Benzo(a)pyrene [B(a)P] is classified as one of the PAHs by IARC. B(a)P is mostly produced from cigarette smoking or car exhausts and act as a potent carcinogen which leads to lung carcinogenesis (Humans, 2010 and Yeo et?al., 2014). Due to lipophilic nature it is easily absorbed through biological membranes and bioactivated via a variety of metabolic pathways (Jacques et al., 2010). B(a)P binds with the aryl hydrocarbon receptor (AHR) and get activated, which induces the transcription of many genes that are involved in its metabolism, including cytochrome P450 1A1 (CYP1A1). It is metabolized into epoxide which induces DNA adduct formation and reactive oxygen species (ROS) production in cells (Miller and Ramos, 2001). Due to the formation of excessive free radicals, this in turn reacts with lipids causing lipid peroxidation. An enhancement in ROS also disrupts the redox homeostasis which results in an oxidative stress (Toyokuni et al., 1995). Therefore, maintaining ROS homeostasis is critical for normal development and survival. Regular exposure of agents that damage DNA may lead to chronic inflammation which has been associated with the development of several pathological conditions such as emphysema, pulmonary fibrosis, chronic obstructive pulmonary disease (COPD) and lung cancer (Baumgartner et?al., 1997, Jahangir and Sultana, 2008 and Young et?al., 2009). In-vitro and in-vivo studies have shown that B(a)P induces genetic mutations, chromosome damage and single strand breaks in DNA ( Alvarez-Gonzalez et?al., 2011 and MacLeod et?al., 1991). In the light of earlier studies and literature, B(a)P shows detrimental effects on lungs in the short-term exposure and thus established as a model to study adverse effects on pulmonary system ( Baumgartner et?al., 1997 and Qamar et?al., 2012).

In contrast to red and processed

In src inhibitors to red and processed meat (which is mainly produced from red meat), white meat has not been associated with increased risks for CRC (Larsson and Wolk, 2006). This difference was proposed to be mainly due to the absence of endogenous formation of intestinal carcinogenic NOCs. In controlled dietary intervention studies, intake of red meat, but not white meat, has been shown to increase endogenous formation of NOCs in a dose-response manner (Norat et al., 2002). It has been suggested that this effect is mediated by heme iron, which is highly abundant in red meat (Cross et al., 2010).
We must bear in mind that the main determinant of the reddish color of the flesh, and therefore responsible for its classification as red meat, is the concentration of myoglobin, which can be up to 2% in the case of ox or lamb meat, and much lower in pork and veal (Carr et al., 2016b). Thus, if the potential carcinogenic effect of red meat is driven by the above mentioned carcinogenic potential of the heme iron, the risk might vary according to red meat subtype consumed. However, studies evaluating individual associations between specific red meat subtypes and risks for CRC are scarce and with controversial results. Nonetheless, a recent meta-analysis has summarized the overall epidemiological evidence related to meat subtypes, and has reported that beef and lamb consumption is associated with an increased CRC risk (RR 1.11, 95% CI 1.01 to 1.22; and RR 1.24, 95% CI 1.08 to 1.44, respectively), while no association was observed for pork (RR 0.95, 95% CI 0.78 to 1.16) (Carr et al., 2016b).
Because CRC risk factors, clinic pathological features, incidence rates, stage at diagnosis, prognosis, and response to risk factors and to treatment may vary across anatomic sub-sites, spinal cord is necessary to evaluate if the association between meat intake and CRC is homogenous in all CRC sub-sites. To date, evidence on the relation between red meat and CRC risk by sub-site location is scarce. In fact, although only a limited number of studies have investigated such a relation (i.e. rectal vs. proximal, defined as extending from the cecum up to the splenic flexure, and vs. distal colon, that includes the descending and the sigmoid colon and the recto-sigmoid junction). In a meta-analysis of three cohorts by Larsson and Wolk (2006) the authors observed that associations for red meat tend to be stronger for rectal cancers, whereas processed meat associations were stronger for distal colon cancers (Larsson and Wolk, 2006). Nevertheless as mentioned the data are scarce and future studies are needed to elucidate potentially distinct mechanisms underlying the relationship between the intake of processed meat and unprocessed red meat and CRC subtypes risk. In addition, it has been recently suggested that the duration (time) of the high meat consumption could be a relevant factor in the association between CRC and meat intake although the results are inconclusive, recent processed meat intake within the past four years has not been associated with risk of distal colon cancer (Bernstein et al., 2015). However, more studies are necessary because scarce information exists regarding time of intake and CRC risk.

Behavior of individually identified eels

Behavior of individually identified eels was quantified by integrating PIT detection data at all four antennas with behaviors recorded at the bypass entrance via underwater video. Behavioral events were classified as Approach (eels detected by the entrance PIT antenna or observed within 0.5 m of the bypass entrance, but not entering the bypass pipe), Entry–Reject (entering the bypass pipe but exiting back upstream out of the bypass pipe), or Entry–Pass (entering the bypass pipe and passing downstream through the entire airlift system). Approach and Entry–Reject events were considered as “avoidance” behaviors; i.e., eels encountered the entrance structure (or associated accelerating flow) but avoided entrainment into the bypass pipe. Kruskal–Wallis one-way analysis of variance on ranks was performed to compare the effect of airlift entrance velocity on median number of approach or Entry–Reject behaviors per eel before passing, number of Entry–Pass events per eel during the entire 3 h test, and number of Entry–Pass events per hour for the first, second and third hour of the test. Cox\’s proportional hazard regression was used to test for differences in approach and passage rates (percentage of first approach and first pass events over time) under each of the treatment velocities; dependent variables were time to approach and time to pass. Eels that squalene epoxidase failed to approach or pass during the trial were included as censored observations, with time set to trial duration (3 h). Eel length and eye indices were also compared to behavioral and passage metrics using Pearson product-moment correlation.
3. Results
3.1. Airlift hydraulics and physical characteristics
Water velocity at the airlift entrance increased nonlinearly with airflow (Fig. 2); correlation between the input airflow and water velocity was high and significant (Pearson’s R = 0.99; P < 0.0001). Nominal average cross-sectional water velocities for biological tests were established at the bypass entrance as 0.91, 1.22, and 1.52 m s?1 at airflow rates of 1.68, 2.24, and 3.64 m3 min?1 respectively, with corresponding water flows through the airlift of 0.066, 0.089, and 0.111 m3 s?1. In small (100 mm diameter) airlifts, a gas-liquid ratio exceeding a threshold of 0.1 results in transition from bubbly to bubbly-slug flow (Todoroki et al., 1973). At the test airflow rates, the gas-liquid ratio was 0.424, 0.420, and 0.547, respectively; therefore all biological tests were assumed to have been conducted in the bubbly-slug flow regime, although bicarbonate ions could not be verified in the larger diameter airlift. Passive particle and passive drifter transit times between PIT antennas are shown in Table 1. Measured squalene epoxidase transit times of the passive drifter at an entrance velocity of 1.22 m s?1 were comparable to calculated passive particle transit times.
Relationship between airflow (ln transformed) and within-airlift water velocity for 20.3 cm and 30.5 cm diameter (entrance) pipe sections.
Figure options

View the MathML sourcedqdt k qe qt

View the MathML sourcedqdt=k2(qe?qt)2
View the MathML sourcetqt=1k2qe2+tqe
where k2 is the rate constant of pseudo-second-order kinetic model (g?1 min?1) and qe is the adsorption capacity at equilibrium, which were determined from the slope and intercept of plot t/qt versus time, respectively. Pseudo-first-order and pseudo-second-order rate constants and corresponding R2 values are shown in Table 3 and plots are shown in Fig. 4(a–d). The adsorption capacity (qe) at equilibrium obtained experimentally does not correlate with the pseudo-first-order kinetic model. The R2 value calculated was also low, leading to the assumption that the pseudo-first-order kinetics model did not explain the adsorption experimental data. The experimentally determined qe values and R2 correlated well with the pseudo-second-order kinetic model, the calculated qe values were in good agreement with experimental qe values and the corresponding R2 value was also higher in comparison to pseudo-first-order kinetic model, which suggests that Ni(II) and Zn(II) adsorption onto FBC followed pseudo-second-order kinetic model ( Iqbal et al., 2013, Manzoor et al., 2013 and Ullah et al., 2013).
Pseudo-first order, Pseudo-second order and Intraparticle direct renin inhibitors models parameters for adsorption of Zn(II) and Ni(II) ions onto composite (fungal with bentonite).
Metal Pseudo-first order Pseudo-second order Intraparticle diffusion model
Table options
(A) Ni(II) pseudo-first-order plot, (B) Zn(II) pseudo-first-order plot, (C) Ni(II) pseudo-second-order plot, (D) Zn(II) pseudo-second-order plot, (E) Ni(II) intraparticle diffusion plot and (F) Zn(II) intraparticle diffusion plot.
Figure options
3.8.1. Intraparticle diffusion model
Weber-Morris intraparticle diffusion model was tested for the evaluation of adsorption mechanism which describes the diffusion mechanism (Weber and Morris, 1962) and relation for this model can be seen in Eq. (12).
Where, “Kid” is the intraparticle diffusion rate constant and “C” is a constant, which gives an idea about the thickness of the boundary layer and can be calculated from the plot between qt and t0.5. Generally, adsorption mechanism is presented by transport of the adsorbate to the external surface of the adsorbent, diffusion within the pores of the adsorbent and adsorption rate at the interior sites (Ofomaja, 2010). The curve obtained in case of Ni(II) and Zn(II) are shown in Fig. 4(e and f), respectively and data is depicted in Table 3. It is clear from C and R2 values that the Ni(II) and Zn(II) adsorption onto FBC proceeded by surface adsorption instead of intraparticle diffusion. The intercept C of the linear portion of the intraparticle diffusion curve gives an idea about boundary layer thickness, larger the value of the intercept, greater is the boundary effect ( Kannan and Meenakshisundaram, 2002). These results are comparable with the previous reports for metal ions adsorption onto different biomasses ( Noeline et al., 2005, Ofomaja, 2010, Selatnia et al., 2004 and Yadava et al., 1991).

This paper will summarise past work

This paper will summarise past work and will attempt to show that fouling in falling-film evaporators can be minimised by appropriate attention to design.
2. Preheating
To obtain suitable functional properties in the final milk powder, the milk is preheated at temperatures from 72 to 120 °C (Singh, 2007) using plate heat exchangers, shell and tube heat exchangers and/or direct steam injection. Given the high temperatures required, the build-up of deposit in these heat exchangers can be significant. Murphy et al. (1999) showed the tubular preheaters operating in the range 45-65 °C were a significant source of thermophilic bacteria, and that the number of bacteria could be significantly reduced by replacing the tubular heat exchanger with direct steam injection. However such a change is not effective for the orexin efficiency of the evaporator. They concluded that a short mid-run clean was effective in controlling growth. Similarly Scott et al. (2007) found significant growth of thermophilic bacteria after about nine hours of operation. Westergaard (2004) discussed a number of options to avoid fouling and thermophile growth in preheaters. These include the use of two preheating units in parallel to enable changeover and cleaning during operation, or direct contact heaters which avoid heat surfaces and hence minimise biofilm growth.
3. Tube wetting
The minimum wetting rate, defined as the mass flow rate per unit circumference (kg s?1 m?1), is the minimum liquid flow rate required to ensure complete wetting. The selection of average wetting rates for the evaporator tubes is a key design step, and they can be determined using mass balance calculations. If the wetting rate is too low, each evaporator effect can be split into two or more passes. An example is given by Morison and Hartel (2007). Ward (1994) stated “the evaporator designer normally selects the tube wetting rate based on personal experience”, but previous and subsequent research discussed below shows that designers can use a range of data and correlations and not rely solely on personal experience.
3.1. Minimum wetting rates
The minimum wetting rates for different liquids are subject to some uncertainty. An early significant work on film wetting was that of Hartley and Murgatroyd (1964) which established the physical phenomena involved and suggested equations for determination of the flow rates required to achieve complete wetting. Paramalingam et al. (2000) determined minimum wetting rates for concentrated milk on flat plates, and they also confirmed that the apparent overall heat transfer coefficient in a commercial evaporator decreases if the flow rate is lower than the required minimum. This would have been due to the reduction in heat transfer area caused by dry patch formation. Morison et al. (2006) used a short evaporator tube to determine the minimum wetting rates for fluids with orexin a wide range of properties.