More systematic exploration and collection of pine germplasm was

More systematic exploration and collection of pine germplasm was done in Central America and Mexico between the late 1950s and the early 1970s, focusing on Pinuscaribaea, Pinusmaximinoi, Pinusoocarpa, Pinusgreggii, Pinustecunumanii and P. patula.

Subsequently, P. caribaea and P. oocarpa, for example, have been introduced to 79 and 34 countries, respectively ( Table 1). The past germplasm PI3K inhibitor transfer patterns of tropical hardwoods are more diverse when compared to the above-discussed categories of species. Some tropical hardwoods were introduced for production purposes outside their natural ranges several hundred years ago, long before systematic R&D efforts started. More recently, however, germplasm of several tropical hardwoods was first transferred for R&D, and the results of this work then created interest and demand for further transferring germplasm for production purposes. Tectona grandis is a well-known example of the first category of tropical hardwoods. The large-scale transfer of its germplasm from Asia to other continents started more than one hundred years ago. Today, the species is estimated to be planted in a total

of 65 countries outside of its native range ( Table 1). Transferred germplasm of T. grandis originated from multiple sources and this contributed to the development of landraces in Africa and Central America. The origins of selleck compound these landraces are poorly understood, but historical records and genetic studies have shed some light on the possible routes of introduction, and the likely sources of germplasm. In Africa, it appears that T. grandis was first introduced to Tanzania at the end of the 19th century, and from there to other countries in East and (later) West Africa. The African landraces are reported to originate from multiple and rather diverse seed sources in India, Myanmar and possibly Java ( Wood, 1967). These landraces have a relatively high level of genetic diversity ( Kjaer and Siegismund, 1996). Phospholipase D1 No clear genetic relationship with T. grandis populations in South India has

been found ( Fofana et al., 2008), but Verhaegen et al. (2010) indicated that North India may have been an important seed source for many African introductions. Several other studies on the genetic diversity of T. grandis (e.g., Kertadikara and Prat, 1995, Shrestha et al., 2005 and Sreekanth et al., 2012) have also increased our understanding of the African landraces, but they have not been able to reveal their exact origins. In Central America, the first introductions of T. grandis occurred in Trinidad, where the seed probably originated from Myanmar and India ( Keogh, 1980). In the early 20th century, T. grandis was also planted in Panama using a small seed lot presumed to originate from India ( Keogh, 1980).

Once treated, PCR amplification mix was added to the well and amp

Once treated, PCR amplification mix was added to the well and amplification performed. The species cross-reactivity study was performed using a number of commercially available non-human DNAs. Ten nanograms of each domestic animal or microbial species was amplified in duplicate. Species samples included chicken, pig, mouse, bovine, cat, dog, rabbit, deer, horse, Escherichia coli, Enterococcus faecalis, Lactobacillus acidophilis, Streptococcus mutans, Staphylococcus epidermidis,

Micrococcus luteus, Fusobacterium nucleatum, Streptococcus salivarius, Streptococcus mitis, Acinetobacter lwoffi, Pseudomonas aeruginosa, Candida albicans, and Saccharomyces cerevisiae. Three primate species, chimpanzee (male; Coriell Institute), macaque (male; Coriell Navitoclax price Institute), and gorilla (gender unknown; privately obtained), were evaluated using 500 pg. The sensitivity selleckchem study utilized two DNA dilution series provided to all test sites. Test quantities included 500 pg, 200 pg, 100 pg, and 50 pg. An inhibitor study evaluated hematin (Sigma–Aldrich), humic acid (Sigma–Aldrich), tannic acid (Sigma–Aldrich), and EDTA (Sigma–Aldrich) titrations. Each inhibitor study site prepared its own extracted DNA, inhibitor stocks and dilutions. Two mixture series, one male–male and one male–female, were prepared and distributed. Mixture ratios included 0:1, 1:19, 1:9, 1:5, 1:2, 1:1, 2:1, 5:1, 9:1, 19:1, and 1:0 for each series. The

total template quantity was 500 pg per reaction. Concordance was performed with extracted DNA from 652 unrelated individuals from Caucasian, Hispanic, African-American, and Asian-American ethnic groups. Reaction volume studies used 1.2 mm punches of blood on Indicating FTA® cards, in addition to buccal Indicating FTA®

cards described previously. Amplification reactions were performed at 25 μl volumes on a GeneAmp® PCR System 9700 thermal cycler using a 96-well silver or gold block and max ramp rates as described in the PowerPlex® Fusion System Technical Manual [9], unless otherwise noted. The thermal cycling method for extracted DNA samples was: 96 °C for 1 min; 30 cycles of 94 °C for 10 s, 59 °C for 1 min, 4��8C and 72 °C for 30 s, followed by a 60 °C final extension for 10 min and a 4 °C soak. The cycle number and final extension hold time was modified for solid support materials due to the substantial increase in template amount with these materials. FTA® card punches were amplified for 27 cycles, swab lysates were amplified for 27 or 25 cycles, and treated nonFTA punches were amplified for 25 or 26 cycles. All amplification reactions with solid support substrates utilized a 20 min final extension. Further cycle number optimization was evaluated in a cycle number study. Within that study extracted DNA samples were amplified for 29, 30, and 31 cycles, FTA® card punches for 26, 27, and 28 cycles, and treated nonFTA punches for 25, 26, and 27 cycles.

06) The co-exposure to cigarette smoke did not increase IL-5 lev

06). The co-exposure to cigarette smoke did not increase IL-5 levels in the lung tissue or the number of IL-5 positive cells in the peribronchovascular space (Fig. 4C and D, respectively). The OVA groups showed a significant increase Alpelisib order in IL-5 levels in the lung tissue when compared with all of the other groups (p = 0.004); however, this difference could not be detected in the peribronchovascular

space, despite graphic similarities (p = 0.06). Cigarette smoke exposure did not increase eotaxin levels in the lung tissue (Fig. 4E). The OVA group showed a significant increase in eotaxin when compared with all of the other groups (p = 0.01). In contrast, an increase in IFN-γ levels in the lung tissue was observed in the OVA + CS group when compared with all of the other groups (p = 0.001) ( Fig. 4F). Fig. 5 shows a panel with the levels of IL-10 measured in the Bio-Plex assay and the numbers of IL-10-positive

cells in the Olaparib order bronchial epithelium (Fig. 5A and B, respectively). There was an increase in IL-10 levels in the CS, OVA and OVA + CS groups, with the OVA + CS group significantly different from all of the other groups (p = 0.001). The CS and OVA groups also showed significant differences compared with the Control group (p < 0.05) ( Fig. 5A). The abundance of IL-10-positive cells was also increased in the groups exposed to cigarette smoke when compared with the Control group (p < 0.05) ( Fig. 5B). Exposure to ovalbumin

resulted in a non-significant increase in collagen fiber content in the peribronchovascular area (p = 0.06 compared with the control group, Fig. 6). Only the OVA + CS group showed a significant increase of collagen fiber content in the peribronchovascular area (p = 0.001 compared with the other three groups). Panels A–D show representative photomicrographs of collagen content in the bronchovascular structures in the four experimental groups following staining Temsirolimus order for collagen fibers. The OVA + CS group showed a significant increase in the abundance of TGF-β-positive cells in the bronchial epithelium (p < 0.005 compared with the Control and CS groups, Fig. 7A). Isolated exposure to either OVA or cigarette smoke did not increase the density of TGF-β-positive cells in the epithelium. In addition, there was a strong correlation between TGF-β-positive epithelial cells and peribronchovascular collagen fiber content ( Fig. 6) in the OVA + CS group (r = 0.91; p = 0.01). The cytokine assay also showed a significant increase in GM-CSF levels in the OVA + CS group compared with all of the other groups (p = 0.004) ( Fig. 7B). Cigarette smoke exposure also increased VEGF levels, as indicated in Fig. 7C. The OVA + CS group showed a significant difference in VEGF levels compared with the Control and OVA groups (p = 0.03). The CS group showed a similar increase in VEGF levels when compared with the control mice (p = 0.01).

A total of four fibre optic sensors were tested: one sensor was d

A total of four fibre optic sensors were tested: one sensor was deployed in a femoral

artery and one in an ear check details vein in each of the two animals, to gather evidence of clot formation or other fouling. The animals were part of a separate study being performed at Charles University, Plzen, and the insertion and presence of the fibre optic sensors did not compromise those studies in any way. After intravascular deployment for 24 h, the sensors were removed, stored in a plastic tube and returned to Oxford for analysis. Each sensor was examined by scanning electron microscopy (SEM) in Oxford, both in the unused state and after 24 h of continuous in vivo deployment. SEM Energy Dispersive X-ray (EDX) analysis was performed by means of a JEOL 6480 LV SEM equipped with an Oxford Instruments ABT-263 chemical structure X-MAX80 SD X-ray detector and INCA X-ray analysis system. The analysis was performed

using EDX, which investigates the characteristic X-rays produced by the interaction between the primary electron beam and the sample. The technique identifies all elements present with atomic numbers of 5 and greater (boron) with a detection limit of approximately 0.1 wt%. In this case the analysis was carried out in Low Vacuum mode with a gas pressure of 40 Pa (using air) to prevent charging on the uncoated samples. Differences between experimental ΔPaO2 values were assessed statistically using ANOVA, followed by post hoc comparisons between conditions (IBM SPSS Statistics for Windows, Version 20.0; Armonk, NY, USA). Statistical significance was assumed at values of p < 0.05. Variables are presented as

means ± SD, unless otherwise stated. A PMMA sensor was tested for its response to the simulated RRs, together with an AL300 commercial sensor, over a five-hour period, at 39 °C. Because the blood in the test rig was heparinised, there were no concerns about blood clots forming on the sensor surface. The in-house PMMA and AL300 sensors were used to monitor continuous ΔPO2 oscillations of 45 kPa peak-to-peak amplitude, from 5 kPa to 50 kPa Edoxaban (37–375 mmHg) at simulated respiratory rates from 10 to 60 bpm, over the five-hour period. Sensor output recording were taken at 20 min and 5 h during the experiments. Fig. 1 shows PO2PO2 values recorded in vitro   by both the PMMA and AL300 sensors in response to amplitude-stable PO2PO2 oscillations at six simulated RRs in flowing blood at 39 °C. These values were recorded approximately 20 min after the sensors were immersed in blood. The response of the PMMA sensor was always faster than that of the AL300 sensor, and this was evident for all simulated RRs.

In the chronic phase, our data show that ginseng treatment very s

In the chronic phase, our data show that ginseng treatment very significantly reduced colon tumor number and load. The H&E staining histological observations support these pharmacological observations. We used HPLC analysis to determine the major ginsenosides in the AG used in this study. Previously, we evaluated the effects of another herb in the ginseng family, notoginseng,

on experimental colitis for up to 14 days. We reported that notoginseng attenuated the acute colitis [34] comparable to what was observed using AG in this study. Although the ginseng saponin profiles are different between AG and notoginseng, the two botanicals also share a number of common ginsenosides. It would be interesting to identify which is/are see more the key ginsenoside(s) responsible for the observed effects reported in these two studies. AG and Asian ginseng are two major ginseng species. These two ginsengs, especially Asian ginseng, are the most studied TSA HDAC in vitro natural products in the world [35] and [36]. It is generally accepted that the main bioactive constituents of both ginsengs are ginsenosides [37] and [38]. Over 80 ginsenosides have been identified, and nearly all these ginsenosides can be found in the two species. However, the ginsenoside profile between the two ginseng species is different, and this difference may contribute to their different pharmacological effects [18] and [35]. Of note, AG has approximately two times higher total

ginsenoside content than Asian ginseng, largely due to its obvious high levels of Rb1, Re,

and Rd [35]. Using the extract of AG, Cui et al [39] showed that the extract suppressed colon cancer associated with colitis in the AOM/DSS model. In clonidine particular, these authors investigated the molecular mechanisms of ginseng’s anticancer effects using antibody array observations on colon cells isolated at a precancerous stage. Our study also used oral ginseng administration, and it is likely that enteric microbiome plays a role in ginseng metabolism and bioavailability. After AG is ingested orally, the bioavailability of its saponins is low. This is due to incomplete absorption of the parent compounds and their conversion into metabolites by the enteric microbiome, mainly via step-wise cleavage of sugar moieties [35] and [40]. The ginseng metabolites may possess more significant pharmacological benefits than their parent compounds such as Rb1 [41], including the effects observed in this study. Because the diarrhea induced by DSS is likely to affect the activity and/or profile of enteric microbiome, AOM/DSS-induced, colitis-associated colorectal carcinogenesis may not be an ideal in vivo model to study the botanical chemoprevention of colorectal cancer in relation to the enteric microbiome. Future study should be extended to other colon cancer animal models, especially the APC mutant Min (multiple intestinal neoplasia) mice with detailed mechanisms of action [42] and [43].

A fruitbat,

A fruitbat, LBH589 Pteropus tonganus, shows significant declines in frequency, although it survived on the island. Similar impacts are recorded for marine fish and shellfish ( Butler, 2001), including measurable resource depression in several species. These impacts on the local biota were accompanied by the introduction of the Pacific rat, pig, dog, and chicken. Pig husbandry became important during the island’s middle phase, but as with the Tikopia case, pigs were later eliminated from the

subsistence system. This is presumed to reflect trophic competition with humans for carbohydrates as human population densities increased ( Kirch, 2001). Whereas Tonga, Tikopia, and Mangaia are all relatively small islands, the Hawaiian Islands are a subtropical archipelago rich in a variety of microenvironments LY2109761 mw and resources that incorporate eight major islands and many smaller islets with 16,698 km2 of land area. Unsurprisingly,

the extent of Polynesian impact on the Hawaiian Islands was not as total as on the smaller islands; significant parts of the Hawaiian landscape remained relatively unaffected by human land use and resource exploitation at the time of initial European contact. Nonetheless, the lowland zones (i.e., land below ca. 600–900 m) of the main islands exhibited extensive anthropogenic modification, in some areas with almost complete human conversion and manipulation of the land surface in intensive food production systems. Extensive multidisciplinary research on Polynesian ecodynamics in Hawai’i has resulted in a richly documented record that we cannot do full justice Amrubicin to here (Olson and James, 1984, Athens, 1997, Burney et al., 2001, Athens et al., 2002, Vitousek et al., 2004, Kirch, 2007 and Kirch et al., 2012). Pollen records from O‘ahu and Kaua’i islands document major transformations in the lowland vegetation communities

of those islands soon after Polynesian arrival ca. A.D. 1000, including the elimination of coastal Pritchardia palm forests on O‘ahu. These dramatic vegetation changes were probably due to a combination of clearing for gardens and other land use activities, combined with the effects of introduced rats on vulnerable native seeds and seedlings. Such forest clearance also led to localized erosion and deposition of sediments in the lowlands, in-filling valley bottoms and embayments. The lowland forests were habitats for a number of flightless birds, including four endemic genera of anatids (ducks or geese) and one ibis, all of which became extinct within a relatively short period following Polynesian arrival. The Hawaiian land snails, a classic case of adaptive radiation and high degree of endemism (in such families as Achatinellidae, Amastridae, and Endodontidae), also saw significant extinction or local extirpation episodes related to forest clearance, and possibly to direct predation by Polynesian introduced ants ( Christensen and Kirch, 1986).

As currently defined, the Holocene is by far the shortest geologi

As currently defined, the Holocene is by far the shortest geological epoch within the established geological time scale, limited to roughly the last 11,500 calendar years (10,000 14C years). As Zalasiewicz et al. (2011b) noted, the “Holocene

is really just the last of a series of interglacial climate phases that have punctuated the severe icehouse climate of the past 2 Myr. We distinguish it as an epoch for practical purposes, in that many of the surface bodies of sediment selleck chemical on which we live—the soils, river deposits, deltas, coastal plains and so on—were formed during this time.” As such, the Holocene is a relatively arbitrary construct that would not have appeared Raf inhibitor particularly dramatic or lasted long if humans had not contributed

to biological and ecological changes around the world. Defining an Anthropocene epoch that begins in AD 1850, AD 2000, or another very recent date would ignore a host of archeological and paleoecological data sets. It will also exacerbate the arbitrary and short-lived nature of the Holocene. In examining the evidence for human transformation of the global biosphere during three phases of human history—the Paleolithic, Neolithic, and Industrial ages—Ellis (2011:1012–1013) had this to say of the Neolithic: Agricultural human systems set the stage for sustained human population growth for millennia, from a few million in 10,000 BCE to billions today. More importantly, these systems are sustained by an entirely novel biological process—the Pyruvate dehydrogenase lipoamide kinase isozyme 1 clearing of native vegetation and herbivores

and their replacement by engineered ecosystems populated with domesticated plant and/or animal species whose evolution is controlled by human systems. Were these agroecosystems to attain sufficient global extent, endure long enough and alter ecosystem structure and biogeochemical processes intensively enough, these alone may represent a novel transformation of the biosphere justifying a new geological epoch (references omitted from original). In this paper, I have added to the widespread changes caused by early agricultural and pastoral peoples to Earth’s terrestrial ecosystems, documenting a post-Pleistocene proliferation of anthropogenic shell midden soils in coastal and other aquatic settings worldwide. The global intensification of fishing and maritime economies near the end of the Pleistocene adds nearshore marine habitats to the list of ecosystems Homo sapiens has altered for millennia. By the Terminal Pleistocene or Early Holocene, agricultural and maritime peoples together had widespread and transformative effects on the terrestrial and nearshore ecosystems they lived in.

The data was sampled at a rate of 1000 Hz The data were analyzed

The data was sampled at a rate of 1000 Hz. The data were analyzed online by the experimenter learn more and if participants did not keep fixation the trial was discarded and repeated. The results are presented in Fig. 3. All data were tested for normality using the Shapiro–Wilk statistic; the data were normal unless otherwise stated. Inferential statistics used a significance level of p < .05, except when multiple comparisons were performed, where a Bonferonni correction of p < .016 was applied. For both tasks less than 1% of trials were redone because participants failed to keep fixation (CBT: 0.58%; Visual Patterns: 0.56%). Analyses are concerned with the mean span for each condition.

A 2 × 2 × 3 repeated measures ANOVA with the factors Task (Visual, Spatial), Side of Presentation (Temporal, Nasal), and Eye Position (Frontal, MDV3100 Abducted 20, Abducted 40) was performed. A significant

main effect of Task was found, F(1,13) = 235.68; p = .00, with memory span being higher in the visual patterns task (M = 7.38, SE = .26) compared to the Corsi Blocks task (M = 4.72; SE = .22); therefore, the two tasks are analyzed separately. The only statistically significant result was the interaction between Task and Side of Presentation, F(1,13) = 6.27; p = .026. A 2 × 3 repeated measures ANOVA with the factors Side of Presentation (Temporal, Nasal), and Eye Position (Frontal, Abducted 20, Abducted 40) revealed no significant main effects (Side of Presentation: p = .625; Eye Position: p = .280). The interaction was also not statistically significant (p = .682, η2 = 0.2). The same 2 × 3 repeated measures ANOVA was performed for Corsi spans. While the main effect

of Eye Position was not statistically significant (p = .145, η2 = 0.14), the main effect of Side of Presentation was, F(1,13) = 11.56; p = .005, η2 = 0.47 with span being higher in the nasal conditions (M = 4.86, SE = .22) compared to the temporal conditions (M = 4.58, SE = .23). The interaction was not significant (p = .393, η2 = 0.069). Bonferroni-corrected planned comparisons (paired samples t-tests; corrected alpha level p < .016) revealed that Corsi span in the temporal hemifield was significantly impaired compared to span in the nasal hemifield, but only in the Abducted 40 condition t(13) = 2.84; p = .014, d = .78; span reduced Unoprostone by .42 (SE = .15). There was a trend in the same direction in the Abducted 20 condition that did not approach significance when corrected for multiple comparisons (t(13) = 2.12; p = .053; d = .59). There was no difference in performance in the Frontal condition condition t(13) = .89; p = .39, d = .23). Memory span on the Corsi Blocks task was significantly reduced only when presented locations could not be encoded as the goal of saccadic eye movements; i.e., when memoranda were presented in the temporal hemifield in the 40° eye-abducted condition.

( Happ et al , 1940, Wolman and Leopold, 1957 and Florsheim and M

( Happ et al., 1940, Wolman and Leopold, 1957 and Florsheim and Mount, 2002). Sediment transport capacity (TC) is the cumulative ability to convey sediment over time, which can be expressed by various hydraulic parameters such as stream power

or energy of flows available to carry the sediment. The applied hydraulic forces are driven by the magnitude and frequency of flows, so they are scale-dependent and time-variant. Thus, TC is variable in space downstream and laterally across the floodplain and is sensitive to climate and hydrologic changes to the basin. The flow regime may Neratinib solubility dmso be influenced by human activities that alter runoff; i.e., land-use changes that introduce sediment may also increase flood magnitudes and TC. One way to conceptualize the potential for LS storage at a site is as a storage potential ratio of sediment delivery CX-5461 nmr to sediment transport

capacity over time: equation(1) SP=fDSTCwhere SP is storage potential. When sediment delivery is equal to transport capacity over time, then the reach is transporting the load available and the stream at that location can be considered to be graded ( Mackin, 1948) ( Fig. 7). Under graded conditions, the product of sediment discharge and caliber should be proportional to the water and sediment load of the stream ( Lane, 1955). If deliveries exceed transfer capacity (DS/TC > 1), however, some storage is likely. If deliveries greatly exceed transport capacity through time (DS/TC ≫ 1), abundant deposition and channel aggradation is likely, even without barriers or sinks ( Fig. 7b). Thus, the likelihood of LS being stored at a site is a function of a variety of processes and conditions governing sediment production, transport, and deposition, flow hydraulics over time, valley bottom characteristics upstream and Branched chain aminotransferase at the site, and sediment characteristics. These relationships explain why thick graded LS deposits are common in the Southern Piedmont of the USA where erosion of thick residual soils produced large volumes of sediment, but LS deposits are punctuated and less

common in glaciated basins with thin soils. For application to longer time scales, DS and TC can be defined to include variability in exogenous variables such as climate or tectonics. The sediment delivery ratio (SDR) is defined as the sediment yield at a point (YS) as a proportion of the sediment produced upstream by hill-slope erosion ( Roehl, 1962, Vanoni, 1975, Renfro, 1975, Dickinson and Wall, 1977 and Robinson, 1977): equation(2) SDR=YSPS Due to storage between hill-slope sources and floodplains down-valley, the SDR is usually less than one and decreases downvalley systematically with drainage area (Roehl, 1962, Novotny, 1980 and Shen and Julien, 1993) (Fig. 8). The decrease in SDRs downvalley was conceptualized as the ‘sediment delivery problem’ by Walling (1983) and recently restated by Fryirs (2013).

Fig 14 provides a useful example Fig 14b shows the morphology

Fig. 14 provides a useful example. Fig. 14b shows the morphology captured by a 5 m DTM, and in Fig. 14c, the derived drainage upslope area is displayed. Fig. 14d and e depict the airborne lidar 1 m DTM and the derived drainage upslope area, respectively. We used the D∞ flow direction algorithm (Tarboton, 1997) for the calculation of

the drainage area because of its advantages over the methods that restrict flow to eight possible directions (D8, introducing grid bias) or proportion flow according to slope (introducing unrealistic dispersion). It is clear from the figure that it is possible to correctly detect the terraces only with high-resolution topography (∼1 m DTM, Fig. 14d), thus providing a tool to identify the terrace-induced flow direction changes with more detail. Another interesting result can be extracted from this picture. Significant parts of the surveyed terrace failures mapped in the field through DGPS (red points) are located exactly (yellow arrows) where there is an evident flow direction change due to terrace feature (Fig. 14e). However, this approach (purely topographically based), while providing a first useful overview of the problem needs to be improved with other specific and physically based analyses because some of the surveyed wall failures are not located on

flow direction changes (Fig. 14e). To automatically identify the location of terraces, we applied a feature extraction technique based Nutlin-3 ic50 on a statistical threshold. Recent studies underlined how physical processes and anthropic features leave topographic signatures that can be derived from the lidar DTMs (Tarolli, 2014). Statistics can be used to automatically detect or extract particular features (e.g., Cazorzi et al., 2013 and Sofia et al., 2014). To automatically detect terraces, we represented surface morphology with a quadratic approximation of the original surface (Eq. (1)) as proposed by Evans (1979).

equation(1) Z=ax2+by2+cxy+dx+ey+fZ=ax2+by2+cxy+dx+ey+fwhere x, y, and Z are local coordinates, filipin and a through f are quadratic coefficients. The same quadratic approach has been successfully applied by Sofia et al. (2013), and Sofia et al. (2014). Giving that terraces can be considered as ridges on the side of the hill, we then computed the maximum curvature (C  max, Eq. (2)) by solving and differentiating Eq. (1) considering a local moving window, as proposed by Wood (1996). equation(2) Cmax=k⋅g⋅(−a−b+(a−b)2+C2)where C  max is the value of maximum curvature, the coefficients a  , b, and c   are computed by solving Eq. (1) within the moving window, k   is the size of the moving window and g   is the DTM resolution. The moving window used in this study is 5 m because it was demonstrated in recent studies (e.g., Tarolli et al., 2012) that the moving window size has to be related to the feature width under investigation.