27 ± 1 85 to 265 47 ± 120 86 nm after 21 days at 4 °C, respective

27 ± 1.85 to 265.47 ± 120.86 nm after 21 days at 4 °C, respectively. They attributed the instability of the β-carotene nanoemulsions to the Brownian motion. The bixin nanocapsule suspension was also considered physically stable regarding the mean diameter during the storage evaluated by laser diffraction

(Fig. 5a) and dynamic light scattering (Fig. 5b), since no significant changes (p < 0.05) were observed in the mean diameter and the particle size distributions also remained constant, with no significant changes (p < 0.05), at 0, 28, 63, 91 and 119 days of storage. Other authors attributed to the steric effect provided by the surfactant polysorbate 80 the Selleck Kinase Inhibitor Library responsibility for the stability of this type of nanocapsule formulation ( Jäger et al., 2009 and Venturini et al., 2011). Yuan et al. (2008) studying the effects of production parameters, developed β-carotene nanoemulsions with mean diameter (z-average) ranging from 132 to 184 nm that were stable for four weeks in amber bottle flushed with nitrogen and stored at 4 and 25 °C. Tan and Nakajima (2005) verified that click here β-carotene nanodispersions prepared using only Tween 20 as the emulsifier remained stable after 12 weeks of storage at 4 °C in amber bottles. Ribeiro et al. (2008) reported that the β-carotene

nanoparticles prepared using poly-d,l lactic acid and poly-d,l-lactic-co-glycolic acid were stable over 5 months of storage at 4 °C in the dark. The decrease in bixin content during the first days of storage most likely occurred due to the

formation of free radicals in the oil (CCT) during the solubilisation of bixin in the organic phase (40 °C) (Tan & Nakajima, 2005) the presence of oxygen in the amber bottles and the bixin release from the nanocapsule structure during storage, which means free or unprotected bixin in the continuous phase (Jäger et al., 2009). From the 7th to the 28th day of storage, there was no significant variation in the bixin content (p < 0.05) ( Fig. 6). After 119 days of storage, a bixin content of 45.7 ± 1.1% was observed. next This indicates that nanoencapsulation is highly effective in inhibiting carotenoid loss during storage, although, decrease in carotenoid content has also been demonstrated. Over 12 weeks of storage at 4 °C, the residual content of β-carotene in the nanodispersions varied from 25.2% to 56% (Tan & Nakajima, 2005). Using different parameters of encapsulation, Yuan et al. (2008) produced and evaluated the stability of β-carotene nanoemulsions. After 4 weeks of storage at 4 and 25 °C, the residual β-carotene concentration ranged from 75% to 86% of β-carotene. Yin, Chu, Bobayashi, and Nakajima (2009) studied the effects of different emulsifiers on the stability of β-carotene nanodispersions. After 4 months, the content of β-carotene fell from 45.6 to 63.3%.

Only the best model

for each fruit is shown on Table 2 I

Only the best model

for each fruit is shown on Table 2. Initially, very different trends were observed for the evolution of the calibration error (RMSECV), according to the number of LVs, between the three species (Fig. 2). For passion and tomato fruits, evolution of RMSECV with Navitoclax number of LVs showed no consistent trend (Fig. 2a). The behavior of the unstressed calibration error for the passion and tomato fruits was characterized by low correlation coefficients between predicted and measured values. The best PLS model developed for the passion fruit used pre-processing multiple scatter correction (MSC) and 5 LVs which provided the lowest cross validation error of 1.62 °Brix. When the model was applied to predict the 12 internal validation samples, a low correlation (R2 = 0.63) and a high error of prediction (RMSEP% = 9.8%) were found ( Fig. 2b). For tomatoes, results were similar to the results found for passion fruits ( Fig. 2c). The lowest cross validation error (0.13 °Brix) was observed for models using 10 LVs and MSC pre-processing. When the model was used to predict the 32 internal validation samples, the prediction error was 8.85% and the correlation

coefficient was 0.52 ( Fig. 2d). However, in apricot, the relationship between RMSECV and number of LVs followed a regular profile, and a good correlation was found ( Fig. 2e). The same ratio was observed by Camps and Christen (2009). The lowest cross validation error (0.69 °Brix) was observed for models using 6 LVs Evodiamine and MSC pre-processing followed by smoothing. A high Selleck CHIR99021 correlation coefficient (R2 = 0.93) and a low prediction error (RMSEP 3.3%) were observed, when the model was used to predict the 24 internal validation samples ( Fig. 2f). Measurement of acidity-related parameters in intact fruits is notoriously

difficult (Flores, Sánchez, Pérez-Marín, Guerrero, & Garrido-Varo, 2009). Such difficulties can be observed in Fig. 3. Similarly to what was found for the soluble solids content, when the cross validation error was plotted against the number of LVs for passion fruit and tomato (Fig. 3a and c), the correlation coefficients were below 0.49 and 0.51, respectively, indicating a poor relationship between measured and predicted values for titratable acidity. The best PLS model developed for the passion fruit used pre-processing first derivative and 5 LVs, which resulted in a cross validation error of 14.69 mmol H+·100 g FW−1. When the model was used to predict the 11 internal validation samples, a low correlation (R2 = 0.49) and a high value for the error of prediction (RMSEP% = 11.4%) were found ( Fig. 3b). For tomatoes, a minor cross validation error (0.35 mmol H+·100 g FW−1) was observed for a model using 8 LVs and MSC pre-processing. When the model was used to predict the 32 internal validation samples, a prediction error of 10.43% and a correlation coefficient of 0.

, 2001), PDMS–DVB (San Juan et al , 2007) and DVB–CAR–PDMS fibre

, 2001), PDMS–DVB (San Juan et al., 2007) and DVB–CAR–PDMS fibre (Lara-Gonzalo, Sánchez-Uría, Segovia-García, & Sanz-Medel, 2008) for the extraction of THMs from water. However, many authors agree that the CAR–PDMS fibre provides the best extraction efficiency. In this study, six types of fibres were investigated to extract THMs from a 10 mL soft drink sample spiked with 10 μg L−1 of each compound with the addition of 80 μL of NaOH 6 mol L−1. The extractions were carried out in triplicate for each fibre studied. The extraction time was 10 min at 20 °C with magnetic stirring speed BMS-354825 clinical trial of 500 rpm. The extraction efficiency of THMs increased in the following sequence

of fibres: PA 85 μm < PDMS 100 μm < CW–DVB 65 μm < PDMS–DVB Palbociclib manufacturer 65 μm < DVB–CAR–PDMS

50/30 μm < CAR–PDMS. The CAR–PDMS fibre clearly shows superior extraction efficiency in relation to the other fibres. This superiority can be attributed to the porous phase of carboxen that captures small analytes contained between two and twelve carbon atoms. Comparing with the second better fibre, CAR–PDMS is 2, 3 and 1.5-folds better than DVB–CAR–PDMS for CHCl3, CHCl2Br and CHClBr2, respectively. The CAR–PDMS fibre was selected and applied to other experiments. The extraction temperature effect on the THM extraction was performed in the range between 10 °C and 80 °C. Increasing the extraction temperature increases the diffusion of the analytes to the fibre surface. Consequently, the time necessary to reach the equilibrium of partition between the sample and extractor

phase is reduced. However, the sorption process is exothermic and high extraction temperatures can decrease the partition coefficient decreasing the mass of analytes extracted at equilibrium. Generally, an optimum extraction temperature can be observed during the SPME procedure (Budziak et al., 2007 and Jia et al., 1998). The best conditions are 20 °C for CHCl3, 30 °C for CHCl2Br, 50 °C for CHClBr2 and the response was similar Racecadotril for CHBr3 between 30 °C and 60 °C, already considering experimental errors. It can be observed that after 60 °C, the efficiency of THM extraction decays rapidly. For further studies an extraction temperature of 30 °C was selected for all analytes. The extraction of analytes can be affected by headspace volume in which each compound diffuses. The theory of SPME dictates that for greater sensitivity for the headspace extraction mode, the volume of the gaseous phase should be minimised. In this study the headspace volume was in the range of 15–39 mL (sample volume range of 25–1 mL) using 40 mL vials. The soft drink sample was spiked with 10 μg L−1 of each target analyte. Different volumes of NaOH were added according to the volume of the sample studied (until pH 6.1). The best extraction condition for all the THMs occurs using 20 mL of headspace volume (sample volume of 20 mL).

In 2010, 4 4% of women said they had not had antenatal visits or

In 2010, 4.4% of women said they had not had antenatal visits or examinations for financial reasons. For this pregnancy, 2.3% of the women had had in vitro fertilisation and 2.3% ovarian induction alone (Table

2). The mean prepregnancy weight of women increased continuously over the study period, and the percentage with moderate to severe obesity rose from 6.0% in 1998 to 9.9% in SP600125 2010. The proportion of women who smoked during the third trimester of their pregnancy fell from 24.8% in 1998 to 17.1% in 2010. In 1995, 64.7% of the nulliparas attended antenatal classes, and in 2010, 73.2%, but this trend was not regular over the study period. Moreover 21.4% of the women had the recently recommended ‘4th month appointment’. This appointment

is intended to allow each woman to meet at a relatively early stage with a midwife or doctor, who would identify any problems she has or is likely to encounter and provide her with important prevention information to optimise her health and the baby’s. The mean number of antenatal visits was 9.9 (± 3.7) in 2010. Although this number was higher than for the preceding survey the question in 2010 specified “including visits to the emergency department” (Table 3). Almost all the women had seen medical staff at their maternity unit or the obstetrician who delivered their baby at least once before labour. The rate of late filing of the medical pregnancy certificates (which should be submitted to the health insurance fund) increased over time, and this difference was substantial and significant between 2003 and 2010. The healthcare provider

seen for the certification selleck products and for the rest of antenatal care was most often an obstetrician. Nonetheless, compared with 2003, women saw midwives much more often in 2010, either at the maternity ward or in private practice. The mean number of ultrasound examinations increased regularly from 4.0 (± 1.9) in 1995 to 5.0 (± 2.5) in 2010 (Table 4). Changes in the questions about HIV screening over the years make it difficult to analyse changes in practices; nonetheless, we found that the percentage of women who did not know if they had had this examination increased slightly. Compared with 2003, women in 2010 were much more familiar with nuchal translucency C59 concentration measurements and reported less frequently that serum screening for Down syndrome was not offered. Finally the amniocentesis rate was 9.0%; it fell notably between 2003 and 2010, especially for women aged 38 years or older. After an increase between 1995 and 1998, antenatal hospitalisations dropped slightly between 1998 and 2003, and then remained stable between 2003 and 2010 (Table 5). On the other hand, the duration of hospitalisation decreased regularly for the entire period. Gestational diabetes required treatment for 6.8% of the women, by insulin for 1.7% and by diet for 5.1%. Threatened preterm delivery was diagnosed and led to hospitalisation in 6.5% of the women.

Thus, we would expect sandy, poorly developed (sesquioxide- and c

Thus, we would expect sandy, poorly developed (sesquioxide- and clay-poor) to saturate with N fairly quickly compared to finer textured volcanic or highly weathered soils (sesquioxide-

and clay-rich) Secondly, the theories on processes of organic matter derived N input to soil are poorly known. Far more studies have focused on forest floor organic matter turnover Selleckchem Saracatinib and release of N but there is evidence that most soil organic matter (and therefore N) increases are fine root derived (Oades, 1988). The vast majority of forest ecosystems contain less N than would be expected from even modest inputs of N from atmospheric deposition and N fixation. We suspect that the reason for this is periodic fire, which can remove substantial amounts of N by volatilization, and can occur even in humid

ecosystems during droughts. Research over the last two CP-690550 molecular weight decades has suggested that N retained within forest ecosystems is not slowly bled away by leaching after inputs have been reduced, but remains within the system unless it is harvested or burned. Cases of occult N inputs – where apparent net increments of N exceed known inputs – still occur but not in all cases. We suspect that unmeasured inputs by dry deposition, non-symbiotic N fixation, and weathering of N from sedimentary rocks may account for this occult N when it occurs. This research was supported by the National Science Foundation, the U.S. Forest Service, and the Nevada Agricultural Experiment Station, University of Nevada, Reno. “
“The authors regret that some data in Table 2 contained incorrectly labelled data and should be replaced with the table below. The authors

would like to apologise for any inconvenience caused. “
“The cognitive approach to Artificial Intelligence emerged in the early days of the discipline: it borrowed its original inspiration from the methodological approach developed by scholars in Cybernetics. In this setting, the computational simulation Pomalidomide nmr of biological processes played a central epistemological role in the development and refinement of theories, and in the realization of intelligent machines. Likewise, thanks to a computational approach to Cognitive Science, intelligent systems have been proposed based on plausible models of human cognition and computational cognitive models and architectures, and aimed at a deeper understanding of human thinking. In the last few years, these approaches gained new consideration in wide areas of research such as Knowledge Representation and Reasoning, Robotics, Machine Learning, Bio-Inspired Cognitive Computing, Computational Creativity and further research fields that are now targeting Human Level Intelligence (also called AGI, Artificial General Intelligence) in computational artifacts. This special issue is intended to provide a fair overview of the research being carried out in the interdisciplinary area of cognitively inspired AI systems.

fsl orst edu/lemma) The 30 m pixel GNN datasets derive from a pr

fsl.orst.edu/lemma). The 30 m pixel GNN datasets derive from a process integrating regional inventory field plots, environmental gradients, and Landsat imagery. Within Oregon and Washington the GNN datasets have become a common regional-scale measurement of present-day forest conditions (Moeur et al., 2011). Due to the Landsat imagery used to produce the GNN datasets “present-day” is year 2006 within southwest Oregon, eastern Oregon Cascades, and eastern

Washington Cascades and year 2000 in all other map zones (Fig. 1). To compare present-day forest vegetation to the NRV reference conditions, we mapped the current distribution of s-classes for each biophysical setting using GNN data. S-class mapping was based upon C59 selleckchem tree canopy

cover and tree size thresholds provided for each s-class in the biophysical setting model descriptions (Appendix A.2). Quadratic mean diameter has been used in previous applications of the GNN data to classify forest size class (Moeur et al., 2011). However, simply using the GNN dataset’s reported quadratic mean diameter to represent forest stand size class has been found to over represent the abundance of large and extra-large size class stands in eastern Oregon and Washington forests (M. Hemstrom and K. Mellen-McLean personal observations). Consequently, we used total canopy cover accounting for canopy overlap, and a combination of canopy cover and trees per acre by size class to classify GNN data into successional stages. We first applied a customized decision process developed to assign one of the 7 regional forest stand size classes (USDA Forest Service, 2004) to each pixel based on the GNN plot-related attributes Rolziracetam of trees per acre by diameter class and canopy cover by diameter

class (Appendix A.4). We then assigned biophysical setting s-classes by size class and total canopy cover. The first two steps of the size class decision process sets a density threshold for the number of trees >50.8 cm or >76.2 cm diameter breast height in order for a pixel to be classified as large or extra-large, respectively. These threshold values vary by biophysical setting from approximately 20–50 trees per hectare and were determined by US Forest Service Pacific Northwest Region Ecology Program specialists. We evaluated our “GNN size class decision process” using stand exam and forest inventory and analysis plot data from the Mahleur National Forest. Estimated abundance of large and very large size classes using our “GNN size class decision process” were very close to levels based stand exam and plot data (76,897 ha. versus 74,244 ha respectively; M. Hemstrom unpublished data).

Previous research has shown (a) that parents referred to

Previous research has shown (a) that parents referred to

mental health settings for their children’s externalizing problems are often resistant to parenting interventions (e.g., Nock and Kazdin, 2005 and Patterson and Chamberlain, 1994) and (b) that parents recruited into prevention-based interventions can be difficult to engage and are less successfully treated than families who are seeking treatment (Dumas et al., 2007 and Weisz et al., 2005). In describing the use of PMT-based strategies in IBHC, we make several assumptions. The first set of assumptions addresses the unique characteristics of many primary care patients that impact the adaptations we recommend, while the second set addresses assumptions about the knowledge and skills of the practicing clinician. Regarding assumptions for patients, we assume Selleck Selinexor patients who present to primary care GPCR Compound Library cell line settings with behavior problems have not been experiencing the problems for a prolonged period of time; rather, caregivers

may have only recently noted changes in their child’s behavior that are of concern. In our experience, there are times when parents were not yet thinking of seeking help for these newly emerging problems, but the help seeking is prompted when a pediatrician asks about the child’s behavior. In contrast to parents who are seeking specialty mental health care for a child’s behavior problems, and who may be exasperated with the child and frustrated by numerous unsuccessful attempts at change, the patients we often see in primary care are agreeable to interventions and exhibit high efficacy for their implementation. Second, we assume parents are invested in their child’s care, as evidenced by their having taken time to bring the child to the

doctor’s office. In comparison to primary prevention programs (where parents were not seeking help at all), parents may be more willing to engage with a BHC to address child problems. Third, we assume many parents who receive services for externalizing behavior problems from a BHC in a primary care setting will have had little to no contact with specialty mental health providers. Regarding assumptions for behavioral health clinicians, we first assume that they Tyrosine-protein kinase BLK have prior experience with and knowledge of PMT, as well as more general competence in using cognitive-behavioral approaches to working with children and families. Materials and recommendations offered here are not basic instructions in the delivery of PMT, but are guides for implementing PMT-based strategies in IBHC. Second, we assume that behavioral health clinicians will have assessed for the appropriateness of using PMT-based interventions in a given case. As shown in Figure 1, patients are first triaged to determine appropriateness of being seen in an IBHC setting.

Despite the major progress made in HBV therapy, there remain vari

Despite the major progress made in HBV therapy, there remain various challenges. One is cost, about $60,000–$72,000 for 5-year TDF therapy. Pharmacy claims show that adherence is a problem; doses used are less than doses prescribed. There is a lack of accurate prediction of how HBV disease will progress in individuals. HBV DNA can be integrated into the human genome at an early stage of infection. Fortunately, the integrated

viral DNA is usually not the complete viral genome and patients, who achieve HBsAg loss, rarely relapse. Stefan Mehrle, TSA HDAC in vitro University of Heidelberg, Germany (Stephan Urban, Head of Hepatitis B Research Group, University of Heidelberg, was originally scheduled to give this presentation). Some chronic HBV-infected subjects are co-infected with hepatitis delta virus (HDV). This is a defective virus that replicates only in the presence of HBV. Current antiviral drugs do not inhibit HDV. Recently, heparan sulphate proteoglycan (HSPG) has been shown to be essential for binding both HBV and HDV to primary hepatocytes. In 2012, human sodium taurocholate co-transporting polypeptide (hNTCP) was Akt assay identified as a functional receptor for HBV and HDV. hNTCP is also designated as a solute carrier protein 10A1 (SLC10A1). hNTCP was shown to be a binding factor for the preS1 domain of the HBV L envelope protein. This interaction

was found to be essential for HBV and HDV infection. Whereas HBV replication is poor in cell lines derived from hepatocytes (e.g. HepG2 and Huh-7) in which hNTCP is usually weakly expressed, HBV replication is possible in primary human hepatocytes. The critical discovery was that over-expression of hNTCP in HepG2 or Huh-7 cells conferred susceptibility to HBV and HDV infection. Myrcludex-B is a lipopeptide derived from amino acid residues 2–48 of the preS1 region of the HBV L protein. Because it quickly (within 5 min) targets the liver, it is being developed for liver imaging and for drug targeting. It also

acts as an entry inhibitor for HBV and HDV by Glutamate dehydrogenase interrupting binding between the HBV L protein and hNTCP. It specifically inhibits hNTCP-mediated taurocholate transport but the effect on HBV replication is much greater. Myrcludex-B activity has been investigated in vivo using SCID mice reconstituted with human hepatocytes. With prophylactic treatment, not one infected hepatocyte was seen. Following therapeutic treatment, at week 6 post-infection, there were a few isolated infected cells. After the end of therapy, the infection seems to spread but only to neighboring cells. Myrcludex-B has been synthesised on a 100 g scale. Toxicology evaluation in 3 chimpanzees has been completed and clinical trials have been initiated. In a Phase I trial using a 20 mg dose, myrcludex-B was well tolerated. Results of a further Phase I trial are due to be reported later this year (2014). A dose-ranging Phase II trial has been started.

This highlights the remarkable plasticity of hexameric structures

This highlights the remarkable plasticity of hexameric structures. Following recognition and binding to the origin of replication, melting of the DNA helix surrounding the origin, and oligomerization into two hexamers at the origin of replication, the LT-ag then recruits the cellular DNA replication factors: RPA, topoisomerase I and polymerase

α primase. Type I topoisomerases are essential to relieve supercoiling stress as the strands unwind (Lin et al., 2002). Podophyllotoxin (Condylox) is a topoisomerase I inhibitor in clinical use against HPV lesions to block DZNeP clinical trial viral DNA replication (Stern et al., 2012). As podophyllotoxin is also active against host chromosomal replication, it is cytotoxic. Following

the initiation events, the clamp loader, replication factor C (RFC), and the polymerase processivity factor, PCNA (proliferating cell nuclear antigen), are recruited and loaded leading to the binding and activity of DNA polymerase δ, which extends both lagging and leading strands. After PyV DNA replication and the expression of late structural proteins, new progeny virions Tyrosine Kinase Inhibitor Library cell assay are assembled and are released from the infected cell. Papillomaviruses are highly diverse and have been discovered in a wide array of vertebrates and their host range includes all amniotes (Rector and Van Ranst, 2013). Papillomaviruses are highly host-restricted, and cause abortive infections in non-host species. The HPV life cycle is closely linked to the differentiation state of the epithelial

cells and the initial step involves the infection of keratinocytes in the basal layer of squamous epithelia (Fig. 8) (Stanley, 2012, Carbohydrate Chow et al., 2010, Duensing and Munger, 2004 and zur Hausen, 2002). Similarly to PyVs, HPVs do not encode for their own DNA polymerases but they encode for viral proteins (i.e. E1 and E2) that are required for viral genome replication during the HPV productive cycle (Fig. 9A) (D’Abramo and Archambault, 2011, McBride, 2013 and Bergvall et al., 2013). E1 is the most highly conserved HPV protein and the only one with enzymatic activity. E1 is the replicative helicase of HPV and is essential for viral replication and pathogenesis. Both LT-ag and the E1 protein are structurally related members of the helicase superfamily III (SF3). E1 binds to the origin of replication together with E2 protein. In fact, the E2 protein assists and directs faithful viral origin recognition of E1 while E1 is the replicative DNA helicase, melting the DNA around the origin of replication and establishing itself as a double hexameric helicase. The formation of the E1–E2-origin of replication complex involves not only the binding of E1 and E2 to specific viral DNA elements in the origin of replication but also a protein–protein interaction between the N-terminal transactivation domain of E2 and the helicase/ATPase domain of E1 (Fig. 9B).

10 Despite the amount of uncertainty placed on these volume–weig

10. Despite the amount of uncertainty placed on these volume–weight calculations it appears

OSI-906 concentration that published C-factors nonetheless underestimate the effects of urban forest cover in the region ( Fig. 11); however, in order to elucidate an appropriate C-value range for the area, an assessment of the contributions to the pond’s sediment budget unaffiliated with sheet and rill erosion from the surrounding landscape is required. The following two sources offer explanation for the inclusion of materials not accounted for by the USLE, which contribute to the overestimation of pond sedimentation due to inter-rill and rill processes: (1) sediment transported into the pond by anthropogenic means, and (2) gully erosion from surrounding hillsides. The pond is the final resting place for 17-AAG chemical structure all materials derived from surrounding hillslopes and the footpath. A small source of error that could explain some of the variance between field and numerical model results is presented by the unknown factors associated with the upkeep of the footpath around the pond. Sand and gravel are replaced here on a regular basis as hillslope runoff not only carries materials from the slopes,

but also from the footpath into the pond. Evidence for this process is found in cores, which contained scattered pebbles found concentrated on the footpath. Since no records exist that would allow for quantification of this sediment source, the extent to which very these materials

offset measurements cannot be pursued; however, based on an assessment of collected sediment cores and a comparison of pond-sediment volume against path dimensions, it is assumed this contribution is negligible. It is likely that gullies are a significant contributor to the USLE model deviation; however, they provide an unquantified volume of sediment to the pond’s budget and pursuing their contribution from a process-oriented perspective would be time-consuming. It is estimated based on field reconnaissance of gully dimensions (width and depth) and their extent (derived from the flow-accumulation model) that the volume represented by gullies along the steep slopes north of the pond corresponds to ∼100 m3. This Gully-volume estimate is an order of magnitude smaller than the volume of sediment emplaced into the pond (∼6228 m3) and therefore would do little to close the gap between USLE model estimates of inter-rill and rill erosion and quantified pond sedimentation (Fig. 11). Regardless of how much gullying and anthropogenic contributions may add to the pond’s sediment budget, evidence suggests that urban forest cover promotes high erosion rates, which translate to high sediment flux and deposition within the pond. This is a function of the absent sediment storage anywhere along route within the watershed (Fig. 3); the study area thus provides a suitable location for a qualitative assessment of the C-value for this land-cover type.